Merge branch 'develop' into feature/add-nsfw-filter

args_manager.py

@@ -31,6 +31,9 @@ args_parser.parser.add_argument("--disable-metadata", action='store_true',
 args_parser.parser.add_argument("--disable-preset-download", action='store_true',
                                 help="Disables downloading models for presets", default=False)
 
+args_parser.parser.add_argument("--enable-describe-uov-image", action='store_true',
+                                help="Disables automatic description of uov images when prompt is empty", default=False)
+
 args_parser.parser.add_argument("--always-download-new-model", action='store_true',
                                 help="Always download newer models ", default=False)
 

css/style.css

@@ -391,6 +391,6 @@ progress::after {
   background-color: #fff8;
   font-family: monospace;
   text-align: center;
-  border-radius-top: 5px;
+  border-radius: 5px 5px 0px 0px;
   display: none; /* remove this to enable tooltip in preview image */
 }

docker.md

@@ -54,6 +54,7 @@ Docker specified environments are there. They are used by 'entrypoint.sh'
 |CMDARGS|Arguments for [entry_with_update.py](entry_with_update.py) which is called by [entrypoint.sh](entrypoint.sh)|
 |config_path|'config.txt' location|
 |config_example_path|'config_modification_tutorial.txt' location|
+|HF_MIRROR| huggingface mirror site domain| 
 
 You can also use the same json key names and values explained in the 'config_modification_tutorial.txt' as the environments.
 See examples in the [docker-compose.yml](docker-compose.yml)

extras/vae_interpose.py

@@ -1,69 +1,85 @@
 # https://github.com/city96/SD-Latent-Interposer/blob/main/interposer.py
 
 import os
-import torch
-import safetensors.torch as sf
-import torch.nn as nn
-import ldm_patched.modules.model_management
 
+import safetensors.torch as sf
+import torch
+import torch.nn as nn
+
+import ldm_patched.modules.model_management
 from ldm_patched.modules.model_patcher import ModelPatcher
 from modules.config import path_vae_approx
 
 
-class Block(nn.Module):
+class ResBlock(nn.Module):
-    def __init__(self, size):
+    """Block with residuals"""
+
+    def __init__(self, ch):
         super().__init__()
         self.join = nn.ReLU()
+        self.norm = nn.BatchNorm2d(ch)
         self.long = nn.Sequential(
-            nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
-            nn.LeakyReLU(0.1),
+            nn.SiLU(),
-            nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
-            nn.LeakyReLU(0.1),
+            nn.SiLU(),
-            nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
+            nn.Dropout(0.1)
         )
 
     def forward(self, x):
-        y = self.long(x)
+        x = self.norm(x)
-        z = self.join(y + x)
+        return self.join(self.long(x) + x)
-        return z
 
 
-class Interposer(nn.Module):
+class ExtractBlock(nn.Module):
-    def __init__(self):
+    """Increase no. of channels by [out/in]"""
+
+    def __init__(self, ch_in, ch_out):
         super().__init__()
-        self.chan = 4
+        self.join = nn.ReLU()
-        self.hid = 128
+        self.short = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)
-
+        self.long = nn.Sequential(
-        self.head_join = nn.ReLU()
+            nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1),
-        self.head_short = nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1)
+            nn.SiLU(),
-        self.head_long = nn.Sequential(
+            nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
-            nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1),
+            nn.SiLU(),
-            nn.LeakyReLU(0.1),
+            nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
-            nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=1),
+            nn.Dropout(0.1)
-            nn.LeakyReLU(0.1),
-            nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=1),
-        )
-        self.core = nn.Sequential(
-            Block(self.hid),
-            Block(self.hid),
-            Block(self.hid),
-        )
-        self.tail = nn.Sequential(
-            nn.ReLU(),
-            nn.Conv2d(self.hid, self.chan, kernel_size=3, stride=1, padding=1)
         )
 
