better caster (#1480)

related to mps/rocm/cpu casting for fp16 and etc on clip

.github/ISSUE_TEMPLATE/bug_report.md

@@ -7,6 +7,10 @@ assignees: ''
 
 ---
 
+**Read Troubleshoot**
+
+[x] I admit that I have read the [Troubleshoot](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md) before making this issue.
+
 **Describe the problem**
 A clear and concise description of what the bug is.
 

.gitignore

@@ -7,13 +7,17 @@ __pycache__
 *.patch
 *.backup
 *.corrupted
+*.partial
+*.onnx
+sorted_styles.json
+/input
+/cache
 /language/default.json
-lena.png
-lena_result.png
-lena_test.py
+/test_imgs
+config.txt
+config_modification_tutorial.txt
 user_path_config.txt
-build_chb.py
-experiment.py
+user_path_config-deprecated.txt
 /modules/*.png
 /repositories
 /venv

args_manager.py

@@ -1,20 +1,38 @@
-from fcbh.options import enable_args_parsing
-enable_args_parsing(False)
-import fcbh.cli_args as fcbh_cli
+import ldm_patched.modules.args_parser as args_parser
 
 
-fcbh_cli.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")
-fcbh_cli.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.")
+args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")
+args_parser.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.")
 
-fcbh_cli.parser.add_argument("--language", type=str, default='default',
-                             help="Translate UI using json files in [language] folder. "
+args_parser.parser.add_argument("--language", type=str, default='default',
+                                help="Translate UI using json files in [language] folder. "
                                   "For example, [--language example] will use [language/example.json] for translation.")
 
-fcbh_cli.args = fcbh_cli.parser.parse_args()
-fcbh_cli.args.disable_cuda_malloc = True
-fcbh_cli.args.auto_launch = True
+# For example, https://github.com/lllyasviel/Fooocus/issues/849
+args_parser.parser.add_argument("--disable-offload-from-vram", action="store_true",
+                                help="Force loading models to vram when the unload can be avoided. "
+                                  "Some Mac users may need this.")
 
-if getattr(fcbh_cli.args, 'port', 8188) == 8188:
-    fcbh_cli.args.port = None
+args_parser.parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
+args_parser.parser.add_argument("--disable-image-log", action='store_true',
+                                help="Prevent writing images and logs to hard drive.")
 
-args = fcbh_cli.args
+args_parser.parser.add_argument("--disable-analytics", action='store_true',
+                                help="Disables analytics for Gradio", default=False)
+
+args_parser.parser.set_defaults(
+    disable_cuda_malloc=True,
+    in_browser=True,
+    port=None
+)
+
+args_parser.args = args_parser.parser.parse_args()
+
+# (Disable by default because of issues like https://github.com/lllyasviel/Fooocus/issues/724)
+args_parser.args.always_offload_from_vram = not args_parser.args.disable_offload_from_vram
+
+if args_parser.args.disable_analytics:
+    import os
+    os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+
+args = args_parser.args

backend/doc

@@ -1,5 +0,0 @@
-# Fooocus' Comfy Backend Headless (FCBH)
-
-This is a Comfy Backend from StabilityAI. This pre-complied backend makes it easier for people who have trouble using pygit2.
-
-FCBH is maintained by Fooocus's reviewing upon StabilityAI's changes.

backend/headless/LICENSE

@@ -1,674 +0,0 @@
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-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
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-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

backend/headless/fcbh/cli_args.py

@@ -1,111 +0,0 @@
-import argparse
-import enum
-import fcbh.options
-
-class EnumAction(argparse.Action):
-    """
-    Argparse action for handling Enums
-    """
-    def __init__(self, **kwargs):
-        # Pop off the type value
-        enum_type = kwargs.pop("type", None)
-
-        # Ensure an Enum subclass is provided
-        if enum_type is None:
-            raise ValueError("type must be assigned an Enum when using EnumAction")
-        if not issubclass(enum_type, enum.Enum):
-            raise TypeError("type must be an Enum when using EnumAction")
-
-        # Generate choices from the Enum
-        choices = tuple(e.value for e in enum_type)
-        kwargs.setdefault("choices", choices)
-        kwargs.setdefault("metavar", f"[{','.join(list(choices))}]")
-
-        super(EnumAction, self).__init__(**kwargs)
-
-        self._enum = enum_type
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        # Convert value back into an Enum
-        value = self._enum(values)
-        setattr(namespace, self.dest, value)
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)")
-parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
-parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
-parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.")
-
-parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
-parser.add_argument("--output-directory", type=str, default=None, help="Set the fcbh_backend output directory.")
-parser.add_argument("--temp-directory", type=str, default=None, help="Set the fcbh_backend temp directory (default is in the fcbh_backend directory).")
-parser.add_argument("--input-directory", type=str, default=None, help="Set the fcbh_backend input directory.")
-parser.add_argument("--auto-launch", action="store_true", help="Automatically launch fcbh_backend in the default browser.")
-parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
-parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
-cm_group = parser.add_mutually_exclusive_group()
-cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
-cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
-
-parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.")
-
-fp_group = parser.add_mutually_exclusive_group()
-fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
-fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
-
-parser.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.")
-
-fpvae_group = parser.add_mutually_exclusive_group()
-fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.")
-fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.")
-fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.")
-
-parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
-
-parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.")
-
-class LatentPreviewMethod(enum.Enum):
-    NoPreviews = "none"
-    Auto = "auto"
-    Latent2RGB = "latent2rgb"
-    TAESD = "taesd"
-
-parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
-
-attn_group = parser.add_mutually_exclusive_group()
-attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
-attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.")
-attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.")
-
-parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
-
-vram_group = parser.add_mutually_exclusive_group()
-vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
-vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
-vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
-vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
-vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
-vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
-
-
-parser.add_argument("--disable-smart-memory", action="store_true", help="Force fcbh_backend to agressively offload to regular ram instead of keeping models in vram when it can.")
-
-
-parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
-parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
-parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")
-
-parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
-
-if fcbh.options.args_parsing:
-    args = parser.parse_args()
-else:
-    args = parser.parse_args([])
-
-if args.windows_standalone_build:
-    args.auto_launch = True
-
-if args.disable_auto_launch:
-    args.auto_launch = False

backend/headless/fcbh/k_diffusion/external.py

@@ -1,194 +0,0 @@
-import math
-
-import torch
-from torch import nn
-
-from . import sampling, utils
-
-
-class VDenoiser(nn.Module):
-    """A v-diffusion-pytorch model wrapper for k-diffusion."""
-
-    def __init__(self, inner_model):
-        super().__init__()
-        self.inner_model = inner_model
-        self.sigma_data = 1.
-
-    def get_scalings(self, sigma):
-        c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
-        c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        return c_skip, c_out, c_in
-
-    def sigma_to_t(self, sigma):
-        return sigma.atan() / math.pi * 2
-
-    def t_to_sigma(self, t):
-        return (t * math.pi / 2).tan()
-
-    def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        model_output = self.inner_model(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
-        target = (input - c_skip * noised_input) / c_out
-        return (model_output - target).pow(2).flatten(1).mean(1)
-
-    def forward(self, input, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        return self.inner_model(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
-
-
-class DiscreteSchedule(nn.Module):
-    """A mapping between continuous noise levels (sigmas) and a list of discrete noise
-    levels."""
-
-    def __init__(self, sigmas, quantize):
-        super().__init__()
-        self.register_buffer('sigmas', sigmas)
-        self.register_buffer('log_sigmas', sigmas.log())
-        self.quantize = quantize
-
-    @property
-    def sigma_min(self):
-        return self.sigmas[0]
-
-    @property
-    def sigma_max(self):
-        return self.sigmas[-1]
-
-    def get_sigmas(self, n=None):
-        if n is None:
-            return sampling.append_zero(self.sigmas.flip(0))
-        t_max = len(self.sigmas) - 1
-        t = torch.linspace(t_max, 0, n, device=self.sigmas.device)
-        return sampling.append_zero(self.t_to_sigma(t))
-
-    def sigma_to_discrete_timestep(self, sigma):
-        log_sigma = sigma.log()
-        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
-        return dists.abs().argmin(dim=0).view(sigma.shape)
-
-    def sigma_to_t(self, sigma, quantize=None):
-        quantize = self.quantize if quantize is None else quantize
-        if quantize:
-            return self.sigma_to_discrete_timestep(sigma)
-        log_sigma = sigma.log()
-        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
-        low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
-        high_idx = low_idx + 1
-        low, high = self.log_sigmas[low_idx], self.log_sigmas[high_idx]
-        w = (low - log_sigma) / (low - high)
-        w = w.clamp(0, 1)
-        t = (1 - w) * low_idx + w * high_idx
-        return t.view(sigma.shape)
-
-    def t_to_sigma(self, t):
-        t = t.float()
-        low_idx = t.floor().long()
-        high_idx = t.ceil().long()
-        w = t-low_idx if t.device.type == 'mps' else t.frac()
-        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
-        return log_sigma.exp()
-
-    def predict_eps_discrete_timestep(self, input, t, **kwargs):
-        if t.dtype != torch.int64 and t.dtype != torch.int32:
-            t = t.round()
-        sigma = self.t_to_sigma(t)
-        input = input * ((utils.append_dims(sigma, input.ndim) ** 2 + 1.0) ** 0.5)
-        return  (input - self(input, sigma, **kwargs)) / utils.append_dims(sigma, input.ndim)
-
-    def predict_eps_sigma(self, input, sigma, **kwargs):
-        input = input * ((utils.append_dims(sigma, input.ndim) ** 2 + 1.0) ** 0.5)
-        return  (input - self(input, sigma, **kwargs)) / utils.append_dims(sigma, input.ndim)
-
-class DiscreteEpsDDPMDenoiser(DiscreteSchedule):
-    """A wrapper for discrete schedule DDPM models that output eps (the predicted
-    noise)."""
-
-    def __init__(self, model, alphas_cumprod, quantize):
-        super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize)
-        self.inner_model = model
-        self.sigma_data = 1.
-
-    def get_scalings(self, sigma):
-        c_out = -sigma
-        c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        return c_out, c_in
-
-    def get_eps(self, *args, **kwargs):
-        return self.inner_model(*args, **kwargs)
-
-    def loss(self, input, noise, sigma, **kwargs):
-        c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        eps = self.get_eps(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
-        return (eps - noise).pow(2).flatten(1).mean(1)
-
-    def forward(self, input, sigma, **kwargs):
-        c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
-        return input + eps * c_out
-
-
-class OpenAIDenoiser(DiscreteEpsDDPMDenoiser):
-    """A wrapper for OpenAI diffusion models."""
-
-    def __init__(self, model, diffusion, quantize=False, has_learned_sigmas=True, device='cpu'):
-        alphas_cumprod = torch.tensor(diffusion.alphas_cumprod, device=device, dtype=torch.float32)
-        super().__init__(model, alphas_cumprod, quantize=quantize)
-        self.has_learned_sigmas = has_learned_sigmas
-
-    def get_eps(self, *args, **kwargs):
-        model_output = self.inner_model(*args, **kwargs)
-        if self.has_learned_sigmas:
-            return model_output.chunk(2, dim=1)[0]
-        return model_output
-
-
-class CompVisDenoiser(DiscreteEpsDDPMDenoiser):
-    """A wrapper for CompVis diffusion models."""
-
-    def __init__(self, model, quantize=False, device='cpu'):
-        super().__init__(model, model.alphas_cumprod, quantize=quantize)
-
-    def get_eps(self, *args, **kwargs):
-        return self.inner_model.apply_model(*args, **kwargs)
-
-
-class DiscreteVDDPMDenoiser(DiscreteSchedule):
-    """A wrapper for discrete schedule DDPM models that output v."""
-
-    def __init__(self, model, alphas_cumprod, quantize):
-        super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize)
-        self.inner_model = model
-        self.sigma_data = 1.
-
-    def get_scalings(self, sigma):
-        c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
-        c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        return c_skip, c_out, c_in
-
-    def get_v(self, *args, **kwargs):
-        return self.inner_model(*args, **kwargs)
-
-    def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        model_output = self.get_v(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
-        target = (input - c_skip * noised_input) / c_out
-        return (model_output - target).pow(2).flatten(1).mean(1)
-
-    def forward(self, input, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        return self.get_v(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
-
-
-class CompVisVDenoiser(DiscreteVDDPMDenoiser):
-    """A wrapper for CompVis diffusion models that output v."""
-
-    def __init__(self, model, quantize=False, device='cpu'):
-        super().__init__(model, model.alphas_cumprod, quantize=quantize)
-
-    def get_v(self, x, t, cond, **kwargs):
-        return self.inner_model.apply_model(x, t, cond)

backend/headless/fcbh/ldm/models/diffusion/ddim.py

@@ -1,418 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-
-from fcbh.ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
-
-
-class DDIMSampler(object):
-    def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-        self.device = device
-        self.parameterization = kwargs.get("parameterization", "eps")
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != self.device:
-                attr = attr.float().to(self.device)
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        self.make_schedule_timesteps(ddim_timesteps, ddim_eta=ddim_eta, verbose=verbose)
-
-    def make_schedule_timesteps(self, ddim_timesteps, ddim_eta=0., verbose=True):
-        self.ddim_timesteps = torch.tensor(ddim_timesteps)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.device)
-
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample_custom(self,
-                      ddim_timesteps,
-                      conditioning=None,
-                      callback=None,
-                      img_callback=None,
-                      quantize_x0=False,
-                      eta=0.,
-                      mask=None,
-                      x0=None,
-                      temperature=1.,
-                      noise_dropout=0.,
-                      score_corrector=None,
-                      corrector_kwargs=None,
-                      verbose=True,
-                      x_T=None,
-                      log_every_t=100,
-                      unconditional_guidance_scale=1.,
-                      unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-                      dynamic_threshold=None,
-                      ucg_schedule=None,
-                      denoise_function=None,
-                      extra_args=None,
-                      to_zero=True,
-                      end_step=None,
-                      disable_pbar=False,
-                      **kwargs
-                      ):
-        self.make_schedule_timesteps(ddim_timesteps=ddim_timesteps, ddim_eta=eta, verbose=verbose)
-        samples, intermediates = self.ddim_sampling(conditioning, x_T.shape,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    dynamic_threshold=dynamic_threshold,
-                                                    ucg_schedule=ucg_schedule,
-                                                    denoise_function=denoise_function,
-                                                    extra_args=extra_args,
-                                                    to_zero=to_zero,
-                                                    end_step=end_step,
-                                                    disable_pbar=disable_pbar
-                                                    )
-        return samples, intermediates
-
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               dynamic_threshold=None,
-               ucg_schedule=None,
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                ctmp = conditioning[list(conditioning.keys())[0]]
-                while isinstance(ctmp, list): ctmp = ctmp[0]
-                cbs = ctmp.shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-
-            elif isinstance(conditioning, list):
-                for ctmp in conditioning:
-                    if ctmp.shape[0] != batch_size:
-                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
-        samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    dynamic_threshold=dynamic_threshold,
-                                                    ucg_schedule=ucg_schedule,
-                                                    denoise_function=None,
-                                                    extra_args=None
-                                                    )
-        return samples, intermediates
-
-    def q_sample(self, x_start, t, noise=None):
-        if noise is None:
-            noise = torch.randn_like(x_start)
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    @torch.no_grad()
-    def ddim_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
-                      ucg_schedule=None, denoise_function=None, extra_args=None, to_zero=True, end_step=None, disable_pbar=False):
-        device = self.model.alphas_cumprod.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else timesteps.flip(0)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        # print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range[:end_step], desc='DDIM Sampler', total=end_step, disable=disable_pbar)
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            if ucg_schedule is not None:
-                assert len(ucg_schedule) == len(time_range)
-                unconditional_guidance_scale = ucg_schedule[i]
-
-            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      dynamic_threshold=dynamic_threshold, denoise_function=denoise_function, extra_args=extra_args)
-            img, pred_x0 = outs
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        if to_zero:
-            img = pred_x0
-        else:
-            if ddim_use_original_steps:
-                sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
-            else:
-                sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
-            img /= sqrt_alphas_cumprod[index - 1]
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,
-                      dynamic_threshold=None, denoise_function=None, extra_args=None):
-        b, *_, device = *x.shape, x.device
-
-        if denoise_function is not None:
-            model_output = denoise_function(x, t, **extra_args)
-        elif unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            model_output = self.model.apply_model(x, t, c)
-        else:
-            x_in = torch.cat([x] * 2)
-            t_in = torch.cat([t] * 2)
-            if isinstance(c, dict):
-                assert isinstance(unconditional_conditioning, dict)
-                c_in = dict()
-                for k in c:
-                    if isinstance(c[k], list):
-                        c_in[k] = [torch.cat([
-                            unconditional_conditioning[k][i],
-                            c[k][i]]) for i in range(len(c[k]))]
-                    else:
-                        c_in[k] = torch.cat([
-                                unconditional_conditioning[k],
-                                c[k]])
-            elif isinstance(c, list):
-                c_in = list()
-                assert isinstance(unconditional_conditioning, list)
-                for i in range(len(c)):
-                    c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
-            else:
-                c_in = torch.cat([unconditional_conditioning, c])
-            model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
-
-        if self.parameterization == "v":
-            e_t = extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * model_output + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
-        else:
-            e_t = model_output
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps", 'not implemented'
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-        # select parameters corresponding to the currently considered timestep
-        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        if self.parameterization != "v":
-            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        else:
-            pred_x0 = extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * x - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * model_output
-
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-
-        if dynamic_threshold is not None:
-            raise NotImplementedError()
-
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-        return x_prev, pred_x0
-
-    @torch.no_grad()
-    def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
-               unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
-        num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
-
-        assert t_enc <= num_reference_steps
-        num_steps = t_enc
-
-        if use_original_steps:
-            alphas_next = self.alphas_cumprod[:num_steps]
-            alphas = self.alphas_cumprod_prev[:num_steps]
-        else:
-            alphas_next = self.ddim_alphas[:num_steps]
-            alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
-
-        x_next = x0
-        intermediates = []
-        inter_steps = []
-        for i in tqdm(range(num_steps), desc='Encoding Image'):
-            t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
-            if unconditional_guidance_scale == 1.:
-                noise_pred = self.model.apply_model(x_next, t, c)
-            else:
-                assert unconditional_conditioning is not None
-                e_t_uncond, noise_pred = torch.chunk(
-                    self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
-                                           torch.cat((unconditional_conditioning, c))), 2)
-                noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
-
-            xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
-            weighted_noise_pred = alphas_next[i].sqrt() * (
-                    (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
-            x_next = xt_weighted + weighted_noise_pred
-            if return_intermediates and i % (
-                    num_steps // return_intermediates) == 0 and i < num_steps - 1:
-                intermediates.append(x_next)
-                inter_steps.append(i)
-            elif return_intermediates and i >= num_steps - 2:
-                intermediates.append(x_next)
-                inter_steps.append(i)
-            if callback: callback(i)
-
-        out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
-        if return_intermediates:
-            out.update({'intermediates': intermediates})
-        return x_next, out
-
-    @torch.no_grad()
-    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None, max_denoise=False):
-        # fast, but does not allow for exact reconstruction
-        # t serves as an index to gather the correct alphas
-        if use_original_steps:
-            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
-            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
-        else:
-            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
-            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
-
-        if noise is None:
-            noise = torch.randn_like(x0)
-        if max_denoise:
-            noise_multiplier = 1.0
-        else:
-            noise_multiplier = extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape)
-
-        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + noise_multiplier * noise)
-
-    @torch.no_grad()
-    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
-               use_original_steps=False, callback=None):
-
-        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
-        timesteps = timesteps[:t_start]
-
-        time_range = np.flip(timesteps)
-        total_steps = timesteps.shape[0]
-        print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
-        x_dec = x_latent
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
-            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
-                                          unconditional_guidance_scale=unconditional_guidance_scale,
-                                          unconditional_conditioning=unconditional_conditioning)
-            if callback: callback(i)
-        return x_dec

backend/headless/fcbh/ldm/models/diffusion/dpm_solver/__init__.py

@@ -1 +0,0 @@
-from .sampler import DPMSolverSampler

backend/headless/fcbh/ldm/models/diffusion/dpm_solver/sampler.py

@@ -1,96 +0,0 @@
-"""SAMPLING ONLY."""
-import torch
-
-from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
-
-MODEL_TYPES = {
-    "eps": "noise",
-    "v": "v"
-}
-
-
-class DPMSolverSampler(object):
-    def __init__(self, model, device=torch.device("cuda"), **kwargs):
-        super().__init__()
-        self.model = model
-        self.device = device
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
-        self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != self.device:
-                attr = attr.to(self.device)
-        setattr(self, name, attr)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                ctmp = conditioning[list(conditioning.keys())[0]]
-                while isinstance(ctmp, list): ctmp = ctmp[0]
-                if isinstance(ctmp, torch.Tensor):
-                    cbs = ctmp.shape[0]
-                    if cbs != batch_size:
-                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            elif isinstance(conditioning, list):
-                for ctmp in conditioning:
-                    if ctmp.shape[0] != batch_size:
-                        print(f"Warning: Got {ctmp.shape[0]} conditionings but batch-size is {batch_size}")
-            else:
-                if isinstance(conditioning, torch.Tensor):
-                    if conditioning.shape[0] != batch_size:
-                        print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-
-        print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
-
-        device = self.model.betas.device
-        if x_T is None:
-            img = torch.randn(size, device=device)
-        else:
-            img = x_T
-
-        ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
-
-        model_fn = model_wrapper(
-            lambda x, t, c: self.model.apply_model(x, t, c),
-            ns,
-            model_type=MODEL_TYPES[self.model.parameterization],
-            guidance_type="classifier-free",
-            condition=conditioning,
-            unconditional_condition=unconditional_conditioning,
-            guidance_scale=unconditional_guidance_scale,
-        )
-
-        dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
-        x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2,
-                              lower_order_final=True)
-
-        return x.to(device), None

backend/headless/fcbh/ldm/models/diffusion/plms.py

@@ -1,245 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
-from ldm.models.diffusion.sampling_util import norm_thresholding
-
-
-class PLMSSampler(object):
-    def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-        self.device = device
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != self.device:
-                attr = attr.to(self.device)
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        if ddim_eta != 0:
-            raise ValueError('ddim_eta must be 0 for PLMS')
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               dynamic_threshold=None,
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        print(f'Data shape for PLMS sampling is {size}')
-
-        samples, intermediates = self.plms_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    dynamic_threshold=dynamic_threshold,
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def plms_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,
-                      dynamic_threshold=None):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        print(f"Running PLMS Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
-        old_eps = []
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      old_eps=old_eps, t_next=ts_next,
-                                      dynamic_threshold=dynamic_threshold)
-            img, pred_x0, e_t = outs
-            old_eps.append(e_t)
-            if len(old_eps) >= 4:
-                old_eps.pop(0)
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
-                      dynamic_threshold=None):
-        b, *_, device = *x.shape, x.device
-
-        def get_model_output(x, t):
-            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-                e_t = self.model.apply_model(x, t, c)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t] * 2)
-                c_in = torch.cat([unconditional_conditioning, c])
-                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-            if score_corrector is not None:
-                assert self.model.parameterization == "eps"
-                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-            return e_t
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
-        def get_x_prev_and_pred_x0(e_t, index):
-            # select parameters corresponding to the currently considered timestep
-            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-            # current prediction for x_0
-            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-            if quantize_denoised:
-                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-            if dynamic_threshold is not None:
-                pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
-            # direction pointing to x_t
-            dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-            if noise_dropout > 0.:
-                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-            return x_prev, pred_x0
-
-        e_t = get_model_output(x, t)
-        if len(old_eps) == 0:
-            # Pseudo Improved Euler (2nd order)
-            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-            e_t_next = get_model_output(x_prev, t_next)
-            e_t_prime = (e_t + e_t_next) / 2
-        elif len(old_eps) == 1:
-            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (3 * e_t - old_eps[-1]) / 2
-        elif len(old_eps) == 2:
-            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-        elif len(old_eps) >= 3:
-            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-        return x_prev, pred_x0, e_t

backend/headless/fcbh/ldm/models/diffusion/sampling_util.py

@@ -1,22 +0,0 @@
-import torch
-import numpy as np
-
-
-def append_dims(x, target_dims):
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions.
-    From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
-    dims_to_append = target_dims - x.ndim
-    if dims_to_append < 0:
-        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
-    return x[(...,) + (None,) * dims_to_append]
-
-
-def norm_thresholding(x0, value):
-    s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
-    return x0 * (value / s)
-
-
-def spatial_norm_thresholding(x0, value):
-    # b c h w
-    s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
-    return x0 * (value / s)

backend/headless/fcbh/ops.py

@@ -1,46 +0,0 @@
-import torch
-from contextlib import contextmanager
-
-class Linear(torch.nn.Module):
-    def __init__(self, in_features: int, out_features: int, bias: bool = True,
-                 device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__()
-        self.in_features = in_features
-        self.out_features = out_features
-        self.weight = torch.nn.Parameter(torch.empty((out_features, in_features), **factory_kwargs))
-        if bias:
-            self.bias = torch.nn.Parameter(torch.empty(out_features, **factory_kwargs))
-        else:
-            self.register_parameter('bias', None)
-
-    def forward(self, input):
-        return torch.nn.functional.linear(input, self.weight, self.bias)
-
-class Conv2d(torch.nn.Conv2d):
-    def reset_parameters(self):
-        return None
-
-def conv_nd(dims, *args, **kwargs):
-    if dims == 2:
-        return Conv2d(*args, **kwargs)
-    else:
-        raise ValueError(f"unsupported dimensions: {dims}")
-
-@contextmanager
-def use_fcbh_ops(device=None, dtype=None): # Kind of an ugly hack but I can't think of a better way
-    old_torch_nn_linear = torch.nn.Linear
-    force_device = device
-    force_dtype = dtype
-    def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None):
-        if force_device is not None:
-            device = force_device
-        if force_dtype is not None:
-            dtype = force_dtype
-        return Linear(in_features, out_features, bias=bias, device=device, dtype=dtype)
-
-    torch.nn.Linear = linear_with_dtype
-    try:
-        yield
-    finally:
-        torch.nn.Linear = old_torch_nn_linear

backend/headless/fcbh/samplers.py

@@ -1,728 +0,0 @@
-from .k_diffusion import sampling as k_diffusion_sampling
-from .k_diffusion import external as k_diffusion_external
-from .extra_samplers import uni_pc
-import torch
-import enum
-from fcbh import model_management
-from .ldm.models.diffusion.ddim import DDIMSampler
-from .ldm.modules.diffusionmodules.util import make_ddim_timesteps
-import math
-from fcbh import model_base
-import fcbh.utils
-import fcbh.conds
-
-
-#The main sampling function shared by all the samplers
-#Returns predicted noise
-def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
-        def get_area_and_mult(conds, x_in, timestep_in):
-            area = (x_in.shape[2], x_in.shape[3], 0, 0)
-            strength = 1.0
-
-            if 'timestep_start' in conds:
-                timestep_start = conds['timestep_start']
-                if timestep_in[0] > timestep_start:
-                    return None
-            if 'timestep_end' in conds:
-                timestep_end = conds['timestep_end']
-                if timestep_in[0] < timestep_end:
-                    return None
-            if 'area' in conds:
-                area = conds['area']
-            if 'strength' in conds:
-                strength = conds['strength']
-
-            input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
-            if 'mask' in conds:
-                # Scale the mask to the size of the input
-                # The mask should have been resized as we began the sampling process
-                mask_strength = 1.0
-                if "mask_strength" in conds:
-                    mask_strength = conds["mask_strength"]
-                mask = conds['mask']
-                assert(mask.shape[1] == x_in.shape[2])
-                assert(mask.shape[2] == x_in.shape[3])
-                mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength
-                mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
-            else:
-                mask = torch.ones_like(input_x)
-            mult = mask * strength
-
-            if 'mask' not in conds:
-                rr = 8
-                if area[2] != 0:
-                    for t in range(rr):
-                        mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1))
-                if (area[0] + area[2]) < x_in.shape[2]:
-                    for t in range(rr):
-                        mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1))
-                if area[3] != 0:
-                    for t in range(rr):
-                        mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1))
-                if (area[1] + area[3]) < x_in.shape[3]:
-                    for t in range(rr):
-                        mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1))
-
-            conditionning = {}
-            model_conds = conds["model_conds"]
-            for c in model_conds:
-                conditionning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
-
-            control = None
-            if 'control' in conds:
-                control = conds['control']
-
-            patches = None
-            if 'gligen' in conds:
-                gligen = conds['gligen']
-                patches = {}
-                gligen_type = gligen[0]
-                gligen_model = gligen[1]
-                if gligen_type == "position":
-                    gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)
-                else:
-                    gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)
-
-                patches['middle_patch'] = [gligen_patch]
-
-            return (input_x, mult, conditionning, area, control, patches)
-
-        def cond_equal_size(c1, c2):
-            if c1 is c2:
-                return True
-            if c1.keys() != c2.keys():
-                return False
-            for k in c1:
-                if not c1[k].can_concat(c2[k]):
-                    return False
-            return True
-
-        def can_concat_cond(c1, c2):
-            if c1[0].shape != c2[0].shape:
-                return False
-
-            #control
-            if (c1[4] is None) != (c2[4] is None):
-                return False
-            if c1[4] is not None:
-                if c1[4] is not c2[4]:
-                    return False
-
-            #patches
-            if (c1[5] is None) != (c2[5] is None):
-                return False
-            if (c1[5] is not None):
-                if c1[5] is not c2[5]:
-                    return False
-
-            return cond_equal_size(c1[2], c2[2])
-
-        def cond_cat(c_list):
-            c_crossattn = []
-            c_concat = []
-            c_adm = []
-            crossattn_max_len = 0
-
-            temp = {}
-            for x in c_list:
-                for k in x:
-                    cur = temp.get(k, [])
-                    cur.append(x[k])
-                    temp[k] = cur
-
-            out = {}
-            for k in temp:
-                conds = temp[k]
-                out[k] = conds[0].concat(conds[1:])
-
-            return out
-
-        def calc_cond_uncond_batch(model_function, cond, uncond, x_in, timestep, max_total_area, model_options):
-            out_cond = torch.zeros_like(x_in)
-            out_count = torch.ones_like(x_in)/100000.0
-
-            out_uncond = torch.zeros_like(x_in)
-            out_uncond_count = torch.ones_like(x_in)/100000.0
-
-            COND = 0
-            UNCOND = 1
-
-            to_run = []
-            for x in cond:
-                p = get_area_and_mult(x, x_in, timestep)
-                if p is None:
-                    continue
-
-                to_run += [(p, COND)]
-            if uncond is not None:
-                for x in uncond:
-                    p = get_area_and_mult(x, x_in, timestep)
-                    if p is None:
-                        continue
-
-                    to_run += [(p, UNCOND)]
-
-            while len(to_run) > 0:
-                first = to_run[0]
-                first_shape = first[0][0].shape
-                to_batch_temp = []
-                for x in range(len(to_run)):
-                    if can_concat_cond(to_run[x][0], first[0]):
-                        to_batch_temp += [x]
-
-                to_batch_temp.reverse()
-                to_batch = to_batch_temp[:1]
-
-                for i in range(1, len(to_batch_temp) + 1):
-                    batch_amount = to_batch_temp[:len(to_batch_temp)//i]
-                    if (len(batch_amount) * first_shape[0] * first_shape[2] * first_shape[3] < max_total_area):
-                        to_batch = batch_amount
-                        break
-
-                input_x = []
-                mult = []
-                c = []
-                cond_or_uncond = []
-                area = []
-                control = None
-                patches = None
-                for x in to_batch:
-                    o = to_run.pop(x)
-                    p = o[0]
-                    input_x += [p[0]]
-                    mult += [p[1]]
-                    c += [p[2]]
-                    area += [p[3]]
-                    cond_or_uncond += [o[1]]
-                    control = p[4]
-                    patches = p[5]
-
-                batch_chunks = len(cond_or_uncond)
-                input_x = torch.cat(input_x)
-                c = cond_cat(c)
-                timestep_ = torch.cat([timestep] * batch_chunks)
-
-                if control is not None:
-                    c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
-
-                transformer_options = {}
-                if 'transformer_options' in model_options:
-                    transformer_options = model_options['transformer_options'].copy()
-
-                if patches is not None:
-                    if "patches" in transformer_options:
-                        cur_patches = transformer_options["patches"].copy()
-                        for p in patches:
-                            if p in cur_patches:
-                                cur_patches[p] = cur_patches[p] + patches[p]
-                            else:
-                                cur_patches[p] = patches[p]
-                    else:
-                        transformer_options["patches"] = patches
-
-                transformer_options["cond_or_uncond"] = cond_or_uncond[:]
-                c['transformer_options'] = transformer_options
-
-                if 'model_function_wrapper' in model_options:
-                    output = model_options['model_function_wrapper'](model_function, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
-                else:
-                    output = model_function(input_x, timestep_, **c).chunk(batch_chunks)
-                del input_x
-
-                for o in range(batch_chunks):
-                    if cond_or_uncond[o] == COND:
-                        out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
-                        out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
-                    else:
-                        out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
-                        out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
-                del mult
-
-            out_cond /= out_count
-            del out_count
-            out_uncond /= out_uncond_count
-            del out_uncond_count
-
-            return out_cond, out_uncond
-
-
-        max_total_area = model_management.maximum_batch_area()
-        if math.isclose(cond_scale, 1.0):
-            uncond = None
-
-        cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, model_options)
-        if "sampler_cfg_function" in model_options:
-            args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}
-            return model_options["sampler_cfg_function"](args)
-        else:
-            return uncond + (cond - uncond) * cond_scale
-
-
-class CompVisVDenoiser(k_diffusion_external.DiscreteVDDPMDenoiser):
-    def __init__(self, model, quantize=False, device='cpu'):
-        super().__init__(model, model.alphas_cumprod, quantize=quantize)
-
-    def get_v(self, x, t, cond, **kwargs):
-        return self.inner_model.apply_model(x, t, cond, **kwargs)
-
-
-class CFGNoisePredictor(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.inner_model = model
-        self.alphas_cumprod = model.alphas_cumprod
-    def apply_model(self, x, timestep, cond, uncond, cond_scale, model_options={}, seed=None):
-        out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed)
-        return out
-
-
-class KSamplerX0Inpaint(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.inner_model = model
-    def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None):
-        if denoise_mask is not None:
-            latent_mask = 1. - denoise_mask
-            x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask
-        out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed)
-        if denoise_mask is not None:
-            out *= denoise_mask
-
-        if denoise_mask is not None:
-            out += self.latent_image * latent_mask
-        return out
-
-def simple_scheduler(model, steps):
-    sigs = []
-    ss = len(model.sigmas) / steps
-    for x in range(steps):
-        sigs += [float(model.sigmas[-(1 + int(x * ss))])]
-    sigs += [0.0]
-    return torch.FloatTensor(sigs)
-
-def ddim_scheduler(model, steps):
-    sigs = []
-    ddim_timesteps = make_ddim_timesteps(ddim_discr_method="uniform", num_ddim_timesteps=steps, num_ddpm_timesteps=model.inner_model.inner_model.num_timesteps, verbose=False)
-    for x in range(len(ddim_timesteps) - 1, -1, -1):
-        ts = ddim_timesteps[x]
-        if ts > 999:
-            ts = 999
-        sigs.append(model.t_to_sigma(torch.tensor(ts)))
-    sigs += [0.0]
-    return torch.FloatTensor(sigs)
-
-def sgm_scheduler(model, steps):
-    sigs = []
-    timesteps = torch.linspace(model.inner_model.inner_model.num_timesteps - 1, 0, steps + 1)[:-1].type(torch.int)
-    for x in range(len(timesteps)):
-        ts = timesteps[x]
-        if ts > 999:
-            ts = 999
-        sigs.append(model.t_to_sigma(torch.tensor(ts)))
-    sigs += [0.0]
-    return torch.FloatTensor(sigs)
-
-def get_mask_aabb(masks):
-    if masks.numel() == 0:
-        return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
-
-    b = masks.shape[0]
-
-    bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int)
-    is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool)
-    for i in range(b):
-        mask = masks[i]
-        if mask.numel() == 0:
-            continue
-        if torch.max(mask != 0) == False:
-            is_empty[i] = True
-            continue
-        y, x = torch.where(mask)
-        bounding_boxes[i, 0] = torch.min(x)
-        bounding_boxes[i, 1] = torch.min(y)
-        bounding_boxes[i, 2] = torch.max(x)
-        bounding_boxes[i, 3] = torch.max(y)
-
-    return bounding_boxes, is_empty
-
-def resolve_areas_and_cond_masks(conditions, h, w, device):
-    # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.
-    # While we're doing this, we can also resolve the mask device and scaling for performance reasons
-    for i in range(len(conditions)):
-        c = conditions[i]
-        if 'area' in c:
-            area = c['area']
-            if area[0] == "percentage":
-                modified = c.copy()
-                area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w))
-                modified['area'] = area
-                c = modified
-                conditions[i] = c
-
-        if 'mask' in c:
-            mask = c['mask']
-            mask = mask.to(device=device)
-            modified = c.copy()
-            if len(mask.shape) == 2:
-                mask = mask.unsqueeze(0)
-            if mask.shape[1] != h or mask.shape[2] != w:
-                mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1)
-
-            if modified.get("set_area_to_bounds", False):
-                bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)
-                boxes, is_empty = get_mask_aabb(bounds)
-                if is_empty[0]:
-                    # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway)
-                    modified['area'] = (8, 8, 0, 0)
-                else:
-                    box = boxes[0]
-                    H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0])
-                    H = max(8, H)
-                    W = max(8, W)
-                    area = (int(H), int(W), int(Y), int(X))
-                    modified['area'] = area
-
-            modified['mask'] = mask
-            conditions[i] = modified
-
-def create_cond_with_same_area_if_none(conds, c):
-    if 'area' not in c:
-        return
-
-    c_area = c['area']
-    smallest = None
-    for x in conds:
-        if 'area' in x:
-            a = x['area']
-            if c_area[2] >= a[2] and c_area[3] >= a[3]:
-                if a[0] + a[2] >= c_area[0] + c_area[2]:
-                    if a[1] + a[3] >= c_area[1] + c_area[3]:
-                        if smallest is None:
-                            smallest = x
-                        elif 'area' not in smallest:
-                            smallest = x
-                        else:
-                            if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]:
-                                smallest = x
-        else:
-            if smallest is None:
-                smallest = x
-    if smallest is None:
-        return
-    if 'area' in smallest:
-        if smallest['area'] == c_area:
-            return
-
-    out = c.copy()
-    out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied?
-    conds += [out]
-
-def calculate_start_end_timesteps(model, conds):
-    for t in range(len(conds)):
-        x = conds[t]
-
-        timestep_start = None
-        timestep_end = None
-        if 'start_percent' in x:
-            timestep_start = model.sigma_to_t(model.t_to_sigma(torch.tensor(x['start_percent'] * 999.0)))
-        if 'end_percent' in x:
-            timestep_end = model.sigma_to_t(model.t_to_sigma(torch.tensor(x['end_percent'] * 999.0)))
-
-        if (timestep_start is not None) or (timestep_end is not None):
-            n = x.copy()
-            if (timestep_start is not None):
-                n['timestep_start'] = timestep_start
-            if (timestep_end is not None):
-                n['timestep_end'] = timestep_end
-            conds[t] = n
-
-def pre_run_control(model, conds):
-    for t in range(len(conds)):
-        x = conds[t]
-
-        timestep_start = None
-        timestep_end = None
-        percent_to_timestep_function = lambda a: model.sigma_to_t(model.t_to_sigma(torch.tensor(a) * 999.0))
-        if 'control' in x:
-            x['control'].pre_run(model.inner_model.inner_model, percent_to_timestep_function)
-
-def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
-    cond_cnets = []
-    cond_other = []
-    uncond_cnets = []
-    uncond_other = []
-    for t in range(len(conds)):
-        x = conds[t]
-        if 'area' not in x:
-            if name in x and x[name] is not None:
-                cond_cnets.append(x[name])
-            else:
-                cond_other.append((x, t))
-    for t in range(len(uncond)):
-        x = uncond[t]
-        if 'area' not in x:
-            if name in x and x[name] is not None:
-                uncond_cnets.append(x[name])
-            else:
-                uncond_other.append((x, t))
-
-    if len(uncond_cnets) > 0:
-        return
-
-    for x in range(len(cond_cnets)):
-        temp = uncond_other[x % len(uncond_other)]
-        o = temp[0]
-        if name in o and o[name] is not None:
-            n = o.copy()
-            n[name] = uncond_fill_func(cond_cnets, x)
-            uncond += [n]
-        else:
-            n = o.copy()
-            n[name] = uncond_fill_func(cond_cnets, x)
-            uncond[temp[1]] = n
-
-def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs):
-    for t in range(len(conds)):
-        x = conds[t]
-        params = x.copy()
-        params["device"] = device
-        params["noise"] = noise
-        params["width"] = params.get("width", noise.shape[3] * 8)
-        params["height"] = params.get("height", noise.shape[2] * 8)
-        params["prompt_type"] = params.get("prompt_type", prompt_type)
-        for k in kwargs:
-            if k not in params:
-                params[k] = kwargs[k]
-
-        out = model_function(**params)
-        x = x.copy()
-        model_conds = x['model_conds'].copy()
-        for k in out:
-            model_conds[k] = out[k]
-        x['model_conds'] = model_conds
-        conds[t] = x
-    return conds
-
-class Sampler:
-    def sample(self):
-        pass
-
-    def max_denoise(self, model_wrap, sigmas):
-        return math.isclose(float(model_wrap.sigma_max), float(sigmas[0]), rel_tol=1e-05)
-
-class DDIM(Sampler):
-    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
-        timesteps = []
-        for s in range(sigmas.shape[0]):
-            timesteps.insert(0, model_wrap.sigma_to_discrete_timestep(sigmas[s]))
-        noise_mask = None
-        if denoise_mask is not None:
-            noise_mask = 1.0 - denoise_mask
-
-        ddim_callback = None
-        if callback is not None:
-            total_steps = len(timesteps) - 1
-            ddim_callback = lambda pred_x0, i: callback(i, pred_x0, None, total_steps)
-
-        max_denoise = self.max_denoise(model_wrap, sigmas)
-
-        ddim_sampler = DDIMSampler(model_wrap.inner_model.inner_model, device=noise.device)
-        ddim_sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
-        z_enc = ddim_sampler.stochastic_encode(latent_image, torch.tensor([len(timesteps) - 1] * noise.shape[0]).to(noise.device), noise=noise, max_denoise=max_denoise)
-        samples, _ = ddim_sampler.sample_custom(ddim_timesteps=timesteps,
-                                                batch_size=noise.shape[0],
-                                                shape=noise.shape[1:],
-                                                verbose=False,
-                                                eta=0.0,
-                                                x_T=z_enc,
-                                                x0=latent_image,
-                                                img_callback=ddim_callback,
-                                                denoise_function=model_wrap.predict_eps_discrete_timestep,
-                                                extra_args=extra_args,
-                                                mask=noise_mask,
-                                                to_zero=sigmas[-1]==0,
-                                                end_step=sigmas.shape[0] - 1,
-                                                disable_pbar=disable_pbar)
-        return samples
-
-class UNIPC(Sampler):
-    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
-        return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar)
-
-class UNIPCBH2(Sampler):
-    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
-        return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)
-
-KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
-                  "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
-                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm"]
-
-def ksampler(sampler_name, extra_options={}):
-    class KSAMPLER(Sampler):
-        def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
-            extra_args["denoise_mask"] = denoise_mask
-            model_k = KSamplerX0Inpaint(model_wrap)
-            model_k.latent_image = latent_image
-            model_k.noise = noise
-
-            if self.max_denoise(model_wrap, sigmas):
-                noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)
-            else:
-                noise = noise * sigmas[0]
-
-            k_callback = None
-            total_steps = len(sigmas) - 1
-            if callback is not None:
-                k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
-
-            sigma_min = sigmas[-1]
-            if sigma_min == 0:
-                sigma_min = sigmas[-2]
-
-            if latent_image is not None:
-                noise += latent_image
-            if sampler_name == "dpm_fast":
-                samples = k_diffusion_sampling.sample_dpm_fast(model_k, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
-            elif sampler_name == "dpm_adaptive":
-                samples = k_diffusion_sampling.sample_dpm_adaptive(model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback, disable=disable_pbar)
-            else:
-                samples = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **extra_options)
-            return samples
-    return KSAMPLER
-
-def wrap_model(model):
-    model_denoise = CFGNoisePredictor(model)
-    if model.model_type == model_base.ModelType.V_PREDICTION:
-        model_wrap = CompVisVDenoiser(model_denoise, quantize=True)
-    else:
-        model_wrap = k_diffusion_external.CompVisDenoiser(model_denoise, quantize=True)
-    return model_wrap
-
-def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
-    positive = positive[:]
-    negative = negative[:]
-
-    resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device)
-    resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device)
-
-    model_wrap = wrap_model(model)
-
-    calculate_start_end_timesteps(model_wrap, negative)
-    calculate_start_end_timesteps(model_wrap, positive)
-
-    #make sure each cond area has an opposite one with the same area
-    for c in positive:
-        create_cond_with_same_area_if_none(negative, c)
-    for c in negative:
-        create_cond_with_same_area_if_none(positive, c)
-
-    pre_run_control(model_wrap, negative + positive)
-
-    apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
-    apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
-
-    if latent_image is not None:
-        latent_image = model.process_latent_in(latent_image)
-
-    if hasattr(model, 'extra_conds'):
-        positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask)
-        negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask)
-
-    extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed}
-
-    samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
-    return model.process_latent_out(samples.to(torch.float32))
-
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
-SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
-
-def calculate_sigmas_scheduler(model, scheduler_name, steps):
-    model_wrap = wrap_model(model)
-    if scheduler_name == "karras":
-        sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_wrap.sigma_min), sigma_max=float(model_wrap.sigma_max))
-    elif scheduler_name == "exponential":
-        sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_wrap.sigma_min), sigma_max=float(model_wrap.sigma_max))
-    elif scheduler_name == "normal":
-        sigmas = model_wrap.get_sigmas(steps)
-    elif scheduler_name == "simple":
-        sigmas = simple_scheduler(model_wrap, steps)
-    elif scheduler_name == "ddim_uniform":
-        sigmas = ddim_scheduler(model_wrap, steps)
-    elif scheduler_name == "sgm_uniform":
-        sigmas = sgm_scheduler(model_wrap, steps)
-    else:
-        print("error invalid scheduler", self.scheduler)
-    return sigmas
-
-def sampler_class(name):
-    if name == "uni_pc":
-        sampler = UNIPC
-    elif name == "uni_pc_bh2":
-        sampler = UNIPCBH2
-    elif name == "ddim":
-        sampler = DDIM
-    else:
-        sampler = ksampler(name)
-    return sampler
-
-class KSampler:
-    SCHEDULERS = SCHEDULER_NAMES
-    SAMPLERS = SAMPLER_NAMES
-
-    def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
-        self.model = model
-        self.device = device
-        if scheduler not in self.SCHEDULERS:
-            scheduler = self.SCHEDULERS[0]
-        if sampler not in self.SAMPLERS:
-            sampler = self.SAMPLERS[0]
-        self.scheduler = scheduler
-        self.sampler = sampler
-        self.set_steps(steps, denoise)
-        self.denoise = denoise
-        self.model_options = model_options
-
-    def calculate_sigmas(self, steps):
-        sigmas = None
-
-        discard_penultimate_sigma = False
-        if self.sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']:
-            steps += 1
-            discard_penultimate_sigma = True
-
-        sigmas = calculate_sigmas_scheduler(self.model, self.scheduler, steps)
-
-        if discard_penultimate_sigma:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-        return sigmas
-
-    def set_steps(self, steps, denoise=None):
-        self.steps = steps
-        if denoise is None or denoise > 0.9999:
-            self.sigmas = self.calculate_sigmas(steps).to(self.device)
-        else:
-            new_steps = int(steps/denoise)
-            sigmas = self.calculate_sigmas(new_steps).to(self.device)
-            self.sigmas = sigmas[-(steps + 1):]
-
-    def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
-        if sigmas is None:
-            sigmas = self.sigmas
-
-        if last_step is not None and last_step < (len(sigmas) - 1):
-            sigmas = sigmas[:last_step + 1]
-            if force_full_denoise:
-                sigmas[-1] = 0
-
-        if start_step is not None:
-            if start_step < (len(sigmas) - 1):
-                sigmas = sigmas[start_step:]
-            else:
-                if latent_image is not None:
-                    return latent_image
-                else:
-                    return torch.zeros_like(noise)
-
-        sampler = sampler_class(self.sampler)
-
-        return sample(self.model, noise, positive, negative, cfg, self.device, sampler(), sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)

