release: bump version number to 2.4.0-rc2

now follows general Fooocus model management principles + includes code optimisations for reusability

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -21,6 +21,7 @@ body:
         　5. Try a fresh installation of Fooocus in a different directory - see if a clean installation solves the issue
         Before making a issue report please, check that the issue hasn't been reported recently.
       options:
+        - label: The issue has not been resolved by following the [troubleshooting guide](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md)
         - label: The issue exists on a clean installation of Fooocus
         - label: The issue exists in the current version of Fooocus
         - label: The issue has not been reported before recently

Dockerfile

@@ -10,7 +10,7 @@ RUN apt-get update -y && \
 COPY requirements_docker.txt requirements_versions.txt /tmp/
 RUN pip install --no-cache-dir -r /tmp/requirements_docker.txt -r /tmp/requirements_versions.txt && \
 	rm -f /tmp/requirements_docker.txt /tmp/requirements_versions.txt
-RUN pip install --no-cache-dir xformers==0.0.22 --no-dependencies
+RUN pip install --no-cache-dir xformers==0.0.23 --no-dependencies
 RUN curl -fsL -o /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2 https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_amd64 && \
 	chmod +x /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2
 

args_manager.py

@@ -4,7 +4,10 @@ import os
 from tempfile import gettempdir
 
 args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")
+
 args_parser.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.")
+args_parser.parser.add_argument("--disable-preset-selection", action='store_true',
+                                help="Disables preset selection in Gradio.")
 
 args_parser.parser.add_argument("--language", type=str, default='default',
                                 help="Translate UI using json files in [language] folder. "
@@ -28,6 +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)
 
@@ -49,7 +55,4 @@ if args_parser.args.disable_analytics:
 if args_parser.args.disable_in_browser:
     args_parser.args.in_browser = False
 
-if args_parser.args.temp_path is None:
-    args_parser.args.temp_path = os.path.join(gettempdir(), 'Fooocus')
-
 args = args_parser.args

css/style.css

@@ -1,5 +1,136 @@
 /* based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/style.css */
 
+.loader-container {
+  display: flex; /* Use flex to align items horizontally */
+  align-items: center; /* Center items vertically within the container */
+  white-space: nowrap; /* Prevent line breaks within the container */
+}
+
+.loader {
+  border: 8px solid #f3f3f3; /* Light grey */
+  border-top: 8px solid #3498db; /* Blue */
+  border-radius: 50%;
+  width: 30px;
+  height: 30px;
+  animation: spin 2s linear infinite;
+}
+
+@keyframes spin {
+  0% { transform: rotate(0deg); }
+  100% { transform: rotate(360deg); }
+}
+
+/* Style the progress bar */
+progress {
+  appearance: none; /* Remove default styling */
+  height: 20px; /* Set the height of the progress bar */
+  border-radius: 5px; /* Round the corners of the progress bar */
+  background-color: #f3f3f3; /* Light grey background */
+  width: 100%;
+}
+
+/* Style the progress bar container */
+.progress-container {
+  margin-left: 20px;
+  margin-right: 20px;
+  flex-grow: 1; /* Allow the progress container to take up remaining space */
+}
+
+/* Set the color of the progress bar fill */
+progress::-webkit-progress-value {
+  background-color: #3498db; /* Blue color for the fill */
+}
+
+progress::-moz-progress-bar {
+  background-color: #3498db; /* Blue color for the fill in Firefox */
+}
+
+/* Style the text on the progress bar */
+progress::after {
+  content: attr(value '%'); /* Display the progress value followed by '%' */
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%);
+  color: white; /* Set text color */
+  font-size: 14px; /* Set font size */
+}
+
+/* Style other texts */
+.loader-container > span {
+  margin-left: 5px; /* Add spacing between the progress bar and the text */
+}
+
+.progress-bar > .generating {
+  display: none !important;
+}
+
+.progress-bar{
+  height: 30px !important;
+}
+
+.type_row{
+  height: 80px !important;
+}
+
+.type_row_half{
+  height: 32px !important;
+}
+
+.scroll-hide{
+  resize: none !important;
+}
+
+.refresh_button{
+  border: none !important;
+  background: none !important;
+  font-size: none !important;
+  box-shadow: none !important;
+}
+
+.advanced_check_row{
+  width: 250px !important;
+}
+
+.min_check{
+  min-width: min(1px, 100%) !important;
+}
+
+.resizable_area {
+  resize: vertical;
+  overflow: auto !important;
+}
+
+.aspect_ratios label {
+    width: 140px !important;
+}
+
+.aspect_ratios label span {
+    white-space: nowrap !important;
+}
+
+.aspect_ratios label input {
+    margin-left: -5px !important;
+}
+
+.lora_enable label {
+  height: 100%;
+}
+
+.lora_enable label input {
+  margin: auto;
+}
+
+.lora_enable label span {
+  display: none;
+}
+
+@-moz-document url-prefix() {
+  .lora_weight input[type=number] {
+    width: 80px;
+  }
+}
+
 #context-menu{
     z-index:9999;
     position:absolute;
@@ -218,3 +349,48 @@
 #stylePreviewOverlay.lower-half {
     transform: translate(-140px, -140px);
 }
+
+/* scrollable box for style selections */
+.contain .tabs {
+  height: 100%;
+}
+
+.contain .tabs .tabitem.style_selections_tab {
+  height: 100%;
+}
+
+.contain .tabs .tabitem.style_selections_tab > div:first-child {
+  height: 100%;
+}
+
+.contain .tabs .tabitem.style_selections_tab .style_selections {
+  min-height: 200px;
+  height: 100%;
+}
+
+.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] {
+  position: absolute; /* remove this to disable scrolling within the checkbox-group */
+  overflow: auto;
+  padding-right: 2px;
+  max-height: 100%;
+}
+
+.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] label {
+  /* max-width: calc(35% - 15px) !important; */ /* add this to enable 3 columns layout */
+  flex: calc(50% - 5px) !important;
+}
+
+.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] label span {
+  /* white-space:nowrap; */ /* add this to disable text wrapping (better choice for 3 columns layout) */
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+/* styles preview tooltip */
+.preview-tooltip {
+  background-color: #fff8;
+  font-family: monospace;
+  text-align: center;
+  border-radius: 5px 5px 0px 0px;
+  display: none; /* remove this to enable tooltip in preview image */
+}

development.md

@@ -0,0 +1,11 @@
+## Running unit tests
+
+Native python:
+```
+python -m unittest tests/
+```
+
+Embedded python (Windows zip file installation method):
+```
+..\python_embeded\python.exe -m unittest
+```

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/censor.py

@@ -0,0 +1,60 @@
+import os
+
+import numpy as np
+import torch
+from transformers import CLIPConfig, CLIPImageProcessor
+
+import ldm_patched.modules.model_management as model_management
+import modules.config
+from extras.safety_checker.models.safety_checker import StableDiffusionSafetyChecker
+from ldm_patched.modules.model_patcher import ModelPatcher
+
+safety_checker_repo_root = os.path.join(os.path.dirname(__file__), 'safety_checker')
+config_path = os.path.join(safety_checker_repo_root, "configs", "config.json")
+preprocessor_config_path = os.path.join(safety_checker_repo_root, "configs", "preprocessor_config.json")
+
+
+class Censor:
+    def __init__(self):
+        self.safety_checker_model: ModelPatcher | None = None
+        self.clip_image_processor: CLIPImageProcessor | None = None
+        self.load_device = torch.device('cpu')
+        self.offload_device = torch.device('cpu')
+
+    def init(self):
+        if self.safety_checker_model is None and self.clip_image_processor is None:
+            safety_checker_model = modules.config.downloading_safety_checker_model()
+            self.clip_image_processor = CLIPImageProcessor.from_json_file(preprocessor_config_path)
+            clip_config = CLIPConfig.from_json_file(config_path)
+            model = StableDiffusionSafetyChecker.from_pretrained(safety_checker_model, config=clip_config)
+            model.eval()
+
+            self.load_device = model_management.text_encoder_device()
+            self.offload_device = model_management.text_encoder_offload_device()
+
+            model.to(self.offload_device)
+
+            self.safety_checker_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)
+
+    def censor(self, images: list | np.ndarray) -> list | np.ndarray:
+        self.init()
+        model_management.load_model_gpu(self.safety_checker_model)
+
+        single = False
+        if not isinstance(images, list) or isinstance(images, np.ndarray):
+            images = [images]
+            single = True
+
+        safety_checker_input = self.clip_image_processor(images, return_tensors="pt")
+        safety_checker_input.to(device=self.load_device)
+        checked_images, has_nsfw_concept = self.safety_checker_model.model(images=images,
+                                                                           clip_input=safety_checker_input.pixel_values)
+        checked_images = [image.astype(np.uint8) for image in checked_images]
+
+        if single:
+            checked_images = checked_images[0]
+
+        return checked_images
+
+
+default_censor = Censor().censor

extras/safety_checker/configs/config.json

@@ -0,0 +1,171 @@
+{
+  "_name_or_path": "clip-vit-large-patch14/",
+  "architectures": [
+    "SafetyChecker"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 768,
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 77,
+    "min_length": 0,
+    "model_type": "clip_text_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.21.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "vocab_size": 49408
+  },
+  "text_config_dict": {
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12
+  },
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "vision_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "clip_vision_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 14,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.21.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "vision_config_dict": {
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14
+  }
+}

extras/safety_checker/configs/preprocessor_config.json

@@ -0,0 +1,20 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}

extras/safety_checker/models/safety_checker.py

@@ -0,0 +1,126 @@
+# from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py
+
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import torch
+import torch.nn as nn
+from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+def cosine_distance(image_embeds, text_embeds):
+    normalized_image_embeds = nn.functional.normalize(image_embeds)
+    normalized_text_embeds = nn.functional.normalize(text_embeds)
+    return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
+
+
+class StableDiffusionSafetyChecker(PreTrainedModel):
+    config_class = CLIPConfig
+    main_input_name = "clip_input"
+
+    _no_split_modules = ["CLIPEncoderLayer"]
+
+    def __init__(self, config: CLIPConfig):
+        super().__init__(config)
+
+        self.vision_model = CLIPVisionModel(config.vision_config)
+        self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)
+
+        self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)
+        self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)
+
+        self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
+        self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
+
+    @torch.no_grad()
+    def forward(self, clip_input, images):
+        pooled_output = self.vision_model(clip_input)[1]  # pooled_output
+        image_embeds = self.visual_projection(pooled_output)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()
+        cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()
+
+        result = []
+        batch_size = image_embeds.shape[0]
+        for i in range(batch_size):
+            result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
+
+            # increase this value to create a stronger `nfsw` filter
+            # at the cost of increasing the possibility of filtering benign images
+            adjustment = 0.0
+
+            for concept_idx in range(len(special_cos_dist[0])):
+                concept_cos = special_cos_dist[i][concept_idx]
+                concept_threshold = self.special_care_embeds_weights[concept_idx].item()
+                result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
+                if result_img["special_scores"][concept_idx] > 0:
+                    result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]})
+                    adjustment = 0.01
+
+            for concept_idx in range(len(cos_dist[0])):
+                concept_cos = cos_dist[i][concept_idx]
+                concept_threshold = self.concept_embeds_weights[concept_idx].item()
+                result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
+                if result_img["concept_scores"][concept_idx] > 0:
+                    result_img["bad_concepts"].append(concept_idx)
+
+            result.append(result_img)
+
+        has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
+
+        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
+            if has_nsfw_concept:
+                if torch.is_tensor(images) or torch.is_tensor(images[0]):
+                    images[idx] = torch.zeros_like(images[idx])  # black image
+                else:
+                    images[idx] = np.zeros(images[idx].shape)  # black image
+
+        if any(has_nsfw_concepts):
+            logger.warning(
+                "Potential NSFW content was detected in one or more images. A black image will be returned instead."
+                " Try again with a different prompt and/or seed."
+            )
+
+        return images, has_nsfw_concepts
+
+    @torch.no_grad()
+    def forward_onnx(self, clip_input: torch.Tensor, images: torch.Tensor):
+        pooled_output = self.vision_model(clip_input)[1]  # pooled_output
+        image_embeds = self.visual_projection(pooled_output)
+
+        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
+        cos_dist = cosine_distance(image_embeds, self.concept_embeds)
+
+        # increase this value to create a stronger `nsfw` filter
+        # at the cost of increasing the possibility of filtering benign images
+        adjustment = 0.0
+
+        special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
+        # special_scores = special_scores.round(decimals=3)
+        special_care = torch.any(special_scores > 0, dim=1)
+        special_adjustment = special_care * 0.01
+        special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
+
+        concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
+        # concept_scores = concept_scores.round(decimals=3)
+        has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
+
+        images[has_nsfw_concepts] = 0.0  # black image
+
+        return images, has_nsfw_concepts