     def forward(self, x):
-        y = self.head_join(
+        return self.join(self.long(x) + self.short(x))
-            self.head_long(x) +
+
-            self.head_short(x)
+
+class InterposerModel(nn.Module):
+    """Main neural network"""
+
+    def __init__(self, ch_in=4, ch_out=4, ch_mid=64, scale=1.0, blocks=12):
+        super().__init__()
+        self.ch_in = ch_in
+        self.ch_out = ch_out
+        self.ch_mid = ch_mid
+        self.blocks = blocks
+        self.scale = scale
+
+        self.head = ExtractBlock(self.ch_in, self.ch_mid)
+        self.core = nn.Sequential(
+            nn.Upsample(scale_factor=self.scale, mode="nearest"),
+            *[ResBlock(self.ch_mid) for _ in range(blocks)],
+            nn.BatchNorm2d(self.ch_mid),
+            nn.SiLU(),
         )
+        self.tail = nn.Conv2d(self.ch_mid, self.ch_out, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        y = self.head(x)
         z = self.core(y)
         return self.tail(z)
 
 
 vae_approx_model = None
-vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v3.1.safetensors')
+vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v4.0.safetensors')
 
 
 def parse(x):
@@ -72,7 +88,7 @@ def parse(x):
     x_origin = x.clone()
 
     if vae_approx_model is None:
-        model = Interposer()
+        model = InterposerModel()
         model.eval()
         sd = sf.load_file(vae_approx_filename)
         model.load_state_dict(sd)

javascript/script.js

@@ -122,6 +122,43 @@ document.addEventListener("DOMContentLoaded", function() {
     initStylePreviewOverlay();
 });
 
+var onAppend = function(elem, f) {
+    var observer = new MutationObserver(function(mutations) {
+        mutations.forEach(function(m) {
+            if (m.addedNodes.length) {
+                f(m.addedNodes);
+            }
+        });
+    });
+    observer.observe(elem, {childList: true});
+}
+
+function addObserverIfDesiredNodeAvailable(querySelector, callback) {
+    var elem = document.querySelector(querySelector);
+    if (!elem) {
+        window.setTimeout(() => addObserverIfDesiredNodeAvailable(querySelector, callback), 1000);
+        return;
+    }
+
+    onAppend(elem, callback);
+}
+
+/**
+ * Show reset button on toast "Connection errored out."
+ */
+addObserverIfDesiredNodeAvailable(".toast-wrap", function(added) {
+    added.forEach(function(element) {
+         if (element.innerText.includes("Connection errored out.")) {
+             window.setTimeout(function() {
+                document.getElementById("reset_button").classList.remove("hidden");
+                document.getElementById("generate_button").classList.add("hidden");
+                document.getElementById("skip_button").classList.add("hidden");
+                document.getElementById("stop_button").classList.add("hidden");
+            });
+         }
+    });
+});
+
 /**
  * Add a ctrl+enter as a shortcut to start a generation
  */

language/en.json

@@ -4,6 +4,7 @@
     "Generate": "Generate",
     "Skip": "Skip",
     "Stop": "Stop",
+    "Reconnect": "Reconnect",
     "Input Image": "Input Image",
     "Advanced": "Advanced",
     "Upscale or Variation": "Upscale or Variation",
@@ -59,6 +60,7 @@
     "\ud83d\udcda History Log": "\uD83D\uDCDA History Log",
     "Image Style": "Image Style",
     "Fooocus V2": "Fooocus V2",
+    "Random Style": "Random Style",
     "Default (Slightly Cinematic)": "Default (Slightly Cinematic)",
     "Fooocus Masterpiece": "Fooocus Masterpiece",
     "Fooocus Photograph": "Fooocus Photograph",
@@ -341,6 +343,8 @@
     "sgm_uniform": "sgm_uniform",
     "simple": "simple",
     "ddim_uniform": "ddim_uniform",
+    "VAE": "VAE",
+    "Default (model)": "Default (model)",
     "Forced Overwrite of Sampling Step": "Forced Overwrite of Sampling Step",
     "Set as -1 to disable. For developer debugging.": "Set as -1 to disable. For developer debugging.",
     "Forced Overwrite of Refiner Switch Step": "Forced Overwrite of Refiner Switch Step",