backend/headless/fcbh_extras/nodes_latent.py

@@ -1,74 +0,0 @@
-import fcbh.utils
-
-def reshape_latent_to(target_shape, latent):
-    if latent.shape[1:] != target_shape[1:]:
-        latent.movedim(1, -1)
-        latent = fcbh.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
-        latent.movedim(-1, 1)
-    return fcbh.utils.repeat_to_batch_size(latent, target_shape[0])
-
-
-class LatentAdd:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
-
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples1, samples2):
-        samples_out = samples1.copy()
-
-        s1 = samples1["samples"]
-        s2 = samples2["samples"]
-
-        s2 = reshape_latent_to(s1.shape, s2)
-        samples_out["samples"] = s1 + s2
-        return (samples_out,)
-
-class LatentSubtract:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
-
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples1, samples2):
-        samples_out = samples1.copy()
-
-        s1 = samples1["samples"]
-        s2 = samples2["samples"]
-
-        s2 = reshape_latent_to(s1.shape, s2)
-        samples_out["samples"] = s1 - s2
-        return (samples_out,)
-
-class LatentMultiply:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT",),
-                              "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
-                             }}
-
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples, multiplier):
-        samples_out = samples.copy()
-
-        s1 = samples["samples"]
-        samples_out["samples"] = s1 * multiplier
-        return (samples_out,)
-
-NODE_CLASS_MAPPINGS = {
-    "LatentAdd": LatentAdd,
-    "LatentSubtract": LatentSubtract,
-    "LatentMultiply": LatentMultiply,
-}

css/style.css

@@ -29,8 +29,8 @@
 
 .canvas-tooltip-info {
   position: absolute;
-  top: 10px;
-  left: 10px;
+  top: 28px;
+  left: 2px;
   cursor: help;
   background-color: rgba(0, 0, 0, 0.3);
   width: 20px;
@@ -93,3 +93,106 @@
 .styler {
   overflow:inherit !important;
 }
+
+.gradio-container{
+  overflow: visible;
+}
+
+/* fullpage image viewer */
+
+#lightboxModal{
+    display: none;
+    position: fixed;
+    z-index: 1001;
+    left: 0;
+    top: 0;
+    width: 100%;
+    height: 100%;
+    overflow: auto;
+    background-color: rgba(20, 20, 20, 0.95);
+    user-select: none;
+    -webkit-user-select: none;
+    flex-direction: column;
+}
+
+.modalControls {
+    display: flex;
+    position: absolute;
+    right: 0px;
+    left: 0px;
+    gap: 1em;
+    padding: 1em;
+    background-color:rgba(0,0,0,0);
+    z-index: 1;
+    transition: 0.2s ease background-color;
+}
+.modalControls:hover {
+    background-color:rgba(0,0,0,0.9);
+}
+.modalClose {
+    margin-left: auto;
+}
+.modalControls span{
+    color: white;
+    text-shadow: 0px 0px 0.25em black;
+    font-size: 35px;
+    font-weight: bold;
+    cursor: pointer;
+    width: 1em;
+}
+
+.modalControls span:hover, .modalControls span:focus{
+    color: #999;
+    text-decoration: none;
+}
+
+#lightboxModal > img {
+    display: block;
+    margin: auto;
+    width: auto;
+}
+
+#lightboxModal > img.modalImageFullscreen{
+    object-fit: contain;
+    height: 100%;
+    width: 100%;
+    min-height: 0;
+}
+
+.modalPrev,
+.modalNext {
+  cursor: pointer;
+  position: absolute;
+  top: 50%;
+  width: auto;
+  padding: 16px;
+  margin-top: -50px;
+  color: white;
+  font-weight: bold;
+  font-size: 20px;
+  transition: 0.6s ease;
+  border-radius: 0 3px 3px 0;
+  user-select: none;
+  -webkit-user-select: none;
+}
+
+.modalNext {
+  right: 0;
+  border-radius: 3px 0 0 3px;
+}
+
+.modalPrev:hover,
+.modalNext:hover {
+  background-color: rgba(0, 0, 0, 0.8);
+}
+
+#imageARPreview {
+    position: absolute;
+    top: 0px;
+    left: 0px;
+    border: 2px solid red;
+    background: rgba(255, 0, 0, 0.3);
+    z-index: 900;
+    pointer-events: none;
+    display: none;
+}

entry_with_update.py

@@ -37,7 +37,7 @@ try:
         repo.reset(local_branch.target, pygit2.GIT_RESET_HARD)
         print("Fast-forward merge")
     elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL:
-        print("Update failed - Did you modified any file?")
+        print("Update failed - Did you modify any file?")
 except Exception as e:
     print('Update failed.')
     print(str(e))

experiments_face.py

@@ -0,0 +1,7 @@
+import cv2
+import extras.face_crop as cropper
+
+
+img = cv2.imread('lena.png')
+result = cropper.crop_image(img)
+cv2.imwrite('lena_result.png', result)

experiments_interrogate.py

@@ -0,0 +1,8 @@
+import cv2
+from extras.interrogate import default_interrogator as default_interrogator_photo
+from extras.wd14tagger import default_interrogator as default_interrogator_anime
+
+img = cv2.imread('./test_imgs/red_box.jpg')[:, :, ::-1].copy()
+print(default_interrogator_photo(img))
+img = cv2.imread('./test_imgs/miku.jpg')[:, :, ::-1].copy()
+print(default_interrogator_anime(img))

extras/BLIP/configs/bert_config.json

@@ -0,0 +1,21 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "type_vocab_size": 2,
+  "vocab_size": 30522,
+  "encoder_width": 768,
+  "add_cross_attention": true   
+}

extras/BLIP/configs/caption_coco.yaml

@@ -0,0 +1,33 @@
+image_root: '/export/share/datasets/vision/coco/images/'
+ann_root: 'annotation'
+coco_gt_root: 'annotation/coco_gt'
+
+# set pretrained as a file path or an url
+pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
+
+# size of vit model; base or large
+vit: 'base'
+vit_grad_ckpt: False
+vit_ckpt_layer: 0
+batch_size: 32
+init_lr: 1e-5
+
+# vit: 'large'
+# vit_grad_ckpt: True
+# vit_ckpt_layer: 5
+# batch_size: 16
+# init_lr: 2e-6
+
+image_size: 384
+
+# generation configs
+max_length: 20  
+min_length: 5
+num_beams: 3
+prompt: 'a picture of '
+
+# optimizer
+weight_decay: 0.05
+min_lr: 0
+max_epoch: 5
+

extras/BLIP/configs/med_config.json

@@ -0,0 +1,21 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "type_vocab_size": 2,
+  "vocab_size": 30524,
+  "encoder_width": 768,
+  "add_cross_attention": true   
+}

extras/BLIP/configs/nlvr.yaml

@@ -0,0 +1,21 @@
+image_root: '/export/share/datasets/vision/NLVR2/' 
+ann_root: 'annotation'
+
+# set pretrained as a file path or an url
+pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth'
+
+#size of vit model; base or large
+vit: 'base'
+batch_size_train: 16 
+batch_size_test: 64 
+vit_grad_ckpt: False
+vit_ckpt_layer: 0
+max_epoch: 15
+
+image_size: 384
+
+# optimizer
+weight_decay: 0.05
+init_lr: 3e-5
+min_lr: 0
+

extras/BLIP/configs/nocaps.yaml

@@ -0,0 +1,15 @@
+image_root: '/export/share/datasets/vision/nocaps/'
+ann_root: 'annotation'
+
+# set pretrained as a file path or an url
+pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
+
+vit: 'base'
+batch_size: 32
+
+image_size: 384
+
+max_length: 20
+min_length: 5
+num_beams: 3
+prompt: 'a picture of '

extras/BLIP/configs/pretrain.yaml

@@ -0,0 +1,27 @@
+train_file: ['/export/share/junnan-li/VL_pretrain/annotation/coco_karpathy_train.json',
+             '/export/share/junnan-li/VL_pretrain/annotation/vg_caption.json',
+             ]
+laion_path: ''   
+
+# size of vit model; base or large
+vit: 'base'
+vit_grad_ckpt: False
+vit_ckpt_layer: 0
+
+image_size: 224
+batch_size: 75
+
+queue_size: 57600
+alpha: 0.4
+
+# optimizer
+weight_decay: 0.05
+init_lr: 3e-4
+min_lr: 1e-6
+warmup_lr: 1e-6
+lr_decay_rate: 0.9
+max_epoch: 20
+warmup_steps: 3000
+
+
+

extras/BLIP/configs/retrieval_coco.yaml

@@ -0,0 +1,34 @@
+image_root: '/export/share/datasets/vision/coco/images/'
+ann_root: 'annotation'
+dataset: 'coco'
+
+# set pretrained as a file path or an url
+pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
+
+# size of vit model; base or large
+
+vit: 'base'
+batch_size_train: 32
+batch_size_test: 64
+vit_grad_ckpt: True
+vit_ckpt_layer: 4
+init_lr: 1e-5
+
+# vit: 'large'
+# batch_size_train: 16
+# batch_size_test: 32
+# vit_grad_ckpt: True
+# vit_ckpt_layer: 12
+# init_lr: 5e-6
+
+image_size: 384
+queue_size: 57600
+alpha: 0.4
+k_test: 256
+negative_all_rank: True
+
+# optimizer
+weight_decay: 0.05
+min_lr: 0
+max_epoch: 6
+

extras/BLIP/configs/retrieval_flickr.yaml

@@ -0,0 +1,34 @@
+image_root: '/export/share/datasets/vision/flickr30k/'
+ann_root: 'annotation'
+dataset: 'flickr'
+
+# set pretrained as a file path or an url
+pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth'
+
+# size of vit model; base or large
+
+vit: 'base'
+batch_size_train: 32
+batch_size_test: 64
+vit_grad_ckpt: True
+vit_ckpt_layer: 4
+init_lr: 1e-5
+
+# vit: 'large'
+# batch_size_train: 16
+# batch_size_test: 32
+# vit_grad_ckpt: True
+# vit_ckpt_layer: 10
+# init_lr: 5e-6
+
+image_size: 384
+queue_size: 57600
+alpha: 0.4
+k_test: 128
+negative_all_rank: False
+
+# optimizer
+weight_decay: 0.05
+min_lr: 0
+max_epoch: 6
+

extras/BLIP/configs/retrieval_msrvtt.yaml

@@ -0,0 +1,12 @@
+video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos'
+ann_root: 'annotation'
+
+# set pretrained as a file path or an url
+pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
+
+# size of vit model; base or large
+vit: 'base'
+batch_size: 64
+k_test: 128
+image_size: 384
+num_frm_test: 8

extras/BLIP/configs/vqa.yaml

@@ -0,0 +1,25 @@
+vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #followed by train2014/
+vg_root: '/export/share/datasets/vision/visual-genome/'  #followed by image/
+train_files: ['vqa_train','vqa_val','vg_qa']
+ann_root: 'annotation'
+
+# set pretrained as a file path or an url
+pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
+
+# size of vit model; base or large
+vit: 'base'
+batch_size_train: 16 
+batch_size_test: 32 
+vit_grad_ckpt: False
+vit_ckpt_layer: 0
+init_lr: 2e-5
+
+image_size: 480
+
+k_test: 128
+inference: 'rank'
+
+# optimizer
+weight_decay: 0.05
+min_lr: 0
+max_epoch: 10

extras/BLIP/models/bert_tokenizer/config.json

@@ -0,0 +1,23 @@
+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.6.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

extras/BLIP/models/bert_tokenizer/tokenizer_config.json

@@ -0,0 +1,3 @@
+{
+  "do_lower_case": true
+}

extras/BLIP/models/blip.py

@@ -0,0 +1,239 @@
+'''
+ * Copyright (c) 2022, salesforce.com, inc.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+ * By Junnan Li
+'''
+import warnings
+warnings.filterwarnings("ignore")
+
+from extras.BLIP.models.vit import VisionTransformer, interpolate_pos_embed
+from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel
+from transformers import BertTokenizer
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+import os
+from urllib.parse import urlparse
+from timm.models.hub import download_cached_file
+
+class BLIP_Base(nn.Module):
+    def __init__(self,                 
+                 med_config = 'configs/med_config.json',  
+                 image_size = 224,
+                 vit = 'base',
+                 vit_grad_ckpt = False,
+                 vit_ckpt_layer = 0,                 
+                 ):
+        """
+        Args:
+            med_config (str): path for the mixture of encoder-decoder model's configuration file
+            image_size (int): input image size
+            vit (str): model size of vision transformer
+        """               
+        super().__init__()
+        
+        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
+        self.tokenizer = init_tokenizer()   
+        med_config = BertConfig.from_json_file(med_config)
+        med_config.encoder_width = vision_width
+        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)  
+
+        
+    def forward(self, image, caption, mode):
+        
+        assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal"
+        text = self.tokenizer(caption, return_tensors="pt").to(image.device) 
+        
+        if mode=='image':    
+            # return image features
+            image_embeds = self.visual_encoder(image)             
+            return image_embeds
+        
+        elif mode=='text':
+            # return text features
+            text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      
+                                            return_dict = True, mode = 'text')  
+            return text_output.last_hidden_state
+        
+        elif mode=='multimodal':
+            # return multimodel features
+            image_embeds = self.visual_encoder(image)    
+            image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)      
+            
+            text.input_ids[:,0] = self.tokenizer.enc_token_id
+            output = self.text_encoder(text.input_ids,
+                                       attention_mask = text.attention_mask,
+                                       encoder_hidden_states = image_embeds,
+                                       encoder_attention_mask = image_atts,      
+                                       return_dict = True,
+                                      )              
+            return output.last_hidden_state
+        
+        
+        
+class BLIP_Decoder(nn.Module):
+    def __init__(self,                 
+                 med_config = 'configs/med_config.json',  
+                 image_size = 384,
+                 vit = 'base',
+                 vit_grad_ckpt = False,
+                 vit_ckpt_layer = 0,
+                 prompt = 'a picture of ',
+                 ):
+        """
+        Args:
+            med_config (str): path for the mixture of encoder-decoder model's configuration file
+            image_size (int): input image size
+            vit (str): model size of vision transformer
+        """            
+        super().__init__()
+        
+        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
+        self.tokenizer = init_tokenizer()   
+        med_config = BertConfig.from_json_file(med_config)
+        med_config.encoder_width = vision_width
+        self.text_decoder = BertLMHeadModel(config=med_config)    
+        
+        self.prompt = prompt
+        self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1
+
+        
+    def forward(self, image, caption):
+        
+        image_embeds = self.visual_encoder(image) 
+        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
+        
+        text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device) 
+        
+        text.input_ids[:,0] = self.tokenizer.bos_token_id
+        
+        decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100)         
+        decoder_targets[:,:self.prompt_length] = -100
+     
+        decoder_output = self.text_decoder(text.input_ids, 
+                                           attention_mask = text.attention_mask, 
+                                           encoder_hidden_states = image_embeds,
+                                           encoder_attention_mask = image_atts,                  
+                                           labels = decoder_targets,
+                                           return_dict = True,   
+                                          )   
+        loss_lm = decoder_output.loss
+        
+        return loss_lm
+        
+    def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0):
+        image_embeds = self.visual_encoder(image)
+
+        if not sample:
+            image_embeds = image_embeds.repeat_interleave(num_beams,dim=0)
+            
+        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
+        model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts}
+        
+        prompt = [self.prompt] * image.size(0)
+        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) 
+        input_ids[:,0] = self.tokenizer.bos_token_id
+        input_ids = input_ids[:, :-1] 
+
+        if sample:
+            #nucleus sampling
+            outputs = self.text_decoder.generate(input_ids=input_ids,
+                                                  max_length=max_length,
+                                                  min_length=min_length,
+                                                  do_sample=True,
+                                                  top_p=top_p,
+                                                  num_return_sequences=1,
+                                                  eos_token_id=self.tokenizer.sep_token_id,
+                                                  pad_token_id=self.tokenizer.pad_token_id, 
+                                                  repetition_penalty=1.1,                                            
+                                                  **model_kwargs)
+        else:
+            #beam search
+            outputs = self.text_decoder.generate(input_ids=input_ids,
+                                                  max_length=max_length,
+                                                  min_length=min_length,
+                                                  num_beams=num_beams,
+                                                  eos_token_id=self.tokenizer.sep_token_id,
+                                                  pad_token_id=self.tokenizer.pad_token_id,     
+                                                  repetition_penalty=repetition_penalty,
+                                                  **model_kwargs)            
+            
+        captions = []    
+        for output in outputs:
+            caption = self.tokenizer.decode(output, skip_special_tokens=True)    
+            captions.append(caption[len(self.prompt):])
+        return captions
+    
+
+def blip_decoder(pretrained='',**kwargs):
+    model = BLIP_Decoder(**kwargs)
+    if pretrained:
+        model,msg = load_checkpoint(model,pretrained)
+        assert(len(msg.missing_keys)==0)
+    return model    
+    
+def blip_feature_extractor(pretrained='',**kwargs):
+    model = BLIP_Base(**kwargs)
+    if pretrained:
+        model,msg = load_checkpoint(model,pretrained)
+        assert(len(msg.missing_keys)==0)
+    return model        
+
+def init_tokenizer():
+    tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "bert_tokenizer")
+    tokenizer = BertTokenizer.from_pretrained(tokenizer_path)
+    tokenizer.add_special_tokens({'bos_token':'[DEC]'})
+    tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})       
+    tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]  
+    return tokenizer
+
+
+def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
+        
+    assert vit in ['base', 'large'], "vit parameter must be base or large"
+    if vit=='base':
+        vision_width = 768
+        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, 
+                                           num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
+                                           drop_path_rate=0 or drop_path_rate
+                                          )   
+    elif vit=='large':
+        vision_width = 1024
+        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, 
+                                           num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
+                                           drop_path_rate=0.1 or drop_path_rate
+                                          )   
+    return visual_encoder, vision_width
+
+def is_url(url_or_filename):
+    parsed = urlparse(url_or_filename)
+    return parsed.scheme in ("http", "https")
+
+def load_checkpoint(model,url_or_filename):
+    if is_url(url_or_filename):
+        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
+        checkpoint = torch.load(cached_file, map_location='cpu') 
+    elif os.path.isfile(url_or_filename):        
+        checkpoint = torch.load(url_or_filename, map_location='cpu') 
+    else:
+        raise RuntimeError('checkpoint url or path is invalid')
+        
+    state_dict = checkpoint['model']
+    
+    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) 
+    if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
+        state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
+                                                                         model.visual_encoder_m)    
+    for key in model.state_dict().keys():
+        if key in state_dict.keys():
+            if state_dict[key].shape!=model.state_dict()[key].shape:
+                del state_dict[key]
+    
+    msg = model.load_state_dict(state_dict,strict=False)
+    print('load checkpoint from %s'%url_or_filename)  
+    return model,msg
+    

extras/BLIP/models/blip_itm.py

@@ -0,0 +1,76 @@
+from extras.BLIP.models.med import BertConfig, BertModel
+from transformers import BertTokenizer
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
+
+class BLIP_ITM(nn.Module):
+    def __init__(self,                 
+                 med_config = 'configs/med_config.json',  
+                 image_size = 384,
+                 vit = 'base',
+                 vit_grad_ckpt = False,
+                 vit_ckpt_layer = 0,                      
+                 embed_dim = 256,     
+                 ):
+        """
+        Args:
+            med_config (str): path for the mixture of encoder-decoder model's configuration file
+            image_size (int): input image size
+            vit (str): model size of vision transformer
+        """               
+        super().__init__()
+        
+        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
+        self.tokenizer = init_tokenizer()   
+        med_config = BertConfig.from_json_file(med_config)
+        med_config.encoder_width = vision_width
+        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)          
+
+        text_width = self.text_encoder.config.hidden_size
+        
+        self.vision_proj = nn.Linear(vision_width, embed_dim)
+        self.text_proj = nn.Linear(text_width, embed_dim)
+
+        self.itm_head = nn.Linear(text_width, 2) 
+        
+        
+    def forward(self, image, caption, match_head='itm'):
+
+        image_embeds = self.visual_encoder(image) 
+        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
+      
+        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, 
+                              return_tensors="pt").to(image.device) 
+
+                 
+        if match_head=='itm':
+            output = self.text_encoder(text.input_ids,
+                                       attention_mask = text.attention_mask,
+                                       encoder_hidden_states = image_embeds,
+                                       encoder_attention_mask = image_atts,      
+                                       return_dict = True,
+                                      )
+            itm_output = self.itm_head(output.last_hidden_state[:,0,:])     
+            return itm_output
+            
+        elif match_head=='itc':
+            text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      
+                                            return_dict = True, mode = 'text')                     
+            image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)   
+            text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)    
+            
+            sim = image_feat @ text_feat.t()
+            return sim
+        
+        
+def blip_itm(pretrained='',**kwargs):
+    model = BLIP_ITM(**kwargs)
+    if pretrained:
+        model,msg = load_checkpoint(model,pretrained)
+        assert(len(msg.missing_keys)==0)
+    return model         
+            

extras/BLIP/models/blip_nlvr.py

@@ -0,0 +1,105 @@
+from extras.BLIP.models.med import BertConfig
+from extras.BLIP.models.nlvr_encoder import BertModel
+from extras.BLIP.models.vit import interpolate_pos_embed
+from extras.BLIP.models.blip import create_vit, init_tokenizer, is_url
+
+from timm.models.hub import download_cached_file
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from transformers import BertTokenizer
+import numpy as np
+import os
+
+
+class BLIP_NLVR(nn.Module):
+    def __init__(self,                 
+                 med_config = 'configs/med_config.json',  
+                 image_size = 480,
+                 vit = 'base',
+                 vit_grad_ckpt = False,
+                 vit_ckpt_layer = 0,                   
+                 ):
+        """
+        Args:
+            med_config (str): path for the mixture of encoder-decoder model's configuration file
+            image_size (int): input image size
+            vit (str): model size of vision transformer
+        """               
+        super().__init__()
+        
+        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1)
+        self.tokenizer = init_tokenizer()   
+        med_config = BertConfig.from_json_file(med_config)
+        med_config.encoder_width = vision_width
+        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) 
+                    
+        self.cls_head = nn.Sequential(
+                  nn.Linear(self.text_encoder.config.hidden_size, self.text_encoder.config.hidden_size),
+                  nn.ReLU(),
+                  nn.Linear(self.text_encoder.config.hidden_size, 2)
+                )  
+
+    def forward(self, image, text, targets, train=True):
+        
+        image_embeds = self.visual_encoder(image) 
+        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
+        image0_embeds, image1_embeds = torch.split(image_embeds,targets.size(0))     
+
+        text = self.tokenizer(text, padding='longest', return_tensors="pt").to(image.device) 
+        text.input_ids[:,0] = self.tokenizer.enc_token_id        
+
+        output = self.text_encoder(text.input_ids, 
+                                   attention_mask = text.attention_mask, 
+                                   encoder_hidden_states = [image0_embeds,image1_embeds],
+                                   encoder_attention_mask = [image_atts[:image0_embeds.size(0)],
+                                                             image_atts[image0_embeds.size(0):]],        
+                                   return_dict = True,
+                                  )  
+        hidden_state = output.last_hidden_state[:,0,:]        
+        prediction = self.cls_head(hidden_state)
+
+        if train:            
+            loss = F.cross_entropy(prediction, targets)   
+            return loss
+        else:
+            return prediction
+    
+def blip_nlvr(pretrained='',**kwargs):
+    model = BLIP_NLVR(**kwargs)
+    if pretrained:
+        model,msg = load_checkpoint(model,pretrained)
+        print("missing keys:")
+        print(msg.missing_keys)
+    return model  
+
+        
+def load_checkpoint(model,url_or_filename):
+    if is_url(url_or_filename):
+        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
+        checkpoint = torch.load(cached_file, map_location='cpu') 
+    elif os.path.isfile(url_or_filename):        
+        checkpoint = torch.load(url_or_filename, map_location='cpu') 
+    else:
+        raise RuntimeError('checkpoint url or path is invalid')
+    state_dict = checkpoint['model']
+    
+    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) 
+    
+    for key in list(state_dict.keys()):
+        if 'crossattention.self.' in key:
+            new_key0 = key.replace('self','self0')
+            new_key1 = key.replace('self','self1')
+            state_dict[new_key0] = state_dict[key]
+            state_dict[new_key1] = state_dict[key]
+        elif 'crossattention.output.dense.' in key:
+            new_key0 = key.replace('dense','dense0')
+            new_key1 = key.replace('dense','dense1')
+            state_dict[new_key0] = state_dict[key]
+            state_dict[new_key1] = state_dict[key]  
+                
+    msg = model.load_state_dict(state_dict,strict=False)
+    print('load checkpoint from %s'%url_or_filename)  
+    return model,msg
+            

extras/BLIP/models/blip_pretrain.py

@@ -0,0 +1,339 @@
+'''
+ * Copyright (c) 2022, salesforce.com, inc.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+ * By Junnan Li
+'''
+from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel
+from transformers import BertTokenizer
+import transformers
+transformers.logging.set_verbosity_error()
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
+
+class BLIP_Pretrain(nn.Module):
+    def __init__(self,                 
+                 med_config = 'configs/bert_config.json',  
+                 image_size = 224,
+                 vit = 'base',
+                 vit_grad_ckpt = False,
+                 vit_ckpt_layer = 0,                    
+                 embed_dim = 256,     
+                 queue_size = 57600,
+                 momentum = 0.995,
+                 ):
+        """
+        Args:
+            med_config (str): path for the mixture of encoder-decoder model's configuration file
+            image_size (int): input image size
+            vit (str): model size of vision transformer
+        """               
+        super().__init__()
+        
+        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0)
+        
+        if vit=='base':
+            checkpoint = torch.hub.load_state_dict_from_url(
+                url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
+                map_location="cpu", check_hash=True)
+            state_dict = checkpoint["model"]     
+            msg = self.visual_encoder.load_state_dict(state_dict,strict=False)
+        elif vit=='large':
+            from timm.models.helpers import load_custom_pretrained
+            from timm.models.vision_transformer import default_cfgs
+            load_custom_pretrained(self.visual_encoder,default_cfgs['vit_large_patch16_224_in21k'])        
+               
+        self.tokenizer = init_tokenizer()   
+        encoder_config = BertConfig.from_json_file(med_config)
+        encoder_config.encoder_width = vision_width
+        self.text_encoder = BertModel.from_pretrained('bert-base-uncased',config=encoder_config, add_pooling_layer=False)
+        self.text_encoder.resize_token_embeddings(len(self.tokenizer)) 
+
+        text_width = self.text_encoder.config.hidden_size
+        
+        self.vision_proj = nn.Linear(vision_width, embed_dim)
+        self.text_proj = nn.Linear(text_width, embed_dim)
+
+        self.itm_head = nn.Linear(text_width, 2) 
+        
+        # create momentum encoders  
+        self.visual_encoder_m, vision_width = create_vit(vit,image_size)              
+        self.vision_proj_m = nn.Linear(vision_width, embed_dim)
+        self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False)      
+        self.text_proj_m = nn.Linear(text_width, embed_dim)
+        
+        self.model_pairs = [[self.visual_encoder,self.visual_encoder_m],
+                            [self.vision_proj,self.vision_proj_m],
+                            [self.text_encoder,self.text_encoder_m],
+                            [self.text_proj,self.text_proj_m],
+                           ]       
+        self.copy_params()
+
+        # create the queue
+        self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
+        self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
+        self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long))  
+
+        self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
+        self.text_queue = nn.functional.normalize(self.text_queue, dim=0)
+        
+        self.queue_size = queue_size
+        self.momentum = momentum
+        self.temp = nn.Parameter(0.07*torch.ones([]))   
+        
+        # create the decoder
+        decoder_config = BertConfig.from_json_file(med_config)
+        decoder_config.encoder_width = vision_width        
+        self.text_decoder = BertLMHeadModel.from_pretrained('bert-base-uncased',config=decoder_config)    
+        self.text_decoder.resize_token_embeddings(len(self.tokenizer)) 
+        tie_encoder_decoder_weights(self.text_encoder,self.text_decoder.bert,'','/attention')
+        
+        
+    def forward(self, image, caption, alpha):
+        with torch.no_grad():
+            self.temp.clamp_(0.001,0.5)
+        
+        image_embeds = self.visual_encoder(image) 
+        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
+        image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)          
+        
+        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=30, 
+                              return_tensors="pt").to(image.device)  
+        text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      
+                                        return_dict = True, mode = 'text')            
+        text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)                 
+             
+        # get momentum features
+        with torch.no_grad():
+            self._momentum_update()
+            image_embeds_m = self.visual_encoder_m(image) 
+            image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1)  
+            image_feat_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1)                   
+            
+            text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask,                      
+                                                return_dict = True, mode = 'text')    
+            text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) 
+            text_feat_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1)
+
+            sim_i2t_m = image_feat_m @ text_feat_all / self.temp  
+            sim_t2i_m = text_feat_m @ image_feat_all / self.temp 
+
+            sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device)
+            sim_targets.fill_diagonal_(1)          
+
+            sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
+            sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets        
+
+        sim_i2t = image_feat @ text_feat_all / self.temp
+        sim_t2i = text_feat @ image_feat_all / self.temp
+                             
+        loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean()
+        loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() 
+
+        loss_ita = (loss_i2t+loss_t2i)/2
+
+        self._dequeue_and_enqueue(image_feat_m, text_feat_m)        
+
+        ###============== Image-text Matching ===================###
+        encoder_input_ids = text.input_ids.clone()
+        encoder_input_ids[:,0] = self.tokenizer.enc_token_id
+        
+        # forward the positve image-text pair
+        bs = image.size(0)
+        output_pos = self.text_encoder(encoder_input_ids,
+                                       attention_mask = text.attention_mask,
+                                       encoder_hidden_states = image_embeds,
+                                       encoder_attention_mask = image_atts,      
+                                       return_dict = True,
+                                      )            
+        with torch.no_grad():       
+            weights_t2i = F.softmax(sim_t2i[:,:bs],dim=1)+1e-4 
+            weights_t2i.fill_diagonal_(0)            
+            weights_i2t = F.softmax(sim_i2t[:,:bs],dim=1)+1e-4  
+            weights_i2t.fill_diagonal_(0)   
+            
+        # select a negative image for each text
+        image_embeds_neg = []    
+        for b in range(bs):
+            neg_idx = torch.multinomial(weights_t2i[b], 1).item()
+            image_embeds_neg.append(image_embeds[neg_idx])
+        image_embeds_neg = torch.stack(image_embeds_neg,dim=0)   
+
+        # select a negative text for each image
+        text_ids_neg = []
+        text_atts_neg = []
+        for b in range(bs):
+            neg_idx = torch.multinomial(weights_i2t[b], 1).item()
+            text_ids_neg.append(encoder_input_ids[neg_idx])
+            text_atts_neg.append(text.attention_mask[neg_idx])
+
+        text_ids_neg = torch.stack(text_ids_neg,dim=0)   
+        text_atts_neg = torch.stack(text_atts_neg,dim=0)      
+
+        text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0)     
+        text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0)     
+
+        image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0)
+        image_atts_all = torch.cat([image_atts,image_atts],dim=0)
+
+        output_neg = self.text_encoder(text_ids_all,
+                                       attention_mask = text_atts_all,
+                                       encoder_hidden_states = image_embeds_all,
+                                       encoder_attention_mask = image_atts_all,      
+                                       return_dict = True,
+                                      )                            
+
+        vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0)
+        vl_output = self.itm_head(vl_embeddings)            
+
+        itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)],
+                               dim=0).to(image.device)
+        loss_itm = F.cross_entropy(vl_output, itm_labels)  
+        
+        ##================= LM ========================##     
+        decoder_input_ids = text.input_ids.clone()      
+        decoder_input_ids[:,0] = self.tokenizer.bos_token_id
+        decoder_targets = decoder_input_ids.masked_fill(decoder_input_ids == self.tokenizer.pad_token_id, -100) 
+
+        decoder_output = self.text_decoder(decoder_input_ids, 
+                                           attention_mask = text.attention_mask, 
+                                           encoder_hidden_states = image_embeds,
+                                           encoder_attention_mask = image_atts,                  
+                                           labels = decoder_targets,
+                                           return_dict = True,   
+                                          )   
+          
+        loss_lm = decoder_output.loss                
+        return loss_ita, loss_itm, loss_lm
+ 
+
+
+    @torch.no_grad()    
+    def copy_params(self):
+        for model_pair in self.model_pairs:           
+            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
+                param_m.data.copy_(param.data)  # initialize
+                param_m.requires_grad = False  # not update by gradient    
+
+            
+    @torch.no_grad()        
+    def _momentum_update(self):
+        for model_pair in self.model_pairs:           
+            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
+                param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum)
+
+                        
+    @torch.no_grad()
+    def _dequeue_and_enqueue(self, image_feat, text_feat):
+        # gather keys before updating queue
+        image_feats = concat_all_gather(image_feat)
+        text_feats = concat_all_gather(text_feat)
+
+        batch_size = image_feats.shape[0]
+
+        ptr = int(self.queue_ptr)
+        assert self.queue_size % batch_size == 0  # for simplicity
+
+        # replace the keys at ptr (dequeue and enqueue)
+        self.image_queue[:, ptr:ptr + batch_size] = image_feats.T
+        self.text_queue[:, ptr:ptr + batch_size] = text_feats.T
+        ptr = (ptr + batch_size) % self.queue_size  # move pointer
+
+        self.queue_ptr[0] = ptr 
+
+
+def blip_pretrain(**kwargs):
+    model = BLIP_Pretrain(**kwargs)
+    return model 
+
+
+@torch.no_grad()
+def concat_all_gather(tensor):
+    """
+    Performs all_gather operation on the provided tensors.
+    *** Warning ***: torch.distributed.all_gather has no gradient.
+    """
+    tensors_gather = [torch.ones_like(tensor)
+        for _ in range(torch.distributed.get_world_size())]
+    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
+
+    output = torch.cat(tensors_gather, dim=0)
+    return output     
+
+
+from typing import List
+def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str):
+    uninitialized_encoder_weights: List[str] = []
+    if decoder.__class__ != encoder.__class__:
+        print(
+            f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
+        )
+
+    def tie_encoder_to_decoder_recursively(
+        decoder_pointer: nn.Module,
+        encoder_pointer: nn.Module,
+        module_name: str,
+        uninitialized_encoder_weights: List[str],
+        skip_key: str,
+        depth=0,
+    ):
+        assert isinstance(decoder_pointer, nn.Module) and isinstance(
+            encoder_pointer, nn.Module
+        ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module"
+        if hasattr(decoder_pointer, "weight") and skip_key not in module_name:
+            assert hasattr(encoder_pointer, "weight")
+            encoder_pointer.weight = decoder_pointer.weight
+            if hasattr(decoder_pointer, "bias"):
+                assert hasattr(encoder_pointer, "bias")
+                encoder_pointer.bias = decoder_pointer.bias                
+            print(module_name+' is tied')    
+            return
+
+        encoder_modules = encoder_pointer._modules
+        decoder_modules = decoder_pointer._modules
+        if len(decoder_modules) > 0:
+            assert (
+                len(encoder_modules) > 0
+            ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
+
+            all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()])
+            encoder_layer_pos = 0
+            for name, module in decoder_modules.items():
+                if name.isdigit():
+                    encoder_name = str(int(name) + encoder_layer_pos)
+                    decoder_name = name
+                    if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len(
+                        encoder_modules
+                    ) != len(decoder_modules):
+                        # this can happen if the name corresponds to the position in a list module list of layers
+                        # in this case the decoder has added a cross-attention that the encoder does not have
+                        # thus skip this step and subtract one layer pos from encoder
+                        encoder_layer_pos -= 1
+                        continue
+                elif name not in encoder_modules:
+                    continue
+                elif depth > 500:
+                    raise ValueError(
+                        "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model."
+                    )
+                else:
+                    decoder_name = encoder_name = name
+                tie_encoder_to_decoder_recursively(
+                    decoder_modules[decoder_name],
+                    encoder_modules[encoder_name],
+                    module_name + "/" + name,
+                    uninitialized_encoder_weights,
+                    skip_key,
+                    depth=depth + 1,
+                )
+                all_encoder_weights.remove(module_name + "/" + encoder_name)
+
+            uninitialized_encoder_weights += list(all_encoder_weights)
+
+    # tie weights recursively
+    tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key)  

