Add custom nodes, Civitai loras (LFS), and vast.ai setup script

Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

custom_nodes/comfyui_ultimatesdupscale/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintainted in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Other
+*.ipynb
+*.code-workspace
+/test/test_images
+/.vscode
+config.json

custom_nodes/comfyui_ultimatesdupscale/LICENSE

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+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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custom_nodes/comfyui_ultimatesdupscale/README.md

@@ -0,0 +1,44 @@
+# ComfyUI_UltimateSDUpscale
+
+[ComfyUI](https://github.com/comfyanonymous/ComfyUI) nodes for performing the image-to-image diffusion process on large images in tiles. This approach improves the details that is commonly found on upscaled images while reducing hardware requirements and maintaining an image size that the diffusion model is trained on.
+
+## Installation
+
+
+### Using Git
+1. Git must be installed on your system. Verify by running `git -v` in a terminal.
+2. Enter the following command from the terminal starting in ComfyUI/custom_nodes/
+    ```
+    git clone https://github.com/ssitu/ComfyUI_UltimateSDUpscale
+    ```
+
+### ComfyUI Manager
+1. [ComfyUI Manager](https://github.com/Comfy-Org/ComfyUI-Manager) must be installed.
+2. After launching ComfyUI, open ComfyUI Manager and select the "Custom Nodes Manager" option.
+3. Search for "UltimateSDUpscale" and install the node. Select latest for the most up-to-date version.
+4. Follow any prompts to restart ComfyUI.
+
+### comfy-cli
+
+1. [comfy-cli](https://github.com/Comfy-Org/comfy-cli) must be installed.
+2. Run this command from the terminal: `comfy node install comfyui_ultimatesdupscale`
+
+### Manual Download
+1. Download the zip file from https://registry.comfy.org/nodes/comfyui_ultimatesdupscale to select the version you want, or obtain the current nightly version by clicking the green "Code" button on the GitHub repository page and selecting "Download ZIP".
+2. Create a new folder in the `ComfyUI/custom_nodes/` directory to hold the extracted files (e.g. `ComfyUI/custom_nodes/ComfyUI_UltimateSDUpscale`).
+3. Extract the contents of the zip file into the `ComfyUI/custom_nodes/ComfyUI_UltimateSDUpscale` folder.
+
+
+## Usage
+
+Nodes can be found in the node menu under `image/upscaling`.
+
+Documentation for the nodes can be found in the [`js/docs/`](js/docs/) folder, or viewed within the application by right-clicking the relevant node and selecting the info icon.
+
+Details about most of the parameters can be found [here](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki/FAQ#parameters-descriptions).
+
+Example workflows can be found in the [`example_workflows/`](example_workflows/) folder. You can also find them in the ComfyUI application under the Templates menu, scroll down the left sidebar to find the Extensions section, then selecting this repository.
+
+## References
+* Ultimate Stable Diffusion Upscale script for the Automatic1111 Web UI: https://github.com/Coyote-A/ultimate-upscale-for-automatic1111
+* ComfyUI: https://github.com/comfyanonymous/ComfyUI

custom_nodes/comfyui_ultimatesdupscale/__init__.py

@@ -0,0 +1,77 @@
+import sys
+import os
+
+# Check for original USDU script
+current_dir = os.path.dirname(os.path.realpath(__file__))
+repos_dir = os.path.join(current_dir, "repositories")
+usdu_dir = os.path.join(repos_dir, "ultimate_sd_upscale")
+if not len(os.listdir(usdu_dir)):
+    print("[USDU] Original USDU script not found, downloading it from https://github.com/Coyote-A/ultimate-upscale-for-automatic1111")
+    import urllib.request
+    import zipfile
+    import shutil
+
+    url = "https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/archive/master.zip"
+    zip_path = os.path.join(current_dir, "usdu_temp.zip")
+
+    urllib.request.urlretrieve(url, zip_path)
+
+    with zipfile.ZipFile(zip_path, "r") as zip_ref:
+        top_folder = zip_ref.namelist()[0].split('/')[0] + '/'
+        for member in zip_ref.namelist():
+            if member.startswith(top_folder) and not member.endswith('/'):
+                target_path = os.path.join(usdu_dir, member[len(top_folder):])
+                os.makedirs(os.path.dirname(target := os.path.join(usdu_dir, member[len(top_folder):])), exist_ok=True)
+                with zip_ref.open(member) as source, open(target, 'wb') as target_file:
+                    shutil.copyfileobj(fsrc=zip_ref.open(member), fdst=target_file)
+
+    os.remove(zip_path)
+    print("[USDU] Original USDU script downloaded successfully")
+
+# Remove other custom_node paths from sys.path to avoid conflicts
+custom_node_paths = [path for path in sys.path if "custom_node" in path]
+original_sys_path = sys.path.copy()
+for path in custom_node_paths:
+    sys.path.remove(path)
+
+# Add this repository's path to sys.path for third-party imports
+repo_dir = os.path.dirname(os.path.realpath(__file__))
+sys.path.insert(0, repo_dir)
+original_modules = sys.modules.copy()
+
+# Place aside potentially conflicting modules
+modules_used = [
+    "modules",
+    "modules.devices",
+    "modules.images",
+    "modules.processing",
+    "modules.scripts",
+    "modules.shared",
+    "modules.upscaler",
+    "utils",
+]
+original_imported_modules = {}
+for module in modules_used:
+    if module in sys.modules:
+        original_imported_modules[module] = sys.modules.pop(module)
+
+# Proceed with node setup
+from .usdu_nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+WEB_DIRECTORY = "./js"
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
+
+# Clean up imports
+# Remove any new modules
+modules_to_remove = []
+for module in sys.modules:
+    if module not in original_modules:
+        modules_to_remove.append(module)
+for module in modules_to_remove:
+    del sys.modules[module]
+
+# Restore original modules
+sys.modules.update(original_imported_modules)
+
+# Restore original sys.path
+sys.path = original_sys_path

