Merge branch 'main_upstream' into feature/update-anime-preset

2024-06-30 14:32:57 +02:00 · 2024-06-30 14:32:57 +02:00 · 8e13eae7ec
parent c908b0083b 9178aa8ebb
commit 8e13eae7ec
57 changed files with 2086 additions and 387 deletions
--- a/.dockerignore
+++ b/.dockerignore
@ -1 +1,54 @@
-.idea
+__pycache__
+*.ckpt
+*.safetensors
+*.pth
+*.pt
+*.bin
+*.patch
+*.backup
+*.corrupted
+*.partial
+*.onnx
+sorted_styles.json
+/input
+/cache
+/language/default.json
+/test_imgs
+config.txt
+config_modification_tutorial.txt
+user_path_config.txt
+user_path_config-deprecated.txt
+/modules/*.png
+/repositories
+/fooocus_env
+/venv
+/tmp
+/ui-config.json
+/outputs
+/config.json
+/log
+/webui.settings.bat
+/embeddings
+/styles.csv
+/params.txt
+/styles.csv.bak
+/webui-user.bat
+/webui-user.sh
+/interrogate
+/user.css
+/.idea
+/notification.ogg
+/notification.mp3
+/SwinIR
+/textual_inversion
+.vscode
+/extensions
+/test/stdout.txt
+/test/stderr.txt
+/cache.json*
+/config_states/
+/node_modules
+/package-lock.json
+/.coverage*
+/auth.json
+.DS_Store
--- a/.gitattributes
+++ b/.gitattributes
@ -0,0 +1,3 @@
+# Ensure that shell scripts always use lf line endings, e.g. entrypoint.sh for docker
+* text=auto
+*.sh text eol=lf
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@ -1 +1 @@
-*       @lllyasviel
+*       @mashb1t
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@ -0,0 +1,6 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "monthly"
--- a/.github/workflows/build_container.yml
+++ b/.github/workflows/build_container.yml
@ -0,0 +1,47 @@
+name: Docker image build
+
+on:
+  push:
+    branches:
+      - main
+    tags:
+      - v*
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ghcr.io/${{ github.repository_owner }}/${{ github.event.repository.name }}
+          tags: |
+            type=semver,pattern={{version}}
+            type=semver,pattern={{major}}.{{minor}}
+            type=semver,pattern={{major}}
+            type=edge,branch=main
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: ./Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
--- a/4
+++ b/4
@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.3.1-base-ubuntu22.04
+FROM nvidia/cuda:12.4.1-base-ubuntu22.04
 ENV DEBIAN_FRONTEND noninteractive
 ENV CMDARGS --listen

@ -23,7 +23,7 @@ RUN chown -R user:user /content
 WORKDIR /content
 USER user

-RUN git clone https://github.com/lllyasviel/Fooocus /content/app
+COPY --chown=user:user . /content/app
 RUN mv /content/app/models /content/app/models.org

 CMD [ "sh", "-c", "/content/entrypoint.sh ${CMDARGS}" ]
--- a/args_manager.py
+++ b/args_manager.py
@ -1,7 +1,4 @@
 import ldm_patched.modules.args_parser as args_parser
-import os
-
-from tempfile import gettempdir

 args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")

@ -31,6 +28,9 @@ 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)

--- a/css/style.css
+++ b/css/style.css
@ -27,6 +27,7 @@ progress {
  border-radius: 5px; /* Round the corners of the progress bar */
  background-color: #f3f3f3; /* Light grey background */
  width: 100%;
+  vertical-align: middle !important;
 }

 /* Style the progress bar container */
@ -69,30 +70,39 @@ progress::after {
  height: 30px !important;
 }

-.type_row{
-  height: 80px !important;
+.progress-bar span {
+    text-align: right;
+    width: 215px;
+}
+div:has(> #positive_prompt) {
+    border: none;
 }

-.type_row_half{
-  height: 32px !important;
+#positive_prompt {
+    padding: 1px;
+    background: var(--background-fill-primary);
 }

-.scroll-hide{
-  resize: none !important;
+.type_row {
+  height: 84px !important;
 }

-.refresh_button{
+.type_row_half {
+  height: 34px !important;
+}
+
+.refresh_button {
  border: none !important;
  background: none !important;
  font-size: none !important;
  box-shadow: none !important;
 }

-.advanced_check_row{
+.advanced_check_row {
  width: 250px !important;
 }

-.min_check{
+.min_check {
  min-width: min(1px, 100%) !important;
 }

@ -101,10 +111,14 @@ progress::after {
  overflow: auto !important;
 }

-.aspect_ratios label {
+.performance_selection label {
    width: 140px !important;
 }

+.aspect_ratios label {
+    flex: calc(50% - 5px) !important;
+}
+
 .aspect_ratios label span {
    white-space: nowrap !important;
 }
@ -391,6 +405,14 @@ progress::after {
  background-color: #fff8;
  font-family: monospace;
  text-align: center;
-  border-radius-top: 5px;
+  border-radius: 5px 5px 0px 0px;
  display: none; /* remove this to enable tooltip in preview image */
+}
+
+#inpaint_canvas .canvas-tooltip-info {
+  top: 2px;
+}
+
+#inpaint_brush_color input[type=color]{
+  background: none;
 }
--- a/development.md
+++ b/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
+```
--- a/docker-compose.yml
+++ b/docker-compose.yml
@ -1,12 +1,10 @@
-version: '3.9'
-
 volumes:
  fooocus-data:

 services:
  app:
    build: .
-    image: fooocus
+    image: ghcr.io/lllyasviel/fooocus
    ports:
     - "7865:7865"
    environment:
--- a/docker.md
+++ b/docker.md
@ -1,35 +1,99 @@
 # Fooocus on Docker

-The docker image is based on NVIDIA CUDA 12.3 and PyTorch 2.0, see [Dockerfile](Dockerfile) and [requirements_docker.txt](requirements_docker.txt) for details.
+The docker image is based on NVIDIA CUDA 12.4 and PyTorch 2.1, see [Dockerfile](Dockerfile) and [requirements_docker.txt](requirements_docker.txt) for details.
+
+## Requirements
+
+- A computer with specs good enough to run Fooocus, and proprietary Nvidia drivers
+- Docker, Docker Compose, or Podman

 ## Quick start

-**This is just an easy way for testing. Please find more information in the [notes](#notes).**
+**More information in the [notes](#notes).**
+
+### Running with Docker Compose

 1. Clone this repository
-2. Build the image with `docker compose build`
-3. Run the docker container with `docker compose up`. Building the image takes some time.
+2. Run the docker container with `docker compose up`.
+
+### Running with Docker
+
+```sh
+docker run -p 7865:7865 -v fooocus-data:/content/data -it \
+--gpus all \
+-e CMDARGS=--listen \
+-e DATADIR=/content/data \
+-e config_path=/content/data/config.txt \
+-e config_example_path=/content/data/config_modification_tutorial.txt \
+-e path_checkpoints=/content/data/models/checkpoints/ \
+-e path_loras=/content/data/models/loras/ \
+-e path_embeddings=/content/data/models/embeddings/ \
+-e path_vae_approx=/content/data/models/vae_approx/ \
+-e path_upscale_models=/content/data/models/upscale_models/ \
+-e path_inpaint=/content/data/models/inpaint/ \
+-e path_controlnet=/content/data/models/controlnet/ \
+-e path_clip_vision=/content/data/models/clip_vision/ \
+-e path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ \
+-e path_outputs=/content/app/outputs/ \
+ghcr.io/lllyasviel/fooocus
+```
+### Running with Podman
+
+```sh
+podman run -p 7865:7865 -v fooocus-data:/content/data -it \
+--security-opt=no-new-privileges --cap-drop=ALL --security-opt label=type:nvidia_container_t --device=nvidia.com/gpu=all \
+-e CMDARGS=--listen \
+-e DATADIR=/content/data \
+-e config_path=/content/data/config.txt \
+-e config_example_path=/content/data/config_modification_tutorial.txt \
+-e path_checkpoints=/content/data/models/checkpoints/ \
+-e path_loras=/content/data/models/loras/ \
+-e path_embeddings=/content/data/models/embeddings/ \
+-e path_vae_approx=/content/data/models/vae_approx/ \
+-e path_upscale_models=/content/data/models/upscale_models/ \
+-e path_inpaint=/content/data/models/inpaint/ \
+-e path_controlnet=/content/data/models/controlnet/ \
+-e path_clip_vision=/content/data/models/clip_vision/ \
+-e path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ \
+-e path_outputs=/content/app/outputs/ \
+ghcr.io/lllyasviel/fooocus
+```

 When you see the message  `Use the app with http://0.0.0.0:7865/` in the console, you can access the URL in your browser.

-Your models and outputs are stored in the `fooocus-data` volume, which, depending on OS, is stored in `/var/lib/docker/volumes`.
+Your models and outputs are stored in the `fooocus-data` volume, which, depending on OS, is stored in `/var/lib/docker/volumes/` (or `~/.local/share/containers/storage/volumes/` when using `podman`).
+
+## Building the container locally
+
+Clone the repository first, and open a terminal in the folder.
+
+Build with `docker`:
+```sh
+docker build . -t fooocus
+```
+
+Build with `podman`:
+```sh
+podman build . -t fooocus
+```

 ## Details

-### Update the container manually
+### Update the container manually (`docker compose`)

 When you are using `docker compose up` continuously, the container is not updated to the latest version of Fooocus automatically.
 Run `git pull` before executing `docker compose build --no-cache` to build an image with the latest Fooocus version.
 You can then start it with `docker compose up`

 ### Import models, outputs
-If you want to import files from models or the outputs folder, you can uncomment the following settings in the [docker-compose.yml](docker-compose.yml):
+
+If you want to import files from models or the outputs folder, you can add the following bind mounts in the [docker-compose.yml](docker-compose.yml) or your preferred method of running the container:
 ```
 #- ./models:/import/models   # Once you import files, you don't need to mount again.
 #- ./outputs:/import/outputs  # Once you import files, you don't need to mount again.
 ```
-After running `docker compose up`, your files will be copied into `/content/data/models` and `/content/data/outputs`
-Since `/content/data` is a persistent volume folder, your files will be persisted even when you re-run `docker compose up --build` without above volume settings.
+After running the container, your files will be copied into `/content/data/models` and `/content/data/outputs`
+Since `/content/data` is a persistent volume folder, your files will be persisted even when you re-run the container without the above mounts.


