docs: bump version number to 2.2.0-rc1

easier debugging and issue handling

.dockerignore

@@ -0,0 +1 @@
+.idea

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,18 +0,0 @@
----
-name: Bug report
-about: Describe a problem
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Read Troubleshoot**
-
-[x] I confirm that I have read the [Troubleshoot](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md) guide before making this issue.
-
-**Describe the problem**
-A clear and concise description of what the bug is.
-
-**Full Console Log**
-Paste the **full** console log here. You will make our job easier if you give a **full** log.

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,106 @@
+name: Bug Report
+description: You think something is broken in Fooocus
+title: "[Bug]: "
+labels: ["bug", "triage"]
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        > The title of the bug report should be short and descriptive.
+        > Use relevant keywords for searchability.
+        > Do not leave it blank, but also do not put an entire error log in it.
+  - type: checkboxes
+    attributes:
+      label: Checklist
+      description: |
+        Please perform basic debugging to see if your configuration is the cause of the issue.
+        Basic debug procedure
+        　2. Update Fooocus - sometimes things just need to be updated
+        　3. Backup and remove your config.txt - check if the issue is caused by bad configuration
+        　5. Try a fresh installation of Fooocus in a different directory - see if a clean installation solves the issue
+        Before making a issue report please, check that the issue hasn't been reported recently.
+      options:
+        - label: The issue exists on a clean installation of Fooocus
+        - label: The issue exists in the current version of Fooocus
+        - label: The issue has not been reported before recently
+        - label: The issue has been reported before but has not been fixed yet
+  - type: markdown
+    attributes:
+      value: |
+        > Please fill this form with as much information as possible. Don't forget to add information about "What browsers" and provide screenshots if possible
+  - type: textarea
+    id: what-did
+    attributes:
+      label: What happened?
+      description: Tell us what happened in a very clear and simple way
+      placeholder: |
+        image generation is not working as intended.
+    validations:
+      required: true
+  - type: textarea
+    id: steps
+    attributes:
+      label: Steps to reproduce the problem
+      description: Please provide us with precise step by step instructions on how to reproduce the bug
+      placeholder: |
+        1. Go to ...
+        2. Press ...
+        3. ...
+    validations:
+      required: true
+  - type: textarea
+    id: what-should
+    attributes:
+      label: What should have happened?
+      description: Tell us what you think the normal behavior should be
+      placeholder: |
+        Fooocus should ...
+    validations:
+      required: true
+  - type: dropdown
+    id: browsers
+    attributes:
+      label: What browsers do you use to access Fooocus?
+      multiple: true
+      options:
+        - Mozilla Firefox
+        - Google Chrome
+        - Brave
+        - Apple Safari
+        - Microsoft Edge
+        - Android
+        - iOS
+        - Other
+  - type: dropdown
+    id: hosting
+    attributes:
+      label: Where are you running Fooocus?
+      multiple: false
+      options:
+        - Locally
+        - Locally with virtualization (e.g. Docker)
+        - Cloud (Google Colab)
+        - Cloud (other)
+  - type: input
+    id: operating-system
+    attributes:
+      label: What operating system are you using?
+      placeholder: |
+        Windows 10
+  - type: textarea
+    id: logs
+    attributes:
+      label: Console logs
+      description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service.
+      render: Shell
+    validations:
+      required: true
+  - type: textarea
+    id: misc
+    attributes:
+      label: Additional information
+      description: | 
+        Please provide us with any relevant additional info or context.
+        Examples:
+        　I have updated my GPU driver recently.

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,5 @@
+blank_issues_enabled: false
+contact_links:
+  - name: Ask a question
+    url: https://github.com/lllyasviel/Fooocus/discussions/new?category=q-a
+    about: Ask the community for help

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,14 +0,0 @@
----
-name: Feature request
-about: Suggest an idea for this project
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Is your feature request related to a problem? Please describe.**
-A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
-
-**Describe the idea you'd like**
-A clear and concise description of what you want to happen.

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,40 @@
+name: Feature request
+description: Suggest an idea for this project
+title: "[Feature Request]: "
+labels: ["enhancement", "triage"]
+
+body:
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit.
+      options:
+        - label: I have searched the existing issues and checked the recent builds/commits
+          required: true
+  - type: markdown
+    attributes:
+      value: |
+        *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible*
+  - type: textarea
+    id: feature
+    attributes:
+      label: What would your feature do?
+      description: Tell us about your feature in a very clear and simple way, and what problem it would solve
+    validations:
+      required: true
+  - type: textarea
+    id: workflow
+    attributes:
+      label: Proposed workflow
+      description: Please provide us with step by step information on how you'd like the feature to be accessed and used
+      value: |
+        1. Go to .... 
+        2. Press ....
+        3. ...
+    validations:
+      required: true
+  - type: textarea
+    id: misc
+    attributes:
+      label: Additional information
+      description: Add any other context or screenshots about the feature request here.

.gitignore

@@ -51,3 +51,4 @@ user_path_config-deprecated.txt
 /package-lock.json
 /.coverage*
 /auth.json
+.DS_Store

Dockerfile

@@ -0,0 +1,29 @@
+FROM nvidia/cuda:12.3.1-base-ubuntu22.04
+ENV DEBIAN_FRONTEND noninteractive
+ENV CMDARGS --listen
+
+RUN apt-get update -y && \
+	apt-get install -y curl libgl1 libglib2.0-0 python3-pip python-is-python3 git && \
+	apt-get clean && \
+	rm -rf /var/lib/apt/lists/*
+
+COPY requirements_docker.txt requirements_versions.txt /tmp/
+RUN pip install --no-cache-dir -r /tmp/requirements_docker.txt -r /tmp/requirements_versions.txt && \
+	rm -f /tmp/requirements_docker.txt /tmp/requirements_versions.txt
+RUN pip install --no-cache-dir xformers==0.0.22 --no-dependencies
+RUN curl -fsL -o /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2 https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_amd64 && \
+	chmod +x /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2
+
+RUN adduser --disabled-password --gecos '' user && \
+	mkdir -p /content/app /content/data
+
+COPY entrypoint.sh /content/
+RUN chown -R user:user /content
+
+WORKDIR /content
+USER user
+
+RUN git clone https://github.com/lllyasviel/Fooocus /content/app
+RUN mv /content/app/models /content/app/models.org
+
+CMD [ "sh", "-c", "/content/entrypoint.sh ${CMDARGS}" ]

args_manager.py

@@ -1,5 +1,7 @@
 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.")
 args_parser.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.")
@@ -18,7 +20,10 @@ args_parser.parser.add_argument("--disable-image-log", action='store_true',
                                 help="Prevent writing images and logs to hard drive.")
 
 args_parser.parser.add_argument("--disable-analytics", action='store_true',
-                                help="Disables analytics for Gradio", default=False)
+                                help="Disables analytics for Gradio.")
+
+args_parser.parser.add_argument("--disable-metadata", action='store_true',
+                                help="Disables saving metadata to images.")
 
 args_parser.parser.add_argument("--disable-preset-download", action='store_true',
                                 help="Disables downloading models for presets", default=False)
@@ -40,7 +45,11 @@ args_parser.args.always_offload_from_vram = not args_parser.args.disable_offload
 if args_parser.args.disable_analytics:
     import os
     os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+
 if args_parser.args.disable_in_browser:
     args_parser.args.in_browser = False
 
+if args_parser.args.temp_path is None:
+    args_parser.args.temp_path = os.path.join(gettempdir(), 'Fooocus')
+
 args = args_parser.args

docker-compose.yml

@@ -0,0 +1,38 @@
+version: '3.9'
+
+volumes:
+  fooocus-data:
+
+services:
+  app:
+    build: .
+    image: fooocus
+    ports:
+     - "7865:7865"
+    environment:
+      - CMDARGS=--listen    # Arguments for launch.py.
+      - DATADIR=/content/data   # Directory which stores models, outputs dir
+      - config_path=/content/data/config.txt
+      - config_example_path=/content/data/config_modification_tutorial.txt
+      - path_checkpoints=/content/data/models/checkpoints/
+      - path_loras=/content/data/models/loras/
+      - path_embeddings=/content/data/models/embeddings/
+      - path_vae_approx=/content/data/models/vae_approx/
+      - path_upscale_models=/content/data/models/upscale_models/
+      - path_inpaint=/content/data/models/inpaint/
+      - path_controlnet=/content/data/models/controlnet/
+      - path_clip_vision=/content/data/models/clip_vision/
+      - path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/
+      - path_outputs=/content/app/outputs/    # Warning: If it is not located under '/content/app', you can't see history log!
+    volumes:
+      - fooocus-data:/content/data
+      #- ./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.
+    tty: true
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids: ['0']
+              capabilities: [compute, utility]

docker.md

@@ -0,0 +1,66 @@
+# 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.
+
+## Quick start
+
+**This is just an easy way for testing. Please find more information in the [notes](#notes).**
+
+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.
+
+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`.
+
+## Details
+
+### Update the container manually
+
+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):
+```
+#- ./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.
+
+
+### Paths inside the container
+
+|Path|Details|
+|-|-|
+|/content/app|The application stored folder|
+|/content/app/models.org|Original 'models' folder.<br> Files are copied to the '/content/app/models' which is symlinked to '/content/data/models' every time the container boots. (Existing files will not be overwritten.) |
+|/content/data|Persistent volume mount point|
+|/content/data/models|The folder is symlinked to '/content/app/models'|
+|/content/data/outputs|The folder is symlinked to '/content/app/outputs'|
+
+### Environments
+
+You can change `config.txt` parameters by using environment variables.
+**The priority of using the environments is higher than the values defined in `config.txt`, and they will be saved to the `config_modification_tutorial.txt`**
+
+Docker specified environments are there. They are used by 'entrypoint.sh'
+|Environment|Details|
+|-|-|
+|DATADIR|'/content/data' location.|
+|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|
+
+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)
+
+## Notes
+
+- Please keep 'path_outputs' under '/content/app'. Otherwise, you may get an error when you open the history log.
+- Docker on Mac/Windows still has issues in the form of slow volume access when you use "bind mount" volumes. Please refer to [this article](https://docs.docker.com/storage/volumes/#use-a-volume-with-docker-compose) for not using "bind mount".
+- The MPS backend (Metal Performance Shaders, Apple Silicon M1/M2/etc.) is not yet supported in Docker, see https://github.com/pytorch/pytorch/issues/81224
+- You can also use `docker compose up -d` to start the container detached and connect to the logs with `docker compose logs -f`. This way you can also close the terminal and keep the container running.

entrypoint.sh

@@ -0,0 +1,33 @@
+#!/bin/bash
+
+ORIGINALDIR=/content/app
+# Use predefined DATADIR if it is defined
+[[ x"${DATADIR}" == "x" ]] && DATADIR=/content/data
+
+# Make persistent dir from original dir
+function mklink () {
+	mkdir -p $DATADIR/$1
+	ln -s $DATADIR/$1 $ORIGINALDIR
+}
+
+# Copy old files from import dir
+function import () {
+	(test -d /import/$1 && cd /import/$1 && cp -Rpn . $DATADIR/$1/)
+}
+
+cd $ORIGINALDIR
+
+# models
+mklink models
+# Copy original files
+(cd $ORIGINALDIR/models.org && cp -Rpn . $ORIGINALDIR/models/)
+# Import old files
+import models
+
+# outputs
+mklink outputs
+# Import old files
+import outputs
+
+# Start application
+python launch.py $*

extras/expansion.py

@@ -112,6 +112,9 @@ class FooocusExpansion:
         max_token_length = 75 * int(math.ceil(float(current_token_length) / 75.0))
         max_new_tokens = max_token_length - current_token_length
 
+        if max_new_tokens == 0:
+            return prompt[:-1]
+
         # https://huggingface.co/blog/introducing-csearch
         # https://huggingface.co/docs/transformers/generation_strategies
         features = self.model.generate(**tokenized_kwargs,

extras/preprocessors.py

@@ -1,27 +1,26 @@
 import cv2
 import numpy as np
-import modules.advanced_parameters as advanced_parameters
 
 
-def centered_canny(x: np.ndarray):
+def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 2 and x.dtype == np.uint8
 
-    y = cv2.Canny(x, int(advanced_parameters.canny_low_threshold), int(advanced_parameters.canny_high_threshold))
+    y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold))
     y = y.astype(np.float32) / 255.0
     return y
 
 
-def centered_canny_color(x: np.ndarray):
+def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 3 and x.shape[2] == 3
 
-    result = [centered_canny(x[..., i]) for i in range(3)]
+    result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)]
     result = np.stack(result, axis=2)
     return result
 
 
-def pyramid_canny_color(x: np.ndarray):
+def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 3 and x.shape[2] == 3
 
@@ -31,7 +30,7 @@ def pyramid_canny_color(x: np.ndarray):
     for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
         Hs, Ws = int(H * k), int(W * k)
         small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA)
-        edge = centered_canny_color(small)
+        edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold)
         if acc_edge is None:
             acc_edge = edge
         else:
@@ -54,11 +53,11 @@ def norm255(x, low=4, high=96):
     return x * 255.0
 
 
-def canny_pyramid(x):
+def canny_pyramid(x, canny_low_threshold, canny_high_threshold):
     # For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions.
     # Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions.
 