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):
-    def __init__(self, size):
+class ResBlock(nn.Module):
+    """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.LeakyReLU(0.1),
-            nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
-            nn.LeakyReLU(0.1),
-            nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
+            nn.Dropout(0.1)
         )
 
     def forward(self, x):
-        y = self.long(x)
-        z = self.join(y + x)
-        return z
+        x = self.norm(x)
+        return self.join(self.long(x) + x)
 
 
-class Interposer(nn.Module):
-    def __init__(self):
+class ExtractBlock(nn.Module):
+    """Increase no. of channels by [out/in]"""
+
+    def __init__(self, ch_in, ch_out):
         super().__init__()
-        self.chan = 4
-        self.hid = 128
-
-        self.head_join = nn.ReLU()
-        self.head_short = nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1)
-        self.head_long = nn.Sequential(
-            nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1),
-            nn.LeakyReLU(0.1),
-            nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=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)
+        self.join = nn.ReLU()
+        self.short = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)
+        self.long = nn.Sequential(
+            nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
+            nn.Dropout(0.1)
         )
 
     def forward(self, x):
-        y = self.head_join(
-            self.head_long(x) +
-            self.head_short(x)
+        return self.join(self.long(x) + self.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)

fooocus_colab.ipynb

@@ -12,7 +12,7 @@
     "%cd /content\n",
     "!git clone https://github.com/lllyasviel/Fooocus.git\n",
     "%cd /content/Fooocus\n",
-    "!python entry_with_update.py --share\n"
+    "!python entry_with_update.py --share --always-high-vram\n"
    ]
   }
  ],

fooocus_version.py

@@ -1 +1 @@
-version = '2.2.0'
+version = '2.4.0-rc2'

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
  */
@@ -150,9 +187,12 @@ function initStylePreviewOverlay() {
     let overlayVisible = false;
     const samplesPath = document.querySelector("meta[name='samples-path']").getAttribute("content")
     const overlay = document.createElement('div');
+    const tooltip = document.createElement('div');
+    tooltip.className = 'preview-tooltip';
+    overlay.appendChild(tooltip);
     overlay.id = 'stylePreviewOverlay';
     document.body.appendChild(overlay);
-    document.addEventListener('mouseover', function(e) {
+    document.addEventListener('mouseover', function (e) {
         const label = e.target.closest('.style_selections label');
         if (!label) return;
         label.removeEventListener("mouseout", onMouseLeave);
@@ -162,9 +202,12 @@ function initStylePreviewOverlay() {
         const originalText = label.querySelector("span").getAttribute("data-original-text");
         const name = originalText || label.querySelector("span").textContent;
         overlay.style.backgroundImage = `url("${samplesPath.replace(
-          "fooocus_v2",
-          name.toLowerCase().replaceAll(" ", "_")
+            "fooocus_v2",
+            name.toLowerCase().replaceAll(" ", "_")
         ).replaceAll("\\", "\\\\")}")`;
+
+        tooltip.textContent = name;
+
         function onMouseLeave() {
             overlayVisible = false;
             overlay.style.opacity = "0";
@@ -172,8 +215,8 @@ function initStylePreviewOverlay() {
             label.removeEventListener("mouseout", onMouseLeave);
         }
     });
-    document.addEventListener('mousemove', function(e) {
-        if(!overlayVisible) return;
+    document.addEventListener('mousemove', function (e) {
+        if (!overlayVisible) return;
         overlay.style.left = `${e.clientX}px`;
         overlay.style.top = `${e.clientY}px`;
         overlay.className = e.clientY > window.innerHeight / 2 ? "lower-half" : "upper-half";

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",
@@ -38,9 +39,12 @@
     "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)": "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)",
     "Setting": "Setting",
     "Style": "Style",
+    "Preset": "Preset",
     "Performance": "Performance",
     "Speed": "Speed",
     "Quality": "Quality",
+    "Extreme Speed": "Extreme Speed",
+    "Lightning": "Lightning",
     "Aspect Ratios": "Aspect Ratios",
     "width \u00d7 height": "width \u00d7 height",
     "Image Number": "Image Number",
@@ -50,9 +54,13 @@
     "Seed": "Seed",
     "Disable seed increment": "Disable seed increment",
     "Disable automatic seed increment when image number is > 1.": "Disable automatic seed increment when image number is > 1.",
+    "Read wildcards in order": "Read wildcards in order",
+    "Black Out NSFW": "Black Out NSFW",
+    "Use black image if NSFW is detected.": "Use black image if NSFW is detected.",
     "\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",
@@ -335,6 +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",
@@ -367,7 +377,6 @@
     "B2": "B2",
     "S1": "S1",
     "S2": "S2",
-    "Extreme Speed": "Extreme Speed",
     "\uD83D\uDD0E Type here to search styles ...": "\uD83D\uDD0E Type here to search styles ...",
     "Type prompt here.": "Type prompt here.",
     "Outpaint Expansion Direction:": "Outpaint Expansion Direction:",
@@ -379,7 +388,8 @@
     "Metadata": "Metadata",
     "Apply Metadata": "Apply Metadata",
     "Metadata Scheme": "Metadata Scheme",
-    "Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.": "Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.",
+    "Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.": "Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.",
     "fooocus (json)": "fooocus (json)",
-    "a1111 (plain text)": "a1111 (plain text)"
+    "a1111 (plain text)": "a1111 (plain text)",
+    "Unsupported image type in input": "Unsupported image type in input"
 }

launch.py

@@ -1,6 +1,6 @@
 import os
-import sys
 import ssl
+import sys
 
 print('[System ARGV] ' + str(sys.argv))
 
@@ -15,15 +15,13 @@ if "GRADIO_SERVER_PORT" not in os.environ:
 
 ssl._create_default_https_context = ssl._create_unverified_context
 
-
 import platform
 import fooocus_version
 
 from build_launcher import build_launcher
-from modules.launch_util import is_installed, run, python, run_pip, requirements_met
+from modules.launch_util import is_installed, run, python, run_pip, requirements_met, delete_folder_content
 from modules.model_loader import load_file_from_url
 
-
 REINSTALL_ALL = False
 TRY_INSTALL_XFORMERS = False
 
@@ -42,7 +40,7 @@ def prepare_environment():
 
     if TRY_INSTALL_XFORMERS:
         if REINSTALL_ALL or not is_installed("xformers"):
-            xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
+            xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23')
             if platform.system() == "Windows":
                 if platform.python_version().startswith("3.10"):
                     run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
@@ -64,10 +62,11 @@ 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',
-     'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors')
+    ('xl-to-v1_interposer-v4.0.safetensors',
+     'https://huggingface.co/mashb1t/misc/resolve/main/xl-to-v1_interposer-v4.0.safetensors')
 ]
 
+
 def ini_args():
     from args_manager import args
     return args
@@ -77,15 +76,28 @@ prepare_environment()
 build_launcher()
 args = ini_args()
 
-
 if args.gpu_device_id is not None:
     os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id)
     print("Set device to:", args.gpu_device_id)
 
+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
 
-def download_models():
+os.environ['GRADIO_TEMP_DIR'] = config.temp_path
+
+if config.temp_path_cleanup_on_launch:
+    print(f'[Cleanup] Attempting to delete content of temp dir {config.temp_path}')
+    result = delete_folder_content(config.temp_path, '[Cleanup] ')
+    if result:
+        print("[Cleanup] Cleanup successful")
+    else:
+        print(f"[Cleanup] Failed to delete content of temp dir.")
+
+
+def download_models(default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads):
     for file_name, url in vae_approx_filenames:
         load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name)
 
@@ -97,31 +109,32 @@ def download_models():
 
     if args.disable_preset_download:
         print('Skipped model download.')
-        return
+        return default_model, checkpoint_downloads
 
     if not args.always_download_new_model:
-        if not os.path.exists(os.path.join(config.paths_checkpoints[0], config.default_base_model_name)):
-            for alternative_model_name in config.previous_default_models:
+        if not os.path.exists(os.path.join(config.paths_checkpoints[0], default_model)):
+            for alternative_model_name in previous_default_models:
                 if os.path.exists(os.path.join(config.paths_checkpoints[0], alternative_model_name)):
-                    print(f'You do not have [{config.default_base_model_name}] but you have [{alternative_model_name}].')
+                    print(f'You do not have [{default_model}] but you have [{alternative_model_name}].')
                     print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, '
-                          f'but you are not using latest models.')
+                          f'but you are not using the latest models.')
                     print('Use --always-download-new-model to avoid fallback and always get new models.')
-                    config.checkpoint_downloads = {}
-                    config.default_base_model_name = alternative_model_name
+                    checkpoint_downloads = {}
+                    default_model = alternative_model_name
                     break
 
-    for file_name, url in config.checkpoint_downloads.items():
+    for file_name, url in checkpoint_downloads.items():
         load_file_from_url(url=url, model_dir=config.paths_checkpoints[0], file_name=file_name)
-    for file_name, url in config.embeddings_downloads.items():
+    for file_name, url in embeddings_downloads.items():
         load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name)
-    for file_name, url in config.lora_downloads.items():
+    for file_name, url in lora_downloads.items():
         load_file_from_url(url=url, model_dir=config.paths_loras[0], file_name=file_name)
 
-    return
+    return default_model, checkpoint_downloads
 
 
-download_models()
-
+config.default_base_model_name, config.checkpoint_downloads = download_models(
+    config.default_base_model_name, config.previous_default_models, config.checkpoint_downloads,
+    config.embeddings_downloads, config.lora_downloads)
 
 from webui import *

ldm_patched/contrib/external_align_your_steps.py

@@ -0,0 +1,55 @@
+# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
+
+#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
+import numpy as np
+import torch
+
+def loglinear_interp(t_steps, num_steps):
+    """
+    Performs log-linear interpolation of a given array of decreasing numbers.
+    """
+    xs = np.linspace(0, 1, len(t_steps))
+    ys = np.log(t_steps[::-1])
+
+    new_xs = np.linspace(0, 1, num_steps)
+    new_ys = np.interp(new_xs, xs, ys)
+
+    interped_ys = np.exp(new_ys)[::-1].copy()
+    return interped_ys
+
+NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956,  3.8636745985,  2.6946151520, 1.8841921177,  1.3943805092,  0.9642583904,  0.6523686016, 0.3977456272,  0.1515232662,  0.0291671582],
+                "SDXL":[14.6146412293, 6.3184485287,  3.7681790315,  2.1811480769, 1.3405244945,  0.8620721141,  0.5550693289,  0.3798540708, 0.2332364134,  0.1114188177,  0.0291671582],
+                "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]}
+
+class AlignYourStepsScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model_type": (["SD1", "SDXL", "SVD"], ),
+                     "steps": ("INT", {"default": 10, "min": 10, "max": 10000}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                      }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, model_type, steps, denoise):
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return (torch.FloatTensor([]),)
+            total_steps = round(steps * denoise)
+
+        sigmas = NOISE_LEVELS[model_type][:]
+        if (steps + 1) != len(sigmas):
+            sigmas = loglinear_interp(sigmas, steps + 1)
+
+        sigmas = sigmas[-(total_steps + 1):]
+        sigmas[-1] = 0
+        return (torch.FloatTensor(sigmas), )
+
+NODE_CLASS_MAPPINGS = {
+    "AlignYourStepsScheduler": AlignYourStepsScheduler,
+}

ldm_patched/contrib/external_custom_sampler.py

@@ -230,6 +230,25 @@ class SamplerDPMPP_SDE:
         sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
         return (sampler, )
 
+
+class SamplerTCD:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "eta": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
+            }
+        }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, eta=0.3):
+        sampler = ldm_patched.modules.samplers.ksampler("tcd", {"eta": eta})
+        return (sampler, )
+
+
 class SamplerCustom:
     @classmethod
     def INPUT_TYPES(s):
@@ -292,6 +311,7 @@ NODE_CLASS_MAPPINGS = {
     "KSamplerSelect": KSamplerSelect,
     "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
     "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
+    "SamplerTCD": SamplerTCD,
     "SplitSigmas": SplitSigmas,
     "FlipSigmas": FlipSigmas,
 }

ldm_patched/contrib/external_model_advanced.py

@@ -70,7 +70,7 @@ class ModelSamplingDiscrete:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["eps", "v_prediction", "lcm"],),
+                              "sampling": (["eps", "v_prediction", "lcm", "tcd"]),
                               "zsnr": ("BOOLEAN", {"default": False}),
                               }}
 