launch.py

@@ -62,8 +62,8 @@ def prepare_environment():
 vae_approx_filenames = [
     ('xlvaeapp.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/xlvaeapp.pth'),
     ('vaeapp_sd15.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/vaeapp_sd15.pt'),
-    ('xl-to-v1_interposer-v3.1.safetensors',
+    ('xl-to-v1_interposer-v4.0.safetensors',
-     'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors')
+     'https://huggingface.co/mashb1t/misc/resolve/main/xl-to-v1_interposer-v4.0.safetensors')
 ]
 
 
@@ -80,6 +80,10 @@ 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)
 
+if args.hf_mirror is not None : 
+    os.environ['HF_MIRROR'] = str(args.hf_mirror)
+    print("Set hf_mirror to:", args.hf_mirror)
+
 from modules import config
 
 os.environ['GRADIO_TEMP_DIR'] = config.temp_path

ldm_patched/modules/args_parser.py

@@ -37,6 +37,7 @@ parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nar
 parser.add_argument("--port", type=int, default=8188)
 parser.add_argument("--disable-header-check", type=str, default=None, metavar="ORIGIN", nargs="?", const="*")
 parser.add_argument("--web-upload-size", type=float, default=100)
+parser.add_argument("--hf-mirror", type=str, default=None)
 
 parser.add_argument("--external-working-path", type=str, default=None, metavar="PATH", nargs='+', action='append')
 parser.add_argument("--output-path", type=str, default=None)

ldm_patched/modules/sd.py

@@ -427,12 +427,13 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
 
     return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
 
-def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
+def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, vae_filename_param=None):
     sd = ldm_patched.modules.utils.load_torch_file(ckpt_path)
     sd_keys = sd.keys()
     clip = None
     clipvision = None
     vae = None
+    vae_filename = None
     model = None
     model_patcher = None
     clip_target = None
@@ -462,8 +463,12 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         model.load_model_weights(sd, "model.diffusion_model.")
 
     if output_vae:
-        vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
+        if vae_filename_param is None:
-        vae_sd = model_config.process_vae_state_dict(vae_sd)
+            vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
+            vae_sd = model_config.process_vae_state_dict(vae_sd)
+        else:
+            vae_sd = ldm_patched.modules.utils.load_torch_file(vae_filename_param)
+            vae_filename = vae_filename_param
         vae = VAE(sd=vae_sd)
 
     if output_clip:
@@ -485,7 +490,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
             print("loaded straight to GPU")
             model_management.load_model_gpu(model_patcher)
 
-    return (model_patcher, clip, vae, clipvision)
+    return model_patcher, clip, vae, vae_filename, clipvision
 
 
 def load_unet_state_dict(sd): #load unet in diffusers format

modules/async_worker.py

@@ -44,7 +44,7 @@ def worker():
     import args_manager
 
     from extras.censor import censor_batch, censor_single
-    from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
+    from modules.sdxl_styles import apply_style, get_random_style, apply_wildcards, fooocus_expansion, apply_arrays, random_style_name
     from modules.private_logger import log
     from extras.expansion import safe_str
     from modules.util import remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, \
@@ -172,6 +172,7 @@ def worker():
         adaptive_cfg = args.pop()
         sampler_name = args.pop()
         scheduler_name = args.pop()
+        vae_name = args.pop()
         overwrite_step = args.pop()
         overwrite_switch = args.pop()
         overwrite_width = args.pop()
@@ -434,7 +435,7 @@ def worker():
             progressbar(async_task, 3, 'Loading models ...')
             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)
+                                        use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
 
             progressbar(async_task, 3, 'Processing prompts ...')
             tasks = []
@@ -455,8 +456,12 @@ def worker():
                 positive_basic_workloads = []
                 negative_basic_workloads = []
 