extras/BLIP/models/blip_retrieval.py

@@ -0,0 +1,319 @@
+from extras.BLIP.models.med import BertConfig, BertModel
+from transformers import BertTokenizer
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
+
+class BLIP_Retrieval(nn.Module):
+    def __init__(self,                 
+                 med_config = 'configs/med_config.json',  
+                 image_size = 384,
+                 vit = 'base',
+                 vit_grad_ckpt = False,
+                 vit_ckpt_layer = 0,                      
+                 embed_dim = 256,     
+                 queue_size = 57600,
+                 momentum = 0.995,
+                 negative_all_rank = False,
+                 ):
+        """
+        Args:
+            med_config (str): path for the mixture of encoder-decoder model's configuration file
+            image_size (int): input image size
+            vit (str): model size of vision transformer
+        """               
+        super().__init__()
+        
+        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
+        self.tokenizer = init_tokenizer()   
+        med_config = BertConfig.from_json_file(med_config)
+        med_config.encoder_width = vision_width
+        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)          
+
+        text_width = self.text_encoder.config.hidden_size
+        
+        self.vision_proj = nn.Linear(vision_width, embed_dim)
+        self.text_proj = nn.Linear(text_width, embed_dim)
+
+        self.itm_head = nn.Linear(text_width, 2) 
+        
+        # create momentum encoders  
+        self.visual_encoder_m, vision_width = create_vit(vit,image_size)              
+        self.vision_proj_m = nn.Linear(vision_width, embed_dim)
+        self.text_encoder_m = BertModel(config=med_config, add_pooling_layer=False)    
+        self.text_proj_m = nn.Linear(text_width, embed_dim)
+        
+        self.model_pairs = [[self.visual_encoder,self.visual_encoder_m],
+                            [self.vision_proj,self.vision_proj_m],
+                            [self.text_encoder,self.text_encoder_m],
+                            [self.text_proj,self.text_proj_m],
+                           ]       
+        self.copy_params()
+
+        # create the queue
+        self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
+        self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
+        self.register_buffer("idx_queue", torch.full((1,queue_size),-100))
+        self.register_buffer("ptr_queue", torch.zeros(1, dtype=torch.long))  
+
+        self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
+        self.text_queue = nn.functional.normalize(self.text_queue, dim=0)
+        
+        self.queue_size = queue_size
+        self.momentum = momentum
+        self.temp = nn.Parameter(0.07*torch.ones([]))   
+        
+        self.negative_all_rank = negative_all_rank
+        
+        
+    def forward(self, image, caption, alpha, idx):
+        with torch.no_grad():
+            self.temp.clamp_(0.001,0.5)
+        
+        image_embeds = self.visual_encoder(image) 
+        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
+        image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)    
+        
+        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, 
+                              return_tensors="pt").to(image.device) 
+        
+        text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      
+                                        return_dict = True, mode = 'text')            
+        text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)        
+        
+        ###============== Image-text Contrastive Learning ===================###
+        idx = idx.view(-1,1)
+        idx_all = torch.cat([idx.t(), self.idx_queue.clone().detach()],dim=1)  
+        pos_idx = torch.eq(idx, idx_all).float()       
+        sim_targets = pos_idx / pos_idx.sum(1,keepdim=True)   
+        
+        # get momentum features
+        with torch.no_grad():
+            self._momentum_update()
+            image_embeds_m = self.visual_encoder_m(image) 
+            image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1)  
+            image_feat_m_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1)                   
+            
+            text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask,                      
+                                                return_dict = True, mode = 'text')    
+            text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) 
+            text_feat_m_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1)
+
+            sim_i2t_m = image_feat_m @ text_feat_m_all / self.temp  
+            sim_t2i_m = text_feat_m @ image_feat_m_all / self.temp   
+
+            sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
+            sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets        
+
+        sim_i2t = image_feat @ text_feat_m_all / self.temp 
+        sim_t2i = text_feat @ image_feat_m_all / self.temp 
+                             
+        loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean()
+        loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() 
+
+        loss_ita = (loss_i2t+loss_t2i)/2
+        
+        idxs = concat_all_gather(idx)
+        self._dequeue_and_enqueue(image_feat_m, text_feat_m, idxs)        
+
+        ###============== Image-text Matching ===================###
+        encoder_input_ids = text.input_ids.clone()
+        encoder_input_ids[:,0] = self.tokenizer.enc_token_id
+
+        # forward the positve image-text pair
+        bs = image.size(0)
+        output_pos = self.text_encoder(encoder_input_ids,
+                                       attention_mask = text.attention_mask,
+                                       encoder_hidden_states = image_embeds,
+                                       encoder_attention_mask = image_atts,      
+                                       return_dict = True,
+                                      )  
+        
+        
+        if self.negative_all_rank:    
+            # compute sample similarity
+            with torch.no_grad():                
+                mask = torch.eq(idx, idxs.t())
+
+                image_feat_world = concat_all_gather(image_feat)
+                text_feat_world = concat_all_gather(text_feat)
+
+                sim_i2t = image_feat @ text_feat_world.t() / self.temp 
+                sim_t2i = text_feat @ image_feat_world.t() / self.temp 
+
+                weights_i2t = F.softmax(sim_i2t,dim=1)
+                weights_i2t.masked_fill_(mask, 0)            
+
+                weights_t2i = F.softmax(sim_t2i,dim=1)
+                weights_t2i.masked_fill_(mask, 0)     
+
+            image_embeds_world = all_gather_with_grad(image_embeds) 
+
+            # select a negative image (from all ranks) for each text
+            image_embeds_neg = []    
+            for b in range(bs):
+                neg_idx = torch.multinomial(weights_t2i[b], 1).item()
+                image_embeds_neg.append(image_embeds_world[neg_idx])
+            image_embeds_neg = torch.stack(image_embeds_neg,dim=0)   
+
+            # select a negative text (from all ranks) for each image
+            input_ids_world = concat_all_gather(encoder_input_ids)
+            att_mask_world = concat_all_gather(text.attention_mask)        
+
+            text_ids_neg = []
+            text_atts_neg = []
+            for b in range(bs):
+                neg_idx = torch.multinomial(weights_i2t[b], 1).item()
+                text_ids_neg.append(input_ids_world[neg_idx])
+                text_atts_neg.append(att_mask_world[neg_idx])
+                
+        else:
+            with torch.no_grad():                
+                mask = torch.eq(idx, idx.t())
+                
+                sim_i2t = image_feat @ text_feat.t() / self.temp 
+                sim_t2i = text_feat @ image_feat.t() / self.temp 
+
+                weights_i2t = F.softmax(sim_i2t,dim=1)
+                weights_i2t.masked_fill_(mask, 0)            
+
+                weights_t2i = F.softmax(sim_t2i,dim=1)
+                weights_t2i.masked_fill_(mask, 0)     
+
+            # select a negative image (from same rank) for each text
+            image_embeds_neg = []    
+            for b in range(bs):
+                neg_idx = torch.multinomial(weights_t2i[b], 1).item()
+                image_embeds_neg.append(image_embeds[neg_idx])
+            image_embeds_neg = torch.stack(image_embeds_neg,dim=0)   
+
+            # select a negative text (from same rank) for each image    
+            text_ids_neg = []
+            text_atts_neg = []
+            for b in range(bs):
+                neg_idx = torch.multinomial(weights_i2t[b], 1).item()
+                text_ids_neg.append(encoder_input_ids[neg_idx])
+                text_atts_neg.append(text.attention_mask[neg_idx])            
+            
+        text_ids_neg = torch.stack(text_ids_neg,dim=0)   
+        text_atts_neg = torch.stack(text_atts_neg,dim=0)      
+
+        text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0)     
+        text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0)     
+
+        image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0)
+        image_atts_all = torch.cat([image_atts,image_atts],dim=0)
+
+        output_neg = self.text_encoder(text_ids_all,
+                                       attention_mask = text_atts_all,
+                                       encoder_hidden_states = image_embeds_all,
+                                       encoder_attention_mask = image_atts_all,      
+                                       return_dict = True,
+                                      )                         
+          
+
+        vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0)
+        vl_output = self.itm_head(vl_embeddings)            
+
+        itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)],
+                               dim=0).to(image.device)
+        loss_itm = F.cross_entropy(vl_output, itm_labels)     
+
+        return loss_ita, loss_itm 
+ 
+
+    @torch.no_grad()    
+    def copy_params(self):
+        for model_pair in self.model_pairs:           
+            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
+                param_m.data.copy_(param.data)  # initialize
+                param_m.requires_grad = False  # not update by gradient    
+
+            
+    @torch.no_grad()        
+    def _momentum_update(self):
+        for model_pair in self.model_pairs:           
+            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
+                param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum)
+                
+                
+    @torch.no_grad()
+    def _dequeue_and_enqueue(self, image_feat, text_feat, idxs):
+        # gather keys before updating queue
+        image_feats = concat_all_gather(image_feat)
+        text_feats = concat_all_gather(text_feat)
+        
+
+        batch_size = image_feats.shape[0]
+
+        ptr = int(self.ptr_queue)
+        assert self.queue_size % batch_size == 0  # for simplicity
+
+        # replace the keys at ptr (dequeue and enqueue)
+        self.image_queue[:, ptr:ptr + batch_size] = image_feats.T
+        self.text_queue[:, ptr:ptr + batch_size] = text_feats.T
+        self.idx_queue[:, ptr:ptr + batch_size] = idxs.T
+        ptr = (ptr + batch_size) % self.queue_size # move pointer
+
+        self.ptr_queue[0] = ptr  
+
+
+def blip_retrieval(pretrained='',**kwargs):
+    model = BLIP_Retrieval(**kwargs)
+    if pretrained:
+        model,msg = load_checkpoint(model,pretrained)
+        print("missing keys:")
+        print(msg.missing_keys)
+    return model 
+
+
+@torch.no_grad()
+def concat_all_gather(tensor):
+    """
+    Performs all_gather operation on the provided tensors.
+    *** Warning ***: torch.distributed.all_gather has no gradient.
+    """
+    tensors_gather = [torch.ones_like(tensor)
+        for _ in range(torch.distributed.get_world_size())]
+    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
+
+    output = torch.cat(tensors_gather, dim=0)
+    return output      
+
+
+class GatherLayer(torch.autograd.Function):
+    """
+    Gather tensors from all workers with support for backward propagation:
+    This implementation does not cut the gradients as torch.distributed.all_gather does.
+    """
+
+    @staticmethod
+    def forward(ctx, x):
+        output = [torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())]
+        torch.distributed.all_gather(output, x)
+        return tuple(output)
+
+    @staticmethod
+    def backward(ctx, *grads):
+        all_gradients = torch.stack(grads)
+        torch.distributed.all_reduce(all_gradients)
+        return all_gradients[torch.distributed.get_rank()]
+
+
+def all_gather_with_grad(tensors):
+    """
+    Performs all_gather operation on the provided tensors.
+    Graph remains connected for backward grad computation.
+    """
+    # Queue the gathered tensors
+    world_size = torch.distributed.get_world_size()
+    # There is no need for reduction in the single-proc case
+    if world_size == 1:
+        return tensors
+
+    tensor_all = GatherLayer.apply(tensors)
+
+    return torch.cat(tensor_all, dim=0)

extras/BLIP/models/blip_vqa.py

@@ -0,0 +1,186 @@
+from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from transformers import BertTokenizer
+import numpy as np
+
+class BLIP_VQA(nn.Module):
+    def __init__(self,                 
+                 med_config = 'configs/med_config.json',  
+                 image_size = 480,
+                 vit = 'base',
+                 vit_grad_ckpt = False,
+                 vit_ckpt_layer = 0,                   
+                 ):
+        """
+        Args:
+            med_config (str): path for the mixture of encoder-decoder model's configuration file
+            image_size (int): input image size
+            vit (str): model size of vision transformer
+        """               
+        super().__init__()
+        
+        self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1)
+        self.tokenizer = init_tokenizer()  
+        
+        encoder_config = BertConfig.from_json_file(med_config)
+        encoder_config.encoder_width = vision_width
+        self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) 
+        
+        decoder_config = BertConfig.from_json_file(med_config)        
+        self.text_decoder = BertLMHeadModel(config=decoder_config)          
+
+
+    def forward(self, image, question, answer=None, n=None, weights=None, train=True, inference='rank', k_test=128):
+        
+        image_embeds = self.visual_encoder(image) 
+        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
+        
+        question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, 
+                                  return_tensors="pt").to(image.device) 
+        question.input_ids[:,0] = self.tokenizer.enc_token_id
+        
+        if train:               
+            '''
+            n: number of answers for each question
+            weights: weight for each answer
+            '''                     
+            answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device) 
+            answer.input_ids[:,0] = self.tokenizer.bos_token_id
+            answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100)      
+
+            question_output = self.text_encoder(question.input_ids, 
+                                                attention_mask = question.attention_mask, 
+                                                encoder_hidden_states = image_embeds,
+                                                encoder_attention_mask = image_atts,                             
+                                                return_dict = True)    
+
+            question_states = []                
+            question_atts = []  
+            for b, n in enumerate(n):
+                question_states += [question_output.last_hidden_state[b]]*n
+                question_atts += [question.attention_mask[b]]*n                
+            question_states = torch.stack(question_states,0)    
+            question_atts = torch.stack(question_atts,0)     
+
+            answer_output = self.text_decoder(answer.input_ids, 
+                                              attention_mask = answer.attention_mask, 
+                                              encoder_hidden_states = question_states,
+                                              encoder_attention_mask = question_atts,                  
+                                              labels = answer_targets,
+                                              return_dict = True,   
+                                              reduction = 'none',
+                                             )      
+            
+            loss = weights * answer_output.loss
+            loss = loss.sum()/image.size(0)
+
+            return loss
+            
+
+        else: 
+            question_output = self.text_encoder(question.input_ids, 
+                                                attention_mask = question.attention_mask, 
+                                                encoder_hidden_states = image_embeds,
+                                                encoder_attention_mask = image_atts,                                    
+                                                return_dict = True) 
+            
+            if inference=='generate':
+                num_beams = 3
+                question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0)
+                question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device)
+                model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts}
+                
+                bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device)
+                
+                outputs = self.text_decoder.generate(input_ids=bos_ids,
+                                                     max_length=10,
+                                                     min_length=1,
+                                                     num_beams=num_beams,
+                                                     eos_token_id=self.tokenizer.sep_token_id,
+                                                     pad_token_id=self.tokenizer.pad_token_id, 
+                                                     **model_kwargs)
+                
+                answers = []    
+                for output in outputs:
+                    answer = self.tokenizer.decode(output, skip_special_tokens=True)    
+                    answers.append(answer)
+                return answers
+            
+            elif inference=='rank':
+                max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, 
+                                           answer.input_ids, answer.attention_mask, k_test) 
+                return max_ids
+ 
+                
+                
+    def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k):
+        
+        num_ques = question_states.size(0)
+        start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token
+        
+        start_output = self.text_decoder(start_ids, 
+                                         encoder_hidden_states = question_states,
+                                         encoder_attention_mask = question_atts,                                      
+                                         return_dict = True,
+                                         reduction = 'none')              
+        logits = start_output.logits[:,0,:] # first token's logit
+        
+        # topk_probs: top-k probability 
+        # topk_ids: [num_question, k]        
+        answer_first_token = answer_ids[:,1]
+        prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) 
+        topk_probs, topk_ids = prob_first_token.topk(k,dim=1) 
+        
+        # answer input: [num_question*k, answer_len]                 
+        input_ids = []
+        input_atts = []
+        for b, topk_id in enumerate(topk_ids):
+            input_ids.append(answer_ids.index_select(dim=0, index=topk_id))
+            input_atts.append(answer_atts.index_select(dim=0, index=topk_id))
+        input_ids = torch.cat(input_ids,dim=0)  
+        input_atts = torch.cat(input_atts,dim=0)  
+
+        targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100)
+
+        # repeat encoder's output for top-k answers
+        question_states = tile(question_states, 0, k)
+        question_atts = tile(question_atts, 0, k)
+        
+        output = self.text_decoder(input_ids, 
+                                   attention_mask = input_atts, 
+                                   encoder_hidden_states = question_states,
+                                   encoder_attention_mask = question_atts,     
+                                   labels = targets_ids,
+                                   return_dict = True, 
+                                   reduction = 'none')   
+        
+        log_probs_sum = -output.loss
+        log_probs_sum = log_probs_sum.view(num_ques,k)
+
+        max_topk_ids = log_probs_sum.argmax(dim=1) 
+        max_ids = topk_ids[max_topk_ids>=0,max_topk_ids]
+
+        return max_ids
+    
+    
+def blip_vqa(pretrained='',**kwargs):
+    model = BLIP_VQA(**kwargs)
+    if pretrained:
+        model,msg = load_checkpoint(model,pretrained)
+#         assert(len(msg.missing_keys)==0)
+    return model  
+
+
+def tile(x, dim, n_tile):
+    init_dim = x.size(dim)
+    repeat_idx = [1] * x.dim()
+    repeat_idx[dim] = n_tile
+    x = x.repeat(*(repeat_idx))
+    order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)]))
+    return torch.index_select(x, dim, order_index.to(x.device))    
+        
+        

extras/BLIP/models/med.py

@@ -0,0 +1,955 @@
+'''
+ * Copyright (c) 2022, salesforce.com, inc.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+ * By Junnan Li
+ * Based on huggingface code base
+ * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
+'''
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor, device, dtype, nn
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+import torch.nn.functional as F
+
+from transformers.activations import ACT2FN
+from transformers.file_utils import (
+    ModelOutput,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    NextSentencePredictorOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import (
+    PreTrainedModel,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
+from transformers.utils import logging
+from transformers.models.bert.configuration_bert import BertConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word and position embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        
+        self.config = config
+
+    def forward(
+        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
+    ):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        embeddings = inputs_embeds
+
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings += position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config, is_cross_attention):
+        super().__init__()
+        self.config = config
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+            )
+        
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        if is_cross_attention:
+            self.key = nn.Linear(config.encoder_width, self.all_head_size)
+            self.value = nn.Linear(config.encoder_width, self.all_head_size)
+        else:
+            self.key = nn.Linear(config.hidden_size, self.all_head_size)
+            self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
+        self.save_attention = False   
+            
+    def save_attn_gradients(self, attn_gradients):
+        self.attn_gradients = attn_gradients
+        
+    def get_attn_gradients(self):
+        return self.attn_gradients
+    
+    def save_attention_map(self, attention_map):
+        self.attention_map = attention_map
+        
+    def get_attention_map(self):
+        return self.attention_map
+    
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention:
+            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+            attention_mask = encoder_attention_mask
+        elif past_key_value is not None:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+        else:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        past_key_value = (key_layer, value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            seq_length = hidden_states.size()[1]
+            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
+            distance = position_ids_l - position_ids_r
+            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
+            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
+
+            if self.position_embedding_type == "relative_key":
+                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores
+            elif self.position_embedding_type == "relative_key_query":
+                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+        
+        if is_cross_attention and self.save_attention:
+            self.save_attention_map(attention_probs)
+            attention_probs.register_hook(self.save_attn_gradients)         
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs_dropped = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs_dropped = attention_probs_dropped * head_mask
+
+        context_layer = torch.matmul(attention_probs_dropped, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+        outputs = outputs + (past_key_value,)
+        return outputs
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config, is_cross_attention=False):
+        super().__init__()
+        self.self = BertSelfAttention(config, is_cross_attention)
+        self.output = BertSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        self_outputs = self.self(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config, layer_num):
+        super().__init__()
+        self.config = config
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = BertAttention(config)      
+        self.layer_num = layer_num          
+        if self.config.add_cross_attention:
+            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
+        self.intermediate = BertIntermediate(config)
+        self.output = BertOutput(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+        mode=None,
+    ):
+        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+            past_key_value=self_attn_past_key_value,
+        )
+        attention_output = self_attention_outputs[0]
+
+        outputs = self_attention_outputs[1:-1]
+        present_key_value = self_attention_outputs[-1]
+
+        if mode=='multimodal':
+            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
+
+            cross_attention_outputs = self.crossattention(
+                attention_output,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                output_attentions=output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               
+        layer_output = apply_chunking_to_forward(
+            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+        )
+        outputs = (layer_output,) + outputs
+
+        outputs = outputs + (present_key_value,)
+
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=True,
+        mode='multimodal',
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+
+        next_decoder_cache = () if use_cache else None
+               
+        for i in range(self.config.num_hidden_layers):
+            layer_module = self.layer[i]
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                if use_cache:
+                    logger.warn(
+                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    mode=mode,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                    mode=mode,
+                )
+
+            hidden_states = layer_outputs[0]
+            if use_cache:
+                next_decoder_cache += (layer_outputs[-1],)
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_decoder_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertLMPredictionHead(config)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = BertConfig
+    base_model_prefix = "bert"
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def _init_weights(self, module):
+        """ Initialize the weights """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+
+class BertModel(BertPreTrainedModel):
+    """
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
+    input to the forward pass.
+    """
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = BertEmbeddings(config)
+        
+        self.encoder = BertEncoder(config)
+
+        self.pooler = BertPooler(config) if add_pooling_layer else None
+
+        self.init_weights()
+ 
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    
+    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
+        """
+        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
+
+        Arguments:
+            attention_mask (:obj:`torch.Tensor`):
+                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
+            input_shape (:obj:`Tuple[int]`):
+                The shape of the input to the model.
+            device: (:obj:`torch.device`):
+                The device of the input to the model.
+
+        Returns:
+            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
+        """
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        if attention_mask.dim() == 3:
+            extended_attention_mask = attention_mask[:, None, :, :]
+        elif attention_mask.dim() == 2:
+            # Provided a padding mask of dimensions [batch_size, seq_length]
+            # - if the model is a decoder, apply a causal mask in addition to the padding mask
+            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
+            if is_decoder:
+                batch_size, seq_length = input_shape
+
+                seq_ids = torch.arange(seq_length, device=device)
+                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
+                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
+                # causal and attention masks must have same type with pytorch version < 1.3
+                causal_mask = causal_mask.to(attention_mask.dtype)
+   
+                if causal_mask.shape[1] < attention_mask.shape[1]:
+                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
+                    causal_mask = torch.cat(
+                        [
+                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
+                            causal_mask,
+                        ],
+                        axis=-1,
+                    )                     
+
+                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
+            else:
+                extended_attention_mask = attention_mask[:, None, None, :]
+        else:
+            raise ValueError(
+                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
+                    input_shape, attention_mask.shape
+                )
+            )
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+        return extended_attention_mask
+    
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        is_decoder=False,
+        mode='multimodal',
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            batch_size, seq_length = input_shape
+            device = input_ids.device
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size, seq_length = input_shape
+            device = inputs_embeds.device
+        elif encoder_embeds is not None:    
+            input_shape = encoder_embeds.size()[:-1]
+            batch_size, seq_length = input_shape 
+            device = encoder_embeds.device
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
+
+        # past_key_values_length
+        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+
+        if attention_mask is None:
+            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
+            
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, 
+                                                                                 device, is_decoder)
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if encoder_hidden_states is not None:
+            if type(encoder_hidden_states) == list:
+                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
+            else:
+                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            
+            if type(encoder_attention_mask) == list:
+                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
+            elif encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+            else:    
+                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_extended_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+        
+        if encoder_embeds is None:
+            embedding_output = self.embeddings(
+                input_ids=input_ids,
+                position_ids=position_ids,
+                inputs_embeds=inputs_embeds,
+                past_key_values_length=past_key_values_length,
+            )
+        else:
+            embedding_output = encoder_embeds
+            
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            mode=mode,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+
+class BertLMHeadModel(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config, add_pooling_layer=False)
+        self.cls = BertOnlyMLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        return_logits=False,            
+        is_decoder=True,
+        reduction='mean',
+        mode='multimodal', 
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
+            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
+            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        Returns:
+        Example::
+            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
+            >>> import torch
+            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+            >>> config = BertConfig.from_pretrained("bert-base-cased")
+            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
+            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+            >>> outputs = model(**inputs)
+            >>> prediction_logits = outputs.logits
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        if labels is not None:
+            use_cache = False
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            is_decoder=is_decoder,
+            mode=mode,
+        )
+        
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+        
+        if return_logits:
+            return prediction_scores[:, :-1, :].contiguous()  
+
+        lm_loss = None
+        if labels is not None:
+            # we are doing next-token prediction; shift prediction scores and input ids by one
+            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
+            labels = labels[:, 1:].contiguous()
+            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) 
+            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+            if reduction=='none':
+                lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)               
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((lm_loss,) + output) if lm_loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=lm_loss,
+            logits=prediction_scores,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
+        input_shape = input_ids.shape
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones(input_shape)
+
+        # cut decoder_input_ids if past is used
+        if past is not None:
+            input_ids = input_ids[:, -1:]
+
+        return {
+            "input_ids": input_ids, 
+            "attention_mask": attention_mask, 
+            "past_key_values": past,
+            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
+            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
+            "is_decoder": True,
+        }
+
+    def _reorder_cache(self, past, beam_idx):
+        reordered_past = ()
+        for layer_past in past:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past

extras/BLIP/models/nlvr_encoder.py

@@ -0,0 +1,843 @@
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor, device, dtype, nn
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+import torch.nn.functional as F
+
+from transformers.activations import ACT2FN
+from transformers.file_utils import (
+    ModelOutput,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    NextSentencePredictorOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import (
+    PreTrainedModel,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
+from transformers.utils import logging
+from transformers.models.bert.configuration_bert import BertConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word and position embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        
+        self.config = config
+
+    def forward(
+        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
+    ):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        embeddings = inputs_embeds
+
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings += position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config, is_cross_attention):
+        super().__init__()
+        self.config = config
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+            )
+        
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        if is_cross_attention:
+            self.key = nn.Linear(config.encoder_width, self.all_head_size)
+            self.value = nn.Linear(config.encoder_width, self.all_head_size)
+        else:
+            self.key = nn.Linear(config.hidden_size, self.all_head_size)
+            self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
+        self.save_attention = False   
+            
+    def save_attn_gradients(self, attn_gradients):
+        self.attn_gradients = attn_gradients
+        
+    def get_attn_gradients(self):
+        return self.attn_gradients
+    
+    def save_attention_map(self, attention_map):
+        self.attention_map = attention_map
+        
+    def get_attention_map(self):
+        return self.attention_map
+    
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention:
+            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+            attention_mask = encoder_attention_mask
+        elif past_key_value is not None:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+        else:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        past_key_value = (key_layer, value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            seq_length = hidden_states.size()[1]
+            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
+            distance = position_ids_l - position_ids_r
+            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
+            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
+
+            if self.position_embedding_type == "relative_key":
+                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores
+            elif self.position_embedding_type == "relative_key_query":
+                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+        
+        if is_cross_attention and self.save_attention:
+            self.save_attention_map(attention_probs)
+            attention_probs.register_hook(self.save_attn_gradients)         
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs_dropped = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs_dropped = attention_probs_dropped * head_mask
+
+        context_layer = torch.matmul(attention_probs_dropped, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+        outputs = outputs + (past_key_value,)
+        return outputs
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config, twin=False, merge=False):     
+        super().__init__()
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)        
+        if twin:
+            self.dense0 = nn.Linear(config.hidden_size, config.hidden_size)
+            self.dense1 = nn.Linear(config.hidden_size, config.hidden_size)         
+        else:
+            self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if merge:
+            self.act =  ACT2FN[config.hidden_act]
+            self.merge_layer = nn.Linear(config.hidden_size * 2, config.hidden_size)
+            self.merge = True
+        else:
+            self.merge = False
+
+    def forward(self, hidden_states, input_tensor):
+        if type(hidden_states) == list:
+            hidden_states0 = self.dense0(hidden_states[0])
+            hidden_states1 = self.dense1(hidden_states[1])        
+            if self.merge:  
+                #hidden_states = self.merge_layer(self.act(torch.cat([hidden_states0,hidden_states1],dim=-1)))
+                hidden_states = self.merge_layer(torch.cat([hidden_states0,hidden_states1],dim=-1))
+            else:
+                hidden_states = (hidden_states0+hidden_states1)/2
+        else:    
+            hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config, is_cross_attention=False, layer_num=-1):
+        super().__init__()
+        if is_cross_attention:
+            self.self0 = BertSelfAttention(config, is_cross_attention)
+            self.self1 = BertSelfAttention(config, is_cross_attention)
+        else:    
+            self.self = BertSelfAttention(config, is_cross_attention)
+        self.output = BertSelfOutput(config, twin=is_cross_attention, merge=(is_cross_attention and layer_num>=6))
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):        
+        if type(encoder_hidden_states)==list:   
+            self_outputs0 = self.self0(
+                hidden_states,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states[0],
+                encoder_attention_mask[0],
+                past_key_value,
+                output_attentions,
+            )
+            self_outputs1 = self.self1(
+                hidden_states,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states[1],
+                encoder_attention_mask[1],
+                past_key_value,
+                output_attentions,
+            )                        
+            attention_output = self.output([self_outputs0[0],self_outputs1[0]], hidden_states)
+    
+            outputs = (attention_output,) + self_outputs0[1:]  # add attentions if we output them
+        else:        
+            self_outputs = self.self(
+                hidden_states,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                past_key_value,
+                output_attentions,
+            )
+            attention_output = self.output(self_outputs[0], hidden_states)
+            outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config, layer_num):
+        super().__init__()
+        self.config = config
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = BertAttention(config)      
+        self.layer_num = layer_num          
+        if self.config.add_cross_attention:
+            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention, layer_num=layer_num)
+        self.intermediate = BertIntermediate(config)
+        self.output = BertOutput(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+        mode=None,
+    ):
+        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+            past_key_value=self_attn_past_key_value,
+        )
+        attention_output = self_attention_outputs[0]
+
+        outputs = self_attention_outputs[1:-1]
+        present_key_value = self_attention_outputs[-1]
+
+        if mode=='multimodal':
+            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
+            cross_attention_outputs = self.crossattention(
+                attention_output,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                output_attentions=output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               
+        layer_output = apply_chunking_to_forward(
+            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+        )
+        outputs = (layer_output,) + outputs
+
+        outputs = outputs + (present_key_value,)
+
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=True,
+        mode='multimodal',
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+
+        next_decoder_cache = () if use_cache else None
+               
+        for i in range(self.config.num_hidden_layers):
+            layer_module = self.layer[i]
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                if use_cache:
+                    logger.warn(
+                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    mode=mode,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                    mode=mode,
+                )
+
+            hidden_states = layer_outputs[0]
+            if use_cache:
+                next_decoder_cache += (layer_outputs[-1],)
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_decoder_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertLMPredictionHead(config)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = BertConfig
+    base_model_prefix = "bert"
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def _init_weights(self, module):
+        """ Initialize the weights """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+
+class BertModel(BertPreTrainedModel):
+    """
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
+    input to the forward pass.
+    """
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = BertEmbeddings(config)
+        
+        self.encoder = BertEncoder(config)
+
+        self.pooler = BertPooler(config) if add_pooling_layer else None
+
+        self.init_weights()
+ 
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    
+    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
+        """
+        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
+
+        Arguments:
+            attention_mask (:obj:`torch.Tensor`):
+                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
+            input_shape (:obj:`Tuple[int]`):
+                The shape of the input to the model.
+            device: (:obj:`torch.device`):
+                The device of the input to the model.
+
+        Returns:
+            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
+        """
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        if attention_mask.dim() == 3:
+            extended_attention_mask = attention_mask[:, None, :, :]
+        elif attention_mask.dim() == 2:
+            # Provided a padding mask of dimensions [batch_size, seq_length]
+            # - if the model is a decoder, apply a causal mask in addition to the padding mask
+            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
+            if is_decoder:
+                batch_size, seq_length = input_shape
+
+                seq_ids = torch.arange(seq_length, device=device)
+                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
+                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
+                # causal and attention masks must have same type with pytorch version < 1.3
+                causal_mask = causal_mask.to(attention_mask.dtype)
+   
+                if causal_mask.shape[1] < attention_mask.shape[1]:
+                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
+                    causal_mask = torch.cat(
+                        [
+                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
+                            causal_mask,
+                        ],
+                        axis=-1,
+                    )                     
+
+                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
+            else:
+                extended_attention_mask = attention_mask[:, None, None, :]
+        else:
+            raise ValueError(
+                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
+                    input_shape, attention_mask.shape
+                )
+            )
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+        return extended_attention_mask
+    
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        is_decoder=False,
+        mode='multimodal',
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            batch_size, seq_length = input_shape
+            device = input_ids.device
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size, seq_length = input_shape
+            device = inputs_embeds.device
+        elif encoder_embeds is not None:    
+            input_shape = encoder_embeds.size()[:-1]
+            batch_size, seq_length = input_shape 
+            device = encoder_embeds.device
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
+
+        # past_key_values_length
+        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+
+        if attention_mask is None:
+            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
+            
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, 
+                                                                                 device, is_decoder)
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if encoder_hidden_states is not None:
+            if type(encoder_hidden_states) == list:
+                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
+            else:
+                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            
+            if type(encoder_attention_mask) == list:
+                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
+            elif encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+            else:    
+                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_extended_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+        
+        if encoder_embeds is None:
+            embedding_output = self.embeddings(
+                input_ids=input_ids,
+                position_ids=position_ids,
+                inputs_embeds=inputs_embeds,
+                past_key_values_length=past_key_values_length,
+            )
+        else:
+            embedding_output = encoder_embeds
+            
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            mode=mode,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+

extras/BLIP/models/vit.py

@@ -0,0 +1,308 @@
+'''
+ * Copyright (c) 2022, salesforce.com, inc.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+ * By Junnan Li
+ * Based on timm code base
+ * https://github.com/rwightman/pytorch-image-models/tree/master/timm
+'''
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from functools import partial
+
+from timm.models.vision_transformer import _cfg, PatchEmbed
+from timm.models.registry import register_model
+from timm.models.layers import trunc_normal_, DropPath
+from timm.models.helpers import named_apply, adapt_input_conv
+
+
+def checkpoint_wrapper(x):
+    return x
+
+
+class Mlp(nn.Module):
+    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
+    """
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim ** -0.5
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.attn_gradients = None
+        self.attention_map = None
+        
+    def save_attn_gradients(self, attn_gradients):
+        self.attn_gradients = attn_gradients
+        
+    def get_attn_gradients(self):
+        return self.attn_gradients
+    
+    def save_attention_map(self, attention_map):
+        self.attention_map = attention_map
+        
+    def get_attention_map(self):
+        return self.attention_map
+    
+    def forward(self, x, register_hook=False):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+                
+        if register_hook:
+            self.save_attention_map(attn)
+            attn.register_hook(self.save_attn_gradients)        
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if use_grad_checkpointing:
+            self.attn = checkpoint_wrapper(self.attn)
+            self.mlp = checkpoint_wrapper(self.mlp)
+
+    def forward(self, x, register_hook=False):
+        x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+    
+class VisionTransformer(nn.Module):
+    """ Vision Transformer
+    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
+        https://arxiv.org/abs/2010.11929
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, 
+                 use_grad_checkpointing=False, ckpt_layer=0):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            num_classes (int): number of classes for classification head
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
+            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
+            drop_rate (float): dropout rate
+            attn_drop_rate (float): attention dropout rate
+            drop_path_rate (float): stochastic depth rate
+            norm_layer: (nn.Module): normalization layer
+        """
+        super().__init__()
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
+                use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer)
+            )
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+
+        trunc_normal_(self.pos_embed, std=.02)
+        trunc_normal_(self.cls_token, std=.02)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_embed', 'cls_token'}
+
+    def forward(self, x, register_blk=-1):
+        B = x.shape[0]
+        x = self.patch_embed(x)
+
+        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+  
+        x = x + self.pos_embed[:,:x.size(1),:]
+        x = self.pos_drop(x)
+
+        for i,blk in enumerate(self.blocks):
+            x = blk(x, register_blk==i)
+        x = self.norm(x)
+        
+        return x
+
+    @torch.jit.ignore()
+    def load_pretrained(self, checkpoint_path, prefix=''):
+        _load_weights(self, checkpoint_path, prefix)
+        
+
+@torch.no_grad()
+def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
+    """ Load weights from .npz checkpoints for official Google Brain Flax implementation
+    """
+    import numpy as np
+
+    def _n2p(w, t=True):
+        if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
+            w = w.flatten()
+        if t:
+            if w.ndim == 4:
+                w = w.transpose([3, 2, 0, 1])
+            elif w.ndim == 3:
+                w = w.transpose([2, 0, 1])
+            elif w.ndim == 2:
+                w = w.transpose([1, 0])
+        return torch.from_numpy(w)
+
+    w = np.load(checkpoint_path)
+    if not prefix and 'opt/target/embedding/kernel' in w:
+        prefix = 'opt/target/'
+
+    if hasattr(model.patch_embed, 'backbone'):
+        # hybrid
+        backbone = model.patch_embed.backbone
+        stem_only = not hasattr(backbone, 'stem')
+        stem = backbone if stem_only else backbone.stem
+        stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
+        stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
+        stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
+        if not stem_only:
+            for i, stage in enumerate(backbone.stages):
+                for j, block in enumerate(stage.blocks):
+                    bp = f'{prefix}block{i + 1}/unit{j + 1}/'
+                    for r in range(3):
+                        getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
+                        getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
+                        getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
+                    if block.downsample is not None:
+                        block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
+                        block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
+                        block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
+        embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
+    else:
+        embed_conv_w = adapt_input_conv(
+            model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
+    model.patch_embed.proj.weight.copy_(embed_conv_w)
+    model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
+    model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
+    pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
+    if pos_embed_w.shape != model.pos_embed.shape:
+        pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights
+            pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
+    model.pos_embed.copy_(pos_embed_w)
+    model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
+    model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
+#     if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
+#         model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
+#         model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
+#     if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
+#         model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
+#         model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
+    for i, block in enumerate(model.blocks.children()):
+        block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
+        mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
+        block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
+        block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
+        block.attn.qkv.weight.copy_(torch.cat([
+            _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
+        block.attn.qkv.bias.copy_(torch.cat([
+            _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
+        block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
+        block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
+        for r in range(2):
+            getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
+            getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
+        block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
+        block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
+
+            
+def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):        
+    # interpolate position embedding
+    embedding_size = pos_embed_checkpoint.shape[-1]
+    num_patches = visual_encoder.patch_embed.num_patches
+    num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
+    # height (== width) for the checkpoint position embedding
+    orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+    # height (== width) for the new position embedding
+    new_size = int(num_patches ** 0.5)
+
+    if orig_size!=new_size:
+        # class_token and dist_token are kept unchanged
+        extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+        # only the position tokens are interpolated
+        pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+        pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+        pos_tokens = torch.nn.functional.interpolate(
+            pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+        new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+        print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2))
+        
+        return new_pos_embed    
+    else:
+        return pos_embed_checkpoint

extras/expansion.py

@@ -8,12 +8,12 @@
 import os
 import torch
 import math
-import fcbh.model_management as model_management
+import ldm_patched.modules.model_management as model_management
 
 from transformers.generation.logits_process import LogitsProcessorList
 from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
-from modules.path import fooocus_expansion_path
-from fcbh.model_patcher import ModelPatcher
+from modules.config import path_fooocus_expansion
+from ldm_patched.modules.model_patcher import ModelPatcher
 
 
 # limitation of np.random.seed(), called from transformers.set_seed()
@@ -36,9 +36,9 @@ def remove_pattern(x, pattern):
 
 class FooocusExpansion:
     def __init__(self):
-        self.tokenizer = AutoTokenizer.from_pretrained(fooocus_expansion_path)
+        self.tokenizer = AutoTokenizer.from_pretrained(path_fooocus_expansion)
 
-        positive_words = open(os.path.join(fooocus_expansion_path, 'positive.txt'),
+        positive_words = open(os.path.join(path_fooocus_expansion, 'positive.txt'),
                               encoding='utf-8').read().splitlines()
         positive_words = ['Ġ' + x.lower() for x in positive_words if x != '']
 
@@ -59,7 +59,7 @@ class FooocusExpansion:
         # t198 = self.tokenizer('\n', return_tensors="np")
         # eos = self.tokenizer.eos_token_id
 