-    color_canny = pyramid_canny_color(x)
+    color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold)
     result = np.sum(color_canny, axis=2)
 
     return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)

fooocus_version.py

@@ -1 +1 @@
-version = '2.1.865'
+version = '2.2.0-rc1'

language/en.json

@@ -48,6 +48,8 @@
     "Describing what you do not want to see.": "Describing what you do not want to see.",
     "Random": "Random",
     "Seed": "Seed",
+    "Disable seed increment": "Disable seed increment",
+    "Disable automatic seed increment when image number is > 1.": "Disable automatic seed increment when image number is > 1.",
     "\ud83d\udcda History Log": "\uD83D\uDCDA History Log",
     "Image Style": "Image Style",
     "Fooocus V2": "Fooocus V2",
@@ -342,6 +344,10 @@
     "Forced Overwrite of Denoising Strength of \"Vary\"": "Forced Overwrite of Denoising Strength of \"Vary\"",
     "Set as negative number to disable. For developer debugging.": "Set as negative number to disable. For developer debugging.",
     "Forced Overwrite of Denoising Strength of \"Upscale\"": "Forced Overwrite of Denoising Strength of \"Upscale\"",
+    "Disable Preview": "Disable Preview",
+    "Disable preview during generation.": "Disable preview during generation.",
+    "Disable Intermediate Results": "Disable Intermediate Results",
+    "Disable intermediate results during generation, only show final gallery.": "Disable intermediate results during generation, only show final gallery.",
     "Inpaint Engine": "Inpaint Engine",
     "v1": "v1",
     "Version of Fooocus inpaint model": "Version of Fooocus inpaint model",
@@ -368,5 +374,12 @@
     "* Powered by Fooocus Inpaint Engine (beta)": "* Powered by Fooocus Inpaint Engine (beta)",
     "Fooocus Enhance": "Fooocus Enhance",
     "Fooocus Cinematic": "Fooocus Cinematic",
-    "Fooocus Sharp": "Fooocus Sharp"
+    "Fooocus Sharp": "Fooocus Sharp",
+    "Drag any image generated by Fooocus here": "Drag any image generated by Fooocus here",
+    "Metadata": "Metadata",
+    "Apply Metadata": "Apply Metadata",
+    "Metadata Scheme": "Metadata Scheme",
+    "Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.": "Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.",
+    "fooocus (json)": "fooocus (json)",
+    "a1111 (plain text)": "a1111 (plain text)"
 }

launch.py

@@ -68,7 +68,6 @@ vae_approx_filenames = [
      'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors')
 ]
 
-
 def ini_args():
     from args_manager import args
     return args
@@ -101,9 +100,9 @@ def download_models():
         return
 
     if not args.always_download_new_model:
-        if not os.path.exists(os.path.join(config.path_checkpoints, config.default_base_model_name)):
+        if not os.path.exists(os.path.join(config.paths_checkpoints[0], config.default_base_model_name)):
             for alternative_model_name in config.previous_default_models:
-                if os.path.exists(os.path.join(config.path_checkpoints, alternative_model_name)):
+                if os.path.exists(os.path.join(config.paths_checkpoints[0], alternative_model_name)):
                     print(f'You do not have [{config.default_base_model_name}] but you have [{alternative_model_name}].')
                     print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, '
                           f'but you are not using latest models.')
@@ -113,11 +112,11 @@ def download_models():
                     break
 
     for file_name, url in config.checkpoint_downloads.items():
-        load_file_from_url(url=url, model_dir=config.path_checkpoints, file_name=file_name)
+        load_file_from_url(url=url, model_dir=config.paths_checkpoints[0], file_name=file_name)
     for file_name, url in config.embeddings_downloads.items():
         load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name)
     for file_name, url in config.lora_downloads.items():
-        load_file_from_url(url=url, model_dir=config.path_loras, file_name=file_name)
+        load_file_from_url(url=url, model_dir=config.paths_loras[0], file_name=file_name)
 
     return
 

ldm_patched/modules/args_parser.py

@@ -100,8 +100,7 @@ vram_group.add_argument("--always-high-vram", action="store_true")
 vram_group.add_argument("--always-normal-vram", action="store_true")
 vram_group.add_argument("--always-low-vram", action="store_true")
 vram_group.add_argument("--always-no-vram", action="store_true")
-vram_group.add_argument("--always-cpu", action="store_true")
+vram_group.add_argument("--always-cpu", type=int, nargs="?", metavar="CPU_NUM_THREADS", const=-1)
-
 
 parser.add_argument("--always-offload-from-vram", action="store_true")
 parser.add_argument("--pytorch-deterministic", action="store_true")

ldm_patched/modules/model_management.py

@@ -60,6 +60,9 @@ except:
     pass
 
 if args.always_cpu:
+    if args.always_cpu > 0:
+        torch.set_num_threads(args.always_cpu)
+    print(f"Running on {torch.get_num_threads()} CPU threads")
     cpu_state = CPUState.CPU
 
 def is_intel_xpu():

modules/advanced_parameters.py

@@ -1,33 +0,0 @@
-disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name,  \
-    scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
-    overwrite_vary_strength, overwrite_upscale_strength, \
-    mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
-    debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
-    refiner_swap_method, \
-    freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
-    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 = [None] * 35
-
-
-def set_all_advanced_parameters(*args):
-    global disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
-        scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
-        overwrite_vary_strength, overwrite_upscale_strength, \
-        mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
-        debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
-        refiner_swap_method, \
-        freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
-        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
-
-    disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
-        scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
-        overwrite_vary_strength, overwrite_upscale_strength, \
-        mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
-        debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
-        refiner_swap_method, \
-        freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
-        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 = args
-
-    return

modules/async_worker.py

@@ -1,11 +1,15 @@
 import threading
+from modules.patch import PatchSettings, patch_settings, patch_all
 
+patch_all()
 
 class AsyncTask:
     def __init__(self, args):
         self.args = args
         self.yields = []
         self.results = []
+        self.last_stop = False
+        self.processing = False
 
 
 async_tasks = []
@@ -14,6 +18,7 @@ async_tasks = []
 def worker():
     global async_tasks
 
+    import os
     import traceback
     import math
     import numpy as np
@@ -31,17 +36,22 @@ def worker():
     import extras.preprocessors as preprocessors
     import modules.inpaint_worker as inpaint_worker
     import modules.constants as constants
-    import modules.advanced_parameters as advanced_parameters
     import extras.ip_adapter as ip_adapter
     import extras.face_crop
     import fooocus_version
+    import args_manager
 
-    from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion
+    from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
     from modules.private_logger import log
     from extras.expansion import safe_str
     from modules.util import remove_empty_str, HWC3, resize_image, \
         get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix
     from modules.upscaler import perform_upscale
+    from modules.flags import Performance
+    from modules.meta_parser import get_metadata_parser, MetadataScheme
+
+    pid = os.getpid()
+    print(f'Started worker with PID {pid}')
 
     try:
         async_gradio_app = shared.gradio_root
@@ -69,9 +79,6 @@ def worker():
         return
 
     def build_image_wall(async_task):
-        if not advanced_parameters.generate_image_grid:
-            return
-
         results = async_task.results
 
         if len(results) < 2:
@@ -111,10 +118,19 @@ def worker():
         async_task.results = async_task.results + [wall]
         return
 
+    def apply_enabled_loras(loras):
+        enabled_loras = []
+        for lora_enabled, lora_model, lora_weight in loras:
+            if lora_enabled:
+                enabled_loras.append([lora_model, lora_weight])
+
+        return enabled_loras
+
     @torch.no_grad()
     @torch.inference_mode()
     def handler(async_task):
         execution_start_time = time.perf_counter()
+        async_task.processing = True
 
         args = async_task.args
         args.reverse()
@@ -122,16 +138,17 @@ def worker():
         prompt = args.pop()
         negative_prompt = args.pop()
         style_selections = args.pop()
-        performance_selection = args.pop()
+        performance_selection = Performance(args.pop())
         aspect_ratios_selection = args.pop()
         image_number = args.pop()
+        output_format = args.pop()
         image_seed = args.pop()
         sharpness = args.pop()
         guidance_scale = args.pop()
         base_model_name = args.pop()
         refiner_model_name = args.pop()
         refiner_switch = args.pop()
-        loras = [[str(args.pop()), float(args.pop())] for _ in range(5)]
+        loras = apply_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()
@@ -141,8 +158,48 @@ def worker():
         inpaint_additional_prompt = args.pop()
         inpaint_mask_image_upload = args.pop()
 
+        disable_preview = args.pop()
+        disable_intermediate_results = args.pop()
+        disable_seed_increment = args.pop()
+        adm_scaler_positive = args.pop()
+        adm_scaler_negative = args.pop()
+        adm_scaler_end = args.pop()
+        adaptive_cfg = args.pop()
+        sampler_name = args.pop()
+        scheduler_name = args.pop()
+        overwrite_step = args.pop()
+        overwrite_switch = args.pop()
+        overwrite_width = args.pop()
+        overwrite_height = args.pop()
+        overwrite_vary_strength = args.pop()
+        overwrite_upscale_strength = args.pop()
+        mixing_image_prompt_and_vary_upscale = args.pop()
+        mixing_image_prompt_and_inpaint = args.pop()
+        debugging_cn_preprocessor = args.pop()
+        skipping_cn_preprocessor = args.pop()
+        canny_low_threshold = args.pop()
+        canny_high_threshold = args.pop()
+        refiner_swap_method = args.pop()
+        controlnet_softness = args.pop()
+        freeu_enabled = args.pop()
+        freeu_b1 = args.pop()
+        freeu_b2 = args.pop()
+        freeu_s1 = args.pop()
+        freeu_s2 = args.pop()
+        debugging_inpaint_preprocessor = args.pop()
+        inpaint_disable_initial_latent = args.pop()
+        inpaint_engine = args.pop()
+        inpaint_strength = args.pop()
+        inpaint_respective_field = args.pop()
+        inpaint_mask_upload_checkbox = args.pop()
+        invert_mask_checkbox = args.pop()
+        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
+
         cn_tasks = {x: [] for x in flags.ip_list}
-        for _ in range(4):
+        for _ in range(flags.controlnet_image_count):
             cn_img = args.pop()
             cn_stop = args.pop()
             cn_weight = args.pop()
@@ -167,17 +224,9 @@ def worker():
             print(f'Refiner disabled because base model and refiner are same.')
             refiner_model_name = 'None'
 
-        assert performance_selection in ['Speed', 'Quality', 'Extreme Speed']
+        steps = performance_selection.steps()
 
-        steps = 30
+        if performance_selection == Performance.EXTREME_SPEED:
-
-        if performance_selection == 'Speed':
-            steps = 30
-
-        if performance_selection == 'Quality':
-            steps = 60
-
-        if performance_selection == 'Extreme Speed':
             print('Enter LCM mode.')
             progressbar(async_task, 1, 'Downloading LCM components ...')
             loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
@@ -186,30 +235,32 @@ def worker():
                 print(f'Refiner disabled in LCM mode.')
 
             refiner_model_name = 'None'
-            sampler_name = advanced_parameters.sampler_name = 'lcm'
+            sampler_name = 'lcm'
-            scheduler_name = advanced_parameters.scheduler_name = 'lcm'
+            scheduler_name = 'lcm'
-            modules.patch.sharpness = sharpness = 0.0
+            sharpness = 0.0
-            cfg_scale = guidance_scale = 1.0
+            guidance_scale = 1.0
-            modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg = 1.0
+            adaptive_cfg = 1.0
             refiner_switch = 1.0
-            modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive = 1.0
+            adm_scaler_positive = 1.0
-            modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative = 1.0
+            adm_scaler_negative = 1.0
-            modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end = 0.0
+            adm_scaler_end = 0.0
-            steps = 8
 
-        modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg
+        print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
-        print(f'[Parameters] Adaptive CFG = {modules.patch.adaptive_cfg}')
+        print(f'[Parameters] Sharpness = {sharpness}')
-
+        print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
-        modules.patch.sharpness = sharpness
-        print(f'[Parameters] Sharpness = {modules.patch.sharpness}')
-
-        modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive
-        modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative
-        modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end
         print(f'[Parameters] ADM Scale = '
-              f'{modules.patch.positive_adm_scale} : '
+              f'{adm_scaler_positive} : '
-              f'{modules.patch.negative_adm_scale} : '
+              f'{adm_scaler_negative} : '
-              f'{modules.patch.adm_scaler_end}')
+              f'{adm_scaler_end}')
+
+        patch_settings[pid] = PatchSettings(
+            sharpness,
+            adm_scaler_end,
+            adm_scaler_positive,
+            adm_scaler_negative,
+            controlnet_softness,
+            adaptive_cfg
+        )
 
         cfg_scale = float(guidance_scale)
         print(f'[Parameters] CFG = {cfg_scale}')
@@ -222,10 +273,9 @@ def worker():
         width, height = int(width), int(height)
 
         skip_prompt_processing = False
-        refiner_swap_method = advanced_parameters.refiner_swap_method
 
         inpaint_worker.current_task = None
-        inpaint_parameterized = advanced_parameters.inpaint_engine != 'None'
+        inpaint_parameterized = inpaint_engine != 'None'
         inpaint_image = None
         inpaint_mask = None
         inpaint_head_model_path = None
@@ -239,15 +289,12 @@ def worker():
         seed = int(image_seed)
         print(f'[Parameters] Seed = {seed}')
 
-        sampler_name = advanced_parameters.sampler_name
-        scheduler_name = advanced_parameters.scheduler_name
-
         goals = []
         tasks = []
 
         if input_image_checkbox:
             if (current_tab == 'uov' or (
-                    current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
+                    current_tab == 'ip' and mixing_image_prompt_and_vary_upscale)) \
                     and uov_method != flags.disabled and uov_input_image is not None:
                 uov_input_image = HWC3(uov_input_image)
                 if 'vary' in uov_method:
@@ -257,26 +304,17 @@ def worker():
                     if 'fast' in uov_method:
                         skip_prompt_processing = True
                     else:
-                        steps = 18
+                        steps = performance_selection.steps_uov()
-
-                        if performance_selection == 'Speed':
-                            steps = 18
-
-                        if performance_selection == 'Quality':
-                            steps = 36
-
-                        if performance_selection == 'Extreme Speed':
-                            steps = 8
 
                     progressbar(async_task, 1, 'Downloading upscale models ...')
                     modules.config.downloading_upscale_model()
             if (current_tab == 'inpaint' or (
-                    current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \
+                    current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \
                     and isinstance(inpaint_input_image, dict):
                 inpaint_image = inpaint_input_image['image']
                 inpaint_mask = inpaint_input_image['mask'][:, :, 0]
 