 ### Paths inside the container
@ -54,6 +118,7 @@ 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)
--- a/extras/censor.py
+++ b/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
--- a/extras/safety_checker/configs/config.json
+++ b/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
+  }
+}
--- a/extras/safety_checker/configs/preprocessor_config.json
+++ b/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
+}
--- a/extras/safety_checker/models/safety_checker.py
+++ b/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
--- a/extras/vae_interpose.py
+++ b/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)
--- a/fooocus_version.py
+++ b/fooocus_version.py
@ -1 +1 @@
-version = '2.3.1'
+version = '2.4.3'
--- a/javascript/localization.js
+++ b/javascript/localization.js
@ -80,6 +80,15 @@ function refresh_style_localization() {
    processNode(document.querySelector('.style_selections'));
 }

+function refresh_aspect_ratios_label(value) {
+    label = document.querySelector('#aspect_ratios_accordion div span');
+    translation = getTranslation("Aspect Ratios");
+    if (typeof translation == "undefined") {
+        translation = "Aspect Ratios";
+    }
+    label.textContent = translation + " " + htmlDecode(value);
+}
+
 function localizeWholePage() {
    processNode(gradioApp());

--- a/javascript/script.js
+++ b/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
 */
@ -219,3 +256,8 @@ function set_theme(theme) {
        window.location.replace(gradioURL + '?__theme=' + theme);
    }
 }
+
+function htmlDecode(input) {
+  var doc = new DOMParser().parseFromString(input, "text/html");
+  return doc.documentElement.textContent;
+}
--- a/language/en.json
+++ b/language/en.json
@ -4,12 +4,20 @@
    "Generate": "Generate",
    "Skip": "Skip",
    "Stop": "Stop",
+    "Reconnect": "Reconnect",
    "Input Image": "Input Image",
    "Advanced": "Advanced",
    "Upscale or Variation": "Upscale or Variation",
    "Image Prompt": "Image Prompt",
-    "Inpaint or Outpaint (beta)": "Inpaint or Outpaint (beta)",
-    "Drag above image to here": "Drag above image to here",
+    "Inpaint or Outpaint": "Inpaint or Outpaint",
+    "Outpaint Direction": "Outpaint Direction",
+    "Method": "Method",
+    "Describe": "Describe",
+    "Content Type": "Content Type",
+    "Photograph": "Photograph",
+    "Art/Anime": "Art/Anime",
+    "Describe this Image into Prompt": "Describe this Image into Prompt",
+    "Image Size and Recommended Size": "Image Size and Recommended Size",
    "Upscale or Variation:": "Upscale or Variation:",
    "Disabled": "Disabled",
    "Vary (Subtle)": "Vary (Subtle)",
@ -54,9 +62,12 @@
    "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",
@ -309,6 +320,8 @@
    "vae": "vae",
    "CFG Mimicking from TSNR": "CFG Mimicking from TSNR",
    "Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).": "Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).",
+    "CLIP Skip": "CLIP Skip",
+    "Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).": "Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).",
    "Sampler": "Sampler",
    "dpmpp_2m_sde_gpu": "dpmpp_2m_sde_gpu",
    "Only effective in non-inpaint mode.": "Only effective in non-inpaint mode.",
@ -339,6 +352,8 @@
    "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",
@ -378,7 +393,7 @@
    "Fooocus Enhance": "Fooocus Enhance",
    "Fooocus Cinematic": "Fooocus Cinematic",
    "Fooocus Sharp": "Fooocus Sharp",
-    "Drag any image generated by Fooocus here": "Drag any image generated by Fooocus here",
+    "For images created by Fooocus": "For images created by Fooocus",
    "Metadata": "Metadata",
    "Apply Metadata": "Apply Metadata",
    "Metadata Scheme": "Metadata Scheme",
--- a/launch.py
+++ b/launch.py
@ -62,8 +62,8 @@ def prepare_environment():
 vae_approx_filenames = [
    ('xlvaeapp.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/xlvaeapp.pth'),
    ('vaeapp_sd15.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/vaeapp_sd15.pt'),
-    ('xl-to-v1_interposer-v3.1.safetensors',
-     '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')
 ]


@ -80,6 +80,10 @@ if args.gpu_device_id is not None:
    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id)
    print("Set device to:", args.gpu_device_id)

+if args.hf_mirror is not None : 
+    os.environ['HF_MIRROR'] = str(args.hf_mirror)
+    print("Set hf_mirror to:", args.hf_mirror)
+
 from modules import config

 os.environ['GRADIO_TEMP_DIR'] = config.temp_path
--- a/ldm_patched/contrib/external_align_your_steps.py
+++ b/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,
+}
--- a/ldm_patched/contrib/external_custom_sampler.py
+++ b/ldm_patched/contrib/external_custom_sampler.py
@ -107,8 +107,7 @@ class SDTurboScheduler:
    def get_sigmas(self, model, steps, denoise):
        start_step = 10 - int(10 * denoise)
        timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
-        ldm_patched.modules.model_management.load_models_gpu([model])
-        sigmas = model.model.model_sampling.sigma(timesteps)
+        sigmas = model.model_sampling.sigma(timesteps)
        sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
        return (sigmas, )

@ -230,6 +229,25 @@ 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 +310,7 @@ NODE_CLASS_MAPPINGS = {
    "KSamplerSelect": KSamplerSelect,
    "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
    "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
+    "SamplerTCD": SamplerTCD,
    "SplitSigmas": SplitSigmas,
    "FlipSigmas": FlipSigmas,
 }
--- a/ldm_patched/contrib/external_model_advanced.py
+++ b/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
@ -105,7 +108,7 @@ class ModelSamplingContinuousEDM:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
-                              "sampling": (["v_prediction", "eps"],),
+                              "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
                              "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                              "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                              }}
@ -118,17 +121,25 @@ class ModelSamplingContinuousEDM:
    def patch(self, model, sampling, sigma_max, sigma_min):
        m = model.clone()

+        latent_format = None
+        sigma_data = 1.0
        if sampling == "eps":
            sampling_type = ldm_patched.modules.model_sampling.EPS
        elif sampling == "v_prediction":
            sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION
+        elif sampling == "edm_playground_v2.5":
+            sampling_type = ldm_patched.modules.model_sampling.EDM
+            sigma_data = 0.5
+            latent_format = ldm_patched.modules.latent_formats.SDXL_Playground_2_5()

        class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type):
            pass

        model_sampling = ModelSamplingAdvanced(model.model.model_config)
-        model_sampling.set_sigma_range(sigma_min, sigma_max)
+        model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
        m.add_object_patch("model_sampling", model_sampling)
+        if latent_format is not None:
+            m.add_object_patch("latent_format", latent_format)
        return (m, )

 class RescaleCFG:
--- a/ldm_patched/k_diffusion/sampling.py
+++ b/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 +807,32 @@ 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()
+        else:
+            x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2)
+
+    return x
--- a/ldm_patched/modules/args_parser.py
+++ b/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)
--- a/ldm_patched/modules/latent_formats.py
+++ b/ldm_patched/modules/latent_formats.py
@ -1,3 +1,4 @@
+import torch

 class LatentFormat:
    scale_factor = 1.0
@ -34,6 +35,70 @@ class SDXL(LatentFormat):
                ]
        self.taesd_decoder_name = "taesdxl_decoder"

+class SDXL_Playground_2_5(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 0.5
+        self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)
+        self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)
+
+        self.latent_rgb_factors = [
+                    #   R        G        B
+                    [ 0.3920,  0.4054,  0.4549],
+                    [-0.2634, -0.0196,  0.0653],
+                    [ 0.0568,  0.1687, -0.0755],
+                    [-0.3112, -0.2359, -0.2076]
+                ]
+        self.taesd_decoder_name = "taesdxl_decoder"
+
+    def process_in(self, latent):
+        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+        latents_std = self.latents_std.to(latent.device, latent.dtype)
+        return (latent - latents_mean) * self.scale_factor / latents_std
+
+    def process_out(self, latent):
+        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+        latents_std = self.latents_std.to(latent.device, latent.dtype)
+        return latent * latents_std / self.scale_factor + latents_mean
+
+
 class SD_X4(LatentFormat):
    def __init__(self):
        self.scale_factor = 0.08333
+        self.latent_rgb_factors = [
+            [-0.2340, -0.3863, -0.3257],
+            [ 0.0994,  0.0885, -0.0908],
+            [-0.2833, -0.2349, -0.3741],
+            [ 0.2523, -0.0055, -0.1651]
+        ]
+
+class SC_Prior(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 1.0
+        self.latent_rgb_factors = [
+            [-0.0326, -0.0204, -0.0127],
+            [-0.1592, -0.0427,  0.0216],
+            [ 0.0873,  0.0638, -0.0020],
+            [-0.0602,  0.0442,  0.1304],
+            [ 0.0800, -0.0313, -0.1796],
+            [-0.0810, -0.0638, -0.1581],
+            [ 0.1791,  0.1180,  0.0967],
+            [ 0.0740,  0.1416,  0.0432],
+            [-0.1745, -0.1888, -0.1373],
+            [ 0.2412,  0.1577,  0.0928],
+            [ 0.1908,  0.0998,  0.0682],
+            [ 0.0209,  0.0365, -0.0092],
+            [ 0.0448, -0.0650, -0.1728],
+            [-0.1658, -0.1045, -0.1308],
+            [ 0.0542,  0.1545,  0.1325],
+            [-0.0352, -0.1672, -0.2541]
+        ]
+
+class SC_B(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 1.0 / 0.43
+        self.latent_rgb_factors = [
+            [ 0.1121,  0.2006,  0.1023],
+            [-0.2093, -0.0222, -0.0195],
+            [-0.3087, -0.1535,  0.0366],
+            [ 0.0290, -0.1574, -0.4078]
+        ]
--- a/ldm_patched/modules/model_sampling.py
+++ b/ldm_patched/modules/model_sampling.py
@ -1,7 +1,7 @@
 import torch
-import numpy as np
 from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule
 import math
+import numpy as np

 class EPS:
    def calculate_input(self, sigma, noise):
@ -12,12 +12,28 @@ class EPS:
        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
        return model_input - model_output * sigma

+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        if max_denoise:
+            noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
+        else:
+            noise = noise * sigma
+
+        noise += latent_image
+        return noise
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return latent

 class V_PREDICTION(EPS):
    def calculate_denoised(self, sigma, model_output, model_input):
        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5

+class EDM(V_PREDICTION):
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
+

 class ModelSamplingDiscrete(torch.nn.Module):
    def __init__(self, model_config=None):
@ -42,8 +58,7 @@ class ModelSamplingDiscrete(torch.nn.Module):
        else:
            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
        alphas = 1. - betas
-        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
-        # alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+        alphas_cumprod = torch.cumprod(alphas, dim=0)

        timesteps, = betas.shape
        self.num_timesteps = int(timesteps)
@ -55,11 +70,16 @@ class ModelSamplingDiscrete(torch.nn.Module):
        # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))

        sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
+        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
        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())
+        self.register_buffer('sigmas', sigmas.float())
+        self.register_buffer('log_sigmas', sigmas.log().float())
+
+    def set_alphas_cumprod(self, alphas_cumprod):
+        self.register_buffer("alphas_cumprod", alphas_cumprod.float())

    @property
    def sigma_min(self):
@ -94,8 +114,6 @@ class ModelSamplingDiscrete(torch.nn.Module):
 class ModelSamplingContinuousEDM(torch.nn.Module):
    def __init__(self, model_config=None):
        super().__init__()
-        self.sigma_data = 1.0
-
        if model_config is not None:
            sampling_settings = model_config.sampling_settings
        else:
@ -103,9 +121,11 @@ class ModelSamplingContinuousEDM(torch.nn.Module):

        sigma_min = sampling_settings.get("sigma_min", 0.002)
        sigma_max = sampling_settings.get("sigma_max", 120.0)
-        self.set_sigma_range(sigma_min, sigma_max)
+        sigma_data = sampling_settings.get("sigma_data", 1.0)
+        self.set_parameters(sigma_min, sigma_max, sigma_data)

-    def set_sigma_range(self, sigma_min, sigma_max):
+    def set_parameters(self, sigma_min, sigma_max, sigma_data):
+        self.sigma_data = sigma_data
        sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()

        self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
@ -134,3 +154,56 @@ class ModelSamplingContinuousEDM(torch.nn.Module):

        log_sigma_min = math.log(self.sigma_min)
        return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
+
+class StableCascadeSampling(ModelSamplingDiscrete):
+    def __init__(self, model_config=None):
+        super().__init__()
+
+        if model_config is not None:
+            sampling_settings = model_config.sampling_settings
+        else:
+            sampling_settings = {}
+
+        self.set_parameters(sampling_settings.get("shift", 1.0))
+
+    def set_parameters(self, shift=1.0, cosine_s=8e-3):
+        self.shift = shift
+        self.cosine_s = torch.tensor(cosine_s)
+        self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
+
+        #This part is just for compatibility with some schedulers in the codebase
+        self.num_timesteps = 10000
+        sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
+        for x in range(self.num_timesteps):
+            t = (x + 1) / self.num_timesteps
+            sigmas[x] = self.sigma(t)
+
+        self.set_sigmas(sigmas)
+
+    def sigma(self, timestep):
+        alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod)
+
+        if self.shift != 1.0:
+            var = alpha_cumprod
+            logSNR = (var/(1-var)).log()
+            logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))
+            alpha_cumprod = logSNR.sigmoid()
+
+        alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999)
+        return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5
+
+    def timestep(self, sigma):
+        var = 1 / ((sigma * sigma) + 1)
+        var = var.clamp(0, 1.0)
+        s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device)
+        t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
+        return t
+
+    def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return 999999999.9
+        if percent >= 1.0:
+            return 0.0
+
+        percent = 1.0 - percent
+        return self.sigma(torch.tensor(percent))
--- a/ldm_patched/modules/samplers.py
+++ b/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", "edm_playground_v2.5"]

 class KSAMPLER(Sampler):
    def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
--- a/ldm_patched/modules/sd.py
+++ b/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
--- a/models/safety_checker/put_safety_checker_models_here
+++ b/models/safety_checker/put_safety_checker_models_here
--- a/modules/init.py
+++ b/modules/init.py
--- a/modules/async_worker.py
+++ b/modules/async_worker.py
@ -4,6 +4,7 @@ from modules.patch import PatchSettings, patch_settings, patch_all

 patch_all()