@@ -90,6 +90,9 @@ class ModelSamplingDiscrete:
         elif sampling == "lcm":
             sampling_type = LCM
             sampling_base = ModelSamplingDiscreteDistilled
+        elif sampling == "tcd":
+            sampling_type = ldm_patched.modules.model_sampling.EPS
+            sampling_base = ModelSamplingDiscreteDistilled
 
         class ModelSamplingAdvanced(sampling_base, sampling_type):
             pass

ldm_patched/k_diffusion/sampling.py

@@ -752,7 +752,6 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n
     return x
 
 
-
 @torch.no_grad()
 def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
     # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
@@ -808,3 +807,30 @@ def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=Non
             d_prime = w1 * d + w2 * d_2 + w3 * d_3
             x = x + d_prime * dt
     return x
+
+
+@torch.no_grad()
+def sample_tcd(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, eta=0.3):
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+
+    model_sampling = model.inner_model.inner_model.model_sampling
+    timesteps_s = torch.floor((1 - eta) * model_sampling.timestep(sigmas)).to(dtype=torch.long).detach().cpu()
+    timesteps_s[-1] = 0
+    alpha_prod_s = model_sampling.alphas_cumprod[timesteps_s]
+    beta_prod_s = 1 - alpha_prod_s
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)  # predicted_original_sample
+        eps = (x - denoised) / sigmas[i]
+        denoised = alpha_prod_s[i + 1].sqrt() * denoised + beta_prod_s[i + 1].sqrt() * eps
+
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+
+        x = denoised
+        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()
+
+    return x

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/model_sampling.py

@@ -50,17 +50,17 @@ class ModelSamplingDiscrete(torch.nn.Module):
         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('log_sigmas', sigmas.log())
 
+    def set_alphas_cumprod(self, alphas_cumprod):
+        self.register_buffer("alphas_cumprod", alphas_cumprod.float())
+
     @property
     def sigma_min(self):
         return self.sigmas[0]

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"]
+                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "tcd"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

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)
-        vae_sd = model_config.process_vae_state_dict(vae_sd)
+        if vae_filename_param is None:
+            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

@@ -1,8 +1,10 @@
 import threading
+import re
 from modules.patch import PatchSettings, patch_settings, patch_all
 
 patch_all()
 
+
 class AsyncTask:
     def __init__(self, args):
         self.args = args
@@ -22,6 +24,7 @@ def worker():
     import traceback
     import math
     import numpy as np
+    import cv2
     import torch
     import time
     import shared
@@ -41,11 +44,13 @@ def worker():
     import fooocus_version
     import args_manager
 
-    from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
+    from extras.censor import default_censor
+    from modules.sdxl_styles import apply_style, get_random_style, 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, get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix
+    from modules.util import (remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil,
+                              get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras,
+                              parse_lora_references_from_prompt, apply_wildcards)
     from modules.upscaler import perform_upscale
     from modules.flags import Performance
     from modules.meta_parser import get_metadata_parser, MetadataScheme
@@ -66,10 +71,15 @@ def worker():
         print(f'[Fooocus] {text}')
         async_task.yields.append(['preview', (number, text, None)])
 
-    def yield_result(async_task, imgs, do_not_show_finished_images=False):
+    def yield_result(async_task, imgs, black_out_nsfw, censor=True, do_not_show_finished_images=False,
+                     progressbar_index=13):
         if not isinstance(imgs, list):
             imgs = [imgs]
 
+        if censor and (modules.config.default_black_out_nsfw or black_out_nsfw):
+            progressbar(async_task, progressbar_index, 'Checking for NSFW content ...')
+            imgs = default_censor(imgs)
+
         async_task.results = async_task.results + imgs
 
         if do_not_show_finished_images:
@@ -79,16 +89,20 @@ def worker():
         return
 
     def build_image_wall(async_task):
-        results = async_task.results
+        results = []
 
-        if len(results) < 2:
+        if len(async_task.results) < 2:
             return
 
-        for img in results:
+        for img in async_task.results:
+            if isinstance(img, str) and os.path.exists(img):
+                img = cv2.imread(img)
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
             if not isinstance(img, np.ndarray):
                 return
             if img.ndim != 3:
                 return
+            results.append(img)
 
         H, W, C = results[0].shape
 
@@ -118,14 +132,6 @@ def worker():
         async_task.results = async_task.results + [wall]
         return
 
-    def apply_enabled_loras(loras):
-        enabled_loras = []
-        for lora_enabled, lora_model, lora_weight in loras:
-            if lora_enabled:
-                enabled_loras.append([lora_model, lora_weight])
-
-        return enabled_loras
-
     @torch.no_grad()
     @torch.inference_mode()
     def handler(async_task):
@@ -143,12 +149,14 @@ def worker():
         image_number = args.pop()
         output_format = args.pop()
         image_seed = args.pop()
+        read_wildcards_in_order = args.pop()
         sharpness = args.pop()
         guidance_scale = args.pop()
         base_model_name = args.pop()
         refiner_model_name = args.pop()
         refiner_switch = args.pop()
-        loras = apply_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop()), ] for _ in range(modules.config.default_max_lora_number)])
+        loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in
+                                   range(modules.config.default_max_lora_number)])
         input_image_checkbox = args.pop()
         current_tab = args.pop()
         uov_method = args.pop()
@@ -161,12 +169,14 @@ def worker():
         disable_preview = args.pop()
         disable_intermediate_results = args.pop()
         disable_seed_increment = args.pop()
+        black_out_nsfw = args.pop()
         adm_scaler_positive = args.pop()
         adm_scaler_negative = args.pop()
         adm_scaler_end = args.pop()
         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()
@@ -196,7 +206,8 @@ def worker():
         inpaint_erode_or_dilate = args.pop()
 
         save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
-        metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
+        metadata_scheme = MetadataScheme(
+            args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
 
         cn_tasks = {x: [] for x in flags.ip_list}
         for _ in range(flags.controlnet_image_count):
@@ -245,6 +256,44 @@ def worker():
             adm_scaler_negative = 1.0
             adm_scaler_end = 0.0
 
+        elif performance_selection == Performance.LIGHTNING:
+            print('Enter Lightning mode.')
+            progressbar(async_task, 1, 'Downloading Lightning components ...')
+            loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]
+
+            if refiner_model_name != 'None':
+                print(f'Refiner disabled in Lightning mode.')
+
+            refiner_model_name = 'None'
+            sampler_name = 'euler'
+            scheduler_name = 'sgm_uniform'
+            sharpness = 0.0
+            guidance_scale = 1.0
+            adaptive_cfg = 1.0
+            refiner_switch = 1.0
+            adm_scaler_positive = 1.0
+            adm_scaler_negative = 1.0
+            adm_scaler_end = 0.0
+
+        elif performance_selection == Performance.HYPER_SD:
+            print('Enter Hyper-SD mode.')
+            progressbar(async_task, 1, 'Downloading Hyper-SD components ...')
+            loras += [(modules.config.downloading_sdxl_hyper_sd_lora(), 0.8)]
+
+            if refiner_model_name != 'None':
+                print(f'Refiner disabled in Hyper-SD mode.')
+
+            refiner_model_name = 'None'
+            sampler_name = 'dpmpp_sde_gpu'
+            scheduler_name = 'karras'
+            sharpness = 0.0
+            guidance_scale = 1.0
+            adaptive_cfg = 1.0
+            refiner_switch = 1.0
+            adm_scaler_positive = 1.0
+            adm_scaler_negative = 1.0
+            adm_scaler_end = 0.0
+
         print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
         print(f'[Parameters] Sharpness = {sharpness}')
         print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
@@ -342,7 +391,7 @@ def worker():
                         print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
                         if refiner_model_name == 'None':
                             use_synthetic_refiner = True
-                            refiner_switch = 0.5
+                            refiner_switch = 0.8
                     else:
                         inpaint_head_model_path, inpaint_patch_model_path = None, None
                         print(f'[Inpaint] Parameterized inpaint is disabled.')
@@ -408,31 +457,40 @@ def worker():
             extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
 
             progressbar(async_task, 3, 'Loading models ...')
+
+            loras = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number)
+
             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 = []
-            
+
             for i in range(image_number):
                 if disable_seed_increment:
-                    task_seed = seed
+                    task_seed = seed % (constants.MAX_SEED + 1)
                 else:
                     task_seed = (seed + i) % (constants.MAX_SEED + 1)  # randint is inclusive, % is not
 
                 task_rng = random.Random(task_seed)  # may bind to inpaint noise in the future
-                task_prompt = apply_wildcards(prompt, task_rng)
+                task_prompt = apply_wildcards(prompt, task_rng, i, read_wildcards_in_order)
                 task_prompt = apply_arrays(task_prompt, i)
-                task_negative_prompt = apply_wildcards(negative_prompt, task_rng)
-                task_extra_positive_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_positive_prompts]
-                task_extra_negative_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_negative_prompts]
+                task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order)
+                task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in
+                                               extra_positive_prompts]
+                task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in
+                                               extra_negative_prompts]
 
                 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
@@ -460,6 +518,7 @@ 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:
@@ -553,9 +612,13 @@ def worker():
                 direct_return = False
 
             if direct_return:
-                d = [('Upscale (Fast)', '2x')]
-                uov_input_image_path = log(uov_input_image, d, output_format)
-                yield_result(async_task, uov_input_image_path, do_not_show_finished_images=True)
+                d = [('Upscale (Fast)', 'upscale_fast', '2x')]
+                if modules.config.default_black_out_nsfw or black_out_nsfw:
+                    progressbar(async_task, 100, 'Checking for NSFW content ...')
+                    uov_input_image = default_censor(uov_input_image)
+                progressbar(async_task, 100, 'Saving image to system ...')
+                uov_input_image_path = log(uov_input_image, d, output_format=output_format)
+                yield_result(async_task, uov_input_image_path, black_out_nsfw, False, do_not_show_finished_images=True)
                 return
 
             tiled = True
@@ -596,12 +659,12 @@ def worker():
 
                 H, W, C = inpaint_image.shape
                 if 'left' in outpaint_selections:
-                    inpaint_image = np.pad(inpaint_image, [[0, 0], [int(H * 0.3), 0], [0, 0]], mode='edge')
-                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(H * 0.3), 0]], mode='constant',
+                    inpaint_image = np.pad(inpaint_image, [[0, 0], [int(W * 0.3), 0], [0, 0]], mode='edge')
+                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(W * 0.3), 0]], mode='constant',
                                           constant_values=255)
                 if 'right' in outpaint_selections:
-                    inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(H * 0.3)], [0, 0]], mode='edge')
-                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(H * 0.3)]], mode='constant',
+                    inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(W * 0.3)], [0, 0]], mode='edge')
+                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(W * 0.3)]], mode='constant',
                                           constant_values=255)
 
                 inpaint_image = np.ascontiguousarray(inpaint_image.copy())
@@ -619,7 +682,7 @@ def worker():
             )
 
             if debugging_inpaint_preprocessor:
-                yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
+                yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), black_out_nsfw,
                              do_not_show_finished_images=True)
                 return
 
@@ -683,7 +746,7 @@ def worker():
                 cn_img = HWC3(cn_img)
                 task[0] = core.numpy_to_pytorch(cn_img)
                 if debugging_cn_preprocessor:
-                    yield_result(async_task, cn_img, do_not_show_finished_images=True)
+                    yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_cpds]:
                 cn_img, cn_stop, cn_weight = task
@@ -695,7 +758,7 @@ def worker():
                 cn_img = HWC3(cn_img)
                 task[0] = core.numpy_to_pytorch(cn_img)
                 if debugging_cn_preprocessor:
-                    yield_result(async_task, cn_img, do_not_show_finished_images=True)
+                    yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_ip]:
                 cn_img, cn_stop, cn_weight = task
@@ -706,7 +769,7 @@ def worker():
 
                 task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)
                 if debugging_cn_preprocessor:
-                    yield_result(async_task, cn_img, do_not_show_finished_images=True)
+                    yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_ip_face]:
                 cn_img, cn_stop, cn_weight = task
@@ -720,7 +783,7 @@ def worker():
 
                 task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)
                 if debugging_cn_preprocessor:
-                    yield_result(async_task, cn_img, do_not_show_finished_images=True)
+                    yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
                     return
 
             all_ip_tasks = cn_tasks[flags.cn_ip] + cn_tasks[flags.cn_ip_face]
@@ -755,19 +818,19 @@ def worker():
         final_sampler_name = sampler_name
         final_scheduler_name = scheduler_name
 