custom_nodes/comfyui_ultimatesdupscale/config.json.example

@@ -0,0 +1,3 @@
+{
+    "per_tile_progress": true
+}

custom_nodes/comfyui_ultimatesdupscale/gradio.py

@@ -0,0 +1 @@
+# Empty gradio module for the ultimate-upscale.py import because gradio is not needed

custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscale.md

@@ -0,0 +1,71 @@
+`Ultimate SD Upscale` combines image upscaling with tiled image-to-image processing to create high-quality, detail-enhanced upscaled images.
+
+This is the main node that interfaces with the original code for the Ultimate SD Upscale script. An image is supplied for upscaling, determined by the `upscale_by` parameter. The upscale is performed using the upscale model input.
+
+After the upscaling, the image goes through the redraw step if the tiling order is not set to "None". A tile is selected from the image, defined by the tiling order and tile parameters from the node widgets. The tile is used as input for an image-to-image process, using the sampling-related parameters given by the node widgets. The tile is then pasted back onto the upscaled image at the appropriate position. This continues until all tiles have been processed.
+
+After the redraw step, the seam fix step is applied if enabled. There are various strategies for fixing seams, defined by the `seam_fix_mode` parameter from the node widgets. The seam fix step uses the same image-to-image process as the redraw step, but applied to areas between tiles from the redraw step.
+
+## Inputs
+
+| Parameter | Data Type | Input Method | Default | Range | Description |
+|-----------|-----------|--------------|---------|--------|-------------|
+| `image` | IMAGE | Image Input | None | - | The image to upscale. |
+| `model` | MODEL | Model Selection | None | - | The model to use for image-to-image processing on each tile. |
+| `positive` | CONDITIONING | Conditioning Input | None | - | The positive conditioning for each tile during the redraw step. |
+| `negative` | CONDITIONING | Conditioning Input | None | - | The negative conditioning for each tile during the redraw step. |
+| `vae` | VAE | Model Selection | None | - | The VAE model to use for encoding and decoding tiles. |
+| `upscale_by` | FLOAT | Slider | 2.0 | 0.05-4.0 (step 0.05) | The factor to multiply the height and width of the input image(s) by. |
+| `seed` | INT | Number Input | 0 | 0-18446744073709551615 | The seed to use for image-to-image processing, ensuring reproducible results. |
+| `steps` | INT | Number Input | 20 | 1-10000 | The number of sampling steps to use for each tile during the redraw step and seam fix step. |
+| `cfg` | FLOAT | Slider | 8.0 | 0.0-100.0 | The CFG (Classifier Free Guidance) scale to use for each tile. Higher values make the output follow the prompt more closely. The recommended values depend on the model. |
+| `sampler_name` | COMBO | Dropdown | - | Available samplers | The sampler to use for each tile during the image-to-image process. |
+| `scheduler` | COMBO | Dropdown | - | Available schedulers | The scheduler to use for each tile during the sampling process. |
+| `denoise` | FLOAT | Slider | 0.2 | 0.0-1.0 (step 0.01) | The denoising strength to use for each tile. Higher values allow more creative changes, but more chance of seams. |
+| `upscale_model` | UPSCALE_MODEL | Model Selection | None | - | The upscaler model for upscaling the image before the tiled redraw step. |
+| `mode_type` | COMBO | Dropdown | - | Linear, Chess, None | The tiling order to use for the redraw step. Linear processes tiles row by row, Chess uses a checkerboard pattern, and None skips the redraw step. |
+| `tile_width` | INT | Number Input | 512 | 64-8192 (step 8) | The base width of each tile during the redraw step. |
+| `tile_height` | INT | Number Input | 512 | 64-8192 (step 8) | The base height of each tile during the redraw step. |
+| `mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the mask applied to tiles, helping blend tiles seamlessly. A higher value means more of the original image is retained near the seams when pasting the refined tiles back on the upscaled image. |
+| `tile_padding` | INT | Number Input | 32 | 0-8192 (step 8) | The padding to apply to tiles, providing more context for better blending. Adds to tile size (e.g. (`tile_width` + `tile_padding`)x(`tile_height` + `tile_padding`)). |
+| `seam_fix_mode` | COMBO | Dropdown | - | None, Band Pass, Half Tile, Half Tile + Intersections | The seam fix mode to use. Different modes apply different strategies to fix visible seams between tiles. |
+| `seam_fix_denoise` | FLOAT | Slider | 1.0 | 0.0-1.0 (step 0.01) | The denoising strength to use for the seam fix step. |
+| `seam_fix_width` | INT | Number Input | 64 | 0-8192 (step 8) | The width of the bands used for the Band Pass seam fix mode. |
+| `seam_fix_mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the seam fix mask, ensuring smooth blending. |
+| `seam_fix_padding` | INT | Number Input | 16 | 0-8192 (step 8) | The padding to apply for the seam fix step. Adds to tile size. |
+| `force_uniform_tiles` | BOOLEAN | Toggle | True | True/False | If enabled, tiles that would be cut off by the edges of the image will expand using context around the tile to keep the same tile size determined by `tile_width`, `tile_height`, and `tile_padding`. This is what happens in the A1111 Web UI. If disabled, the minimal size for tiles will be used, which may make the sampling faster but may cause artifacts due to irregular tile sizes. |
+| `tiled_decode` | BOOLEAN | Toggle | False | True/False | Whether to use tiled decoding when decoding tiles. Useful when you know the ComfyUI engine will attempt a normal decode and run into an Out Of Memory error, and resorts to tiled decoding anyway. |
+
+## Outputs
+
+| Output Name | Data Type | Description |
+|-------------|-----------|-------------|
+| `IMAGE` | IMAGE | The final upscaled image. |
+
+## Usage Tips
+
+1. **Basic Usage**
+	- Typical tile sizes are based on the model resolutions that it is trained on, such as 512x512 for SD1.5 models. If you can generate a coherent image at that resolution, then it is a good choice for the tile size.
+	- If the workflow involves generating a base image, then using USDU to upscale and refine, it is common to take the base image size as the tile size for the USDU node. For example, generating a 512x512 image, then using USDU with 2x upscale and 512x512 tiles to get a final 1024x1024 image.
+	- If you want to specify an exact output size, use the "No Upscale" variant of the node and perform the upscaling separately (e.g., ImageUpscaleWithModel -> ImageScale -> UltimateSDUpscaleNoUpscale).
+
+2. **Tiling Modes**
+	- **Linear**: Processes tiles sequentially row by row.
+	- **Chess**: Uses a checkerboard pattern, processing every other tile first. Can help reduce visible seams.
+	- **None**: Skips the redraw step entirely, only performs the initial upscale. Useful if you have an image upscaled by USDU and see seams, and only want to use the seam fix step.
+
+3. **Denoise Settings**
+	- Use a lower denoise (0.05-0.2) to refine the upscaled image to be less blurry, while avoiding seams and hallucinations.
+	- Higher denoise values are only usable when using something like a ControlNet tile model to avoid tiles and seams.
+
+4. **Seam Fix Modes**
+	- **None**: No seam fixing applied
+	- **Band Pass**: Applies processing to band-like areas between tiles
+	- **Half Tile**: Processes half-tile overlapping regions
+	- **Half Tile + Intersections**: Most thorough, processes half-tiles and their intersections
+
+5. **Performance Optimization**
+    - Enable `tiled_decode` if you're running out of VRAM during decoding, and want to skip the default behavior of attempting normal decoding.
+    - Use the largest tile size that the model and VRAM can handle to reduce the number of tiles needed.
+    - Disable `force_uniform_tiles` to only denoise what will be visible after pasting back the tile. This can save processing time, but the model used may not be trained for the resulting tile sizes, and the model will be missing the context around the tile that may otherwise be available with this option enabled.6. **Important Notes**
+    - The seam fix step significantly increases processing time. If seams are a problem, it may be better to reduce the denoise or increase tile size instead to avoid the increase in processing time.

custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscaleCustomSample.md

@@ -0,0 +1,106 @@
+`Ultimate SD Upscale (Custom Sample)` combines image upscaling with tiled image-to-image processing using custom samplers and sigmas for advanced control over the sampling process.
+
+This variant of the Ultimate SD Upscale node is designed for advanced users who want to use custom samplers and sigma schedules instead of the built-in ComfyUI samplers. This allows for more experimental and fine-tuned control over the sampling process during the tiled redraw and seam fix steps. The upscale model is optional; if not provided, the Lanczos algorithm will be used instead.
+
+An image is supplied for upscaling, determined by the `upscale_by` parameter. The upscale is performed using the upscale model input if provided, otherwise a Lanczos scaling is applied.
+
+After the upscaling, the image goes through the redraw step if the tiling order is not set to "None". A tile is selected from the image, defined by the tiling order and tile parameters from the node widgets. The tile is used as input for an image-to-image process, using the sampling-related parameters given by the node widgets, including custom sampler and sigmas if provided. The tile is then pasted back onto the upscaled image at the appropriate position. This continues until all tiles have been processed.
+
+After the redraw step, the seam fix step is applied if enabled. There are various strategies for fixing seams, defined by the `seam_fix_mode` parameter from the node widgets. The seam fix step uses the same image-to-image process as the redraw step, but applied to areas between tiles from the redraw step.
+
+## Inputs
+
+### Required Inputs
+
+| Parameter | Data Type | Input Method | Default | Range | Description |
+|-----------|-----------|--------------|---------|--------|-------------|
+| `image` | IMAGE | Image Input | None | - | The image to upscale. |
+| `model` | MODEL | Model Selection | None | - | The model to use for image-to-image processing on each tile. |
+| `positive` | CONDITIONING | Conditioning Input | None | - | The positive conditioning for each tile during the redraw step. |
+| `negative` | CONDITIONING | Conditioning Input | None | - | The negative conditioning for each tile during the redraw step. |
+| `vae` | VAE | Model Selection | None | - | The VAE model to use for encoding and decoding tiles. |
+| `upscale_by` | FLOAT | Slider | 2.0 | 0.05-4.0 (step 0.05) | The factor to multiply the height and width of the input image(s) by. |
+| `seed` | INT | Number Input | 0 | 0-18446744073709551615 | The seed to use for image-to-image processing, ensuring reproducible results. |
+| `steps` | INT | Number Input | 20 | 1-10000 | The number of sampling steps to use for each tile during the redraw step and seam fix step. |
+| `cfg` | FLOAT | Slider | 8.0 | 0.0-100.0 | The CFG (Classifier Free Guidance) scale to use for each tile. Higher values make the output follow the prompt more closely. The recommended values depend on the model. |
+| `sampler_name` | COMBO | Dropdown | - | Available samplers | The sampler to use for each tile during the image-to-image process. |
+| `scheduler` | COMBO | Dropdown | - | Available schedulers | The scheduler to use for each tile during the sampling process. |
+| `denoise` | FLOAT | Slider | 0.2 | 0.0-1.0 (step 0.01) | The denoising strength to use for each tile. Higher values allow more creative changes, but more chance of seams. |
+| `mode_type` | COMBO | Dropdown | - | Linear, Chess, None | The tiling order to use for the redraw step. Linear processes tiles row by row, Chess uses a checkerboard pattern, and None skips the redraw step. |
+| `tile_width` | INT | Number Input | 512 | 64-8192 (step 8) | The base width of each tile during the redraw step. |
+| `tile_height` | INT | Number Input | 512 | 64-8192 (step 8) | The base height of each tile during the redraw step. |
+| `mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the mask applied to tiles, helping blend tiles seamlessly. A higher value means more of the original image is retained near the seams when pasting the refined tiles back on the upscaled image. |
+| `tile_padding` | INT | Number Input | 32 | 0-8192 (step 8) | The padding to apply to tiles, providing more context for better blending. Adds to tile size (e.g. (`tile_width` + `tile_padding`)x(`tile_height` + `tile_padding`)). |
+| `seam_fix_mode` | COMBO | Dropdown | - | None, Band Pass, Half Tile, Half Tile + Intersections | The seam fix mode to use. Different modes apply different strategies to fix visible seams between tiles. |
+| `seam_fix_denoise` | FLOAT | Slider | 1.0 | 0.0-1.0 (step 0.01) | The denoising strength to use for the seam fix step. |
+| `seam_fix_width` | INT | Number Input | 64 | 0-8192 (step 8) | The width of the bands used for the Band Pass seam fix mode. |
+| `seam_fix_mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the seam fix mask, ensuring smooth blending. |
+| `seam_fix_padding` | INT | Number Input | 16 | 0-8192 (step 8) | The padding to apply for the seam fix step. Adds to tile size. |