+
 class AsyncTask:
    def __init__(self, args):
        self.args = args
@ -43,11 +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, get_enabled_loras
+    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, 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
@ -68,10 +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=flags.preparation_step_count):
        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:
@ -147,7 +155,8 @@ def worker():
        base_model_name = args.pop()
        refiner_model_name = args.pop()
        refiner_switch = args.pop()
-        loras = get_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()
@ -160,12 +169,15 @@ 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()
+        clip_skip = 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()
@ -195,7 +207,8 @@ 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):
@ -225,10 +238,12 @@ def worker():

        steps = performance_selection.steps()

+        performance_loras = []
+
        if performance_selection == Performance.EXTREME_SPEED:
            print('Enter LCM mode.')
            progressbar(async_task, 1, 'Downloading LCM components ...')
-            loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
+            performance_loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]

            if refiner_model_name != 'None':
                print(f'Refiner disabled in LCM mode.')
@ -247,7 +262,7 @@ def worker():
        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)]
+            performance_loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]

            if refiner_model_name != 'None':
                print(f'Refiner disabled in Lightning mode.')
@ -263,7 +278,27 @@ def worker():
            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 ...')
+            performance_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] CLIP Skip = {clip_skip}')
        print(f'[Parameters] Sharpness = {sharpness}')
        print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
        print(f'[Parameters] ADM Scale = '
@ -425,14 +460,21 @@ def worker():
            extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
            extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []

-            progressbar(async_task, 3, 'Loading models ...')
+            progressbar(async_task, 2, 'Loading models ...')
+
+            lora_filenames = modules.util.remove_performance_lora(modules.config.lora_filenames, performance_selection)
+            loras, prompt = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number, lora_filenames=lora_filenames)
+            loras += performance_loras
+
            pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
                                        loras=loras, base_model_additional_loras=base_model_additional_loras,
-                                        use_synthetic_refiner=use_synthetic_refiner)
+                                        use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
+
+            pipeline.set_clip_skip(clip_skip)

            progressbar(async_task, 3, 'Processing prompts ...')
            tasks = []
-            
+
            for i in range(image_number):
                if disable_seed_increment:
                    task_seed = seed % (constants.MAX_SEED + 1)
@ -443,14 +485,20 @@ def worker():
                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, 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]
+                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
@ -478,29 +526,30 @@ 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:
                for i, t in enumerate(tasks):
-                    progressbar(async_task, 5, f'Preparing Fooocus text #{i + 1} ...')
+                    progressbar(async_task, 4, f'Preparing Fooocus text #{i + 1} ...')
                    expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed'])
                    print(f'[Prompt Expansion] {expansion}')
                    t['expansion'] = expansion
                    t['positive'] = copy.deepcopy(t['positive']) + [expansion]  # Deep copy.

            for i, t in enumerate(tasks):
-                progressbar(async_task, 7, f'Encoding positive #{i + 1} ...')
+                progressbar(async_task, 5, f'Encoding positive #{i + 1} ...')
                t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])

            for i, t in enumerate(tasks):
                if abs(float(cfg_scale) - 1.0) < 1e-4:
                    t['uc'] = pipeline.clone_cond(t['c'])
                else:
-                    progressbar(async_task, 10, f'Encoding negative #{i + 1} ...')
+                    progressbar(async_task, 6, f'Encoding negative #{i + 1} ...')
                    t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])

        if len(goals) > 0:
-            progressbar(async_task, 13, 'Image processing ...')
+            progressbar(async_task, 7, 'Image processing ...')

        if 'vary' in goals:
            if 'subtle' in uov_method:
@ -521,7 +570,7 @@ def worker():
            uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)

            initial_pixels = core.numpy_to_pytorch(uov_input_image)
-            progressbar(async_task, 13, 'VAE encoding ...')
+            progressbar(async_task, 8, 'VAE encoding ...')

            candidate_vae, _ = pipeline.get_candidate_vae(
                steps=steps,
@ -538,7 +587,7 @@ def worker():

        if 'upscale' in goals:
            H, W, C = uov_input_image.shape
-            progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...')
+            progressbar(async_task, 9, f'Upscaling image from {str((H, W))} ...')
            uov_input_image = perform_upscale(uov_input_image)
            print(f'Image upscaled.')

@ -572,8 +621,12 @@ def worker():

            if direct_return:
                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, do_not_show_finished_images=True)
+                yield_result(async_task, uov_input_image_path, black_out_nsfw, False, do_not_show_finished_images=True)
                return

            tiled = True
@ -583,7 +636,7 @@ def worker():
                denoising_strength = overwrite_upscale_strength

            initial_pixels = core.numpy_to_pytorch(uov_input_image)
-            progressbar(async_task, 13, 'VAE encoding ...')
+            progressbar(async_task, 10, 'VAE encoding ...')

            candidate_vae, _ = pipeline.get_candidate_vae(
                steps=steps,
@ -637,11 +690,11 @@ 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

-            progressbar(async_task, 13, 'VAE Inpaint encoding ...')
+            progressbar(async_task, 11, 'VAE Inpaint encoding ...')

            inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
            inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
@ -661,7 +714,7 @@ def worker():

            latent_swap = None
            if candidate_vae_swap is not None:
-                progressbar(async_task, 13, 'VAE SD15 encoding ...')
+                progressbar(async_task, 12, 'VAE SD15 encoding ...')
                latent_swap = core.encode_vae(
                    vae=candidate_vae_swap,
                    pixels=inpaint_pixel_fill)['samples']
@ -701,7 +754,7 @@ 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
@ -713,7 +766,7 @@ 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
@ -724,7 +777,7 @@ 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
@ -738,7 +791,7 @@ 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]
@ -773,29 +826,48 @@ 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',
-                    zsnr=False)[0]
-            if pipeline.final_refiner_unet is not None:
-                pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch(
-                    pipeline.final_refiner_unet,
-                    sampling='lcm',
-                    zsnr=False)[0]
-            print('Using lcm scheduler.')

-        async_task.yields.append(['preview', (13, 'Moving model to GPU ...', None)])
+            def patch_discrete(unet):
+                return core.opModelSamplingDiscrete.patch(
+                    pipeline.final_unet,
+                    sampling=scheduler_name,
+                    zsnr=False)[0]
+
+            if pipeline.final_unet is not None:
+                pipeline.final_unet = patch_discrete(pipeline.final_unet)
+            if pipeline.final_refiner_unet is not None:
+                pipeline.final_refiner_unet = patch_discrete(pipeline.final_refiner_unet)
+            print(f'Using {scheduler_name} scheduler.')
+        elif scheduler_name == 'edm_playground_v2.5':
+            final_scheduler_name = 'karras'
+
+            def patch_edm(unet):
+                return core.opModelSamplingContinuousEDM.patch(
+                    unet,
+                    sampling=scheduler_name,
+                    sigma_max=120.0,
+                    sigma_min=0.002)[0]
+
+            if pipeline.final_unet is not None:
+                pipeline.final_unet = patch_edm(pipeline.final_unet)
+            if pipeline.final_refiner_unet is not None:
+                pipeline.final_refiner_unet = patch_edm(pipeline.final_refiner_unet)
+
+            print(f'Using {scheduler_name} scheduler.')
+
+        async_task.yields.append(['preview', (flags.preparation_step_count, 'Moving model to GPU ...', None)])

        def callback(step, x0, x, total_steps, y):
            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)])
+                int(flags.preparation_step_count + (100 - flags.preparation_step_count) * float(done_steps) / float(all_steps)),
+                f'Sampling step {step + 1}/{total_steps}, image {current_task_id + 1}/{image_number} ...', y)])

        for current_task_id, task in enumerate(tasks):
+            current_progress = int(flags.preparation_step_count + (100 - flags.preparation_step_count) * float(current_task_id * steps) / float(all_steps))
+            progressbar(async_task, current_progress, f'Preparing task {current_task_id + 1}/{image_number} ...')
            execution_start_time = time.perf_counter()

            try:
@ -838,11 +910,18 @@ def worker():
                    imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]

                img_paths = []
+                current_progress = int(flags.preparation_step_count + (100 - flags.preparation_step_count) * 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, f'Saving image {current_task_id + 1}/{image_number} 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)),
+                         ('Styles', 'styles',
+                          str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
                         ('Performance', 'performance', performance_selection.value)]

                    if performance_selection.steps() != steps:
@ -865,10 +944,14 @@ 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))

+                    if clip_skip > 1:
+                        d.append(('CLIP Skip', 'clip_skip', clip_skip))
                    d.append(('Sampler', 'sampler', sampler_name))
                    d.append(('Scheduler', 'scheduler', scheduler_name))
+                    d.append(('VAE', 'vae', vae_name))
                    d.append(('Seed', 'seed', str(task['task_seed'])))

                    if freeu_enabled:
@ -883,12 +966,14 @@ def worker():
                        metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
                        metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
                                                 task['log_negative_prompt'], task['negative'],
-                                                 steps, base_model_name, refiner_model_name, loras)
-                    d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
+                                                 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')
--- a/modules/config.py
+++ b/modules/config.py
@ -2,13 +2,14 @@ import os
 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.extra_utils import makedirs_with_log, get_files_from_folder, try_eval_env_var
 from modules.flags import OutputFormat, Performance, MetadataScheme


@ -20,7 +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 = {}
@ -189,16 +190,18 @@ 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):
+def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False, expected_type=None):
    global config_dict, visited_keys

    if key not in visited_keys:
@ -206,6 +209,7 @@ def get_config_item_or_set_default(key, default_value, validator, disable_empty_
    
    v = os.getenv(key)
    if v is not None:
+        v = try_eval_env_var(v, expected_type)
        print(f"Environment: {key} = {v}")
        config_dict[key] = v