-        if scheduler_name == 'lcm':
+        if scheduler_name in ['lcm', 'tcd']:
             final_scheduler_name = 'sgm_uniform'
             if pipeline.final_unet is not None:
                 pipeline.final_unet = core.opModelSamplingDiscrete.patch(
                     pipeline.final_unet,
-                    sampling='lcm',
+                    sampling=scheduler_name,
                     zsnr=False)[0]
             if pipeline.final_refiner_unet is not None:
                 pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch(
                     pipeline.final_refiner_unet,
-                    sampling='lcm',
+                    sampling=scheduler_name,
                     zsnr=False)[0]
-            print('Using lcm scheduler.')
+            print(f'Using {scheduler_name} scheduler.')
 
         async_task.yields.append(['preview', (13, 'Moving model to GPU ...', None)])
 
@@ -775,14 +838,15 @@ def worker():
             done_steps = current_task_id * steps + step
             async_task.yields.append(['preview', (
                 int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
-                f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling', y)])
+                f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling',
+                y)])
 
         for current_task_id, task in enumerate(tasks):
             execution_start_time = time.perf_counter()
 
             try:
                 if async_task.last_stop is not False:
-                    ldm_patched.model_management.interrupt_current_processing()
+                    ldm_patched.modules.model_management.interrupt_current_processing()
                 positive_cond, negative_cond = task['c'], task['uc']
 
                 if 'cn' in goals:
@@ -820,22 +884,33 @@ def worker():
                     imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
 
                 img_paths = []
+                current_progress = int(15.0 + 85.0 * float((current_task_id + 1) * steps) / float(all_steps))
+                if modules.config.default_black_out_nsfw or black_out_nsfw:
+                    progressbar(async_task, current_progress, 'Checking for NSFW content ...')
+                    imgs = default_censor(imgs)
+
+                progressbar(async_task, current_progress, 'Saving image to system ...')
                 for x in imgs:
                     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)),
-                         ('Performance', 'performance', performance_selection.value),
-                         ('Resolution', 'resolution', str((width, height))),
-                         ('Guidance Scale', 'guidance_scale', guidance_scale),
-                         ('Sharpness', 'sharpness', sharpness),
-                         ('ADM Guidance', 'adm_guidance', str((
-                             modules.patch.patch_settings[pid].positive_adm_scale,
-                             modules.patch.patch_settings[pid].negative_adm_scale,
-                             modules.patch.patch_settings[pid].adm_scaler_end))),
-                         ('Base Model', 'base_model', base_model_name),
-                         ('Refiner Model', 'refiner_model', refiner_model_name),
-                         ('Refiner Switch', 'refiner_switch', refiner_switch)]
+                         ('Styles', 'styles',
+                          str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
+                         ('Performance', 'performance', performance_selection.value)]
+
+                    if performance_selection.steps() != steps:
+                        d.append(('Steps', 'steps', steps))
+
+                    d += [('Resolution', 'resolution', str((width, height))),
+                          ('Guidance Scale', 'guidance_scale', guidance_scale),
+                          ('Sharpness', 'sharpness', sharpness),
+                          ('ADM Guidance', 'adm_guidance', str((
+                              modules.patch.patch_settings[pid].positive_adm_scale,
+                              modules.patch.patch_settings[pid].negative_adm_scale,
+                              modules.patch.patch_settings[pid].adm_scaler_end))),
+                          ('Base Model', 'base_model', base_model_name),
+                          ('Refiner Model', 'refiner_model', refiner_model_name),
+                          ('Refiner Switch', 'refiner_switch', refiner_switch)]
 
                     if refiner_model_name != 'None':
                         if overwrite_switch > 0:
@@ -843,30 +918,34 @@ def worker():
                         if refiner_swap_method != flags.refiner_swap_method:
                             d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method))
                     if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
-                        d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
+                        d.append(
+                            ('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
 
                     d.append(('Sampler', 'sampler', sampler_name))
                     d.append(('Scheduler', 'scheduler', scheduler_name))
-                    d.append(('Seed', 'seed', task['task_seed']))
+                    d.append(('VAE', 'vae', vae_name))
+                    d.append(('Seed', 'seed', str(task['task_seed'])))
 
                     if freeu_enabled:
                         d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))
 
+                    for li, (n, w) in enumerate(loras):
+                        if n != 'None':
+                            d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
+
                     metadata_parser = None
                     if save_metadata_to_images:
                         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)
-
-                    for li, (n, w) in enumerate(loras):
-                        if n != 'None':
-                            d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
-
+                                                 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, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
+                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:
                 if async_task.last_stop == 'skip':
                     print('User skipped')

modules/config.py

@@ -3,12 +3,15 @@ import json
 import math
 import numbers
 import args_manager
+import tempfile
 import modules.flags
 import modules.sdxl_styles
 
 from modules.model_loader import load_file_from_url
-from modules.util import get_files_from_folder, makedirs_with_log
-from modules.flags import Performance, MetadataScheme
+from modules.util import makedirs_with_log
+from modules.extra_utils import get_files_from_folder
+from modules.flags import OutputFormat, Performance, MetadataScheme
+
 
 def get_config_path(key, default_value):
     env = os.getenv(key)
@@ -18,6 +21,7 @@ def get_config_path(key, default_value):
     else:
         return os.path.abspath(default_value)
 
+wildcards_max_bfs_depth = 64
 config_path = get_config_path('config_path', "./config.txt")
 config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt")
 config_dict = {}
@@ -94,21 +98,36 @@ def try_load_deprecated_user_path_config():
 
 try_load_deprecated_user_path_config()
 
+
+def get_presets():
+    preset_folder = 'presets'
+    presets = ['initial']
+    if not os.path.exists(preset_folder):
+        print('No presets found.')
+        return presets
+
+    return presets + [f[:f.index('.json')] for f in os.listdir(preset_folder) if f.endswith('.json')]
+
+
+def try_get_preset_content(preset):
+    if isinstance(preset, str):
+        preset_path = os.path.abspath(f'./presets/{preset}.json')
+        try:
+            if os.path.exists(preset_path):
+                with open(preset_path, "r", encoding="utf-8") as json_file:
+                    json_content = json.load(json_file)
+                    print(f'Loaded preset: {preset_path}')
+                    return json_content
+            else:
+                raise FileNotFoundError
+        except Exception as e:
+            print(f'Load preset [{preset_path}] failed')
+            print(e)
+    return {}
+
+available_presets = get_presets()
 preset = args_manager.args.preset
-
-if isinstance(preset, str):
-    preset_path = os.path.abspath(f'./presets/{preset}.json')
-    try:
-        if os.path.exists(preset_path):
-            with open(preset_path, "r", encoding="utf-8") as json_file:
-                config_dict.update(json.load(json_file))
-                print(f'Loaded preset: {preset_path}')
-        else:
-            raise FileNotFoundError
-    except Exception as e:
-        print(f'Load preset [{preset_path}] failed')
-        print(e)
-
+config_dict.update(try_get_preset_content(preset))
 
 def get_path_output() -> str:
     """
@@ -117,7 +136,7 @@ def get_path_output() -> str:
     global config_dict
     path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True)
     if args_manager.args.output_path:
-        print(f'[CONFIG] Overriding config value path_outputs with {args_manager.args.output_path}')
+        print(f'Overriding config value path_outputs with {args_manager.args.output_path}')
         config_dict['path_outputs'] = path_output = args_manager.args.output_path
     return path_output
 
@@ -171,13 +190,17 @@ 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/')
 path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vision/')
 path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion')
+path_wildcards = get_dir_or_set_default('path_wildcards', '../wildcards/')
+path_safety_checker = get_dir_or_set_default('path_safety_checker', '../models/safety_checker/')
 path_outputs = get_path_output()
 
+
 def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False):
     global config_dict, visited_keys
 
@@ -206,7 +229,37 @@ def get_config_item_or_set_default(key, default_value, validator, disable_empty_
         return default_value
 
 
-default_base_model_name = get_config_item_or_set_default(
+def init_temp_path(path: str | None, default_path: str) -> str:
+    if args_manager.args.temp_path:
+        path = args_manager.args.temp_path
+
+    if path != '' and path != default_path:
+        try:
+            if not os.path.isabs(path):
+                path = os.path.abspath(path)
+            os.makedirs(path, exist_ok=True)
+            print(f'Using temp path {path}')
+            return path
+        except Exception as e:
+            print(f'Could not create temp path {path}. Reason: {e}')
+            print(f'Using default temp path {default_path} instead.')
+
+    os.makedirs(default_path, exist_ok=True)
+    return default_path
+
+
+default_temp_path = os.path.join(tempfile.gettempdir(), 'fooocus')
+temp_path = init_temp_path(get_config_item_or_set_default(
+    key='temp_path',
+    default_value=default_temp_path,
+    validator=lambda x: isinstance(x, str),
+), default_temp_path)
+temp_path_cleanup_on_launch = get_config_item_or_set_default(
+    key='temp_path_cleanup_on_launch',
+    default_value=True,
+    validator=lambda x: isinstance(x, bool)
+)
+default_base_model_name = default_model = get_config_item_or_set_default(
     key='default_model',
     default_value='model.safetensors',
     validator=lambda x: isinstance(x, str)
@@ -216,7 +269,7 @@ previous_default_models = get_config_item_or_set_default(
     default_value=[],
     validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x)
 )
-default_refiner_model_name = get_config_item_or_set_default(
+default_refiner_model_name = default_refiner = get_config_item_or_set_default(
     key='default_refiner',
     default_value='None',
     validator=lambda x: isinstance(x, str)
@@ -240,31 +293,40 @@ default_loras = get_config_item_or_set_default(
     key='default_loras',
     default_value=[
         [
+            True,
             "None",
             1.0
         ],
         [
+            True,
             "None",
             1.0
         ],
         [
+            True,
             "None",
             1.0
         ],
         [
+            True,
             "None",
             1.0
         ],
         [
+            True,
             "None",
             1.0
         ]
     ],
-    validator=lambda x: isinstance(x, list) and all(len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number) for y in x)
+    validator=lambda x: isinstance(x, list) and all(
+        len(y) == 3 and isinstance(y[0], bool) and isinstance(y[1], str) and isinstance(y[2], numbers.Number)
+        or len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number)
+        for y in x)
 )
+default_loras = [(y[0], y[1], y[2]) if len(y) == 3 else (True, y[0], y[1]) for y in default_loras]
 default_max_lora_number = get_config_item_or_set_default(
     key='default_max_lora_number',
-    default_value=len(default_loras),
+    default_value=len(default_loras) if isinstance(default_loras, list) and len(default_loras) > 0 else 5,
     validator=lambda x: isinstance(x, int) and x >= 1
 )
 default_cfg_scale = get_config_item_or_set_default(
@@ -287,6 +349,11 @@ 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=[
@@ -326,7 +393,7 @@ default_max_image_number = get_config_item_or_set_default(
 default_output_format = get_config_item_or_set_default(
     key='default_output_format',
     default_value='png',
-    validator=lambda x: x in modules.flags.output_formats
+    validator=lambda x: x in OutputFormat.list()
 )
 default_image_number = get_config_item_or_set_default(
     key='default_image_number',
@@ -391,6 +458,11 @@ example_inpaint_prompts = get_config_item_or_set_default(
     ],
     validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x)
 )
+default_black_out_nsfw = get_config_item_or_set_default(
+    key='default_black_out_nsfw',
+    default_value=False,
+    validator=lambda x: isinstance(x, bool)
+)
 default_save_metadata_to_images = get_config_item_or_set_default(
     key='default_save_metadata_to_images',
     default_value=False,
@@ -409,31 +481,33 @@ metadata_created_by = get_config_item_or_set_default(
 
 example_inpaint_prompts = [[x] for x in example_inpaint_prompts]
 
-config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [['None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]
-
-possible_preset_keys = [
-    "default_model",
-    "default_refiner",
-    "default_refiner_switch",
-    "default_loras_min_weight",
-    "default_loras_max_weight",
-    "default_loras",
-    "default_max_lora_number",
-    "default_cfg_scale",
-    "default_sample_sharpness",
-    "default_sampler",
-    "default_scheduler",
-    "default_performance",
-    "default_prompt",
-    "default_prompt_negative",
-    "default_styles",
-    "default_aspect_ratio",
-    "default_save_metadata_to_images",
-    "checkpoint_downloads",
-    "embeddings_downloads",
-    "lora_downloads",
-]
+config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [[True, 'None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]
 