+                task_styles = style_selections.copy()
                 if use_style:
-                    for s in style_selections:
+                    for i, s in enumerate(task_styles):
+                        if s == random_style_name:
+                            s = get_random_style(task_rng)
+                            task_styles[i] = s
                         p, n = apply_style(s, positive=task_prompt)
                         positive_basic_workloads = positive_basic_workloads + p
                         negative_basic_workloads = negative_basic_workloads + n
@@ -484,6 +489,7 @@ def worker():
                     negative_top_k=len(negative_basic_workloads),
                     log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts),
                     log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts),
+                    styles=task_styles
                 ))
 
             if use_expansion:
@@ -856,7 +862,7 @@ def worker():
                     d = [('Prompt', 'prompt', task['log_positive_prompt']),
                          ('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
                          ('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
-                         ('Styles', 'styles', str(raw_style_selections)),
+                         ('Styles', 'styles', str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
                          ('Performance', 'performance', performance_selection.value)]
 
                     if performance_selection.steps() != steps:
@@ -883,6 +889,7 @@ def worker():
 
                     d.append(('Sampler', 'sampler', sampler_name))
                     d.append(('Scheduler', 'scheduler', scheduler_name))
+                    d.append(('VAE', 'vae', vae_name))
                     d.append(('Seed', 'seed', str(task['task_seed'])))
 
                     if freeu_enabled:
@@ -897,10 +904,10 @@ def worker():
                         metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
                         metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
                                                  task['log_negative_prompt'], task['negative'],
-                                                 steps, base_model_name, refiner_model_name, loras)
+                                                 steps, base_model_name, refiner_model_name, loras, vae_name)
                     d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
                     d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
-                    img_paths.append(log(x, d, metadata_parser, output_format))
+                    img_paths.append(log(x, d, metadata_parser, output_format, task))
 
                 yield_result(async_task, img_paths, black_out_nsfw, False, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
             except ldm_patched.modules.model_management.InterruptProcessingException as e:

modules/config.py

@@ -189,6 +189,7 @@ paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/check
 paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True)
 path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/')
 path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/')
+path_vae = get_dir_or_set_default('path_vae', '../models/vae/')
 path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/')
 path_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/')
 path_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/')
@@ -347,6 +348,11 @@ default_scheduler = get_config_item_or_set_default(
     default_value='karras',
     validator=lambda x: x in modules.flags.scheduler_list
 )
+default_vae = get_config_item_or_set_default(
+    key='default_vae',
+    default_value=modules.flags.default_vae,
+    validator=lambda x: isinstance(x, str)
+)
 default_styles = get_config_item_or_set_default(
     key='default_styles',
     default_value=[
@@ -541,6 +547,7 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
 
 model_filenames = []
 lora_filenames = []
+vae_filenames = []
 wildcard_filenames = []
 
 sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors'
@@ -552,15 +559,20 @@ def get_model_filenames(folder_paths, extensions=None, name_filter=None):
     if extensions is None:
         extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']
     files = []
+
+    if not isinstance(folder_paths, list):
+        folder_paths = [folder_paths]
     for folder in folder_paths:
         files += get_files_from_folder(folder, extensions, name_filter)
+
     return files
 
 
 def update_files():
-    global model_filenames, lora_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)
+    vae_filenames = get_model_filenames(path_vae)
     wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
     available_presets = get_presets()
     return

modules/core.py

@@ -35,12 +35,13 @@ opModelSamplingDiscrete = ModelSamplingDiscrete()
 
 
 class StableDiffusionModel:
-    def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None):
+    def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None, vae_filename=None):
         self.unet = unet
         self.vae = vae
         self.clip = clip
         self.clip_vision = clip_vision
         self.filename = filename
+        self.vae_filename = vae_filename
         self.unet_with_lora = unet
         self.clip_with_lora = clip
         self.visited_loras = ''
@@ -142,9 +143,10 @@ def apply_controlnet(positive, negative, control_net, image, strength, start_per
 