@ -250,41 +254,49 @@ 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),
+    expected_type=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)
+    validator=lambda x: isinstance(x, bool),
+    expected_type=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)
+    validator=lambda x: isinstance(x, str),
+    expected_type=str
 )
 previous_default_models = get_config_item_or_set_default(
    key='previous_default_models',
    default_value=[],
-    validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x)
+    validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x),
+    expected_type=list
 )
 default_refiner_model_name = default_refiner = get_config_item_or_set_default(
    key='default_refiner',
    default_value='None',
-    validator=lambda x: isinstance(x, str)
+    validator=lambda x: isinstance(x, str),
+    expected_type=str
 )
 default_refiner_switch = get_config_item_or_set_default(
    key='default_refiner_switch',
    default_value=0.8,
-    validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1
+    validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1,
+    expected_type=numbers.Number
 )
 default_loras_min_weight = get_config_item_or_set_default(
    key='default_loras_min_weight',
    default_value=-2,
-    validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10
+    validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10,
+    expected_type=numbers.Number
 )
 default_loras_max_weight = get_config_item_or_set_default(
    key='default_loras_max_weight',
    default_value=2,
-    validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10
+    validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10,
+    expected_type=numbers.Number
 )
 default_loras = get_config_item_or_set_default(
    key='default_loras',
@ -318,33 +330,45 @@ default_loras = get_config_item_or_set_default(
    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)
+        for y in x),
+    expected_type=list
 )
 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) if isinstance(default_loras, list) and len(default_loras) > 0 else 5,
-    validator=lambda x: isinstance(x, int) and x >= 1
+    validator=lambda x: isinstance(x, int) and x >= 1,
+    expected_type=int
 )
 default_cfg_scale = get_config_item_or_set_default(
    key='default_cfg_scale',
    default_value=7.0,
-    validator=lambda x: isinstance(x, numbers.Number)
+    validator=lambda x: isinstance(x, numbers.Number),
+    expected_type=numbers.Number
 )
 default_sample_sharpness = get_config_item_or_set_default(
    key='default_sample_sharpness',
    default_value=2.0,
-    validator=lambda x: isinstance(x, numbers.Number)
+    validator=lambda x: isinstance(x, numbers.Number),
+    expected_type=numbers.Number
 )
 default_sampler = get_config_item_or_set_default(
    key='default_sampler',
    default_value='dpmpp_2m_sde_gpu',
-    validator=lambda x: x in modules.flags.sampler_list
+    validator=lambda x: x in modules.flags.sampler_list,
+    expected_type=str
 )
 default_scheduler = get_config_item_or_set_default(
    key='default_scheduler',
    default_value='karras',
-    validator=lambda x: x in modules.flags.scheduler_list
+    validator=lambda x: x in modules.flags.scheduler_list,
+    expected_type=str
+)
+default_vae = get_config_item_or_set_default(
+    key='default_vae',
+    default_value=modules.flags.default_vae,
+    validator=lambda x: isinstance(x, str),
+    expected_type=str
 )
 default_styles = get_config_item_or_set_default(
    key='default_styles',
@ -353,117 +377,144 @@ default_styles = get_config_item_or_set_default(
        "Fooocus Enhance",
        "Fooocus Sharp"
    ],
-    validator=lambda x: isinstance(x, list) and all(y in modules.sdxl_styles.legal_style_names for y in x)
+    validator=lambda x: isinstance(x, list) and all(y in modules.sdxl_styles.legal_style_names for y in x),
+    expected_type=list
 )
 default_prompt_negative = get_config_item_or_set_default(
    key='default_prompt_negative',
    default_value='',
    validator=lambda x: isinstance(x, str),
-    disable_empty_as_none=True
+    disable_empty_as_none=True,
+    expected_type=str
 )
 default_prompt = get_config_item_or_set_default(
    key='default_prompt',
    default_value='',
    validator=lambda x: isinstance(x, str),
-    disable_empty_as_none=True
+    disable_empty_as_none=True,
+    expected_type=str
 )
 default_performance = get_config_item_or_set_default(
    key='default_performance',
    default_value=Performance.SPEED.value,
-    validator=lambda x: x in Performance.list()
+    validator=lambda x: x in Performance.list(),
+    expected_type=str
 )
 default_advanced_checkbox = get_config_item_or_set_default(
    key='default_advanced_checkbox',
    default_value=False,
-    validator=lambda x: isinstance(x, bool)
+    validator=lambda x: isinstance(x, bool),
+    expected_type=bool
 )
 default_max_image_number = get_config_item_or_set_default(
    key='default_max_image_number',
    default_value=32,
-    validator=lambda x: isinstance(x, int) and x >= 1
+    validator=lambda x: isinstance(x, int) and x >= 1,
+    expected_type=int
 )
 default_output_format = get_config_item_or_set_default(
    key='default_output_format',
    default_value='png',
-    validator=lambda x: x in OutputFormat.list()
+    validator=lambda x: x in OutputFormat.list(),
+    expected_type=str
 )
 default_image_number = get_config_item_or_set_default(
    key='default_image_number',
    default_value=2,
-    validator=lambda x: isinstance(x, int) and 1 <= x <= default_max_image_number
+    validator=lambda x: isinstance(x, int) and 1 <= x <= default_max_image_number,
+    expected_type=int
 )
 checkpoint_downloads = get_config_item_or_set_default(
    key='checkpoint_downloads',
    default_value={},
-    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items())
+    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),
+    expected_type=dict
 )
 lora_downloads = get_config_item_or_set_default(
    key='lora_downloads',
    default_value={},
-    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items())
+    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),
+    expected_type=dict
 )
 embeddings_downloads = get_config_item_or_set_default(
    key='embeddings_downloads',
    default_value={},
-    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items())
+    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),
+    expected_type=dict
 )
 available_aspect_ratios = get_config_item_or_set_default(
    key='available_aspect_ratios',
-    default_value=[
-        '704*1408', '704*1344', '768*1344', '768*1280', '832*1216', '832*1152',
-        '896*1152', '896*1088', '960*1088', '960*1024', '1024*1024', '1024*960',
-        '1088*960', '1088*896', '1152*896', '1152*832', '1216*832', '1280*768',
-        '1344*768', '1344*704', '1408*704', '1472*704', '1536*640', '1600*640',
-        '1664*576', '1728*576'
-    ],
-    validator=lambda x: isinstance(x, list) and all('*' in v for v in x) and len(x) > 1
+    default_value=modules.flags.sdxl_aspect_ratios,
+    validator=lambda x: isinstance(x, list) and all('*' in v for v in x) and len(x) > 1,
+    expected_type=list
 )
 default_aspect_ratio = get_config_item_or_set_default(
    key='default_aspect_ratio',
    default_value='1152*896' if '1152*896' in available_aspect_ratios else available_aspect_ratios[0],
-    validator=lambda x: x in available_aspect_ratios
+    validator=lambda x: x in available_aspect_ratios,
+    expected_type=str
 )
 default_inpaint_engine_version = get_config_item_or_set_default(
    key='default_inpaint_engine_version',
    default_value='v2.6',
-    validator=lambda x: x in modules.flags.inpaint_engine_versions
+    validator=lambda x: x in modules.flags.inpaint_engine_versions,
+    expected_type=str
 )
 default_cfg_tsnr = get_config_item_or_set_default(
    key='default_cfg_tsnr',
    default_value=7.0,
-    validator=lambda x: isinstance(x, numbers.Number)
+    validator=lambda x: isinstance(x, numbers.Number),
+    expected_type=numbers.Number
+)
+default_clip_skip = get_config_item_or_set_default(
+    key='default_clip_skip',
+    default_value=2,
+    validator=lambda x: isinstance(x, int) and 1 <= x <= modules.flags.clip_skip_max,
+    expected_type=int
 )
 default_overwrite_step = get_config_item_or_set_default(
    key='default_overwrite_step',
    default_value=-1,
-    validator=lambda x: isinstance(x, int)
+    validator=lambda x: isinstance(x, int),
+    expected_type=int
 )
 default_overwrite_switch = get_config_item_or_set_default(
    key='default_overwrite_switch',
    default_value=-1,
-    validator=lambda x: isinstance(x, int)
+    validator=lambda x: isinstance(x, int),
+    expected_type=int
 )
 example_inpaint_prompts = get_config_item_or_set_default(
    key='example_inpaint_prompts',
    default_value=[
        'highly detailed face', 'detailed girl face', 'detailed man face', 'detailed hand', 'beautiful eyes'
    ],
-    validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x)
+    validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x),
+    expected_type=list
+)
+default_black_out_nsfw = get_config_item_or_set_default(
+    key='default_black_out_nsfw',
+    default_value=False,
+    validator=lambda x: isinstance(x, bool),
+    expected_type=bool
 )
 default_save_metadata_to_images = get_config_item_or_set_default(
    key='default_save_metadata_to_images',
    default_value=False,
-    validator=lambda x: isinstance(x, bool)
+    validator=lambda x: isinstance(x, bool),
+    expected_type=bool
 )
 default_metadata_scheme = get_config_item_or_set_default(
    key='default_metadata_scheme',
    default_value=MetadataScheme.FOOOCUS.value,
-    validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x]
+    validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x],
+    expected_type=str
 )
 metadata_created_by = get_config_item_or_set_default(
    key='metadata_created_by',
    default_value='',
-    validator=lambda x: isinstance(x, str)
+    validator=lambda x: isinstance(x, str),
+    expected_type=str
 )

 example_inpaint_prompts = [[x] for x in example_inpaint_prompts]
@ -481,6 +532,8 @@ possible_preset_keys = {
    "default_loras": "<processed>",
    "default_cfg_scale": "guidance_scale",
    "default_sample_sharpness": "sharpness",
+    "default_cfg_tsnr": "adaptive_cfg",
+    "default_clip_skip": "clip_skip",
    "default_sampler": "sampler",
    "default_scheduler": "scheduler",
    "default_overwrite_step": "steps",
@ -493,7 +546,8 @@ possible_preset_keys = {
    "default_save_metadata_to_images": "default_save_metadata_to_images",
    "checkpoint_downloads": "checkpoint_downloads",
    "embeddings_downloads": "embeddings_downloads",
-    "lora_downloads": "lora_downloads"
+    "lora_downloads": "lora_downloads",
+    "default_vae": "vae"
 }

 REWRITE_PRESET = False
@ -514,7 +568,7 @@ def add_ratio(x):


 default_aspect_ratio = add_ratio(default_aspect_ratio)
-available_aspect_ratios = [add_ratio(x) for x in available_aspect_ratios]
+available_aspect_ratios_labels = [add_ratio(x) for x in available_aspect_ratios]


 # Only write config in the first launch.
@ -535,26 +589,28 @@ 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'
-loras_metadata_remove = [sdxl_lcm_lora, sdxl_lightning_lora]
-

 def get_model_filenames(folder_paths, extensions=None, name_filter=None):
    if extensions is None:
        extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']
    files = []
+
+    if not isinstance(folder_paths, list):
+        folder_paths = [folder_paths]
    for folder in folder_paths:
        files += get_files_from_folder(folder, extensions, name_filter)
+
    return files


 def update_files():
-    global model_filenames, lora_filenames, wildcard_filenames, available_presets
+    global model_filenames, lora_filenames, vae_filenames, wildcard_filenames, available_presets
    model_filenames = get_model_filenames(paths_checkpoints)
    lora_filenames = get_model_filenames(paths_loras)
+    vae_filenames = get_model_filenames(path_vae)
    wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
    available_presets = get_presets()
    return
@ -602,17 +658,27 @@ def downloading_sdxl_lcm_lora():
    load_file_from_url(
        url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors',
        model_dir=paths_loras[0],
-        file_name=sdxl_lcm_lora
+        file_name=modules.flags.PerformanceLoRA.EXTREME_SPEED.value
    )
-    return sdxl_lcm_lora
+    return modules.flags.PerformanceLoRA.EXTREME_SPEED.value
+

 def downloading_sdxl_lightning_lora():
    load_file_from_url(
-        url='https://huggingface.co/ByteDance/SDXL-Lightning/resolve/main/sdxl_lightning_4step_lora.safetensors',
+        url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_lightning_4step_lora.safetensors',
        model_dir=paths_loras[0],
-        file_name=sdxl_lightning_lora
+        file_name=modules.flags.PerformanceLoRA.LIGHTNING.value
    )
-    return sdxl_lightning_lora
+    return modules.flags.PerformanceLoRA.LIGHTNING.value
+
+
+def downloading_sdxl_hyper_sd_lora():
+    load_file_from_url(
+        url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_hyper_sd_4step_lora.safetensors',
+        model_dir=paths_loras[0],
+        file_name=modules.flags.PerformanceLoRA.HYPER_SD.value
+    )
+    return modules.flags.PerformanceLoRA.HYPER_SD.value


 def downloading_controlnet_canny():
@ -679,5 +745,13 @@ 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_files()
--- a/modules/core.py
+++ b/modules/core.py
@ -21,8 +21,7 @@ from modules.lora import match_lora
 from modules.util import get_file_from_folder_list
 from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip
 from modules.config import path_embeddings
-from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete
-
+from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete, ModelSamplingContinuousEDM

 opEmptyLatentImage = EmptyLatentImage()
 opVAEDecode = VAEDecode()
@ -32,15 +31,17 @@ opVAEEncodeTiled = VAEEncodeTiled()
 opControlNetApplyAdvanced = ControlNetApplyAdvanced()
 opFreeU = FreeU_V2()
 opModelSamplingDiscrete = ModelSamplingDiscrete()
+opModelSamplingContinuousEDM = ModelSamplingContinuousEDM()


 class StableDiffusionModel:
-    def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None):
+    def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None, vae_filename=None):
        self.unet = unet
        self.vae = vae
        self.clip = clip
        self.clip_vision = clip_vision
        self.filename = filename
+        self.vae_filename = vae_filename
        self.unet_with_lora = unet
        self.clip_with_lora = clip
        self.visited_loras = ''
@ -142,9 +143,10 @@ def apply_controlnet(positive, negative, control_net, image, strength, start_per