+# mapping config to meta parameter 
+possible_preset_keys = {
+    "default_model": "base_model",
+    "default_refiner": "refiner_model",
+    "default_refiner_switch": "refiner_switch",
+    "previous_default_models": "previous_default_models",
+    "default_loras_min_weight": "default_loras_min_weight",
+    "default_loras_max_weight": "default_loras_max_weight",
+    "default_loras": "<processed>",
+    "default_cfg_scale": "guidance_scale",
+    "default_sample_sharpness": "sharpness",
+    "default_sampler": "sampler",
+    "default_scheduler": "scheduler",
+    "default_overwrite_step": "steps",
+    "default_performance": "performance",
+    "default_image_number": "image_number",
+    "default_prompt": "prompt",
+    "default_prompt_negative": "negative_prompt",
+    "default_styles": "styles",
+    "default_aspect_ratio": "resolution",
+    "default_save_metadata_to_images": "default_save_metadata_to_images",
+    "checkpoint_downloads": "checkpoint_downloads",
+    "embeddings_downloads": "embeddings_downloads",
+    "lora_downloads": "lora_downloads"
+}
 
 REWRITE_PRESET = False
 
@@ -474,21 +548,35 @@ 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'
+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 get_model_filenames(folder_paths, name_filter=None):
-    extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']
+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_all_model_names():
-    global model_filenames, lora_filenames
+def update_files():
+    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
 
 
@@ -538,6 +626,23 @@ def downloading_sdxl_lcm_lora():
     )
     return sdxl_lcm_lora
 
+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
+    )
+    return sdxl_lightning_lora
+
+
+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
+    )
+    return sdxl_hyper_sd_lora
+
 
 def downloading_controlnet_canny():
     load_file_from_url(
@@ -603,5 +708,13 @@ def downloading_upscale_model():
     )
     return os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin')
 
+def downloading_safety_checker_model():
+    load_file_from_url(
+        url='https://huggingface.co/mashb1t/misc/resolve/main/stable-diffusion-safety-checker.bin',
+        model_dir=path_safety_checker,
+        file_name='stable-diffusion-safety-checker.bin'
+    )
+    return os.path.join(path_safety_checker, 'stable-diffusion-safety-checker.bin')
 
-update_all_model_names()
+
+update_files()

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 = ''
@@ -73,14 +74,14 @@ class StableDiffusionModel:
 
         loras_to_load = []
 
-        for name, weight in loras:
-            if name == 'None':
+        for filename, weight in loras:
+            if filename == 'None':
                 continue
 
-            if os.path.exists(name):
-                lora_filename = name
+            if os.path.exists(filename):
+                lora_filename = filename
             else:
-                lora_filename = get_file_from_folder_list(name, modules.config.paths_loras)
+                lora_filename = get_file_from_folder_list(filename, modules.config.paths_loras)
 
             if not os.path.exists(lora_filename):
                 print(f'Lora file not found: {lora_filename}')
@@ -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):
-    unet, clip, vae, 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)
+def load_model(ckpt_filename, vae_filename=None):
+    unet, clip, vae, vae_filename, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings,
+                                                                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
@@ -11,7 +12,7 @@ from extras.expansion import FooocusExpansion
 
 from ldm_patched.modules.model_base import SDXL, SDXLRefiner
 from modules.sample_hijack import clip_separate
-from modules.util import get_file_from_folder_list
+from modules.util import get_file_from_folder_list, get_enabled_loras
 
 
 model_base = core.StableDiffusionModel()
@@ -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)
+    model_base = core.load_model(filename, vae_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=modules.config.default_loras
+    loras=get_enabled_loras(modules.config.default_loras),
+    vae_name=modules.config.default_vae,
 )
 
 

modules/extra_utils.py

@@ -0,0 +1,20 @@
+import os
+
+
+def get_files_from_folder(folder_path, extensions=None, name_filter=None):
+    if not os.path.isdir(folder_path):
+        raise ValueError("Folder path is not a valid directory.")
+
+    filenames = []
+
+    for root, _, files in os.walk(folder_path, topdown=False):
+        relative_path = os.path.relpath(root, folder_path)
+        if relative_path == ".":
+            relative_path = ""
+        for filename in sorted(files, key=lambda s: s.casefold()):
+            _, file_extension = os.path.splitext(filename)
+            if (extensions is None or file_extension.lower() in extensions) and (name_filter is None or name_filter in _):
+                path = os.path.join(relative_path, filename)
+                filenames.append(path)
+
+    return filenames

modules/flags.py

@@ -34,7 +34,8 @@ KSAMPLER = {
     "dpmpp_3m_sde": "",
     "dpmpp_3m_sde_gpu": "",
     "ddpm": "",
-    "lcm": "LCM"
+    "lcm": "LCM",
+    "tcd": "TCD"
 }
 
 SAMPLER_EXTRA = {
@@ -47,12 +48,14 @@ SAMPLERS = KSAMPLER | SAMPLER_EXTRA
 
 KSAMPLER_NAMES = list(KSAMPLER.keys())
 
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo"]
+SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo", "align_your_steps", "tcd"]
 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"
@@ -67,7 +70,7 @@ default_parameters = {
     cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0)
 }  # stop, weight
 
-output_formats = ['png', 'jpg', 'webp']
+output_formats = ['png', 'jpeg', 'webp']
 
 inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']
 inpaint_option_default = 'Inpaint or Outpaint (default)'
@@ -89,37 +92,54 @@ metadata_scheme = [
     (f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value),
 ]
 
-lora_count = 5
-
 controlnet_image_count = 4
 
 
+class OutputFormat(Enum):
+    PNG = 'png'
+    JPEG = 'jpeg'
+    WEBP = 'webp'
+
+    @classmethod
+    def list(cls) -> list:
+        return list(map(lambda c: c.value, cls))
+
+
 class Steps(IntEnum):
     QUALITY = 60
     SPEED = 30
     EXTREME_SPEED = 8
+    LIGHTNING = 4
+    HYPER_SD = 4
 
 
 class StepsUOV(IntEnum):
     QUALITY = 36
     SPEED = 18
     EXTREME_SPEED = 8
+    LIGHTNING = 4
+    HYPER_SD = 4
 
 
 class Performance(Enum):
     QUALITY = 'Quality'
     SPEED = 'Speed'
     EXTREME_SPEED = 'Extreme Speed'
+    LIGHTNING = 'Lightning'
+    HYPER_SD = 'Hyper-SD'
 
     @classmethod
     def list(cls) -> list:
         return list(map(lambda c: c.value, cls))
 
+    @classmethod
+    def has_restricted_features(cls, x) -> bool:
+        if isinstance(x, Performance):
+            x = x.value
+        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
 
     def steps_uov(self) -> int | None:
         return StepsUOV[self.name].value if Steps[self.name] else None
-
-
-performance_selections = Performance.list()

modules/gradio_hijack.py

@@ -17,7 +17,7 @@ from gradio_client.documentation import document, set_documentation_group
 from gradio_client.serializing import ImgSerializable
 from PIL import Image as _Image  # using _ to minimize namespace pollution
 
-from gradio import processing_utils, utils
+from gradio import processing_utils, utils, Error
 from gradio.components.base import IOComponent, _Keywords, Block
 from gradio.deprecation import warn_style_method_deprecation
 from gradio.events import (
@@ -275,7 +275,10 @@ class Image(
                 x, mask = x["image"], x["mask"]
 
         assert isinstance(x, str)
-        im = processing_utils.decode_base64_to_image(x)
+        try:
+            im = processing_utils.decode_base64_to_image(x)
+        except PIL.UnidentifiedImageError:
+            raise Error("Unsupported image type in input")
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             im = im.convert(self.image_mode)

modules/html.py

@@ -1,142 +1,3 @@
-css = '''
-.loader-container {
-  display: flex; /* Use flex to align items horizontally */
-  align-items: center; /* Center items vertically within the container */
-  white-space: nowrap; /* Prevent line breaks within the container */
-}
-
-.loader {
-  border: 8px solid #f3f3f3; /* Light grey */
-  border-top: 8px solid #3498db; /* Blue */
-  border-radius: 50%;
-  width: 30px;
-  height: 30px;
-  animation: spin 2s linear infinite;
-}
-
-@keyframes spin {
-  0% { transform: rotate(0deg); }
-  100% { transform: rotate(360deg); }
-}
-
-/* Style the progress bar */
-progress {
-  appearance: none; /* Remove default styling */
-  height: 20px; /* Set the height of the progress bar */
-  border-radius: 5px; /* Round the corners of the progress bar */
-  background-color: #f3f3f3; /* Light grey background */
-  width: 100%;
-}
-
-/* Style the progress bar container */
-.progress-container {
-  margin-left: 20px;
-  margin-right: 20px;
-  flex-grow: 1; /* Allow the progress container to take up remaining space */
-}
-
-/* Set the color of the progress bar fill */
-progress::-webkit-progress-value {
-  background-color: #3498db; /* Blue color for the fill */
-}
-
-progress::-moz-progress-bar {
-  background-color: #3498db; /* Blue color for the fill in Firefox */
-}
-
-/* Style the text on the progress bar */
-progress::after {
-  content: attr(value '%'); /* Display the progress value followed by '%' */
-  position: absolute;
-  top: 50%;
-  left: 50%;
-  transform: translate(-50%, -50%);
-  color: white; /* Set text color */
-  font-size: 14px; /* Set font size */
-}
-
-/* Style other texts */
-.loader-container > span {
-  margin-left: 5px; /* Add spacing between the progress bar and the text */
-}
-
-.progress-bar > .generating {
-  display: none !important;
-}
-
-.progress-bar{
-  height: 30px !important;
-}
-
-.type_row{
-  height: 80px !important;
-}
-
-.type_row_half{
-  height: 32px !important;
-}
-
-.scroll-hide{
-  resize: none !important;
-}
-
-.refresh_button{
-  border: none !important;
-  background: none !important;
-  font-size: none !important;
-  box-shadow: none !important;
-}
-
-.advanced_check_row{
-  width: 250px !important;
-}
-
-.min_check{
-  min-width: min(1px, 100%) !important;
-}
-
-.resizable_area {
-  resize: vertical;
-  overflow: auto !important;
-}
-
-.aspect_ratios label {
-    width: 140px !important;
-}
-
-.aspect_ratios label span {
-    white-space: nowrap !important;
-}
-
-.aspect_ratios label input {
-    margin-left: -5px !important;
-}
-
-.lora_enable {
-  flex-grow: 1 !important;
-}
-
-.lora_enable label {
-  height: 100%;
-}
-
-.lora_enable label input {
-  margin: auto;
-}
-
-.lora_enable label span {
-  display: none;
-}
-
-.lora_model {
-  flex-grow: 5 !important;
-}
-
-.lora_weight {
-  flex-grow: 5 !important;
-}
-
-'''
 progress_html = '''
 <div class="loader-container">
   <div class="loader"></div>

modules/launch_util.py

@@ -1,6 +1,7 @@
 import os
 import importlib
 import importlib.util
+import shutil
 import subprocess
 import sys
 import re
@@ -9,9 +10,6 @@ import importlib.metadata
 import packaging.version
 from packaging.requirements import Requirement
 
-
-
-
 logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR)  # sshh...
 logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
 
@@ -101,3 +99,19 @@ def requirements_met(requirements_file):
 
     return True
 
+
+def delete_folder_content(folder, prefix=None):
+    result = True
+
+    for filename in os.listdir(folder):
+        file_path = os.path.join(folder, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+            elif os.path.isdir(file_path):
+                shutil.rmtree(file_path)
+        except Exception as e:
+            print(f'{prefix}Failed to delete {file_path}. Reason: {e}')
+            result = False
+
+    return result

modules/meta_parser.py

@@ -1,5 +1,4 @@
 import json
-import os
 import re
 from abc import ABC, abstractmethod
 from pathlib import Path
@@ -12,7 +11,7 @@ import modules.config
 import modules.sdxl_styles
 from modules.flags import MetadataScheme, Performance, Steps
 from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS
-from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, calculate_sha256
+from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, sha256
 
 re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
@@ -27,8 +26,9 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
         loaded_parameter_dict = json.loads(raw_metadata)
     assert isinstance(loaded_parameter_dict, dict)
 
-    results = [len(loaded_parameter_dict) > 0, 1]
+    results = [len(loaded_parameter_dict) > 0]
 
+    get_image_number('image_number', 'Image Number', loaded_parameter_dict, results)
     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)
@@ -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:
@@ -92,13 +93,25 @@ def get_float(key: str, fallback: str | None, source_dict: dict, results: list,
         results.append(gr.update())
 