+| `force_uniform_tiles` | BOOLEAN | Toggle | True | True/False | If enabled, tiles that would be cut off by the edges of the image will expand using context around the tile to keep the same tile size determined by `tile_width`, `tile_height`, and `tile_padding`. This is what happens in the A1111 Web UI. If disabled, the minimal size for tiles will be used, which may make the sampling faster but may cause artifacts due to irregular tile sizes. |
+| `tiled_decode` | BOOLEAN | Toggle | False | True/False | Whether to use tiled decoding when decoding tiles. Useful when you know the ComfyUI engine will attempt a normal decode and run into an Out Of Memory error, and resorts to tiled decoding anyway. |
+
+### Optional Inputs
+
+| Parameter | Data Type | Input Method | Default | Description |
+|-----------|-----------|--------------|---------|-------------|
+| `upscale_model` | UPSCALE_MODEL | Model Selection | None | The upscaler model for upscaling the image before the tiled redraw step. If not provided, the Lanczos algorithm will be used instead. |
+| `custom_sampler` | SAMPLER | Sampler Input | None | A custom sampler to use instead of the built-in ComfyUI sampler specified by `sampler_name`. Only used if both `custom_sampler` and `custom_sigmas` are provided. |
+| `custom_sigmas` | SIGMAS | Sigmas Input | None | A custom noise schedule to use during sampling. Only used if both `custom_sampler` and `custom_sigmas` are provided. |
+
+## Outputs
+
+| Output Name | Data Type | Description |
+|-------------|-----------|-------------|
+| `IMAGE` | IMAGE | The final upscaled image. |
+
+## Usage Tips
+
+1. **When to Use This Node**
+    - You want to experiment with custom samplers not available in the standard node.
+    - You need precise control over sigma schedules.
+    - You're working with advanced sampling techniques or research implementations.
+    - You want the flexibility to skip the upscale model and use Lanczos instead.
+    - You're combining USDU with custom sampling workflows.
+
+2. **Basic Usage**
+    - Typical tile sizes are based on the model resolutions that it is trained on, such as 512x512 for SD1.5 models. If you can generate a coherent image at that resolution, then it is probably a good choice for the tile size.
+    - If the workflow involves generating a base image, then using USDU to upscale and refine, it is common to take the base image size as the tile size for the USDU node. For example, generating a 512x512 image, then using USDU with 2x upscale and 512x512 tiles to get a final 1024x1024 image.
+
+3. **Custom Sampler Usage**
+    - When both `custom_sampler` and `custom_sigmas` are provided, `custom_sampler` will be used instead of the `sampler_name` parameter
+    - Custom samplers can implement experimental or specialized sampling algorithms
+    - Ensure your custom sampler is compatible with the model and VAE being used
+    - Custom samplers typically require `custom_sigmas` to be provided as well
+
+4. **Custom Sigmas Usage**
+    - When both `custom_sampler` and `custom_sigmas` are provided, `custom_sigmas` will be used instead of the default noise schedule.
+    - Sigma schedules control the noise levels during the denoising process
+    - Custom sigmas allow you to fine-tune the denoising trajectory
+    - Different sigma schedules can produce different aesthetic results
+    - When using `custom_sigmas`, ensure they're appropriate for your `steps` parameter
+
+5. **Tiling Modes**
+    - **Linear**: Processes tiles sequentially row by row.
+    - **Chess**: Uses a checkerboard pattern, processing every other tile first. Can help reduce visible seams.
+    - **None**: Skips the redraw step entirely, only performs the initial upscale. Useful if you have an image upscaled by USDU and see seams, and only want to use the seam fix step.
+
+6. **Denoise Settings**
+    - Use a lower denoise (0.05-0.2) to refine the upscaled image to be less blurry, while avoiding seams and hallucinations.
+    - Higher denoise values are only usable when using something like a ControlNet tile model to avoid tiles and seams.
+
+7. **Seam Fix Modes**
+    - **None**: No seam fixing applied
+    - **Band Pass**: Applies processing to band-like areas between tiles
+    - **Half Tile**: Processes half-tile overlapping regions
+    - **Half Tile + Intersections**: Most thorough, processes half-tiles and their intersections
+
+8. **Performance Optimization**
+    - Enable `tiled_decode` if you're running out of VRAM during decoding, and want to skip the default behavior of attempting normal decoding.
+    - Use the largest tile size that the model and VRAM can handle to reduce the number of tiles needed.
+    - Disable `force_uniform_tiles` to only denoise what will be visible after pasting back the tile. This can save processing time, but the model used may not be trained for the resulting tile sizes, and the model will be missing the context around the tile that may otherwise be available with this option enabled.
+
+9. **Important Notes**
+    - When no `upscale_model` is provided, Lanczos is used to scale by the `upscale_by` factor instead.
+    - Custom sampler and sigmas should be compatible with each other, and also the model being used.
+    - `custom_sampler` and `custom_sigmas` are both optional, but must be provided together to take effect.
+    - The seam fix step significantly increases processing time. If seams are a problem, it may be better to reduce the denoise or increase tile size instead to avoid the increase in processing time.

