Merge pull request #3095 from lllyasviel/develop

release v2.4.2

.github/workflows/build_container.yml

@@ -1,9 +1,11 @@
-name: Create and publish a container image
+name: Docker image build
 
 on:
   push:
+    branches:
+      - main
     tags:
-      - 'v*'
+      - v*
 
 jobs:
   build-and-push-image:
@@ -33,6 +35,7 @@ jobs:
             type=semver,pattern={{version}}
             type=semver,pattern={{major}}.{{minor}}
             type=semver,pattern={{major}}
+            type=edge,branch=main
 
       - name: Build and push Docker image
         uses: docker/build-push-action@v5

Dockerfile

@@ -23,7 +23,7 @@ RUN chown -R user:user /content
 WORKDIR /content
 USER user
 
-COPY . /content/app
+COPY --chown=user:user . /content/app
 RUN mv /content/app/models /content/app/models.org
 
 CMD [ "sh", "-c", "/content/entrypoint.sh ${CMDARGS}" ]

args_manager.py

@@ -1,7 +1,4 @@
 import ldm_patched.modules.args_parser as args_parser
-import os
-
-from tempfile import gettempdir
 
 args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")
 

css/style.css

@@ -74,17 +74,21 @@ progress::after {
     text-align: right;
     width: 215px;
 }
+div:has(> #positive_prompt) {
+    border: none;
+}
+
+#positive_prompt {
+    padding: 1px;
+    background: var(--background-fill-primary);
+}
 
 .type_row {
-  height: 80px !important;
+  height: 84px !important;
 }
 
 .type_row_half {
-  height: 32px !important;
+  height: 34px !important;
-}
-
-.scroll-hide{
-  resize: none !important;
 }
 
 .refresh_button {

fooocus_version.py

@@ -1 +1 @@
-version = '2.4.0'
+version = '2.4.2'

javascript/localization.js

@@ -81,9 +81,12 @@ function refresh_style_localization() {
 }
 
 function refresh_aspect_ratios_label(value) {
-    label = document.querySelector('#aspect_ratios_accordion div span[data-original-text="Aspect Ratios"]')
+    label = document.querySelector('#aspect_ratios_accordion div span');
-    translation = getTranslation("Aspect Ratios")
+    translation = getTranslation("Aspect Ratios");
-    label.textContent = translation + " " + htmlDecode(value)
+    if (typeof translation == "undefined") {
+        translation = "Aspect Ratios";
+    }
+    label.textContent = translation + " " + htmlDecode(value);
 }
 
 function localizeWholePage() {

language/en.json

@@ -321,6 +321,7 @@
     "CFG Mimicking from TSNR": "CFG Mimicking from TSNR",
     "Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).": "Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).",
     "CLIP Skip": "CLIP Skip",
+    "Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).": "Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).",
     "Sampler": "Sampler",
     "dpmpp_2m_sde_gpu": "dpmpp_2m_sde_gpu",
     "Only effective in non-inpaint mode.": "Only effective in non-inpaint mode.",

ldm_patched/contrib/external_custom_sampler.py

@@ -107,8 +107,7 @@ class SDTurboScheduler:
     def get_sigmas(self, model, steps, denoise):
         start_step = 10 - int(10 * denoise)
         timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
-        ldm_patched.modules.model_management.load_models_gpu([model])
+        sigmas = model.model_sampling.sigma(timesteps)
-        sigmas = model.model.model_sampling.sigma(timesteps)
         sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
         return (sigmas, )
 

ldm_patched/contrib/external_model_advanced.py

@@ -108,7 +108,7 @@ class ModelSamplingContinuousEDM:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["v_prediction", "eps"],),
+                              "sampling": (["v_prediction", "edm_playground_v2.5", "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}),
                               }}
@@ -121,17 +121,25 @@ class ModelSamplingContinuousEDM:
     def patch(self, model, sampling, sigma_max, sigma_min):
         m = model.clone()
 
+        latent_format = None
+        sigma_data = 1.0
         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 == "edm_playground_v2.5":
+            sampling_type = ldm_patched.modules.model_sampling.EDM
+            sigma_data = 0.5
+            latent_format = ldm_patched.modules.latent_formats.SDXL_Playground_2_5()
 
         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)
+        model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
         m.add_object_patch("model_sampling", model_sampling)
+        if latent_format is not None:
+            m.add_object_patch("latent_format", latent_format)
         return (m, )
 
 class RescaleCFG:

ldm_patched/k_diffusion/sampling.py

@@ -832,5 +832,7 @@ def sample_tcd(model, x, sigmas, extra_args=None, callback=None, disable=None, n
         if eta > 0 and sigmas[i + 1] > 0:
             noise = noise_sampler(sigmas[i], sigmas[i + 1])
             x = x / alpha_prod_s[i+1].sqrt() + noise * (sigmas[i+1]**2 + 1 - 1/alpha_prod_s[i+1]).sqrt()
+        else:
+            x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2)
 
     return x

ldm_patched/modules/latent_formats.py

@@ -1,3 +1,4 @@
+import torch
 
 class LatentFormat:
     scale_factor = 1.0
@@ -34,6 +35,70 @@ class SDXL(LatentFormat):
                 ]
         self.taesd_decoder_name = "taesdxl_decoder"
 