 @torch.no_grad()
 @torch.inference_mode()
-def load_model(ckpt_filename):
+def load_model(ckpt_filename, vae_filename=None):
-    unet, clip, vae, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings)
+    unet, clip, vae, vae_filename, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings,
-    return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename)
+                                                                vae_filename_param=vae_filename)
+    return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename, vae_filename=vae_filename)
 
 
 @torch.no_grad()

modules/default_pipeline.py

@@ -3,6 +3,7 @@ import os
 import torch
 import modules.patch
 import modules.config
+import modules.flags
 import ldm_patched.modules.model_management
 import ldm_patched.modules.latent_formats
 import modules.inpaint_worker
@@ -58,17 +59,21 @@ def assert_model_integrity():
 
 @torch.no_grad()
 @torch.inference_mode()
-def refresh_base_model(name):
+def refresh_base_model(name, vae_name=None):
     global model_base
 
     filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
 
-    if model_base.filename == filename:
+    vae_filename = None
+    if vae_name is not None and vae_name != modules.flags.default_vae:
+        vae_filename = get_file_from_folder_list(vae_name, modules.config.path_vae)
+
+    if model_base.filename == filename and model_base.vae_filename == vae_filename:
         return
 
-    model_base = core.StableDiffusionModel()
+    model_base = core.load_model(filename, vae_filename)
-    model_base = core.load_model(filename)
     print(f'Base model loaded: {model_base.filename}')
+    print(f'VAE loaded: {model_base.vae_filename}')
     return
 
 
@@ -216,7 +221,7 @@ def prepare_text_encoder(async_call=True):
 @torch.no_grad()
 @torch.inference_mode()
 def refresh_everything(refiner_model_name, base_model_name, loras,
-                       base_model_additional_loras=None, use_synthetic_refiner=False):
+                       base_model_additional_loras=None, use_synthetic_refiner=False, vae_name=None):
     global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion
 
     final_unet = None
@@ -227,11 +232,11 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
 
     if use_synthetic_refiner and refiner_model_name == 'None':
         print('Synthetic Refiner Activated')
-        refresh_base_model(base_model_name)
+        refresh_base_model(base_model_name, vae_name)
         synthesize_refiner_model()
     else:
         refresh_refiner_model(refiner_model_name)
-        refresh_base_model(base_model_name)
+        refresh_base_model(base_model_name, vae_name)
 
     refresh_loras(loras, base_model_additional_loras=base_model_additional_loras)
     assert_model_integrity()
@@ -254,7 +259,8 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
 refresh_everything(
     refiner_model_name=modules.config.default_refiner_model_name,
     base_model_name=modules.config.default_base_model_name,
-    loras=get_enabled_loras(modules.config.default_loras)
+    loras=get_enabled_loras(modules.config.default_loras),
+    vae_name=modules.config.default_vae,
 )
 
 

modules/flags.py

@@ -53,6 +53,8 @@ SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
 sampler_list = SAMPLER_NAMES
 scheduler_list = SCHEDULER_NAMES
 
+default_vae = 'Default (model)'
+
 refiner_swap_method = 'joint'
 
 cn_ip = "ImagePrompt"

modules/meta_parser.py

@@ -46,6 +46,7 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
     get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
     get_str('sampler', 'Sampler', loaded_parameter_dict, results)
     get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
+    get_str('vae', 'VAE', loaded_parameter_dict, results)
     get_seed('seed', 'Seed', loaded_parameter_dict, results)
 
     if is_generating:
@@ -253,6 +254,7 @@ class MetadataParser(ABC):
         self.refiner_model_name: str = ''
         self.refiner_model_hash: str = ''
         self.loras: list = []
+        self.vae_name: str = ''
 
     @abstractmethod
     def get_scheme(self) -> MetadataScheme:
@@ -267,7 +269,7 @@ class MetadataParser(ABC):
         raise NotImplementedError
 
     def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
-                 refiner_model_name, loras):
+                 refiner_model_name, loras, vae_name):
         self.raw_prompt = raw_prompt
         self.full_prompt = full_prompt
         self.raw_negative_prompt = raw_negative_prompt
@@ -289,6 +291,7 @@ class MetadataParser(ABC):
                 lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras)
                 lora_hash = get_sha256(lora_path)
                 self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
+        self.vae_name = Path(vae_name).stem
 