custom_nodes/comfyui_ultimatesdupscale/js/docs/UltimateSDUpscaleNoUpscale.md

@@ -0,0 +1,80 @@
+`Ultimate SD Upscale (No Upscale)` applies tiled image-to-image proc5. **Performance Optimization**
+    - Enable `tiled_decode` if you're running out of VRAM during decoding, and want to skip the default behavior of attempting normal decoding.
+    - Use the largest tile size that the model and VRAM can handle to reduce the number of tiles needed.
+    - Disable `force_uniform_tiles` to only denoise what will be visible after pasting back the tile. This can save processing time, but the model used may not be trained for the resulting tile sizes, and the model will be missing the context around the tile that may otherwise be available with this option enabled.ng to an already upscaled image to enhance details and fix seams, without performing the initial upscaling step with an upscale model.
+
+This variant of the Ultimate SD Upscale node is designed for situations where you already have an upscaled image and only want to apply the tiled redraw and seam fix steps. This is useful when you've upscaled an image using a different method or upscaler and want to use USDU's tiled refinement capabilities to add details and remove artifacts.
+
+The image goes through the redraw step if the tiling order is not set to "None". A tile is selected from the image, defined by the tiling order and tile parameters from the node widgets. The tile is used as input for an image-to-image process, using the sampling-related parameters given by the node widgets. The tile is then pasted back onto the image at the appropriate position. This continues until all tiles have been processed.
+
+After the redraw step, the seam fix step is applied if enabled. There are various strategies for fixing seams, defined by the `seam_fix_mode` parameter from the node widgets. The seam fix step uses the same image-to-image process as the redraw step, but applied to areas between tiles from the redraw step.
+
+## Inputs
+
+| Parameter | Data Type | Input Method | Default | Range | Description |
+|-----------|-----------|--------------|---------|--------|-------------|
+| `upscaled_image` | IMAGE | Image Input | None | - | The already upscaled image to refine with tiled processing. |
+| `model` | MODEL | Model Selection | None | - | The model to use for image-to-image processing on each tile. |
+| `positive` | CONDITIONING | Conditioning Input | None | - | The positive conditioning for each tile during the redraw step. |
+| `negative` | CONDITIONING | Conditioning Input | None | - | The negative conditioning for each tile during the redraw step. |
+| `vae` | VAE | Model Selection | None | - | The VAE model to use for encoding and decoding tiles. |
+| `seed` | INT | Number Input | 0 | 0-18446744073709551615 | The seed to use for image-to-image processing, ensuring reproducible results. |
+| `steps` | INT | Number Input | 20 | 1-10000 | The number of sampling steps to use for each tile during the redraw step and seam fix step. |
+| `cfg` | FLOAT | Slider | 8.0 | 0.0-100.0 | The CFG (Classifier Free Guidance) scale to use for each tile. Higher values make the output follow the prompt more closely. The recommended values depend on the model. |
+| `sampler_name` | COMBO | Dropdown | - | Available samplers | The sampler to use for each tile during the image-to-image process. |
+| `scheduler` | COMBO | Dropdown | - | Available schedulers | The scheduler to use for each tile during the sampling process. |
+| `denoise` | FLOAT | Slider | 0.2 | 0.0-1.0 (step 0.01) | The denoising strength to use for each tile. Higher values allow more creative changes, but more chance of seams. |
+| `mode_type` | COMBO | Dropdown | - | Linear, Chess, None | The tiling order to use for the redraw step. Linear processes tiles row by row, Chess uses a checkerboard pattern, and None skips the redraw step. |
+| `tile_width` | INT | Number Input | 512 | 64-8192 (step 8) | The base width of each tile during the redraw step. |
+| `tile_height` | INT | Number Input | 512 | 64-8192 (step 8) | The base height of each tile during the redraw step. |
+| `mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the mask applied to tiles, helping blend tiles seamlessly. A higher value means more of the original image is retained near the seams when pasting the refined tiles back on the upscaled image. |
+| `tile_padding` | INT | Number Input | 32 | 0-8192 (step 8) | The padding to apply to tiles, providing more context for better blending. Adds to tile size (e.g. (`tile_width` + `tile_padding`)x(`tile_height` + `tile_padding`)). |
+| `seam_fix_mode` | COMBO | Dropdown | - | None, Band Pass, Half Tile, Half Tile + Intersections | The seam fix mode to use. Different modes apply different strategies to fix visible seams between tiles. |
+| `seam_fix_denoise` | FLOAT | Slider | 1.0 | 0.0-1.0 (step 0.01) | The denoising strength to use for the seam fix step. |
+| `seam_fix_width` | INT | Number Input | 64 | 0-8192 (step 8) | The width of the bands used for the Band Pass seam fix mode. |
+| `seam_fix_mask_blur` | INT | Number Input | 8 | 0-64 | The blur radius for the seam fix mask, ensuring smooth blending. |
+| `seam_fix_padding` | INT | Number Input | 16 | 0-8192 (step 8) | The padding to apply for the seam fix step. Adds to tile size. |
+| `force_uniform_tiles` | BOOLEAN | Toggle | True | True/False | If enabled, tiles that would be cut off by the edges of the image will expand using context around the tile to keep the same tile size determined by `tile_width`, `tile_height`, and `tile_padding`. This is what happens in the A1111 Web UI. If disabled, the minimal size for tiles will be used, which may make the sampling faster but may cause artifacts due to irregular tile sizes. |
+| `tiled_decode` | BOOLEAN | Toggle | False | True/False | Whether to use tiled decoding when decoding tiles. Useful when you know the ComfyUI engine will attempt a normal decode and run into an Out Of Memory error, and resorts to tiled decoding anyway. |
+
+## Outputs
+
+| Output Name | Data Type | Description |
+|-------------|-----------|-------------|
+| `IMAGE` | IMAGE | The final refined image. |
+
+## Usage Tips
+
+1. **When to Use This Node**
+    - You've already upscaled an image with a different upscaler and want to add details.
+    - You want to fix seams or artifacts in an existing high-resolution image.
+    - You want more control by separating the upscaling and refinement steps.
+    - You want to skip the use of an upscale model, and do a simple upscale with an algorithm like Lanczos or Nearest Neighbor beforehand.
+
+2. **Basic Usage**
+    - Typical tile sizes are based on the model resolutions that it is trained on, such as 512x512 for SD1.5 models. If you can generate a coherent image at that resolution, then it is probably a good choice for the tile size.
+
+3. **Tiling Modes**
+    - **Linear**: Processes tiles sequentially row by row.
+    - **Chess**: Uses a checkerboard pattern, processing every other tile first. Can help reduce visible seams.
+    - **None**: Skips the redraw step entirely. Useful if you only want to use the seam fix step to fix visible seams without adding new details.
+
+4. **Denoise Settings**
+    - Use a lower denoise (0.05-0.2) to refine the upscaled image to be less blurry, while avoiding seams and hallucinations.
+    - Higher denoise values are only usable when using something like a ControlNet tile model to avoid tiles and seams.
+
+5. **Seam Fix Modes**
+    - **None**: No seam fixing applied
+    - **Band Pass**: Applies processing to band-like areas between tiles
+    - **Half Tile**: Processes half-tile overlapping regions
+    - **Half Tile + Intersections**: Most thorough, processes half-tiles and their intersections
+
+6. **Performance Optimization**
+    - Enable `tiled_decode` if you're running out of VRAM during decoding, and want to skip the default behavior of attempting normal decoding.
+    - Use the largest tile size that the model and VRAM can handle to reduce the number of tiles needed.
+    - Disable force_uniform_tiles to only denoise what will be visible after pasting back the tile. This can save processing time, but the model used may not be trained for the resulting tile sizes, and the model will be missing the context around the tile that may otherwise be available with this option enabled.
+
+7. **Important Notes**
+    - This node does not perform any upscaling; it expects an already upscaled image as input
+    - The input image size determines the output size (no scaling is applied)
+    - The seam fix step significantly increases processing time. If seams are a problem, it may be better to reduce the denoise or increase tile size instead to avoid the increase in processing time.