+class SDXL_Playground_2_5(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 0.5
+        self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)
+        self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)
+
+        self.latent_rgb_factors = [
+                    #   R        G        B
+                    [ 0.3920,  0.4054,  0.4549],
+                    [-0.2634, -0.0196,  0.0653],
+                    [ 0.0568,  0.1687, -0.0755],
+                    [-0.3112, -0.2359, -0.2076]
+                ]
+        self.taesd_decoder_name = "taesdxl_decoder"
+
+    def process_in(self, latent):
+        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+        latents_std = self.latents_std.to(latent.device, latent.dtype)
+        return (latent - latents_mean) * self.scale_factor / latents_std
+
+    def process_out(self, latent):
+        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+        latents_std = self.latents_std.to(latent.device, latent.dtype)
+        return latent * latents_std / self.scale_factor + latents_mean
+
+
 class SD_X4(LatentFormat):
     def __init__(self):
         self.scale_factor = 0.08333
+        self.latent_rgb_factors = [
+            [-0.2340, -0.3863, -0.3257],
+            [ 0.0994,  0.0885, -0.0908],
+            [-0.2833, -0.2349, -0.3741],
+            [ 0.2523, -0.0055, -0.1651]
+        ]
+
+class SC_Prior(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 1.0
+        self.latent_rgb_factors = [
+            [-0.0326, -0.0204, -0.0127],
+            [-0.1592, -0.0427,  0.0216],
+            [ 0.0873,  0.0638, -0.0020],
+            [-0.0602,  0.0442,  0.1304],
+            [ 0.0800, -0.0313, -0.1796],
+            [-0.0810, -0.0638, -0.1581],
+            [ 0.1791,  0.1180,  0.0967],
+            [ 0.0740,  0.1416,  0.0432],
+            [-0.1745, -0.1888, -0.1373],
+            [ 0.2412,  0.1577,  0.0928],
+            [ 0.1908,  0.0998,  0.0682],
+            [ 0.0209,  0.0365, -0.0092],
+            [ 0.0448, -0.0650, -0.1728],
+            [-0.1658, -0.1045, -0.1308],
+            [ 0.0542,  0.1545,  0.1325],
+            [-0.0352, -0.1672, -0.2541]
+        ]
+
+class SC_B(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 1.0 / 0.43
+        self.latent_rgb_factors = [
+            [ 0.1121,  0.2006,  0.1023],
+            [-0.2093, -0.0222, -0.0195],
+            [-0.3087, -0.1535,  0.0366],
+            [ 0.0290, -0.1574, -0.4078]
+        ]

ldm_patched/modules/model_sampling.py

@@ -1,5 +1,4 @@
 import torch
-import numpy as np
 from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule
 import math
 
@@ -12,12 +11,28 @@ class EPS:
         sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
         return model_input - model_output * sigma
 
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        if max_denoise:
+            noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
+        else:
+            noise = noise * sigma
+
+        noise += latent_image
+        return noise
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return latent
 
 class V_PREDICTION(EPS):
     def calculate_denoised(self, sigma, model_output, model_input):
         sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
         return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
+class EDM(V_PREDICTION):
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
+
 
 class ModelSamplingDiscrete(torch.nn.Module):
     def __init__(self, model_config=None):
@@ -42,24 +57,23 @@ class ModelSamplingDiscrete(torch.nn.Module):
         else:
             betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
         alphas = 1. - betas
-        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
+        alphas_cumprod = torch.cumprod(alphas, dim=0)
-        # alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
 
         timesteps, = betas.shape
         self.num_timesteps = int(timesteps)
         self.linear_start = linear_start
         self.linear_end = linear_end
 
+        # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
+        # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
+        # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
+
         sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
         self.set_sigmas(sigmas)
-        self.set_alphas_cumprod(alphas_cumprod.float())
 
     def set_sigmas(self, sigmas):
-        self.register_buffer('sigmas', sigmas)
+        self.register_buffer('sigmas', sigmas.float())
-        self.register_buffer('log_sigmas', sigmas.log())
+        self.register_buffer('log_sigmas', sigmas.log().float())
-
-    def set_alphas_cumprod(self, alphas_cumprod):
-        self.register_buffer("alphas_cumprod", alphas_cumprod.float())
 
     @property
     def sigma_min(self):
@@ -94,8 +108,6 @@ class ModelSamplingDiscrete(torch.nn.Module):
 class ModelSamplingContinuousEDM(torch.nn.Module):
     def __init__(self, model_config=None):
         super().__init__()
-        self.sigma_data = 1.0
-
         if model_config is not None:
             sampling_settings = model_config.sampling_settings
         else:
@@ -103,9 +115,11 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
 
         sigma_min = sampling_settings.get("sigma_min", 0.002)
         sigma_max = sampling_settings.get("sigma_max", 120.0)
-        self.set_sigma_range(sigma_min, sigma_max)
+        sigma_data = sampling_settings.get("sigma_data", 1.0)
+        self.set_parameters(sigma_min, sigma_max, sigma_data)
 