@torch.no_grad()
@torch.inference_mode()
-def load_model(ckpt_filename):
-    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()
--- a/modules/default_pipeline.py
+++ b/modules/default_pipeline.py
@ -3,6 +3,7 @@ import os
 import torch
 import modules.patch
 import modules.config
+import modules.flags
 import ldm_patched.modules.model_management
 import ldm_patched.modules.latent_formats
 import modules.inpaint_worker
@ -58,17 +59,21 @@ def assert_model_integrity():

@torch.no_grad()
@torch.inference_mode()
-def refresh_base_model(name):
+def refresh_base_model(name, vae_name=None):
    global model_base

    filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)

-    if model_base.filename == filename:
+    vae_filename = None
+    if vae_name is not None and vae_name != modules.flags.default_vae:
+        vae_filename = get_file_from_folder_list(vae_name, modules.config.path_vae)
+
+    if model_base.filename == filename and model_base.vae_filename == vae_filename:
        return

-    model_base = core.StableDiffusionModel()
-    model_base = core.load_model(filename)
+    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


@ -196,6 +201,17 @@ def clip_encode(texts, pool_top_k=1):
    return [[torch.cat(cond_list, dim=1), {"pooled_output": pooled_acc}]]


+@torch.no_grad()
+@torch.inference_mode()
+def set_clip_skip(clip_skip: int):
+    global final_clip
+
+    if final_clip is None:
+        return
+
+    final_clip.clip_layer(-abs(clip_skip))
+    return
+
@torch.no_grad()
@torch.inference_mode()
 def clear_all_caches():
@ -216,7 +232,7 @@ 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 +243,11 @@ 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 +270,8 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
 refresh_everything(
    refiner_model_name=modules.config.default_refiner_model_name,
    base_model_name=modules.config.default_base_model_name,
-    loras=get_enabled_loras(modules.config.default_loras)
+    loras=get_enabled_loras(modules.config.default_loras),
+    vae_name=modules.config.default_vae,
 )


--- a/modules/extra_utils.py
+++ b/modules/extra_utils.py
@ -0,0 +1,41 @@
+import os
+from ast import literal_eval
+
+
+def makedirs_with_log(path):
+    try:
+        os.makedirs(path, exist_ok=True)
+    except OSError as error:
+        print(f'Directory {path} could not be created, reason: {error}')
+
+
+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
+
+
+def try_eval_env_var(value: str, expected_type=None):
+    try:
+        value_eval = value
+        if expected_type is bool:
+            value_eval = value.title()
+        value_eval = literal_eval(value_eval)
+        if expected_type is not None and not isinstance(value_eval, expected_type):
+            return value
+        return value_eval
+    except:
+        return value
--- a/modules/flags.py
+++ b/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 +48,16 @@ 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", "edm_playground_v2.5"]
 SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())

 sampler_list = SAMPLER_NAMES
 scheduler_list = SCHEDULER_NAMES

+clip_skip_max = 12
+
+default_vae = 'Default (model)'
+
 refiner_swap_method = 'joint'

 cn_ip = "ImagePrompt"
@ -78,6 +83,14 @@ inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option
 desc_type_photo = 'Photograph'
 desc_type_anime = 'Art/Anime'

+sdxl_aspect_ratios = [
+    '704*1408', '704*1344', '768*1344', '768*1280', '832*1216', '832*1152',
+    '896*1152', '896*1088', '960*1088', '960*1024', '1024*1024', '1024*960',
+    '1088*960', '1088*896', '1152*896', '1152*832', '1216*832', '1280*768',
+    '1344*768', '1344*704', '1408*704', '1472*704', '1536*640', '1600*640',
+    '1664*576', '1728*576'
+]
+

 class MetadataScheme(Enum):
    FOOOCUS = 'fooocus'
@ -90,6 +103,7 @@ metadata_scheme = [
 ]

 controlnet_image_count = 4
+preparation_step_count = 13


 class OutputFormat(Enum):
@ -102,11 +116,24 @@ class OutputFormat(Enum):
        return list(map(lambda c: c.value, cls))


+class PerformanceLoRA(Enum):
+    QUALITY = None
+    SPEED = None
+    EXTREME_SPEED = 'sdxl_lcm_lora.safetensors'
+    LIGHTNING = 'sdxl_lightning_4step_lora.safetensors'
+    HYPER_SD = 'sdxl_hyper_sd_4step_lora.safetensors'
+
+
 class Steps(IntEnum):
    QUALITY = 60
    SPEED = 30
    EXTREME_SPEED = 8
    LIGHTNING = 4
+    HYPER_SD = 4
+
+    @classmethod
+    def keys(cls) -> list:
+        return list(map(lambda c: c, Steps.__members__))


 class StepsUOV(IntEnum):
@ -114,6 +141,7 @@ class StepsUOV(IntEnum):
    SPEED = 18
    EXTREME_SPEED = 8
    LIGHTNING = 4
+    HYPER_SD = 4


 class Performance(Enum):
@ -121,19 +149,31 @@ class Performance(Enum):
    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 values(cls) -> list:
+        return list(map(lambda c: c.value, cls))
+
+    @classmethod
+    def by_steps(cls, steps: int | str):
+        return cls[Steps(int(steps)).name]
+
    @classmethod
    def has_restricted_features(cls, x) -> bool:
        if isinstance(x, Performance):
            x = x.value
-        return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.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
+        return Steps[self.name].value if self.name in Steps.__members__ else None

    def steps_uov(self) -> int | None:
-        return StepsUOV[self.name].value if Steps[self.name] else None
+        return StepsUOV[self.name].value if self.name in StepsUOV.__members__ else None
+
+    def lora_filename(self) -> str | None:
+        return PerformanceLoRA[self.name].value if self.name in PerformanceLoRA.__members__ else None
--- a/modules/meta_parser.py
+++ b/modules/meta_parser.py
@ -32,20 +32,22 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
    get_str('prompt', 'Prompt', loaded_parameter_dict, results)
    get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results)
    get_list('styles', 'Styles', loaded_parameter_dict, results)
-    get_str('performance', 'Performance', loaded_parameter_dict, results)
+    performance = get_str('performance', 'Performance', loaded_parameter_dict, results)
    get_steps('steps', 'Steps', loaded_parameter_dict, results)
-    get_float('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
+    get_number('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
    get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)
-    get_float('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)
-    get_float('sharpness', 'Sharpness', loaded_parameter_dict, results)
+    get_number('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)
+    get_number('sharpness', 'Sharpness', loaded_parameter_dict, results)
    get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results)
    get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results)
-    get_float('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results)
+    get_number('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results)
+    get_number('clip_skip', 'CLIP Skip', loaded_parameter_dict, results, cast_type=int)
    get_str('base_model', 'Base Model', loaded_parameter_dict, results)
    get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results)
-    get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
+    get_number('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:
@ -57,19 +59,27 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):

    get_freeu('freeu', 'FreeU', loaded_parameter_dict, results)

+    # prevent performance LoRAs to be added twice, by performance and by lora
+    performance_filename = None
+    if performance is not None and performance in Performance.values():
+        performance = Performance(performance)
+        performance_filename = performance.lora_filename()
+
    for i in range(modules.config.default_max_lora_number):
-        get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results)
+        get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results, performance_filename)

    return results


-def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
+def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None) -> str | None:
    try:
        h = source_dict.get(key, source_dict.get(fallback, default))
        assert isinstance(h, str)
        results.append(h)
+        return h
    except:
        results.append(gr.update())
+        return None


 def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
@ -82,11 +92,11 @@ def get_list(key: str, fallback: str | None, source_dict: dict, results: list, d
        results.append(gr.update())


-def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
+def get_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None, cast_type=float):
    try:
        h = source_dict.get(key, source_dict.get(fallback, default))
        assert h is not None
-        h = float(h)
+        h = cast_type(h)
        results.append(h)
    except:
        results.append(gr.update())
@ -109,8 +119,9 @@ def get_steps(key: str, fallback: str | None, source_dict: dict, results: list,
        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():
+        performance_name = source_dict.get('performance', '').replace(' ', '_').replace('-', '_').casefold()
+        performance_candidates = [key for key in Steps.keys() if key.casefold() == performance_name and Steps[key] == h]
+        if len(performance_candidates) == 0:
            results.append(h)
            return
        results.append(-1)
@ -123,7 +134,7 @@ def get_resolution(key: str, fallback: str | None, source_dict: dict, results: l
        h = source_dict.get(key, source_dict.get(fallback, default))
        width, height = eval(h)
        formatted = modules.config.add_ratio(f'{width}*{height}')
-        if formatted in modules.config.available_aspect_ratios:
+        if formatted in modules.config.available_aspect_ratios_labels:
            results.append(formatted)
            results.append(-1)
            results.append(-1)
@ -179,7 +190,7 @@ def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list,
        results.append(gr.update())


-def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
+def get_lora(key: str, fallback: str | None, source_dict: dict, results: list, performance_filename: str | None):
    try:
        split_data = source_dict.get(key, source_dict.get(fallback)).split(' : ')
        enabled = True
@ -191,6 +202,9 @@ def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
            name = split_data[1]
            weight = split_data[2]

+        if name == performance_filename:
+            raise Exception
+
        weight = float(weight)
        results.append(enabled)
        results.append(name)
@ -204,7 +218,6 @@ 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:
-        # is_safetensors = os.path.splitext(filepath)[1].lower() == '.safetensors'
        hash_cache[filepath] = sha256(filepath)

    return hash_cache[filepath]
@ -220,7 +233,7 @@ def parse_meta_from_preset(preset_content):
            loras = getattr(modules.config, settings_key)
            if settings_key in items:
                loras = items[settings_key]
-            for index, lora in enumerate(loras[:5]):
+            for index, lora in enumerate(loras[:modules.config.default_max_lora_number]):
                preset_prepared[f'lora_combined_{index + 1}'] = ' : '.join(map(str, lora))
        elif settings_key == "default_aspect_ratio":
            if settings_key in items and items[settings_key] is not None:
@ -247,27 +260,28 @@ class MetadataParser(ABC):
        self.full_prompt: str = ''
        self.raw_negative_prompt: str = ''
        self.full_negative_prompt: str = ''
-        self.steps: int = 30
+        self.steps: int = Steps.SPEED.value
        self.base_model_name: str = ''
        self.base_model_hash: str = ''
        self.refiner_model_name: str = ''
        self.refiner_model_hash: str = ''
        self.loras: list = []
+        self.vae_name: str = ''

    @abstractmethod
    def get_scheme(self) -> MetadataScheme:
        raise NotImplementedError

    @abstractmethod
-    def parse_json(self, metadata: dict | str) -> dict:
+    def to_json(self, metadata: dict | str) -> dict:
        raise NotImplementedError