custom_nodes/comfyui_ultimatesdupscale/modules/devices.py

@@ -0,0 +1,2 @@
+def torch_gc():
+    pass

custom_nodes/comfyui_ultimatesdupscale/modules/images.py

@@ -0,0 +1,8 @@
+from PIL import Image
+
+
+def flatten(img, bgcolor):
+    # Replace transparency with bgcolor
+    if img.mode in ("RGB"):
+        return img
+    return Image.alpha_composite(Image.new("RGBA", img.size, bgcolor), img).convert("RGB")

custom_nodes/comfyui_ultimatesdupscale/modules/processing.py

@@ -0,0 +1,277 @@
+from PIL import Image, ImageFilter
+import torch
+import math
+from nodes import common_ksampler, VAEEncode, VAEDecode, VAEDecodeTiled
+from comfy_extras.nodes_custom_sampler import SamplerCustom
+from usdu_utils import pil_to_tensor, tensor_to_pil, get_crop_region, expand_crop, crop_cond
+from modules import shared
+from tqdm import tqdm
+import comfy
+from enum import Enum
+import json
+import os
+
+if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
+    Image.Resampling = Image
+
+# Taken from the USDU script
+class USDUMode(Enum):
+    LINEAR = 0
+    CHESS = 1
+    NONE = 2
+
+class USDUSFMode(Enum):
+    NONE = 0
+    BAND_PASS = 1
+    HALF_TILE = 2
+    HALF_TILE_PLUS_INTERSECTIONS = 3
+
+class StableDiffusionProcessing:
+
+    def __init__(
+        self,
+        init_img,
+        model,
+        positive,
+        negative,
+        vae,
+        seed,
+        steps,
+        cfg,
+        sampler_name,
+        scheduler,
+        denoise,
+        upscale_by,
+        uniform_tile_mode,
+        tiled_decode,
+        tile_width,
+        tile_height,
+        redraw_mode,
+        seam_fix_mode,
+        custom_sampler=None,
+        custom_sigmas=None,
+    ):
+        # Variables used by the USDU script
+        self.init_images = [init_img]
+        self.image_mask = None
+        self.mask_blur = 0
+        self.inpaint_full_res_padding = 0
+        self.width = init_img.width * upscale_by
+        self.height = init_img.height * upscale_by
+        self.rows = round(self.height / tile_height)
+        self.cols = round(self.width / tile_width)
+
+        # ComfyUI Sampler inputs
+        self.model = model
+        self.positive = positive
+        self.negative = negative
+        self.vae = vae
+        self.seed = seed
+        self.steps = steps
+        self.cfg = cfg
+        self.sampler_name = sampler_name
+        self.scheduler = scheduler
+        self.denoise = denoise
+
+        # Optional custom sampler and sigmas
+        self.custom_sampler = custom_sampler
+        self.custom_sigmas = custom_sigmas
+
+        if (custom_sampler is not None) ^ (custom_sigmas is not None):
+            print("[USDU] Both custom sampler and custom sigmas must be provided, defaulting to widget sampler and sigmas")
+
+        # Variables used only by this script
+        self.init_size = init_img.width, init_img.height
+        self.upscale_by = upscale_by
+        self.uniform_tile_mode = uniform_tile_mode
+        self.tiled_decode = tiled_decode
+        self.vae_decoder = VAEDecode()
+        self.vae_encoder = VAEEncode()
+        self.vae_decoder_tiled = VAEDecodeTiled()
+
+        if self.tiled_decode:
+            print("[USDU] Using tiled decode")
+
+        # Other required A1111 variables for the USDU script that is currently unused in this script
+        self.extra_generation_params = {}
+
+        # Load config file for USDU
+        config_path = os.path.join(os.path.dirname(__file__), os.pardir, 'config.json')
+        config = {}
+        if os.path.exists(config_path):
+            with open(config_path, 'r') as f:
+                config = json.load(f)
+
+        # Progress bar for the entire process instead of per tile
+        self.progress_bar_enabled = False
+        if comfy.utils.PROGRESS_BAR_ENABLED:
+            self.progress_bar_enabled = True
+            comfy.utils.PROGRESS_BAR_ENABLED = config.get('per_tile_progress', True)
+            self.tiles = 0
+            if redraw_mode.value != USDUMode.NONE.value:
+                self.tiles += self.rows * self.cols
+            if seam_fix_mode.value == USDUSFMode.BAND_PASS.value:
+                self.tiles += (self.rows - 1) + (self.cols - 1)
+            elif seam_fix_mode.value == USDUSFMode.HALF_TILE.value:
+                self.tiles += (self.rows - 1) * self.cols + (self.cols - 1) * self.rows
+            elif seam_fix_mode.value == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS.value:
+                self.tiles += (self.rows - 1) * self.cols + (self.cols - 1) * self.rows + (self.rows - 1) * (self.cols - 1)
+            self.pbar = None
+            # self.pbar = tqdm(total=self.tiles, desc='USDU') # Creating the pbar here will cause an empty progress bar to be displayed
+
+    def __del__(self):
+        # Undo changes to progress bar flag when node is done or cancelled
+        if self.progress_bar_enabled:
+            comfy.utils.PROGRESS_BAR_ENABLED = True
+    
+class Processed:
+
+    def __init__(self, p: StableDiffusionProcessing, images: list, seed: int, info: str):
+        self.images = images
+        self.seed = seed
+        self.info = info
+
+    def infotext(self, p: StableDiffusionProcessing, index):
+        return None
+
+
+def fix_seed(p: StableDiffusionProcessing):
+    pass
+
+
+def sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise, custom_sampler, custom_sigmas):