-    def set_sigma_range(self, sigma_min, sigma_max):
+    def set_parameters(self, sigma_min, sigma_max, sigma_data):
+        self.sigma_data = sigma_data
         sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
 
         self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
@@ -134,3 +148,56 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
 
         log_sigma_min = math.log(self.sigma_min)
         return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
+
+class StableCascadeSampling(ModelSamplingDiscrete):
+    def __init__(self, model_config=None):
+        super().__init__()
+
+        if model_config is not None:
+            sampling_settings = model_config.sampling_settings
+        else:
+            sampling_settings = {}
+
+        self.set_parameters(sampling_settings.get("shift", 1.0))
+
+    def set_parameters(self, shift=1.0, cosine_s=8e-3):
+        self.shift = shift
+        self.cosine_s = torch.tensor(cosine_s)
+        self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
+
+        #This part is just for compatibility with some schedulers in the codebase
+        self.num_timesteps = 10000
+        sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
+        for x in range(self.num_timesteps):
+            t = (x + 1) / self.num_timesteps
+            sigmas[x] = self.sigma(t)
+
+        self.set_sigmas(sigmas)
+
+    def sigma(self, timestep):
+        alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod)
+
+        if self.shift != 1.0:
+            var = alpha_cumprod
+            logSNR = (var/(1-var)).log()
+            logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))
+            alpha_cumprod = logSNR.sigmoid()
+
+        alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999)
+        return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5
+
+    def timestep(self, sigma):
+        var = 1 / ((sigma * sigma) + 1)
+        var = var.clamp(0, 1.0)
+        s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device)
+        t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
+        return t
+
+    def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return 999999999.9
+        if percent >= 1.0:
+            return 0.0
+
+        percent = 1.0 - percent
+        return self.sigma(torch.tensor(percent))

ldm_patched/modules/samplers.py

@@ -523,7 +523,7 @@ class UNIPCBH2(Sampler):
 
 KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","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", "lcm", "tcd"]
+                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "tcd", "edm_playground_v2.5"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

modules/async_worker.py

@@ -462,8 +462,10 @@ def worker():
 
             progressbar(async_task, 2, 'Loading models ...')
 
-            loras, prompt = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number)
+            lora_filenames = modules.util.remove_performance_lora(modules.config.lora_filenames, performance_selection)
+            loras, prompt = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number, lora_filenames=lora_filenames)
             loras += performance_loras
+
             pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
                                         loras=loras, base_model_additional_loras=base_model_additional_loras,
                                         use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
@@ -826,16 +828,33 @@ def worker():
 
         if scheduler_name in ['lcm', 'tcd']:
             final_scheduler_name = 'sgm_uniform'
-            if pipeline.final_unet is not None:
+
-                pipeline.final_unet = core.opModelSamplingDiscrete.patch(
+            def patch_discrete(unet):
+                return core.opModelSamplingDiscrete.patch(
                     pipeline.final_unet,
                     sampling=scheduler_name,
                     zsnr=False)[0]
+
+            if pipeline.final_unet is not None:
+                pipeline.final_unet = patch_discrete(pipeline.final_unet)
             if pipeline.final_refiner_unet is not None:
-                pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch(
+                pipeline.final_refiner_unet = patch_discrete(pipeline.final_refiner_unet)
-                    pipeline.final_refiner_unet,
+            print(f'Using {scheduler_name} scheduler.')
+        elif scheduler_name == 'edm_playground_v2.5':
+            final_scheduler_name = 'karras'
+
+            def patch_edm(unet):
+                return core.opModelSamplingContinuousEDM.patch(
+                    unet,
                     sampling=scheduler_name,
-                    zsnr=False)[0]
+                    sigma_max=120.0,
+                    sigma_min=0.002)[0]
+
+            if pipeline.final_unet is not None:
+                pipeline.final_unet = patch_edm(pipeline.final_unet)
+            if pipeline.final_refiner_unet is not None:
+                pipeline.final_refiner_unet = patch_edm(pipeline.final_refiner_unet)
+
             print(f'Using {scheduler_name} scheduler.')
 
         async_task.yields.append(['preview', (flags.preparation_step_count, 'Moving model to GPU ...', None)])

modules/config.py

@@ -436,8 +436,8 @@ default_cfg_tsnr = get_config_item_or_set_default(
 )
 default_clip_skip = get_config_item_or_set_default(
     key='default_clip_skip',
-    default_value=1,
+    default_value=2,
-    validator=lambda x: isinstance(x, numbers.Number)
+    validator=lambda x: isinstance(x, int) and 1 <= x <= modules.flags.clip_skip_max
 )
 default_overwrite_step = get_config_item_or_set_default(
     key='default_overwrite_step',
@@ -548,25 +548,9 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
 
 model_filenames = []
 lora_filenames = []
-lora_filenames_no_special = []
 vae_filenames = []
 wildcard_filenames = []
 
-sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors'
-sdxl_lightning_lora = 'sdxl_lightning_4step_lora.safetensors'
-sdxl_hyper_sd_lora = 'sdxl_hyper_sd_4step_lora.safetensors'
-loras_metadata_remove = [sdxl_lcm_lora, sdxl_lightning_lora, sdxl_hyper_sd_lora]
-
-
-def remove_special_loras(lora_filenames):
-    global loras_metadata_remove
-
-    loras_no_special = lora_filenames.copy()
-    for lora_to_remove in loras_metadata_remove:
-        if lora_to_remove in loras_no_special:
-            loras_no_special.remove(lora_to_remove)
-    return loras_no_special
-
 
 def get_model_filenames(folder_paths, extensions=None, name_filter=None):
     if extensions is None:
@@ -582,10 +566,9 @@ def get_model_filenames(folder_paths, extensions=None, name_filter=None):
 
 
 def update_files():
-    global model_filenames, lora_filenames, lora_filenames_no_special, vae_filenames, wildcard_filenames, available_presets
+    global model_filenames, lora_filenames, vae_filenames, wildcard_filenames, available_presets
     model_filenames = get_model_filenames(paths_checkpoints)
     lora_filenames = get_model_filenames(paths_loras)
-    lora_filenames_no_special = remove_special_loras(lora_filenames)
     vae_filenames = get_model_filenames(path_vae)
     wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
     available_presets = get_presets()
@@ -634,26 +617,27 @@ def downloading_sdxl_lcm_lora():
     load_file_from_url(
         url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors',
         model_dir=paths_loras[0],
-        file_name=sdxl_lcm_lora
+        file_name=modules.flags.PerformanceLoRA.EXTREME_SPEED.value
     )
-    return sdxl_lcm_lora
+    return modules.flags.PerformanceLoRA.EXTREME_SPEED.value
+
 
 def downloading_sdxl_lightning_lora():
     load_file_from_url(
         url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_lightning_4step_lora.safetensors',
         model_dir=paths_loras[0],
-        file_name=sdxl_lightning_lora
+        file_name=modules.flags.PerformanceLoRA.LIGHTNING.value
     )
-    return sdxl_lightning_lora
+    return modules.flags.PerformanceLoRA.LIGHTNING.value
 
 
 def downloading_sdxl_hyper_sd_lora():
     load_file_from_url(
         url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_hyper_sd_4step_lora.safetensors',
         model_dir=paths_loras[0],
-        file_name=sdxl_hyper_sd_lora
+        file_name=modules.flags.PerformanceLoRA.HYPER_SD.value
     )
-    return sdxl_hyper_sd_lora
+    return modules.flags.PerformanceLoRA.HYPER_SD.value
 
 
 def downloading_controlnet_canny():

modules/core.py

@@ -21,8 +21,7 @@ from modules.lora import match_lora
 from modules.util import get_file_from_folder_list
 from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip
 from modules.config import path_embeddings
-from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete
+from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete, ModelSamplingContinuousEDM
-
 
 opEmptyLatentImage = EmptyLatentImage()
 opVAEDecode = VAEDecode()
@@ -32,6 +31,7 @@ opVAEEncodeTiled = VAEEncodeTiled()
 opControlNetApplyAdvanced = ControlNetApplyAdvanced()
 opFreeU = FreeU_V2()
 opModelSamplingDiscrete = ModelSamplingDiscrete()
+opModelSamplingContinuousEDM = ModelSamplingContinuousEDM()
 
 
 class StableDiffusionModel:

modules/flags.py

@@ -48,12 +48,14 @@ SAMPLERS = KSAMPLER | SAMPLER_EXTRA
 
 KSAMPLER_NAMES = list(KSAMPLER.keys())
 
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo", "align_your_steps", "tcd"]
+SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo", "align_your_steps", "tcd", "edm_playground_v2.5"]
 SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
 
 sampler_list = SAMPLER_NAMES
 scheduler_list = SCHEDULER_NAMES
 
+clip_skip_max = 12
+
 default_vae = 'Default (model)'
 
 refiner_swap_method = 'joint'
@@ -89,6 +91,7 @@ sdxl_aspect_ratios = [
     '1664*576', '1728*576'
 ]
 
+
 class MetadataScheme(Enum):
     FOOOCUS = 'fooocus'
     A1111 = 'a1111'
@@ -113,6 +116,14 @@ class OutputFormat(Enum):
         return list(map(lambda c: c.value, cls))
 
 
+class PerformanceLoRA(Enum):
+    QUALITY = None
+    SPEED = None
+    EXTREME_SPEED = 'sdxl_lcm_lora.safetensors'
+    LIGHTNING = 'sdxl_lightning_4step_lora.safetensors'
+    HYPER_SD = 'sdxl_hyper_sd_4step_lora.safetensors'
+
+
 class Steps(IntEnum):
     QUALITY = 60
     SPEED = 30
@@ -140,6 +151,10 @@ class Performance(Enum):
     def list(cls) -> list:
         return list(map(lambda c: c.value, cls))
 