     @staticmethod
     def remove_special_loras(lora_filenames):
@@ -310,6 +313,7 @@ class A1111MetadataParser(MetadataParser):
         'steps': 'Steps',
         'sampler': 'Sampler',
         'scheduler': 'Scheduler',
+        'vae': 'VAE',
         'guidance_scale': 'CFG scale',
         'seed': 'Seed',
         'resolution': 'Size',
@@ -397,13 +401,12 @@ class A1111MetadataParser(MetadataParser):
                     data['sampler'] = k
                     break
 
-        for key in ['base_model', 'refiner_model']:
+        for key in ['base_model', 'refiner_model', 'vae']:
             if key in data:
-                for filename in modules.config.model_filenames:
+                if key == 'vae':
-                    path = Path(filename)
+                    self.add_extension_to_filename(data, modules.config.vae_filenames, 'vae')
-                    if data[key] == path.stem:
+                else:
-                        data[key] = filename
+                    self.add_extension_to_filename(data, modules.config.model_filenames, key)
-                        break
 
         lora_data = ''
         if 'lora_weights' in data and data['lora_weights'] != '':
@@ -433,6 +436,7 @@ class A1111MetadataParser(MetadataParser):
 
         sampler = data['sampler']
         scheduler = data['scheduler']
+
         if sampler in SAMPLERS and SAMPLERS[sampler] != '':
             sampler = SAMPLERS[sampler]
             if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras':
@@ -451,6 +455,7 @@ class A1111MetadataParser(MetadataParser):
 
             self.fooocus_to_a1111['performance']: data['performance'],
             self.fooocus_to_a1111['scheduler']: scheduler,
+            self.fooocus_to_a1111['vae']: Path(data['vae']).stem,
             # workaround for multiline prompts
             self.fooocus_to_a1111['raw_prompt']: self.raw_prompt,
             self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt,
@@ -491,6 +496,14 @@ class A1111MetadataParser(MetadataParser):
         negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
         return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
 
+    @staticmethod
+    def add_extension_to_filename(data, filenames, key):
+        for filename in filenames:
+            path = Path(filename)
+            if data[key] == path.stem:
+                data[key] = filename
+                break
+
 
 class FooocusMetadataParser(MetadataParser):
     def get_scheme(self) -> MetadataScheme:
@@ -499,6 +512,7 @@ class FooocusMetadataParser(MetadataParser):
     def parse_json(self, metadata: dict) -> dict:
         model_filenames = modules.config.model_filenames.copy()
         lora_filenames = modules.config.lora_filenames.copy()
+        vae_filenames = modules.config.vae_filenames.copy()
         self.remove_special_loras(lora_filenames)
         for key, value in metadata.items():
             if value in ['', 'None']:
@@ -507,6 +521,8 @@ class FooocusMetadataParser(MetadataParser):
                 metadata[key] = self.replace_value_with_filename(key, value, model_filenames)
             elif key.startswith('lora_combined_'):
                 metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
+            elif key == 'vae':
+                metadata[key] = self.replace_value_with_filename(key, value, vae_filenames)
             else:
                 continue
 
@@ -533,6 +549,7 @@ class FooocusMetadataParser(MetadataParser):
             res['refiner_model'] = self.refiner_model_name
             res['refiner_model_hash'] = self.refiner_model_hash
 
+        res['vae'] = self.vae_name
         res['loras'] = self.loras
 
         if modules.config.metadata_created_by != '':

modules/model_loader.py

@@ -14,6 +14,8 @@ def load_file_from_url(
 
     Returns the path to the downloaded file.
     """
+    domain = os.environ.get("HF_MIRROR", "https://huggingface.co").rstrip('/')
+    url = str.replace(url, "https://huggingface.co", domain, 1)
     os.makedirs(model_dir, exist_ok=True)
     if not file_name:
         parts = urlparse(url)

modules/private_logger.py

@@ -21,7 +21,7 @@ def get_current_html_path(output_format=None):
     return html_name
 