-        self.model = AutoModelForCausalLM.from_pretrained(fooocus_expansion_path)
+        self.model = AutoModelForCausalLM.from_pretrained(path_fooocus_expansion)
         self.model.eval()
 
         load_device = model_management.text_encoder_device()

extras/face_crop.py

@@ -0,0 +1,50 @@
+import cv2
+import numpy as np
+import modules.config
+
+
+faceRestoreHelper = None
+
+
+def align_warp_face(self, landmark, border_mode='constant'):
+    affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0]
+    self.affine_matrices.append(affine_matrix)
+    if border_mode == 'constant':
+        border_mode = cv2.BORDER_CONSTANT
+    elif border_mode == 'reflect101':
+        border_mode = cv2.BORDER_REFLECT101
+    elif border_mode == 'reflect':
+        border_mode = cv2.BORDER_REFLECT
+    input_img = self.input_img
+    cropped_face = cv2.warpAffine(input_img, affine_matrix, self.face_size,
+                                  borderMode=border_mode, borderValue=(135, 133, 132))
+    return cropped_face
+
+
+def crop_image(img_rgb):
+    global faceRestoreHelper
+    
+    if faceRestoreHelper is None:
+        from extras.facexlib.utils.face_restoration_helper import FaceRestoreHelper
+        faceRestoreHelper = FaceRestoreHelper(
+            upscale_factor=1,
+            model_rootpath=modules.config.path_controlnet,
+            device='cpu'  # use cpu is safer since we are out of memory management
+        )
+
+    faceRestoreHelper.clean_all()
+    faceRestoreHelper.read_image(np.ascontiguousarray(img_rgb[:, :, ::-1].copy()))
+    faceRestoreHelper.get_face_landmarks_5()
+
+    landmarks = faceRestoreHelper.all_landmarks_5
+    # landmarks are already sorted with confidence.
+
+    if len(landmarks) == 0:
+        print('No face detected')
+        return img_rgb
+    else:
+        print(f'Detected {len(landmarks)} faces')
+
+    result = align_warp_face(faceRestoreHelper, landmarks[0])
+
+    return np.ascontiguousarray(result[:, :, ::-1].copy())

extras/facexlib/detection/__init__.py

@@ -0,0 +1,31 @@
+import torch
+from copy import deepcopy
+
+from extras.facexlib.utils import load_file_from_url
+from .retinaface import RetinaFace
+
+
+def init_detection_model(model_name, half=False, device='cuda', model_rootpath=None):
+    if model_name == 'retinaface_resnet50':
+        model = RetinaFace(network_name='resnet50', half=half, device=device)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth'
+    elif model_name == 'retinaface_mobile0.25':
+        model = RetinaFace(network_name='mobile0.25', half=half, device=device)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth'
+    else:
+        raise NotImplementedError(f'{model_name} is not implemented.')
+
+    model_path = load_file_from_url(
+        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
+
+    # TODO: clean pretrained model
+    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+    # remove unnecessary 'module.'
+    for k, v in deepcopy(load_net).items():
+        if k.startswith('module.'):
+            load_net[k[7:]] = v
+            load_net.pop(k)
+    model.load_state_dict(load_net, strict=True)
+    model.eval()
+    model = model.to(device)
+    return model

extras/facexlib/detection/align_trans.py

@@ -0,0 +1,219 @@
+import cv2
+import numpy as np
+
+from .matlab_cp2tform import get_similarity_transform_for_cv2
+
+# reference facial points, a list of coordinates (x,y)
+REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278],
+                           [33.54930115, 92.3655014], [62.72990036, 92.20410156]]
+
+DEFAULT_CROP_SIZE = (96, 112)
+
+
+class FaceWarpException(Exception):
+
+    def __str__(self):
+        return 'In File {}:{}'.format(__file__, super.__str__(self))
+
+
+def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):
+    """
+    Function:
+    ----------
+        get reference 5 key points according to crop settings:
+        0. Set default crop_size:
+            if default_square:
+                crop_size = (112, 112)
+            else:
+                crop_size = (96, 112)
+        1. Pad the crop_size by inner_padding_factor in each side;
+        2. Resize crop_size into (output_size - outer_padding*2),
+            pad into output_size with outer_padding;
+        3. Output reference_5point;
+    Parameters:
+    ----------
+        @output_size: (w, h) or None
+            size of aligned face image
+        @inner_padding_factor: (w_factor, h_factor)
+            padding factor for inner (w, h)
+        @outer_padding: (w_pad, h_pad)
+            each row is a pair of coordinates (x, y)
+        @default_square: True or False
+            if True:
+                default crop_size = (112, 112)
+            else:
+                default crop_size = (96, 112);
+        !!! make sure, if output_size is not None:
+                (output_size - outer_padding)
+                = some_scale * (default crop_size * (1.0 +
+                inner_padding_factor))
+    Returns:
+    ----------
+        @reference_5point: 5x2 np.array
+            each row is a pair of transformed coordinates (x, y)
+    """
+
+    tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
+    tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
+
+    # 0) make the inner region a square
+    if default_square:
+        size_diff = max(tmp_crop_size) - tmp_crop_size
+        tmp_5pts += size_diff / 2
+        tmp_crop_size += size_diff
+
+    if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]):
+
+        return tmp_5pts
+
+    if (inner_padding_factor == 0 and outer_padding == (0, 0)):
+        if output_size is None:
+            return tmp_5pts
+        else:
+            raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
+
+    # check output size
+    if not (0 <= inner_padding_factor <= 1.0):
+        raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
+
+    if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None):
+        output_size = tmp_crop_size * \
+            (1 + inner_padding_factor * 2).astype(np.int32)
+        output_size += np.array(outer_padding)
+    if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]):
+        raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])')
+
+    # 1) pad the inner region according inner_padding_factor
+    if inner_padding_factor > 0:
+        size_diff = tmp_crop_size * inner_padding_factor * 2
+        tmp_5pts += size_diff / 2
+        tmp_crop_size += np.round(size_diff).astype(np.int32)
+
+    # 2) resize the padded inner region
+    size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
+
+    if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
+        raise FaceWarpException('Must have (output_size - outer_padding)'
+                                '= some_scale * (crop_size * (1.0 + inner_padding_factor)')
+
+    scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
+    tmp_5pts = tmp_5pts * scale_factor
+    #    size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
+    #    tmp_5pts = tmp_5pts + size_diff / 2
+    tmp_crop_size = size_bf_outer_pad
+
+    # 3) add outer_padding to make output_size
+    reference_5point = tmp_5pts + np.array(outer_padding)
+    tmp_crop_size = output_size
+
+    return reference_5point
+
+
+def get_affine_transform_matrix(src_pts, dst_pts):
+    """
+    Function:
+    ----------
+        get affine transform matrix 'tfm' from src_pts to dst_pts
+    Parameters:
+    ----------
+        @src_pts: Kx2 np.array
+            source points matrix, each row is a pair of coordinates (x, y)
+        @dst_pts: Kx2 np.array
+            destination points matrix, each row is a pair of coordinates (x, y)
+    Returns:
+    ----------
+        @tfm: 2x3 np.array
+            transform matrix from src_pts to dst_pts
+    """
+
+    tfm = np.float32([[1, 0, 0], [0, 1, 0]])
+    n_pts = src_pts.shape[0]
+    ones = np.ones((n_pts, 1), src_pts.dtype)
+    src_pts_ = np.hstack([src_pts, ones])
+    dst_pts_ = np.hstack([dst_pts, ones])
+
+    A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
+
+    if rank == 3:
+        tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]])
+    elif rank == 2:
+        tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])
+
+    return tfm
+
+
+def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'):
+    """
+    Function:
+    ----------
+        apply affine transform 'trans' to uv
+    Parameters:
+    ----------
+        @src_img: 3x3 np.array
+            input image
+        @facial_pts: could be
+            1)a list of K coordinates (x,y)
+        or
+            2) Kx2 or 2xK np.array
+            each row or col is a pair of coordinates (x, y)
+        @reference_pts: could be
+            1) a list of K coordinates (x,y)
+        or
+            2) Kx2 or 2xK np.array
+            each row or col is a pair of coordinates (x, y)
+        or
+            3) None
+            if None, use default reference facial points
+        @crop_size: (w, h)
+            output face image size
+        @align_type: transform type, could be one of
+            1) 'similarity': use similarity transform
+            2) 'cv2_affine': use the first 3 points to do affine transform,
+                    by calling cv2.getAffineTransform()
+            3) 'affine': use all points to do affine transform
+    Returns:
+    ----------
+        @face_img: output face image with size (w, h) = @crop_size
+    """
+
+    if reference_pts is None:
+        if crop_size[0] == 96 and crop_size[1] == 112:
+            reference_pts = REFERENCE_FACIAL_POINTS
+        else:
+            default_square = False
+            inner_padding_factor = 0
+            outer_padding = (0, 0)
+            output_size = crop_size
+
+            reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding,
+                                                        default_square)
+
+    ref_pts = np.float32(reference_pts)
+    ref_pts_shp = ref_pts.shape
+    if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
+        raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2')
+
+    if ref_pts_shp[0] == 2:
+        ref_pts = ref_pts.T
+
+    src_pts = np.float32(facial_pts)
+    src_pts_shp = src_pts.shape
+    if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
+        raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2')
+
+    if src_pts_shp[0] == 2:
+        src_pts = src_pts.T
+
+    if src_pts.shape != ref_pts.shape:
+        raise FaceWarpException('facial_pts and reference_pts must have the same shape')
+
+    if align_type == 'cv2_affine':
+        tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
+    elif align_type == 'affine':
+        tfm = get_affine_transform_matrix(src_pts, ref_pts)
+    else:
+        tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
+
+    face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
+
+    return face_img

extras/facexlib/detection/matlab_cp2tform.py

@@ -0,0 +1,317 @@
+import numpy as np
+from numpy.linalg import inv, lstsq
+from numpy.linalg import matrix_rank as rank
+from numpy.linalg import norm
+
+
+class MatlabCp2tormException(Exception):
+
+    def __str__(self):
+        return 'In File {}:{}'.format(__file__, super.__str__(self))
+
+
+def tformfwd(trans, uv):
+    """
+    Function:
+    ----------
+        apply affine transform 'trans' to uv
+
+    Parameters:
+    ----------
+        @trans: 3x3 np.array
+            transform matrix
+        @uv: Kx2 np.array
+            each row is a pair of coordinates (x, y)
+
+    Returns:
+    ----------
+        @xy: Kx2 np.array
+            each row is a pair of transformed coordinates (x, y)
+    """
+    uv = np.hstack((uv, np.ones((uv.shape[0], 1))))
+    xy = np.dot(uv, trans)
+    xy = xy[:, 0:-1]
+    return xy
+
+
+def tforminv(trans, uv):
+    """
+    Function:
+    ----------
+        apply the inverse of affine transform 'trans' to uv
+
+    Parameters:
+    ----------
+        @trans: 3x3 np.array
+            transform matrix
+        @uv: Kx2 np.array
+            each row is a pair of coordinates (x, y)
+
+    Returns:
+    ----------
+        @xy: Kx2 np.array
+            each row is a pair of inverse-transformed coordinates (x, y)
+    """
+    Tinv = inv(trans)
+    xy = tformfwd(Tinv, uv)
+    return xy
+
+
+def findNonreflectiveSimilarity(uv, xy, options=None):
+    options = {'K': 2}
+
+    K = options['K']
+    M = xy.shape[0]
+    x = xy[:, 0].reshape((-1, 1))  # use reshape to keep a column vector
+    y = xy[:, 1].reshape((-1, 1))  # use reshape to keep a column vector
+
+    tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1))))
+    tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1))))
+    X = np.vstack((tmp1, tmp2))
+
+    u = uv[:, 0].reshape((-1, 1))  # use reshape to keep a column vector
+    v = uv[:, 1].reshape((-1, 1))  # use reshape to keep a column vector
+    U = np.vstack((u, v))
+
+    # We know that X * r = U
+    if rank(X) >= 2 * K:
+        r, _, _, _ = lstsq(X, U, rcond=-1)
+        r = np.squeeze(r)
+    else:
+        raise Exception('cp2tform:twoUniquePointsReq')
+    sc = r[0]
+    ss = r[1]
+    tx = r[2]
+    ty = r[3]
+
+    Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]])
+    T = inv(Tinv)
+    T[:, 2] = np.array([0, 0, 1])
+
+    return T, Tinv
+
+
+def findSimilarity(uv, xy, options=None):
+    options = {'K': 2}
+
+    #    uv = np.array(uv)
+    #    xy = np.array(xy)
+
+    # Solve for trans1
+    trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options)
+
+    # Solve for trans2
+
+    # manually reflect the xy data across the Y-axis
+    xyR = xy
+    xyR[:, 0] = -1 * xyR[:, 0]
+
+    trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options)
+
+    # manually reflect the tform to undo the reflection done on xyR
+    TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])
+
+    trans2 = np.dot(trans2r, TreflectY)
+
+    # Figure out if trans1 or trans2 is better
+    xy1 = tformfwd(trans1, uv)
+    norm1 = norm(xy1 - xy)
+
+    xy2 = tformfwd(trans2, uv)
+    norm2 = norm(xy2 - xy)
+
+    if norm1 <= norm2:
+        return trans1, trans1_inv
+    else:
+        trans2_inv = inv(trans2)
+        return trans2, trans2_inv
+
+
+def get_similarity_transform(src_pts, dst_pts, reflective=True):
+    """
+    Function:
+    ----------
+        Find Similarity Transform Matrix 'trans':
+            u = src_pts[:, 0]
+            v = src_pts[:, 1]
+            x = dst_pts[:, 0]
+            y = dst_pts[:, 1]
+            [x, y, 1] = [u, v, 1] * trans
+
+    Parameters:
+    ----------
+        @src_pts: Kx2 np.array
+            source points, each row is a pair of coordinates (x, y)
+        @dst_pts: Kx2 np.array
+            destination points, each row is a pair of transformed
+            coordinates (x, y)
+        @reflective: True or False
+            if True:
+                use reflective similarity transform
+            else:
+                use non-reflective similarity transform
+
+    Returns:
+    ----------
+       @trans: 3x3 np.array
+            transform matrix from uv to xy
+        trans_inv: 3x3 np.array
+            inverse of trans, transform matrix from xy to uv
+    """
+
+    if reflective:
+        trans, trans_inv = findSimilarity(src_pts, dst_pts)
+    else:
+        trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts)
+
+    return trans, trans_inv
+
+
+def cvt_tform_mat_for_cv2(trans):
+    """
+    Function:
+    ----------
+        Convert Transform Matrix 'trans' into 'cv2_trans' which could be
+        directly used by cv2.warpAffine():
+            u = src_pts[:, 0]
+            v = src_pts[:, 1]
+            x = dst_pts[:, 0]
+            y = dst_pts[:, 1]
+            [x, y].T = cv_trans * [u, v, 1].T
+
+    Parameters:
+    ----------
+        @trans: 3x3 np.array
+            transform matrix from uv to xy
+
+    Returns:
+    ----------
+        @cv2_trans: 2x3 np.array
+            transform matrix from src_pts to dst_pts, could be directly used
+            for cv2.warpAffine()
+    """
+    cv2_trans = trans[:, 0:2].T
+
+    return cv2_trans
+
+
+def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True):
+    """
+    Function:
+    ----------
+        Find Similarity Transform Matrix 'cv2_trans' which could be
+        directly used by cv2.warpAffine():
+            u = src_pts[:, 0]
+            v = src_pts[:, 1]
+            x = dst_pts[:, 0]
+            y = dst_pts[:, 1]
+            [x, y].T = cv_trans * [u, v, 1].T
+
+    Parameters:
+    ----------
+        @src_pts: Kx2 np.array
+            source points, each row is a pair of coordinates (x, y)
+        @dst_pts: Kx2 np.array
+            destination points, each row is a pair of transformed
+            coordinates (x, y)
+        reflective: True or False
+            if True:
+                use reflective similarity transform
+            else:
+                use non-reflective similarity transform
+
+    Returns:
+    ----------
+        @cv2_trans: 2x3 np.array
+            transform matrix from src_pts to dst_pts, could be directly used
+            for cv2.warpAffine()
+    """
+    trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective)
+    cv2_trans = cvt_tform_mat_for_cv2(trans)
+
+    return cv2_trans
+
+
+if __name__ == '__main__':
+    """
+    u = [0, 6, -2]
+    v = [0, 3, 5]
+    x = [-1, 0, 4]
+    y = [-1, -10, 4]
+
+    # In Matlab, run:
+    #
+    #   uv = [u'; v'];
+    #   xy = [x'; y'];
+    #   tform_sim=cp2tform(uv,xy,'similarity');
+    #
+    #   trans = tform_sim.tdata.T
+    #   ans =
+    #       -0.0764   -1.6190         0
+    #        1.6190   -0.0764         0
+    #       -3.2156    0.0290    1.0000
+    #   trans_inv = tform_sim.tdata.Tinv
+    #    ans =
+    #
+    #       -0.0291    0.6163         0
+    #       -0.6163   -0.0291         0
+    #       -0.0756    1.9826    1.0000
+    #    xy_m=tformfwd(tform_sim, u,v)
+    #
+    #    xy_m =
+    #
+    #       -3.2156    0.0290
+    #        1.1833   -9.9143
+    #        5.0323    2.8853
+    #    uv_m=tforminv(tform_sim, x,y)
+    #
+    #    uv_m =
+    #
+    #        0.5698    1.3953
+    #        6.0872    2.2733
+    #       -2.6570    4.3314
+    """
+    u = [0, 6, -2]
+    v = [0, 3, 5]
+    x = [-1, 0, 4]
+    y = [-1, -10, 4]
+
+    uv = np.array((u, v)).T
+    xy = np.array((x, y)).T
+
+    print('\n--->uv:')
+    print(uv)
+    print('\n--->xy:')
+    print(xy)
+
+    trans, trans_inv = get_similarity_transform(uv, xy)
+
+    print('\n--->trans matrix:')
+    print(trans)
+
+    print('\n--->trans_inv matrix:')
+    print(trans_inv)
+
+    print('\n---> apply transform to uv')
+    print('\nxy_m = uv_augmented * trans')
+    uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1))))
+    xy_m = np.dot(uv_aug, trans)
+    print(xy_m)
+
+    print('\nxy_m = tformfwd(trans, uv)')
+    xy_m = tformfwd(trans, uv)
+    print(xy_m)
+
+    print('\n---> apply inverse transform to xy')
+    print('\nuv_m = xy_augmented * trans_inv')
+    xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1))))
+    uv_m = np.dot(xy_aug, trans_inv)
+    print(uv_m)
+
+    print('\nuv_m = tformfwd(trans_inv, xy)')
+    uv_m = tformfwd(trans_inv, xy)
+    print(uv_m)
+
+    uv_m = tforminv(trans, xy)
+    print('\nuv_m = tforminv(trans, xy)')
+    print(uv_m)

extras/facexlib/detection/retinaface.py

@@ -0,0 +1,366 @@
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter
+
+from extras.facexlib.detection.align_trans import get_reference_facial_points, warp_and_crop_face
+from extras.facexlib.detection.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head
+from extras.facexlib.detection.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm,
+                                                 py_cpu_nms)
+
+
+def generate_config(network_name):
+
+    cfg_mnet = {
+        'name': 'mobilenet0.25',
+        'min_sizes': [[16, 32], [64, 128], [256, 512]],
+        'steps': [8, 16, 32],
+        'variance': [0.1, 0.2],
+        'clip': False,
+        'loc_weight': 2.0,
+        'gpu_train': True,
+        'batch_size': 32,
+        'ngpu': 1,
+        'epoch': 250,
+        'decay1': 190,
+        'decay2': 220,
+        'image_size': 640,
+        'return_layers': {
+            'stage1': 1,
+            'stage2': 2,
+            'stage3': 3
+        },
+        'in_channel': 32,
+        'out_channel': 64
+    }
+
+    cfg_re50 = {
+        'name': 'Resnet50',
+        'min_sizes': [[16, 32], [64, 128], [256, 512]],
+        'steps': [8, 16, 32],
+        'variance': [0.1, 0.2],
+        'clip': False,
+        'loc_weight': 2.0,
+        'gpu_train': True,
+        'batch_size': 24,
+        'ngpu': 4,
+        'epoch': 100,
+        'decay1': 70,
+        'decay2': 90,
+        'image_size': 840,
+        'return_layers': {
+            'layer2': 1,
+            'layer3': 2,
+            'layer4': 3
+        },
+        'in_channel': 256,
+        'out_channel': 256
+    }
+
+    if network_name == 'mobile0.25':
+        return cfg_mnet
+    elif network_name == 'resnet50':
+        return cfg_re50
+    else:
+        raise NotImplementedError(f'network_name={network_name}')
+
+
+class RetinaFace(nn.Module):
+
+    def __init__(self, network_name='resnet50', half=False, phase='test', device=None):
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
+
+        super(RetinaFace, self).__init__()
+        self.half_inference = half
+        cfg = generate_config(network_name)
+        self.backbone = cfg['name']
+
+        self.model_name = f'retinaface_{network_name}'
+        self.cfg = cfg
+        self.phase = phase
+        self.target_size, self.max_size = 1600, 2150
+        self.resize, self.scale, self.scale1 = 1., None, None
+        self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]], device=self.device)
+        self.reference = get_reference_facial_points(default_square=True)
+        # Build network.
+        backbone = None
+        if cfg['name'] == 'mobilenet0.25':
+            backbone = MobileNetV1()
+            self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
+        elif cfg['name'] == 'Resnet50':
+            import torchvision.models as models
+            backbone = models.resnet50(weights=None)
+            self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
+
+        in_channels_stage2 = cfg['in_channel']
+        in_channels_list = [
+            in_channels_stage2 * 2,
+            in_channels_stage2 * 4,
+            in_channels_stage2 * 8,
+        ]
+
+        out_channels = cfg['out_channel']
+        self.fpn = FPN(in_channels_list, out_channels)
+        self.ssh1 = SSH(out_channels, out_channels)
+        self.ssh2 = SSH(out_channels, out_channels)
+        self.ssh3 = SSH(out_channels, out_channels)
+
+        self.ClassHead = make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
+        self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
+        self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
+
+        self.to(self.device)
+        self.eval()
+        if self.half_inference:
+            self.half()
+
+    def forward(self, inputs):
+        out = self.body(inputs)
+
+        if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50':
+            out = list(out.values())
+        # FPN
+        fpn = self.fpn(out)
+
+        # SSH
+        feature1 = self.ssh1(fpn[0])
+        feature2 = self.ssh2(fpn[1])
+        feature3 = self.ssh3(fpn[2])
+        features = [feature1, feature2, feature3]
+
+        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
+        classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1)
+        tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)]
+        ldm_regressions = (torch.cat(tmp, dim=1))
+
+        if self.phase == 'train':
+            output = (bbox_regressions, classifications, ldm_regressions)
+        else:
+            output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
+        return output
+
+    def __detect_faces(self, inputs):
+        # get scale
+        height, width = inputs.shape[2:]
+        self.scale = torch.tensor([width, height, width, height], dtype=torch.float32, device=self.device)
+        tmp = [width, height, width, height, width, height, width, height, width, height]
+        self.scale1 = torch.tensor(tmp, dtype=torch.float32, device=self.device)
+
+        # forawrd
+        inputs = inputs.to(self.device)
+        if self.half_inference:
+            inputs = inputs.half()
+        loc, conf, landmarks = self(inputs)
+
+        # get priorbox
+        priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:])
+        priors = priorbox.forward().to(self.device)
+
+        return loc, conf, landmarks, priors
+
+    # single image detection
+    def transform(self, image, use_origin_size):
+        # convert to opencv format
+        if isinstance(image, Image.Image):
+            image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+        image = image.astype(np.float32)
+
+        # testing scale
+        im_size_min = np.min(image.shape[0:2])
+        im_size_max = np.max(image.shape[0:2])
+        resize = float(self.target_size) / float(im_size_min)
+
+        # prevent bigger axis from being more than max_size
+        if np.round(resize * im_size_max) > self.max_size:
+            resize = float(self.max_size) / float(im_size_max)
+        resize = 1 if use_origin_size else resize
+
+        # resize
+        if resize != 1:
+            image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+
+        # convert to torch.tensor format
+        # image -= (104, 117, 123)
+        image = image.transpose(2, 0, 1)
+        image = torch.from_numpy(image).unsqueeze(0)
+
+        return image, resize
+
+    def detect_faces(
+        self,
+        image,
+        conf_threshold=0.8,
+        nms_threshold=0.4,
+        use_origin_size=True,
+    ):
+        image, self.resize = self.transform(image, use_origin_size)
+        image = image.to(self.device)
+        if self.half_inference:
+            image = image.half()
+        image = image - self.mean_tensor
+
+        loc, conf, landmarks, priors = self.__detect_faces(image)
+
+        boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance'])
+        boxes = boxes * self.scale / self.resize
+        boxes = boxes.cpu().numpy()
+
+        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+
+        landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance'])
+        landmarks = landmarks * self.scale1 / self.resize
+        landmarks = landmarks.cpu().numpy()
+
+        # ignore low scores
+        inds = np.where(scores > conf_threshold)[0]
+        boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds]
+
+        # sort
+        order = scores.argsort()[::-1]
+        boxes, landmarks, scores = boxes[order], landmarks[order], scores[order]
+
+        # do NMS
+        bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+        keep = py_cpu_nms(bounding_boxes, nms_threshold)
+        bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep]
+        # self.t['forward_pass'].toc()
+        # print(self.t['forward_pass'].average_time)
+        # import sys
+        # sys.stdout.flush()
+        return np.concatenate((bounding_boxes, landmarks), axis=1)
+
+    def __align_multi(self, image, boxes, landmarks, limit=None):
+
+        if len(boxes) < 1:
+            return [], []
+
+        if limit:
+            boxes = boxes[:limit]
+            landmarks = landmarks[:limit]
+
+        faces = []
+        for landmark in landmarks:
+            facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)]
+
+            warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112))
+            faces.append(warped_face)
+
+        return np.concatenate((boxes, landmarks), axis=1), faces
+
+    def align_multi(self, img, conf_threshold=0.8, limit=None):
+
+        rlt = self.detect_faces(img, conf_threshold=conf_threshold)
+        boxes, landmarks = rlt[:, 0:5], rlt[:, 5:]
+
+        return self.__align_multi(img, boxes, landmarks, limit)
+
+    # batched detection
+    def batched_transform(self, frames, use_origin_size):
+        """
+        Arguments:
+            frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c],
+                type=np.float32, BGR format).
+            use_origin_size: whether to use origin size.
+        """
+        from_PIL = True if isinstance(frames[0], Image.Image) else False
+
+        # convert to opencv format
+        if from_PIL:
+            frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames]
+            frames = np.asarray(frames, dtype=np.float32)
+
+        # testing scale
+        im_size_min = np.min(frames[0].shape[0:2])
+        im_size_max = np.max(frames[0].shape[0:2])
+        resize = float(self.target_size) / float(im_size_min)
+
+        # prevent bigger axis from being more than max_size
+        if np.round(resize * im_size_max) > self.max_size:
+            resize = float(self.max_size) / float(im_size_max)
+        resize = 1 if use_origin_size else resize
+
+        # resize
+        if resize != 1:
+            if not from_PIL:
+                frames = F.interpolate(frames, scale_factor=resize)
+            else:
+                frames = [
+                    cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+                    for frame in frames
+                ]
+
+        # convert to torch.tensor format
+        if not from_PIL:
+            frames = frames.transpose(1, 2).transpose(1, 3).contiguous()
+        else:
+            frames = frames.transpose((0, 3, 1, 2))
+            frames = torch.from_numpy(frames)
+
+        return frames, resize
+
+    def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True):
+        """
+        Arguments:
+            frames: a list of PIL.Image, or np.array(shape=[n, h, w, c],
+                type=np.uint8, BGR format).
+            conf_threshold: confidence threshold.
+            nms_threshold: nms threshold.
+            use_origin_size: whether to use origin size.
+        Returns:
+            final_bounding_boxes: list of np.array ([n_boxes, 5],
+                type=np.float32).
+            final_landmarks: list of np.array ([n_boxes, 10], type=np.float32).
+        """
+        # self.t['forward_pass'].tic()
+        frames, self.resize = self.batched_transform(frames, use_origin_size)
+        frames = frames.to(self.device)
+        frames = frames - self.mean_tensor
+
+        b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames)
+
+        final_bounding_boxes, final_landmarks = [], []
+
+        # decode
+        priors = priors.unsqueeze(0)
+        b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize
+        b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize
+        b_conf = b_conf[:, :, 1]
+
+        # index for selection
+        b_indice = b_conf > conf_threshold
+
+        # concat
+        b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float()
+
+        for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice):
+
+            # ignore low scores
+            pred, landm = pred[inds, :], landm[inds, :]
+            if pred.shape[0] == 0:
+                final_bounding_boxes.append(np.array([], dtype=np.float32))
+                final_landmarks.append(np.array([], dtype=np.float32))
+                continue
+
+            # sort
+            # order = score.argsort(descending=True)
+            # box, landm, score = box[order], landm[order], score[order]
+
+            # to CPU
+            bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy()
+
+            # NMS
+            keep = py_cpu_nms(bounding_boxes, nms_threshold)
+            bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep]
+
+            # append
+            final_bounding_boxes.append(bounding_boxes)
+            final_landmarks.append(landmarks)
+        # self.t['forward_pass'].toc(average=True)
+        # self.batch_time += self.t['forward_pass'].diff
+        # self.total_frame += len(frames)
+        # print(self.batch_time / self.total_frame)
+
+        return final_bounding_boxes, final_landmarks

extras/facexlib/detection/retinaface_net.py

@@ -0,0 +1,196 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def conv_bn(inp, oup, stride=1, leaky=0):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True))
+
+
+def conv_bn_no_relu(inp, oup, stride):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+        nn.BatchNorm2d(oup),
+    )
+
+
+def conv_bn1X1(inp, oup, stride, leaky=0):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True))
+
+
+def conv_dw(inp, oup, stride, leaky=0.1):
+    return nn.Sequential(
+        nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
+        nn.BatchNorm2d(inp),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True),
+        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+        nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True),
+    )
+
+
+class SSH(nn.Module):
+
+    def __init__(self, in_channel, out_channel):
+        super(SSH, self).__init__()
+        assert out_channel % 4 == 0
+        leaky = 0
+        if (out_channel <= 64):
+            leaky = 0.1
+        self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1)
+
+        self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky)
+        self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
+
+        self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky)
+        self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
+
+    def forward(self, input):
+        conv3X3 = self.conv3X3(input)
+
+        conv5X5_1 = self.conv5X5_1(input)
+        conv5X5 = self.conv5X5_2(conv5X5_1)
+
+        conv7X7_2 = self.conv7X7_2(conv5X5_1)
+        conv7X7 = self.conv7x7_3(conv7X7_2)
+
+        out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
+        out = F.relu(out)
+        return out
+
+
+class FPN(nn.Module):
+
+    def __init__(self, in_channels_list, out_channels):
+        super(FPN, self).__init__()
+        leaky = 0
+        if (out_channels <= 64):
+            leaky = 0.1
+        self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky)
+        self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky)
+        self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky)
+
+        self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky)
+        self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky)
+
+    def forward(self, input):
+        # names = list(input.keys())
+        # input = list(input.values())
+
+        output1 = self.output1(input[0])
+        output2 = self.output2(input[1])
+        output3 = self.output3(input[2])
+
+        up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest')
+        output2 = output2 + up3
+        output2 = self.merge2(output2)
+
+        up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest')
+        output1 = output1 + up2
+        output1 = self.merge1(output1)
+
+        out = [output1, output2, output3]
+        return out
+
+
+class MobileNetV1(nn.Module):
+
+    def __init__(self):
+        super(MobileNetV1, self).__init__()
+        self.stage1 = nn.Sequential(
+            conv_bn(3, 8, 2, leaky=0.1),  # 3
+            conv_dw(8, 16, 1),  # 7
+            conv_dw(16, 32, 2),  # 11
+            conv_dw(32, 32, 1),  # 19
+            conv_dw(32, 64, 2),  # 27
+            conv_dw(64, 64, 1),  # 43
+        )
+        self.stage2 = nn.Sequential(
+            conv_dw(64, 128, 2),  # 43 + 16 = 59
+            conv_dw(128, 128, 1),  # 59 + 32 = 91
+            conv_dw(128, 128, 1),  # 91 + 32 = 123
+            conv_dw(128, 128, 1),  # 123 + 32 = 155
+            conv_dw(128, 128, 1),  # 155 + 32 = 187
+            conv_dw(128, 128, 1),  # 187 + 32 = 219
+        )
+        self.stage3 = nn.Sequential(
+            conv_dw(128, 256, 2),  # 219 +3 2 = 241
+            conv_dw(256, 256, 1),  # 241 + 64 = 301
+        )
+        self.avg = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(256, 1000)
+
+    def forward(self, x):
+        x = self.stage1(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.avg(x)
+        # x = self.model(x)
+        x = x.view(-1, 256)
+        x = self.fc(x)
+        return x
+
+
+class ClassHead(nn.Module):
+
+    def __init__(self, inchannels=512, num_anchors=3):
+        super(ClassHead, self).__init__()
+        self.num_anchors = num_anchors
+        self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0)
+
+    def forward(self, x):
+        out = self.conv1x1(x)
+        out = out.permute(0, 2, 3, 1).contiguous()
+
+        return out.view(out.shape[0], -1, 2)
+
+
+class BboxHead(nn.Module):
+
+    def __init__(self, inchannels=512, num_anchors=3):
+        super(BboxHead, self).__init__()
+        self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0)
+
+    def forward(self, x):
+        out = self.conv1x1(x)
+        out = out.permute(0, 2, 3, 1).contiguous()
+
+        return out.view(out.shape[0], -1, 4)
+
+
+class LandmarkHead(nn.Module):
+
+    def __init__(self, inchannels=512, num_anchors=3):
+        super(LandmarkHead, self).__init__()
+        self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0)
+
+    def forward(self, x):
+        out = self.conv1x1(x)
+        out = out.permute(0, 2, 3, 1).contiguous()
+
+        return out.view(out.shape[0], -1, 10)
+
+
+def make_class_head(fpn_num=3, inchannels=64, anchor_num=2):
+    classhead = nn.ModuleList()
+    for i in range(fpn_num):
+        classhead.append(ClassHead(inchannels, anchor_num))
+    return classhead
+
+
+def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2):
+    bboxhead = nn.ModuleList()
+    for i in range(fpn_num):
+        bboxhead.append(BboxHead(inchannels, anchor_num))
+    return bboxhead
+
+
+def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2):
+    landmarkhead = nn.ModuleList()
+    for i in range(fpn_num):
+        landmarkhead.append(LandmarkHead(inchannels, anchor_num))
+    return landmarkhead

extras/facexlib/detection/retinaface_utils.py

@@ -0,0 +1,421 @@
+import numpy as np
+import torch
+import torchvision
+from itertools import product as product
+from math import ceil
+
+
+class PriorBox(object):
+
+    def __init__(self, cfg, image_size=None, phase='train'):
+        super(PriorBox, self).__init__()
+        self.min_sizes = cfg['min_sizes']
+        self.steps = cfg['steps']
+        self.clip = cfg['clip']
+        self.image_size = image_size
+        self.feature_maps = [[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)] for step in self.steps]
+        self.name = 's'
+
+    def forward(self):
+        anchors = []
+        for k, f in enumerate(self.feature_maps):
+            min_sizes = self.min_sizes[k]
+            for i, j in product(range(f[0]), range(f[1])):
+                for min_size in min_sizes:
+                    s_kx = min_size / self.image_size[1]
+                    s_ky = min_size / self.image_size[0]
+                    dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
+                    dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
+                    for cy, cx in product(dense_cy, dense_cx):
+                        anchors += [cx, cy, s_kx, s_ky]
+
+        # back to torch land
+        output = torch.Tensor(anchors).view(-1, 4)
+        if self.clip:
+            output.clamp_(max=1, min=0)
+        return output
+
+
+def py_cpu_nms(dets, thresh):
+    """Pure Python NMS baseline."""
+    keep = torchvision.ops.nms(
+        boxes=torch.Tensor(dets[:, :4]),
+        scores=torch.Tensor(dets[:, 4]),
+        iou_threshold=thresh,
+    )
+
+    return list(keep)
+
+
+def point_form(boxes):
+    """ Convert prior_boxes to (xmin, ymin, xmax, ymax)
+    representation for comparison to point form ground truth data.
+    Args:
+        boxes: (tensor) center-size default boxes from priorbox layers.
+    Return:
+        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+    """
+    return torch.cat(
+        (
+            boxes[:, :2] - boxes[:, 2:] / 2,  # xmin, ymin
+            boxes[:, :2] + boxes[:, 2:] / 2),
+        1)  # xmax, ymax
+
+
+def center_size(boxes):
+    """ Convert prior_boxes to (cx, cy, w, h)
+    representation for comparison to center-size form ground truth data.
+    Args:
+        boxes: (tensor) point_form boxes
+    Return:
+        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+    """
+    return torch.cat(
+        (boxes[:, 2:] + boxes[:, :2]) / 2,  # cx, cy
+        boxes[:, 2:] - boxes[:, :2],
+        1)  # w, h
+
+
+def intersect(box_a, box_b):
+    """ We resize both tensors to [A,B,2] without new malloc:
+    [A,2] -> [A,1,2] -> [A,B,2]
+    [B,2] -> [1,B,2] -> [A,B,2]
+    Then we compute the area of intersect between box_a and box_b.
+    Args:
+      box_a: (tensor) bounding boxes, Shape: [A,4].
+      box_b: (tensor) bounding boxes, Shape: [B,4].
+    Return:
+      (tensor) intersection area, Shape: [A,B].
+    """
+    A = box_a.size(0)
+    B = box_b.size(0)
+    max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
+    min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2))
+    inter = torch.clamp((max_xy - min_xy), min=0)
+    return inter[:, :, 0] * inter[:, :, 1]
+
+
+def jaccard(box_a, box_b):
+    """Compute the jaccard overlap of two sets of boxes.  The jaccard overlap
+    is simply the intersection over union of two boxes.  Here we operate on
+    ground truth boxes and default boxes.
+    E.g.:
+        A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
+    Args:
+        box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
+        box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
+    Return:
+        jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
+    """
+    inter = intersect(box_a, box_b)
+    area_a = ((box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter)  # [A,B]
+    area_b = ((box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter)  # [A,B]
+    union = area_a + area_b - inter
+    return inter / union  # [A,B]
+
+
+def matrix_iou(a, b):
+    """
+    return iou of a and b, numpy version for data augenmentation
+    """
+    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+    area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
+    return area_i / (area_a[:, np.newaxis] + area_b - area_i)
+
+
+def matrix_iof(a, b):
+    """
+    return iof of a and b, numpy version for data augenmentation
+    """
+    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+    return area_i / np.maximum(area_a[:, np.newaxis], 1)
+
+
+def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):
+    """Match each prior box with the ground truth box of the highest jaccard
+    overlap, encode the bounding boxes, then return the matched indices
+    corresponding to both confidence and location preds.
+    Args:
+        threshold: (float) The overlap threshold used when matching boxes.
+        truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].
+        priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
+        variances: (tensor) Variances corresponding to each prior coord,
+            Shape: [num_priors, 4].
+        labels: (tensor) All the class labels for the image, Shape: [num_obj].
+        landms: (tensor) Ground truth landms, Shape [num_obj, 10].
+        loc_t: (tensor) Tensor to be filled w/ encoded location targets.
+        conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
+        landm_t: (tensor) Tensor to be filled w/ encoded landm targets.
+        idx: (int) current batch index
+    Return:
+        The matched indices corresponding to 1)location 2)confidence
+        3)landm preds.
+    """
+    # jaccard index
+    overlaps = jaccard(truths, point_form(priors))
+    # (Bipartite Matching)
+    # [1,num_objects] best prior for each ground truth
+    best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
+
+    # ignore hard gt
+    valid_gt_idx = best_prior_overlap[:, 0] >= 0.2
+    best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]
+    if best_prior_idx_filter.shape[0] <= 0:
+        loc_t[idx] = 0
+        conf_t[idx] = 0
+        return
+
+    # [1,num_priors] best ground truth for each prior
+    best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
+    best_truth_idx.squeeze_(0)
+    best_truth_overlap.squeeze_(0)
+    best_prior_idx.squeeze_(1)
+    best_prior_idx_filter.squeeze_(1)
+    best_prior_overlap.squeeze_(1)
+    best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2)  # ensure best prior
+    # TODO refactor: index  best_prior_idx with long tensor
+    # ensure every gt matches with its prior of max overlap
+    for j in range(best_prior_idx.size(0)):  # 判别此anchor是预测哪一个boxes
+        best_truth_idx[best_prior_idx[j]] = j
+    matches = truths[best_truth_idx]  # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来
+    conf = labels[best_truth_idx]  # Shape: [num_priors]      此处为每一个anchor对应的label取出来
+    conf[best_truth_overlap < threshold] = 0  # label as background   overlap<0.35的全部作为负样本
+    loc = encode(matches, priors, variances)
+
+    matches_landm = landms[best_truth_idx]
+    landm = encode_landm(matches_landm, priors, variances)
+    loc_t[idx] = loc  # [num_priors,4] encoded offsets to learn
+    conf_t[idx] = conf  # [num_priors] top class label for each prior
+    landm_t[idx] = landm
+
+
+def encode(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 4].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded boxes (tensor), Shape: [num_priors, 4]
+    """
+
+    # dist b/t match center and prior's center
+    g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, 2:])
+    # match wh / prior wh
+    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
+    g_wh = torch.log(g_wh) / variances[1]
+    # return target for smooth_l1_loss
+    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]
+
+
+def encode_landm(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 10].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded landm (tensor), Shape: [num_priors, 10]
+    """
+
+    # dist b/t match center and prior's center
+    matched = torch.reshape(matched, (matched.size(0), 5, 2))
+    priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2)
+    g_cxcy = matched[:, :, :2] - priors[:, :, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, :, 2:])
+    # g_cxcy /= priors[:, :, 2:]
+    g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1)
+    # return target for smooth_l1_loss
+    return g_cxcy
+
+
+# Adapted from https://github.com/Hakuyume/chainer-ssd
+def decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+                       priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+    boxes[:, :2] -= boxes[:, 2:] / 2
+    boxes[:, 2:] += boxes[:, :2]
+    return boxes
+
+
+def decode_landm(pre, priors, variances):
+    """Decode landm from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        pre (tensor): landm predictions for loc layers,
+            Shape: [num_priors,10]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded landm predictions
+    """
+    tmp = (
+        priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
+        priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
+        priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
+        priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
+        priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
+    )
+    landms = torch.cat(tmp, dim=1)
+    return landms
+
+
+def batched_decode(b_loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        b_loc (tensor): location predictions for loc layers,
+            Shape: [num_batches,num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [1,num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+    boxes = (
+        priors[:, :, :2] + b_loc[:, :, :2] * variances[0] * priors[:, :, 2:],
+        priors[:, :, 2:] * torch.exp(b_loc[:, :, 2:] * variances[1]),
+    )
+    boxes = torch.cat(boxes, dim=2)
+
+    boxes[:, :, :2] -= boxes[:, :, 2:] / 2
+    boxes[:, :, 2:] += boxes[:, :, :2]
+    return boxes
+
+
+def batched_decode_landm(pre, priors, variances):
+    """Decode landm from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        pre (tensor): landm predictions for loc layers,
+            Shape: [num_batches,num_priors,10]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [1,num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded landm predictions
+    """
+    landms = (
+        priors[:, :, :2] + pre[:, :, :2] * variances[0] * priors[:, :, 2:],
+        priors[:, :, :2] + pre[:, :, 2:4] * variances[0] * priors[:, :, 2:],
+        priors[:, :, :2] + pre[:, :, 4:6] * variances[0] * priors[:, :, 2:],
+        priors[:, :, :2] + pre[:, :, 6:8] * variances[0] * priors[:, :, 2:],
+        priors[:, :, :2] + pre[:, :, 8:10] * variances[0] * priors[:, :, 2:],
+    )
+    landms = torch.cat(landms, dim=2)
+    return landms
+
+
+def log_sum_exp(x):
+    """Utility function for computing log_sum_exp while determining
+    This will be used to determine unaveraged confidence loss across
+    all examples in a batch.
+    Args:
+        x (Variable(tensor)): conf_preds from conf layers
+    """
+    x_max = x.data.max()
+    return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max
+
+
+# Original author: Francisco Massa:
+# https://github.com/fmassa/object-detection.torch
+# Ported to PyTorch by Max deGroot (02/01/2017)
+def nms(boxes, scores, overlap=0.5, top_k=200):
+    """Apply non-maximum suppression at test time to avoid detecting too many
+    overlapping bounding boxes for a given object.
+    Args:
+        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
+        scores: (tensor) The class predscores for the img, Shape:[num_priors].
+        overlap: (float) The overlap thresh for suppressing unnecessary boxes.
+        top_k: (int) The Maximum number of box preds to consider.
+    Return:
+        The indices of the kept boxes with respect to num_priors.
+    """
+
+    keep = torch.Tensor(scores.size(0)).fill_(0).long()
+    if boxes.numel() == 0:
+        return keep
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+    area = torch.mul(x2 - x1, y2 - y1)
+    v, idx = scores.sort(0)  # sort in ascending order
+    # I = I[v >= 0.01]
+    idx = idx[-top_k:]  # indices of the top-k largest vals
+    xx1 = boxes.new()
+    yy1 = boxes.new()
+    xx2 = boxes.new()
+    yy2 = boxes.new()
+    w = boxes.new()
+    h = boxes.new()
+
+    # keep = torch.Tensor()
+    count = 0
+    while idx.numel() > 0:
+        i = idx[-1]  # index of current largest val
+        # keep.append(i)
+        keep[count] = i
+        count += 1
+        if idx.size(0) == 1:
+            break
+        idx = idx[:-1]  # remove kept element from view
+        # load bboxes of next highest vals
+        torch.index_select(x1, 0, idx, out=xx1)
+        torch.index_select(y1, 0, idx, out=yy1)
+        torch.index_select(x2, 0, idx, out=xx2)
+        torch.index_select(y2, 0, idx, out=yy2)
+        # store element-wise max with next highest score
+        xx1 = torch.clamp(xx1, min=x1[i])
+        yy1 = torch.clamp(yy1, min=y1[i])
+        xx2 = torch.clamp(xx2, max=x2[i])
+        yy2 = torch.clamp(yy2, max=y2[i])
+        w.resize_as_(xx2)
+        h.resize_as_(yy2)
+        w = xx2 - xx1
+        h = yy2 - yy1
+        # check sizes of xx1 and xx2.. after each iteration
+        w = torch.clamp(w, min=0.0)
+        h = torch.clamp(h, min=0.0)
+        inter = w * h
+        # IoU = i / (area(a) + area(b) - i)
+        rem_areas = torch.index_select(area, 0, idx)  # load remaining areas)
+        union = (rem_areas - inter) + area[i]
+        IoU = inter / union  # store result in iou
+        # keep only elements with an IoU <= overlap
+        idx = idx[IoU.le(overlap)]
+    return keep, count