+    # Choose way to sample based on given inputs
+
+    # Custom sampler and sigmas
+    if custom_sampler is not None and custom_sigmas is not None:
+        kwargs = dict(
+            model=model,
+            add_noise=True,
+            noise_seed=seed,
+            cfg=cfg,
+            positive=positive,
+            negative=negative,
+            sampler=custom_sampler,
+            sigmas=custom_sigmas,
+            latent_image=latent
+        )
+        if "execute" in dir(SamplerCustom):
+            (samples, _) = SamplerCustom.execute(**kwargs)
+        else:
+            custom_sample = SamplerCustom()
+            (samples, _) = getattr(custom_sample, custom_sample.FUNCTION)(**kwargs)
+        return samples
+
+    # Default
+    (samples,) = common_ksampler(model, seed, steps, cfg, sampler_name,
+                                 scheduler, positive, negative, latent, denoise=denoise)
+    return samples
+
+
+def process_images(p: StableDiffusionProcessing) -> Processed:
+    # Where the main image generation happens in A1111
+
+    # Show the progress bar
+    if p.progress_bar_enabled and p.pbar is None:
+        p.pbar = tqdm(total=p.tiles, desc='USDU', unit='tile')
+
+    # Setup
+    image_mask = p.image_mask.convert('L')
+    init_image = p.init_images[0]
+
+    # Locate the white region of the mask outlining the tile and add padding
+    crop_region = get_crop_region(image_mask, p.inpaint_full_res_padding)
+
+    if p.uniform_tile_mode:
+        # Expand the crop region to match the processing size ratio and then resize it to the processing size
+        x1, y1, x2, y2 = crop_region
+        crop_width = x2 - x1
+        crop_height = y2 - y1
+        crop_ratio = crop_width / crop_height
+        p_ratio = p.width / p.height
+        if crop_ratio > p_ratio:
+            target_width = crop_width
+            target_height = round(crop_width / p_ratio)
+        else:
+            target_width = round(crop_height * p_ratio)
+            target_height = crop_height
+        crop_region, _ = expand_crop(crop_region, image_mask.width, image_mask.height, target_width, target_height)
+        tile_size = p.width, p.height
+    else:
+        # Uses the minimal size that can fit the mask, minimizes tile size but may lead to image sizes that the model is not trained on
+        x1, y1, x2, y2 = crop_region
+        crop_width = x2 - x1
+        crop_height = y2 - y1
+        target_width = math.ceil(crop_width / 8) * 8
+        target_height = math.ceil(crop_height / 8) * 8
+        crop_region, tile_size = expand_crop(crop_region, image_mask.width,
+                                             image_mask.height, target_width, target_height)
+
+    # Blur the mask
+    if p.mask_blur > 0:
+        image_mask = image_mask.filter(ImageFilter.GaussianBlur(p.mask_blur))
+
+    # Crop the images to get the tiles that will be used for generation
+    tiles = [img.crop(crop_region) for img in shared.batch]
+
+    # Assume the same size for all images in the batch
+    initial_tile_size = tiles[0].size
+
+    # Resize if necessary
+    for i, tile in enumerate(tiles):
+        if tile.size != tile_size:
+            tiles[i] = tile.resize(tile_size, Image.Resampling.LANCZOS)
+
+    # Crop conditioning
+    positive_cropped = crop_cond(p.positive, crop_region, p.init_size, init_image.size, tile_size)
+    negative_cropped = crop_cond(p.negative, crop_region, p.init_size, init_image.size, tile_size)
+
+    # Encode the image
+    batched_tiles = torch.cat([pil_to_tensor(tile) for tile in tiles], dim=0)
+    (latent,) = p.vae_encoder.encode(p.vae, batched_tiles)
+
+    # Generate samples
+    samples = sample(p.model, p.seed, p.steps, p.cfg, p.sampler_name, p.scheduler, positive_cropped,
+                     negative_cropped, latent, p.denoise, p.custom_sampler, p.custom_sigmas)
+
+    # Update the progress bar
+    if p.progress_bar_enabled:
+        p.pbar.update(1)
+
+    # Decode the sample
+    if not p.tiled_decode:
+        (decoded,) = p.vae_decoder.decode(p.vae, samples)
+    else:
+        (decoded,) = p.vae_decoder_tiled.decode(p.vae, samples, 512)  # Default tile size is 512
+
+    # Convert the sample to a PIL image
+    tiles_sampled = [tensor_to_pil(decoded, i) for i in range(len(decoded))]
+
+    for i, tile_sampled in enumerate(tiles_sampled):
+        init_image = shared.batch[i]
+
+        # Resize back to the original size
+        if tile_sampled.size != initial_tile_size:
+            tile_sampled = tile_sampled.resize(initial_tile_size, Image.Resampling.LANCZOS)
+
+        # Put the tile into position
+        image_tile_only = Image.new('RGBA', init_image.size)
+        image_tile_only.paste(tile_sampled, crop_region[:2])
+
+        # Add the mask as an alpha channel
+        # Must make a copy due to the possibility of an edge becoming black
+        temp = image_tile_only.copy()
+        temp.putalpha(image_mask)
+        image_tile_only.paste(temp, image_tile_only)
+
+        # Add back the tile to the initial image according to the mask in the alpha channel
+        result = init_image.convert('RGBA')
+        result.alpha_composite(image_tile_only)
+
+        # Convert back to RGB
+        result = result.convert('RGB')
+
+        shared.batch[i] = result
+
+    processed = Processed(p, [shared.batch[0]], p.seed, None)
+    return processed