-                if advanced_parameters.inpaint_mask_upload_checkbox:
+                if inpaint_mask_upload_checkbox:
                     if isinstance(inpaint_mask_image_upload, np.ndarray):
                         if inpaint_mask_image_upload.ndim == 3:
                             H, W, C = inpaint_image.shape
@@ -285,10 +323,10 @@ def worker():
                             inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255
                             inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload)
 
-                if int(advanced_parameters.inpaint_erode_or_dilate) != 0:
+                if int(inpaint_erode_or_dilate) != 0:
-                    inpaint_mask = erode_or_dilate(inpaint_mask, advanced_parameters.inpaint_erode_or_dilate)
+                    inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate)
 
-                if advanced_parameters.invert_mask_checkbox:
+                if invert_mask_checkbox:
                     inpaint_mask = 255 - inpaint_mask
 
                 inpaint_image = HWC3(inpaint_image)
@@ -299,7 +337,7 @@ def worker():
                     if inpaint_parameterized:
                         progressbar(async_task, 1, 'Downloading inpainter ...')
                         inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
-                            advanced_parameters.inpaint_engine)
+                            inpaint_engine)
                         base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
                         print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
                         if refiner_model_name == 'None':
@@ -315,8 +353,8 @@ def worker():
                             prompt = inpaint_additional_prompt + '\n' + prompt
                     goals.append('inpaint')
             if current_tab == 'ip' or \
-                    advanced_parameters.mixing_image_prompt_and_inpaint or \
+                    mixing_image_prompt_and_vary_upscale or \
-                    advanced_parameters.mixing_image_prompt_and_vary_upscale:
+                    mixing_image_prompt_and_inpaint:
                 goals.append('cn')
                 progressbar(async_task, 1, 'Downloading control models ...')
                 if len(cn_tasks[flags.cn_canny]) > 0:
@@ -335,19 +373,19 @@ def worker():
         ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path)
         ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path)
 
-        if advanced_parameters.overwrite_step > 0:
+        if overwrite_step > 0:
-            steps = advanced_parameters.overwrite_step
+            steps = overwrite_step
 
         switch = int(round(steps * refiner_switch))
 
-        if advanced_parameters.overwrite_switch > 0:
+        if overwrite_switch > 0:
-            switch = advanced_parameters.overwrite_switch
+            switch = overwrite_switch
 
-        if advanced_parameters.overwrite_width > 0:
+        if overwrite_width > 0:
-            width = advanced_parameters.overwrite_width
+            width = overwrite_width
 
-        if advanced_parameters.overwrite_height > 0:
+        if overwrite_height > 0:
-            height = advanced_parameters.overwrite_height
+            height = overwrite_height
 
         print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
         print(f'[Parameters] Steps = {steps} - {switch}')
@@ -376,11 +414,16 @@ def worker():
 
             progressbar(async_task, 3, 'Processing prompts ...')
             tasks = []
-            for i in range(image_number):
-                task_seed = (seed + i) % (constants.MAX_SEED + 1)  # randint is inclusive, % is not
-                task_rng = random.Random(task_seed)  # may bind to inpaint noise in the future
             
+            for i in range(image_number):
+                if disable_seed_increment:
+                    task_seed = seed
+                else:
+                    task_seed = (seed + i) % (constants.MAX_SEED + 1)  # randint is inclusive, % is not
+
+                task_rng = random.Random(task_seed)  # may bind to inpaint noise in the future
                 task_prompt = apply_wildcards(prompt, task_rng)
+                task_prompt = apply_arrays(task_prompt, i)
                 task_negative_prompt = apply_wildcards(negative_prompt, task_rng)
                 task_extra_positive_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_positive_prompts]
                 task_extra_negative_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_negative_prompts]
@@ -446,8 +489,8 @@ def worker():
                 denoising_strength = 0.5
             if 'strong' in uov_method:
                 denoising_strength = 0.85
-            if advanced_parameters.overwrite_vary_strength > 0:
+            if overwrite_vary_strength > 0:
-                denoising_strength = advanced_parameters.overwrite_vary_strength
+                denoising_strength = overwrite_vary_strength
 
             shape_ceil = get_image_shape_ceil(uov_input_image)
             if shape_ceil < 1024:
@@ -511,15 +554,15 @@ def worker():
 
             if direct_return:
                 d = [('Upscale (Fast)', '2x')]
-                log(uov_input_image, d)
+                uov_input_image_path = log(uov_input_image, d, output_format)
-                yield_result(async_task, uov_input_image, do_not_show_finished_images=True)
+                yield_result(async_task, uov_input_image_path, do_not_show_finished_images=True)
                 return
 
             tiled = True
             denoising_strength = 0.382
 
-            if advanced_parameters.overwrite_upscale_strength > 0:
+            if overwrite_upscale_strength > 0:
-                denoising_strength = advanced_parameters.overwrite_upscale_strength
+                denoising_strength = overwrite_upscale_strength
 
             initial_pixels = core.numpy_to_pytorch(uov_input_image)
             progressbar(async_task, 13, 'VAE encoding ...')
@@ -563,19 +606,19 @@ def worker():
 
                 inpaint_image = np.ascontiguousarray(inpaint_image.copy())
                 inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
-                advanced_parameters.inpaint_strength = 1.0
+                inpaint_strength = 1.0
-                advanced_parameters.inpaint_respective_field = 1.0
+                inpaint_respective_field = 1.0
 
-            denoising_strength = advanced_parameters.inpaint_strength
+            denoising_strength = inpaint_strength
 
             inpaint_worker.current_task = inpaint_worker.InpaintWorker(
                 image=inpaint_image,
                 mask=inpaint_mask,
                 use_fill=denoising_strength > 0.99,
-                k=advanced_parameters.inpaint_respective_field
+                k=inpaint_respective_field
             )
 
-            if advanced_parameters.debugging_inpaint_preprocessor:
+            if debugging_inpaint_preprocessor:
                 yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
                              do_not_show_finished_images=True)
                 return
@@ -621,7 +664,7 @@ def worker():
                     model=pipeline.final_unet
                 )
 
-            if not advanced_parameters.inpaint_disable_initial_latent:
+            if not inpaint_disable_initial_latent:
                 initial_latent = {'samples': latent_fill}
 
             B, C, H, W = latent_fill.shape
@@ -634,24 +677,24 @@ def worker():
                 cn_img, cn_stop, cn_weight = task
                 cn_img = resize_image(HWC3(cn_img), width=width, height=height)
 
-                if not advanced_parameters.skipping_cn_preprocessor:
+                if not skipping_cn_preprocessor:
-                    cn_img = preprocessors.canny_pyramid(cn_img)
+                    cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold)
 
                 cn_img = HWC3(cn_img)
                 task[0] = core.numpy_to_pytorch(cn_img)
-                if advanced_parameters.debugging_cn_preprocessor:
+                if debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_cpds]:
                 cn_img, cn_stop, cn_weight = task
                 cn_img = resize_image(HWC3(cn_img), width=width, height=height)
 
-                if not advanced_parameters.skipping_cn_preprocessor:
+                if not skipping_cn_preprocessor:
                     cn_img = preprocessors.cpds(cn_img)
 
                 cn_img = HWC3(cn_img)
                 task[0] = core.numpy_to_pytorch(cn_img)
-                if advanced_parameters.debugging_cn_preprocessor:
+                if debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_ip]:
@@ -662,21 +705,21 @@ def worker():
                 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
 
                 task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)
-                if advanced_parameters.debugging_cn_preprocessor:
+                if debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_ip_face]:
                 cn_img, cn_stop, cn_weight = task
                 cn_img = HWC3(cn_img)
 
-                if not advanced_parameters.skipping_cn_preprocessor:
+                if not skipping_cn_preprocessor:
                     cn_img = extras.face_crop.crop_image(cn_img)
 
                 # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75
                 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
 
                 task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)
-                if advanced_parameters.debugging_cn_preprocessor:
+                if debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
 
@@ -685,14 +728,14 @@ def worker():
             if len(all_ip_tasks) > 0:
                 pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
 
-        if advanced_parameters.freeu_enabled:
+        if freeu_enabled:
             print(f'FreeU is enabled!')
             pipeline.final_unet = core.apply_freeu(
                 pipeline.final_unet,
-                advanced_parameters.freeu_b1,
+                freeu_b1,
-                advanced_parameters.freeu_b2,
+                freeu_b2,
-                advanced_parameters.freeu_s1,
+                freeu_s1,
-                advanced_parameters.freeu_s2
+                freeu_s2
             )
 
         all_steps = steps * image_number
@@ -738,6 +781,8 @@ def worker():
             execution_start_time = time.perf_counter()
 
             try:
+                if async_task.last_stop is not False:
+                    ldm_patched.model_management.interrupt_current_processing()
                 positive_cond, negative_cond = task['c'], task['uc']
 
                 if 'cn' in goals:
@@ -765,7 +810,8 @@ def worker():
                     denoise=denoising_strength,
                     tiled=tiled,
                     cfg_scale=cfg_scale,
-                    refiner_swap_method=refiner_swap_method
+                    refiner_swap_method=refiner_swap_method,
+                    disable_preview=disable_preview
                 )
 
                 del task['c'], task['uc'], positive_cond, negative_cond  # Save memory
@@ -773,37 +819,58 @@ def worker():
                 if inpaint_worker.current_task is not None:
                     imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
 
+                img_paths = []
                 for x in imgs:
-                    d = [
+                    d = [('Prompt', 'prompt', task['log_positive_prompt']),
-                        ('Prompt', task['log_positive_prompt']),
+                         ('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
-                        ('Negative Prompt', task['log_negative_prompt']),
+                         ('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
-                        ('Fooocus V2 Expansion', task['expansion']),
+                         ('Styles', 'styles', str(raw_style_selections)),
-                        ('Styles', str(raw_style_selections)),
+                         ('Performance', 'performance', performance_selection.value),
-                        ('Performance', performance_selection),
+                         ('Resolution', 'resolution', str((width, height))),
-                        ('Resolution', str((width, height))),
+                         ('Guidance Scale', 'guidance_scale', guidance_scale),
-                        ('Sharpness', sharpness),
+                         ('Sharpness', 'sharpness', sharpness),
-                        ('Guidance Scale', guidance_scale),
+                         ('ADM Guidance', 'adm_guidance', str((
-                        ('ADM Guidance', str((
+                             modules.patch.patch_settings[pid].positive_adm_scale,
-                            modules.patch.positive_adm_scale,
+                             modules.patch.patch_settings[pid].negative_adm_scale,
-                            modules.patch.negative_adm_scale,
+                             modules.patch.patch_settings[pid].adm_scaler_end))),
-                            modules.patch.adm_scaler_end))),
+                         ('Base Model', 'base_model', base_model_name),
-                        ('Base Model', base_model_name),
+                         ('Refiner Model', 'refiner_model', refiner_model_name),
-                        ('Refiner Model', refiner_model_name),
+                         ('Refiner Switch', 'refiner_switch', refiner_switch)]
-                        ('Refiner Switch', refiner_switch),
+
-                        ('Sampler', sampler_name),
+                    if refiner_model_name != 'None':
-                        ('Scheduler', scheduler_name),
+                        if overwrite_switch > 0:
-                        ('Seed', task['task_seed']),
+                            d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch))
-                    ]
+                        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(('Sampler', 'sampler', sampler_name))
+                    d.append(('Scheduler', 'scheduler', scheduler_name))
+                    d.append(('Seed', 'seed', task['task_seed']))
+
+                    if freeu_enabled:
+                        d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))
+
+                    metadata_parser = None
+                    if save_metadata_to_images:
+                        metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
+                        metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
+                                                 task['log_negative_prompt'], task['negative'],
+                                                 steps, base_model_name, refiner_model_name, loras)
+
                     for li, (n, w) in enumerate(loras):
                         if n != 'None':
-                            d.append((f'LoRA {li + 1}', f'{n} : {w}'))
+                            d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
-                    d.append(('Version', 'v' + fooocus_version.version))
-                    log(x, d)
 
-                yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1)
+                    d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
+                    img_paths.append(log(x, d, metadata_parser, output_format))
+
+                yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
             except ldm_patched.modules.model_management.InterruptProcessingException as e:
-                if shared.last_stop == 'skip':
+                if async_task.last_stop == 'skip':
                     print('User skipped')
+                    async_task.last_stop = False
                     continue
                 else:
                     print('User stopped')
@@ -811,21 +878,27 @@ def worker():
 
             execution_time = time.perf_counter() - execution_start_time
             print(f'Generating and saving time: {execution_time:.2f} seconds')
-
+        async_task.processing = False
         return
 
     while True:
         time.sleep(0.01)
         if len(async_tasks) > 0:
             task = async_tasks.pop(0)
+            generate_image_grid = task.args.pop(0)
+
             try:
                 handler(task)
-                build_image_wall(task)
+                if generate_image_grid:
+                    build_image_wall(task)
                 task.yields.append(['finish', task.results])
                 pipeline.prepare_text_encoder(async_call=True)
             except:
                 traceback.print_exc()
                 task.yields.append(['finish', task.results])
+            finally:
+                if pid in modules.patch.patch_settings:
+                    del modules.patch.patch_settings[pid]
     pass
 
 

modules/config.py

@@ -7,11 +7,19 @@ import modules.flags
 import modules.sdxl_styles
 
 from modules.model_loader import load_file_from_url
-from modules.util import get_files_from_folder
+from modules.util import get_files_from_folder, makedirs_with_log
+from modules.flags import Performance, MetadataScheme
 
+def get_config_path(key, default_value):
+    env = os.getenv(key)
+    if env is not None and isinstance(env, str):
+        print(f"Environment: {key} = {env}")
+        return env
+    else:
+        return os.path.abspath(default_value)
 
-config_path = os.path.abspath("./config.txt")
+config_path = get_config_path('config_path', "./config.txt")
-config_example_path = os.path.abspath("config_modification_tutorial.txt")
+config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt")
 config_dict = {}
 always_save_keys = []
 visited_keys = []
@@ -107,14 +115,14 @@ def get_path_output() -> str:
     Checking output path argument and overriding default path.
     """
     global config_dict
-    path_output = get_dir_or_set_default('path_outputs', '../outputs/')
+    path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True)
     if args_manager.args.output_path:
         print(f'[CONFIG] Overriding config value path_outputs with {args_manager.args.output_path}')
         config_dict['path_outputs'] = path_output = args_manager.args.output_path
     return path_output
 