extras/facexlib/parsing/__init__.py

@@ -0,0 +1,24 @@
+import torch
+
+from extras.facexlib.utils import load_file_from_url
+from .bisenet import BiSeNet
+from .parsenet import ParseNet
+
+
+def init_parsing_model(model_name='bisenet', half=False, device='cuda', model_rootpath=None):
+    if model_name == 'bisenet':
+        model = BiSeNet(num_class=19)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/parsing_bisenet.pth'
+    elif model_name == 'parsenet':
+        model = ParseNet(in_size=512, out_size=512, parsing_ch=19)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth'
+    else:
+        raise NotImplementedError(f'{model_name} is not implemented.')
+
+    model_path = load_file_from_url(
+        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
+    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+    model.load_state_dict(load_net, strict=True)
+    model.eval()
+    model = model.to(device)
+    return model

extras/facexlib/parsing/bisenet.py

@@ -0,0 +1,140 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .resnet import ResNet18
+
+
+class ConvBNReLU(nn.Module):
+
+    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False)
+        self.bn = nn.BatchNorm2d(out_chan)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(self.bn(x))
+        return x
+
+
+class BiSeNetOutput(nn.Module):
+
+    def __init__(self, in_chan, mid_chan, num_class):
+        super(BiSeNetOutput, self).__init__()
+        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+        self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        feat = self.conv(x)
+        out = self.conv_out(feat)
+        return out, feat
+
+
+class AttentionRefinementModule(nn.Module):
+
+    def __init__(self, in_chan, out_chan):
+        super(AttentionRefinementModule, self).__init__()
+        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False)
+        self.bn_atten = nn.BatchNorm2d(out_chan)
+        self.sigmoid_atten = nn.Sigmoid()
+
+    def forward(self, x):
+        feat = self.conv(x)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv_atten(atten)
+        atten = self.bn_atten(atten)
+        atten = self.sigmoid_atten(atten)
+        out = torch.mul(feat, atten)
+        return out
+
+
+class ContextPath(nn.Module):
+
+    def __init__(self):
+        super(ContextPath, self).__init__()
+        self.resnet = ResNet18()
+        self.arm16 = AttentionRefinementModule(256, 128)
+        self.arm32 = AttentionRefinementModule(512, 128)
+        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+    def forward(self, x):
+        feat8, feat16, feat32 = self.resnet(x)
+        h8, w8 = feat8.size()[2:]
+        h16, w16 = feat16.size()[2:]
+        h32, w32 = feat32.size()[2:]
+
+        avg = F.avg_pool2d(feat32, feat32.size()[2:])
+        avg = self.conv_avg(avg)
+        avg_up = F.interpolate(avg, (h32, w32), mode='nearest')
+
+        feat32_arm = self.arm32(feat32)
+        feat32_sum = feat32_arm + avg_up
+        feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest')
+        feat32_up = self.conv_head32(feat32_up)
+
+        feat16_arm = self.arm16(feat16)
+        feat16_sum = feat16_arm + feat32_up
+        feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest')
+        feat16_up = self.conv_head16(feat16_up)
+
+        return feat8, feat16_up, feat32_up  # x8, x8, x16
+
+
+class FeatureFusionModule(nn.Module):
+
+    def __init__(self, in_chan, out_chan):
+        super(FeatureFusionModule, self).__init__()
+        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+        self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False)
+        self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, fsp, fcp):
+        fcat = torch.cat([fsp, fcp], dim=1)
+        feat = self.convblk(fcat)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv1(atten)
+        atten = self.relu(atten)
+        atten = self.conv2(atten)
+        atten = self.sigmoid(atten)
+        feat_atten = torch.mul(feat, atten)
+        feat_out = feat_atten + feat
+        return feat_out
+
+
+class BiSeNet(nn.Module):
+
+    def __init__(self, num_class):
+        super(BiSeNet, self).__init__()
+        self.cp = ContextPath()
+        self.ffm = FeatureFusionModule(256, 256)
+        self.conv_out = BiSeNetOutput(256, 256, num_class)
+        self.conv_out16 = BiSeNetOutput(128, 64, num_class)
+        self.conv_out32 = BiSeNetOutput(128, 64, num_class)
+
+    def forward(self, x, return_feat=False):
+        h, w = x.size()[2:]
+        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # return res3b1 feature
+        feat_sp = feat_res8  # replace spatial path feature with res3b1 feature
+        feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+        out, feat = self.conv_out(feat_fuse)
+        out16, feat16 = self.conv_out16(feat_cp8)
+        out32, feat32 = self.conv_out32(feat_cp16)
+
+        out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True)
+        out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True)
+        out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True)
+
+        if return_feat:
+            feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True)
+            feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True)
+            feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True)
+            return out, out16, out32, feat, feat16, feat32
+        else:
+            return out, out16, out32

extras/facexlib/parsing/parsenet.py

@@ -0,0 +1,194 @@
+"""Modified from https://github.com/chaofengc/PSFRGAN
+"""
+import numpy as np
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class NormLayer(nn.Module):
+    """Normalization Layers.
+
+    Args:
+        channels: input channels, for batch norm and instance norm.
+        input_size: input shape without batch size, for layer norm.
+    """
+
+    def __init__(self, channels, normalize_shape=None, norm_type='bn'):
+        super(NormLayer, self).__init__()
+        norm_type = norm_type.lower()
+        self.norm_type = norm_type
+        if norm_type == 'bn':
+            self.norm = nn.BatchNorm2d(channels, affine=True)
+        elif norm_type == 'in':
+            self.norm = nn.InstanceNorm2d(channels, affine=False)
+        elif norm_type == 'gn':
+            self.norm = nn.GroupNorm(32, channels, affine=True)
+        elif norm_type == 'pixel':
+            self.norm = lambda x: F.normalize(x, p=2, dim=1)
+        elif norm_type == 'layer':
+            self.norm = nn.LayerNorm(normalize_shape)
+        elif norm_type == 'none':
+            self.norm = lambda x: x * 1.0
+        else:
+            assert 1 == 0, f'Norm type {norm_type} not support.'
+
+    def forward(self, x, ref=None):
+        if self.norm_type == 'spade':
+            return self.norm(x, ref)
+        else:
+            return self.norm(x)
+
+
+class ReluLayer(nn.Module):
+    """Relu Layer.
+
+    Args:
+        relu type: type of relu layer, candidates are
+            - ReLU
+            - LeakyReLU: default relu slope 0.2
+            - PRelu
+            - SELU
+            - none: direct pass
+    """
+
+    def __init__(self, channels, relu_type='relu'):
+        super(ReluLayer, self).__init__()
+        relu_type = relu_type.lower()
+        if relu_type == 'relu':
+            self.func = nn.ReLU(True)
+        elif relu_type == 'leakyrelu':
+            self.func = nn.LeakyReLU(0.2, inplace=True)
+        elif relu_type == 'prelu':
+            self.func = nn.PReLU(channels)
+        elif relu_type == 'selu':
+            self.func = nn.SELU(True)
+        elif relu_type == 'none':
+            self.func = lambda x: x * 1.0
+        else:
+            assert 1 == 0, f'Relu type {relu_type} not support.'
+
+    def forward(self, x):
+        return self.func(x)
+
+
+class ConvLayer(nn.Module):
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 scale='none',
+                 norm_type='none',
+                 relu_type='none',
+                 use_pad=True,
+                 bias=True):
+        super(ConvLayer, self).__init__()
+        self.use_pad = use_pad
+        self.norm_type = norm_type
+        if norm_type in ['bn']:
+            bias = False
+
+        stride = 2 if scale == 'down' else 1
+
+        self.scale_func = lambda x: x
+        if scale == 'up':
+            self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest')
+
+        self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2)))
+        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias)
+
+        self.relu = ReluLayer(out_channels, relu_type)
+        self.norm = NormLayer(out_channels, norm_type=norm_type)
+
+    def forward(self, x):
+        out = self.scale_func(x)
+        if self.use_pad:
+            out = self.reflection_pad(out)
+        out = self.conv2d(out)
+        out = self.norm(out)
+        out = self.relu(out)
+        return out
+
+
+class ResidualBlock(nn.Module):
+    """
+    Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html
+    """
+
+    def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'):
+        super(ResidualBlock, self).__init__()
+
+        if scale == 'none' and c_in == c_out:
+            self.shortcut_func = lambda x: x
+        else:
+            self.shortcut_func = ConvLayer(c_in, c_out, 3, scale)
+
+        scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']}
+        scale_conf = scale_config_dict[scale]
+
+        self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type)
+        self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none')
+
+    def forward(self, x):
+        identity = self.shortcut_func(x)
+
+        res = self.conv1(x)
+        res = self.conv2(res)
+        return identity + res
+
+
+class ParseNet(nn.Module):
+
+    def __init__(self,
+                 in_size=128,
+                 out_size=128,
+                 min_feat_size=32,
+                 base_ch=64,
+                 parsing_ch=19,
+                 res_depth=10,
+                 relu_type='LeakyReLU',
+                 norm_type='bn',
+                 ch_range=[32, 256]):
+        super().__init__()
+        self.res_depth = res_depth
+        act_args = {'norm_type': norm_type, 'relu_type': relu_type}
+        min_ch, max_ch = ch_range
+
+        ch_clip = lambda x: max(min_ch, min(x, max_ch))  # noqa: E731
+        min_feat_size = min(in_size, min_feat_size)
+
+        down_steps = int(np.log2(in_size // min_feat_size))
+        up_steps = int(np.log2(out_size // min_feat_size))
+
+        # =============== define encoder-body-decoder ====================
+        self.encoder = []
+        self.encoder.append(ConvLayer(3, base_ch, 3, 1))
+        head_ch = base_ch
+        for i in range(down_steps):
+            cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2)
+            self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args))
+            head_ch = head_ch * 2
+
+        self.body = []
+        for i in range(res_depth):
+            self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args))
+
+        self.decoder = []
+        for i in range(up_steps):
+            cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2)
+            self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args))
+            head_ch = head_ch // 2
+
+        self.encoder = nn.Sequential(*self.encoder)
+        self.body = nn.Sequential(*self.body)
+        self.decoder = nn.Sequential(*self.decoder)
+        self.out_img_conv = ConvLayer(ch_clip(head_ch), 3)
+        self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch)
+
+    def forward(self, x):
+        feat = self.encoder(x)
+        x = feat + self.body(feat)
+        x = self.decoder(x)
+        out_img = self.out_img_conv(x)
+        out_mask = self.out_mask_conv(x)
+        return out_mask, out_img

extras/facexlib/parsing/resnet.py

@@ -0,0 +1,69 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+
+    def __init__(self, in_chan, out_chan, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(in_chan, out_chan, stride)
+        self.bn1 = nn.BatchNorm2d(out_chan)
+        self.conv2 = conv3x3(out_chan, out_chan)
+        self.bn2 = nn.BatchNorm2d(out_chan)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = None
+        if in_chan != out_chan or stride != 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_chan),
+            )
+
+    def forward(self, x):
+        residual = self.conv1(x)
+        residual = F.relu(self.bn1(residual))
+        residual = self.conv2(residual)
+        residual = self.bn2(residual)
+
+        shortcut = x
+        if self.downsample is not None:
+            shortcut = self.downsample(x)
+
+        out = shortcut + residual
+        out = self.relu(out)
+        return out
+
+
+def create_layer_basic(in_chan, out_chan, bnum, stride=1):
+    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
+    for i in range(bnum - 1):
+        layers.append(BasicBlock(out_chan, out_chan, stride=1))
+    return nn.Sequential(*layers)
+
+
+class ResNet18(nn.Module):
+
+    def __init__(self):
+        super(ResNet18, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
+        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
+        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
+        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(self.bn1(x))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        feat8 = self.layer2(x)  # 1/8
+        feat16 = self.layer3(feat8)  # 1/16
+        feat32 = self.layer4(feat16)  # 1/32
+        return feat8, feat16, feat32

extras/facexlib/utils/__init__.py

@@ -0,0 +1,7 @@
+from .face_utils import align_crop_face_landmarks, compute_increased_bbox, get_valid_bboxes, paste_face_back
+from .misc import img2tensor, load_file_from_url, scandir
+
+__all__ = [
+    'align_crop_face_landmarks', 'compute_increased_bbox', 'get_valid_bboxes', 'load_file_from_url', 'paste_face_back',
+    'img2tensor', 'scandir'
+]

extras/facexlib/utils/face_restoration_helper.py

@@ -0,0 +1,374 @@
+import cv2
+import numpy as np
+import os
+import torch
+from torchvision.transforms.functional import normalize
+
+from extras.facexlib.detection import init_detection_model
+from extras.facexlib.parsing import init_parsing_model
+from extras.facexlib.utils.misc import img2tensor, imwrite
+
+
+def get_largest_face(det_faces, h, w):
+
+    def get_location(val, length):
+        if val < 0:
+            return 0
+        elif val > length:
+            return length
+        else:
+            return val
+
+    face_areas = []
+    for det_face in det_faces:
+        left = get_location(det_face[0], w)
+        right = get_location(det_face[2], w)
+        top = get_location(det_face[1], h)
+        bottom = get_location(det_face[3], h)
+        face_area = (right - left) * (bottom - top)
+        face_areas.append(face_area)
+    largest_idx = face_areas.index(max(face_areas))
+    return det_faces[largest_idx], largest_idx
+
+
+def get_center_face(det_faces, h=0, w=0, center=None):
+    if center is not None:
+        center = np.array(center)
+    else:
+        center = np.array([w / 2, h / 2])
+    center_dist = []
+    for det_face in det_faces:
+        face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2])
+        dist = np.linalg.norm(face_center - center)
+        center_dist.append(dist)
+    center_idx = center_dist.index(min(center_dist))
+    return det_faces[center_idx], center_idx
+
+
+class FaceRestoreHelper(object):
+    """Helper for the face restoration pipeline (base class)."""
+
+    def __init__(self,
+                 upscale_factor,
+                 face_size=512,
+                 crop_ratio=(1, 1),
+                 det_model='retinaface_resnet50',
+                 save_ext='png',
+                 template_3points=False,
+                 pad_blur=False,
+                 use_parse=False,
+                 device=None,
+                 model_rootpath=None):
+        self.template_3points = template_3points  # improve robustness
+        self.upscale_factor = upscale_factor
+        # the cropped face ratio based on the square face
+        self.crop_ratio = crop_ratio  # (h, w)
+        assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1'
+        self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0]))
+
+        if self.template_3points:
+            self.face_template = np.array([[192, 240], [319, 240], [257, 371]])
+        else:
+            # standard 5 landmarks for FFHQ faces with 512 x 512
+            self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935],
+                                           [201.26117, 371.41043], [313.08905, 371.15118]])
+        self.face_template = self.face_template * (face_size / 512.0)
+        if self.crop_ratio[0] > 1:
+            self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2
+        if self.crop_ratio[1] > 1:
+            self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2
+        self.save_ext = save_ext
+        self.pad_blur = pad_blur
+        if self.pad_blur is True:
+            self.template_3points = False
+
+        self.all_landmarks_5 = []
+        self.det_faces = []
+        self.affine_matrices = []
+        self.inverse_affine_matrices = []
+        self.cropped_faces = []
+        self.restored_faces = []
+        self.pad_input_imgs = []
+
+        if device is None:
+            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        else:
+            self.device = device
+
+        # init face detection model
+        self.face_det = init_detection_model(det_model, half=False, device=self.device, model_rootpath=model_rootpath)
+
+        # init face parsing model
+        self.use_parse = use_parse
+        self.face_parse = init_parsing_model(model_name='parsenet', device=self.device, model_rootpath=model_rootpath)
+
+    def set_upscale_factor(self, upscale_factor):
+        self.upscale_factor = upscale_factor
+
+    def read_image(self, img):
+        """img can be image path or cv2 loaded image."""
+        # self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255]
+        if isinstance(img, str):
+            img = cv2.imread(img)
+
+        if np.max(img) > 256:  # 16-bit image
+            img = img / 65535 * 255
+        if len(img.shape) == 2:  # gray image
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        elif img.shape[2] == 4:  # RGBA image with alpha channel
+            img = img[:, :, 0:3]
+
+        self.input_img = img
+
+    def get_face_landmarks_5(self,
+                             only_keep_largest=False,
+                             only_center_face=False,
+                             resize=None,
+                             blur_ratio=0.01,
+                             eye_dist_threshold=None):
+        if resize is None:
+            scale = 1
+            input_img = self.input_img
+        else:
+            h, w = self.input_img.shape[0:2]
+            scale = min(h, w) / resize
+            h, w = int(h / scale), int(w / scale)
+            input_img = cv2.resize(self.input_img, (w, h), interpolation=cv2.INTER_LANCZOS4)
+
+        with torch.no_grad():
+            bboxes = self.face_det.detect_faces(input_img, 0.97) * scale
+        for bbox in bboxes:
+            # remove faces with too small eye distance: side faces or too small faces
+            eye_dist = np.linalg.norm([bbox[5] - bbox[7], bbox[6] - bbox[8]])
+            if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold):
+                continue
+
+            if self.template_3points:
+                landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)])
+            else:
+                landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)])
+            self.all_landmarks_5.append(landmark)
+            self.det_faces.append(bbox[0:5])
+        if len(self.det_faces) == 0:
+            return 0
+        if only_keep_largest:
+            h, w, _ = self.input_img.shape
+            self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w)
+            self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]]
+        elif only_center_face:
+            h, w, _ = self.input_img.shape
+            self.det_faces, center_idx = get_center_face(self.det_faces, h, w)
+            self.all_landmarks_5 = [self.all_landmarks_5[center_idx]]
+
+        # pad blurry images
+        if self.pad_blur:
+            self.pad_input_imgs = []
+            for landmarks in self.all_landmarks_5:
+                # get landmarks
+                eye_left = landmarks[0, :]
+                eye_right = landmarks[1, :]
+                eye_avg = (eye_left + eye_right) * 0.5
+                mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5
+                eye_to_eye = eye_right - eye_left
+                eye_to_mouth = mouth_avg - eye_avg
+
+                # Get the oriented crop rectangle
+                # x: half width of the oriented crop rectangle
+                x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
+                #  - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise
+                # norm with the hypotenuse: get the direction
+                x /= np.hypot(*x)  # get the hypotenuse of a right triangle
+                rect_scale = 1.5
+                x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)
+                # y: half height of the oriented crop rectangle
+                y = np.flipud(x) * [-1, 1]
+
+                # c: center
+                c = eye_avg + eye_to_mouth * 0.1
+                # quad: (left_top, left_bottom, right_bottom, right_top)
+                quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+                # qsize: side length of the square
+                qsize = np.hypot(*x) * 2
+                border = max(int(np.rint(qsize * 0.1)), 3)
+
+                # get pad
+                # pad: (width_left, height_top, width_right, height_bottom)
+                pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
+                       int(np.ceil(max(quad[:, 1]))))
+                pad = [
+                    max(-pad[0] + border, 1),
+                    max(-pad[1] + border, 1),
+                    max(pad[2] - self.input_img.shape[0] + border, 1),
+                    max(pad[3] - self.input_img.shape[1] + border, 1)
+                ]
+
+                if max(pad) > 1:
+                    # pad image
+                    pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
+                    # modify landmark coords
+                    landmarks[:, 0] += pad[0]
+                    landmarks[:, 1] += pad[1]
+                    # blur pad images
+                    h, w, _ = pad_img.shape
+                    y, x, _ = np.ogrid[:h, :w, :1]
+                    mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],
+                                                       np.float32(w - 1 - x) / pad[2]),
+                                      1.0 - np.minimum(np.float32(y) / pad[1],
+                                                       np.float32(h - 1 - y) / pad[3]))
+                    blur = int(qsize * blur_ratio)
+                    if blur % 2 == 0:
+                        blur += 1
+                    blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur))
+                    # blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0)
+
+                    pad_img = pad_img.astype('float32')
+                    pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
+                    pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0)
+                    pad_img = np.clip(pad_img, 0, 255)  # float32, [0, 255]
+                    self.pad_input_imgs.append(pad_img)
+                else:
+                    self.pad_input_imgs.append(np.copy(self.input_img))
+
+        return len(self.all_landmarks_5)
+
+    def align_warp_face(self, save_cropped_path=None, border_mode='constant'):
+        """Align and warp faces with face template.
+        """
+        if self.pad_blur:
+            assert len(self.pad_input_imgs) == len(
+                self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}'
+        for idx, landmark in enumerate(self.all_landmarks_5):
+            # use 5 landmarks to get affine matrix
+            # use cv2.LMEDS method for the equivalence to skimage transform
+            # ref: https://blog.csdn.net/yichxi/article/details/115827338
+            affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0]
+            self.affine_matrices.append(affine_matrix)
+            # warp and crop faces
+            if border_mode == 'constant':
+                border_mode = cv2.BORDER_CONSTANT
+            elif border_mode == 'reflect101':
+                border_mode = cv2.BORDER_REFLECT101
+            elif border_mode == 'reflect':
+                border_mode = cv2.BORDER_REFLECT
+            if self.pad_blur:
+                input_img = self.pad_input_imgs[idx]
+            else:
+                input_img = self.input_img
+            cropped_face = cv2.warpAffine(
+                input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132))  # gray
+            self.cropped_faces.append(cropped_face)
+            # save the cropped face
+            if save_cropped_path is not None:
+                path = os.path.splitext(save_cropped_path)[0]
+                save_path = f'{path}_{idx:02d}.{self.save_ext}'
+                imwrite(cropped_face, save_path)
+
+    def get_inverse_affine(self, save_inverse_affine_path=None):
+        """Get inverse affine matrix."""
+        for idx, affine_matrix in enumerate(self.affine_matrices):
+            inverse_affine = cv2.invertAffineTransform(affine_matrix)
+            inverse_affine *= self.upscale_factor
+            self.inverse_affine_matrices.append(inverse_affine)
+            # save inverse affine matrices
+            if save_inverse_affine_path is not None:
+                path, _ = os.path.splitext(save_inverse_affine_path)
+                save_path = f'{path}_{idx:02d}.pth'
+                torch.save(inverse_affine, save_path)
+
+    def add_restored_face(self, face):
+        self.restored_faces.append(face)
+
+    def paste_faces_to_input_image(self, save_path=None, upsample_img=None):
+        h, w, _ = self.input_img.shape
+        h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor)
+
+        if upsample_img is None:
+            # simply resize the background
+            upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
+        else:
+            upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
+
+        assert len(self.restored_faces) == len(
+            self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.')
+        for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices):
+            # Add an offset to inverse affine matrix, for more precise back alignment
+            if self.upscale_factor > 1:
+                extra_offset = 0.5 * self.upscale_factor
+            else:
+                extra_offset = 0
+            inverse_affine[:, 2] += extra_offset
+            inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up))
+
+            if self.use_parse:
+                # inference
+                face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR)
+                face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True)
+                normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+                face_input = torch.unsqueeze(face_input, 0).to(self.device)
+                with torch.no_grad():
+                    out = self.face_parse(face_input)[0]
+                out = out.argmax(dim=1).squeeze().cpu().numpy()
+
+                mask = np.zeros(out.shape)
+                MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0]
+                for idx, color in enumerate(MASK_COLORMAP):
+                    mask[out == idx] = color
+                #  blur the mask
+                mask = cv2.GaussianBlur(mask, (101, 101), 11)
+                mask = cv2.GaussianBlur(mask, (101, 101), 11)
+                # remove the black borders
+                thres = 10
+                mask[:thres, :] = 0
+                mask[-thres:, :] = 0
+                mask[:, :thres] = 0
+                mask[:, -thres:] = 0
+                mask = mask / 255.
+
+                mask = cv2.resize(mask, restored_face.shape[:2])
+                mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3)
+                inv_soft_mask = mask[:, :, None]
+                pasted_face = inv_restored
+
+            else:  # use square parse maps
+                mask = np.ones(self.face_size, dtype=np.float32)
+                inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
+                # remove the black borders
+                inv_mask_erosion = cv2.erode(
+                    inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))
+                pasted_face = inv_mask_erosion[:, :, None] * inv_restored
+                total_face_area = np.sum(inv_mask_erosion)  # // 3
+                # compute the fusion edge based on the area of face
+                w_edge = int(total_face_area**0.5) // 20
+                erosion_radius = w_edge * 2
+                inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
+                blur_size = w_edge * 2
+                inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
+                if len(upsample_img.shape) == 2:  # upsample_img is gray image
+                    upsample_img = upsample_img[:, :, None]
+                inv_soft_mask = inv_soft_mask[:, :, None]
+
+            if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4:  # alpha channel
+                alpha = upsample_img[:, :, 3:]
+                upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3]
+                upsample_img = np.concatenate((upsample_img, alpha), axis=2)
+            else:
+                upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img
+
+        if np.max(upsample_img) > 256:  # 16-bit image
+            upsample_img = upsample_img.astype(np.uint16)
+        else:
+            upsample_img = upsample_img.astype(np.uint8)
+        if save_path is not None:
+            path = os.path.splitext(save_path)[0]
+            save_path = f'{path}.{self.save_ext}'
+            imwrite(upsample_img, save_path)
+        return upsample_img
+
+    def clean_all(self):
+        self.all_landmarks_5 = []
+        self.restored_faces = []
+        self.affine_matrices = []
+        self.cropped_faces = []
+        self.inverse_affine_matrices = []
+        self.det_faces = []
+        self.pad_input_imgs = []

extras/facexlib/utils/face_utils.py

@@ -0,0 +1,250 @@
+import cv2
+import numpy as np
+import torch
+
+
+def compute_increased_bbox(bbox, increase_area, preserve_aspect=True):
+    left, top, right, bot = bbox
+    width = right - left
+    height = bot - top
+
+    if preserve_aspect:
+        width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))
+        height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))
+    else:
+        width_increase = height_increase = increase_area
+    left = int(left - width_increase * width)
+    top = int(top - height_increase * height)
+    right = int(right + width_increase * width)
+    bot = int(bot + height_increase * height)
+    return (left, top, right, bot)
+
+
+def get_valid_bboxes(bboxes, h, w):
+    left = max(bboxes[0], 0)
+    top = max(bboxes[1], 0)
+    right = min(bboxes[2], w)
+    bottom = min(bboxes[3], h)
+    return (left, top, right, bottom)
+
+
+def align_crop_face_landmarks(img,
+                              landmarks,
+                              output_size,
+                              transform_size=None,
+                              enable_padding=True,
+                              return_inverse_affine=False,
+                              shrink_ratio=(1, 1)):
+    """Align and crop face with landmarks.
+
+    The output_size and transform_size are based on width. The height is
+    adjusted based on shrink_ratio_h/shring_ration_w.
+
+    Modified from:
+    https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
+
+    Args:
+        img (Numpy array): Input image.
+        landmarks (Numpy array): 5 or 68 or 98 landmarks.
+        output_size (int): Output face size.
+        transform_size (ing): Transform size. Usually the four time of
+            output_size.
+        enable_padding (float): Default: True.
+        shrink_ratio (float | tuple[float] | list[float]): Shring the whole
+            face for height and width (crop larger area). Default: (1, 1).
+
+    Returns:
+        (Numpy array): Cropped face.
+    """
+    lm_type = 'retinaface_5'  # Options: dlib_5, retinaface_5
+
+    if isinstance(shrink_ratio, (float, int)):
+        shrink_ratio = (shrink_ratio, shrink_ratio)
+    if transform_size is None:
+        transform_size = output_size * 4
+
+    # Parse landmarks
+    lm = np.array(landmarks)
+    if lm.shape[0] == 5 and lm_type == 'retinaface_5':
+        eye_left = lm[0]
+        eye_right = lm[1]
+        mouth_avg = (lm[3] + lm[4]) * 0.5
+    elif lm.shape[0] == 5 and lm_type == 'dlib_5':
+        lm_eye_left = lm[2:4]
+        lm_eye_right = lm[0:2]
+        eye_left = np.mean(lm_eye_left, axis=0)
+        eye_right = np.mean(lm_eye_right, axis=0)
+        mouth_avg = lm[4]
+    elif lm.shape[0] == 68:
+        lm_eye_left = lm[36:42]
+        lm_eye_right = lm[42:48]
+        eye_left = np.mean(lm_eye_left, axis=0)
+        eye_right = np.mean(lm_eye_right, axis=0)
+        mouth_avg = (lm[48] + lm[54]) * 0.5
+    elif lm.shape[0] == 98:
+        lm_eye_left = lm[60:68]
+        lm_eye_right = lm[68:76]
+        eye_left = np.mean(lm_eye_left, axis=0)
+        eye_right = np.mean(lm_eye_right, axis=0)
+        mouth_avg = (lm[76] + lm[82]) * 0.5
+
+    eye_avg = (eye_left + eye_right) * 0.5
+    eye_to_eye = eye_right - eye_left
+    eye_to_mouth = mouth_avg - eye_avg
+
+    # Get the oriented crop rectangle
+    # x: half width of the oriented crop rectangle
+    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
+    #  - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise
+    # norm with the hypotenuse: get the direction
+    x /= np.hypot(*x)  # get the hypotenuse of a right triangle
+    rect_scale = 1  # TODO: you can edit it to get larger rect
+    x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)
+    # y: half height of the oriented crop rectangle
+    y = np.flipud(x) * [-1, 1]
+
+    x *= shrink_ratio[1]  # width
+    y *= shrink_ratio[0]  # height
+
+    # c: center
+    c = eye_avg + eye_to_mouth * 0.1
+    # quad: (left_top, left_bottom, right_bottom, right_top)
+    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+    # qsize: side length of the square
+    qsize = np.hypot(*x) * 2
+
+    quad_ori = np.copy(quad)
+    # Shrink, for large face
+    # TODO: do we really need shrink
+    shrink = int(np.floor(qsize / output_size * 0.5))
+    if shrink > 1:
+        h, w = img.shape[0:2]
+        rsize = (int(np.rint(float(w) / shrink)), int(np.rint(float(h) / shrink)))
+        img = cv2.resize(img, rsize, interpolation=cv2.INTER_AREA)
+        quad /= shrink
+        qsize /= shrink
+
+    # Crop
+    h, w = img.shape[0:2]
+    border = max(int(np.rint(qsize * 0.1)), 3)
+    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
+            int(np.ceil(max(quad[:, 1]))))
+    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, w), min(crop[3] + border, h))
+    if crop[2] - crop[0] < w or crop[3] - crop[1] < h:
+        img = img[crop[1]:crop[3], crop[0]:crop[2], :]
+        quad -= crop[0:2]
+
+    # Pad
+    # pad: (width_left, height_top, width_right, height_bottom)
+    h, w = img.shape[0:2]
+    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
+           int(np.ceil(max(quad[:, 1]))))
+    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - w + border, 0), max(pad[3] - h + border, 0))
+    if enable_padding and max(pad) > border - 4:
+        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
+        img = np.pad(img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
+        h, w = img.shape[0:2]
+        y, x, _ = np.ogrid[:h, :w, :1]
+        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],
+                                           np.float32(w - 1 - x) / pad[2]),
+                          1.0 - np.minimum(np.float32(y) / pad[1],
+                                           np.float32(h - 1 - y) / pad[3]))
+        blur = int(qsize * 0.02)
+        if blur % 2 == 0:
+            blur += 1
+        blur_img = cv2.boxFilter(img, 0, ksize=(blur, blur))
+
+        img = img.astype('float32')
+        img += (blur_img - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
+        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
+        img = np.clip(img, 0, 255)  # float32, [0, 255]
+        quad += pad[:2]
+
+    # Transform use cv2
+    h_ratio = shrink_ratio[0] / shrink_ratio[1]
+    dst_h, dst_w = int(transform_size * h_ratio), transform_size
+    template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])
+    # use cv2.LMEDS method for the equivalence to skimage transform
+    # ref: https://blog.csdn.net/yichxi/article/details/115827338
+    affine_matrix = cv2.estimateAffinePartial2D(quad, template, method=cv2.LMEDS)[0]
+    cropped_face = cv2.warpAffine(
+        img, affine_matrix, (dst_w, dst_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132))  # gray
+
+    if output_size < transform_size:
+        cropped_face = cv2.resize(
+            cropped_face, (output_size, int(output_size * h_ratio)), interpolation=cv2.INTER_LINEAR)
+
+    if return_inverse_affine:
+        dst_h, dst_w = int(output_size * h_ratio), output_size
+        template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])
+        # use cv2.LMEDS method for the equivalence to skimage transform
+        # ref: https://blog.csdn.net/yichxi/article/details/115827338
+        affine_matrix = cv2.estimateAffinePartial2D(
+            quad_ori, np.array([[0, 0], [0, output_size], [dst_w, dst_h], [dst_w, 0]]), method=cv2.LMEDS)[0]
+        inverse_affine = cv2.invertAffineTransform(affine_matrix)
+    else:
+        inverse_affine = None
+    return cropped_face, inverse_affine
+
+
+def paste_face_back(img, face, inverse_affine):
+    h, w = img.shape[0:2]
+    face_h, face_w = face.shape[0:2]
+    inv_restored = cv2.warpAffine(face, inverse_affine, (w, h))
+    mask = np.ones((face_h, face_w, 3), dtype=np.float32)
+    inv_mask = cv2.warpAffine(mask, inverse_affine, (w, h))
+    # remove the black borders
+    inv_mask_erosion = cv2.erode(inv_mask, np.ones((2, 2), np.uint8))
+    inv_restored_remove_border = inv_mask_erosion * inv_restored
+    total_face_area = np.sum(inv_mask_erosion) // 3
+    # compute the fusion edge based on the area of face
+    w_edge = int(total_face_area**0.5) // 20
+    erosion_radius = w_edge * 2
+    inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
+    blur_size = w_edge * 2
+    inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
+    img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * img
+    # float32, [0, 255]
+    return img
+
+
+if __name__ == '__main__':
+    import os
+
+    from extras.facexlib.detection import init_detection_model
+    from extras.facexlib.utils.face_restoration_helper import get_largest_face
+    from extras.facexlib.visualization import visualize_detection
+
+    img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png'
+    img_name = os.splitext(os.path.basename(img_path))[0]
+
+    # initialize model
+    det_net = init_detection_model('retinaface_resnet50', half=False)
+    img_ori = cv2.imread(img_path)
+    h, w = img_ori.shape[0:2]
+    # if larger than 800, scale it
+    scale = max(h / 800, w / 800)
+    if scale > 1:
+        img = cv2.resize(img_ori, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_LINEAR)
+
+    with torch.no_grad():
+        bboxes = det_net.detect_faces(img, 0.97)
+    if scale > 1:
+        bboxes *= scale  # the score is incorrect
+    bboxes = get_largest_face(bboxes, h, w)[0]
+    visualize_detection(img_ori, [bboxes], f'tmp/{img_name}_det.png')
+
+    landmarks = np.array([[bboxes[i], bboxes[i + 1]] for i in range(5, 15, 2)])
+
+    cropped_face, inverse_affine = align_crop_face_landmarks(
+        img_ori,
+        landmarks,
+        output_size=512,
+        transform_size=None,
+        enable_padding=True,
+        return_inverse_affine=True,
+        shrink_ratio=(1, 1))
+
+    cv2.imwrite(f'tmp/{img_name}_cropeed_face.png', cropped_face)
+    img = paste_face_back(img_ori, cropped_face, inverse_affine)
+    cv2.imwrite(f'tmp/{img_name}_back.png', img)

extras/facexlib/utils/misc.py

@@ -0,0 +1,118 @@
+import cv2
+import os
+import os.path as osp
+import torch
+from torch.hub import download_url_to_file, get_dir
+from urllib.parse import urlparse
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+
+def imwrite(img, file_path, params=None, auto_mkdir=True):
+    """Write image to file.
+
+    Args:
+        img (ndarray): Image array to be written.
+        file_path (str): Image file path.
+        params (None or list): Same as opencv's :func:`imwrite` interface.
+        auto_mkdir (bool): If the parent folder of `file_path` does not exist,
+            whether to create it automatically.
+
+    Returns:
+        bool: Successful or not.
+    """
+    if auto_mkdir:
+        dir_name = os.path.abspath(os.path.dirname(file_path))
+        os.makedirs(dir_name, exist_ok=True)
+    return cv2.imwrite(file_path, img, params)
+
+
+def img2tensor(imgs, bgr2rgb=True, float32=True):
+    """Numpy array to tensor.
+
+    Args:
+        imgs (list[ndarray] | ndarray): Input images.
+        bgr2rgb (bool): Whether to change bgr to rgb.
+        float32 (bool): Whether to change to float32.
+
+    Returns:
+        list[tensor] | tensor: Tensor images. If returned results only have
+            one element, just return tensor.
+    """
+
+    def _totensor(img, bgr2rgb, float32):
+        if img.shape[2] == 3 and bgr2rgb:
+            if img.dtype == 'float64':
+                img = img.astype('float32')
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = torch.from_numpy(img.transpose(2, 0, 1))
+        if float32:
+            img = img.float()
+        return img
+
+    if isinstance(imgs, list):
+        return [_totensor(img, bgr2rgb, float32) for img in imgs]
+    else:
+        return _totensor(imgs, bgr2rgb, float32)
+
+
+def load_file_from_url(url, model_dir=None, progress=True, file_name=None, save_dir=None):
+    """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
+    """
+    if model_dir is None:
+        hub_dir = get_dir()
+        model_dir = os.path.join(hub_dir, 'checkpoints')
+
+    if save_dir is None:
+        save_dir = os.path.join(ROOT_DIR, model_dir)
+    os.makedirs(save_dir, exist_ok=True)
+
+    parts = urlparse(url)
+    filename = os.path.basename(parts.path)
+    if file_name is not None:
+        filename = file_name
+    cached_file = os.path.abspath(os.path.join(save_dir, filename))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
+    return cached_file
+
+
+def scandir(dir_path, suffix=None, recursive=False, full_path=False):
+    """Scan a directory to find the interested files.
+    Args:
+        dir_path (str): Path of the directory.
+        suffix (str | tuple(str), optional): File suffix that we are
+            interested in. Default: None.
+        recursive (bool, optional): If set to True, recursively scan the
+            directory. Default: False.
+        full_path (bool, optional): If set to True, include the dir_path.
+            Default: False.
+    Returns:
+        A generator for all the interested files with relative paths.
+    """
+
+    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
+        raise TypeError('"suffix" must be a string or tuple of strings')
+
+    root = dir_path
+
+    def _scandir(dir_path, suffix, recursive):
+        for entry in os.scandir(dir_path):
+            if not entry.name.startswith('.') and entry.is_file():
+                if full_path:
+                    return_path = entry.path
+                else:
+                    return_path = osp.relpath(entry.path, root)
+
+                if suffix is None:
+                    yield return_path
+                elif return_path.endswith(suffix):
+                    yield return_path
+            else:
+                if recursive:
+                    yield from _scandir(entry.path, suffix=suffix, recursive=recursive)
+                else:
+                    continue
+
+    return _scandir(dir_path, suffix=suffix, recursive=recursive)

extras/interrogate.py

@@ -0,0 +1,63 @@
+import os
+import torch
+import ldm_patched.modules.model_management as model_management
+
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+from modules.model_loader import load_file_from_url
+from modules.config import path_clip_vision
+from ldm_patched.modules.model_patcher import ModelPatcher
+from extras.BLIP.models.blip import blip_decoder
+
+
+blip_image_eval_size = 384
+blip_repo_root = os.path.join(os.path.dirname(__file__), 'BLIP')
+
+
+class Interrogator:
+    def __init__(self):
+        self.blip_model = None
+        self.load_device = torch.device('cpu')
+        self.offload_device = torch.device('cpu')
+        self.dtype = torch.float32
+
+    @torch.no_grad()
+    @torch.inference_mode()
+    def interrogate(self, img_rgb):
+        if self.blip_model is None:
+            filename = load_file_from_url(
+                url='https://huggingface.co/lllyasviel/misc/resolve/main/model_base_caption_capfilt_large.pth',
+                model_dir=path_clip_vision,
+                file_name='model_base_caption_capfilt_large.pth',
+            )
+
+            model = blip_decoder(pretrained=filename, image_size=blip_image_eval_size, vit='base',
+                                 med_config=os.path.join(blip_repo_root, "configs", "med_config.json"))
+            model.eval()
+
+            self.load_device = model_management.text_encoder_device()
+            self.offload_device = model_management.text_encoder_offload_device()
+            self.dtype = torch.float32
+
+            model.to(self.offload_device)
+
+            if model_management.should_use_fp16(device=self.load_device):
+                model.half()
+                self.dtype = torch.float16
+
+            self.blip_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)
+
+        model_management.load_model_gpu(self.blip_model)
+
+        gpu_image = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
+            transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+        ])(img_rgb).unsqueeze(0).to(device=self.load_device, dtype=self.dtype)
+
+        caption = self.blip_model.model.generate(gpu_image, sample=True, num_beams=1, max_length=75)[0]
+
+        return caption
+
+
+default_interrogator = Interrogator().interrogate

extras/ip_adapter.py

@@ -1,12 +1,12 @@
 import torch
-import fcbh.clip_vision
+import ldm_patched.modules.clip_vision
 import safetensors.torch as sf
-import fcbh.model_management as model_management
+import ldm_patched.modules.model_management as model_management
 import contextlib
-import fcbh.ldm.modules.attention as attention
+import ldm_patched.ldm.modules.attention as attention
 
-from fooocus_extras.resampler import Resampler
-from fcbh.model_patcher import ModelPatcher
+from extras.resampler import Resampler
+from ldm_patched.modules.model_patcher import ModelPatcher
 from modules.core import numpy_to_pytorch
 
 
@@ -82,28 +82,22 @@ class IPAdapterModel(torch.nn.Module):
         self.ip_layers.load_state_dict_ordered(state_dict["ip_adapter"])
 
 
-clip_vision: fcbh.clip_vision.ClipVisionModel = None
+clip_vision: ldm_patched.modules.clip_vision.ClipVisionModel = None
 ip_negative: torch.Tensor = None
-image_proj_model: ModelPatcher = None
-ip_layers: ModelPatcher = None
-ip_adapter: IPAdapterModel = None
-ip_unconds = None
+ip_adapters: dict = {}
 
 
 def load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path):
-    global clip_vision, image_proj_model, ip_layers, ip_negative, ip_adapter, ip_unconds
+    global clip_vision, ip_negative, ip_adapters
 
-    if clip_vision_path is None:
-        return
-    if ip_negative_path is None:
-        return
-    if ip_adapter_path is None:
-        return
-    if clip_vision is not None and image_proj_model is not None and ip_layers is not None and ip_negative is not None:
-        return
+    if clip_vision is None and isinstance(clip_vision_path, str):
+        clip_vision = ldm_patched.modules.clip_vision.load(clip_vision_path)
 
-    ip_negative = sf.load_file(ip_negative_path)['data']
-    clip_vision = fcbh.clip_vision.load(clip_vision_path)
+    if ip_negative is None and isinstance(ip_negative_path, str):
+        ip_negative = sf.load_file(ip_negative_path)['data']
+
+    if not isinstance(ip_adapter_path, str) or ip_adapter_path in ip_adapters:
+        return
 
     load_device = model_management.get_torch_device()
     offload_device = torch.device('cpu')
@@ -141,7 +135,13 @@ def load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path):
     ip_layers = ModelPatcher(model=ip_adapter.ip_layers, load_device=load_device,
                              offload_device=offload_device)
 
-    ip_unconds = None
+    ip_adapters[ip_adapter_path] = dict(
+        ip_adapter=ip_adapter,
+        image_proj_model=image_proj_model,
+        ip_layers=ip_layers,
+        ip_unconds=None
+    )
+
     return
 
 
@@ -161,19 +161,18 @@ def clip_preprocess(image):
 
 @torch.no_grad()
 @torch.inference_mode()
-def preprocess(img):
-    global ip_unconds
+def preprocess(img, ip_adapter_path):
+    global ip_adapters
+    entry = ip_adapters[ip_adapter_path]
 
-    fcbh.model_management.load_model_gpu(clip_vision.patcher)
+    ldm_patched.modules.model_management.load_model_gpu(clip_vision.patcher)
     pixel_values = clip_preprocess(numpy_to_pytorch(img).to(clip_vision.load_device))
+    outputs = clip_vision.model(pixel_values=pixel_values, output_hidden_states=True)
 
-    if clip_vision.dtype != torch.float32:
-        precision_scope = torch.autocast
-    else:
-        precision_scope = lambda a, b: contextlib.nullcontext(a)
-
-    with precision_scope(fcbh.model_management.get_autocast_device(clip_vision.load_device), torch.float32):
-        outputs = clip_vision.model(pixel_values=pixel_values, output_hidden_states=True)
+    ip_adapter = entry['ip_adapter']
+    ip_layers = entry['ip_layers']
+    image_proj_model = entry['image_proj_model']
+    ip_unconds = entry['ip_unconds']
 
     if ip_adapter.plus:
         cond = outputs.hidden_states[-2]
@@ -182,17 +181,19 @@ def preprocess(img):
 
     cond = cond.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)
 
-    fcbh.model_management.load_model_gpu(image_proj_model)
+    ldm_patched.modules.model_management.load_model_gpu(image_proj_model)
     cond = image_proj_model.model(cond).to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)
 
-    fcbh.model_management.load_model_gpu(ip_layers)
+    ldm_patched.modules.model_management.load_model_gpu(ip_layers)
 
     if ip_unconds is None:
         uncond = ip_negative.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)
         ip_unconds = [m(uncond).cpu() for m in ip_layers.model.to_kvs]
+        entry['ip_unconds'] = ip_unconds
 
     ip_conds = [m(cond).cpu() for m in ip_layers.model.to_kvs]
-    return ip_conds
+
+    return ip_conds, ip_unconds
 
 
 @torch.no_grad()
@@ -206,46 +207,46 @@ def patch_model(model, tasks):
             current_step = float(model.model.diffusion_model.current_step.detach().cpu().numpy()[0])
             cond_or_uncond = extra_options['cond_or_uncond']
 
-            with torch.autocast("cuda", dtype=ip_adapter.dtype):
-                q = n
-                k = [context_attn2]
-                v = [value_attn2]
-                b, _, _ = q.shape
+            q = n
+            k = [context_attn2]
+            v = [value_attn2]
+            b, _, _ = q.shape
 
-                for ip_conds, cn_stop, cn_weight in tasks:
-                    if current_step < cn_stop:
-                        ip_k_c = ip_conds[ip_index * 2].to(q)
-                        ip_v_c = ip_conds[ip_index * 2 + 1].to(q)
-                        ip_k_uc = ip_unconds[ip_index * 2].to(q)
-                        ip_v_uc = ip_unconds[ip_index * 2 + 1].to(q)
+            for (cs, ucs), cn_stop, cn_weight in tasks:
+                if current_step < cn_stop:
+                    ip_k_c = cs[ip_index * 2].to(q)
+                    ip_v_c = cs[ip_index * 2 + 1].to(q)
+                    ip_k_uc = ucs[ip_index * 2].to(q)
+                    ip_v_uc = ucs[ip_index * 2 + 1].to(q)
 
-                        ip_k = torch.cat([(ip_k_c, ip_k_uc)[i] for i in cond_or_uncond], dim=0)
-                        ip_v = torch.cat([(ip_v_c, ip_v_uc)[i] for i in cond_or_uncond], dim=0)
+                    ip_k = torch.cat([(ip_k_c, ip_k_uc)[i] for i in cond_or_uncond], dim=0)
+                    ip_v = torch.cat([(ip_v_c, ip_v_uc)[i] for i in cond_or_uncond], dim=0)
 
-                        # Midjourney's attention formulation of image prompt (non-official reimplementation)
-                        # Written by Lvmin Zhang at Stanford University, 2023 Dec
-                        # For non-commercial use only - if you use this in commercial project then
-                        # probably it has some intellectual property issues.
-                        # Contact lvminzhang@acm.org if you are not sure.
+                    # Midjourney's attention formulation of image prompt (non-official reimplementation)
+                    # Written by Lvmin Zhang at Stanford University, 2023 Dec
+                    # For non-commercial use only - if you use this in commercial project then
+                    # probably it has some intellectual property issues.
+                    # Contact lvminzhang@acm.org if you are not sure.
 
