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sync (#658)
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lllyasviel
+63
-63
@@ -1,27 +1,27 @@
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import torch
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import comfy.model_base
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import comfy.ldm.modules.diffusionmodules.openaimodel
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import comfy.samplers
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import comfy.k_diffusion.external
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import comfy.model_management
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import fcbh.model_base
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import fcbh.ldm.modules.diffusionmodules.openaimodel
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import fcbh.samplers
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import fcbh.k_diffusion.external
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import fcbh.model_management
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import modules.anisotropic as anisotropic
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import comfy.ldm.modules.attention
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import comfy.k_diffusion.sampling
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import comfy.sd1_clip
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import fcbh.ldm.modules.attention
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import fcbh.k_diffusion.sampling
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import fcbh.sd1_clip
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import modules.inpaint_worker as inpaint_worker
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import comfy.ldm.modules.diffusionmodules.openaimodel
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import comfy.ldm.modules.diffusionmodules.model
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import comfy.sd
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import comfy.cldm.cldm
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import comfy.model_patcher
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import comfy.samplers
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import comfy.cli_args
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import fcbh.ldm.modules.diffusionmodules.openaimodel
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import fcbh.ldm.modules.diffusionmodules.model
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import fcbh.sd
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import fcbh.cldm.cldm
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import fcbh.model_patcher
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import fcbh.samplers
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import fcbh.cli_args
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import args_manager
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import modules.advanced_parameters as advanced_parameters
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from comfy.k_diffusion import utils
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from comfy.k_diffusion.sampling import BrownianTreeNoiseSampler, trange
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from comfy.ldm.modules.diffusionmodules.openaimodel import timestep_embedding, forward_timestep_embed
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from fcbh.k_diffusion import utils
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from fcbh.k_diffusion.sampling import BrownianTreeNoiseSampler, trange
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from fcbh.ldm.modules.diffusionmodules.openaimodel import timestep_embedding, forward_timestep_embed
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sharpness = 2.0
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@@ -54,26 +54,26 @@ def calculate_weight_patched(self, patches, weight, key):
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if w1.shape != weight.shape:
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print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
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else:
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weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
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weight += alpha * fcbh.model_management.cast_to_device(w1, weight.device, weight.dtype)
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elif len(v) == 3:
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# fooocus
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w1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
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w_min = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
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w_max = comfy.model_management.cast_to_device(v[2], weight.device, torch.float32)
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w1 = fcbh.model_management.cast_to_device(v[0], weight.device, torch.float32)
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w_min = fcbh.model_management.cast_to_device(v[1], weight.device, torch.float32)
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w_max = fcbh.model_management.cast_to_device(v[2], weight.device, torch.float32)
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w1 = (w1 / 255.0) * (w_max - w_min) + w_min
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if alpha != 0.0:
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if w1.shape != weight.shape:
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print("WARNING SHAPE MISMATCH {} FOOOCUS WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
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else:
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weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
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weight += alpha * fcbh.model_management.cast_to_device(w1, weight.device, weight.dtype)
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elif len(v) == 4: # lora/locon
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mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
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mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
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mat1 = fcbh.model_management.cast_to_device(v[0], weight.device, torch.float32)
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mat2 = fcbh.model_management.cast_to_device(v[1], weight.device, torch.float32)
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if v[2] is not None:
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alpha *= v[2] / mat2.shape[0]
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if v[3] is not None:
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# locon mid weights, hopefully the math is fine because I didn't properly test it
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mat3 = comfy.model_management.cast_to_device(v[3], weight.device, torch.float32)
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mat3 = fcbh.model_management.cast_to_device(v[3], weight.device, torch.float32)
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final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
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mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1),
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mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
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@@ -94,23 +94,23 @@ def calculate_weight_patched(self, patches, weight, key):
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if w1 is None:
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dim = w1_b.shape[0]
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w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, torch.float32),
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comfy.model_management.cast_to_device(w1_b, weight.device, torch.float32))
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w1 = torch.mm(fcbh.model_management.cast_to_device(w1_a, weight.device, torch.float32),
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fcbh.model_management.cast_to_device(w1_b, weight.device, torch.float32))
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else:
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w1 = comfy.model_management.cast_to_device(w1, weight.device, torch.float32)
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w1 = fcbh.model_management.cast_to_device(w1, weight.device, torch.float32)
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if w2 is None:
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dim = w2_b.shape[0]
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if t2 is None:
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w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32),
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comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32))
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w2 = torch.mm(fcbh.model_management.cast_to_device(w2_a, weight.device, torch.float32),
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fcbh.model_management.cast_to_device(w2_b, weight.device, torch.float32))
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else:
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w2 = torch.einsum('i j k l, j r, i p -> p r k l',
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comfy.model_management.cast_to_device(t2, weight.device, torch.float32),
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comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32),
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comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32))
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fcbh.model_management.cast_to_device(t2, weight.device, torch.float32),