 
-def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str:
+def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None, task=None) -> str:
     path_outputs = modules.config.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs
     output_format = output_format if output_format else modules.config.default_output_format
     date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)
@@ -111,9 +111,15 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
     for label, key, value in metadata:
         value_txt = str(value).replace('\n', ' </br> ')
         item += f"<tr><td class='label'>{label}</td><td class='value'>{value_txt}</td></tr>\n"
+
+    if task is not None and 'positive' in task and 'negative' in task:
+        full_prompt_details = f"""<details><summary>Positive</summary>{', '.join(task['positive'])}</details>
+        <details><summary>Negative</summary>{', '.join(task['negative'])}</details>"""
+        item += f"<tr><td class='label'>Full raw prompt</td><td class='value'>{full_prompt_details}</td></tr>\n"
+
     item += "</table>"
 
-    js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v in metadata}, indent=0), safe='')
+    js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v, in metadata}, indent=0), safe='')
     item += f"</br><button onclick=\"to_clipboard('{js_txt}')\">Copy to Clipboard</button>"
 
     item += "</td>"

modules/sdxl_styles.py

@@ -5,6 +5,7 @@ import math
 import modules.config
 
 from modules.util import get_files_from_folder
+from random import Random
 
 # cannot use modules.config - validators causing circular imports
 styles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/'))
@@ -50,8 +51,13 @@ for styles_file in styles_files:
         print(f'Failed to load style file {styles_file}')
 
 style_keys = list(styles.keys())
-fooocus_expansion = "Fooocus V2"
+fooocus_expansion = 'Fooocus V2'
-legal_style_names = [fooocus_expansion] + style_keys
+random_style_name = 'Random Style'
+legal_style_names = [fooocus_expansion, random_style_name] + style_keys
+
+
+def get_random_style(rng: Random) -> str:
+    return rng.choice(list(styles.items()))[0]
 
 
 def apply_style(style, positive):

modules/ui_gradio_extensions.py

@@ -39,7 +39,7 @@ def javascript_html():
     head += f'<script type="text/javascript" src="{edit_attention_js_path}"></script>\n'
     head += f'<script type="text/javascript" src="{viewer_js_path}"></script>\n'
     head += f'<script type="text/javascript" src="{image_viewer_js_path}"></script>\n'
-    head += f'<meta name="samples-path" content="{samples_path}"></meta>\n'
+    head += f'<meta name="samples-path" content="{samples_path}">\n'
 
     if args_manager.args.theme:
         head += f'<script type="text/javascript">set_theme(\"{args_manager.args.theme}\");</script>\n'

modules/util.py

@@ -371,6 +371,9 @@ def is_json(data: str) -> bool:
 
 
 def get_file_from_folder_list(name, folders):
+    if not isinstance(folders, list):
+        folders = [folders]
+
     for folder in folders:
         filename = os.path.abspath(os.path.realpath(os.path.join(folder, name)))
         if os.path.isfile(filename):

readme.md

@@ -368,6 +368,7 @@ A safer way is just to try "run_anime.bat" or "run_realistic.bat" - they should
 entry_with_update.py  [-h] [--listen [IP]] [--port PORT]
                       [--disable-header-check [ORIGIN]]
                       [--web-upload-size WEB_UPLOAD_SIZE]
+                      [--hf-mirror HF_MIRROR]
                       [--external-working-path PATH [PATH ...]]
                       [--output-path OUTPUT_PATH] [--temp-path TEMP_PATH]
                       [--cache-path CACHE_PATH] [--in-browser]

webui.py

@@ -123,8 +123,9 @@ with shared.gradio_root:
 