 
-def get_dir_or_set_default(key, default_value):
+def get_dir_or_set_default(key, default_value, as_array=False, make_directory=False):
     global config_dict, visited_keys, always_save_keys
 
     if key not in visited_keys:
@@ -123,20 +131,44 @@ def get_dir_or_set_default(key, default_value):
     if key not in always_save_keys:
         always_save_keys.append(key)
 
-    v = config_dict.get(key, None)
+    v = os.getenv(key)
-    if isinstance(v, str) and os.path.exists(v) and os.path.isdir(v):
+    if v is not None:
-        return v
+        print(f"Environment: {key} = {v}")
+        config_dict[key] = v
+    else:
+        v = config_dict.get(key, None)
+
+    if isinstance(v, str):
+        if make_directory:
+            makedirs_with_log(v)
+        if os.path.exists(v) and os.path.isdir(v):
+            return v if not as_array else [v]
+    elif isinstance(v, list):
+        if make_directory:
+            for d in v:
+                makedirs_with_log(d)
+        if all([os.path.exists(d) and os.path.isdir(d) for d in v]):
+            return v
+
+    if v is not None:
+        print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.')
+    if isinstance(default_value, list):
+        dp = []
+        for path in default_value:
+            abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), path))
+            dp.append(abs_path)
+            os.makedirs(abs_path, exist_ok=True)
     else:
-        if v is not None:
-            print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.')
         dp = os.path.abspath(os.path.join(os.path.dirname(__file__), default_value))
         os.makedirs(dp, exist_ok=True)
-        config_dict[key] = dp
+        if as_array:
-        return dp
+            dp = [dp]
+    config_dict[key] = dp
+    return dp
 
 
-path_checkpoints = get_dir_or_set_default('path_checkpoints', '../models/checkpoints/')
+paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/checkpoints/'], True)
-path_loras = get_dir_or_set_default('path_loras', '../models/loras/')
+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_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/')
@@ -146,13 +178,17 @@ path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vi
 path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion')
 path_outputs = get_path_output()
 
-
 def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False):
     global config_dict, visited_keys
 
     if key not in visited_keys:
         visited_keys.append(key)
     
+    v = os.getenv(key)
+    if v is not None:
+        print(f"Environment: {key} = {v}")
+        config_dict[key] = v
+
     if key not in config_dict:
         config_dict[key] = default_value
         return default_value
@@ -190,6 +226,16 @@ default_refiner_switch = get_config_item_or_set_default(
     default_value=0.8,
     validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1
 )
+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
+)
+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
+)
 default_loras = get_config_item_or_set_default(
     key='default_loras',
     default_value=[
@@ -216,6 +262,11 @@ default_loras = get_config_item_or_set_default(
     ],
     validator=lambda x: isinstance(x, list) and all(len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number) for y in x)
 )
+default_max_lora_number = get_config_item_or_set_default(
+    key='default_max_lora_number',
+    default_value=len(default_loras),
+    validator=lambda x: isinstance(x, int) and x >= 1
+)
 default_cfg_scale = get_config_item_or_set_default(
     key='default_cfg_scale',
     default_value=7.0,
@@ -259,8 +310,8 @@ default_prompt = get_config_item_or_set_default(
 )
 default_performance = get_config_item_or_set_default(
     key='default_performance',
-    default_value='Speed',
+    default_value=Performance.SPEED.value,
-    validator=lambda x: x in modules.flags.performance_selections
+    validator=lambda x: x in Performance.list()
 )
 default_advanced_checkbox = get_config_item_or_set_default(
     key='default_advanced_checkbox',
@@ -272,6 +323,11 @@ default_max_image_number = get_config_item_or_set_default(
     default_value=32,
     validator=lambda x: isinstance(x, int) and x >= 1
 )
+default_output_format = get_config_item_or_set_default(
+    key='default_output_format',
+    default_value='png',
+    validator=lambda x: x in modules.flags.output_formats
+)
 default_image_number = get_config_item_or_set_default(
     key='default_image_number',
     default_value=2,
@@ -335,16 +391,34 @@ example_inpaint_prompts = get_config_item_or_set_default(
     ],
     validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x)
 )
+default_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)
+)
+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]
+)
+metadata_created_by = get_config_item_or_set_default(
+    key='metadata_created_by',
+    default_value='',
+    validator=lambda x: isinstance(x, str)
+)
 
 example_inpaint_prompts = [[x] for x in example_inpaint_prompts]
 
-config_dict["default_loras"] = default_loras = default_loras[:5] + [['None', 1.0] for _ in range(5 - len(default_loras))]
+config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [['None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]
 
 possible_preset_keys = [
     "default_model",
     "default_refiner",
     "default_refiner_switch",
+    "default_loras_min_weight",
+    "default_loras_max_weight",
     "default_loras",
+    "default_max_lora_number",
     "default_cfg_scale",
     "default_sample_sharpness",
     "default_sampler",
@@ -354,6 +428,7 @@ possible_preset_keys = [
     "default_prompt_negative",
     "default_styles",
     "default_aspect_ratio",
+    "default_save_metadata_to_images",
     "checkpoint_downloads",
     "embeddings_downloads",
     "lora_downloads",
@@ -397,21 +472,22 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
                       'and there is no "," before the last "}". \n\n\n')
     json.dump({k: config_dict[k] for k in visited_keys}, json_file, indent=4)
 
-
-os.makedirs(path_outputs, exist_ok=True)
-
 model_filenames = []
 lora_filenames = []
 
 
-def get_model_filenames(folder_path, name_filter=None):
+def get_model_filenames(folder_paths, name_filter=None):
-    return get_files_from_folder(folder_path, ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch'], name_filter)
+    extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']
+    files = []
+    for folder in folder_paths:
+        files += get_files_from_folder(folder, extensions, name_filter)
+    return files
 
 
 def update_all_model_names():
     global model_filenames, lora_filenames
-    model_filenames = get_model_filenames(path_checkpoints)
+    model_filenames = get_model_filenames(paths_checkpoints)
-    lora_filenames = get_model_filenames(path_loras)
+    lora_filenames = get_model_filenames(paths_loras)
     return
 
 
@@ -456,7 +532,7 @@ def downloading_inpaint_models(v):
 def downloading_sdxl_lcm_lora():
     load_file_from_url(
         url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors',
-        model_dir=path_loras,
+        model_dir=paths_loras[0],
         file_name='sdxl_lcm_lora.safetensors'
     )
     return 'sdxl_lcm_lora.safetensors'

modules/core.py

@@ -1,8 +1,3 @@
-from modules.patch import patch_all
-
-patch_all()
-
-
 import os
 import einops
 import torch
@@ -16,7 +11,6 @@ import ldm_patched.modules.controlnet
 import modules.sample_hijack
 import ldm_patched.modules.samplers
 import ldm_patched.modules.latent_formats
-import modules.advanced_parameters
 
 from ldm_patched.modules.sd import load_checkpoint_guess_config
 from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \
@@ -24,6 +18,7 @@ from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode,
 from ldm_patched.contrib.external_freelunch import FreeU_V2
 from ldm_patched.modules.sample import prepare_mask
 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
@@ -85,7 +80,7 @@ class StableDiffusionModel:
             if os.path.exists(name):
                 lora_filename = name
             else:
-                lora_filename = os.path.join(modules.config.path_loras, name)
+                lora_filename = get_file_from_folder_list(name, modules.config.paths_loras)
 
             if not os.path.exists(lora_filename):
                 print(f'Lora file not found: {lora_filename}')
@@ -268,7 +263,7 @@ def get_previewer(model):
 def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
              scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
              force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
-             previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None):
+             previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False):
 
     if sigmas is not None:
         sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device())
@@ -299,7 +294,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
     def callback(step, x0, x, total_steps):
         ldm_patched.modules.model_management.throw_exception_if_processing_interrupted()
         y = None
-        if previewer is not None and not modules.advanced_parameters.disable_preview:
+        if previewer is not None and not disable_preview:
             y = previewer(x0, previewer_start + step, previewer_end)
         if callback_function is not None:
             callback_function(previewer_start + step, x0, x, previewer_end, y)

modules/default_pipeline.py

@@ -11,6 +11,7 @@ from extras.expansion import FooocusExpansion
 
 from ldm_patched.modules.model_base import SDXL, SDXLRefiner
 from modules.sample_hijack import clip_separate
+from modules.util import get_file_from_folder_list
 
 
 model_base = core.StableDiffusionModel()
@@ -60,7 +61,7 @@ def assert_model_integrity():
 def refresh_base_model(name):
     global model_base
 
-    filename = os.path.abspath(os.path.realpath(os.path.join(modules.config.path_checkpoints, name)))
+    filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
 
     if model_base.filename == filename:
         return
@@ -76,7 +77,7 @@ def refresh_base_model(name):
 def refresh_refiner_model(name):
     global model_refiner
 
-    filename = os.path.abspath(os.path.realpath(os.path.join(modules.config.path_checkpoints, name)))
+    filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
 
     if model_refiner.filename == filename:
         return
@@ -315,7 +316,7 @@ def get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'):
 
 @torch.no_grad()
 @torch.inference_mode()
-def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint'):
+def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False):
     target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \
         = final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip
 
@@ -374,6 +375,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             refiner_switch=switch,
             previewer_start=0,
             previewer_end=steps,
+            disable_preview=disable_preview
         )
         decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled)
 
@@ -392,6 +394,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             scheduler=scheduler_name,
             previewer_start=0,
             previewer_end=steps,
+            disable_preview=disable_preview
         )
         print('Refiner swapped by changing ksampler. Noise preserved.')
 
@@ -414,6 +417,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             scheduler=scheduler_name,
             previewer_start=switch,
             previewer_end=steps,
+            disable_preview=disable_preview
         )
 
         target_model = target_refiner_vae
@@ -422,7 +426,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
         decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
 
     if refiner_swap_method == 'vae':
-        modules.patch.eps_record = 'vae'
+        modules.patch.patch_settings[os.getpid()].eps_record = 'vae'
 
         if modules.inpaint_worker.current_task is not None:
             modules.inpaint_worker.current_task.unswap()
@@ -440,7 +444,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             sampler_name=sampler_name,
             scheduler=scheduler_name,
             previewer_start=0,
-            previewer_end=steps
+            previewer_end=steps,
+            disable_preview=disable_preview
         )
         print('Fooocus VAE-based swap.')
 
@@ -459,7 +464,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
                                   denoise=denoise)[switch:] * k_sigmas
         len_sigmas = len(sigmas) - 1
 
-        noise_mean = torch.mean(modules.patch.eps_record, dim=1, keepdim=True)
+        noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True)
 
         if modules.inpaint_worker.current_task is not None:
             modules.inpaint_worker.current_task.swap()
@@ -479,7 +484,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             previewer_start=switch,
             previewer_end=steps,
             sigmas=sigmas,
-            noise_mean=noise_mean
+            noise_mean=noise_mean,
+            disable_preview=disable_preview
         )
 
         target_model = target_refiner_vae
@@ -488,5 +494,5 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
         decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
 
     images = core.pytorch_to_numpy(decoded_latent)
-    modules.patch.eps_record = None
+    modules.patch.patch_settings[os.getpid()].eps_record = None
     return images

modules/flags.py

@@ -1,3 +1,5 @@
+from enum import IntEnum, Enum
+
 disabled = 'Disabled'
 enabled = 'Enabled'
 subtle_variation = 'Vary (Subtle)'
@@ -10,16 +12,49 @@ uov_list = [
     disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast
 ]
 
-KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+CIVITAI_NO_KARRAS = ["euler", "euler_ancestral", "heun", "dpm_fast", "dpm_adaptive", "ddim", "uni_pc"]
-                  "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"]
+# fooocus: a1111 (Civitai)
+KSAMPLER = {
+    "euler": "Euler",
+    "euler_ancestral": "Euler a",
+    "heun": "Heun",
+    "heunpp2": "",
+    "dpm_2": "DPM2",
+    "dpm_2_ancestral": "DPM2 a",
+    "lms": "LMS",
+    "dpm_fast": "DPM fast",
+    "dpm_adaptive": "DPM adaptive",
+    "dpmpp_2s_ancestral": "DPM++ 2S a",
+    "dpmpp_sde": "DPM++ SDE",
+    "dpmpp_sde_gpu": "DPM++ SDE",
+    "dpmpp_2m": "DPM++ 2M",
+    "dpmpp_2m_sde": "DPM++ 2M SDE",
+    "dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
+    "dpmpp_3m_sde": "",
+    "dpmpp_3m_sde_gpu": "",
+    "ddpm": "",
+    "lcm": "LCM"
+}
+
+SAMPLER_EXTRA = {
+    "ddim": "DDIM",
+    "uni_pc": "UniPC",
+    "uni_pc_bh2": ""
+}
+
+SAMPLERS = KSAMPLER | SAMPLER_EXTRA
+
+KSAMPLER_NAMES = list(KSAMPLER.keys())
 
 SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo"]
-SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
+SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
 
 sampler_list = SAMPLER_NAMES
 scheduler_list = SCHEDULER_NAMES
 
+refiner_swap_method = 'joint'
+
 cn_ip = "ImagePrompt"
 cn_ip_face = "FaceSwap"
 cn_canny = "PyraCanny"
@@ -32,9 +67,9 @@ default_parameters = {
     cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0)
 }  # stop, weight
 
-inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']
+output_formats = ['png', 'jpg', 'webp']
-performance_selections = ['Speed', 'Quality', 'Extreme Speed']
 
+inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']
 inpaint_option_default = 'Inpaint or Outpaint (default)'
 inpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)'
 inpaint_option_modify = 'Modify Content (add objects, change background, etc.)'
@@ -42,3 +77,49 @@ inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option
 
 desc_type_photo = 'Photograph'
 desc_type_anime = 'Art/Anime'
+
+
+class MetadataScheme(Enum):
+    FOOOCUS = 'fooocus'
+    A1111 = 'a1111'
+
+
+metadata_scheme = [
+    (f'{MetadataScheme.FOOOCUS.value} (json)', MetadataScheme.FOOOCUS.value),
+    (f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value),
+]
+
+lora_count = 5
+
+controlnet_image_count = 4
+
+
+class Steps(IntEnum):
+    QUALITY = 60
+    SPEED = 30
+    EXTREME_SPEED = 8
+
+
+class StepsUOV(IntEnum):
+    QUALITY = 36
+    SPEED = 18
+    EXTREME_SPEED = 8
+
+
+class Performance(Enum):
+    QUALITY = 'Quality'
+    SPEED = 'Speed'
+    EXTREME_SPEED = 'Extreme Speed'
+
+    @classmethod
+    def list(cls) -> list:
+        return list(map(lambda c: c.value, cls))
+
+    def steps(self) -> int | None:
+        return Steps[self.name].value if Steps[self.name] else None
+
+    def steps_uov(self) -> int | None:
+        return StepsUOV[self.name].value if Steps[self.name] else None
+
+
+performance_selections = Performance.list()

modules/html.py

@@ -112,6 +112,30 @@ progress::after {
     margin-left: -5px !important;
 }
 
+.lora_enable {
+  flex-grow: 1 !important;
+}
+
+.lora_enable label {
+  height: 100%;
+}
+
+.lora_enable label input {
+  margin: auto;
+}
+
+.lora_enable label span {
+  display: none;
+}
+
+.lora_model {
+  flex-grow: 5 !important;
+}
+
+.lora_weight {
+  flex-grow: 5 !important;
+}
+
 '''
 progress_html = '''
 <div class="loader-container">

modules/meta_parser.py

@@ -1,45 +1,114 @@
 import json
+import os
+import re
+from abc import ABC, abstractmethod
+from pathlib import Path
+
 import gradio as gr
+from PIL import Image
+
+import fooocus_version
 import modules.config
+import modules.sdxl_styles
+from modules.flags import MetadataScheme, Performance, Steps
+from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS
+from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, calculate_sha256
+
+re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
+re_param = re.compile(re_param_code)
+re_imagesize = re.compile(r"^(\d+)x(\d+)$")
+
+hash_cache = {}
 
 
-def load_parameter_button_click(raw_prompt_txt, is_generating):
+def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
-    loaded_parameter_dict = json.loads(raw_prompt_txt)
+    loaded_parameter_dict = raw_metadata
+    if isinstance(raw_metadata, str):
+        loaded_parameter_dict = json.loads(raw_metadata)
     assert isinstance(loaded_parameter_dict, dict)
 
-    results = [True, 1]
+    results = [len(loaded_parameter_dict) > 0, 1]
 
+    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)
+    get_steps('steps', 'Steps', loaded_parameter_dict, results)
+    get_float('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_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_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_str('sampler', 'Sampler', loaded_parameter_dict, results)
+    get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
+    get_seed('seed', 'Seed', loaded_parameter_dict, results)
+
+    if is_generating:
+        results.append(gr.update())
+    else:
+        results.append(gr.update(visible=True))
+
+    results.append(gr.update(visible=False))
+
+    get_freeu('freeu', 'FreeU', loaded_parameter_dict, results)
+
+    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)
+
+    return results
+
+
+def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
     try:
-        h = loaded_parameter_dict.get('Prompt', None)
+        h = source_dict.get(key, source_dict.get(fallback, default))
         assert isinstance(h, str)
         results.append(h)
     except:
         results.append(gr.update())
 
-    try:
-        h = loaded_parameter_dict.get('Negative Prompt', None)
-        assert isinstance(h, str)
-        results.append(h)
-    except:
-        results.append(gr.update())
 
+def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
     try:
-        h = loaded_parameter_dict.get('Styles', None)
+        h = source_dict.get(key, source_dict.get(fallback, default))
         h = eval(h)
         assert isinstance(h, list)
         results.append(h)
     except:
         results.append(gr.update())
 
+
+def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
     try:
-        h = loaded_parameter_dict.get('Performance', None)
+        h = source_dict.get(key, source_dict.get(fallback, default))
-        assert isinstance(h, str)
+        assert h is not None
+        h = float(h)
         results.append(h)
     except:
         results.append(gr.update())
 
+
+def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
     try:
-        h = loaded_parameter_dict.get('Resolution', None)
+        h = source_dict.get(key, source_dict.get(fallback, default))
+        assert h is not None
+        h = int(h)
+        # if not in steps or in steps and performance is not the same
+        if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ', '_').casefold():
+            results.append(h)
+            return
+        results.append(-1)
+    except:
+        results.append(-1)
+
+
+def get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
+    try:
+        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:
@@ -55,24 +124,22 @@ def load_parameter_button_click(raw_prompt_txt, is_generating):
         results.append(gr.update())
         results.append(gr.update())
 
+
+def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
     try:
-        h = loaded_parameter_dict.get('Sharpness', None)
+        h = source_dict.get(key, source_dict.get(fallback, default))
         assert h is not None
-        h = float(h)
+        h = int(h)
+        results.append(False)
         results.append(h)
     except:
         results.append(gr.update())
-
-    try:
-        h = loaded_parameter_dict.get('Guidance Scale', None)
-        assert h is not None
-        h = float(h)
-        results.append(h)
-    except:
         results.append(gr.update())
 
+
+def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
     try:
-        h = loaded_parameter_dict.get('ADM Guidance', None)
+        h = source_dict.get(key, source_dict.get(fallback, default))
         p, n, e = eval(h)
         results.append(float(p))
         results.append(float(n))
@@ -82,67 +149,423 @@ def load_parameter_button_click(raw_prompt_txt, is_generating):
         results.append(gr.update())
         results.append(gr.update())
 
-    try:
-        h = loaded_parameter_dict.get('Base Model', None)
-        assert isinstance(h, str)
-        results.append(h)
-    except:
-        results.append(gr.update())
 
+def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
     try:
-        h = loaded_parameter_dict.get('Refiner Model', None)
+        h = source_dict.get(key, source_dict.get(fallback, default))
-        assert isinstance(h, str)
+        b1, b2, s1, s2 = eval(h)
-        results.append(h)
+        results.append(True)
+        results.append(float(b1))
+        results.append(float(b2))
+        results.append(float(s1))
+        results.append(float(s2))
     except:
-        results.append(gr.update())
-
-    try:
-        h = loaded_parameter_dict.get('Refiner Switch', None)
-        assert h is not None
-        h = float(h)
-        results.append(h)
-    except:
-        results.append(gr.update())
-
-    try:
-        h = loaded_parameter_dict.get('Sampler', None)
-        assert isinstance(h, str)
-        results.append(h)
-    except:
-        results.append(gr.update())
-
-    try:
-        h = loaded_parameter_dict.get('Scheduler', None)
-        assert isinstance(h, str)
-        results.append(h)
-    except:
-        results.append(gr.update())
-
-    try:
-        h = loaded_parameter_dict.get('Seed', None)
-        assert h is not None
-        h = int(h)
         results.append(False)
-        results.append(h)
+        results.append(gr.update())
+        results.append(gr.update())
+        results.append(gr.update())
+        results.append(gr.update())
+
+
+def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
+    try:
+        n, w = source_dict.get(key, source_dict.get(fallback)).split(' : ')
+        w = float(w)
+        results.append(True)
+        results.append(n)
+        results.append(w)
     except:
-        results.append(gr.update())
+        results.append(True)
-        results.append(gr.update())
+        results.append('None')
+        results.append(1)
 
-    if is_generating:
-        results.append(gr.update())
-    else:
-        results.append(gr.update(visible=True))
 
-    results.append(gr.update(visible=False))
+def get_sha256(filepath):
+    global hash_cache
+    if filepath not in hash_cache:
+        hash_cache[filepath] = calculate_sha256(filepath)
 
-    for i in range(1, 6):
+    return hash_cache[filepath]
-        try:
-            n, w = loaded_parameter_dict.get(f'LoRA {i}').split(' : ')
-            w = float(w)
-            results.append(n)
-            results.append(w)
-        except:
-            results.append(gr.update())
-            results.append(gr.update())
 