+    @classmethod
+    def by_steps(cls, steps: int | str):
+        return cls[Steps(int(steps)).name]
+
     @classmethod
     def has_restricted_features(cls, x) -> bool:
         if isinstance(x, Performance):
@@ -147,7 +162,10 @@ class Performance(Enum):
         return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.value, cls.HYPER_SD.value]
 
     def steps(self) -> int | None:
-        return Steps[self.name].value if Steps[self.name] else None
+        return Steps[self.name].value if self.name in Steps.__members__ else None
 
     def steps_uov(self) -> int | None:
-        return StepsUOV[self.name].value if Steps[self.name] else None
+        return StepsUOV[self.name].value if self.name in StepsUOV.__members__ else None
+
+    def lora_filename(self) -> str | None:
+        return PerformanceLoRA[self.name].value if self.name in PerformanceLoRA.__members__ else None

modules/meta_parser.py

@@ -32,7 +32,7 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
     get_str('prompt', 'Prompt', loaded_parameter_dict, results)
     get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results)
     get_list('styles', 'Styles', loaded_parameter_dict, results)
-    get_str('performance', 'Performance', loaded_parameter_dict, results)
+    performance = get_str('performance', 'Performance', loaded_parameter_dict, results)
     get_steps('steps', 'Steps', loaded_parameter_dict, results)
     get_number('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
     get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)
@@ -59,19 +59,27 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
 
     get_freeu('freeu', 'FreeU', loaded_parameter_dict, results)
 
+    # prevent performance LoRAs to be added twice, by performance and by lora
+    performance_filename = None
+    if performance is not None and performance in Performance.list():
+        performance = Performance(performance)
+        performance_filename = performance.lora_filename()
+
     for i in range(modules.config.default_max_lora_number):
-        get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results)
+        get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results, performance_filename)
 
     return results
 
 
-def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
+def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None) -> str | None:
     try:
         h = source_dict.get(key, source_dict.get(fallback, default))
         assert isinstance(h, str)
         results.append(h)
+        return h
     except:
         results.append(gr.update())
+        return None
 
 
 def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
@@ -181,7 +189,7 @@ def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list,
         results.append(gr.update())
 
 
-def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
+def get_lora(key: str, fallback: str | None, source_dict: dict, results: list, performance_filename: str | None):
     try:
         split_data = source_dict.get(key, source_dict.get(fallback)).split(' : ')
         enabled = True
@@ -193,6 +201,9 @@ def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
             name = split_data[1]
             weight = split_data[2]
 
+        if name == performance_filename:
+            raise Exception
+
         weight = float(weight)
         results.append(enabled)
         results.append(name)
@@ -248,7 +259,7 @@ class MetadataParser(ABC):
         self.full_prompt: str = ''
         self.raw_negative_prompt: str = ''
         self.full_negative_prompt: str = ''
-        self.steps: int = 30
+        self.steps: int = Steps.SPEED.value
         self.base_model_name: str = ''
         self.base_model_hash: str = ''
         self.refiner_model_name: str = ''
@@ -261,11 +272,11 @@ class MetadataParser(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def parse_json(self, metadata: dict | str) -> dict:
+    def to_json(self, metadata: dict | str) -> dict:
         raise NotImplementedError
 
     @abstractmethod
-    def parse_string(self, metadata: dict) -> str:
+    def to_string(self, metadata: dict) -> str:
         raise NotImplementedError
 
     def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
@@ -328,7 +339,7 @@ class A1111MetadataParser(MetadataParser):
         'version': 'Version'
     }
 
-    def parse_json(self, metadata: str) -> dict:
+    def to_json(self, metadata: str) -> dict:
         metadata_prompt = ''
         metadata_negative_prompt = ''
 
@@ -382,9 +393,9 @@ class A1111MetadataParser(MetadataParser):
         data['styles'] = str(found_styles)
 
         # try to load performance based on steps, fallback for direct A1111 imports
-        if 'steps' in data and 'performance' not in data:
+        if 'steps' in data and 'performance' in data is None:
             try:
-                data['performance'] = Performance[Steps(int(data['steps'])).name].value
+                data['performance'] = Performance.by_steps(data['steps']).value
             except ValueError | KeyError:
                 pass
 
@@ -414,7 +425,7 @@ class A1111MetadataParser(MetadataParser):
                 lora_split = lora.split(': ')
                 lora_name = lora_split[0]
                 lora_weight = lora_split[2] if len(lora_split) == 3 else lora_split[1]
-                for filename in modules.config.lora_filenames_no_special:
+                for filename in modules.config.lora_filenames:
                     path = Path(filename)
                     if lora_name == path.stem:
                         data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'
@@ -422,7 +433,7 @@ class A1111MetadataParser(MetadataParser):
 
         return data
 
-    def parse_string(self, metadata: dict) -> str:
+    def to_string(self, metadata: dict) -> str:
         data = {k: v for _, k, v in metadata}
 
         width, height = eval(data['resolution'])
@@ -502,14 +513,14 @@ class FooocusMetadataParser(MetadataParser):
     def get_scheme(self) -> MetadataScheme:
         return MetadataScheme.FOOOCUS
 