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fcbh.model_management.cast_to_device(w2_b, weight.device, torch.float32),
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fcbh.model_management.cast_to_device(w2_a, weight.device, torch.float32))
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else:
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w2 = comfy.model_management.cast_to_device(w2, weight.device, torch.float32)
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w2 = fcbh.model_management.cast_to_device(w2, weight.device, torch.float32)
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if len(w2.shape) == 4:
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w1 = w1.unsqueeze(2).unsqueeze(2)
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@@ -132,19 +132,19 @@ def calculate_weight_patched(self, patches, weight, key):
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t1 = v[5]
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t2 = v[6]
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m1 = torch.einsum('i j k l, j r, i p -> p r k l',
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comfy.model_management.cast_to_device(t1, weight.device, torch.float32),
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comfy.model_management.cast_to_device(w1b, weight.device, torch.float32),
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comfy.model_management.cast_to_device(w1a, weight.device, torch.float32))
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fcbh.model_management.cast_to_device(t1, weight.device, torch.float32),
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fcbh.model_management.cast_to_device(w1b, weight.device, torch.float32),
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fcbh.model_management.cast_to_device(w1a, weight.device, torch.float32))
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m2 = torch.einsum('i j k l, j r, i p -> p r k l',
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comfy.model_management.cast_to_device(t2, weight.device, torch.float32),
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comfy.model_management.cast_to_device(w2b, weight.device, torch.float32),
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comfy.model_management.cast_to_device(w2a, weight.device, torch.float32))
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fcbh.model_management.cast_to_device(t2, weight.device, torch.float32),
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fcbh.model_management.cast_to_device(w2b, weight.device, torch.float32),
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fcbh.model_management.cast_to_device(w2a, weight.device, torch.float32))
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else:
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m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, torch.float32),
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comfy.model_management.cast_to_device(w1b, weight.device, torch.float32))
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m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, torch.float32),
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comfy.model_management.cast_to_device(w2b, weight.device, torch.float32))
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m1 = torch.mm(fcbh.model_management.cast_to_device(w1a, weight.device, torch.float32),
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fcbh.model_management.cast_to_device(w1b, weight.device, torch.float32))
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m2 = torch.mm(fcbh.model_management.cast_to_device(w2a, weight.device, torch.float32),
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fcbh.model_management.cast_to_device(w2b, weight.device, torch.float32))
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try:
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weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
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@@ -205,7 +205,7 @@ def patched_model_function_wrapper(func, args):
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def sdxl_encode_adm_patched(self, **kwargs):
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global positive_adm_scale, negative_adm_scale
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clip_pooled = comfy.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
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clip_pooled = fcbh.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
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width = kwargs.get("width", 768)
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height = kwargs.get("height", 768)
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target_width = width
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@@ -453,8 +453,8 @@ def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=
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def text_encoder_device_patched():
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# Fooocus's style system uses text encoder much more times than comfy so this makes things much faster.
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return comfy.model_management.get_torch_device()
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# Fooocus's style system uses text encoder much more times than fcbh so this makes things much faster.
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return fcbh.model_management.get_torch_device()
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def patched_get_autocast_device(dev):
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@@ -470,25 +470,25 @@ def patched_get_autocast_device(dev):
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def patch_all():
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if not comfy.model_management.DISABLE_SMART_MEMORY:
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vram_inadequate = comfy.model_management.total_vram < 20 * 1024
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is_old_gpu_arch = not comfy.model_management.should_use_fp16()
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if not fcbh.model_management.DISABLE_SMART_MEMORY:
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vram_inadequate = fcbh.model_management.total_vram < 20 * 1024
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is_old_gpu_arch = not fcbh.model_management.should_use_fp16()
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if vram_inadequate or is_old_gpu_arch:
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# https://github.com/lllyasviel/Fooocus/issues/602
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print(f'[Fooocus Smart Memory] Disabling smart memory, '
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f'vram_inadequate = {vram_inadequate}, is_old_gpu_arch = {is_old_gpu_arch}.')
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comfy.model_management.DISABLE_SMART_MEMORY = True
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fcbh.model_management.DISABLE_SMART_MEMORY = True
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args_manager.args.disable_smart_memory = True
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comfy.cli_args.args.disable_smart_memory = True
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fcbh.cli_args.args.disable_smart_memory = True
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comfy.model_management.get_autocast_device = patched_get_autocast_device
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comfy.samplers.SAMPLER_NAMES += ['dpmpp_fooocus_2m_sde_inpaint_seamless']
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comfy.model_management.text_encoder_device = text_encoder_device_patched
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comfy.model_patcher.ModelPatcher.calculate_weight = calculate_weight_patched
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comfy.cldm.cldm.ControlNet.forward = patched_cldm_forward
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comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward
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comfy.k_diffusion.sampling.sample_dpmpp_fooocus_2m_sde_inpaint_seamless = sample_dpmpp_fooocus_2m_sde_inpaint_seamless
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comfy.k_diffusion.external.DiscreteEpsDDPMDenoiser.forward = patched_discrete_eps_ddpm_denoiser_forward
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comfy.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
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comfy.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = encode_token_weights_patched_with_a1111_method
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fcbh.model_management.get_autocast_device = patched_get_autocast_device
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fcbh.samplers.SAMPLER_NAMES += ['dpmpp_fooocus_2m_sde_inpaint_seamless']
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fcbh.model_management.text_encoder_device = text_encoder_device_patched
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fcbh.model_patcher.ModelPatcher.calculate_weight = calculate_weight_patched
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fcbh.cldm.cldm.ControlNet.forward = patched_cldm_forward
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fcbh.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward
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fcbh.k_diffusion.sampling.sample_dpmpp_fooocus_2m_sde_inpaint_seamless = sample_dpmpp_fooocus_2m_sde_inpaint_seamless
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fcbh.k_diffusion.external.DiscreteEpsDDPMDenoiser.forward = patched_discrete_eps_ddpm_denoiser_forward
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fcbh.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
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fcbh.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = encode_token_weights_patched_with_a1111_method
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return
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