-                        # Below is the sensitive part with potential intellectual property issues.
+                    # Below is the sensitive part with potential intellectual property issues.
 
-                        ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True)
-                        ip_v_offset = ip_v - ip_v_mean
+                    ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True)
+                    ip_v_offset = ip_v - ip_v_mean
 
-                        B, F, C = ip_k.shape
-                        channel_penalty = float(C) / 1280.0
-                        weight = cn_weight * channel_penalty
+                    B, F, C = ip_k.shape
+                    channel_penalty = float(C) / 1280.0
+                    weight = cn_weight * channel_penalty
 
-                        ip_k = ip_k * weight
-                        ip_v = ip_v_offset + ip_v_mean * weight
+                    ip_k = ip_k * weight
+                    ip_v = ip_v_offset + ip_v_mean * weight
 
-                        k.append(ip_k)
-                        v.append(ip_v)
+                    k.append(ip_k)
+                    v.append(ip_v)
+
+            k = torch.cat(k, dim=1)
+            v = torch.cat(v, dim=1)
+            out = sdp(q, k, v, extra_options)
 
-                k = torch.cat(k, dim=1)
-                v = torch.cat(v, dim=1)
-                out = sdp(q, k, v, extra_options)
 
             return out.to(dtype=org_dtype)
         return patcher
@@ -260,27 +261,21 @@ def patch_model(model, tasks):
             to["patches_replace"]["attn2"][key] = make_attn_patcher(number)
 
     number = 0
-    if not ip_adapter.sdxl:
-        for id in [1, 2, 4, 5, 7, 8]:  # id of input_blocks that have cross attention
-            set_model_patch_replace(new_model, number, ("input", id))
-            number += 1
-        for id in [3, 4, 5, 6, 7, 8, 9, 10, 11]:  # id of output_blocks that have cross attention
-            set_model_patch_replace(new_model, number, ("output", id))
-            number += 1
-        set_model_patch_replace(new_model, number, ("middle", 0))
-    else:
-        for id in [4, 5, 7, 8]:  # id of input_blocks that have cross attention
-            block_indices = range(2) if id in [4, 5] else range(10)  # transformer_depth
-            for index in block_indices:
-                set_model_patch_replace(new_model, number, ("input", id, index))
-                number += 1
-        for id in range(6):  # id of output_blocks that have cross attention
-            block_indices = range(2) if id in [3, 4, 5] else range(10)  # transformer_depth
-            for index in block_indices:
-                set_model_patch_replace(new_model, number, ("output", id, index))
-                number += 1
-        for index in range(10):
-            set_model_patch_replace(new_model, number, ("middle", 0, index))
+
+    for id in [4, 5, 7, 8]:
+        block_indices = range(2) if id in [4, 5] else range(10)
+        for index in block_indices:
+            set_model_patch_replace(new_model, number, ("input", id, index))
             number += 1
 
+    for id in range(6):
+        block_indices = range(2) if id in [3, 4, 5] else range(10)
+        for index in block_indices:
+            set_model_patch_replace(new_model, number, ("output", id, index))
+            number += 1
+
+    for index in range(10):
+        set_model_patch_replace(new_model, number, ("middle", 0, index))
+        number += 1
+
     return new_model

extras/vae_interpose.py

@@ -4,10 +4,10 @@ import os
 import torch
 import safetensors.torch as sf
 import torch.nn as nn
-import fcbh.model_management
+import ldm_patched.modules.model_management
 
-from fcbh.model_patcher import ModelPatcher
-from modules.path import vae_approx_path
+from ldm_patched.modules.model_patcher import ModelPatcher
+from modules.config import path_vae_approx
 
 
 class Block(nn.Module):
@@ -63,7 +63,7 @@ class Interposer(nn.Module):
 
 
 vae_approx_model = None
-vae_approx_filename = os.path.join(vae_approx_path, 'xl-to-v1_interposer-v3.1.safetensors')
+vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v3.1.safetensors')
 
 
 def parse(x):
@@ -76,17 +76,17 @@ def parse(x):
         model.eval()
         sd = sf.load_file(vae_approx_filename)
         model.load_state_dict(sd)
-        fp16 = fcbh.model_management.should_use_fp16()
+        fp16 = ldm_patched.modules.model_management.should_use_fp16()
         if fp16:
             model = model.half()
         vae_approx_model = ModelPatcher(
             model=model,
-            load_device=fcbh.model_management.get_torch_device(),
+            load_device=ldm_patched.modules.model_management.get_torch_device(),
             offload_device=torch.device('cpu')
         )
         vae_approx_model.dtype = torch.float16 if fp16 else torch.float32
 
-    fcbh.model_management.load_model_gpu(vae_approx_model)
+    ldm_patched.modules.model_management.load_model_gpu(vae_approx_model)
 
     x = x_origin.to(device=vae_approx_model.load_device, dtype=vae_approx_model.dtype)
     x = vae_approx_model.model(x).to(x_origin)

extras/wd14tagger.py

@@ -0,0 +1,98 @@
+# https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags
+# https://github.com/pythongosssss/ComfyUI-WD14-Tagger/blob/main/wd14tagger.py
+
+# {
+#     "wd-v1-4-moat-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2",
+#     "wd-v1-4-convnextv2-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnextv2-tagger-v2",
+#     "wd-v1-4-convnext-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2",
+#     "wd-v1-4-convnext-tagger": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger",
+#     "wd-v1-4-vit-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2"
+# }
+
+
+import numpy as np
+import csv
+import onnxruntime as ort
+
+from PIL import Image
+from onnxruntime import InferenceSession
+from modules.config import path_clip_vision
+from modules.model_loader import load_file_from_url
+
+
+global_model = None
+global_csv = None
+
+
+def default_interrogator(image_rgb, threshold=0.35, character_threshold=0.85, exclude_tags=""):
+    global global_model, global_csv
+
+    model_name = "wd-v1-4-moat-tagger-v2"
+
+    model_onnx_filename = load_file_from_url(
+        url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.onnx',
+        model_dir=path_clip_vision,
+        file_name=f'{model_name}.onnx',
+    )
+
+    model_csv_filename = load_file_from_url(
+        url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.csv',
+        model_dir=path_clip_vision,
+        file_name=f'{model_name}.csv',
+    )
+
+    if global_model is not None:
+        model = global_model
+    else:
+        model = InferenceSession(model_onnx_filename, providers=ort.get_available_providers())
+        global_model = model
+
+    input = model.get_inputs()[0]
+    height = input.shape[1]
+
+    image = Image.fromarray(image_rgb)  # RGB
+    ratio = float(height)/max(image.size)
+    new_size = tuple([int(x*ratio) for x in image.size])
+    image = image.resize(new_size, Image.LANCZOS)
+    square = Image.new("RGB", (height, height), (255, 255, 255))
+    square.paste(image, ((height-new_size[0])//2, (height-new_size[1])//2))
+
+    image = np.array(square).astype(np.float32)
+    image = image[:, :, ::-1]  # RGB -> BGR
+    image = np.expand_dims(image, 0)
+
+    if global_csv is not None:
+        csv_lines = global_csv
+    else:
+        csv_lines = []
+        with open(model_csv_filename) as f:
+            reader = csv.reader(f)
+            next(reader)
+            for row in reader:
+                csv_lines.append(row)
+        global_csv = csv_lines
+
+    tags = []
+    general_index = None
+    character_index = None
+    for line_num, row in enumerate(csv_lines):
+        if general_index is None and row[2] == "0":
+            general_index = line_num
+        elif character_index is None and row[2] == "4":
+            character_index = line_num
+        tags.append(row[1])
+
+    label_name = model.get_outputs()[0].name
+    probs = model.run([label_name], {input.name: image})[0]
+
+    result = list(zip(tags, probs[0]))
+
+    general = [item for item in result[general_index:character_index] if item[1] > threshold]
+    character = [item for item in result[character_index:] if item[1] > character_threshold]
+
+    all = character + general
+    remove = [s.strip() for s in exclude_tags.lower().split(",")]
+    all = [tag for tag in all if tag[0] not in remove]
+
+    res = ", ".join((item[0].replace("(", "\\(").replace(")", "\\)") for item in all)).replace('_', ' ')
+    return res

fooocus_version.py

@@ -1 +1 @@
-version = '2.1.774'
+version = '2.1.851'

javascript/edit-attention.js

@@ -0,0 +1,128 @@
+function updateInput(target) {
+    let e = new Event("input", {bubbles: true});
+    Object.defineProperty(e, "target", {value: target});
+    target.dispatchEvent(e);
+}
+
+function keyupEditAttention(event) {
+    let target = event.originalTarget || event.composedPath()[0];
+    if (!target.matches("*:is([id*='_prompt'], .prompt) textarea")) return;
+    if (!(event.metaKey || event.ctrlKey)) return;
+
+    let isPlus = event.key == "ArrowUp";
+    let isMinus = event.key == "ArrowDown";
+    if (!isPlus && !isMinus) return;
+
+    let selectionStart = target.selectionStart;
+    let selectionEnd = target.selectionEnd;
+    let text = target.value;
+
+    function selectCurrentParenthesisBlock(OPEN, CLOSE) {
+        if (selectionStart !== selectionEnd) return false;
+
+        // Find opening parenthesis around current cursor
+        const before = text.substring(0, selectionStart);
+        let beforeParen = before.lastIndexOf(OPEN);
+        if (beforeParen == -1) return false;
+        let beforeParenClose = before.lastIndexOf(CLOSE);
+        while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
+            beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
+            beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
+        }
+
+        // Find closing parenthesis around current cursor
+        const after = text.substring(selectionStart);
+        let afterParen = after.indexOf(CLOSE);
+        if (afterParen == -1) return false;
+        let afterParenOpen = after.indexOf(OPEN);
+        while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
+            afterParen = after.indexOf(CLOSE, afterParen + 1);
+            afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
+        }
+        if (beforeParen === -1 || afterParen === -1) return false;
+
+        // Set the selection to the text between the parenthesis
+        const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
+        const lastColon = parenContent.lastIndexOf(":");
+        selectionStart = beforeParen + 1;
+        selectionEnd = selectionStart + lastColon;
+        target.setSelectionRange(selectionStart, selectionEnd);
+        return true;
+    }
+
+    function selectCurrentWord() {
+        if (selectionStart !== selectionEnd) return false;
+        const delimiters = ".,\\/!?%^*;:{}=`~() \r\n\t";
+
+        // seek backward until to find beggining
+        while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
+            selectionStart--;
+        }
+
+        // seek forward to find end
+        while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
+            selectionEnd++;
+        }
+
+        target.setSelectionRange(selectionStart, selectionEnd);
+        return true;
+    }
+
+    // If the user hasn't selected anything, let's select their current parenthesis block or word
+    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+        selectCurrentWord();
+    }
+
+    event.preventDefault();
+
+    var closeCharacter = ')';
+    var delta = 0.1;
+
+    if (selectionStart > 0 && text[selectionStart - 1] == '<') {
+        closeCharacter = '>';
+        delta = 0.05;
+    } else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
+
+        // do not include spaces at the end
+        while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {
+            selectionEnd -= 1;
+        }
+        if (selectionStart == selectionEnd) {
+            return;
+        }
+
+        text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
+
+        selectionStart += 1;
+        selectionEnd += 1;
+    }
+
+    var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
+    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
+    if (isNaN(weight)) return;
+
+    weight += isPlus ? delta : -delta;
+    weight = parseFloat(weight.toPrecision(12));
+    if (String(weight).length == 1) weight += ".0";
+
+    if (closeCharacter == ')' && weight == 1) {
+        var endParenPos = text.substring(selectionEnd).indexOf(')');
+        text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1);
+        selectionStart--;
+        selectionEnd--;
+    } else {
+        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end);
+    }
+
+    target.focus();
+    target.value = text;
+    target.selectionStart = selectionStart;
+    target.selectionEnd = selectionEnd;
+
+    updateInput(target);
+
+}
+
+addEventListener('keydown', (event) => {
+    keyupEditAttention(event);
+});

javascript/imageviewer.js

@@ -0,0 +1,260 @@
+// From A1111
+
+function closeModal() {
+    gradioApp().getElementById("lightboxModal").style.display = "none";
+}
+
+function showModal(event) {
+    const source = event.target || event.srcElement;
+    const modalImage = gradioApp().getElementById("modalImage");
+    const lb = gradioApp().getElementById("lightboxModal");
+    modalImage.src = source.src;
+    if (modalImage.style.display === 'none') {
+        lb.style.setProperty('background-image', 'url(' + source.src + ')');
+    }
+    lb.style.display = "flex";
+    lb.focus();
+
+    event.stopPropagation();
+}
+
+function negmod(n, m) {
+    return ((n % m) + m) % m;
+}
+
+function updateOnBackgroundChange() {
+    const modalImage = gradioApp().getElementById("modalImage");
+    if (modalImage && modalImage.offsetParent) {
+        let currentButton = selected_gallery_button();
+
+        if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
+            modalImage.src = currentButton.children[0].src;
+            if (modalImage.style.display === 'none') {
+                const modal = gradioApp().getElementById("lightboxModal");
+                modal.style.setProperty('background-image', `url(${modalImage.src})`);
+            }
+        }
+    }
+}
+
+function all_gallery_buttons() {
+    var allGalleryButtons = gradioApp().querySelectorAll('.image_gallery .thumbnails > .thumbnail-item.thumbnail-small');
+    var visibleGalleryButtons = [];
+    allGalleryButtons.forEach(function(elem) {
+        if (elem.parentElement.offsetParent) {
+            visibleGalleryButtons.push(elem);
+        }
+    });
+    return visibleGalleryButtons;
+}
+
+function selected_gallery_button() {
+    return all_gallery_buttons().find(elem => elem.classList.contains('selected')) ?? null;
+}
+
+function selected_gallery_index() {
+    return all_gallery_buttons().findIndex(elem => elem.classList.contains('selected'));
+}
+
+function modalImageSwitch(offset) {
+    var galleryButtons = all_gallery_buttons();
+
+    if (galleryButtons.length > 1) {
+        var currentButton = selected_gallery_button();
+
+        var result = -1;
+        galleryButtons.forEach(function(v, i) {
+            if (v == currentButton) {
+                result = i;
+            }
+        });
+
+        if (result != -1) {
+            var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)];
+            nextButton.click();
+            const modalImage = gradioApp().getElementById("modalImage");
+            const modal = gradioApp().getElementById("lightboxModal");
+            modalImage.src = nextButton.children[0].src;
+            if (modalImage.style.display === 'none') {
+                modal.style.setProperty('background-image', `url(${modalImage.src})`);
+            }
+            setTimeout(function() {
+                modal.focus();
+            }, 10);
+        }
+    }
+}
+
+function saveImage() {
+
+}
+
+function modalSaveImage(event) {
+    event.stopPropagation();
+}
+
+function modalNextImage(event) {
+    modalImageSwitch(1);
+    event.stopPropagation();
+}
+
+function modalPrevImage(event) {
+    modalImageSwitch(-1);
+    event.stopPropagation();
+}
+
+function modalKeyHandler(event) {
+    switch (event.key) {
+    case "s":
+        saveImage();
+        break;
+    case "ArrowLeft":
+        modalPrevImage(event);
+        break;
+    case "ArrowRight":
+        modalNextImage(event);
+        break;
+    case "Escape":
+        closeModal();
+        break;
+    }
+}
+
+function setupImageForLightbox(e) {
+    if (e.dataset.modded) {
+        return;
+    }
+
+    e.dataset.modded = true;
+    e.style.cursor = 'pointer';
+    e.style.userSelect = 'none';
+
+    var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1;
+
+    // For Firefox, listening on click first switched to next image then shows the lightbox.
+    // If you know how to fix this without switching to mousedown event, please.
+    // For other browsers the event is click to make it possiblr to drag picture.
+    var event = isFirefox ? 'mousedown' : 'click';
+
+    e.addEventListener(event, function(evt) {
+        if (evt.button == 1) {
+            open(evt.target.src);
+            evt.preventDefault();
+            return;
+        }
+        if (evt.button != 0) return;
+
+        modalZoomSet(gradioApp().getElementById('modalImage'), true);
+        evt.preventDefault();
+        showModal(evt);
+    }, true);
+
+}
+
+function modalZoomSet(modalImage, enable) {
+    if (modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable);
+}
+
+function modalZoomToggle(event) {
+    var modalImage = gradioApp().getElementById("modalImage");
+    modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'));
+    event.stopPropagation();
+}
+
+function modalTileImageToggle(event) {
+    const modalImage = gradioApp().getElementById("modalImage");
+    const modal = gradioApp().getElementById("lightboxModal");
+    const isTiling = modalImage.style.display === 'none';
+    if (isTiling) {
+        modalImage.style.display = 'block';
+        modal.style.setProperty('background-image', 'none');
+    } else {
+        modalImage.style.display = 'none';
+        modal.style.setProperty('background-image', `url(${modalImage.src})`);
+    }
+
+    event.stopPropagation();
+}
+
+onAfterUiUpdate(function() {
+    var fullImg_preview = gradioApp().querySelectorAll('.image_gallery > div > img');
+    if (fullImg_preview != null) {
+        fullImg_preview.forEach(setupImageForLightbox);
+    }
+    updateOnBackgroundChange();
+});
+
+document.addEventListener("DOMContentLoaded", function() {
+    //const modalFragment = document.createDocumentFragment();
+    const modal = document.createElement('div');
+    modal.onclick = closeModal;
+    modal.id = "lightboxModal";
+    modal.tabIndex = 0;
+    modal.addEventListener('keydown', modalKeyHandler, true);
+
+    const modalControls = document.createElement('div');
+    modalControls.className = 'modalControls gradio-container';
+    modal.append(modalControls);
+
+    const modalZoom = document.createElement('span');
+    modalZoom.className = 'modalZoom cursor';
+    modalZoom.innerHTML = '⤡';
+    modalZoom.addEventListener('click', modalZoomToggle, true);
+    modalZoom.title = "Toggle zoomed view";
+    modalControls.appendChild(modalZoom);
+
+    // const modalTileImage = document.createElement('span');
+    // modalTileImage.className = 'modalTileImage cursor';
+    // modalTileImage.innerHTML = '⊞';
+    // modalTileImage.addEventListener('click', modalTileImageToggle, true);
+    // modalTileImage.title = "Preview tiling";
+    // modalControls.appendChild(modalTileImage);
+    //
+    // const modalSave = document.createElement("span");
+    // modalSave.className = "modalSave cursor";
+    // modalSave.id = "modal_save";
+    // modalSave.innerHTML = "🖫";
+    // modalSave.addEventListener("click", modalSaveImage, true);
+    // modalSave.title = "Save Image(s)";
+    // modalControls.appendChild(modalSave);
+
+    const modalClose = document.createElement('span');
+    modalClose.className = 'modalClose cursor';
+    modalClose.innerHTML = '×';
+    modalClose.onclick = closeModal;
+    modalClose.title = "Close image viewer";
+    modalControls.appendChild(modalClose);
+
+    const modalImage = document.createElement('img');
+    modalImage.id = 'modalImage';
+    modalImage.onclick = closeModal;
+    modalImage.tabIndex = 0;
+    modalImage.addEventListener('keydown', modalKeyHandler, true);
+    modal.appendChild(modalImage);
+
+    const modalPrev = document.createElement('a');
+    modalPrev.className = 'modalPrev';
+    modalPrev.innerHTML = '❮';
+    modalPrev.tabIndex = 0;
+    modalPrev.addEventListener('click', modalPrevImage, true);
+    modalPrev.addEventListener('keydown', modalKeyHandler, true);
+    modal.appendChild(modalPrev);
+
+    const modalNext = document.createElement('a');
+    modalNext.className = 'modalNext';
+    modalNext.innerHTML = '❯';
+    modalNext.tabIndex = 0;
+    modalNext.addEventListener('click', modalNextImage, true);
+    modalNext.addEventListener('keydown', modalKeyHandler, true);
+
+    modal.appendChild(modalNext);
+
+    try {
+        gradioApp().appendChild(modal);
+    } catch (e) {
+        gradioApp().body.appendChild(modal);
+    }
+
+    document.body.appendChild(modal);
+
+});

javascript/localization.js

@@ -73,6 +73,10 @@ function processNode(node) {
     });
 }
 
+function refresh_style_localization() {
+    processNode(document.querySelector('.style_selections'));
+}
+
 function localizeWholePage() {
     processNode(gradioApp());
 

javascript/script.js

@@ -125,18 +125,23 @@ document.addEventListener("DOMContentLoaded", function() {
  * Add a ctrl+enter as a shortcut to start a generation
  */
 document.addEventListener('keydown', function(e) {
-    var handled = false;
-    if (e.key !== undefined) {
-        if ((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
-    } else if (e.keyCode !== undefined) {
-        if ((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
-    }
-    if (handled) {
-        var button = gradioApp().querySelector('button[id=generate_button]');
-        if (button) {
-            button.click();
+    const isModifierKey = (e.metaKey || e.ctrlKey || e.altKey);
+    const isEnterKey = (e.key == "Enter" || e.keyCode == 13);
+
+    if(isModifierKey && isEnterKey) {
+        const generateButton = gradioApp().querySelector('button:not(.hidden)[id=generate_button]');
+        if (generateButton) {
+            generateButton.click();
+            e.preventDefault();
+            return;
+        }
+
+        const stopButton = gradioApp().querySelector('button:not(.hidden)[id=stop_button]')
+        if(stopButton) {
+            stopButton.click();
+            e.preventDefault();
+            return;
         }
-        e.preventDefault();
     }
 });
 
@@ -166,3 +171,10 @@ function uiElementInSight(el) {
 function playNotification() {
     gradioApp().querySelector('#audio_notification audio')?.play();
 }
+
+function set_theme(theme) {
+    var gradioURL = window.location.href;
+    if (!gradioURL.includes('?__theme=')) {
+        window.location.replace(gradioURL + '?__theme=' + theme);
+    }
+}

javascript/viewer.js

@@ -0,0 +1,88 @@
+window.main_viewer_height = 512;
+
+function refresh_grid() {
+    let gridContainer = document.querySelector('#final_gallery .grid-container');
+    let final_gallery = document.getElementById('final_gallery');
+
+    if (gridContainer) if (final_gallery) {
+        let rect = final_gallery.getBoundingClientRect();
+        let cols = Math.ceil((rect.width - 16.0) / rect.height);
+        if (cols < 2) cols = 2;
+        gridContainer.style.setProperty('--grid-cols', cols);
+    }
+}
+
+function refresh_grid_delayed() {
+    refresh_grid();
+    setTimeout(refresh_grid, 100);
+    setTimeout(refresh_grid, 500);
+    setTimeout(refresh_grid, 1000);
+}
+
+function resized() {
+    let windowHeight = window.innerHeight - 260;
+    let elements = document.getElementsByClassName('main_view');
+
+    if (windowHeight > 745) windowHeight = 745;
+
+    for (let i = 0; i < elements.length; i++) {
+        elements[i].style.height = windowHeight + 'px';
+    }
+
+    window.main_viewer_height = windowHeight;
+
+    refresh_grid();
+}
+
+function viewer_to_top(delay = 100) {
+    setTimeout(() => window.scrollTo({top: 0, behavior: 'smooth'}), delay);
+}
+
+function viewer_to_bottom(delay = 100) {
+    let element = document.getElementById('positive_prompt');
+    let yPos = window.main_viewer_height;
+
+    if (element) {
+        yPos = element.getBoundingClientRect().top + window.scrollY;
+    }
+
+    setTimeout(() => window.scrollTo({top: yPos - 8, behavior: 'smooth'}), delay);
+}
+
+window.addEventListener('resize', (e) => {
+    resized();
+});
+
+onUiLoaded(async () => {
+    resized();
+});
+
+function on_style_selection_blur() {
+    let target = document.querySelector("#gradio_receiver_style_selections textarea");
+    target.value = "on_style_selection_blur " + Math.random();
+    let e = new Event("input", {bubbles: true})
+    Object.defineProperty(e, "target", {value: target})
+    target.dispatchEvent(e);
+}
+
+onUiLoaded(async () => {
+    let spans = document.querySelectorAll('.aspect_ratios span');
+
+    spans.forEach(function (span) {
+        span.innerHTML = span.innerHTML.replace(/&lt;/g, '<').replace(/&gt;/g, '>');
+    });
+
+    document.querySelector('.style_selections').addEventListener('focusout', function (event) {
+        setTimeout(() => {
+            if (!this.contains(document.activeElement)) {
+                on_style_selection_blur();
+            }
+        }, 200);
+    });
+
+    let inputs = document.querySelectorAll('.lora_weight input[type="range"]');
+
+    inputs.forEach(function (input) {
+        input.style.marginTop = '12px';
+    });
+});

javascript/zoom.js

@@ -1,18 +1,5 @@
 onUiLoaded(async() => {
     // Helper functions
-    // Get active tab
-
-    /**
-     * Waits for an element to be present in the DOM.
-     */
-    const waitForElement = (id) => new Promise(resolve => {
-        const checkForElement = () => {
-            const element = document.querySelector(id);
-            if (element) return resolve(element);
-            setTimeout(checkForElement, 100);
-        };
-        checkForElement();
-    });
 
     // Detect whether the element has a horizontal scroll bar
     function hasHorizontalScrollbar(element) {
@@ -33,140 +20,40 @@ onUiLoaded(async() => {
         }
     }
 
-    // Check if hotkey is valid
-    function isValidHotkey(value) {
-        const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"];
-        return (
-            (typeof value === "string" &&
-                value.length === 1 &&
-                /[a-z]/i.test(value)) ||
-            specialKeys.includes(value)
-        );
-    }
-
-    // Normalize hotkey
-    function normalizeHotkey(hotkey) {
-        return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey;
-    }
-
-    // Format hotkey for display
-    function formatHotkeyForDisplay(hotkey) {
-        return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey;
-    }
-
     // Create hotkey configuration with the provided options
     function createHotkeyConfig(defaultHotkeysConfig) {
         const result = {}; // Resulting hotkey configuration
-
         for (const key in defaultHotkeysConfig) {
             result[key] = defaultHotkeysConfig[key];
         }
-
         return result;
     }
 
-    // Disables functions in the config object based on the provided list of function names
-    function disableFunctions(config, disabledFunctions) {
-        // Bind the hasOwnProperty method to the functionMap object to avoid errors
-        const hasOwnProperty =
-            Object.prototype.hasOwnProperty.bind(functionMap);
-
-        // Loop through the disabledFunctions array and disable the corresponding functions in the config object
-        disabledFunctions.forEach(funcName => {
-            if (hasOwnProperty(funcName)) {
-                const key = functionMap[funcName];
-                config[key] = "disable";
-            }
-        });
-
-        // Return the updated config object
-        return config;
-    }
-
-    /**
-     * The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio.
-     * If the image display property is set to 'none', the mask breaks. To fix this, the function
-     * temporarily sets the display property to 'block' and then hides the mask again after 300 milliseconds
-     * to avoid breaking the canvas. Additionally, the function adjusts the mask to work correctly on
-     * very long images.
-     */
-    function restoreImgRedMask(elements) {
-        const mainTabId = getTabId(elements);
-
-        if (!mainTabId) return;
-
-        const mainTab = gradioApp().querySelector(mainTabId);
-        const img = mainTab.querySelector("img");
-        const imageARPreview = gradioApp().querySelector("#imageARPreview");
-
-        if (!img || !imageARPreview) return;
-
-        imageARPreview.style.transform = "";
-        if (parseFloat(mainTab.style.width) > 865) {
-            const transformString = mainTab.style.transform;
-            const scaleMatch = transformString.match(
-                /scale\(([-+]?[0-9]*\.?[0-9]+)\)/
-            );
-            let zoom = 1; // default zoom
-
-            if (scaleMatch && scaleMatch[1]) {
-                zoom = Number(scaleMatch[1]);
-            }
-
-            imageARPreview.style.transformOrigin = "0 0";
-            imageARPreview.style.transform = `scale(${zoom})`;
-        }
-
-        if (img.style.display !== "none") return;
-
-        img.style.display = "block";
-
-        setTimeout(() => {
-            img.style.display = "none";
-        }, 400);
-    }
-
     // Default config
     const defaultHotkeysConfig = {
-        canvas_hotkey_zoom: "Alt",
+        canvas_hotkey_zoom: "Shift",
         canvas_hotkey_adjust: "Ctrl",
+        canvas_zoom_undo_extra_key: "Ctrl",
+        canvas_zoom_hotkey_undo: "KeyZ",
         canvas_hotkey_reset: "KeyR",
         canvas_hotkey_fullscreen: "KeyS",
         canvas_hotkey_move: "KeyF",
-        canvas_hotkey_overlap: "KeyO",
-        canvas_disabled_functions: [],
         canvas_show_tooltip: true,
         canvas_auto_expand: true,
-        canvas_blur_prompt: false,
-    };
-
-    const functionMap = {
-        "Zoom": "canvas_hotkey_zoom",
-        "Adjust brush size": "canvas_hotkey_adjust",
-        "Moving canvas": "canvas_hotkey_move",
-        "Fullscreen": "canvas_hotkey_fullscreen",
-        "Reset Zoom": "canvas_hotkey_reset",
-        "Overlap": "canvas_hotkey_overlap"
+        canvas_blur_prompt: true,
     };
 
     // Loading the configuration from opts
-    const preHotkeysConfig = createHotkeyConfig(
+    const hotkeysConfig = createHotkeyConfig(
         defaultHotkeysConfig
     );
 
-    // Disable functions that are not needed by the user
-    const hotkeysConfig = disableFunctions(
-        preHotkeysConfig,
-        preHotkeysConfig.canvas_disabled_functions
-    );
-
     let isMoving = false;
-    let mouseX, mouseY;
     let activeElement;
 
     const elemData = {};
 
-    function applyZoomAndPan(elemId, isExtension = true) {
+    function applyZoomAndPan(elemId) {
         const targetElement = gradioApp().querySelector(elemId);
 
         if (!targetElement) {
@@ -181,6 +68,7 @@ onUiLoaded(async() => {
             panX: 0,
             panY: 0
         };
+
         let fullScreenMode = false;
 
         // Create tooltip
@@ -211,44 +99,46 @@ onUiLoaded(async() => {
                     action: "Adjust brush size",
                     keySuffix: " + wheel"
                 },
+                {configKey: "canvas_zoom_hotkey_undo", action: "Undo last action", keyPrefix: `${hotkeysConfig.canvas_zoom_undo_extra_key} + ` },
                 {configKey: "canvas_hotkey_reset", action: "Reset zoom"},
                 {
                     configKey: "canvas_hotkey_fullscreen",
                     action: "Fullscreen mode"
                 },
-                {configKey: "canvas_hotkey_move", action: "Move canvas"},
-                {configKey: "canvas_hotkey_overlap", action: "Overlap"}
+                {configKey: "canvas_hotkey_move", action: "Move canvas"}
             ];
 
-            // Create hotkeys array with disabled property based on the config values
-            const hotkeys = hotkeysInfo.map(info => {
+            // Create hotkeys array based on the config values
+            const hotkeys = hotkeysInfo.map((info) => {
                 const configValue = hotkeysConfig[info.configKey];
-                const key = info.keySuffix ?
-                    `${configValue}${info.keySuffix}` :
-                    configValue.charAt(configValue.length - 1);
-                return {
-                    key,
-                    action: info.action,
-                    disabled: configValue === "disable"
-                };
-            });
-
-            for (const hotkey of hotkeys) {
-                if (hotkey.disabled) {
-                    continue;
+        
+                let key = configValue.slice(-1);
+        
+                if (info.keySuffix) {
+                  key = `${configValue}${info.keySuffix}`;
                 }
+        
+                if (info.keyPrefix && info.keyPrefix !== "None + ") {
+                  key = `${info.keyPrefix}${configValue[3]}`;
+                }
+        
+                return {
+                  key,
+                  action: info.action,
+                };
+              });
+        
+              hotkeys
+                .forEach(hotkey => {
+                  const p = document.createElement("p");
+                  p.innerHTML = `<b>${hotkey.key}</b> - ${hotkey.action}`;
+                  tooltipContent.appendChild(p);
+                });
+        
+              tooltip.append(info, tooltipContent);
 
-                const p = document.createElement("p");
-                p.innerHTML = `<b>${hotkey.key}</b> - ${hotkey.action}`;
-                tooltipContent.appendChild(p);
-            }
-
-            // Add information and content elements to the tooltip element
-            tooltip.appendChild(info);
-            tooltip.appendChild(tooltipContent);
-
-            // Add a hint element to the target element
-            toolTipElemnt.appendChild(tooltip);
+              // Add a hint element to the target element
+              toolTipElemnt.appendChild(tooltip);
         }
 
         //Show tool tip if setting enable
@@ -264,9 +154,7 @@ onUiLoaded(async() => {
                 panY: 0
             };
 
-            if (isExtension) {
-                targetElement.style.overflow = "hidden";
-            }
+            targetElement.style.overflow = "hidden";
 
             targetElement.isZoomed = false;
 
@@ -284,7 +172,7 @@ onUiLoaded(async() => {
                 closeBtn.addEventListener("click", resetZoom);
             }
 
-            if (canvas && isExtension) {
+            if (canvas) {
                 const parentElement = targetElement.closest('[id^="component-"]');
                 if (
                     canvas &&
@@ -297,16 +185,6 @@ onUiLoaded(async() => {
 
             }
 
-            if (
-                canvas &&
-                !isExtension &&
-                parseFloat(canvas.style.width) > 865 &&
-                parseFloat(targetElement.style.width) > 865
-            ) {
-                fitToElement();
-                return;
-            }
-
             targetElement.style.width = "";
         }
 
@@ -372,12 +250,10 @@ onUiLoaded(async() => {
 
             targetElement.style.transformOrigin = "0 0";
             targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`;
+            targetElement.style.overflow = "visible";
 
             toggleOverlap("on");
-            if (isExtension) {
-                targetElement.style.overflow = "visible";
-            }
-
+ 
             return newZoomLevel;
         }
 
@@ -388,6 +264,7 @@ onUiLoaded(async() => {
 
                 let zoomPosX, zoomPosY;
                 let delta = 0.2;
+
                 if (elemData[elemId].zoomLevel > 7) {
                     delta = 0.9;
                 } else if (elemData[elemId].zoomLevel > 2) {
@@ -421,12 +298,7 @@ onUiLoaded(async() => {
 
             let parentElement;
 
-            if (isExtension) {
-                parentElement = targetElement.closest('[id^="component-"]');
-            } else {
-                parentElement = targetElement.parentElement;
-            }
-
+            parentElement = targetElement.closest('[id^="component-"]');
 
             // Get element and screen dimensions
             const elementWidth = targetElement.offsetWidth;
@@ -455,6 +327,26 @@ onUiLoaded(async() => {
             toggleOverlap("off");
         }
 
+        // Undo last action
+        function undoLastAction(e) {
+            let isCtrlPressed = isModifierKey(e, hotkeysConfig.canvas_zoom_undo_extra_key)
+            const isAuxButton = e.button >= 3;
+            
+            if (isAuxButton) {
+              isCtrlPressed = true
+            } else {
+              if (!isModifierKey(e, hotkeysConfig.canvas_zoom_undo_extra_key)) return;
+            }
+
+            // Move undoBtn query outside the if statement to avoid unnecessary queries
+            const undoBtn = document.querySelector(`${activeElement} button[aria-label="Undo"]`);
+        
+            if ((isCtrlPressed) && undoBtn ) {
+                e.preventDefault();
+                undoBtn.click();
+            }
+        }
+
         /**
          * This function fits the target element to the screen by calculating
          * the required scale and offsets. It also updates the global variables
@@ -469,13 +361,8 @@ onUiLoaded(async() => {
 
             if (!canvas) return;
 
-            if (canvas.offsetWidth > 862 || isExtension) {
-                targetElement.style.width = (canvas.offsetWidth + 2) + "px";
-            }
-
-            if (isExtension) {
-                targetElement.style.overflow = "visible";
-            }
+            targetElement.style.width = (canvas.offsetWidth + 2) + "px";
+            targetElement.style.overflow = "visible";
 
             if (fullScreenMode) {
                 resetZoom();
@@ -549,11 +436,11 @@ onUiLoaded(async() => {
                 }
             }
 
-
             const hotkeyActions = {
                 [hotkeysConfig.canvas_hotkey_reset]: resetZoom,
                 [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
-                [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen
+                [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen,
+                [hotkeysConfig.canvas_zoom_hotkey_undo]: undoLastAction,
             };
 
             const action = hotkeyActions[event.code];
@@ -597,26 +484,27 @@ onUiLoaded(async() => {
         }
 
         targetElement.addEventListener("mousemove", getMousePosition);
+        targetElement.addEventListener("auxclick", undoLastAction);
 