-    def parse_json(self, metadata: dict) -> dict:
+    def to_json(self, metadata: dict) -> dict:
         for key, value in metadata.items():
             if value in ['', 'None']:
                 continue
             if key in ['base_model', 'refiner_model']:
                 metadata[key] = self.replace_value_with_filename(key, value, modules.config.model_filenames)
             elif key.startswith('lora_combined_'):
-                metadata[key] = self.replace_value_with_filename(key, value, modules.config.lora_filenames_no_special)
+                metadata[key] = self.replace_value_with_filename(key, value, modules.config.lora_filenames)
             elif key == 'vae':
                 metadata[key] = self.replace_value_with_filename(key, value, modules.config.vae_filenames)
             else:
@@ -517,7 +528,7 @@ class FooocusMetadataParser(MetadataParser):
 
         return metadata
 
-    def parse_string(self, metadata: list) -> str:
+    def to_string(self, metadata: list) -> str:
         for li, (label, key, value) in enumerate(metadata):
             # remove model folder paths from metadata
             if key.startswith('lora_combined_'):
@@ -557,6 +568,8 @@ class FooocusMetadataParser(MetadataParser):
             elif value == path.stem:
                 return filename
 
+        return None
+
 
 def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser:
     match metadata_scheme:

modules/patch_precision.py

@@ -51,8 +51,6 @@ def patched_register_schedule(self, given_betas=None, beta_schedule="linear", ti
     self.linear_end = linear_end
     sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32)
     self.set_sigmas(sigmas)
-    alphas_cumprod = torch.tensor(alphas_cumprod, dtype=torch.float32)
-    self.set_alphas_cumprod(alphas_cumprod)
     return
 
 

modules/private_logger.py

@@ -27,7 +27,7 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
     date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)
     os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
 
-    parsed_parameters = metadata_parser.parse_string(metadata.copy()) if metadata_parser is not None else ''
+    parsed_parameters = metadata_parser.to_string(metadata.copy()) if metadata_parser is not None else ''
     image = Image.fromarray(img)
 
     if output_format == OutputFormat.PNG.value:

modules/sample_hijack.py

@@ -175,7 +175,7 @@ def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps):
     elif scheduler_name == "sgm_uniform":
         sigmas = normal_scheduler(model, steps, sgm=True)
     elif scheduler_name == "turbo":
-        sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps=steps, denoise=1.0)[0]
+        sigmas = SDTurboScheduler().get_sigmas(model=model, steps=steps, denoise=1.0)[0]
     elif scheduler_name == "align_your_steps":
         model_type = 'SDXL' if isinstance(model.latent_format, ldm_patched.modules.latent_formats.SDXL) else 'SD1'
         sigmas = AlignYourStepsScheduler().get_sigmas(model_type=model_type, steps=steps, denoise=1.0)[0]

modules/util.py

@@ -16,6 +16,7 @@ from PIL import Image
 
 import modules.config
 import modules.sdxl_styles
+from modules.flags import Performance
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 
@@ -381,9 +382,6 @@ def get_file_from_folder_list(name, folders):
 
     return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))
 
-def ordinal_suffix(number: int) -> str:
-    return 'th' if 10 <= number % 100 <= 20 else {1: 'st', 2: 'nd', 3: 'rd'}.get(number % 10, 'th')
-
 
 def makedirs_with_log(path):
     try:
@@ -397,10 +395,15 @@ def get_enabled_loras(loras: list, remove_none=True) -> list:
 
 
 def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5,
-                                      skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True) -> tuple[List[Tuple[AnyStr, float]], str]:
+                                      skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True,
+                                      lora_filenames=None) -> tuple[List[Tuple[AnyStr, float]], str]:
+    if lora_filenames is None:
+        lora_filenames = []
+
     found_loras = []
     prompt_without_loras = ''
     cleaned_prompt = ''
+
     for token in prompt.split(','):
         matches = LORAS_PROMPT_PATTERN.findall(token)
 
@@ -410,7 +413,7 @@ def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, flo
         for match in matches:
             lora_name = match[1] + '.safetensors'
             if not skip_file_check:
-                lora_name = get_filname_by_stem(match[1], modules.config.lora_filenames_no_special)
+                lora_name = get_filname_by_stem(match[1], lora_filenames)
             if lora_name is not None:
                 found_loras.append((lora_name, float(match[2])))
             token = token.replace(match[0], '')
@@ -440,6 +443,22 @@ def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, flo
     return updated_loras[:loras_limit], cleaned_prompt
 
 
+def remove_performance_lora(filenames: list, performance: Performance | None):
+    loras_without_performance = filenames.copy()
+
+    if performance is None:
+        return loras_without_performance
+
+    performance_lora = performance.lora_filename()
+
+    for filename in filenames:
+        path = Path(filename)
+        if performance_lora == path.name:
+            loras_without_performance.remove(filename)
+
+    return loras_without_performance
+
+
 def cleanup_prompt(prompt):
     prompt = re.sub(' +', ' ', prompt)
     prompt = re.sub(',+', ',', prompt)

presets/.gitignore

@@ -2,5 +2,6 @@
 !anime.json
 !default.json
 !lcm.json
+!playground_v2.5.json
 !realistic.json
 !sai.json