 
+def get_image_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
+    try:
+        h = source_dict.get(key, source_dict.get(fallback, default))
+        assert h is not None
+        h = int(h)
+        h = min(h, modules.config.default_max_image_number)
+        results.append(h)
+    except:
+        results.append(1)
+
+
 def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
     try:
         h = source_dict.get(key, source_dict.get(fallback, default))
         assert h is not None
         h = int(h)
         # if not in steps or in steps and performance is not the same
-        if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ', '_').casefold():
+        if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ',
+                                                                                                          '_').casefold():
             results.append(h)
             return
         results.append(-1)
@@ -117,8 +130,8 @@ def get_resolution(key: str, fallback: str | None, source_dict: dict, results: l
             results.append(-1)
         else:
             results.append(gr.update())
-            results.append(width)
-            results.append(height)
+            results.append(int(width))
+            results.append(int(height))
     except:
         results.append(gr.update())
         results.append(gr.update())
@@ -169,11 +182,20 @@ def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list,
 
 def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
     try:
-        n, w = source_dict.get(key, source_dict.get(fallback)).split(' : ')
-        w = float(w)
-        results.append(True)
-        results.append(n)
-        results.append(w)
+        split_data = source_dict.get(key, source_dict.get(fallback)).split(' : ')
+        enabled = True
+        name = split_data[0]
+        weight = split_data[1]
+
+        if len(split_data) == 3:
+            enabled = split_data[0] == 'True'
+            name = split_data[1]
+            weight = split_data[2]
+
+        weight = float(weight)
+        results.append(enabled)
+        results.append(name)
+        results.append(weight)
     except:
         results.append(True)
         results.append('None')
@@ -183,7 +205,8 @@ def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
 def get_sha256(filepath):
     global hash_cache
     if filepath not in hash_cache:
-        hash_cache[filepath] = calculate_sha256(filepath)
+        # is_safetensors = os.path.splitext(filepath)[1].lower() == '.safetensors'
+        hash_cache[filepath] = sha256(filepath)
 
     return hash_cache[filepath]
 
@@ -231,6 +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:
@@ -245,7 +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
@@ -267,6 +291,13 @@ 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):
+        for lora_to_remove in modules.config.loras_metadata_remove:
+            if lora_to_remove in lora_filenames:
+                lora_filenames.remove(lora_to_remove)
 
 
 class A1111MetadataParser(MetadataParser):
@@ -282,6 +313,7 @@ class A1111MetadataParser(MetadataParser):
         'steps': 'Steps',
         'sampler': 'Sampler',
         'scheduler': 'Scheduler',
+        'vae': 'VAE',
         'guidance_scale': 'CFG scale',
         'seed': 'Seed',
         'resolution': 'Size',
@@ -369,20 +401,26 @@ 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:
-                    path = Path(filename)
-                    if data[key] == path.stem:
-                        data[key] = filename
-                        break
+                if key == 'vae':
+                    self.add_extension_to_filename(data, modules.config.vae_filenames, 'vae')
+                else:
+                    self.add_extension_to_filename(data, modules.config.model_filenames, key)
 
-        if 'lora_hashes' in data:
+        lora_data = ''
+        if 'lora_weights' in data and data['lora_weights'] != '':
+            lora_data = data['lora_weights']
+        elif 'lora_hashes' in data and data['lora_hashes'] != '' and data['lora_hashes'].split(', ')[0].count(':') == 2:
+            lora_data = data['lora_hashes']
+
+        if lora_data != '':
             lora_filenames = modules.config.lora_filenames.copy()
-            if modules.config.sdxl_lcm_lora in lora_filenames:
-                lora_filenames.remove(modules.config.sdxl_lcm_lora)
-            for li, lora in enumerate(data['lora_hashes'].split(', ')):
-                lora_name, lora_hash, lora_weight = lora.split(': ')
+            self.remove_special_loras(lora_filenames)
+            for li, lora in enumerate(lora_data.split(', ')):
+                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 lora_filenames:
                     path = Path(filename)
                     if lora_name == path.stem:
@@ -398,6 +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':
@@ -416,6 +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,
@@ -431,16 +471,19 @@ class A1111MetadataParser(MetadataParser):
             if key in data:
                 generation_params[self.fooocus_to_a1111[key]] = data[key]
 
-        lora_hashes = []
-        for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras):
-            # workaround for Fooocus not knowing LoRA name in LoRA metadata
-            lora_hashes.append(f'{lora_name}: {lora_hash}: {lora_weight}')
-        lora_hashes_string = ', '.join(lora_hashes)
+        if len(self.loras) > 0:
+            lora_hashes = []
+            lora_weights = []
+            for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras):
+                # workaround for Fooocus not knowing LoRA name in LoRA metadata
+                lora_hashes.append(f'{lora_name}: {lora_hash}')
+                lora_weights.append(f'{lora_name}: {lora_weight}')
+            lora_hashes_string = ', '.join(lora_hashes)
+            lora_weights_string = ', '.join(lora_weights)
+            generation_params[self.fooocus_to_a1111['lora_hashes']] = lora_hashes_string
+            generation_params[self.fooocus_to_a1111['lora_weights']] = lora_weights_string
 
-        generation_params |= {
-            self.fooocus_to_a1111['lora_hashes']: lora_hashes_string,
-            self.fooocus_to_a1111['version']: data['version']
-        }
+        generation_params[self.fooocus_to_a1111['version']] = data['version']
 
         if modules.config.metadata_created_by != '':
             generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by
@@ -453,6 +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:
@@ -461,9 +512,8 @@ class FooocusMetadataParser(MetadataParser):
     def parse_json(self, metadata: dict) -> dict:
         model_filenames = modules.config.model_filenames.copy()
         lora_filenames = modules.config.lora_filenames.copy()
-        if modules.config.sdxl_lcm_lora in lora_filenames:
-            lora_filenames.remove(modules.config.sdxl_lcm_lora)
-
+        vae_filenames = modules.config.vae_filenames.copy()
+        self.remove_special_loras(lora_filenames)
         for key, value in metadata.items():
             if value in ['', 'None']:
                 continue
@@ -471,6 +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
 
@@ -497,6 +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 != '':
@@ -570,4 +623,4 @@ def get_exif(metadata: str | None, metadata_scheme: str):
     exif[0x0131] = 'Fooocus v' + fooocus_version.version
     # 0x927C = MakerNote
     exif[0x927C] = metadata_scheme
-    return exif
+    return exif

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/patch_precision.py

@@ -51,6 +51,8 @@ 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

@@ -6,8 +6,9 @@ import urllib.parse
 
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
-from modules.util import generate_temp_filename
+from modules.flags import OutputFormat
 from modules.meta_parser import MetadataParser, get_exif
+from modules.util import generate_temp_filename
 
 log_cache = {}
 
@@ -20,16 +21,16 @@ def get_current_html_path(output_format=None):
     return html_name
 
 
-def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str:
-    path_outputs = args_manager.args.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs
+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)
     os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
 
-    parsed_parameters = metadata_parser.parse_string(metadata) if metadata_parser is not None else ''
+    parsed_parameters = metadata_parser.parse_string(metadata.copy()) if metadata_parser is not None else ''
     image = Image.fromarray(img)
 
-    if output_format == 'png':
+    if output_format == OutputFormat.PNG.value:
         if parsed_parameters != '':
             pnginfo = PngInfo()
             pnginfo.add_text('parameters', parsed_parameters)
@@ -37,9 +38,9 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
         else:
             pnginfo = None
         image.save(local_temp_filename, pnginfo=pnginfo)
-    elif output_format == 'jpg':
+    elif output_format == OutputFormat.JPEG.value:
         image.save(local_temp_filename, quality=95, optimize=True, progressive=True, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
-    elif output_format == 'webp':
+    elif output_format == OutputFormat.WEBP.value:
         image.save(local_temp_filename, quality=95, lossless=False, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
     else:
         image.save(local_temp_filename)
@@ -90,7 +91,7 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
         </script>"""
     )
 
-    begin_part = f"<!DOCTYPE html><html><head><title>Fooocus Log {date_string}</title>{css_styles}</head><body>{js}<p>Fooocus Log {date_string} (private)</p>\n<p>All images are clean, without any hidden data/meta, and safe to share with others.</p><!--fooocus-log-split-->\n\n"
+    begin_part = f"<!DOCTYPE html><html><head><title>Fooocus Log {date_string}</title>{css_styles}</head><body>{js}<p>Fooocus Log {date_string} (private)</p>\n<p>Metadata is embedded if enabled in the config or developer debug mode. You can find the information for each image in line Metadata Scheme.</p><!--fooocus-log-split-->\n\n"
     end_part = f'\n<!--fooocus-log-split--></body></html>'
 
     middle_part = log_cache.get(html_name, "")
@@ -110,9 +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/sample_hijack.py

@@ -3,6 +3,7 @@ import ldm_patched.modules.samplers
 import ldm_patched.modules.model_management
 
 from collections import namedtuple
+from ldm_patched.contrib.external_align_your_steps import AlignYourStepsScheduler
 from ldm_patched.contrib.external_custom_sampler import SDTurboScheduler
 from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
 from ldm_patched.modules.samplers import normal_scheduler, simple_scheduler, ddim_scheduler
@@ -175,6 +176,9 @@ def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps):
         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]
+    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]
     else:
         raise TypeError("error invalid scheduler")
     return sigmas

modules/sdxl_styles.py

@@ -3,13 +3,11 @@ import re
 import json
 import math
 
-from modules.util import get_files_from_folder
-
+from modules.extra_utils 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/'))
-wildcards_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../wildcards/'))
-wildcards_max_bfs_depth = 64
 
 
 def normalize_key(k):
@@ -25,7 +23,6 @@ def normalize_key(k):
 
 
 styles = {}
-
 styles_files = get_files_from_folder(styles_path, ['.json'])
 
 for x in ['sdxl_styles_fooocus.json',
@@ -51,8 +48,13 @@ for styles_file in styles_files:
         print(f'Failed to load style file {styles_file}')
 
 style_keys = list(styles.keys())
-fooocus_expansion = "Fooocus V2"
-legal_style_names = [fooocus_expansion] + style_keys
+fooocus_expansion = 'Fooocus V2'
+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):
@@ -60,30 +62,8 @@ def apply_style(style, positive):
     return p.replace('{prompt}', positive).splitlines(), n.splitlines()
 
 
-def apply_wildcards(wildcard_text, rng, directory=wildcards_path):
-    for _ in range(wildcards_max_bfs_depth):
-        placeholders = re.findall(r'__([\w-]+)__', wildcard_text)
-        if len(placeholders) == 0:
-            return wildcard_text
-
-        print(f'[Wildcards] processing: {wildcard_text}')
-        for placeholder in placeholders:
-            try:
-                words = open(os.path.join(directory, f'{placeholder}.txt'), encoding='utf-8').read().splitlines()
-                words = [x for x in words if x != '']
-                assert len(words) > 0
-                wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1)
-            except:
-                print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. '
-                      f'Using "{placeholder}" as a normal word.')
-                wildcard_text = wildcard_text.replace(f'__{placeholder}__', placeholder)
-            print(f'[Wildcards] {wildcard_text}')
-
-    print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')
-    return wildcard_text
-
-def get_words(arrays, totalMult, index):
-    if(len(arrays) == 1):
+def get_words(arrays, total_mult, index):
+    if len(arrays) == 1:
         return [arrays[0].split(',')[index]]
     else:
         words = arrays[0].split(',')
@@ -91,12 +71,11 @@ def get_words(arrays, totalMult, index):
         index -= index % len(words)
         index /= len(words)
         index = math.floor(index)
-        return [word] + get_words(arrays[1:], math.floor(totalMult/len(words)), index)
-
+        return [word] + get_words(arrays[1:], math.floor(total_mult / len(words)), index)
 
 
 def apply_arrays(text, index):
-    arrays = re.findall(r'\[\[([\s,\w-]+)\]\]', text)
+    arrays = re.findall(r'\[\[(.*?)\]\]', text)
     if len(arrays) == 0:
         return text
 

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

@@ -1,21 +1,30 @@
-import typing
-
 import numpy as np
 import datetime
 import random
 import math
 import os
 import cv2
+import re
+from typing import List, Tuple, AnyStr, NamedTuple
+
 import json
+import hashlib
 
 from PIL import Image
-from hashlib import sha256
 
 import modules.sdxl_styles
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+
+
+# Regexp compiled once. Matches entries with the following pattern:
+# <lora:some_lora:1>
+# <lora:aNotherLora:-1.6>
+LORAS_PROMPT_PATTERN = re.compile(r".* <lora : ([^:]+) : ([+-]? (?: (?:\d+ (?:\.\d*)?) | (?:\.\d+)))> .*", re.X)
+
 HASH_SHA256_LENGTH = 10
 
+
 def erode_or_dilate(x, k):
     k = int(k)
     if k > 0:
@@ -163,35 +172,44 @@ def generate_temp_filename(folder='./outputs/', extension='png'):
     return date_string, os.path.abspath(result), filename
 