-    return results
+
+def parse_meta_from_preset(preset_content):
+    assert isinstance(preset_content, dict)
+    preset_prepared = {}
+    items = preset_content
+
+    for settings_key, meta_key in modules.config.possible_preset_keys.items():
+        if settings_key == "default_loras":
+            loras = getattr(modules.config, settings_key)
+            if settings_key in items:
+                loras = items[settings_key]
+            for index, lora in enumerate(loras[:5]):
+                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:
+                default_aspect_ratio = items[settings_key]
+                width, height = default_aspect_ratio.split('*')
+            else:
+                default_aspect_ratio = getattr(modules.config, settings_key)
+                width, height = default_aspect_ratio.split('×')
+                height = height[:height.index(" ")]
+            preset_prepared[meta_key] = (width, height)
+        else:
+            preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[
+                settings_key] is not None else getattr(modules.config, settings_key)
+
+        if settings_key == "default_styles" or settings_key == "default_aspect_ratio":
+            preset_prepared[meta_key] = str(preset_prepared[meta_key])
+
+    return preset_prepared
+
+
+class MetadataParser(ABC):
+    def __init__(self):
+        self.raw_prompt: str = ''
+        self.full_prompt: str = ''
+        self.raw_negative_prompt: str = ''
+        self.full_negative_prompt: str = ''
+        self.steps: int = 30
+        self.base_model_name: str = ''
+        self.base_model_hash: str = ''
+        self.refiner_model_name: str = ''
+        self.refiner_model_hash: str = ''
+        self.loras: list = []
+
+    @abstractmethod
+    def get_scheme(self) -> MetadataScheme:
+        raise NotImplementedError
+
+    @abstractmethod
+    def parse_json(self, metadata: dict | str) -> dict:
+        raise NotImplementedError
+
+    @abstractmethod
+    def parse_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):
+        self.raw_prompt = raw_prompt
+        self.full_prompt = full_prompt
+        self.raw_negative_prompt = raw_negative_prompt
+        self.full_negative_prompt = full_negative_prompt
+        self.steps = steps
+        self.base_model_name = Path(base_model_name).stem
+
+        base_model_path = get_file_from_folder_list(base_model_name, modules.config.paths_checkpoints)
+        self.base_model_hash = get_sha256(base_model_path)
+
+        if refiner_model_name not in ['', 'None']:
+            self.refiner_model_name = Path(refiner_model_name).stem
+            refiner_model_path = get_file_from_folder_list(refiner_model_name, modules.config.paths_checkpoints)
+            self.refiner_model_hash = get_sha256(refiner_model_path)
+
+        self.loras = []
+        for (lora_name, lora_weight) in loras:
+            if lora_name != 'None':
+                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))
+
+
+class A1111MetadataParser(MetadataParser):
+    def get_scheme(self) -> MetadataScheme:
+        return MetadataScheme.A1111
+
+    fooocus_to_a1111 = {
+        'raw_prompt': 'Raw prompt',
+        'raw_negative_prompt': 'Raw negative prompt',
+        'negative_prompt': 'Negative prompt',
+        'styles': 'Styles',
+        'performance': 'Performance',
+        'steps': 'Steps',
+        'sampler': 'Sampler',
+        'scheduler': 'Scheduler',
+        'guidance_scale': 'CFG scale',
+        'seed': 'Seed',
+        'resolution': 'Size',
+        'sharpness': 'Sharpness',
+        'adm_guidance': 'ADM Guidance',
+        'refiner_swap_method': 'Refiner Swap Method',
+        'adaptive_cfg': 'Adaptive CFG',
+        'overwrite_switch': 'Overwrite Switch',
+        'freeu': 'FreeU',
+        'base_model': 'Model',
+        'base_model_hash': 'Model hash',
+        'refiner_model': 'Refiner',
+        'refiner_model_hash': 'Refiner hash',
+        'lora_hashes': 'Lora hashes',
+        'lora_weights': 'Lora weights',
+        'created_by': 'User',
+        'version': 'Version'
+    }
+
+    def parse_json(self, metadata: str) -> dict:
+        metadata_prompt = ''
+        metadata_negative_prompt = ''
+
+        done_with_prompt = False
+
+        *lines, lastline = metadata.strip().split("\n")
+        if len(re_param.findall(lastline)) < 3:
+            lines.append(lastline)
+            lastline = ''
+
+        for line in lines:
+            line = line.strip()
+            if line.startswith(f"{self.fooocus_to_a1111['negative_prompt']}:"):
+                done_with_prompt = True
+                line = line[len(f"{self.fooocus_to_a1111['negative_prompt']}:"):].strip()
+            if done_with_prompt:
+                metadata_negative_prompt += ('' if metadata_negative_prompt == '' else "\n") + line
+            else:
+                metadata_prompt += ('' if metadata_prompt == '' else "\n") + line
+
+        found_styles, prompt, negative_prompt = extract_styles_from_prompt(metadata_prompt, metadata_negative_prompt)
+
+        data = {
+            'prompt': prompt,
+            'negative_prompt': negative_prompt
+        }
+
+        for k, v in re_param.findall(lastline):
+            try:
+                if v != '' and v[0] == '"' and v[-1] == '"':
+                    v = unquote(v)
+
+                m = re_imagesize.match(v)
+                if m is not None:
+                    data['resolution'] = str((m.group(1), m.group(2)))
+                else:
+                    data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v
+            except Exception:
+                print(f"Error parsing \"{k}: {v}\"")
+
+        # workaround for multiline prompts
+        if 'raw_prompt' in data:
+            data['prompt'] = data['raw_prompt']
+            raw_prompt = data['raw_prompt'].replace("\n", ', ')
+            if metadata_prompt != raw_prompt and modules.sdxl_styles.fooocus_expansion not in found_styles:
+                found_styles.append(modules.sdxl_styles.fooocus_expansion)
+
+        if 'raw_negative_prompt' in data:
+            data['negative_prompt'] = data['raw_negative_prompt']
+
+        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:
+            try:
+                data['performance'] = Performance[Steps(int(data['steps'])).name].value
+            except ValueError | KeyError:
+                pass
+
+        if 'sampler' in data:
+            data['sampler'] = data['sampler'].replace(' Karras', '')
+            # get key
+            for k, v in SAMPLERS.items():
+                if v == data['sampler']:
+                    data['sampler'] = k
+                    break
+
+        for key in ['base_model', 'refiner_model']:
+            if key in data:
+                for filename in modules.config.model_filenames:
+                    path = Path(filename)
+                    if data[key] == path.stem:
+                        data[key] = filename
+                        break
+
+        if 'lora_hashes' in data:
+            lora_filenames = modules.config.lora_filenames.copy()
+            lora_filenames.remove(modules.config.downloading_sdxl_lcm_lora())
+            for li, lora in enumerate(data['lora_hashes'].split(', ')):
+                lora_name, lora_hash, lora_weight = lora.split(': ')
+                for filename in lora_filenames:
+                    path = Path(filename)
+                    if lora_name == path.stem:
+                        data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'
+                        break
+
+        return data
+
+    def parse_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':
+                sampler += f' Karras'
+
+        generation_params = {
+            self.fooocus_to_a1111['steps']: self.steps,
+            self.fooocus_to_a1111['sampler']: sampler,
+            self.fooocus_to_a1111['seed']: data['seed'],
+            self.fooocus_to_a1111['resolution']: f'{width}x{height}',
+            self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'],
+            self.fooocus_to_a1111['sharpness']: data['sharpness'],
+            self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'],
+            self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem,
+            self.fooocus_to_a1111['base_model_hash']: self.base_model_hash,
+
+            self.fooocus_to_a1111['performance']: data['performance'],
+            self.fooocus_to_a1111['scheduler']: scheduler,
+            # workaround for multiline prompts
+            self.fooocus_to_a1111['raw_prompt']: self.raw_prompt,
+            self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt,
+        }
+
+        if self.refiner_model_name not in ['', 'None']:
+            generation_params |= {
+                self.fooocus_to_a1111['refiner_model']: self.refiner_model_name,
+                self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash
+            }
+
+        for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
+            if key in data:
+                generation_params[self.fooocus_to_a1111[key]] = data[key]
+
+        lora_hashes = []
+        for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras):
+            # workaround for Fooocus not knowing LoRA name in LoRA metadata
+            lora_hashes.append(f'{lora_name}: {lora_hash}: {lora_weight}')
+        lora_hashes_string = ', '.join(lora_hashes)
+
+        generation_params |= {
+            self.fooocus_to_a1111['lora_hashes']: lora_hashes_string,
+            self.fooocus_to_a1111['version']: data['version']
+        }
+
+        if modules.config.metadata_created_by != '':
+            generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by
+
+        generation_params_text = ", ".join(
+            [k if k == v else f'{k}: {quote(v)}' for k, v in generation_params.items() if
+             v is not None])
+        positive_prompt_resolved = ', '.join(self.full_prompt)
+        negative_prompt_resolved = ', '.join(self.full_negative_prompt)
+        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()
+
+
+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()
+        lora_filenames.remove(modules.config.downloading_sdxl_lcm_lora())
+
+        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)
+            elif key.startswith('lora_combined_'):
+                metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
+            else:
+                continue
+
+        return metadata
+
+    def parse_string(self, metadata: list) -> str:
+        for li, (label, key, value) in enumerate(metadata):
+            # remove model folder paths from metadata
+            if key.startswith('lora_combined_'):
+                name, weight = value.split(' : ')
+                name = Path(name).stem
+                value = f'{name} : {weight}'
+                metadata[li] = (label, key, value)
+
+        res = {k: v for _, k, v in metadata}
+
+        res['full_prompt'] = self.full_prompt
+        res['full_negative_prompt'] = self.full_negative_prompt
+        res['steps'] = self.steps
+        res['base_model'] = self.base_model_name
+        res['base_model_hash'] = self.base_model_hash
+
+        if self.refiner_model_name not in ['', 'None']:
+            res['refiner_model'] = self.refiner_model_name
+            res['refiner_model_hash'] = self.refiner_model_hash
+
+        res['loras'] = self.loras
+
+        if modules.config.metadata_created_by != '':
+            res['created_by'] = modules.config.metadata_created_by
+
+        return json.dumps(dict(sorted(res.items())))
+
+    @staticmethod
+    def replace_value_with_filename(key, value, filenames):
+        for filename in filenames:
+            path = Path(filename)
+            if key.startswith('lora_combined_'):
+                name, weight = value.split(' : ')
+                if name == path.stem:
+                    return f'{filename} : {weight}'
+            elif value == path.stem:
+                return filename
+
+
+def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser:
+    match metadata_scheme:
+        case MetadataScheme.FOOOCUS:
+            return FooocusMetadataParser()
+        case MetadataScheme.A1111:
+            return A1111MetadataParser()
+        case _:
+            raise NotImplementedError
+
+
+def read_info_from_image(filepath) -> tuple[str | None, MetadataScheme | None]:
+    with Image.open(filepath) as image:
+        items = (image.info or {}).copy()
+
+    parameters = items.pop('parameters', None)
+    metadata_scheme = items.pop('fooocus_scheme', None)
+    exif = items.pop('exif', None)
+
+    if parameters is not None and is_json(parameters):
+        parameters = json.loads(parameters)
+    elif exif is not None:
+        exif = image.getexif()
+        # 0x9286 = UserComment
+        parameters = exif.get(0x9286, None)
+        # 0x927C = MakerNote
+        metadata_scheme = exif.get(0x927C, None)
+
+        if is_json(parameters):
+            parameters = json.loads(parameters)
+
+    try:
+        metadata_scheme = MetadataScheme(metadata_scheme)
+    except ValueError:
+        metadata_scheme = None
+
+        # broad fallback
+        if isinstance(parameters, dict):
+            metadata_scheme = MetadataScheme.FOOOCUS
+
+        if isinstance(parameters, str):
+            metadata_scheme = MetadataScheme.A1111
+
+    return parameters, metadata_scheme
+
+
+def get_exif(metadata: str | None, metadata_scheme: str):
+    exif = Image.Exif()
+    # tags see see https://github.com/python-pillow/Pillow/blob/9.2.x/src/PIL/ExifTags.py
+    # 0x9286 = UserComment
+    exif[0x9286] = metadata
+    # 0x0131 = Software
+    exif[0x0131] = 'Fooocus v' + fooocus_version.version
+    # 0x927C = MakerNote
+    exif[0x927C] = metadata_scheme
+    return exif

modules/patch.py

@@ -17,7 +17,6 @@ import ldm_patched.controlnet.cldm
 import ldm_patched.modules.model_patcher
 import ldm_patched.modules.samplers
 import ldm_patched.modules.args_parser
-import modules.advanced_parameters as advanced_parameters
 import warnings
 import safetensors.torch
 import modules.constants as constants
@@ -29,15 +28,25 @@ from modules.patch_precision import patch_all_precision
 from modules.patch_clip import patch_all_clip
 
 
-sharpness = 2.0
+class PatchSettings:
+    def __init__(self,
+                 sharpness=2.0,
+                 adm_scaler_end=0.3,
+                 positive_adm_scale=1.5,
+                 negative_adm_scale=0.8,
+                 controlnet_softness=0.25,
+                 adaptive_cfg=7.0):
+        self.sharpness = sharpness
+        self.adm_scaler_end = adm_scaler_end
+        self.positive_adm_scale = positive_adm_scale
+        self.negative_adm_scale = negative_adm_scale
+        self.controlnet_softness = controlnet_softness
+        self.adaptive_cfg = adaptive_cfg
+        self.global_diffusion_progress = 0
+        self.eps_record = None
 
-adm_scaler_end = 0.3
-positive_adm_scale = 1.5
-negative_adm_scale = 0.8
 
-adaptive_cfg = 7.0
+patch_settings = {}
-global_diffusion_progress = 0
-eps_record = None
 
 
 def calculate_weight_patched(self, patches, weight, key):
@@ -201,14 +210,13 @@ class BrownianTreeNoiseSamplerPatched:
 
 
 def compute_cfg(uncond, cond, cfg_scale, t):
-    global adaptive_cfg
+    pid = os.getpid()
-
+    mimic_cfg = float(patch_settings[pid].adaptive_cfg)
-    mimic_cfg = float(adaptive_cfg)
     real_cfg = float(cfg_scale)
 
     real_eps = uncond + real_cfg * (cond - uncond)
 
-    if cfg_scale > adaptive_cfg:
+    if cfg_scale > patch_settings[pid].adaptive_cfg:
         mimicked_eps = uncond + mimic_cfg * (cond - uncond)
         return real_eps * t + mimicked_eps * (1 - t)
     else:
@@ -216,13 +224,13 @@ def compute_cfg(uncond, cond, cfg_scale, t):
 
 
 def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None):
-    global eps_record
+    pid = os.getpid()
 
     if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False):
         final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]
 
-        if eps_record is not None:
+        if patch_settings[pid].eps_record is not None:
-            eps_record = ((x - final_x0) / timestep).cpu()
+            patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu()
 
         return final_x0
 
@@ -231,16 +239,16 @@ def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, mode
     positive_eps = x - positive_x0
     negative_eps = x - negative_x0
 
-    alpha = 0.001 * sharpness * global_diffusion_progress
+    alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress
 
     positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
     positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha)
 
     final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted,
-                            cfg_scale=cond_scale, t=global_diffusion_progress)
+                            cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress)
 
-    if eps_record is not None:
+    if patch_settings[pid].eps_record is not None:
-        eps_record = (final_eps / timestep).cpu()
+        patch_settings[pid].eps_record = (final_eps / timestep).cpu()
 
     return x - final_eps
 
@@ -255,20 +263,19 @@ def round_to_64(x):
 
 
 def sdxl_encode_adm_patched(self, **kwargs):
-    global positive_adm_scale, negative_adm_scale
-
     clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
     width = kwargs.get("width", 1024)
     height = kwargs.get("height", 1024)
     target_width = width
     target_height = height
+    pid = os.getpid()
 
     if kwargs.get("prompt_type", "") == "negative":
-        width = float(width) * negative_adm_scale
+        width = float(width) * patch_settings[pid].negative_adm_scale
-        height = float(height) * negative_adm_scale
+        height = float(height) * patch_settings[pid].negative_adm_scale
     elif kwargs.get("prompt_type", "") == "positive":
-        width = float(width) * positive_adm_scale
+        width = float(width) * patch_settings[pid].positive_adm_scale
-        height = float(height) * positive_adm_scale
+        height = float(height) * patch_settings[pid].positive_adm_scale
 
     def embedder(number_list):
         h = self.embedder(torch.tensor(number_list, dtype=torch.float32))
@@ -322,7 +329,7 @@ def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale,
 
 def timed_adm(y, timesteps):
     if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:
-        y_mask = (timesteps > 999.0 * (1.0 - float(adm_scaler_end))).to(y)[..., None]
+        y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None]
         y_with_adm = y[..., :2816].clone()
         y_without_adm = y[..., 2816:].clone()
         return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask)
@@ -332,6 +339,7 @@ def timed_adm(y, timesteps):
 def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
     t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
     emb = self.time_embed(t_emb)
+    pid = os.getpid()
 
     guided_hint = self.input_hint_block(hint, emb, context)
 
@@ -357,19 +365,17 @@ def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
     h = self.middle_block(h, emb, context)
     outs.append(self.middle_block_out(h, emb, context))
 
-    if advanced_parameters.controlnet_softness > 0:
+    if patch_settings[pid].controlnet_softness > 0:
         for i in range(10):
             k = 1.0 - float(i) / 9.0
-            outs[i] = outs[i] * (1.0 - advanced_parameters.controlnet_softness * k)
+            outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k)
 
     return outs
 
 
 def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
-    global global_diffusion_progress
-
     self.current_step = 1.0 - timesteps.to(x) / 999.0
-    global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
+    patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
 
     y = timed_adm(y, timesteps)
 

modules/private_logger.py

@@ -5,26 +5,48 @@ import json
 import urllib.parse
 
 from PIL import Image
+from PIL.PngImagePlugin import PngInfo
 from modules.util import generate_temp_filename
-
+from modules.meta_parser import MetadataParser, get_exif
 
 log_cache = {}
 
 
-def get_current_html_path():
+def get_current_html_path(output_format=None):
+    output_format = output_format if output_format else modules.config.default_output_format
     date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs,
-                                                                         extension='png')
+                                                                         extension=output_format)
     html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')
     return html_name
 
 
-def log(img, dic):
+def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str:
-    if args_manager.args.disable_image_log:
+    path_outputs = args_manager.args.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs
-        return
+    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)
-    date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs, extension='png')
     os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
-    Image.fromarray(img).save(local_temp_filename)
+
+    parsed_parameters = metadata_parser.parse_string(metadata) if metadata_parser is not None else ''
+    image = Image.fromarray(img)
+
+    if output_format == 'png':
+        if parsed_parameters != '':
+            pnginfo = PngInfo()
+            pnginfo.add_text('parameters', parsed_parameters)
+            pnginfo.add_text('fooocus_scheme', metadata_parser.get_scheme().value)
+        else:
+            pnginfo = None
+        image.save(local_temp_filename, pnginfo=pnginfo)
+    elif output_format == 'jpg':
+        image.save(local_temp_filename, quality=95, optimize=True, progressive=True, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
+    elif output_format == 'webp':
+        image.save(local_temp_filename, quality=95, lossless=False, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
+    else:
+        image.save(local_temp_filename)
+
+    if args_manager.args.disable_image_log:
+        return local_temp_filename
+
     html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')
 
     css_styles = (
@@ -32,7 +54,7 @@ def log(img, dic):
         "body { background-color: #121212; color: #E0E0E0; } "
         "a { color: #BB86FC; } "
         ".metadata { border-collapse: collapse; width: 100%; } "
-        ".metadata .key { width: 15%; } "
+        ".metadata .label { width: 15%; } "
         ".metadata .value { width: 85%; font-weight: bold; } "
         ".metadata th, .metadata td { border: 1px solid #4d4d4d; padding: 4px; } "
         ".image-container img { height: auto; max-width: 512px; display: block; padding-right:10px; } "
@@ -85,13 +107,13 @@ def log(img, dic):
     item = f"<div id=\"{div_name}\" class=\"image-container\"><hr><table><tr>\n"
     item += f"<td><a href=\"{only_name}\" target=\"_blank\"><img src='{only_name}' onerror=\"this.closest('.image-container').style.display='none';\" loading='lazy'/></a><div>{only_name}</div></td>"
     item += "<td><table class='metadata'>"
-    for key, value in dic:
+    for label, key, value in metadata:
-        value_txt = str(value).replace('\n', ' <br/> ')
+        value_txt = str(value).replace('\n', ' </br> ')
-        item += f"<tr><td class='key'>{key}</td><td class='value'>{value_txt}</td></tr>\n"
+        item += f"<tr><td class='label'>{label}</td><td class='value'>{value_txt}</td></tr>\n"
     item += "</table>"
 