    @abstractmethod
-    def parse_string(self, metadata: dict) -> str:
+    def to_string(self, metadata: dict) -> str:
        raise NotImplementedError

    def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
-                 refiner_model_name, loras):
+                 refiner_model_name, loras, vae_name):
        self.raw_prompt = raw_prompt
        self.full_prompt = full_prompt
        self.raw_negative_prompt = raw_negative_prompt
@ -289,12 +303,7 @@ class MetadataParser(ABC):
                lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras)
                lora_hash = get_sha256(lora_path)
                self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
-
-    @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)
+        self.vae_name = Path(vae_name).stem


 class A1111MetadataParser(MetadataParser):
@ -310,6 +319,7 @@ class A1111MetadataParser(MetadataParser):
        'steps': 'Steps',
        'sampler': 'Sampler',
        'scheduler': 'Scheduler',
+        'vae': 'VAE',
        'guidance_scale': 'CFG scale',
        'seed': 'Seed',
        'resolution': 'Size',
@ -317,6 +327,7 @@ class A1111MetadataParser(MetadataParser):
        'adm_guidance': 'ADM Guidance',
        'refiner_swap_method': 'Refiner Swap Method',
        'adaptive_cfg': 'Adaptive CFG',
+        'clip_skip': 'Clip skip',
        'overwrite_switch': 'Overwrite Switch',
        'freeu': 'FreeU',
        'base_model': 'Model',
@ -329,7 +340,7 @@ class A1111MetadataParser(MetadataParser):
        'version': 'Version'
    }

-    def parse_json(self, metadata: str) -> dict:
+    def to_json(self, metadata: str) -> dict:
        metadata_prompt = ''
        metadata_negative_prompt = ''

@ -383,9 +394,9 @@ class A1111MetadataParser(MetadataParser):
        data['styles'] = str(found_styles)

        # try to load performance based on steps, fallback for direct A1111 imports
-        if 'steps' in data and 'performance' not in data:
+        if 'steps' in data and 'performance' in data is None:
            try:
-                data['performance'] = Performance[Steps(int(data['steps'])).name].value
+                data['performance'] = Performance.by_steps(data['steps']).value
            except ValueError | KeyError:
                pass

@ -397,13 +408,12 @@ class A1111MetadataParser(MetadataParser):
                    data['sampler'] = k
                    break

-        for key in ['base_model', 'refiner_model']:
+        for key in ['base_model', 'refiner_model', 'vae']:
            if key in data:
-                for filename in modules.config.model_filenames:
-                    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)

        lora_data = ''
        if 'lora_weights' in data and data['lora_weights'] != '':
@ -412,13 +422,11 @@ class A1111MetadataParser(MetadataParser):
            lora_data = data['lora_hashes']

        if lora_data != '':
-            lora_filenames = modules.config.lora_filenames.copy()
-            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:
+                for filename in modules.config.lora_filenames:
                    path = Path(filename)
                    if lora_name == path.stem:
                        data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'
@ -426,13 +434,14 @@ class A1111MetadataParser(MetadataParser):

        return data

-    def parse_string(self, metadata: dict) -> str:
+    def to_string(self, metadata: dict) -> str:
        data = {k: v for _, k, v in metadata}

        width, height = eval(data['resolution'])

        sampler = data['sampler']
        scheduler = data['scheduler']
+
        if sampler in SAMPLERS and SAMPLERS[sampler] != '':
            sampler = SAMPLERS[sampler]
            if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras':
@ -451,6 +460,7 @@ 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,
@ -462,7 +472,7 @@ class A1111MetadataParser(MetadataParser):
                self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash
            }

-        for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
+        for key in ['adaptive_cfg', 'clip_skip', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
            if key in data:
                generation_params[self.fooocus_to_a1111[key]] = data[key]

@ -491,28 +501,35 @@ 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:
        return MetadataScheme.FOOOCUS

-    def parse_json(self, metadata: dict) -> dict:
-        model_filenames = modules.config.model_filenames.copy()
-        lora_filenames = modules.config.lora_filenames.copy()
-        self.remove_special_loras(lora_filenames)
+    def to_json(self, metadata: dict) -> dict:
        for key, value in metadata.items():
            if value in ['', 'None']:
                continue
            if key in ['base_model', 'refiner_model']:
-                metadata[key] = self.replace_value_with_filename(key, value, model_filenames)
+                metadata[key] = self.replace_value_with_filename(key, value, modules.config.model_filenames)
            elif key.startswith('lora_combined_'):
-                metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
+                metadata[key] = self.replace_value_with_filename(key, value, modules.config.lora_filenames)
+            elif key == 'vae':
+                metadata[key] = self.replace_value_with_filename(key, value, modules.config.vae_filenames)
            else:
                continue

        return metadata

-    def parse_string(self, metadata: list) -> str:
+    def to_string(self, metadata: list) -> str:
        for li, (label, key, value) in enumerate(metadata):
            # remove model folder paths from metadata
            if key.startswith('lora_combined_'):
@ -533,6 +550,7 @@ 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 != '':
@ -551,6 +569,8 @@ class FooocusMetadataParser(MetadataParser):
            elif value == path.stem:
                return filename

+        return None
+

 def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser:
    match metadata_scheme:
--- a/modules/model_loader.py
+++ b/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)
--- a/modules/patch_precision.py
+++ b/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


--- a/modules/private_logger.py
+++ b/modules/private_logger.py
@ -21,13 +21,13 @@ def get_current_html_path(output_format=None):
    return html_name


-def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str:
+def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None, task=None) -> str:
    path_outputs = modules.config.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs
    output_format = output_format if output_format else modules.config.default_output_format
    date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)
    os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)

-    parsed_parameters = metadata_parser.parse_string(metadata.copy()) if metadata_parser is not None else ''
+    parsed_parameters = metadata_parser.to_string(metadata.copy()) if metadata_parser is not None else ''
    image = Image.fromarray(img)

    if output_format == OutputFormat.PNG.value:
@ -111,9 +111,15 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
    for label, key, value in metadata:
        value_txt = str(value).replace('\n', ' </br> ')
        item += f"<tr><td class='label'>{label}</td><td class='value'>{value_txt}</td></tr>\n"
+
+    if task is not None and 'positive' in task and 'negative' in task:
+        full_prompt_details = f"""<details><summary>Positive</summary>{', '.join(task['positive'])}</details>
+        <details><summary>Negative</summary>{', '.join(task['negative'])}</details>"""
+        item += f"<tr><td class='label'>Full raw prompt</td><td class='value'>{full_prompt_details}</td></tr>\n"
+
    item += "</table>"

-    js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v in metadata}, indent=0), safe='')
+    js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v, in metadata}, indent=0), safe='')
    item += f"</br><button onclick=\"to_clipboard('{js_txt}')\">Copy to Clipboard</button>"

    item += "</td>"
--- a/modules/sample_hijack.py
+++ b/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
@ -174,7 +175,10 @@ def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps):
    elif scheduler_name == "sgm_uniform":
        sigmas = normal_scheduler(model, steps, sgm=True)
    elif scheduler_name == "turbo":
-        sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps=steps, denoise=1.0)[0]
+        sigmas = SDTurboScheduler().get_sigmas(model=model, steps=steps, denoise=1.0)[0]
+    elif scheduler_name == "align_your_steps":
+        model_type = 'SDXL' if isinstance(model.latent_format, ldm_patched.modules.latent_formats.SDXL) else 'SD1'
+        sigmas = AlignYourStepsScheduler().get_sigmas(model_type=model_type, steps=steps, denoise=1.0)[0]
    else:
        raise TypeError("error invalid scheduler")
    return sigmas
--- a/modules/sdxl_styles.py
+++ b/modules/sdxl_styles.py
@ -2,13 +2,12 @@ import os
 import re
 import json
 import math
-import modules.config

-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_max_bfs_depth = 64


 def normalize_key(k):
@ -24,7 +23,6 @@ def normalize_key(k):


 styles = {}
-
 styles_files = get_files_from_folder(styles_path, ['.json'])

 for x in ['sdxl_styles_fooocus.json',
@ -50,8 +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):
@ -59,34 +62,7 @@ def apply_style(style, positive):
    return p.replace('{prompt}', positive).splitlines(), n.splitlines()


-def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order):
-    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:
-                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
-
-
-def get_words(arrays, totalMult, index):
+def get_words(arrays, total_mult, index):
    if len(arrays) == 1:
        return [arrays[0].split(',')[index]]
    else:
@ -95,7 +71,7 @@ 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):
--- a/modules/ui_gradio_extensions.py
+++ b/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'
--- a/modules/util.py
+++ b/modules/util.py
@ -1,4 +1,4 @@
-import typing
+from pathlib import Path

 import numpy as np
 import datetime
@ -6,16 +6,28 @@ 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

+import modules.config
 import modules.sdxl_styles
+from modules.flags import Performance

 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:
@ -163,25 +175,6 @@ def generate_temp_filename(folder='./outputs/', extension='png'):
    return date_string, os.path.abspath(result), filename


-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, 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 (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
-
-
 def sha256(filename, use_addnet_hash=False, length=HASH_SHA256_LENGTH):
    print(f"Calculating sha256 for {filename}: ", end='')
    if use_addnet_hash:
@ -355,7 +348,7 @@ 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
@ -370,7 +363,18 @@ def is_json(data: str) -> bool:
    return True


+def get_filname_by_stem(lora_name, filenames: List[str]) -> str | None:
+    for filename in filenames:
+        path = Path(filename)
+        if lora_name == path.stem:
+            return filename
+    return None
+
+
 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):
@ -379,10 +383,6 @@ def get_file_from_folder_list(name, folders):
    return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))