                 with gr.Column(scale=3, min_width=0):
                     generate_button = gr.Button(label="Generate", value="Generate", elem_classes='type_row', elem_id='generate_button', visible=True)
+                    reset_button = gr.Button(label="Reconnect", value="Reconnect", elem_classes='type_row', elem_id='reset_button', visible=False)
                     load_parameter_button = gr.Button(label="Load Parameters", value="Load Parameters", elem_classes='type_row', elem_id='load_parameter_button', visible=False)
-                    skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', visible=False)
+                    skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', elem_id='skip_button', visible=False)
                     stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False)
 
                     def stop_clicked(currentTask):
@@ -406,6 +407,8 @@ with shared.gradio_root:
                                                    value=modules.config.default_sampler)
                         scheduler_name = gr.Dropdown(label='Scheduler', choices=flags.scheduler_list,
                                                      value=modules.config.default_scheduler)
+                        vae_name = gr.Dropdown(label='VAE', choices=[modules.flags.default_vae] + modules.config.vae_filenames,
+                                                     value=modules.config.default_vae, show_label=True)
 
                         generate_image_grid = gr.Checkbox(label='Generate Image Grid for Each Batch',
                                                           info='(Experimental) This may cause performance problems on some computers and certain internet conditions.',
@@ -538,6 +541,7 @@ 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)]
                     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):
@@ -545,7 +549,7 @@ with shared.gradio_root:
                                     gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
                     return results
 
-                refresh_files_output = [base_model, refiner_model]
+                refresh_files_output = [base_model, refiner_model, vae_name]
                 if not args_manager.args.disable_preset_selection:
                     refresh_files_output += [preset_selection]
                 refresh_files.click(refresh_files_clicked, [], refresh_files_output + lora_ctrls,
@@ -557,8 +561,8 @@ with shared.gradio_root:
                              performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection,
                              overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive,
                              adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, base_model,
-                             refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed,
+                             refiner_model, refiner_switch, sampler_name, scheduler_name, vae_name, seed_random,
-                             generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
+                             image_seed, generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
 
         if not args_manager.args.disable_preset_selection:
             def preset_selection_change(preset, is_generating):
@@ -644,7 +648,7 @@ with shared.gradio_root:
         ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image]
         ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment, black_out_nsfw]
         ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg]
-        ctrls += [sampler_name, scheduler_name]
+        ctrls += [sampler_name, scheduler_name, vae_name]
         ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength]
         ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint]
         ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold]
@@ -698,6 +702,14 @@ with shared.gradio_root:
             .then(fn=update_history_link, outputs=history_link) \
             .then(fn=lambda: None, _js='playNotification').then(fn=lambda: None, _js='refresh_grid_delayed')
 
+        reset_button.click(lambda: [worker.AsyncTask(args=[]), False, gr.update(visible=True, interactive=True)] +
+                                   [gr.update(visible=False)] * 6 +
+                                   [gr.update(visible=True, value=[])],
+                           outputs=[currentTask, state_is_generating, generate_button,
+                                    reset_button, stop_button, skip_button,
+                                    progress_html, progress_window, progress_gallery, gallery],
+                           queue=False)
+
         for notification_file in ['notification.ogg', 'notification.mp3']:
             if os.path.exists(notification_file):
                 gr.Audio(interactive=False, value=notification_file, elem_id='audio_notification', visible=False)
@@ -715,6 +727,15 @@ with shared.gradio_root:
         desc_btn.click(trigger_describe, inputs=[desc_method, desc_input_image],
                        outputs=[prompt, style_selections], show_progress=True, queue=True)
 
+        if args_manager.args.enable_describe_uov_image:
+            def trigger_uov_describe(mode, img, prompt):
+                # keep prompt if not empty
+                if prompt == '':
+                    return trigger_describe(mode, img)
+                return gr.update(), gr.update()
+
+            uov_input_image.upload(trigger_uov_describe, inputs=[desc_method, uov_input_image, prompt],
+                           outputs=[prompt, style_selections], show_progress=True, queue=True)
 
 def dump_default_english_config():
     from modules.localization import dump_english_config