-    js_txt = urllib.parse.quote(json.dumps({k: v for k, v in dic}, 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 += f"</br><button onclick=\"to_clipboard('{js_txt}')\">Copy to Clipboard</button>"
 
     item += "</td>"
     item += "</tr></table></div>\n\n"
@@ -105,4 +127,4 @@ def log(img, dic):
 
     log_cache[html_name] = middle_part
 
-    return
+    return local_temp_filename

modules/sdxl_styles.py

@@ -1,6 +1,7 @@
 import os
 import re
 import json
+import math
 
 from modules.util import get_files_from_folder
 
@@ -80,3 +81,38 @@ def apply_wildcards(wildcard_text, rng, directory=wildcards_path):
 
     print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')
     return wildcard_text
+
+def get_words(arrays, totalMult, index):
+    if(len(arrays) == 1):
+        return [arrays[0].split(',')[index]]
+    else:
+        words = arrays[0].split(',')
+        word = words[index % len(words)]
+        index -= index % len(words)
+        index /= len(words)
+        index = math.floor(index)
+        return [word] + get_words(arrays[1:], math.floor(totalMult/len(words)), index)
+
+
+
+def apply_arrays(text, index):
+    arrays = re.findall(r'\[\[([\s,\w-]+)\]\]', text)
+    if len(arrays) == 0:
+        return text
+
+    print(f'[Arrays] processing: {text}')
+    mult = 1
+    for arr in arrays:
+        words = arr.split(',')
+        mult *= len(words)
+    
+    index %= mult
+    chosen_words = get_words(arrays, mult, index)
+    
+    i = 0
+    for arr in arrays:
+        text = text.replace(f'[[{arr}]]', chosen_words[i], 1)   
+        i = i+1
+    
+    return text
+

modules/util.py

@@ -1,15 +1,20 @@
+import typing
+
 import numpy as np
 import datetime
 import random
 import math
 import os
 import cv2
+import json
 
 from PIL import Image
+from hashlib import sha256
 
+import modules.sdxl_styles
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
-
+HASH_SHA256_LENGTH = 10
 
 def erode_or_dilate(x, k):
     k = int(k)
@@ -155,7 +160,7 @@ def generate_temp_filename(folder='./outputs/', extension='png'):
     random_number = random.randint(1000, 9999)
     filename = f"{time_string}_{random_number}.{extension}"
     result = os.path.join(folder, date_string, filename)
-    return date_string, os.path.abspath(os.path.realpath(result)), filename
+    return date_string, os.path.abspath(result), filename
 
 
 def get_files_from_folder(folder_path, exensions=None, name_filter=None):
@@ -168,14 +173,190 @@ def get_files_from_folder(folder_path, exensions=None, name_filter=None):
         relative_path = os.path.relpath(root, folder_path)
         if relative_path == ".":
             relative_path = ""
-        for filename in sorted(files):
+        for filename in sorted(files, key=lambda s: s.casefold()):
             _, file_extension = os.path.splitext(filename)
-            if (exensions == None or file_extension.lower() in exensions) and (name_filter == None or name_filter in _):
+            if (exensions is None or file_extension.lower() in exensions) and (name_filter is None or name_filter in _):
                 path = os.path.join(relative_path, filename)
                 filenames.append(path)
 
     return filenames
 
 
+def calculate_sha256(filename, length=HASH_SHA256_LENGTH) -> str:
+    hash_sha256 = sha256()
+    blksize = 1024 * 1024
+
+    with open(filename, "rb") as f:
+        for chunk in iter(lambda: f.read(blksize), b""):
+            hash_sha256.update(chunk)
+
+    res = hash_sha256.hexdigest()
+    return res[:length] if length else res
+
+
+def quote(text):
+    if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
+        return text
+
+    return json.dumps(text, ensure_ascii=False)
+
+
+def unquote(text):
+    if len(text) == 0 or text[0] != '"' or text[-1] != '"':
+        return text
+
+    try:
+        return json.loads(text)
+    except Exception:
+        return text
+
+
+def unwrap_style_text_from_prompt(style_text, prompt):
+    """
+    Checks the prompt to see if the style text is wrapped around it. If so,
+    returns True plus the prompt text without the style text. Otherwise, returns
+    False with the original prompt.
+
+    Note that the "cleaned" version of the style text is only used for matching
+    purposes here. It isn't returned; the original style text is not modified.
+    """
+    stripped_prompt = prompt
+    stripped_style_text = style_text
+    if "{prompt}" in stripped_style_text:
+        # Work out whether the prompt is wrapped in the style text. If so, we
+        # return True and the "inner" prompt text that isn't part of the style.
+        try:
+            left, right = stripped_style_text.split("{prompt}", 2)
+        except ValueError as e:
+            # If the style text has multple "{prompt}"s, we can't split it into
+            # two parts. This is an error, but we can't do anything about it.
+            print(f"Unable to compare style text to prompt:\n{style_text}")
+            print(f"Error: {e}")
+            return False, prompt, ''
+
+        left_pos = stripped_prompt.find(left)
+        right_pos = stripped_prompt.find(right)
+        if 0 <= left_pos < right_pos:
+            real_prompt = stripped_prompt[left_pos + len(left):right_pos]
+            prompt = stripped_prompt.replace(left + real_prompt + right, '', 1)
+            if prompt.startswith(", "):
+                prompt = prompt[2:]
+            if prompt.endswith(", "):
+                prompt = prompt[:-2]
+            return True, prompt, real_prompt
+    else:
+        # Work out whether the given prompt starts with the style text. If so, we
+        # return True and the prompt text up to where the style text starts.
+        if stripped_prompt.endswith(stripped_style_text):
+            prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)]
+            if prompt.endswith(", "):
+                prompt = prompt[:-2]
+            return True, prompt, prompt
+
+    return False, prompt, ''
+
+
+def extract_original_prompts(style, prompt, negative_prompt):
+    """
+    Takes a style and compares it to the prompt and negative prompt. If the style
+    matches, returns True plus the prompt and negative prompt with the style text
+    removed. Otherwise, returns False with the original prompt and negative prompt.
+    """
+    if not style.prompt and not style.negative_prompt:
+        return False, prompt, negative_prompt
+
+    match_positive, extracted_positive, real_prompt = unwrap_style_text_from_prompt(
+        style.prompt, prompt
+    )
+    if not match_positive:
+        return False, prompt, negative_prompt, ''
+
+    match_negative, extracted_negative, _ = unwrap_style_text_from_prompt(
+        style.negative_prompt, negative_prompt
+    )
+    if not match_negative:
+        return False, prompt, negative_prompt, ''
+
+    return True, extracted_positive, extracted_negative, real_prompt
+
+
+def extract_styles_from_prompt(prompt, negative_prompt):
+    extracted = []
+    applicable_styles = []
+
+    for style_name, (style_prompt, style_negative_prompt) in modules.sdxl_styles.styles.items():
+        applicable_styles.append(PromptStyle(name=style_name, prompt=style_prompt, negative_prompt=style_negative_prompt))
+
+    real_prompt = ''
+
+    while True:
+        found_style = None
+
+        for style in applicable_styles:
+            is_match, new_prompt, new_neg_prompt, new_real_prompt = extract_original_prompts(
+                style, prompt, negative_prompt
+            )
+            if is_match:
+                found_style = style
+                prompt = new_prompt
+                negative_prompt = new_neg_prompt
+                if real_prompt == '' and new_real_prompt != '' and new_real_prompt != prompt:
+                    real_prompt = new_real_prompt
+                break
+
+        if not found_style:
+            break
+
+        applicable_styles.remove(found_style)
+        extracted.append(found_style.name)
+
+    # add prompt expansion if not all styles could be resolved
+    if prompt != '':
+        if real_prompt != '':
+            extracted.append(modules.sdxl_styles.fooocus_expansion)
+        else:
+            # find real_prompt when only prompt expansion is selected
+            first_word = prompt.split(', ')[0]
+            first_word_positions = [i for i in range(len(prompt)) if prompt.startswith(first_word, i)]
+            if len(first_word_positions) > 1:
+                real_prompt = prompt[:first_word_positions[-1]]
+                extracted.append(modules.sdxl_styles.fooocus_expansion)
+                if real_prompt.endswith(', '):
+                    real_prompt = real_prompt[:-2]
+
+    return list(reversed(extracted)), real_prompt, negative_prompt
+
+
+class PromptStyle(typing.NamedTuple):
+    name: str
+    prompt: str
+    negative_prompt: str
+
+
+def is_json(data: str) -> bool:
+    try:
+        loaded_json = json.loads(data)
+        assert isinstance(loaded_json, dict)
+    except (ValueError, AssertionError):
+        return False
+    return True
+
+
+def get_file_from_folder_list(name, folders):
+    for folder in folders:
+        filename = os.path.abspath(os.path.realpath(os.path.join(folder, name)))
+        if os.path.isfile(filename):
+            return filename
+
+    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)
+    except OSError as error:
+        print(f'Directory {path} could not be created, reason: {error}')

readme.md

@@ -237,6 +237,10 @@ You can install Fooocus on Apple Mac silicon (M1 or M2) with macOS 'Catalina' or
 
 Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition.
 
+### Docker
+
+See [docker.md](docker.md)
+
 ### Download Previous Version
 
 See the guidelines [here](https://github.com/lllyasviel/Fooocus/discussions/1405).
@@ -370,7 +374,7 @@ entry_with_update.py  [-h] [--listen [IP]] [--port PORT]
                       [--attention-split | --attention-quad | --attention-pytorch]
                       [--disable-xformers]
                       [--always-gpu | --always-high-vram | --always-normal-vram | 
-                       --always-low-vram | --always-no-vram | --always-cpu]
+                       --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]

requirements_docker.txt

@@ -0,0 +1,5 @@
+torch==2.0.1
+torchvision==0.15.2
+torchaudio==2.0.2
+torchtext==0.15.2
+torchdata==0.6.1

shared.py

@@ -1,2 +1 @@
 gradio_root = None
-last_stop = None

update_log.md

@@ -1,3 +1,8 @@
+# 2.1.865
+
+* Various bugfixes
+* Add authentication to --listen
+
 # 2.1.864
 
 * New model list. See also discussions.

webui.py

@@ -11,7 +11,6 @@ import modules.async_worker as worker
 import modules.constants as constants
 import modules.flags as flags
 import modules.gradio_hijack as grh
-import modules.advanced_parameters as advanced_parameters
 import modules.style_sorter as style_sorter
 import modules.meta_parser
 import args_manager
@@ -21,18 +20,21 @@ from modules.sdxl_styles import legal_style_names
 from modules.private_logger import get_current_html_path
 from modules.ui_gradio_extensions import reload_javascript
 from modules.auth import auth_enabled, check_auth
+from modules.util import is_json
 
+def get_task(*args):
+    args = list(args)
+    args.pop(0)
 
-def generate_clicked(*args):
+    return worker.AsyncTask(args=args)
+
+def generate_clicked(task):
     import ldm_patched.modules.model_management as model_management
 
     with model_management.interrupt_processing_mutex:
         model_management.interrupt_processing = False
-
     # outputs=[progress_html, progress_window, progress_gallery, gallery]
-
     execution_start_time = time.perf_counter()
-    task = worker.AsyncTask(args=list(args))
     finished = False
 
     yield gr.update(visible=True, value=modules.html.make_progress_html(1, 'Waiting for task to start ...')), \
@@ -71,6 +73,11 @@ def generate_clicked(*args):
                     gr.update(visible=True, value=product)
                 finished = True
 
+                # delete Fooocus temp images, only keep gradio temp images
+                if args_manager.args.disable_image_log:
+                    for filepath in product:
+                        os.remove(filepath)
+
     execution_time = time.perf_counter() - execution_start_time
     print(f'Total time: {execution_time:.2f} seconds')
     return
@@ -88,6 +95,7 @@ shared.gradio_root = gr.Blocks(
     css=modules.html.css).queue()
 
 with shared.gradio_root:
+    currentTask = gr.State(worker.AsyncTask(args=[]))
     with gr.Row():
         with gr.Column(scale=2):
             with gr.Row():
@@ -115,21 +123,22 @@ with shared.gradio_root:
                     skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', visible=False)
                     stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False)
 
-                    def stop_clicked():
+                    def stop_clicked(currentTask):
                         import ldm_patched.modules.model_management as model_management
-                        shared.last_stop = 'stop'
+                        currentTask.last_stop = 'stop'
-                        model_management.interrupt_current_processing()
+                        if (currentTask.processing):
-                        return [gr.update(interactive=False)] * 2
+                            model_management.interrupt_current_processing()
+                        return currentTask
 
-                    def skip_clicked():
+                    def skip_clicked(currentTask):
                         import ldm_patched.modules.model_management as model_management
-                        shared.last_stop = 'skip'
+                        currentTask.last_stop = 'skip'
-                        model_management.interrupt_current_processing()
+                        if (currentTask.processing):
-                        return
+                            model_management.interrupt_current_processing()
+                        return currentTask
 