         //observers
         // Creating an observer with a callback function to handle DOM changes
         const observer = new MutationObserver((mutationsList, observer) => {
             for (let mutation of mutationsList) {
-                // If the style attribute of the canvas has changed, by observation it happens only when the picture changes
-                if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&
-                    mutation.target.tagName.toLowerCase() === 'canvas') {
-                    targetElement.isExpanded = false;
-                    setTimeout(resetZoom, 10);
-                }
+              // If the style attribute of the canvas has changed, by observation it happens only when the picture changes
+              if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&
+                mutation.target.tagName.toLowerCase() === 'canvas') {
+                targetElement.isExpanded = false;
+                setTimeout(resetZoom, 10);
+              }
             }
-        });
-
-        // Apply auto expand if enabled
-        if (hotkeysConfig.canvas_auto_expand) {
+          });
+      
+          // Apply auto expand if enabled
+          if (hotkeysConfig.canvas_auto_expand) {
             targetElement.addEventListener("mousemove", autoExpand);
             // Set up an observer to track attribute changes
-            observer.observe(targetElement, {attributes: true, childList: true, subtree: true});
-        }
+            observer.observe(targetElement, { attributes: true, childList: true, subtree: true });
+          }
 
         // Handle events only inside the targetElement
         let isKeyDownHandlerAttached = false;
@@ -661,7 +549,7 @@ onUiLoaded(async() => {
         function handleMoveKeyDown(e) {
 
             // Disable key locks to make pasting from the buffer work correctly
-            if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") {
+            if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && e.code === 'KeyC') || e.code === "F5") {
                 return;
             }
 
@@ -713,11 +601,7 @@ onUiLoaded(async() => {
             if (isMoving && elemId === activeElement) {
                 updatePanPosition(e.movementX, e.movementY);
                 targetElement.style.pointerEvents = "none";
-
-                if (isExtension) {
-                    targetElement.style.overflow = "visible";
-                }
-
+                targetElement.style.overflow = "visible";
             } else {
                 targetElement.style.pointerEvents = "auto";
             }
@@ -745,18 +629,13 @@ onUiLoaded(async() => {
             }
         }
 
-        if (isExtension) {
-            targetElement.addEventListener("mousemove", checkForOutBox);
-        }
-
+        targetElement.addEventListener("mousemove", checkForOutBox);
 
         window.addEventListener('resize', (e) => {
             resetZoom();
 
-            if (isExtension) {
-                targetElement.isExpanded = false;
-                targetElement.isZoomed = false;
-            }
+            targetElement.isExpanded = false;
+            targetElement.isZoomed = false;
         });
 
         gradioApp().addEventListener("mousemove", handleMoveByKey);

language/en.json

@@ -42,32 +42,7 @@
     "Speed": "Speed",
     "Quality": "Quality",
     "Aspect Ratios": "Aspect Ratios",
-    "896\u00d71152": "896\u00d71152",
     "width \u00d7 height": "width \u00d7 height",
-    "704\u00d71408": "704\u00d71408",
-    "704\u00d71344": "704\u00d71344",
-    "768\u00d71344": "768\u00d71344",
-    "768\u00d71280": "768\u00d71280",
-    "832\u00d71216": "832\u00d71216",
-    "832\u00d71152": "832\u00d71152",
-    "896\u00d71088": "896\u00d71088",
-    "960\u00d71088": "960\u00d71088",
-    "960\u00d71024": "960\u00d71024",
-    "1024\u00d71024": "1024\u00d71024",
-    "1024\u00d7960": "1024\u00d7960",
-    "1088\u00d7960": "1088\u00d7960",
-    "1088\u00d7896": "1088\u00d7896",
-    "1152\u00d7832": "1152\u00d7832",
-    "1216\u00d7832": "1216\u00d7832",
-    "1280\u00d7768": "1280\u00d7768",
-    "1344\u00d7768": "1344\u00d7768",
-    "1344\u00d7704": "1344\u00d7704",
-    "1408\u00d7704": "1408\u00d7704",
-    "1472\u00d7704": "1472\u00d7704",
-    "1536\u00d7640": "1536\u00d7640",
-    "1600\u00d7640": "1600\u00d7640",
-    "1664\u00d7576": "1664\u00d7576",
-    "1728\u00d7576": "1728\u00d7576",
     "Image Number": "Image Number",
     "Negative Prompt": "Negative Prompt",
     "Describing what you do not want to see.": "Describing what you do not want to see.",
@@ -385,5 +360,13 @@
     "B1": "B1",
     "B2": "B2",
     "S1": "S1",
-    "S2": "S2"
+    "S2": "S2",
+    "Extreme Speed": "Extreme Speed",
+    "\uD83D\uDD0E Type here to search styles ...": "\uD83D\uDD0E Type here to search styles ...",
+    "Type prompt here.": "Type prompt here.",
+    "Outpaint Expansion Direction:": "Outpaint Expansion Direction:",
+    "* Powered by Fooocus Inpaint Engine (beta)": "* Powered by Fooocus Inpaint Engine (beta)",
+    "Fooocus Enhance": "Fooocus Enhance",
+    "Fooocus Cinematic": "Fooocus Cinematic",
+    "Fooocus Sharp": "Fooocus Sharp"
 }

launch.py

@@ -1,14 +1,28 @@
-from python_hijack import *
-
+import os
 import sys
+import ssl
+
+print('[System ARGV] ' + str(sys.argv))
+
+root = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(root)
+os.chdir(root)
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
+os.environ["GRADIO_SERVER_PORT"] = "7865"
+
+ssl._create_default_https_context = ssl._create_unverified_context
+
+
 import platform
 import fooocus_version
 
 from build_launcher import build_launcher
 from modules.launch_util import is_installed, run, python, run_pip, requirements_met
 from modules.model_loader import load_file_from_url
-from modules.path import modelfile_path, lorafile_path, vae_approx_path, fooocus_expansion_path, \
-    checkpoint_downloads, embeddings_path, embeddings_downloads, lora_downloads
+from modules.config import path_checkpoints, path_loras, path_vae_approx, path_fooocus_expansion, \
+    checkpoint_downloads, path_embeddings, embeddings_downloads, lora_downloads
 
 
 REINSTALL_ALL = False
@@ -58,31 +72,38 @@ vae_approx_filenames = [
 
 def download_models():
     for file_name, url in checkpoint_downloads.items():
-        load_file_from_url(url=url, model_dir=modelfile_path, file_name=file_name)
+        load_file_from_url(url=url, model_dir=path_checkpoints, file_name=file_name)
     for file_name, url in embeddings_downloads.items():
-        load_file_from_url(url=url, model_dir=embeddings_path, file_name=file_name)
+        load_file_from_url(url=url, model_dir=path_embeddings, file_name=file_name)
     for file_name, url in lora_downloads.items():
-        load_file_from_url(url=url, model_dir=lorafile_path, file_name=file_name)
+        load_file_from_url(url=url, model_dir=path_loras, file_name=file_name)
     for file_name, url in vae_approx_filenames:
-        load_file_from_url(url=url, model_dir=vae_approx_path, file_name=file_name)
+        load_file_from_url(url=url, model_dir=path_vae_approx, file_name=file_name)
 
     load_file_from_url(
         url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin',
-        model_dir=fooocus_expansion_path,
+        model_dir=path_fooocus_expansion,
         file_name='pytorch_model.bin'
     )
 
     return
 
 
-def ini_cbh_args():
+def ini_args():
     from args_manager import args
     return args
 
 
 prepare_environment()
 build_launcher()
-ini_cbh_args()
+args = ini_args()
+
+
+if args.gpu_device_id is not None:
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id)
+    print("Set device to:", args.gpu_device_id)
+
+
 download_models()
 
 from webui import *

ldm_patched/contrib/external.py

@@ -1,3 +1,5 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 import torch
 
 import os
@@ -14,31 +16,31 @@ from PIL.PngImagePlugin import PngInfo
 import numpy as np
 import safetensors.torch
 
-sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "fcbh"))
+pass  # sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "ldm_patched"))
 
 
-import fcbh.diffusers_load
-import fcbh.samplers
-import fcbh.sample
-import fcbh.sd
-import fcbh.utils
-import fcbh.controlnet
+import ldm_patched.modules.diffusers_load
+import ldm_patched.modules.samplers
+import ldm_patched.modules.sample
+import ldm_patched.modules.sd
+import ldm_patched.modules.utils
+import ldm_patched.modules.controlnet
 
-import fcbh.clip_vision
+import ldm_patched.modules.clip_vision
 
-import fcbh.model_management
-from fcbh.cli_args import args
+import ldm_patched.modules.model_management
+from ldm_patched.modules.args_parser import args
 
 import importlib
 
-import folder_paths
-import latent_preview
+import ldm_patched.utils.path_utils
+import ldm_patched.utils.latent_visualization
 
 def before_node_execution():
-    fcbh.model_management.throw_exception_if_processing_interrupted()
+    ldm_patched.modules.model_management.throw_exception_if_processing_interrupted()
 
 def interrupt_processing(value=True):
-    fcbh.model_management.interrupt_current_processing(value)
+    ldm_patched.modules.model_management.interrupt_current_processing(value)
 
 MAX_RESOLUTION=8192
 
@@ -248,8 +250,8 @@ class ConditioningSetTimestepRange:
         c = []
         for t in conditioning:
             d = t[1].copy()
-            d['start_percent'] = 1.0 - start
-            d['end_percent'] = 1.0 - end
+            d['start_percent'] = start
+            d['end_percent'] = end
             n = [t[0], d]
             c.append(n)
         return (c, )
@@ -361,12 +363,12 @@ class VAEEncodeForInpaint:
 
 class SaveLatent:
     def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
+        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
 
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT", ),
-                              "filename_prefix": ("STRING", {"default": "latents/fcbh_backend"})},
+                              "filename_prefix": ("STRING", {"default": "latents/ldm_patched"})},
                 "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                 }
     RETURN_TYPES = ()
@@ -376,8 +378,8 @@ class SaveLatent:
 
     CATEGORY = "_for_testing"
 
-    def save(self, samples, filename_prefix="fcbh_backend", prompt=None, extra_pnginfo=None):
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+    def save(self, samples, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None):
+        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir)
 
         # support save metadata for latent sharing
         prompt_info = ""
@@ -385,7 +387,7 @@ class SaveLatent:
             prompt_info = json.dumps(prompt)
 
         metadata = None
-        if not args.disable_metadata:
+        if not args.disable_server_info:
             metadata = {"prompt": prompt_info}
             if extra_pnginfo is not None:
                 for x in extra_pnginfo:
@@ -406,14 +408,14 @@ class SaveLatent:
         output["latent_tensor"] = samples["samples"]
         output["latent_format_version_0"] = torch.tensor([])
 
-        fcbh.utils.save_torch_file(output, file, metadata=metadata)
+        ldm_patched.modules.utils.save_torch_file(output, file, metadata=metadata)
         return { "ui": { "latents": results } }
 
 
 class LoadLatent:
     @classmethod
     def INPUT_TYPES(s):
-        input_dir = folder_paths.get_input_directory()
+        input_dir = ldm_patched.utils.path_utils.get_input_directory()
         files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")]
         return {"required": {"latent": [sorted(files), ]}, }
 
@@ -423,7 +425,7 @@ class LoadLatent:
     FUNCTION = "load"
 
     def load(self, latent):
-        latent_path = folder_paths.get_annotated_filepath(latent)
+        latent_path = ldm_patched.utils.path_utils.get_annotated_filepath(latent)
         latent = safetensors.torch.load_file(latent_path, device="cpu")
         multiplier = 1.0
         if "latent_format_version_0" not in latent:
@@ -433,7 +435,7 @@ class LoadLatent:
 
     @classmethod
     def IS_CHANGED(s, latent):
-        image_path = folder_paths.get_annotated_filepath(latent)
+        image_path = ldm_patched.utils.path_utils.get_annotated_filepath(latent)
         m = hashlib.sha256()
         with open(image_path, 'rb') as f:
             m.update(f.read())
@@ -441,7 +443,7 @@ class LoadLatent:
 
     @classmethod
     def VALIDATE_INPUTS(s, latent):
-        if not folder_paths.exists_annotated_filepath(latent):
+        if not ldm_patched.utils.path_utils.exists_annotated_filepath(latent):
             return "Invalid latent file: {}".format(latent)
         return True
 
@@ -449,22 +451,22 @@ class LoadLatent:
 class CheckpointLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ),
-                              "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}}
+        return {"required": { "config_name": (ldm_patched.utils.path_utils.get_filename_list("configs"), ),
+                              "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), )}}
     RETURN_TYPES = ("MODEL", "CLIP", "VAE")
     FUNCTION = "load_checkpoint"
 
     CATEGORY = "advanced/loaders"
 
     def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
-        config_path = folder_paths.get_full_path("configs", config_name)
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
-        return fcbh.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        config_path = ldm_patched.utils.path_utils.get_full_path("configs", config_name)
+        ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name)
+        return ldm_patched.modules.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings"))
 
 class CheckpointLoaderSimple:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+        return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ),
                              }}
     RETURN_TYPES = ("MODEL", "CLIP", "VAE")
     FUNCTION = "load_checkpoint"
@@ -472,15 +474,15 @@ class CheckpointLoaderSimple:
     CATEGORY = "loaders"
 
     def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
-        out = fcbh.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name)
+        out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings"))
         return out[:3]
 
 class DiffusersLoader:
     @classmethod
     def INPUT_TYPES(cls):
         paths = []
-        for search_path in folder_paths.get_folder_paths("diffusers"):
+        for search_path in ldm_patched.utils.path_utils.get_folder_paths("diffusers"):
             if os.path.exists(search_path):
                 for root, subdir, files in os.walk(search_path, followlinks=True):
                     if "model_index.json" in files:
@@ -493,20 +495,20 @@ class DiffusersLoader:
     CATEGORY = "advanced/loaders/deprecated"
 
     def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
-        for search_path in folder_paths.get_folder_paths("diffusers"):
+        for search_path in ldm_patched.utils.path_utils.get_folder_paths("diffusers"):
             if os.path.exists(search_path):
                 path = os.path.join(search_path, model_path)
                 if os.path.exists(path):
                     model_path = path
                     break
 
-        return fcbh.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return ldm_patched.modules.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings"))
 
 
 class unCLIPCheckpointLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+        return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ),
                              }}
     RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION")
     FUNCTION = "load_checkpoint"
@@ -514,8 +516,8 @@ class unCLIPCheckpointLoader:
     CATEGORY = "loaders"
 
     def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
-        out = fcbh.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name)
+        out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings"))
         return out
 
 class CLIPSetLastLayer:
@@ -542,7 +544,7 @@ class LoraLoader:
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "clip": ("CLIP", ),
-                              "lora_name": (folder_paths.get_filename_list("loras"), ),
+                              "lora_name": (ldm_patched.utils.path_utils.get_filename_list("loras"), ),
                               "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
                               "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
                               }}
@@ -555,7 +557,7 @@ class LoraLoader:
         if strength_model == 0 and strength_clip == 0:
             return (model, clip)
 
-        lora_path = folder_paths.get_full_path("loras", lora_name)
+        lora_path = ldm_patched.utils.path_utils.get_full_path("loras", lora_name)
         lora = None
         if self.loaded_lora is not None:
             if self.loaded_lora[0] == lora_path:
@@ -566,16 +568,75 @@ class LoraLoader:
                 del temp
 
         if lora is None:
-            lora = fcbh.utils.load_torch_file(lora_path, safe_load=True)
+            lora = ldm_patched.modules.utils.load_torch_file(lora_path, safe_load=True)
             self.loaded_lora = (lora_path, lora)
 
-        model_lora, clip_lora = fcbh.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
+        model_lora, clip_lora = ldm_patched.modules.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
         return (model_lora, clip_lora)
 
-class VAELoader:
+class LoraLoaderModelOnly(LoraLoader):
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "vae_name": (folder_paths.get_filename_list("vae"), )}}
+        return {"required": { "model": ("MODEL",),
+                              "lora_name": (ldm_patched.utils.path_utils.get_filename_list("loras"), ),
+                              "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_lora_model_only"
+
+    def load_lora_model_only(self, model, lora_name, strength_model):
+        return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
+
+class VAELoader:
+    @staticmethod
+    def vae_list():
+        vaes = ldm_patched.utils.path_utils.get_filename_list("vae")
+        approx_vaes = ldm_patched.utils.path_utils.get_filename_list("vae_approx")
+        sdxl_taesd_enc = False
+        sdxl_taesd_dec = False
+        sd1_taesd_enc = False
+        sd1_taesd_dec = False
+
+        for v in approx_vaes:
+            if v.startswith("taesd_decoder."):
+                sd1_taesd_dec = True
+            elif v.startswith("taesd_encoder."):
+                sd1_taesd_enc = True
+            elif v.startswith("taesdxl_decoder."):
+                sdxl_taesd_dec = True
+            elif v.startswith("taesdxl_encoder."):
+                sdxl_taesd_enc = True
+        if sd1_taesd_dec and sd1_taesd_enc:
+            vaes.append("taesd")
+        if sdxl_taesd_dec and sdxl_taesd_enc:
+            vaes.append("taesdxl")
+        return vaes
+
+    @staticmethod
+    def load_taesd(name):
+        sd = {}
+        approx_vaes = ldm_patched.utils.path_utils.get_filename_list("vae_approx")
+
+        encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes))
+        decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes))
+
+        enc = ldm_patched.modules.utils.load_torch_file(ldm_patched.utils.path_utils.get_full_path("vae_approx", encoder))
+        for k in enc:
+            sd["taesd_encoder.{}".format(k)] = enc[k]
+
+        dec = ldm_patched.modules.utils.load_torch_file(ldm_patched.utils.path_utils.get_full_path("vae_approx", decoder))
+        for k in dec:
+            sd["taesd_decoder.{}".format(k)] = dec[k]
+
+        if name == "taesd":
+            sd["vae_scale"] = torch.tensor(0.18215)
+        elif name == "taesdxl":
+            sd["vae_scale"] = torch.tensor(0.13025)
+        return sd
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "vae_name": (s.vae_list(), )}}
     RETURN_TYPES = ("VAE",)
     FUNCTION = "load_vae"
 
@@ -583,15 +644,18 @@ class VAELoader:
 
     #TODO: scale factor?
     def load_vae(self, vae_name):
-        vae_path = folder_paths.get_full_path("vae", vae_name)
-        sd = fcbh.utils.load_torch_file(vae_path)
-        vae = fcbh.sd.VAE(sd=sd)
+        if vae_name in ["taesd", "taesdxl"]:
+            sd = self.load_taesd(vae_name)
+        else:
+            vae_path = ldm_patched.utils.path_utils.get_full_path("vae", vae_name)
+            sd = ldm_patched.modules.utils.load_torch_file(vae_path)
+        vae = ldm_patched.modules.sd.VAE(sd=sd)
         return (vae,)
 
 class ControlNetLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}}
+        return {"required": { "control_net_name": (ldm_patched.utils.path_utils.get_filename_list("controlnet"), )}}
 
     RETURN_TYPES = ("CONTROL_NET",)
     FUNCTION = "load_controlnet"
@@ -599,15 +663,15 @@ class ControlNetLoader:
     CATEGORY = "loaders"
 
     def load_controlnet(self, control_net_name):
-        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
-        controlnet = fcbh.controlnet.load_controlnet(controlnet_path)
+        controlnet_path = ldm_patched.utils.path_utils.get_full_path("controlnet", control_net_name)
+        controlnet = ldm_patched.modules.controlnet.load_controlnet(controlnet_path)
         return (controlnet,)
 
 class DiffControlNetLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "control_net_name": (folder_paths.get_filename_list("controlnet"), )}}
+                              "control_net_name": (ldm_patched.utils.path_utils.get_filename_list("controlnet"), )}}
 
     RETURN_TYPES = ("CONTROL_NET",)
     FUNCTION = "load_controlnet"
@@ -615,8 +679,8 @@ class DiffControlNetLoader:
     CATEGORY = "loaders"
 
     def load_controlnet(self, model, control_net_name):
-        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
-        controlnet = fcbh.controlnet.load_controlnet(controlnet_path, model)
+        controlnet_path = ldm_patched.utils.path_utils.get_full_path("controlnet", control_net_name)
+        controlnet = ldm_patched.modules.controlnet.load_controlnet(controlnet_path, model)
         return (controlnet,)
 
 
@@ -685,7 +749,7 @@ class ControlNetApplyAdvanced:
                 if prev_cnet in cnets:
                     c_net = cnets[prev_cnet]
                 else:
-                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (1.0 - start_percent, 1.0 - end_percent))
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))
                     c_net.set_previous_controlnet(prev_cnet)
                     cnets[prev_cnet] = c_net
 
@@ -700,7 +764,7 @@ class ControlNetApplyAdvanced:
 class UNETLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ),
+        return {"required": { "unet_name": (ldm_patched.utils.path_utils.get_filename_list("unet"), ),
                              }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "load_unet"
@@ -708,14 +772,14 @@ class UNETLoader:
     CATEGORY = "advanced/loaders"
 
     def load_unet(self, unet_name):
-        unet_path = folder_paths.get_full_path("unet", unet_name)
-        model = fcbh.sd.load_unet(unet_path)
+        unet_path = ldm_patched.utils.path_utils.get_full_path("unet", unet_name)
+        model = ldm_patched.modules.sd.load_unet(unet_path)
         return (model,)
 
 class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name": (ldm_patched.utils.path_utils.get_filename_list("clip"), ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -723,14 +787,14 @@ class CLIPLoader:
     CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name):
-        clip_path = folder_paths.get_full_path("clip", clip_name)
-        clip = fcbh.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        clip_path = ldm_patched.utils.path_utils.get_full_path("clip", clip_name)
+        clip = ldm_patched.modules.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings"))
         return (clip,)
 
 class DualCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name1": (ldm_patched.utils.path_utils.get_filename_list("clip"), ), "clip_name2": (ldm_patched.utils.path_utils.get_filename_list("clip"), ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -738,15 +802,15 @@ class DualCLIPLoader:
     CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name1, clip_name2):
-        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
-        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
-        clip = fcbh.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        clip_path1 = ldm_patched.utils.path_utils.get_full_path("clip", clip_name1)
+        clip_path2 = ldm_patched.utils.path_utils.get_full_path("clip", clip_name2)
+        clip = ldm_patched.modules.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings"))
         return (clip,)
 
 class CLIPVisionLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ),
+        return {"required": { "clip_name": (ldm_patched.utils.path_utils.get_filename_list("clip_vision"), ),
                              }}
     RETURN_TYPES = ("CLIP_VISION",)
     FUNCTION = "load_clip"
@@ -754,8 +818,8 @@ class CLIPVisionLoader:
     CATEGORY = "loaders"
 
     def load_clip(self, clip_name):
-        clip_path = folder_paths.get_full_path("clip_vision", clip_name)
-        clip_vision = fcbh.clip_vision.load(clip_path)
+        clip_path = ldm_patched.utils.path_utils.get_full_path("clip_vision", clip_name)
+        clip_vision = ldm_patched.modules.clip_vision.load(clip_path)
         return (clip_vision,)
 
 class CLIPVisionEncode:
@@ -776,7 +840,7 @@ class CLIPVisionEncode:
 class StyleModelLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}}
+        return {"required": { "style_model_name": (ldm_patched.utils.path_utils.get_filename_list("style_models"), )}}
 
     RETURN_TYPES = ("STYLE_MODEL",)
     FUNCTION = "load_style_model"
@@ -784,8 +848,8 @@ class StyleModelLoader:
     CATEGORY = "loaders"
 
     def load_style_model(self, style_model_name):
-        style_model_path = folder_paths.get_full_path("style_models", style_model_name)
-        style_model = fcbh.sd.load_style_model(style_model_path)
+        style_model_path = ldm_patched.utils.path_utils.get_full_path("style_models", style_model_name)
+        style_model = ldm_patched.modules.sd.load_style_model(style_model_path)
         return (style_model,)
 
 
@@ -841,7 +905,7 @@ class unCLIPConditioning:
 class GLIGENLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}}
+        return {"required": { "gligen_name": (ldm_patched.utils.path_utils.get_filename_list("gligen"), )}}
 
     RETURN_TYPES = ("GLIGEN",)
     FUNCTION = "load_gligen"
@@ -849,8 +913,8 @@ class GLIGENLoader:
     CATEGORY = "loaders"
 
     def load_gligen(self, gligen_name):
-        gligen_path = folder_paths.get_full_path("gligen", gligen_name)
-        gligen = fcbh.sd.load_gligen(gligen_path)
+        gligen_path = ldm_patched.utils.path_utils.get_full_path("gligen", gligen_name)
+        gligen = ldm_patched.modules.sd.load_gligen(gligen_path)
         return (gligen,)
 
 class GLIGENTextBoxApply:
@@ -885,8 +949,8 @@ class GLIGENTextBoxApply:
         return (c, )
 
 class EmptyLatentImage:
-    def __init__(self, device="cpu"):
-        self.device = device
+    def __init__(self):
+        self.device = ldm_patched.modules.model_management.intermediate_device()
 
     @classmethod
     def INPUT_TYPES(s):
@@ -899,7 +963,7 @@ class EmptyLatentImage:
     CATEGORY = "latent"
 
     def generate(self, width, height, batch_size=1):
-        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
         return ({"samples":latent}, )
 
 
@@ -992,7 +1056,7 @@ class LatentUpscale:
                 width = max(64, width)
                 height = max(64, height)
 
-            s["samples"] = fcbh.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop)
+            s["samples"] = ldm_patched.modules.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop)
         return (s,)
 
 class LatentUpscaleBy:
@@ -1011,7 +1075,7 @@ class LatentUpscaleBy:
         s = samples.copy()
         width = round(samples["samples"].shape[3] * scale_by)
         height = round(samples["samples"].shape[2] * scale_by)
-        s["samples"] = fcbh.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled")
+        s["samples"] = ldm_patched.modules.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled")
         return (s,)
 
 class LatentRotate:
@@ -1127,7 +1191,7 @@ class LatentBlend:
 
         if samples1.shape != samples2.shape:
             samples2.permute(0, 3, 1, 2)
-            samples2 = fcbh.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center')
+            samples2 = ldm_patched.modules.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center')
             samples2.permute(0, 2, 3, 1)
 
         samples_blended = self.blend_mode(samples1, samples2, blend_mode)
@@ -1196,15 +1260,15 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
         noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
     else:
         batch_inds = latent["batch_index"] if "batch_index" in latent else None
-        noise = fcbh.sample.prepare_noise(latent_image, seed, batch_inds)
+        noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds)
 
     noise_mask = None
     if "noise_mask" in latent:
         noise_mask = latent["noise_mask"]
 
-    callback = latent_preview.prepare_callback(model, steps)
-    disable_pbar = not fcbh.utils.PROGRESS_BAR_ENABLED
-    samples = fcbh.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
+    callback = ldm_patched.utils.latent_visualization.prepare_callback(model, steps)
+    disable_pbar = not ldm_patched.modules.utils.PROGRESS_BAR_ENABLED
+    samples = ldm_patched.modules.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
                                   denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
                                   force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
     out = latent.copy()
@@ -1218,9 +1282,9 @@ class KSampler:
                     {"model": ("MODEL",),
                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
-                    "sampler_name": (fcbh.samplers.KSampler.SAMPLERS, ),
-                    "scheduler": (fcbh.samplers.KSampler.SCHEDULERS, ),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+                    "sampler_name": (ldm_patched.modules.samplers.KSampler.SAMPLERS, ),
+                    "scheduler": (ldm_patched.modules.samplers.KSampler.SCHEDULERS, ),
                     "positive": ("CONDITIONING", ),
                     "negative": ("CONDITIONING", ),
                     "latent_image": ("LATENT", ),
@@ -1244,9 +1308,9 @@ class KSamplerAdvanced:
                     "add_noise": (["enable", "disable"], ),
                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
-                    "sampler_name": (fcbh.samplers.KSampler.SAMPLERS, ),
-                    "scheduler": (fcbh.samplers.KSampler.SCHEDULERS, ),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+                    "sampler_name": (ldm_patched.modules.samplers.KSampler.SAMPLERS, ),
+                    "scheduler": (ldm_patched.modules.samplers.KSampler.SCHEDULERS, ),
                     "positive": ("CONDITIONING", ),
                     "negative": ("CONDITIONING", ),
                     "latent_image": ("LATENT", ),
@@ -1272,15 +1336,16 @@ class KSamplerAdvanced:
 
 class SaveImage:
     def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
+        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
         self.type = "output"
         self.prefix_append = ""
+        self.compress_level = 4
 
     @classmethod
     def INPUT_TYPES(s):
         return {"required": 
                     {"images": ("IMAGE", ),
-                     "filename_prefix": ("STRING", {"default": "fcbh_backend"})},
+                     "filename_prefix": ("STRING", {"default": "ldm_patched"})},
                 "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                 }
 
@@ -1291,15 +1356,15 @@ class SaveImage:
 
     CATEGORY = "image"
 
-    def save_images(self, images, filename_prefix="fcbh_backend", prompt=None, extra_pnginfo=None):
+    def save_images(self, images, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None):
         filename_prefix += self.prefix_append
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
         results = list()
         for image in images:
             i = 255. * image.cpu().numpy()
             img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
             metadata = None
-            if not args.disable_metadata:
+            if not args.disable_server_info:
                 metadata = PngInfo()
                 if prompt is not None:
                     metadata.add_text("prompt", json.dumps(prompt))
@@ -1308,7 +1373,7 @@ class SaveImage:
                         metadata.add_text(x, json.dumps(extra_pnginfo[x]))
 
             file = f"{filename}_{counter:05}_.png"
-            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
+            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
             results.append({
                 "filename": file,
                 "subfolder": subfolder,
@@ -1320,9 +1385,10 @@ class SaveImage:
 
 class PreviewImage(SaveImage):
     def __init__(self):
-        self.output_dir = folder_paths.get_temp_directory()
+        self.output_dir = ldm_patched.utils.path_utils.get_temp_directory()
         self.type = "temp"
         self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
+        self.compress_level = 1
 
     @classmethod
     def INPUT_TYPES(s):
@@ -1334,7 +1400,7 @@ class PreviewImage(SaveImage):
 class LoadImage:
     @classmethod
     def INPUT_TYPES(s):
-        input_dir = folder_paths.get_input_directory()
+        input_dir = ldm_patched.utils.path_utils.get_input_directory()
         files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
         return {"required":
                     {"image": (sorted(files), {"image_upload": True})},
@@ -1345,7 +1411,7 @@ class LoadImage:
     RETURN_TYPES = ("IMAGE", "MASK")
     FUNCTION = "load_image"
     def load_image(self, image):
-        image_path = folder_paths.get_annotated_filepath(image)
+        image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image)
         i = Image.open(image_path)
         i = ImageOps.exif_transpose(i)
         image = i.convert("RGB")
@@ -1360,7 +1426,7 @@ class LoadImage:
 
     @classmethod
     def IS_CHANGED(s, image):
-        image_path = folder_paths.get_annotated_filepath(image)
+        image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image)
         m = hashlib.sha256()
         with open(image_path, 'rb') as f:
             m.update(f.read())
@@ -1368,7 +1434,7 @@ class LoadImage:
 
     @classmethod
     def VALIDATE_INPUTS(s, image):
-        if not folder_paths.exists_annotated_filepath(image):
+        if not ldm_patched.utils.path_utils.exists_annotated_filepath(image):
             return "Invalid image file: {}".format(image)
 
         return True
@@ -1377,7 +1443,7 @@ class LoadImageMask:
     _color_channels = ["alpha", "red", "green", "blue"]
     @classmethod
     def INPUT_TYPES(s):
-        input_dir = folder_paths.get_input_directory()
+        input_dir = ldm_patched.utils.path_utils.get_input_directory()
         files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
         return {"required":
                     {"image": (sorted(files), {"image_upload": True}),
@@ -1389,7 +1455,7 @@ class LoadImageMask:
     RETURN_TYPES = ("MASK",)
     FUNCTION = "load_image"
     def load_image(self, image, channel):
-        image_path = folder_paths.get_annotated_filepath(image)
+        image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image)
         i = Image.open(image_path)
         i = ImageOps.exif_transpose(i)
         if i.getbands() != ("R", "G", "B", "A"):
@@ -1407,7 +1473,7 @@ class LoadImageMask:
 
     @classmethod
     def IS_CHANGED(s, image, channel):
-        image_path = folder_paths.get_annotated_filepath(image)
+        image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image)
         m = hashlib.sha256()
         with open(image_path, 'rb') as f:
             m.update(f.read())
@@ -1415,7 +1481,7 @@ class LoadImageMask:
 
     @classmethod
     def VALIDATE_INPUTS(s, image, channel):
-        if not folder_paths.exists_annotated_filepath(image):
+        if not ldm_patched.utils.path_utils.exists_annotated_filepath(image):
             return "Invalid image file: {}".format(image)
 
         if channel not in s._color_channels:
@@ -1449,7 +1515,7 @@ class ImageScale:
             elif height == 0:
                 height = max(1, round(samples.shape[2] * width / samples.shape[3]))
 
-            s = fcbh.utils.common_upscale(samples, width, height, upscale_method, crop)
+            s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, crop)
             s = s.movedim(1,-1)
         return (s,)
 
@@ -1469,7 +1535,7 @@ class ImageScaleBy:
         samples = image.movedim(-1,1)
         width = round(samples.shape[3] * scale_by)
         height = round(samples.shape[2] * scale_by)
-        s = fcbh.utils.common_upscale(samples, width, height, upscale_method, "disabled")
+        s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, "disabled")
         s = s.movedim(1,-1)
         return (s,)
 
@@ -1501,7 +1567,7 @@ class ImageBatch:
 
     def batch(self, image1, image2):
         if image1.shape[1:] != image2.shape[1:]:
-            image2 = fcbh.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1)
+            image2 = ldm_patched.modules.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1)
         s = torch.cat((image1, image2), dim=0)
         return (s,)
 
@@ -1654,6 +1720,7 @@ NODE_CLASS_MAPPINGS = {
 
     "ConditioningZeroOut": ConditioningZeroOut,
     "ConditioningSetTimestepRange": ConditioningSetTimestepRange,
+    "LoraLoaderModelOnly": LoraLoaderModelOnly,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -1756,10 +1823,10 @@ def load_custom_node(module_path, ignore=set()):
 
 def load_custom_nodes():
     base_node_names = set(NODE_CLASS_MAPPINGS.keys())
-    node_paths = folder_paths.get_folder_paths("custom_nodes")
+    node_paths = ldm_patched.utils.path_utils.get_folder_paths("custom_nodes")
     node_import_times = []
     for custom_node_path in node_paths:
-        possible_modules = os.listdir(custom_node_path)
+        possible_modules = os.listdir(os.path.realpath(custom_node_path))
         if "__pycache__" in possible_modules:
             possible_modules.remove("__pycache__")
 
@@ -1782,7 +1849,7 @@ def load_custom_nodes():
         print()
 
 def init_custom_nodes():
-    extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "fcbh_extras")
+    extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "ldm_patched_extras")
     extras_files = [
         "nodes_latent.py",
         "nodes_hypernetwork.py",
@@ -1798,6 +1865,12 @@ def init_custom_nodes():
         "nodes_freelunch.py",
         "nodes_custom_sampler.py",
         "nodes_hypertile.py",
+        "nodes_model_advanced.py",
+        "nodes_model_downscale.py",
+        "nodes_images.py",
+        "nodes_video_model.py",
+        "nodes_sag.py",
+        "nodes_perpneg.py",
     ]
 
     for node_file in extras_files:

ldm_patched/contrib/external_canny.py

@@ -1,9 +1,11 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 #From https://github.com/kornia/kornia
 import math
 
 import torch
 import torch.nn.functional as F
-import fcbh.model_management
+import ldm_patched.modules.model_management
 
 def get_canny_nms_kernel(device=None, dtype=None):
     """Utility function that returns 3x3 kernels for the Canny Non-maximal suppression."""
@@ -290,8 +292,8 @@ class Canny:
     CATEGORY = "image/preprocessors"
 
     def detect_edge(self, image, low_threshold, high_threshold):
-        output = canny(image.to(fcbh.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
-        img_out = output[1].cpu().repeat(1, 3, 1, 1).movedim(1, -1)
+        output = canny(image.to(ldm_patched.modules.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
+        img_out = output[1].to(ldm_patched.modules.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)
         return (img_out,)
 
 NODE_CLASS_MAPPINGS = {

ldm_patched/contrib/external_clip_sdxl.py

@@ -1,5 +1,7 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 import torch
-from nodes import MAX_RESOLUTION
+from ldm_patched.contrib.external import MAX_RESOLUTION
 
 class CLIPTextEncodeSDXLRefiner:
     @classmethod

ldm_patched/contrib/external_compositing.py

@@ -1,6 +1,8 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 import numpy as np
 import torch
-import fcbh.utils
+import ldm_patched.modules.utils
 from enum import Enum
 
 def resize_mask(mask, shape):
@@ -122,15 +124,15 @@ class PorterDuffImageComposite:
 
             if dst_alpha.shape[:2] != dst_image.shape[:2]:
                 upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2)
-                upscale_output = fcbh.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
+                upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
                 dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
             if src_image.shape != dst_image.shape:
                 upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2)
-                upscale_output = fcbh.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
+                upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
                 src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)
             if src_alpha.shape != dst_alpha.shape:
                 upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2)
-                upscale_output = fcbh.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
+                upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
                 src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
 
             out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode])

ldm_patched/contrib/external_custom_sampler.py

@@ -1,9 +1,11 @@
-import fcbh.samplers
-import fcbh.sample
-from fcbh.k_diffusion import sampling as k_diffusion_sampling
-import latent_preview
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
+import ldm_patched.modules.samplers
+import ldm_patched.modules.sample
+from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
+import ldm_patched.utils.latent_visualization
 import torch
-import fcbh.utils
+import ldm_patched.modules.utils
 
 
 class BasicScheduler:
@@ -11,17 +13,17 @@ class BasicScheduler:
     def INPUT_TYPES(s):
         return {"required":
                     {"model": ("MODEL",),
-                     "scheduler": (fcbh.samplers.SCHEDULER_NAMES, ),
+                     "scheduler": (ldm_patched.modules.samplers.SCHEDULER_NAMES, ),
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                       }
                }
     RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "sampling/custom_sampling"
+    CATEGORY = "sampling/custom_sampling/schedulers"
 
     FUNCTION = "get_sigmas"
 
     def get_sigmas(self, model, scheduler, steps):
-        sigmas = fcbh.samplers.calculate_sigmas_scheduler(model.model, scheduler, steps).cpu()
+        sigmas = ldm_patched.modules.samplers.calculate_sigmas_scheduler(model.model, scheduler, steps).cpu()
         return (sigmas, )
 
 
@@ -36,7 +38,7 @@ class KarrasScheduler:
                     }
                }
     RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "sampling/custom_sampling"
+    CATEGORY = "sampling/custom_sampling/schedulers"
 
     FUNCTION = "get_sigmas"
 
@@ -54,7 +56,7 @@ class ExponentialScheduler:
                     }
                }
     RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "sampling/custom_sampling"
+    CATEGORY = "sampling/custom_sampling/schedulers"
 
     FUNCTION = "get_sigmas"
 
@@ -73,7 +75,7 @@ class PolyexponentialScheduler:
                     }
                }
     RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "sampling/custom_sampling"
+    CATEGORY = "sampling/custom_sampling/schedulers"
 
     FUNCTION = "get_sigmas"
 
@@ -81,6 +83,25 @@ class PolyexponentialScheduler:
         sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
         return (sigmas, )
 
+class SDTurboScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                     "steps": ("INT", {"default": 1, "min": 1, "max": 10}),
+                      }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, model, steps):
+        timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[:steps]
+        sigmas = model.model.model_sampling.sigma(timesteps)
+        sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
+        return (sigmas, )
+
 class VPScheduler:
     @classmethod
     def INPUT_TYPES(s):
@@ -92,7 +113,7 @@ class VPScheduler:
                     }
                }
     RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "sampling/custom_sampling"
+    CATEGORY = "sampling/custom_sampling/schedulers"
 
     FUNCTION = "get_sigmas"
 
@@ -109,7 +130,7 @@ class SplitSigmas:
                      }
                 }
     RETURN_TYPES = ("SIGMAS","SIGMAS")
-    CATEGORY = "sampling/custom_sampling"
+    CATEGORY = "sampling/custom_sampling/sigmas"
 
     FUNCTION = "get_sigmas"
 
@@ -118,20 +139,38 @@ class SplitSigmas:
         sigmas2 = sigmas[step:]
         return (sigmas1, sigmas2)
 
+class FlipSigmas:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"sigmas": ("SIGMAS", ),
+                     }
+                }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/sigmas"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, sigmas):
+        sigmas = sigmas.flip(0)
+        if sigmas[0] == 0:
+            sigmas[0] = 0.0001
+        return (sigmas,)
+
 class KSamplerSelect:
     @classmethod
     def INPUT_TYPES(s):
         return {"required":
-                    {"sampler_name": (fcbh.samplers.SAMPLER_NAMES, ),
+                    {"sampler_name": (ldm_patched.modules.samplers.SAMPLER_NAMES, ),
                       }
                }
     RETURN_TYPES = ("SAMPLER",)
-    CATEGORY = "sampling/custom_sampling"
+    CATEGORY = "sampling/custom_sampling/samplers"
 
     FUNCTION = "get_sampler"
 
     def get_sampler(self, sampler_name):
-        sampler = fcbh.samplers.sampler_class(sampler_name)()
+        sampler = ldm_patched.modules.samplers.sampler_object(sampler_name)
         return (sampler, )
 
 class SamplerDPMPP_2M_SDE:
@@ -145,7 +184,7 @@ class SamplerDPMPP_2M_SDE:
                       }
                }
     RETURN_TYPES = ("SAMPLER",)
-    CATEGORY = "sampling/custom_sampling"
+    CATEGORY = "sampling/custom_sampling/samplers"
 
     FUNCTION = "get_sampler"
 
@@ -154,7 +193,7 @@ class SamplerDPMPP_2M_SDE:
             sampler_name = "dpmpp_2m_sde"
         else:
             sampler_name = "dpmpp_2m_sde_gpu"
-        sampler = fcbh.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})()
+        sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})
         return (sampler, )
 
 
@@ -169,7 +208,7 @@ class SamplerDPMPP_SDE:
                       }
                }
     RETURN_TYPES = ("SAMPLER",)
-    CATEGORY = "sampling/custom_sampling"
+    CATEGORY = "sampling/custom_sampling/samplers"
 
     FUNCTION = "get_sampler"
 
@@ -178,7 +217,7 @@ class SamplerDPMPP_SDE:
             sampler_name = "dpmpp_sde"
         else:
             sampler_name = "dpmpp_sde_gpu"
-        sampler = fcbh.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})()
+        sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
         return (sampler, )
 
 class SamplerCustom:
@@ -188,7 +227,7 @@ class SamplerCustom:
                     {"model": ("MODEL",),
                     "add_noise": ("BOOLEAN", {"default": True}),
                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
                     "positive": ("CONDITIONING", ),
                     "negative": ("CONDITIONING", ),
                     "sampler": ("SAMPLER", ),
@@ -211,17 +250,17 @@ class SamplerCustom:
             noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
         else:
             batch_inds = latent["batch_index"] if "batch_index" in latent else None
-            noise = fcbh.sample.prepare_noise(latent_image, noise_seed, batch_inds)
+            noise = ldm_patched.modules.sample.prepare_noise(latent_image, noise_seed, batch_inds)
 
         noise_mask = None
         if "noise_mask" in latent:
             noise_mask = latent["noise_mask"]
 
         x0_output = {}
-        callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+        callback = ldm_patched.utils.latent_visualization.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
 