presets/playground_v2.5.json

@@ -0,0 +1,51 @@
+{
+    "default_model": "playground-v2.5-1024px-aesthetic.fp16.safetensors",
+    "default_refiner": "None",
+    "default_refiner_switch": 0.5,
+    "default_loras": [
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ]
+    ],
+    "default_cfg_scale": 3.0,
+    "default_sample_sharpness": 2.0,
+    "default_sampler": "dpmpp_2m",
+    "default_scheduler": "edm_playground_v2.5",
+    "default_performance": "Speed",
+    "default_prompt": "",
+    "default_prompt_negative": "",
+    "default_styles": [
+        "Fooocus V2",
+        "Fooocus Enhance",
+        "Fooocus Sharp"
+    ],
+    "default_aspect_ratio": "1024*1024",
+    "checkpoint_downloads": {
+        "playground-v2.5-1024px-aesthetic.fp16.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/playground-v2.5-1024px-aesthetic.fp16.safetensors"
+    },
+    "embeddings_downloads": {},
+    "lora_downloads": {},
+    "previous_default_models": []
+}

readme.md

@@ -370,25 +370,36 @@ entry_with_update.py  [-h] [--listen [IP]] [--port PORT]
                       [--web-upload-size WEB_UPLOAD_SIZE]
                       [--hf-mirror HF_MIRROR]
                       [--external-working-path PATH [PATH ...]]
-                      [--output-path OUTPUT_PATH] [--temp-path TEMP_PATH]
+                      [--output-path OUTPUT_PATH]
+                      [--temp-path TEMP_PATH]
                       [--cache-path CACHE_PATH] [--in-browser]
-                      [--disable-in-browser] [--gpu-device-id DEVICE_ID]
+                      [--disable-in-browser]
+                      [--gpu-device-id DEVICE_ID]
                       [--async-cuda-allocation | --disable-async-cuda-allocation]
-                      [--disable-attention-upcast] [--all-in-fp32 | --all-in-fp16]
+                      [--disable-attention-upcast]
+                      [--all-in-fp32 | --all-in-fp16]
                       [--unet-in-bf16 | --unet-in-fp16 | --unet-in-fp8-e4m3fn | --unet-in-fp8-e5m2]
                       [--vae-in-fp16 | --vae-in-fp32 | --vae-in-bf16]   
+                      [--vae-in-cpu]
                       [--clip-in-fp8-e4m3fn | --clip-in-fp8-e5m2 | --clip-in-fp16 | --clip-in-fp32]
-                      [--directml [DIRECTML_DEVICE]] [--disable-ipex-hijack]
+                      [--directml [DIRECTML_DEVICE]]
+                      [--disable-ipex-hijack]
                       [--preview-option [none,auto,fast,taesd]]
                       [--attention-split | --attention-quad | --attention-pytorch]
                       [--disable-xformers]
                       [--always-gpu | --always-high-vram | --always-normal-vram |
                       --always-low-vram | --always-no-vram | --always-cpu [CPU_NUM_THREADS]]
-                      [--always-offload-from-vram] [--disable-server-log]
+                      [--always-offload-from-vram]
+                      [--pytorch-deterministic] [--disable-server-log]  
                       [--debug-mode] [--is-windows-embedded-python]     
-                      [--disable-server-info] [--share] [--preset PRESET]
+                      [--disable-server-info] [--multi-user] [--share]  
-                      [--language LANGUAGE] [--disable-offload-from-vram]
+                      [--preset PRESET] [--disable-preset-selection]    
-                      [--theme THEME] [--disable-image-log]
+                      [--language LANGUAGE]
+                      [--disable-offload-from-vram] [--theme THEME]     
+                      [--disable-image-log] [--disable-analytics]       
+                      [--disable-metadata] [--disable-preset-download]  
+                      [--enable-describe-uov-image]
+                      [--always-download-new-model]
 ```
 
 ## Advanced Features

tests/test_utils.py

@@ -1,5 +1,7 @@
+import os
 import unittest
 
+import modules.flags
 from modules import util
 
 
@@ -77,5 +79,59 @@ class TestUtils(unittest.TestCase):
         for test in test_cases:
             prompt, loras, loras_limit, skip_file_check = test["input"]
             expected = test["output"]
-            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, skip_file_check=skip_file_check)
+            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit,
+                                                            skip_file_check=skip_file_check)
+            self.assertEqual(expected, actual)
+
+    def test_can_parse_tokens_and_strip_performance_lora(self):
+        lora_filenames = [
+            'hey-lora.safetensors',
+            modules.flags.PerformanceLoRA.EXTREME_SPEED.value,
+            modules.flags.PerformanceLoRA.LIGHTNING.value,
+            os.path.join('subfolder', modules.flags.PerformanceLoRA.HYPER_SD.value)
+        ]
+
+        test_cases = [
+            {
+                "input": ("some prompt, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.QUALITY),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.SPEED),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:sdxl_lcm_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.EXTREME_SPEED),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:sdxl_lightning_4step_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.LIGHTNING),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:sdxl_hyper_sd_4step_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.HYPER_SD),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            }
+        ]
+
+        for test in test_cases:
+            prompt, loras, loras_limit, skip_file_check, performance = test["input"]
+            lora_filenames = modules.util.remove_performance_lora(lora_filenames, performance)
+            expected = test["output"]
+            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, lora_filenames=lora_filenames)
             self.assertEqual(expected, actual)

update_log.md

@@ -1,3 +1,17 @@
+# [2.4.2](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.2)
+
+* Fix some small bugs (tcd scheduler when gamma is 0, chown in Dockerfile, update cmd args in readme, translation for aspect ratios, vae default after file reload)