-                    stop_button.click(stop_clicked, outputs=[skip_button, stop_button],
+                    stop_button.click(stop_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False, _js='cancelGenerateForever')
-                                      queue=False, show_progress=False, _js='cancelGenerateForever')
+                    skip_button.click(skip_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False)
-                    skip_button.click(skip_clicked, queue=False, show_progress=False)
             with gr.Row(elem_classes='advanced_check_row'):
                 input_image_checkbox = gr.Checkbox(label='Input Image', value=False, container=False, elem_classes='min_check')
                 advanced_checkbox = gr.Checkbox(label='Advanced', value=modules.config.default_advanced_checkbox, container=False, elem_classes='min_check')
@@ -150,7 +159,7 @@ with shared.gradio_root:
                             ip_weights = []
                             ip_ctrls = []
                             ip_ad_cols = []
-                            for _ in range(4):
+                            for _ in range(flags.controlnet_image_count):
                                 with gr.Column():
                                     ip_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False, height=300)
                                     ip_images.append(ip_image)
@@ -208,6 +217,27 @@ with shared.gradio_root:
                                     value=flags.desc_type_photo)
                                 desc_btn = gr.Button(value='Describe this Image into Prompt')
                                 gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/1363" target="_blank">\U0001F4D4 Document</a>')
+                    with gr.TabItem(label='Metadata') as load_tab:
+                        with gr.Column():
+                            metadata_input_image = grh.Image(label='Drag any image generated by Fooocus here', source='upload', type='filepath')
+                            metadata_json = gr.JSON(label='Metadata')
+                            metadata_import_button = gr.Button(value='Apply Metadata')
+
+                        def trigger_metadata_preview(filepath):
+                            parameters, metadata_scheme = modules.meta_parser.read_info_from_image(filepath)
+
+                            results = {}
+                            if parameters is not None:
+                                results['parameters'] = parameters
+
+                            if isinstance(metadata_scheme, flags.MetadataScheme):
+                                results['metadata_scheme'] = metadata_scheme.value
+
+                            return results
+
+                        metadata_input_image.upload(trigger_metadata_preview, inputs=metadata_input_image,
+                                                    outputs=metadata_json, queue=False, show_progress=True)
+
             switch_js = "(x) => {if(x){viewer_to_bottom(100);viewer_to_bottom(500);}else{viewer_to_top();} return x;}"
             down_js = "() => {viewer_to_bottom();}"
 
@@ -230,6 +260,11 @@ with shared.gradio_root:
                                                    value=modules.config.default_aspect_ratio, info='width × height',
                                                    elem_classes='aspect_ratios')
                 image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number)
+
+                output_format = gr.Radio(label='Output Format',
+                                            choices=modules.flags.output_formats,
+                                            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,
                                              elem_id='negative_prompt',
@@ -259,7 +294,7 @@ with shared.gradio_root:
                     if args_manager.args.disable_image_log:
                         return gr.update(value='')
                     
-                    return gr.update(value=f'<a href="file={get_current_html_path()}" target="_blank">\U0001F4DA History Log</a>')
+                    return gr.update(value=f'<a href="file={get_current_html_path(output_format)}" target="_blank">\U0001F4DA History Log</a>')
 
                 history_link = gr.HTML()
                 shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False)
@@ -319,11 +354,15 @@ with shared.gradio_root:
 
                     for i, (n, v) in enumerate(modules.config.default_loras):
                         with gr.Row():
+                            lora_enabled = gr.Checkbox(label='Enable', value=True,
+                                                       elem_classes=['lora_enable', 'min_check'])
                             lora_model = gr.Dropdown(label=f'LoRA {i + 1}',
-                                                     choices=['None'] + modules.config.lora_filenames, value=n)
+                                                     choices=['None'] + modules.config.lora_filenames, value=n,
-                            lora_weight = gr.Slider(label='Weight', minimum=-2, maximum=2, step=0.01, value=v,
+                                                     elem_classes='lora_model')
+                            lora_weight = gr.Slider(label='Weight', minimum=modules.config.default_loras_min_weight,
+                                                    maximum=modules.config.default_loras_max_weight, step=0.01, value=v,
                                                     elem_classes='lora_weight')
-                            lora_ctrls += [lora_model, lora_weight]
+                            lora_ctrls += [lora_enabled, lora_model, lora_weight]
 
                 with gr.Row():
                     model_refresh = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button')
@@ -347,7 +386,7 @@ with shared.gradio_root:
                                                    step=0.001, value=0.3,
                                                    info='When to end the guidance from positive/negative ADM. ')
 
-                        refiner_swap_method = gr.Dropdown(label='Refiner swap method', value='joint',
+                        refiner_swap_method = gr.Dropdown(label='Refiner swap method', value=flags.refiner_swap_method,
                                                           choices=['joint', 'separate', 'vae'])
 
                         adaptive_cfg = gr.Slider(label='CFG Mimicking from TSNR', minimum=1.0, maximum=30.0, step=0.01,
@@ -387,6 +426,23 @@ with shared.gradio_root:
                                                                info='Set as negative number to disable. For developer debugging.')
                         disable_preview = gr.Checkbox(label='Disable Preview', value=False,
                                                       info='Disable preview during generation.')
+                        disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results', 
+                                                      value=modules.config.default_performance == 'Extreme Speed',
+                                                      interactive=modules.config.default_performance != 'Extreme Speed',
+                                                      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)
+
+                        if not args_manager.args.disable_metadata:
+                            save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=modules.config.default_save_metadata_to_images,
+                                                                  info='Adds parameters to generated images allowing manual regeneration.')
+                            metadata_scheme = gr.Radio(label='Metadata Scheme', choices=flags.metadata_scheme, value=modules.config.default_metadata_scheme,
+                                                       info='Image Prompt parameters are not included. Use a1111 for compatibility with Civitai.',
+                                                       visible=modules.config.default_save_metadata_to_images)
+
+                            save_metadata_to_images.change(lambda x: gr.update(visible=x), inputs=[save_metadata_to_images], outputs=[metadata_scheme], 
+                                                           queue=False, show_progress=False)
 
                     with gr.Tab(label='Control'):
                         debugging_cn_preprocessor = gr.Checkbox(label='Debug Preprocessors', value=False,
@@ -452,15 +508,6 @@ with shared.gradio_root:
                         freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)
                         freeu_ctrls = [freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2]
 
-                adps = [disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name,
-                        scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height,
-                        overwrite_vary_strength, overwrite_upscale_strength,
-                        mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint,
-                        debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness,
-                        canny_low_threshold, canny_high_threshold, refiner_swap_method]
-                adps += freeu_ctrls
-                adps += inpaint_ctrls
-
                 def dev_mode_checked(r):
                     return gr.update(visible=r)
 
@@ -470,24 +517,26 @@ with shared.gradio_root:
 
                 def model_refresh_clicked():
                     modules.config.update_all_model_names()
-                    results = []
+                    results = [gr.update(choices=modules.config.model_filenames)]
-                    results += [gr.update(choices=modules.config.model_filenames), gr.update(choices=['None'] + modules.config.model_filenames)]
+                    results += [gr.update(choices=['None'] + modules.config.model_filenames)]
-                    for i in range(5):
+                    for i in range(modules.config.default_max_lora_number):
-                        results += [gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
+                        results += [gr.update(interactive=True), gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
-                    return results
 
                 model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls,
                                     queue=False, show_progress=False)
 
         performance_selection.change(lambda x: [gr.update(interactive=x != 'Extreme Speed')] * 11 +
-                                               [gr.update(visible=x != 'Extreme Speed')] * 1,
+                                               [gr.update(visible=x != 'Extreme Speed')] * 1 +
+                                               [gr.update(interactive=x != 'Extreme Speed', value=x == 'Extreme Speed', )] * 1,
                                      inputs=performance_selection,
                                      outputs=[
                                          guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive,
                                          adm_scaler_negative, refiner_switch, refiner_model, sampler_name,
-                                         scheduler_name, adaptive_cfg, refiner_swap_method, negative_prompt
+                                         scheduler_name, adaptive_cfg, refiner_swap_method, negative_prompt, disable_intermediate_results
                                      ], queue=False, show_progress=False)
         
+        output_format.input(lambda x: gr.update(output_format=x), inputs=output_format)
+        
         advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, advanced_column,
                                  queue=False, show_progress=False) \
             .then(fn=lambda: None, _js='refresh_grid_delayed', queue=False, show_progress=False)
@@ -525,29 +574,37 @@ with shared.gradio_root:
             inpaint_strength, inpaint_respective_field
         ], show_progress=False, queue=False)
 
-        ctrls = [
+        ctrls = [currentTask, generate_image_grid]
+        ctrls += [
             prompt, negative_prompt, style_selections,
-            performance_selection, aspect_ratios_selection, image_number, image_seed, sharpness, guidance_scale
+            performance_selection, aspect_ratios_selection, image_number, output_format, image_seed, sharpness, guidance_scale
         ]
 
         ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls
         ctrls += [input_image_checkbox, current_tab]
         ctrls += [uov_method, uov_input_image]
         ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image]
+        ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment]
+        ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg]
+        ctrls += [sampler_name, scheduler_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]
+        ctrls += [refiner_swap_method, controlnet_softness]
+        ctrls += freeu_ctrls
+        ctrls += inpaint_ctrls
+
+        if not args_manager.args.disable_metadata:
+            ctrls += [save_metadata_to_images, metadata_scheme]
+
         ctrls += ip_ctrls
 
         state_is_generating = gr.State(False)
 
         def parse_meta(raw_prompt_txt, is_generating):
             loaded_json = None
-            try:
+            if is_json(raw_prompt_txt):
-                if '{' in raw_prompt_txt:
+                loaded_json = json.loads(raw_prompt_txt)
-                    if '}' in raw_prompt_txt:
-                        if ':' in raw_prompt_txt:
-                            loaded_json = json.loads(raw_prompt_txt)
-                            assert isinstance(loaded_json, dict)
-            except:
-                loaded_json = None
 
             if loaded_json is None:
                 if is_generating:
@@ -559,37 +616,35 @@ with shared.gradio_root:
 
         prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False)
 
-        load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=[
+        load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,
-            advanced_checkbox,
+                             performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection,
-            image_number,
+                             overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive,
-            prompt,
+                             adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, base_model,
-            negative_prompt,
+                             refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed,
-            style_selections,
+                             generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
-            performance_selection,
+
-            aspect_ratios_selection,
+        load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False)
-            overwrite_width,
+
-            overwrite_height,
+        def trigger_metadata_import(filepath, state_is_generating):
-            sharpness,
+            parameters, metadata_scheme = modules.meta_parser.read_info_from_image(filepath)
-            guidance_scale,
+            if parameters is None:
-            adm_scaler_positive,
+                print('Could not find metadata in the image!')
-            adm_scaler_negative,
+                parsed_parameters = {}
-            adm_scaler_end,
+            else:
-            base_model,
+                metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
-            refiner_model,
+                parsed_parameters = metadata_parser.parse_json(parameters)
-            refiner_switch,
+
-            sampler_name,
+            return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)
-            scheduler_name,
+
-            seed_random,
+
-            image_seed,
+        metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
-            generate_button,
+            .then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False)
-            load_parameter_button
-        ] + lora_ctrls, queue=False, show_progress=False)
 
         generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True),
                               outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \
             .then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed) \
-            .then(advanced_parameters.set_all_advanced_parameters, inputs=adps) \
+            .then(fn=get_task, inputs=ctrls, outputs=currentTask) \
-            .then(fn=generate_clicked, inputs=ctrls, outputs=[progress_html, progress_window, progress_gallery, gallery]) \
+            .then(fn=generate_clicked, inputs=currentTask, outputs=[progress_html, progress_window, progress_gallery, gallery]) \
             .then(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), gr.update(visible=False, interactive=False), False),
                   outputs=[generate_button, stop_button, skip_button, state_is_generating]) \
             .then(fn=update_history_link, outputs=history_link) \
@@ -626,5 +681,6 @@ shared.gradio_root.launch(
     server_port=args_manager.args.port,
     share=args_manager.args.share,
     auth=check_auth if (args_manager.args.share or args_manager.args.listen) and auth_enabled else None,
+    allowed_paths=[modules.config.path_outputs],
     blocked_paths=[constants.AUTH_FILENAME]
 )

wildcards/animal.txt

@@ -0,0 +1,100 @@
+Alligator
+Ant
+Antelope
+Armadillo
+Badger
+Bat
+Bear
+Beaver
+Bison
+Boar
+Bobcat
+Bull
+Camel
+Chameleon
+Cheetah
+Chicken
+Chihuahua
+Chimpanzee
+Chinchilla
+Chipmunk
+Comodo Dragon
+Cow
+Coyote
+Crocodile
+Crow
+Deer
+Dinosaur
+Dolphin
+Donkey
+Duck
+Eagle
+Eel
+Elephant
+Elk
+Emu
+Falcon
+Ferret
+Flamingo
+Flying Squirrel
+Giraffe
+Goose
+Guinea pig
+Hawk
+Hedgehog
+Hippopotamus
+Horse
+Hummingbird
+Hyena
+Jackal
+Jaguar
+Jellyfish
+Kangaroo
+King Cobra
+Koala bear
+Leopard
+Lion
+Lizard
+Magpie
+Marten
+Meerkat
+Mole
+Monkey
+Moose
+Mouse
+Octopus
+Okapi
+Orangutan
+Ostrich
+Otter
+Owl
+Panda
+Pangolin
+Panther
+Penguin
+Pig
+Porcupine
+Possum
+Puma
+Quokka
+Rabbit
+Raccoon
+Raven
+Reindeer
+Rhinoceros
+Seal
+Shark
+Sheep
+Snail
+Snake
+Sparrow
+Spider
+Squirrel
+Swallow
+Tiger
+Walrus
+Whale
+Wolf
+Wombat
+Yak
+Zebra