 
-def get_files_from_folder(folder_path, exensions=None, name_filter=None):
-    if not os.path.isdir(folder_path):
-        raise ValueError("Folder path is not a valid directory.")
+def sha256(filename, use_addnet_hash=False, length=HASH_SHA256_LENGTH):
+    print(f"Calculating sha256 for {filename}: ", end='')
+    if use_addnet_hash:
+        with open(filename, "rb") as file:
+            sha256_value = addnet_hash_safetensors(file)
+    else:
+        sha256_value = calculate_sha256(filename)
+    print(f"{sha256_value}")
 
-    filenames = []
-
-    for root, dirs, files in os.walk(folder_path, topdown=False):
-        relative_path = os.path.relpath(root, folder_path)
-        if relative_path == ".":
-            relative_path = ""
-        for filename in sorted(files, key=lambda s: s.casefold()):
-            _, file_extension = os.path.splitext(filename)
-            if (exensions is None or file_extension.lower() in exensions) and (name_filter is None or name_filter in _):
-                path = os.path.join(relative_path, filename)
-                filenames.append(path)
-
-    return filenames
+    return sha256_value[:length] if length is not None else sha256_value
 
 
-def calculate_sha256(filename, length=HASH_SHA256_LENGTH) -> str:
-    hash_sha256 = sha256()
+def addnet_hash_safetensors(b):
+    """kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py"""
+    hash_sha256 = hashlib.sha256()
+    blksize = 1024 * 1024
+
+    b.seek(0)
+    header = b.read(8)
+    n = int.from_bytes(header, "little")
+
+    offset = n + 8
+    b.seek(offset)
+    for chunk in iter(lambda: b.read(blksize), b""):
+        hash_sha256.update(chunk)
+
+    return hash_sha256.hexdigest()
+
+
+def calculate_sha256(filename) -> str:
+    hash_sha256 = hashlib.sha256()
     blksize = 1024 * 1024
 
     with open(filename, "rb") as f:
         for chunk in iter(lambda: f.read(blksize), b""):
             hash_sha256.update(chunk)
 
-    res = hash_sha256.hexdigest()
-    return res[:length] if length else res
+    return hash_sha256.hexdigest()
 
 
 def quote(text):
@@ -327,7 +345,7 @@ def extract_styles_from_prompt(prompt, negative_prompt):
     return list(reversed(extracted)), real_prompt, negative_prompt
 
 
-class PromptStyle(typing.NamedTuple):
+class PromptStyle(NamedTuple):
     name: str
     prompt: str
     negative_prompt: str
@@ -343,6 +361,9 @@ 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):
@@ -360,3 +381,50 @@ def makedirs_with_log(path):
         os.makedirs(path, exist_ok=True)
     except OSError as error:
         print(f'Directory {path} could not be created, reason: {error}')
+
+
+def get_enabled_loras(loras: list) -> list:
+    return [(lora[1], lora[2]) for lora in loras if lora[0]]
+
+
+def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5) -> List[Tuple[AnyStr, float]]:
+    new_loras = []
+    updated_loras = []
+    for token in prompt.split(","):
+        m = LORAS_PROMPT_PATTERN.match(token)
+
+        if m:
+            new_loras.append((f"{m.group(1)}.safetensors", float(m.group(2))))
+
+    for lora in loras + new_loras:
+        if lora[0] != "None":
+            updated_loras.append(lora)
+
+    return updated_loras[:loras_limit]
+
+
+def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order) -> str:
+    for _ in range(modules.config.wildcards_max_bfs_depth):
+        placeholders = re.findall(r'__([\w-]+)__', wildcard_text)
+        if len(placeholders) == 0:
+            return wildcard_text
+
+        print(f'[Wildcards] processing: {wildcard_text}')
+        for placeholder in placeholders:
+            try:
+                matches = [x for x in modules.config.wildcard_filenames if os.path.splitext(os.path.basename(x))[0] == placeholder]
+                words = open(os.path.join(modules.config.path_wildcards, matches[0]), encoding='utf-8').read().splitlines()
+                words = [x for x in words if x != '']
+                assert len(words) > 0
+                if read_wildcards_in_order:
+                    wildcard_text = wildcard_text.replace(f'__{placeholder}__', words[i % len(words)], 1)
+                else:
+                    wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1)
+            except:
+                print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. '
+                      f'Using "{placeholder}" as a normal word.')
+                wildcard_text = wildcard_text.replace(f'__{placeholder}__', placeholder)
+            print(f'[Wildcards] {wildcard_text}')
+
+    print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')
+    return wildcard_text

presets/.gitignore

@@ -0,0 +1,6 @@
+*.json
+!anime.json
+!default.json
+!lcm.json
+!realistic.json
+!sai.json

presets/anime.json

@@ -1,46 +1,57 @@
 {
-    "default_model": "animaPencilXL_v100.safetensors",
+    "default_model": "animaPencilXL_v310.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": 7.0,
+    "default_cfg_scale": 6.0,
     "default_sample_sharpness": 2.0,
     "default_sampler": "dpmpp_2m_sde_gpu",
     "default_scheduler": "karras",
     "default_performance": "Speed",
-    "default_prompt": "1girl, ",
+    "default_prompt": "",
     "default_prompt_negative": "",
     "default_styles": [
         "Fooocus V2",
-        "Fooocus Negative",
+        "Fooocus Semi Realistic",
         "Fooocus Masterpiece"
     ],
     "default_aspect_ratio": "896*1152",
     "checkpoint_downloads": {
-        "animaPencilXL_v100.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/animaPencilXL_v100.safetensors"
+        "animaPencilXL_v310.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/animaPencilXL_v310.safetensors"
     },
     "embeddings_downloads": {},
     "lora_downloads": {},
-    "previous_default_models": []
+    "previous_default_models": [
+        "animaPencilXL_v300.safetensors",
+        "animaPencilXL_v260.safetensors",
+        "animaPencilXL_v210.safetensors",
+        "animaPencilXL_v200.safetensors",
+        "animaPencilXL_v100.safetensors"
+    ]
 }

presets/default.json

@@ -4,22 +4,27 @@
     "default_refiner_switch": 0.5,
     "default_loras": [
         [
+            true,
             "sd_xl_offset_example-lora_1.0.safetensors",
             0.1
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ]

presets/lcm.json

@@ -4,22 +4,27 @@
     "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
         ]

presets/lightning.json

@@ -0,0 +1,57 @@
+{
+    "default_model": "juggernautXL_v8Rundiffusion.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": 4.0,
+    "default_sample_sharpness": 2.0,
+    "default_sampler": "dpmpp_2m_sde_gpu",
+    "default_scheduler": "karras",
+    "default_performance": "Lightning",
+    "default_prompt": "",
+    "default_prompt_negative": "",
+    "default_styles": [
+        "Fooocus V2",
+        "Fooocus Enhance",
+        "Fooocus Sharp"
+    ],
+    "default_aspect_ratio": "1152*896",
+    "checkpoint_downloads": {
+        "juggernautXL_v8Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_v8Rundiffusion.safetensors"
+    },
+    "embeddings_downloads": {},
+    "lora_downloads": {},
+    "previous_default_models": [
+        "juggernautXL_version8Rundiffusion.safetensors",
+        "juggernautXL_version7Rundiffusion.safetensors",
+        "juggernautXL_v7Rundiffusion.safetensors",
+        "juggernautXL_version6Rundiffusion.safetensors",
+        "juggernautXL_v6Rundiffusion.safetensors"
+    ]
+}

presets/realistic.json

@@ -1,25 +1,30 @@
 {
     "default_model": "realisticStockPhoto_v20.safetensors",
-    "default_refiner": "",
+    "default_refiner": "None",
     "default_refiner_switch": 0.5,
     "default_loras": [
         [
+            true,
             "SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors",
             0.25
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ]

presets/sai.json

@@ -4,22 +4,27 @@
     "default_refiner_switch": 0.75,
     "default_loras": [
         [
+            true,
             "sd_xl_offset_example-lora_1.0.safetensors",
             0.5
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ]

readme.md

@@ -84,6 +84,10 @@ The first time you launch the software, it will automatically download models:
 
 After Fooocus 2.1.60, you will also have `run_anime.bat` and `run_realistic.bat`. They are different model presets (and require different models, but they will be automatically downloaded). [Check here for more details](https://github.com/lllyasviel/Fooocus/discussions/679).
 
+After Fooocus 2.3.0 you can also switch presets directly in the browser. Keep in mind to add these arguments if you want to change the default behavior:
+* Use `--disable-preset-selection` to disable preset selection in the browser.
+* Use `--always-download-new-model` to download missing models on preset switch. Default is fallback to `previous_default_models` defined in the corresponding preset, also see terminal output.
+
 ![image](https://github.com/lllyasviel/Fooocus/assets/19834515/d386f817-4bd7-490c-ad89-c1e228c23447)
 
 If you already have these files, you can copy them to the above locations to speed up installation.
@@ -115,17 +119,21 @@ See also the common problems and troubleshoots [here](troubleshoot.md).
 
 ### Colab
 
-(Last tested - 2023 Dec 12)
+(Last tested - 2024 Mar 18 by [mashb1t](https://github.com/mashb1t))
 
 | Colab | Info
 | --- | --- |
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lllyasviel/Fooocus/blob/main/fooocus_colab.ipynb) | Fooocus Official
 
-In Colab, you can modify the last line to `!python entry_with_update.py --share` or `!python entry_with_update.py --preset anime --share` or `!python entry_with_update.py --preset realistic --share` for Fooocus Default/Anime/Realistic Edition.
+In Colab, you can modify the last line to `!python entry_with_update.py --share --always-high-vram` or `!python entry_with_update.py --share --always-high-vram --preset anime` or `!python entry_with_update.py --share --always-high-vram --preset realistic` for Fooocus Default/Anime/Realistic Edition.
+
+You can also change the preset in the UI. Please be aware that this may lead to timeouts after 60 seconds. If this is the case, please wait until the download has finished, change the preset to initial and back to the one you've selected or reload the page.
 
 Note that this Colab will disable refiner by default because Colab free's resources are relatively limited (and some "big" features like image prompt may cause free-tier Colab to disconnect). We make sure that basic text-to-image is always working on free-tier Colab.
 
-Thanks to [camenduru](https://github.com/camenduru)!
+Using `--always-high-vram` shifts resource allocation from RAM to VRAM and achieves the overall best balance between performance, flexibility and stability on the default T4 instance. Please find more information [here](https://github.com/lllyasviel/Fooocus/pull/1710#issuecomment-1989185346).
+
+Thanks to [camenduru](https://github.com/camenduru) for the template!
 
 ### Linux (Using Anaconda)
 
@@ -360,6 +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]

sdxl_styles/sdxl_styles_fooocus.json

@@ -3,6 +3,10 @@
         "name": "Fooocus Enhance",
         "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)"
     },
+    {
+        "name": "Fooocus Semi Realistic",
+        "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)"
+    },
     {
         "name": "Fooocus Sharp",
         "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy",

tests/__init__.py

@@ -0,0 +1,4 @@
+import sys
+import pathlib
+
+sys.path.append(pathlib.Path(f'{__file__}/../modules').parent.resolve())

tests/test_utils.py

@@ -0,0 +1,48 @@
+import unittest
+
+from modules import util
+
+
+class TestUtils(unittest.TestCase):
+    def test_can_parse_tokens_with_lora(self):
+        test_cases = [
+            {
+                "input": ("some prompt, very cool, <lora:hey-lora:0.4>,  cool   <lora:you-lora:0.2>", [], 5),
+                "output": [("hey-lora.safetensors", 0.4), ("you-lora.safetensors", 0.2)],
+            },
+            # Test can not exceed limit
+            {
+                "input": ("some prompt, very cool, <lora:hey-lora:0.4>,  cool   <lora:you-lora:0.2>", [], 1),
+                "output": [("hey-lora.safetensors", 0.4)],
+            },
+            # test Loras from UI take precedence over prompt
+            {
+                "input": (
+                    "some prompt, very cool, <lora:l1:0.4>, <lora:l2:-0.2>, <lora:l3:0.3>, <lora:l4:0.5>, <lora:l6:0.24>, <lora:l7:0.1>",
+                    [("hey-lora.safetensors", 0.4)],
+                    5,
+                ),
+                "output": [
+                    ("hey-lora.safetensors", 0.4),
+                    ("l1.safetensors", 0.4),
+                    ("l2.safetensors", -0.2),
+                    ("l3.safetensors", 0.3),
+                    ("l4.safetensors", 0.5),
+                ],
+            },
+            # Test lora specification not separated by comma are ignored, only latest specified is used
+            {
+                "input": ("some prompt, very cool, <lora:hey-lora:0.4><lora:you-lora:0.2>", [], 3),
+                "output": [("you-lora.safetensors", 0.2)],
+            },
+            {
+                "input": ("<lora:foo:1..2>, <lora:bar:.>, <lora:baz:+> and <lora:quux:>", [], 6),
+                "output": []
+            }
+        ]
+
+        for test in test_cases:
+            prompt, loras, loras_limit = test["input"]
+            expected = test["output"]
+            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit)
+            self.assertEqual(expected, actual)