-def ordinal_suffix(number: int) -> str:
-    return 'th' if 10 <= number % 100 <= 20 else {1: 'st', 2: 'nd', 3: 'rd'}.get(number % 10, 'th')
-
-
 def makedirs_with_log(path):
    try:
        os.makedirs(path, exist_ok=True)
@ -390,5 +390,132 @@ def makedirs_with_log(path):
        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 get_enabled_loras(loras: list, remove_none=True) -> list:
+    return [(lora[1], lora[2]) for lora in loras if lora[0] and (lora[1] != 'None' if remove_none else True)]
+
+
+def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5,
+                                      skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True,
+                                      lora_filenames=None) -> tuple[List[Tuple[AnyStr, float]], str]:
+    if lora_filenames is None:
+        lora_filenames = []
+
+    found_loras = []
+    prompt_without_loras = ''
+    cleaned_prompt = ''
+
+    for token in prompt.split(','):
+        matches = LORAS_PROMPT_PATTERN.findall(token)
+
+        if len(matches) == 0:
+            prompt_without_loras += token + ', '
+            continue
+        for match in matches:
+            lora_name = match[1] + '.safetensors'
+            if not skip_file_check:
+                lora_name = get_filname_by_stem(match[1], lora_filenames)
+            if lora_name is not None:
+                found_loras.append((lora_name, float(match[2])))
+            token = token.replace(match[0], '')
+        prompt_without_loras += token + ', '
+
+    if prompt_without_loras != '':
+        cleaned_prompt = prompt_without_loras[:-2]
+
+    if prompt_cleanup:
+        cleaned_prompt = cleanup_prompt(prompt_without_loras)
+
+    new_loras = []
+    lora_names = [lora[0] for lora in loras]
+    for found_lora in found_loras:
+        if deduplicate_loras and (found_lora[0] in lora_names or found_lora in new_loras):
+            continue
+        new_loras.append(found_lora)
+
+    if len(new_loras) == 0:
+        return loras, cleaned_prompt
+
+    updated_loras = []
+    for lora in loras + new_loras:
+        if lora[0] != "None":
+            updated_loras.append(lora)
+
+    return updated_loras[:loras_limit], cleaned_prompt
+
+
+def remove_performance_lora(filenames: list, performance: Performance | None):
+    loras_without_performance = filenames.copy()
+
+    if performance is None:
+        return loras_without_performance
+
+    performance_lora = performance.lora_filename()
+
+    for filename in filenames:
+        path = Path(filename)
+        if performance_lora == path.name:
+            loras_without_performance.remove(filename)
+
+    return loras_without_performance
+
+
+def cleanup_prompt(prompt):
+    prompt = re.sub(' +', ' ', prompt)
+    prompt = re.sub(',+', ',', prompt)
+    cleaned_prompt = ''
+    for token in prompt.split(','):
+        token = token.strip()
+        if token == '':
+            continue
+        cleaned_prompt += token + ', '
+    return cleaned_prompt[:-2]
+
+
+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
+
+
+def get_image_size_info(image: np.ndarray, aspect_ratios: list) -> str:
+    try:
+        image = Image.fromarray(np.uint8(image))
+        width, height = image.size
+        ratio = round(width / height, 2)
+        gcd = math.gcd(width, height)
+        lcm_ratio = f'{width // gcd}:{height // gcd}'
+        size_info = f'Image Size: {width} x {height}, Ratio: {ratio}, {lcm_ratio}'
+
+        closest_ratio = min(aspect_ratios, key=lambda x: abs(ratio - float(x.split('*')[0]) / float(x.split('*')[1])))
+        recommended_width, recommended_height = map(int, closest_ratio.split('*'))
+        recommended_ratio = round(recommended_width / recommended_height, 2)
+        recommended_gcd = math.gcd(recommended_width, recommended_height)
+        recommended_lcm_ratio = f'{recommended_width // recommended_gcd}:{recommended_height // recommended_gcd}'
+
+        size_info = f'{width} x {height}, {ratio}, {lcm_ratio}'
+        size_info += f'\n{recommended_width} x {recommended_height}, {recommended_ratio}, {recommended_lcm_ratio}'
+
+        return size_info
+    except Exception as e:
+        return f'Error reading image: {e}'
--- a/presets/.gitignore
+++ b/presets/.gitignore
@ -2,5 +2,6 @@
 !anime.json
 !default.json
 !lcm.json
+!playground_v2.5.json
 !realistic.json
 !sai.json
--- a/presets/playground_v2.5.json
+++ b/presets/playground_v2.5.json
@ -0,0 +1,49 @@
+{
+    "default_model": "playground-v2.5-1024px-aesthetic.fp16.safetensors",
+    "default_refiner": "None",
+    "default_refiner_switch": 0.5,
+    "default_loras": [
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ]
+    ],
+    "default_cfg_scale": 2.0,
+    "default_sample_sharpness": 2.0,
+    "default_sampler": "dpmpp_2m",
+    "default_scheduler": "edm_playground_v2.5",
+    "default_performance": "Speed",
+    "default_prompt": "",
+    "default_prompt_negative": "",
+    "default_styles": [
+        "Fooocus V2"
+    ],
+    "default_aspect_ratio": "1024*1024",
+    "checkpoint_downloads": {
+        "playground-v2.5-1024px-aesthetic.fp16.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/playground-v2.5-1024px-aesthetic.fp16.safetensors"
+    },
+    "embeddings_downloads": {},
+    "lora_downloads": {},
+    "previous_default_models": []
+}
--- a/readme.md
+++ b/readme.md
@ -368,26 +368,38 @@ 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]
+                      [--output-path OUTPUT_PATH]
+                      [--temp-path TEMP_PATH]
                      [--cache-path CACHE_PATH] [--in-browser]
-                      [--disable-in-browser] [--gpu-device-id DEVICE_ID]
+                      [--disable-in-browser]
+                      [--gpu-device-id DEVICE_ID]
                      [--async-cuda-allocation | --disable-async-cuda-allocation]
-                      [--disable-attention-upcast] [--all-in-fp32 | --all-in-fp16]
+                      [--disable-attention-upcast]
+                      [--all-in-fp32 | --all-in-fp16]
                      [--unet-in-bf16 | --unet-in-fp16 | --unet-in-fp8-e4m3fn | --unet-in-fp8-e5m2]
-                      [--vae-in-fp16 | --vae-in-fp32 | --vae-in-bf16]
+                      [--vae-in-fp16 | --vae-in-fp32 | --vae-in-bf16]   
+                      [--vae-in-cpu]
                      [--clip-in-fp8-e4m3fn | --clip-in-fp8-e5m2 | --clip-in-fp16 | --clip-in-fp32]
-                      [--directml [DIRECTML_DEVICE]] [--disable-ipex-hijack]
+                      [--directml [DIRECTML_DEVICE]]
+                      [--disable-ipex-hijack]
                      [--preview-option [none,auto,fast,taesd]]
                      [--attention-split | --attention-quad | --attention-pytorch]
                      [--disable-xformers]
-                      [--always-gpu | --always-high-vram | --always-normal-vram | 
-                       --always-low-vram | --always-no-vram | --always-cpu [CPU_NUM_THREADS]]
-                      [--always-offload-from-vram] [--disable-server-log]
-                      [--debug-mode] [--is-windows-embedded-python]
-                      [--disable-server-info] [--share] [--preset PRESET]
-                      [--language LANGUAGE] [--disable-offload-from-vram]
-                      [--theme THEME] [--disable-image-log]
+                      [--always-gpu | --always-high-vram | --always-normal-vram |
+                      --always-low-vram | --always-no-vram | --always-cpu [CPU_NUM_THREADS]]
+                      [--always-offload-from-vram]
+                      [--pytorch-deterministic] [--disable-server-log]  
+                      [--debug-mode] [--is-windows-embedded-python]     
+                      [--disable-server-info] [--multi-user] [--share]  
+                      [--preset PRESET] [--disable-preset-selection]    
+                      [--language LANGUAGE]
+                      [--disable-offload-from-vram] [--theme THEME]     
+                      [--disable-image-log] [--disable-analytics]       
+                      [--disable-metadata] [--disable-preset-download]  
+                      [--enable-describe-uov-image]
+                      [--always-download-new-model]
 ```

 ## Advanced Features
--- a/requirements_docker.txt
+++ b/requirements_docker.txt
@ -1,5 +1,2 @@
-torch==2.0.1
-torchvision==0.15.2
-torchaudio==2.0.2
-torchtext==0.15.2
-torchdata==0.6.1
+torch==2.1.0
+torchvision==0.16.0
--- a/sdxl_styles/samples/random_style.jpg
+++ b/sdxl_styles/samples/random_style.jpg
--- a/tests/init.py
+++ b/tests/init.py
@ -0,0 +1,4 @@
+import sys
+import pathlib
+
+sys.path.append(pathlib.Path(f'{__file__}/../modules').parent.resolve())
--- a/tests/test_extra_utils.py
+++ b/tests/test_extra_utils.py
@ -0,0 +1,74 @@
+import numbers
+import os
+import unittest
+
+import modules.flags
+from modules import extra_utils
+
+
+class TestUtils(unittest.TestCase):
+    def test_try_eval_env_var(self):
+        test_cases = [
+            {
+                "input": ("foo", str),
+                "output": "foo"
+            },
+            {
+                "input": ("1", int),
+                "output": 1
+            },
+            {
+                "input": ("1.0", float),
+                "output": 1.0
+            },
+            {
+                "input": ("1", numbers.Number),
+                "output": 1
+            },
+            {
+                "input": ("1.0", numbers.Number),
+                "output": 1.0
+            },
+            {
+                "input": ("true", bool),
+                "output": True
+            },
+            {
+                "input": ("True", bool),
+                "output": True
+            },
+            {
+                "input": ("false", bool),
+                "output": False
+            },
+            {
+                "input": ("False", bool),
+                "output": False
+            },
+            {
+                "input": ("True", str),
+                "output": "True"
+            },
+            {
+                "input": ("False", str),
+                "output": "False"
+            },
+            {
+                "input": ("['a', 'b', 'c']", list),
+                "output": ['a', 'b', 'c']
+            },
+            {
+                "input": ("{'a':1}", dict),
+                "output": {'a': 1}
+            },
+            {
+                "input": ("('foo', 1)", tuple),
+                "output": ('foo', 1)
+            }
+        ]
+
+        for test in test_cases:
+            value, expected_type = test["input"]
+            expected = test["output"]
+            actual = extra_utils.try_eval_env_var(value, expected_type)
+            self.assertEqual(expected, actual)
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@ -0,0 +1,137 @@
+import os
+import unittest
+
+import modules.flags
+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, True),
+                "output": (
+                    [('hey-lora.safetensors', 0.4), ('you-lora.safetensors', 0.2)], 'some prompt, very cool, cool'),
+            },
+            # Test can not exceed limit
+            {
+                "input": ("some prompt, very cool, <lora:hey-lora:0.4>,  cool   <lora:you-lora:0.2>", [], 1, True),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt, very cool, cool'
+                ),
+            },
+            # 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,
+                    True
+                ),
+                "output": (
+                    [
+                        ('hey-lora.safetensors', 0.4),
+                        ('l1.safetensors', 0.4),
+                        ('l2.safetensors', -0.2),
+                        ('l3.safetensors', 0.3),
+                        ('l4.safetensors', 0.5)
+                    ],
+                    'some prompt, very cool'
+                )
+            },
+            # test correct matching even if there is no space separating loras in the same token
+            {
+                "input": ("some prompt, very cool, <lora:hey-lora:0.4><lora:you-lora:0.2>", [], 3, True),
+                "output": (
+                    [
+                        ('hey-lora.safetensors', 0.4),
+                        ('you-lora.safetensors', 0.2)
+                    ],
+                    'some prompt, very cool'
+                ),
+            },
+            # test deduplication, also selected loras are never overridden with loras in prompt
+            {
+                "input": (
+                    "some prompt, very cool, <lora:hey-lora:0.4><lora:hey-lora:0.4><lora:you-lora:0.2>",
+                    [('you-lora.safetensors', 0.3)],
+                    3,
+                    True
+                ),
+                "output": (
+                    [
+                        ('you-lora.safetensors', 0.3),
+                        ('hey-lora.safetensors', 0.4)
+                    ],
+                    'some prompt, very cool'
+                ),
+            },
+            {
+                "input": ("<lora:foo:1..2>, <lora:bar:.>, <test:1.0>, <lora:baz:+> and <lora:quux:>", [], 6, True),
+                "output": (
+                    [],
+                    '<lora:foo:1..2>, <lora:bar:.>, <test:1.0>, <lora:baz:+> and <lora:quux:>'
+                )
+            }
+        ]
+
+        for test in test_cases:
+            prompt, loras, loras_limit, skip_file_check = test["input"]
+            expected = test["output"]
+            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit,
+                                                            skip_file_check=skip_file_check)
+            self.assertEqual(expected, actual)
+
+    def test_can_parse_tokens_and_strip_performance_lora(self):
+        lora_filenames = [
+            'hey-lora.safetensors',
+            modules.flags.PerformanceLoRA.EXTREME_SPEED.value,
+            modules.flags.PerformanceLoRA.LIGHTNING.value,
+            os.path.join('subfolder', modules.flags.PerformanceLoRA.HYPER_SD.value)
+        ]
+
+        test_cases = [
+            {
+                "input": ("some prompt, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.QUALITY),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.SPEED),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:sdxl_lcm_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.EXTREME_SPEED),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:sdxl_lightning_4step_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.LIGHTNING),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            },
+            {
+                "input": ("some prompt, <lora:sdxl_hyper_sd_4step_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.HYPER_SD),
+                "output": (
+                    [('hey-lora.safetensors', 0.4)],
+                    'some prompt'
+                ),
+            }
+        ]
+
+        for test in test_cases:
+            prompt, loras, loras_limit, skip_file_check, performance = test["input"]
+            lora_filenames = modules.util.remove_performance_lora(lora_filenames, performance)
+            expected = test["output"]
+            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, lora_filenames=lora_filenames)
+            self.assertEqual(expected, actual)
--- a/update_log.md
+++ b/update_log.md
@ -1,3 +1,41 @@
+# [2.4.3](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.3)
+
+* Fix alphas_cumprod setter for TCD sampler
+* Add parser for env var strings to expected config value types to allow override of all non-path config keys 
+
+# [2.4.2](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.2)
+
+* Fix some small bugs (tcd scheduler when gamma is 0, chown in Dockerfile, update cmd args in readme, translation for aspect ratios, vae default after file reload)
+* Fix performance LoRA replacement when data is loaded from history log and inline prompt
+* Add support and preset for playground v2.5 (only works with performance Quality or Speed, use with scheduler edm_playground_v2)
+* Make textboxes (incl. positive prompt) resizable
+* Hide intermediate images when performance of Gradio would bottleneck the generation process (Extreme Speed, Lightning, Hyper-SD)
+