+* Fix performance LoRA replacement when data is loaded from history log and inline prompt
+* Add support and preset for playground v2.5 (only works with performance Quality or Speed, use with scheduler edm_playground_v2)
+* Make textboxes (incl. positive prompt) resizable
+* Hide intermediate images when performance of Gradio would bottleneck the generation process (Extreme Speed, Lightning, Hyper-SD)
+
+
+# [2.4.1](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.1)
+
+* Fix some small bugs (e.g. adjust clip skip default value from 1 to 2, add type check to aspect ratios js update function)
+* Add automated docker build on push to main, tagged with `edge`. See [available docker images](https://github.com/lllyasviel/Fooocus/pkgs/container/fooocus).
+
 # [2.4.0](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.0)
 
 * Change settings tab elements to be more compact

webui.py

@@ -112,10 +112,10 @@ with shared.gradio_root:
             gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain', visible=True, height=768,
                                  elem_classes=['resizable_area', 'main_view', 'final_gallery', 'image_gallery'],
                                  elem_id='final_gallery')
-            with gr.Row(elem_classes='type_row'):
+            with gr.Row():
                 with gr.Column(scale=17):
                     prompt = gr.Textbox(show_label=False, placeholder="Type prompt here or paste parameters.", elem_id='positive_prompt',
-                                        container=False, autofocus=True, elem_classes='type_row', lines=1024)
+                                        autofocus=True, lines=3)
 
                     default_prompt = modules.config.default_prompt
                     if isinstance(default_prompt, str) and default_prompt != '':
@@ -421,9 +421,9 @@ with shared.gradio_root:
                                                  value=modules.config.default_cfg_tsnr,
                                                  info='Enabling Fooocus\'s implementation of CFG mimicking for TSNR '
                                                       '(effective when real CFG > mimicked CFG).')
-                        clip_skip = gr.Slider(label='CLIP Skip', minimum=1, maximum=10, step=1,
+                        clip_skip = gr.Slider(label='CLIP Skip', minimum=1, maximum=flags.clip_skip_max, step=1,
                                                  value=modules.config.default_clip_skip,
-                                                 info='Bypass CLIP layers to avoid overfitting (use 1 to disable).')
+                                                 info='Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).')
                         sampler_name = gr.Dropdown(label='Sampler', choices=flags.sampler_list,
                                                    value=modules.config.default_sampler)
                         scheduler_name = gr.Dropdown(label='Scheduler', choices=flags.scheduler_list,
@@ -461,8 +461,7 @@ with shared.gradio_root:
                                                       interactive=not modules.config.default_black_out_nsfw,
                                                       info='Disable preview during generation.')
                         disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results',
-                                                      value=modules.config.default_performance == flags.Performance.EXTREME_SPEED.value,
+                                                      value=flags.Performance.has_restricted_features(modules.config.default_performance),
-                                                      interactive=modules.config.default_performance != flags.Performance.EXTREME_SPEED.value,
                                                       info='Disable intermediate results during generation, only show final gallery.')
                         disable_seed_increment = gr.Checkbox(label='Disable seed increment',
                                                              info='Disable automatic seed increment when image number is > 1.',
@@ -569,7 +568,7 @@ with shared.gradio_root:
                     modules.config.update_files()
                     results = [gr.update(choices=modules.config.model_filenames)]
                     results += [gr.update(choices=['None'] + modules.config.model_filenames)]
-                    results += [gr.update(choices=['None'] + modules.config.vae_filenames)]
+                    results += [gr.update(choices=[flags.default_vae] + modules.config.vae_filenames)]
                     if not args_manager.args.disable_preset_selection:
                         results += [gr.update(choices=modules.config.available_presets)]
                     for i in range(modules.config.default_max_lora_number):
@@ -616,7 +615,7 @@ with shared.gradio_root:
 
         performance_selection.change(lambda x: [gr.update(interactive=not flags.Performance.has_restricted_features(x))] * 11 +
                                                [gr.update(visible=not flags.Performance.has_restricted_features(x))] * 1 +
-                                               [gr.update(interactive=not flags.Performance.has_restricted_features(x), value=flags.Performance.has_restricted_features(x))] * 1,
+                                               [gr.update(value=flags.Performance.has_restricted_features(x))] * 1,
                                      inputs=performance_selection,
                                      outputs=[
                                          guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive,
@@ -713,7 +712,7 @@ with shared.gradio_root:
                 parsed_parameters = {}
             else:
                 metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
-                parsed_parameters = metadata_parser.parse_json(parameters)
+                parsed_parameters = metadata_parser.to_json(parameters)
 
             return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)