-        disable_pbar = not fcbh.utils.PROGRESS_BAR_ENABLED
-        samples = fcbh.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+        disable_pbar = not ldm_patched.modules.utils.PROGRESS_BAR_ENABLED
+        samples = ldm_patched.modules.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
 
         out = latent.copy()
         out["samples"] = samples
@@ -234,13 +273,15 @@ class SamplerCustom:
 
 NODE_CLASS_MAPPINGS = {
     "SamplerCustom": SamplerCustom,
+    "BasicScheduler": BasicScheduler,
     "KarrasScheduler": KarrasScheduler,
     "ExponentialScheduler": ExponentialScheduler,
     "PolyexponentialScheduler": PolyexponentialScheduler,
     "VPScheduler": VPScheduler,
+    "SDTurboScheduler": SDTurboScheduler,
     "KSamplerSelect": KSamplerSelect,
     "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
     "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
-    "BasicScheduler": BasicScheduler,
     "SplitSigmas": SplitSigmas,
+    "FlipSigmas": FlipSigmas,
 }

ldm_patched/contrib/external_freelunch.py

@@ -1,3 +1,5 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 #code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)
 
 import torch

ldm_patched/contrib/external_hypernetwork.py

@@ -1,9 +1,11 @@
-import fcbh.utils
-import folder_paths
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
+import ldm_patched.modules.utils
+import ldm_patched.utils.path_utils
 import torch
 
 def load_hypernetwork_patch(path, strength):
-    sd = fcbh.utils.load_torch_file(path, safe_load=True)
+    sd = ldm_patched.modules.utils.load_torch_file(path, safe_load=True)
     activation_func = sd.get('activation_func', 'linear')
     is_layer_norm = sd.get('is_layer_norm', False)
     use_dropout = sd.get('use_dropout', False)
@@ -97,7 +99,7 @@ class HypernetworkLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "hypernetwork_name": (folder_paths.get_filename_list("hypernetworks"), ),
+                              "hypernetwork_name": (ldm_patched.utils.path_utils.get_filename_list("hypernetworks"), ),
                               "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
                               }}
     RETURN_TYPES = ("MODEL",)
@@ -106,7 +108,7 @@ class HypernetworkLoader:
     CATEGORY = "loaders"
 
     def load_hypernetwork(self, model, hypernetwork_name, strength):
-        hypernetwork_path = folder_paths.get_full_path("hypernetworks", hypernetwork_name)
+        hypernetwork_path = ldm_patched.utils.path_utils.get_full_path("hypernetworks", hypernetwork_name)
         model_hypernetwork = model.clone()
         patch = load_hypernetwork_patch(hypernetwork_path, strength)
         if patch is not None:

ldm_patched/contrib/external_hypertile.py

@@ -1,10 +1,13 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 #Taken from: https://github.com/tfernd/HyperTile/
 
 import math
 from einops import rearrange
-import random
+# Use torch rng for consistency across generations
+from torch import randint
 
-def random_divisor(value: int, min_value: int, /, max_options: int = 1, counter = 0) -> int:
+def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
     min_value = min(min_value, value)
 
     # All big divisors of value (inclusive)
@@ -12,8 +15,10 @@ def random_divisor(value: int, min_value: int, /, max_options: int = 1, counter
 
     ns = [value // i for i in divisors[:max_options]]  # has at least 1 element
 
-    random.seed(counter)
-    idx = random.randint(0, len(ns) - 1)
+    if len(ns) - 1 > 0:
+        idx = randint(low=0, high=len(ns) - 1, size=(1,)).item()
+    else:
+        idx = 0
 
     return ns[idx]
 
@@ -42,7 +47,6 @@ class HyperTile:
 
         latent_tile_size = max(32, tile_size) // 8
         self.temp = None
-        self.counter = 1
 
         def hypertile_in(q, k, v, extra_options):
             if q.shape[-1] in apply_to:
@@ -53,10 +57,8 @@ class HyperTile:
                 h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
 
                 factor = 2**((q.shape[-1] // model_channels) - 1) if scale_depth else 1
-                nh = random_divisor(h, latent_tile_size * factor, swap_size, self.counter)
-                self.counter += 1
-                nw = random_divisor(w, latent_tile_size * factor, swap_size, self.counter)
-                self.counter += 1
+                nh = random_divisor(h, latent_tile_size * factor, swap_size)
+                nw = random_divisor(w, latent_tile_size * factor, swap_size)
 
                 if nh * nw > 1:
                     q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)

ldm_patched/contrib/external_images.py

@@ -0,0 +1,177 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
+import ldm_patched.contrib.external
+import ldm_patched.utils.path_utils
+from ldm_patched.modules.args_parser import args
+
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+
+import numpy as np
+import json
+import os
+
+MAX_RESOLUTION = ldm_patched.contrib.external.MAX_RESOLUTION
+
+class ImageCrop:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",),
+                              "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                              "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "crop"
+
+    CATEGORY = "image/transform"
+
+    def crop(self, image, width, height, x, y):
+        x = min(x, image.shape[2] - 1)
+        y = min(y, image.shape[1] - 1)
+        to_x = width + x
+        to_y = height + y
+        img = image[:,y:to_y, x:to_x, :]
+        return (img,)
+
+class RepeatImageBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",),
+                              "amount": ("INT", {"default": 1, "min": 1, "max": 64}),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "repeat"
+
+    CATEGORY = "image/batch"
+
+    def repeat(self, image, amount):
+        s = image.repeat((amount, 1,1,1))
+        return (s,)
+
+class SaveAnimatedWEBP:
+    def __init__(self):
+        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+
+    methods = {"default": 4, "fastest": 0, "slowest": 6}
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"images": ("IMAGE", ),
+                     "filename_prefix": ("STRING", {"default": "ldm_patched"}),
+                     "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
+                     "lossless": ("BOOLEAN", {"default": True}),
+                     "quality": ("INT", {"default": 80, "min": 0, "max": 100}),
+                     "method": (list(s.methods.keys()),),
+                     # "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}),
+                     },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "image/animation"
+
+    def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None):
+        method = self.methods.get(method)
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+        results = list()
+        pil_images = []
+        for image in images:
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            pil_images.append(img)
+
+        metadata = pil_images[0].getexif()
+        if not args.disable_server_info:
+            if prompt is not None:
+                metadata[0x0110] = "prompt:{}".format(json.dumps(prompt))
+            if extra_pnginfo is not None:
+                inital_exif = 0x010f
+                for x in extra_pnginfo:
+                    metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x]))
+                    inital_exif -= 1
+
+        if num_frames == 0:
+            num_frames = len(pil_images)
+
+        c = len(pil_images)
+        for i in range(0, c, num_frames):
+            file = f"{filename}_{counter:05}_.webp"
+            pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method)
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            counter += 1
+
+        animated = num_frames != 1
+        return { "ui": { "images": results, "animated": (animated,) } }
+
+class SaveAnimatedPNG:
+    def __init__(self):
+        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"images": ("IMAGE", ),
+                     "filename_prefix": ("STRING", {"default": "ldm_patched"}),
+                     "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
+                     "compress_level": ("INT", {"default": 4, "min": 0, "max": 9})
+                     },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "image/animation"
+
+    def save_images(self, images, fps, compress_level, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None):
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+        results = list()
+        pil_images = []
+        for image in images:
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            pil_images.append(img)
+
+        metadata = None
+        if not args.disable_server_info:
+            metadata = PngInfo()
+            if prompt is not None:
+                metadata.add(b"ldm_patched", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True)
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata.add(b"ldm_patched", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True)
+
+        file = f"{filename}_{counter:05}_.png"
+        pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:])
+        results.append({
+            "filename": file,
+            "subfolder": subfolder,
+            "type": self.type
+        })
+
+        return { "ui": { "images": results, "animated": (True,)} }
+
+NODE_CLASS_MAPPINGS = {
+    "ImageCrop": ImageCrop,
+    "RepeatImageBatch": RepeatImageBatch,
+    "SaveAnimatedWEBP": SaveAnimatedWEBP,
+    "SaveAnimatedPNG": SaveAnimatedPNG,
+}

ldm_patched/contrib/external_latent.py

@@ -0,0 +1,133 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
+import ldm_patched.modules.utils
+import torch
+
+def reshape_latent_to(target_shape, latent):
+    if latent.shape[1:] != target_shape[1:]:
+        latent = ldm_patched.modules.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
+    return ldm_patched.modules.utils.repeat_to_batch_size(latent, target_shape[0])
+
+
+class LatentAdd:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced"
+
+    def op(self, samples1, samples2):
+        samples_out = samples1.copy()
+
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+
+        s2 = reshape_latent_to(s1.shape, s2)
+        samples_out["samples"] = s1 + s2
+        return (samples_out,)
+
+class LatentSubtract:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced"
+
+    def op(self, samples1, samples2):
+        samples_out = samples1.copy()
+
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+
+        s2 = reshape_latent_to(s1.shape, s2)
+        samples_out["samples"] = s1 - s2
+        return (samples_out,)
+
+class LatentMultiply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                              "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                             }}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced"
+
+    def op(self, samples, multiplier):
+        samples_out = samples.copy()
+
+        s1 = samples["samples"]
+        samples_out["samples"] = s1 * multiplier
+        return (samples_out,)
+
+class LatentInterpolate:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples1": ("LATENT",),
+                              "samples2": ("LATENT",),
+                              "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced"
+
+    def op(self, samples1, samples2, ratio):
+        samples_out = samples1.copy()
+
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+
+        s2 = reshape_latent_to(s1.shape, s2)
+
+        m1 = torch.linalg.vector_norm(s1, dim=(1))
+        m2 = torch.linalg.vector_norm(s2, dim=(1))
+
+        s1 = torch.nan_to_num(s1 / m1)
+        s2 = torch.nan_to_num(s2 / m2)
+
+        t = (s1 * ratio + s2 * (1.0 - ratio))
+        mt = torch.linalg.vector_norm(t, dim=(1))
+        st = torch.nan_to_num(t / mt)
+
+        samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
+        return (samples_out,)
+
+class LatentBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "batch"
+
+    CATEGORY = "latent/batch"
+
+    def batch(self, samples1, samples2):
+        samples_out = samples1.copy()
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+
+        if s1.shape[1:] != s2.shape[1:]:
+            s2 = ldm_patched.modules.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center")
+        s = torch.cat((s1, s2), dim=0)
+        samples_out["samples"] = s
+        samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])
+        return (samples_out,)
+
+NODE_CLASS_MAPPINGS = {
+    "LatentAdd": LatentAdd,
+    "LatentSubtract": LatentSubtract,
+    "LatentMultiply": LatentMultiply,
+    "LatentInterpolate": LatentInterpolate,
+    "LatentBatch": LatentBatch,
+}

ldm_patched/contrib/external_mask.py

@@ -1,15 +1,17 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 import numpy as np
 import scipy.ndimage
 import torch
-import fcbh.utils
+import ldm_patched.modules.utils
 
-from nodes import MAX_RESOLUTION
+from ldm_patched.contrib.external import MAX_RESOLUTION
 
 def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
     if resize_source:
         source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")
 
-    source = fcbh.utils.repeat_to_batch_size(source, destination.shape[0])
+    source = ldm_patched.modules.utils.repeat_to_batch_size(source, destination.shape[0])
 
     x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
     y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))
@@ -22,7 +24,7 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
     else:
         mask = mask.clone()
         mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
-        mask = fcbh.utils.repeat_to_batch_size(mask, source.shape[0])
+        mask = ldm_patched.modules.utils.repeat_to_batch_size(mask, source.shape[0])
 
     # calculate the bounds of the source that will be overlapping the destination
     # this prevents the source trying to overwrite latent pixels that are out of bounds

ldm_patched/contrib/external_model_advanced.py

@@ -0,0 +1,177 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
+import ldm_patched.utils.path_utils
+import ldm_patched.modules.sd
+import ldm_patched.modules.model_sampling
+import torch
+
+class LCM(ldm_patched.modules.model_sampling.EPS):
+    def calculate_denoised(self, sigma, model_output, model_input):
+        timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        x0 = model_input - model_output * sigma
+
+        sigma_data = 0.5
+        scaled_timestep = timestep * 10.0 #timestep_scaling
+
+        c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
+        c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
+
+        return c_out * x0 + c_skip * model_input
+
+class ModelSamplingDiscreteDistilled(ldm_patched.modules.model_sampling.ModelSamplingDiscrete):
+    original_timesteps = 50
+
+    def __init__(self, model_config=None):
+        super().__init__(model_config)
+
+        self.skip_steps = self.num_timesteps // self.original_timesteps
+
+        sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32)
+        for x in range(self.original_timesteps):
+            sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps]
+
+        self.set_sigmas(sigmas_valid)
+
+    def timestep(self, sigma):
+        log_sigma = sigma.log()
+        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
+        return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device)
+
+    def sigma(self, timestep):
+        t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1))
+        low_idx = t.floor().long()
+        high_idx = t.ceil().long()
+        w = t.frac()
+        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
+        return log_sigma.exp().to(timestep.device)
+
+
+def rescale_zero_terminal_snr_sigmas(sigmas):
+    alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
+    alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+    # Store old values.
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+    # Shift so the last timestep is zero.
+    alphas_bar_sqrt -= (alphas_bar_sqrt_T)
+
+    # Scale so the first timestep is back to the old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+    alphas_bar[-1] = 4.8973451890853435e-08
+    return ((1 - alphas_bar) / alphas_bar) ** 0.5
+
+class ModelSamplingDiscrete:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "sampling": (["eps", "v_prediction", "lcm"],),
+                              "zsnr": ("BOOLEAN", {"default": False}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, sampling, zsnr):
+        m = model.clone()
+
+        sampling_base = ldm_patched.modules.model_sampling.ModelSamplingDiscrete
+        if sampling == "eps":
+            sampling_type = ldm_patched.modules.model_sampling.EPS
+        elif sampling == "v_prediction":
+            sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION
+        elif sampling == "lcm":
+            sampling_type = LCM
+            sampling_base = ModelSamplingDiscreteDistilled
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        if zsnr:
+            model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
+
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
+class ModelSamplingContinuousEDM:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "sampling": (["v_prediction", "eps"],),
+                              "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, sampling, sigma_max, sigma_min):
+        m = model.clone()
+
+        if sampling == "eps":
+            sampling_type = ldm_patched.modules.model_sampling.EPS
+        elif sampling == "v_prediction":
+            sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION
+
+        class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_sigma_range(sigma_min, sigma_max)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
+class RescaleCFG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, multiplier):
+        def rescale_cfg(args):
+            cond = args["cond"]
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+            sigma = args["sigma"]
+            sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))
+            x_orig = args["input"]
+
+            #rescale cfg has to be done on v-pred model output
+            x = x_orig / (sigma * sigma + 1.0)
+            cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
+            uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
+
+            #rescalecfg
+            x_cfg = uncond + cond_scale * (cond - uncond)
+            ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
+            ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)
+
+            x_rescaled = x_cfg * (ro_pos / ro_cfg)
+            x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
+
+            return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5)
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(rescale_cfg)
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "ModelSamplingDiscrete": ModelSamplingDiscrete,
+    "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
+    "RescaleCFG": RescaleCFG,
+}

ldm_patched/contrib/external_model_downscale.py

@@ -0,0 +1,55 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
+import torch
+import ldm_patched.modules.utils
+
+class PatchModelAddDownscale:
+    upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"]
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}),
+                              "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}),
+                              "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                              "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}),
+                              "downscale_after_skip": ("BOOLEAN", {"default": True}),
+                              "downscale_method": (s.upscale_methods,),
+                              "upscale_method": (s.upscale_methods,),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+
+    def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
+        sigma_start = model.model.model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model.model.model_sampling.percent_to_sigma(end_percent)
+
+        def input_block_patch(h, transformer_options):
+            if transformer_options["block"][1] == block_number:
+                sigma = transformer_options["sigmas"][0].item()
+                if sigma <= sigma_start and sigma >= sigma_end:
+                    h = ldm_patched.modules.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled")
+            return h
+
+        def output_block_patch(h, hsp, transformer_options):
+            if h.shape[2] != hsp.shape[2]:
+                h = ldm_patched.modules.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled")
+            return h, hsp
+
+        m = model.clone()
+        if downscale_after_skip:
+            m.set_model_input_block_patch_after_skip(input_block_patch)
+        else:
+            m.set_model_input_block_patch(input_block_patch)
+        m.set_model_output_block_patch(output_block_patch)
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "PatchModelAddDownscale": PatchModelAddDownscale,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    # Sampling
+    "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)",
+}

ldm_patched/contrib/external_model_merging.py

@@ -1,13 +1,15 @@
-import fcbh.sd
-import fcbh.utils
-import fcbh.model_base
-import fcbh.model_management
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
 
-import folder_paths
+import ldm_patched.modules.sd
+import ldm_patched.modules.utils
+import ldm_patched.modules.model_base
+import ldm_patched.modules.model_management
+
+import ldm_patched.utils.path_utils
 import json
 import os
 
-from fcbh.cli_args import args
+from ldm_patched.modules.args_parser import args
 
 class ModelMergeSimple:
     @classmethod
@@ -121,14 +123,14 @@ class ModelMergeBlocks:
 
 class CheckpointSave:
     def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
+        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
 
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "clip": ("CLIP",),
                               "vae": ("VAE",),
-                              "filename_prefix": ("STRING", {"default": "checkpoints/fcbh_backend"}),},
+                              "filename_prefix": ("STRING", {"default": "checkpoints/ldm_patched"}),},
                 "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
     RETURN_TYPES = ()
     FUNCTION = "save"
@@ -137,7 +139,7 @@ class CheckpointSave:
     CATEGORY = "advanced/model_merging"
 
     def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir)
         prompt_info = ""
         if prompt is not None:
             prompt_info = json.dumps(prompt)
@@ -145,9 +147,9 @@ class CheckpointSave:
         metadata = {}
 
         enable_modelspec = True
-        if isinstance(model.model, fcbh.model_base.SDXL):
+        if isinstance(model.model, ldm_patched.modules.model_base.SDXL):
             metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
-        elif isinstance(model.model, fcbh.model_base.SDXLRefiner):
+        elif isinstance(model.model, ldm_patched.modules.model_base.SDXLRefiner):
             metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
         else:
             enable_modelspec = False
@@ -162,12 +164,12 @@ class CheckpointSave:
         # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h",
         # "v2-inpainting"
 
-        if model.model.model_type == fcbh.model_base.ModelType.EPS:
+        if model.model.model_type == ldm_patched.modules.model_base.ModelType.EPS:
             metadata["modelspec.predict_key"] = "epsilon"
-        elif model.model.model_type == fcbh.model_base.ModelType.V_PREDICTION:
+        elif model.model.model_type == ldm_patched.modules.model_base.ModelType.V_PREDICTION:
             metadata["modelspec.predict_key"] = "v"
 
-        if not args.disable_metadata:
+        if not args.disable_server_info:
             metadata["prompt"] = prompt_info
             if extra_pnginfo is not None:
                 for x in extra_pnginfo:
@@ -176,17 +178,17 @@ class CheckpointSave:
         output_checkpoint = f"{filename}_{counter:05}_.safetensors"
         output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
 
-        fcbh.sd.save_checkpoint(output_checkpoint, model, clip, vae, metadata=metadata)
+        ldm_patched.modules.sd.save_checkpoint(output_checkpoint, model, clip, vae, metadata=metadata)
         return {}
 
 class CLIPSave:
     def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
+        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
 
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip": ("CLIP",),
-                              "filename_prefix": ("STRING", {"default": "clip/fcbh_backend"}),},
+                              "filename_prefix": ("STRING", {"default": "clip/ldm_patched"}),},
                 "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
     RETURN_TYPES = ()
     FUNCTION = "save"
@@ -200,13 +202,13 @@ class CLIPSave:
             prompt_info = json.dumps(prompt)
 
         metadata = {}
-        if not args.disable_metadata:
+        if not args.disable_server_info:
             metadata["prompt"] = prompt_info
             if extra_pnginfo is not None:
                 for x in extra_pnginfo:
                     metadata[x] = json.dumps(extra_pnginfo[x])
 
-        fcbh.model_management.load_models_gpu([clip.load_model()])
+        ldm_patched.modules.model_management.load_models_gpu([clip.load_model()])
         clip_sd = clip.get_sd()
 
         for prefix in ["clip_l.", "clip_g.", ""]:
@@ -225,24 +227,24 @@ class CLIPSave:
                 replace_prefix[prefix] = ""
             replace_prefix["transformer."] = ""
 
-            full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir)
+            full_output_folder, filename, counter, subfolder, filename_prefix_ = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix_, self.output_dir)
 
             output_checkpoint = f"{filename}_{counter:05}_.safetensors"
             output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
 
-            current_clip_sd = fcbh.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix)
+            current_clip_sd = ldm_patched.modules.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix)
 
-            fcbh.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata)
+            ldm_patched.modules.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata)
         return {}
 
 class VAESave:
     def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
+        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
 
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "vae": ("VAE",),
-                              "filename_prefix": ("STRING", {"default": "vae/fcbh_backend_vae"}),},
+                              "filename_prefix": ("STRING", {"default": "vae/ldm_patched_vae"}),},
                 "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
     RETURN_TYPES = ()
     FUNCTION = "save"
@@ -251,13 +253,13 @@ class VAESave:
     CATEGORY = "advanced/model_merging"
 
     def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None):
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir)
         prompt_info = ""
         if prompt is not None:
             prompt_info = json.dumps(prompt)
 
         metadata = {}
-        if not args.disable_metadata:
+        if not args.disable_server_info:
             metadata["prompt"] = prompt_info
             if extra_pnginfo is not None:
                 for x in extra_pnginfo:
@@ -266,7 +268,7 @@ class VAESave:
         output_checkpoint = f"{filename}_{counter:05}_.safetensors"
         output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
 
-        fcbh.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata)
+        ldm_patched.modules.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata)
         return {}
 
 NODE_CLASS_MAPPINGS = {

ldm_patched/contrib/external_perpneg.py

@@ -0,0 +1,57 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
+import torch
+import ldm_patched.modules.model_management
+import ldm_patched.modules.sample
+import ldm_patched.modules.samplers
+import ldm_patched.modules.utils
+
+
+class PerpNeg:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL", ),
+                             "empty_conditioning": ("CONDITIONING", ),
+                             "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
+                            }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+
+    def patch(self, model, empty_conditioning, neg_scale):
+        m = model.clone()
+        nocond = ldm_patched.modules.sample.convert_cond(empty_conditioning)
+
+        def cfg_function(args):
+            model = args["model"]
+            noise_pred_pos = args["cond_denoised"]
+            noise_pred_neg = args["uncond_denoised"]
+            cond_scale = args["cond_scale"]
+            x = args["input"]
+            sigma = args["sigma"]
+            model_options = args["model_options"]
+            nocond_processed = ldm_patched.modules.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative")
+
+            (noise_pred_nocond, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, nocond_processed, None, x, sigma, model_options)
+
+            pos = noise_pred_pos - noise_pred_nocond
+            neg = noise_pred_neg - noise_pred_nocond
+            perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg
+            perp_neg = perp * neg_scale
+            cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg)
+            cfg_result = x - cfg_result
+            return cfg_result
+
+        m.set_model_sampler_cfg_function(cfg_function)
+
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "PerpNeg": PerpNeg,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "PerpNeg": "Perp-Neg",
+}

ldm_patched/contrib/external_post_processing.py

@@ -1,10 +1,12 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 import numpy as np
 import torch
 import torch.nn.functional as F
 from PIL import Image
 import math
 
-import fcbh.utils
+import ldm_patched.modules.utils
 
 
 class Blend:
@@ -23,7 +25,7 @@ class Blend:
                     "max": 1.0,
                     "step": 0.01
                 }),
-                "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light"],),
+                "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],),
             },
         }
 
@@ -35,7 +37,7 @@ class Blend:
     def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
         if image1.shape != image2.shape:
             image2 = image2.permute(0, 3, 1, 2)
-            image2 = fcbh.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
+            image2 = ldm_patched.modules.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
             image2 = image2.permute(0, 2, 3, 1)
 
         blended_image = self.blend_mode(image1, image2, blend_mode)
@@ -54,6 +56,8 @@ class Blend:
             return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
         elif mode == "soft_light":
             return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
+        elif mode == "difference":
+            return img1 - img2
         else:
             raise ValueError(f"Unsupported blend mode: {mode}")
 
@@ -126,7 +130,7 @@ class Quantize:
                     "max": 256,
                     "step": 1
                 }),
-                "dither": (["none", "floyd-steinberg"],),
+                "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],),
             },
         }
 
@@ -135,19 +139,47 @@ class Quantize:
 
     CATEGORY = "image/postprocessing"
 
-    def quantize(self, image: torch.Tensor, colors: int = 256, dither: str = "FLOYDSTEINBERG"):
+    def bayer(im, pal_im, order):
+        def normalized_bayer_matrix(n):
+            if n == 0:
+                return np.zeros((1,1), "float32")
+            else:
+                q = 4 ** n
+                m = q * normalized_bayer_matrix(n - 1)
+                return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q
+
+        num_colors = len(pal_im.getpalette()) // 3
+        spread = 2 * 256 / num_colors
+        bayer_n = int(math.log2(order))
+        bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5)
+
+        result = torch.from_numpy(np.array(im).astype(np.float32))
+        tw = math.ceil(result.shape[0] / bayer_matrix.shape[0])
+        th = math.ceil(result.shape[1] / bayer_matrix.shape[1])
+        tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1)
+        result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255)
+        result = result.to(dtype=torch.uint8)
+
+        im = Image.fromarray(result.cpu().numpy())
+        im = im.quantize(palette=pal_im, dither=Image.Dither.NONE)
+        return im
+
+    def quantize(self, image: torch.Tensor, colors: int, dither: str):
         batch_size, height, width, _ = image.shape
         result = torch.zeros_like(image)
 
-        dither_option = Image.Dither.FLOYDSTEINBERG if dither == "floyd-steinberg" else Image.Dither.NONE
-
         for b in range(batch_size):
-            tensor_image = image[b]
-            img = (tensor_image * 255).to(torch.uint8).numpy()
-            pil_image = Image.fromarray(img, mode='RGB')
+            im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB')
 
-            palette = pil_image.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836
-            quantized_image = pil_image.quantize(colors=colors, palette=palette, dither=dither_option)
+            pal_im = im.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836
+
+            if dither == "none":
+                quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE)
+            elif dither == "floyd-steinberg":
+                quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG)
+            elif dither.startswith("bayer"):
+                order = int(dither.split('-')[-1])
+                quantized_image = Quantize.bayer(im, pal_im, order)
 
             quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255
             result[b] = quantized_array
@@ -196,7 +228,7 @@ class Sharpen:
         batch_size, height, width, channels = image.shape
 
         kernel_size = sharpen_radius * 2 + 1
-        kernel = gaussian_kernel(kernel_size, sigma) * -(alpha*10)
+        kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10)
         center = kernel_size // 2
         kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
         kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)
@@ -232,7 +264,7 @@ class ImageScaleToTotalPixels:
         width = round(samples.shape[3] * scale_by)
         height = round(samples.shape[2] * scale_by)
 
-        s = fcbh.utils.common_upscale(samples, width, height, upscale_method, "disabled")
+        s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, "disabled")
         s = s.movedim(1,-1)
         return (s,)
 

ldm_patched/contrib/external_rebatch.py

@@ -1,10 +1,12 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 import torch
 
 class LatentRebatch:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "latents": ("LATENT",),
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
                               }}
     RETURN_TYPES = ("LATENT",)
     INPUT_IS_LIST = True

ldm_patched/contrib/external_sag.py

@@ -0,0 +1,170 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
+import torch
+from torch import einsum
+import torch.nn.functional as F
+import math
+
+from einops import rearrange, repeat
+import os
+from ldm_patched.ldm.modules.attention import optimized_attention, _ATTN_PRECISION
+import ldm_patched.modules.samplers
+
+# from ldm_patched.modules/ldm/modules/attention.py
+# but modified to return attention scores as well as output
+def attention_basic_with_sim(q, k, v, heads, mask=None):
+    b, _, dim_head = q.shape
+    dim_head //= heads
+    scale = dim_head ** -0.5
+
+    h = heads
+    q, k, v = map(
+        lambda t: t.unsqueeze(3)
+        .reshape(b, -1, heads, dim_head)
+        .permute(0, 2, 1, 3)
+        .reshape(b * heads, -1, dim_head)
+        .contiguous(),
+        (q, k, v),
+    )
+
+    # force cast to fp32 to avoid overflowing
+    if _ATTN_PRECISION =="fp32":
+        sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
+    else:
+        sim = einsum('b i d, b j d -> b i j', q, k) * scale
+
+    del q, k
+
+    if mask is not None:
+        mask = rearrange(mask, 'b ... -> b (...)')
+        max_neg_value = -torch.finfo(sim.dtype).max
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
+        sim.masked_fill_(~mask, max_neg_value)
+
+    # attention, what we cannot get enough of
+    sim = sim.softmax(dim=-1)
+
+    out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
+    out = (
+        out.unsqueeze(0)
+        .reshape(b, heads, -1, dim_head)
+        .permute(0, 2, 1, 3)
+        .reshape(b, -1, heads * dim_head)
+    )
+    return (out, sim)
+
+def create_blur_map(x0, attn, sigma=3.0, threshold=1.0):
+    # reshape and GAP the attention map
+    _, hw1, hw2 = attn.shape
+    b, _, lh, lw = x0.shape
+    attn = attn.reshape(b, -1, hw1, hw2)
+    # Global Average Pool
+    mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold
+    ratio = math.ceil(math.sqrt(lh * lw / hw1))
+    mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)]
+
+    # Reshape
+    mask = (
+        mask.reshape(b, *mid_shape)
+        .unsqueeze(1)
+        .type(attn.dtype)
+    )
+    # Upsample
+    mask = F.interpolate(mask, (lh, lw))
+
+    blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma)
+    blurred = blurred * mask + x0 * (1 - mask)
+    return blurred
+
+def gaussian_blur_2d(img, kernel_size, sigma):
+    ksize_half = (kernel_size - 1) * 0.5
+
+    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
+
+    pdf = torch.exp(-0.5 * (x / sigma).pow(2))
+
+    x_kernel = pdf / pdf.sum()
+    x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
+
+    kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
+    kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
+
+    padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
+
+    img = F.pad(img, padding, mode="reflect")
+    img = F.conv2d(img, kernel2d, groups=img.shape[-3])
+    return img
+
+class SelfAttentionGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                             "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.1}),
+                             "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+
+    def patch(self, model, scale, blur_sigma):
+        m = model.clone()
+
+        attn_scores = None
+
+        # TODO: make this work properly with chunked batches
+        #       currently, we can only save the attn from one UNet call
+        def attn_and_record(q, k, v, extra_options):
+            nonlocal attn_scores
+            # if uncond, save the attention scores
+            heads = extra_options["n_heads"]
+            cond_or_uncond = extra_options["cond_or_uncond"]
+            b = q.shape[0] // len(cond_or_uncond)
+            if 1 in cond_or_uncond:
+                uncond_index = cond_or_uncond.index(1)
+                # do the entire attention operation, but save the attention scores to attn_scores
+                (out, sim) = attention_basic_with_sim(q, k, v, heads=heads)
+                # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn]
+                n_slices = heads * b
+                attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)]
+                return out
+            else:
+                return optimized_attention(q, k, v, heads=heads)
+
+        def post_cfg_function(args):
+            nonlocal attn_scores
+            uncond_attn = attn_scores
+
+            sag_scale = scale
+            sag_sigma = blur_sigma
+            sag_threshold = 1.0
+            model = args["model"]
+            uncond_pred = args["uncond_denoised"]
+            uncond = args["uncond"]
+            cfg_result = args["denoised"]
+            sigma = args["sigma"]
+            model_options = args["model_options"]
+            x = args["input"]
+
+            # create the adversarially blurred image
+            degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold)
+            degraded_noised = degraded + x - uncond_pred
+            # call into the UNet
+            (sag, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options)
+            return cfg_result + (degraded - sag) * sag_scale
+
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+
+        # from diffusers:
+        # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch
+        m.set_model_attn1_replace(attn_and_record, "middle", 0, 0)
+
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "SelfAttentionGuidance": SelfAttentionGuidance,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SelfAttentionGuidance": "Self-Attention Guidance",
+}

ldm_patched/contrib/external_tomesd.py

@@ -1,3 +1,5 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 #Taken from: https://github.com/dbolya/tomesd
 
 import torch

ldm_patched/contrib/external_upscale_model.py

@@ -1,14 +1,16 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
 import os
-from fcbh_extras.chainner_models import model_loading
-from fcbh import model_management
+from ldm_patched.pfn import model_loading
+from ldm_patched.modules import model_management
 import torch
-import fcbh.utils
-import folder_paths
+import ldm_patched.modules.utils
+import ldm_patched.utils.path_utils
 
 class UpscaleModelLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ),
+        return {"required": { "model_name": (ldm_patched.utils.path_utils.get_filename_list("upscale_models"), ),
                              }}
     RETURN_TYPES = ("UPSCALE_MODEL",)
     FUNCTION = "load_model"
@@ -16,10 +18,10 @@ class UpscaleModelLoader:
     CATEGORY = "loaders"
 
     def load_model(self, model_name):
-        model_path = folder_paths.get_full_path("upscale_models", model_name)
-        sd = fcbh.utils.load_torch_file(model_path, safe_load=True)
+        model_path = ldm_patched.utils.path_utils.get_full_path("upscale_models", model_name)
+        sd = ldm_patched.modules.utils.load_torch_file(model_path, safe_load=True)
         if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd:
-            sd = fcbh.utils.state_dict_prefix_replace(sd, {"module.":""})
+            sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"module.":""})
         out = model_loading.load_state_dict(sd).eval()
         return (out, )
 
@@ -47,9 +49,9 @@ class ImageUpscaleWithModel:
         oom = True
         while oom:
             try:
-                steps = in_img.shape[0] * fcbh.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
-                pbar = fcbh.utils.ProgressBar(steps)
-                s = fcbh.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
+                steps = in_img.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
+                pbar = ldm_patched.modules.utils.ProgressBar(steps)
+                s = ldm_patched.modules.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
                 oom = False
             except model_management.OOM_EXCEPTION as e:
                 tile //= 2

ldm_patched/contrib/external_video_model.py

@@ -0,0 +1,91 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 
+
+import ldm_patched.contrib.external
+import torch
+import ldm_patched.modules.utils
+import ldm_patched.modules.sd
+import ldm_patched.utils.path_utils
+
+
+class ImageOnlyCheckpointLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ),
+                             }}
+    RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "loaders/video_models"
+
+    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
+        ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name)
+        out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings"))
+        return (out[0], out[3], out[2])
+
+
+class SVD_img2vid_Conditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_vision": ("CLIP_VISION",),
+                              "init_image": ("IMAGE",),
+                              "vae": ("VAE",),
+                              "width": ("INT", {"default": 1024, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 576, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}),
+                              "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}),
+                              "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}),
+                              "fps": ("INT", {"default": 6, "min": 1, "max": 1024}),
+                              "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01})
+                             }}
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/video_models"
+
+    def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level):
+        output = clip_vision.encode_image(init_image)
+        pooled = output.image_embeds.unsqueeze(0)
+        pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
+        encode_pixels = pixels[:,:,:,:3]
+        if augmentation_level > 0:
+            encode_pixels += torch.randn_like(pixels) * augmentation_level
+        t = vae.encode(encode_pixels)
+        positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]]
+        negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]]
+        latent = torch.zeros([video_frames, 4, height // 8, width // 8])
+        return (positive, negative, {"samples":latent})
+
+class VideoLinearCFGGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "sampling/video_models"
+
+    def patch(self, model, min_cfg):
+        def linear_cfg(args):
+            cond = args["cond"]
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+
+            scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1))
+            return uncond + scale * (cond - uncond)
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(linear_cfg)
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader,
+    "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning,
+    "VideoLinearCFGGuidance": VideoLinearCFGGuidance,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)",
+}

ldm_patched/controlnet/cldm.py

@@ -5,15 +5,15 @@ import torch
 import torch as th
 import torch.nn as nn
 
-from ..ldm.modules.diffusionmodules.util import (
+from ldm_patched.ldm.modules.diffusionmodules.util import (
     zero_module,
     timestep_embedding,
 )
 
-from ..ldm.modules.attention import SpatialTransformer
-from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
-from ..ldm.util import exists
-import fcbh.ops
+from ldm_patched.ldm.modules.attention import SpatialTransformer
+from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
+from ldm_patched.ldm.util import exists
+import ldm_patched.modules.ops
 
 class ControlledUnetModel(UNetModel):
     #implemented in the ldm unet
@@ -53,7 +53,8 @@ class ControlNet(nn.Module):
         transformer_depth_middle=None,
         transformer_depth_output=None,
         device=None,
-        operations=fcbh.ops,
+        operations=ldm_patched.modules.ops.disable_weight_init,
+        **kwargs,
     ):
         super().__init__()
         assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
@@ -140,24 +141,24 @@ class ControlNet(nn.Module):
                 )
             ]
         )
-        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations)])
+        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)])
 
         self.input_hint_block = TimestepEmbedSequential(
-                    operations.conv_nd(dims, hint_channels, 16, 3, padding=1),
+                    operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device),
                     nn.SiLU(),
-                    operations.conv_nd(dims, 16, 16, 3, padding=1),
+                    operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device),
                     nn.SiLU(),
-                    operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+                    operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device),
                     nn.SiLU(),
-                    operations.conv_nd(dims, 32, 32, 3, padding=1),
+                    operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device),
                     nn.SiLU(),
-                    operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+                    operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device),
                     nn.SiLU(),
-                    operations.conv_nd(dims, 96, 96, 3, padding=1),
+                    operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device),
                     nn.SiLU(),
-                    operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+                    operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device),
                     nn.SiLU(),
-                    zero_module(operations.conv_nd(dims, 256, model_channels, 3, padding=1))
+                    operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device)
         )
 
         self._feature_size = model_channels
@@ -205,7 +206,7 @@ class ControlNet(nn.Module):
                             )
                         )
                 self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
+                self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
                 self._feature_size += ch
                 input_block_chans.append(ch)
             if level != len(channel_mult) - 1:
@@ -233,7 +234,7 @@ class ControlNet(nn.Module):
                 )
                 ch = out_ch
                 input_block_chans.append(ch)
-                self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
+                self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
                 ds *= 2
                 self._feature_size += ch
 
@@ -275,14 +276,14 @@ class ControlNet(nn.Module):
                 operations=operations
             )]
         self.middle_block = TimestepEmbedSequential(*mid_block)
-        self.middle_block_out = self.make_zero_conv(ch, operations=operations)
+        self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)
         self._feature_size += ch
 
-    def make_zero_conv(self, channels, operations=None):
-        return TimestepEmbedSequential(zero_module(operations.conv_nd(self.dims, channels, channels, 1, padding=0)))
+    def make_zero_conv(self, channels, operations=None, dtype=None, device=None):
+        return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device))
 
     def forward(self, x, hint, timesteps, context, y=None, **kwargs):
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
         emb = self.time_embed(t_emb)
 
         guided_hint = self.input_hint_block(hint, emb, context)
@@ -294,7 +295,7 @@ class ControlNet(nn.Module):
             assert y.shape[0] == x.shape[0]
             emb = emb + self.label_emb(y)
 
-        h = x.type(self.dtype)
+        h = x
         for module, zero_conv in zip(self.input_blocks, self.zero_convs):
             if guided_hint is not None:
                 h = module(h, emb, context)

ldm_patched/k_diffusion/sampling.py

@@ -717,7 +717,6 @@ def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
         mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev)
     return mu
 
-
 def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None):
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@@ -737,3 +736,75 @@ def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disab
 def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
     return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)
 
+@torch.no_grad()
+def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        x = denoised
+        if sigmas[i + 1] > 0:
+            x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1])
+    return x
+
+
+
+@torch.no_grad()
+def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+    # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    s_end = sigmas[-1]
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        if gamma > 0:
+            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        if sigmas[i + 1] == s_end:
+            # Euler method
+            x = x + d * dt
+        elif sigmas[i + 2] == s_end:
+
+            # Heun's method
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
+            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+
+            w = 2 * sigmas[0]
+            w2 = sigmas[i+1]/w
+            w1 = 1 - w2
+
+            d_prime = d * w1 + d_2 * w2
+
+
+            x = x + d_prime * dt
+
+        else:
+            # Heun++
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
+            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+            dt_2 = sigmas[i + 2] - sigmas[i + 1]
+
+            x_3 = x_2 + d_2 * dt_2
+            denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args)
+            d_3 = to_d(x_3, sigmas[i + 2], denoised_3)
+
+            w = 3 * sigmas[0]
+            w2 = sigmas[i + 1] / w
+            w3 = sigmas[i + 2] / w
+            w1 = 1 - w2 - w3
+
+            d_prime = w1 * d + w2 * d_2 + w3 * d_3
+            x = x + d_prime * dt
+    return x

ldm_patched/ldm/models/autoencoder.py

@@ -4,10 +4,10 @@ import torch.nn.functional as F
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Tuple, Union
 
-from fcbh.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+from ldm_patched.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
 
-from fcbh.ldm.util import instantiate_from_config
-from fcbh.ldm.modules.ema import LitEma
+from ldm_patched.ldm.util import instantiate_from_config
+from ldm_patched.ldm.modules.ema import LitEma
 
 class DiagonalGaussianRegularizer(torch.nn.Module):
     def __init__(self, sample: bool = True):
@@ -152,11 +152,11 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
         ddconfig = kwargs.pop("ddconfig")
         super().__init__(
             encoder_config={
-                "target": "fcbh.ldm.modules.diffusionmodules.model.Encoder",
+                "target": "ldm_patched.ldm.modules.diffusionmodules.model.Encoder",
                 "params": ddconfig,
             },
             decoder_config={
-                "target": "fcbh.ldm.modules.diffusionmodules.model.Decoder",
+                "target": "ldm_patched.ldm.modules.diffusionmodules.model.Decoder",
                 "params": ddconfig,
             },
             **kwargs,
@@ -220,7 +220,7 @@ class AutoencoderKL(AutoencodingEngineLegacy):
         super().__init__(
             regularizer_config={
                 "target": (
-                    "fcbh.ldm.models.autoencoder.DiagonalGaussianRegularizer"
+                    "ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer"
                 )
             },
             **kwargs,