+# [2.4.1](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.1)
+
+* Fix some small bugs (e.g. adjust clip skip default value from 1 to 2, add type check to aspect ratios js update function)
+* Add automated docker build on push to main, tagged with `edge`. See [available docker images](https://github.com/lllyasviel/Fooocus/pkgs/container/fooocus).
+
+# [2.4.0](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.0)
+
+* Change settings tab elements to be more compact
+* Add clip skip slider
+* Add select for custom VAE
+* Add new style "Random Style"
+* Update default anime model to animaPencilXL_v310
+* Add button to reconnect the UI after Fooocus crashed without having to configure everything again (no page reload required)
+* Add performance "hyper-sd" (based on [Hyper-SDXL 4 step LoRA](https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-4steps-lora.safetensors))
+* Add [AlignYourSteps](https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/) scheduler by Nvidia, see 
+* Add [TCD](https://github.com/jabir-zheng/TCD) sampler and scheduler (based on sgm_uniform)
+* Add NSFW image censoring (disables intermediate image preview while generating). Set config value `default_black_out_nsfw` to True to always enable.
+* Add argument `--enable-describe-uov-image` to automatically describe uploaded images for upscaling
+* Add inline lora prompt references with subfolder support, example prompt: `colorful bird <lora:toucan:1.2>`
+* Add size and aspect ratio recommendation on image describe
+* Add inpaint brush color picker, helpful when image and mask brush have the same color
+* Add automated Docker image build using Github Actions on each release.
+* Add full raw prompts to history logs
+* Change code ownership from @lllyasviel to @mashb1t for automated issue / MR notification
+
 # [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
--- a/webui.py
+++ b/webui.py
@ -112,10 +112,10 @@ with shared.gradio_root:
            gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain', visible=True, height=768,
                                 elem_classes=['resizable_area', 'main_view', 'final_gallery', 'image_gallery'],
                                 elem_id='final_gallery')
-            with gr.Row(elem_classes='type_row'):
+            with gr.Row():
                with gr.Column(scale=17):
                    prompt = gr.Textbox(show_label=False, placeholder="Type prompt here or paste parameters.", elem_id='positive_prompt',
-                                        container=False, autofocus=True, elem_classes='type_row', lines=1024)
+                                        autofocus=True, lines=3)

                    default_prompt = modules.config.default_prompt
                    if isinstance(default_prompt, str) and default_prompt != '':
@ -123,8 +123,9 @@ with shared.gradio_root:

                with gr.Column(scale=3, min_width=0):
                    generate_button = gr.Button(label="Generate", value="Generate", elem_classes='type_row', elem_id='generate_button', visible=True)
+                    reset_button = gr.Button(label="Reconnect", value="Reconnect", elem_classes='type_row', elem_id='reset_button', visible=False)
                    load_parameter_button = gr.Button(label="Load Parameters", value="Load Parameters", elem_classes='type_row', elem_id='load_parameter_button', visible=False)
-                    skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', visible=False)
+                    skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', elem_id='skip_button', visible=False)
                    stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False)

                    def stop_clicked(currentTask):
@ -151,7 +152,7 @@ with shared.gradio_root:
                    with gr.TabItem(label='Upscale or Variation') as uov_tab:
                        with gr.Row():
                            with gr.Column():
-                                uov_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy')
+                                uov_input_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False)
                            with gr.Column():
                                uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=flags.disabled)
                                gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/390" target="_blank">\U0001F4D4 Document</a>')
@ -200,7 +201,7 @@ with shared.gradio_root:
                                           queue=False, show_progress=False)
                    with gr.TabItem(label='Inpaint or Outpaint') as inpaint_tab:
                        with gr.Row():
-                            inpaint_input_image = grh.Image(label='Drag inpaint or outpaint image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas')
+                            inpaint_input_image = grh.Image(label='Image', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas', show_label=False)
                            inpaint_mask_image = grh.Image(label='Mask Upload', source='upload', type='numpy', height=500, visible=False)

                        with gr.Row():
@ -213,17 +214,26 @@ with shared.gradio_root:
                    with gr.TabItem(label='Describe') as desc_tab:
                        with gr.Row():
                            with gr.Column():
-                                desc_input_image = grh.Image(label='Drag any image to here', source='upload', type='numpy')
+                                desc_input_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False)
                            with gr.Column():
                                desc_method = gr.Radio(
                                    label='Content Type',
                                    choices=[flags.desc_type_photo, flags.desc_type_anime],
                                    value=flags.desc_type_photo)
                                desc_btn = gr.Button(value='Describe this Image into Prompt')
+                                desc_image_size = gr.Textbox(label='Image Size and Recommended Size', elem_id='desc_image_size', visible=False)
                                gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/1363" target="_blank">\U0001F4D4 Document</a>')
-                    with gr.TabItem(label='Metadata') as load_tab:
+
+                                def trigger_show_image_properties(image):
+                                    value = modules.util.get_image_size_info(image, modules.flags.sdxl_aspect_ratios)
+                                    return gr.update(value=value, visible=True)
+
+                                desc_input_image.upload(trigger_show_image_properties, inputs=desc_input_image,
+                                                        outputs=desc_image_size, show_progress=False, queue=False)
+
+                    with gr.TabItem(label='Metadata') as metadata_tab:
                        with gr.Column():
-                            metadata_input_image = grh.Image(label='Drag any image generated by Fooocus here', source='upload', type='filepath')
+                            metadata_input_image = grh.Image(label='For images created by Fooocus', source='upload', type='filepath')
                            metadata_json = gr.JSON(label='Metadata')
                            metadata_import_button = gr.Button(value='Apply Metadata')

@ -254,25 +264,34 @@ with shared.gradio_root:
            inpaint_tab.select(lambda: 'inpaint', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
            ip_tab.select(lambda: 'ip', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
            desc_tab.select(lambda: 'desc', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
+            metadata_tab.select(lambda: 'metadata', outputs=current_tab, queue=False, _js=down_js, show_progress=False)

        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)
+                    preset_selection = gr.Dropdown(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=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',
-                                                   elem_classes='aspect_ratios')
+                                                 value=modules.config.default_performance,
+                                                 elem_classes=['performance_selection'])
+                with gr.Accordion(label='Aspect Ratios', open=False, elem_id='aspect_ratios_accordion') as aspect_ratios_accordion:
+                    aspect_ratios_selection = gr.Radio(label='Aspect Ratios', show_label=False,
+                                                       choices=modules.config.available_aspect_ratios_labels,
+                                                       value=modules.config.default_aspect_ratio,
+                                                       info='width × height',
+                                                       elem_classes='aspect_ratios')
+
+                    aspect_ratios_selection.change(lambda x: None, inputs=aspect_ratios_selection, queue=False, show_progress=False, _js='(x)=>{refresh_aspect_ratios_label(x);}')
+                    shared.gradio_root.load(lambda x: None, inputs=aspect_ratios_selection, queue=False, show_progress=False, _js='(x)=>{refresh_aspect_ratios_label(x);}')
+
                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=flags.OutputFormat.list(),
-                                            value=modules.config.default_output_format)
+                                         choices=flags.OutputFormat.list(),
+                                         value=modules.config.default_output_format)

                negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.",
                                             info='Describing what you do not want to see.', lines=2,
@ -402,10 +421,15 @@ with shared.gradio_root:
                                                 value=modules.config.default_cfg_tsnr,
                                                 info='Enabling Fooocus\'s implementation of CFG mimicking for TSNR '
                                                      '(effective when real CFG > mimicked CFG).')
+                        clip_skip = gr.Slider(label='CLIP Skip', minimum=1, maximum=flags.clip_skip_max, step=1,
+                                                 value=modules.config.default_clip_skip,
+                                                 info='Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).')
                        sampler_name = gr.Dropdown(label='Sampler', choices=flags.sampler_list,
                                                   value=modules.config.default_sampler)
                        scheduler_name = gr.Dropdown(label='Scheduler', choices=flags.scheduler_list,
                                                     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.',
@ -433,17 +457,26 @@ 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 == flags.Performance.EXTREME_SPEED.value,
-                                                      interactive=modules.config.default_performance != flags.Performance.EXTREME_SPEED.value,
+                        disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results',
+                                                      value=flags.Performance.has_restricted_features(modules.config.default_performance),
                                                      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.')
@ -502,13 +535,20 @@ with shared.gradio_root:
                        inpaint_mask_upload_checkbox = gr.Checkbox(label='Enable Mask Upload', value=False)
                        invert_mask_checkbox = gr.Checkbox(label='Invert Mask', value=False)

+                        inpaint_mask_color = gr.ColorPicker(label='Inpaint brush color', value='#FFFFFF', elem_id='inpaint_brush_color')
+
                        inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine,
                                         inpaint_strength, inpaint_respective_field,
                                         inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate]

                        inpaint_mask_upload_checkbox.change(lambda x: gr.update(visible=x),
-                                                           inputs=inpaint_mask_upload_checkbox,
-                                                           outputs=inpaint_mask_image, queue=False, show_progress=False)
+                                                            inputs=inpaint_mask_upload_checkbox,
+                                                            outputs=inpaint_mask_image, queue=False,
+                                                            show_progress=False)
+
+                        inpaint_mask_color.change(lambda x: gr.update(brush_color=x), inputs=inpaint_mask_color,
+                                                  outputs=inpaint_input_image,
+                                                  queue=False, show_progress=False)

                    with gr.Tab(label='FreeU'):
                        freeu_enabled = gr.Checkbox(label='Enabled', value=False)
@ -528,6 +568,7 @@ with shared.gradio_root:
                    modules.config.update_files()
                    results = [gr.update(choices=modules.config.model_filenames)]
                    results += [gr.update(choices=['None'] + modules.config.model_filenames)]
+                    results += [gr.update(choices=[flags.default_vae] + modules.config.vae_filenames)]
                    if not args_manager.args.disable_preset_selection:
                        results += [gr.update(choices=modules.config.available_presets)]
                    for i in range(modules.config.default_max_lora_number):
@ -535,7 +576,7 @@ with shared.gradio_root:
                                    gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
                    return results

-                refresh_files_output = [base_model, refiner_model]
+                refresh_files_output = [base_model, refiner_model, vae_name]
                if not args_manager.args.disable_preset_selection:
                    refresh_files_output += [preset_selection]
                refresh_files.click(refresh_files_clicked, [], refresh_files_output + lora_ctrls,
@ -546,9 +587,9 @@ with shared.gradio_root:
        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
+                             adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, clip_skip,
+                             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):
@ -570,11 +611,11 @@ with shared.gradio_root:
                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) \
+                .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,
+                                               [gr.update(value=flags.Performance.has_restricted_features(x))] * 1,
                                     inputs=performance_selection,
                                     outputs=[
                                         guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive,
@ -632,9 +673,9 @@ with shared.gradio_root:
        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 += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg]
-        ctrls += [sampler_name, scheduler_name]
+        ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment, black_out_nsfw]
+        ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, clip_skip]
+        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]
@ -671,7 +712,7 @@ with shared.gradio_root:
                parsed_parameters = {}
            else:
                metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
-                parsed_parameters = metadata_parser.parse_json(parameters)
+                parsed_parameters = metadata_parser.to_json(parameters)

            return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)

@ -688,6 +729,14 @@ 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)
@ -705,6 +754,15 @@ 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
--- a/wildcards/.gitignore
+++ b/wildcards/.gitignore
@ -0,0 +1,8 @@
+*.txt
+!animal.txt
+!artist.txt
+!color.txt
+!color_flower.txt
+!extended-color.txt
+!flower.txt
+!nationality.txt