update_log.md

@@ -1,3 +1,27 @@
+# [2.3.1](https://github.com/lllyasviel/Fooocus/releases/tag/2.3.1)
+
+* Remove positive prompt from anime prefix to not reset prompt after switching presets
+* Fix image number being reset to 1 when switching preset, now doesn't reset anymore
+* Fix outpainting dimension calculation when extending left/right
+* Fix LoRA compatibility for LoRAs in a1111 metadata scheme
+
+# [2.3.0](https://github.com/lllyasviel/Fooocus/releases/tag/2.3.0)
+
+* Add performance "lightning" (based on [SDXL-Lightning 4 step LoRA](https://huggingface.co/ByteDance/SDXL-Lightning/blob/main/sdxl_lightning_4step_lora.safetensors))
+* Add preset selection to UI, disable with argument `--disable-preset-selection`. Use `--always-download-new-model` to download missing models on preset switch.
+* Improve face swap consistency by switching later in the process to (synthetic) refiner
+* Add temp path cleanup on startup
+* Add support for wildcard subdirectories
+* Add scrollable 2 column layout for styles for better structure
+* Improve Colab resource needs for T4 instances (default), positively tested with all image prompt features
+* Improve anime preset, now uses style `Fooocus Semi Realistic` instead of `Fooocus Negative` (less wet look images)
+
+# [2.2.1](https://github.com/lllyasviel/Fooocus/releases/tag/2.2.1)
+
+* Fix some small bugs (e.g. image grid, upscale fast 2x, LoRA weight width in Firefox)
+* Allow prompt weights in array syntax
+* Add steps override and metadata scheme to history log
+
 # [2.2.0](https://github.com/lllyasviel/Fooocus/releases/tag/2.2.0)
 
 * Isolate every image generation to truly allow multi-user usage

webui.py

@@ -15,6 +15,7 @@ import modules.style_sorter as style_sorter
 import modules.meta_parser
 import args_manager
 import copy
+import launch
 
 from modules.sdxl_styles import legal_style_names
 from modules.private_logger import get_current_html_path
@@ -28,12 +29,16 @@ def get_task(*args):
 
     return worker.AsyncTask(args=args)
 
-def generate_clicked(task):
+def generate_clicked(task: worker.AsyncTask):
     import ldm_patched.modules.model_management as model_management
 
     with model_management.interrupt_processing_mutex:
         model_management.interrupt_processing = False
     # outputs=[progress_html, progress_window, progress_gallery, gallery]
+
+    if len(task.args) == 0:
+        return
+
     execution_start_time = time.perf_counter()
     finished = False
 
@@ -76,7 +81,8 @@ def generate_clicked(task):
                 # delete Fooocus temp images, only keep gradio temp images
                 if args_manager.args.disable_image_log:
                     for filepath in product:
-                        os.remove(filepath)
+                        if isinstance(filepath, str) and os.path.exists(filepath):
+                            os.remove(filepath)
 
     execution_time = time.perf_counter() - execution_start_time
     print(f'Total time: {execution_time:.2f} seconds')
@@ -90,9 +96,7 @@ title = f'Fooocus {fooocus_version.version}'
 if isinstance(args_manager.args.preset, str):
     title += ' ' + args_manager.args.preset
 
-shared.gradio_root = gr.Blocks(
-    title=title,
-    css=modules.html.css).queue()
+shared.gradio_root = gr.Blocks(title=title).queue()
 
 with shared.gradio_root:
     currentTask = gr.State(worker.AsyncTask(args=[]))
@@ -119,8 +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):
@@ -253,8 +258,13 @@ with shared.gradio_root:
 
         with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column:
             with gr.Tab(label='Setting'):
+                if not args_manager.args.disable_preset_selection:
+                    preset_selection = gr.Radio(label='Preset',
+                                                choices=modules.config.available_presets,
+                                                value=args_manager.args.preset if args_manager.args.preset else "initial",
+                                                interactive=True)
                 performance_selection = gr.Radio(label='Performance',
-                                                 choices=modules.flags.performance_selections,
+                                                 choices=flags.Performance.list(),
                                                  value=modules.config.default_performance)
                 aspect_ratios_selection = gr.Radio(label='Aspect Ratios', choices=modules.config.available_aspect_ratios,
                                                    value=modules.config.default_aspect_ratio, info='width × height',
@@ -262,7 +272,7 @@ with shared.gradio_root:
                 image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number)
 
                 output_format = gr.Radio(label='Output Format',
-                                            choices=modules.flags.output_formats,
+                                            choices=flags.OutputFormat.list(),
                                             value=modules.config.default_output_format)
 
                 negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.",
@@ -299,7 +309,7 @@ with shared.gradio_root:
                 history_link = gr.HTML()
                 shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False)
 
-            with gr.Tab(label='Style'):
+            with gr.Tab(label='Style', elem_classes=['style_selections_tab']):
                 style_sorter.try_load_sorted_styles(
                     style_names=legal_style_names,
                     default_selected=modules.config.default_styles)
@@ -352,20 +362,20 @@ with shared.gradio_root:
                 with gr.Group():
                     lora_ctrls = []
 
-                    for i, (n, v) in enumerate(modules.config.default_loras):
+                    for i, (enabled, filename, weight) in enumerate(modules.config.default_loras):
                         with gr.Row():
-                            lora_enabled = gr.Checkbox(label='Enable', value=True,
-                                                       elem_classes=['lora_enable', 'min_check'])
+                            lora_enabled = gr.Checkbox(label='Enable', value=enabled,
+                                                       elem_classes=['lora_enable', 'min_check'], scale=1)
                             lora_model = gr.Dropdown(label=f'LoRA {i + 1}',
-                                                     choices=['None'] + modules.config.lora_filenames, value=n,
-                                                     elem_classes='lora_model')
+                                                     choices=['None'] + modules.config.lora_filenames, value=filename,
+                                                     elem_classes='lora_model', scale=5)
                             lora_weight = gr.Slider(label='Weight', minimum=modules.config.default_loras_min_weight,
-                                                    maximum=modules.config.default_loras_max_weight, step=0.01, value=v,
-                                                    elem_classes='lora_weight')
+                                                    maximum=modules.config.default_loras_max_weight, step=0.01, value=weight,
+                                                    elem_classes='lora_weight', scale=5)
                             lora_ctrls += [lora_enabled, lora_model, lora_weight]
 
                 with gr.Row():
-                    model_refresh = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button')
+                    refresh_files = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button')
             with gr.Tab(label='Advanced'):
                 guidance_scale = gr.Slider(label='Guidance Scale', minimum=1.0, maximum=30.0, step=0.01,
                                            value=modules.config.default_cfg_scale,
@@ -397,6 +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.',
@@ -424,21 +436,32 @@ with shared.gradio_root:
                         overwrite_upscale_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of "Upscale"',
                                                                minimum=-1, maximum=1.0, step=0.001, value=-1,
                                                                info='Set as negative number to disable. For developer debugging.')
-                        disable_preview = gr.Checkbox(label='Disable Preview', value=False,
+                        disable_preview = gr.Checkbox(label='Disable Preview', value=modules.config.default_black_out_nsfw,
+                                                      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 == 'Extreme Speed',
-                                                      interactive=modules.config.default_performance != 'Extreme Speed',
+                                                      value=modules.config.default_performance == flags.Performance.EXTREME_SPEED.value,
+                                                      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.',
                                                              value=False)
+                        read_wildcards_in_order = gr.Checkbox(label="Read wildcards in order", value=False)
+
+                        black_out_nsfw = gr.Checkbox(label='Black Out NSFW',
+                                                     value=modules.config.default_black_out_nsfw,
+                                                     interactive=not modules.config.default_black_out_nsfw,
+                                                     info='Use black image if NSFW is detected.')
+
+                        black_out_nsfw.change(lambda x: gr.update(value=x, interactive=not x),
+                                              inputs=black_out_nsfw, outputs=disable_preview, queue=False,
+                                              show_progress=False)
 
                         if not args_manager.args.disable_metadata:
                             save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=modules.config.default_save_metadata_to_images,
                                                                   info='Adds parameters to generated images allowing manual regeneration.')
                             metadata_scheme = gr.Radio(label='Metadata Scheme', choices=flags.metadata_scheme, value=modules.config.default_metadata_scheme,
-                                                       info='Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.',
+                                                       info='Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.',
                                                        visible=modules.config.default_save_metadata_to_images)
 
                             save_metadata_to_images.change(lambda x: gr.update(visible=x), inputs=[save_metadata_to_images], outputs=[metadata_scheme], 
@@ -511,24 +534,61 @@ with shared.gradio_root:
                 def dev_mode_checked(r):
                     return gr.update(visible=r)
 
-
                 dev_mode.change(dev_mode_checked, inputs=[dev_mode], outputs=[dev_tools],
                                 queue=False, show_progress=False)
 
-                def model_refresh_clicked():
-                    modules.config.update_all_model_names()
+                def refresh_files_clicked():
+                    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):
-                        results += [gr.update(interactive=True), gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
+                        results += [gr.update(interactive=True),
+                                    gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
                     return results
 
-                model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls,
+                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,
                                     queue=False, show_progress=False)
 
-        performance_selection.change(lambda x: [gr.update(interactive=x != 'Extreme Speed')] * 11 +
-                                               [gr.update(visible=x != 'Extreme Speed')] * 1 +
-                                               [gr.update(interactive=x != 'Extreme Speed', value=x == 'Extreme Speed', )] * 1,
+        state_is_generating = gr.State(False)
+
+        load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,
+                             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, vae_name, seed_random,
+                             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):
+                preset_content = modules.config.try_get_preset_content(preset) if preset != 'initial' else {}
+                preset_prepared = modules.meta_parser.parse_meta_from_preset(preset_content)
+
+                default_model = preset_prepared.get('base_model')
+                previous_default_models = preset_prepared.get('previous_default_models', [])
+                checkpoint_downloads = preset_prepared.get('checkpoint_downloads', {})
+                embeddings_downloads = preset_prepared.get('embeddings_downloads', {})
+                lora_downloads = preset_prepared.get('lora_downloads', {})
+
+                preset_prepared['base_model'], preset_prepared['lora_downloads'] = launch.download_models(
+                    default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads)
+
+                if 'prompt' in preset_prepared and preset_prepared.get('prompt') == '':
+                    del preset_prepared['prompt']
+
+                return modules.meta_parser.load_parameter_button_click(json.dumps(preset_prepared), is_generating)
+
+            preset_selection.change(preset_selection_change, inputs=[preset_selection, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
+                .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \
+
+        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,
                                      inputs=performance_selection,
                                      outputs=[
                                          guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive,
@@ -578,16 +638,17 @@ with shared.gradio_root:
         ctrls = [currentTask, generate_image_grid]
         ctrls += [
             prompt, negative_prompt, style_selections,
-            performance_selection, aspect_ratios_selection, image_number, output_format, image_seed, sharpness, guidance_scale
+            performance_selection, aspect_ratios_selection, image_number, output_format, image_seed,
+            read_wildcards_in_order, sharpness, guidance_scale
         ]
 
         ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls
         ctrls += [input_image_checkbox, current_tab]
         ctrls += [uov_method, uov_input_image]
         ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image]
-        ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment]
+        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]
@@ -600,8 +661,6 @@ with shared.gradio_root:
 
         ctrls += ip_ctrls
 
-        state_is_generating = gr.State(False)
-
         def parse_meta(raw_prompt_txt, is_generating):
             loaded_json = None
             if is_json(raw_prompt_txt):
@@ -617,13 +676,6 @@ with shared.gradio_root:
 
         prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False)
 
-        load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,
-                             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,
-                             generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
-
         load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False)
 
         def trigger_metadata_import(filepath, state_is_generating):
@@ -637,7 +689,6 @@ with shared.gradio_root:
 
             return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)
 
-
         metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
             .then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False)
 
@@ -651,6 +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)
@@ -668,6 +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

wildcards/animal.txt

@@ -18,7 +18,7 @@ Chihuahua
 Chimpanzee
 Chinchilla
 Chipmunk
-Comodo Dragon
+Komodo Dragon
 Cow
 Coyote
 Crocodile
@@ -97,4 +97,4 @@ Whale
 Wolf
 Wombat
 Yak
-Zebra
+Zebra