custom_nodes/comfyui_ultimatesdupscale/modules/scripts.py

@@ -0,0 +1,2 @@
+class Script:
+    pass

custom_nodes/comfyui_ultimatesdupscale/modules/shared.py

@@ -0,0 +1,25 @@
+class Options:
+    img2img_background_color = "#ffffff"  # Set to white for now
+
+
+class State:
+    interrupted = False
+
+    def begin(self):
+        pass
+
+    def end(self):
+        pass
+
+
+opts = Options()
+state = State()
+
+# Will only ever hold 1 upscaler
+sd_upscalers = [None]
+# The upscaler usable by ComfyUI nodes
+actual_upscaler = None
+
+# Batch of images to upscale
+batch = None
+batch_as_tensor = None

custom_nodes/comfyui_ultimatesdupscale/modules/upscaler.py

@@ -0,0 +1,31 @@
+from PIL import Image
+from usdu_utils import tensor_to_pil, pil_to_tensor
+from comfy_extras.nodes_upscale_model import ImageUpscaleWithModel
+from modules import shared
+
+if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
+    Image.Resampling = Image
+
+
+class Upscaler:
+
+    def upscale(self, img: Image, scale, selected_model: str = None):
+        if scale == 1.0:
+            return img
+        if (shared.actual_upscaler is None):
+            return img.resize((img.width * scale, img.height * scale), Image.Resampling.LANCZOS)
+        if "execute" in dir(ImageUpscaleWithModel):  
+            # V3 schema: https://github.com/comfyanonymous/ComfyUI/pull/10149
+            (upscaled,) = ImageUpscaleWithModel.execute(shared.actual_upscaler, shared.batch_as_tensor)
+        else:
+            (upscaled,) = ImageUpscaleWithModel().upscale(shared.actual_upscaler, shared.batch_as_tensor)
+        shared.batch = [tensor_to_pil(upscaled, i) for i in range(len(upscaled))]
+        return shared.batch[0]
+
+
+class UpscalerData:
+    name = ""
+    data_path = ""
+
+    def __init__(self):
+        self.scaler = Upscaler()

custom_nodes/comfyui_ultimatesdupscale/pyproject.toml

@@ -0,0 +1,25 @@
+[project]
+name = "comfyui_ultimatesdupscale"
+description = "ComfyUI nodes for the Ultimate Stable Diffusion Upscale script by Coyote-A."
+version = "1.6.5"
+license = { file = "LICENSE" }
+
+[project.optional-dependencies]
+test = [
+    "pytest>=9.0.0",
+    "pytest-cov>=7.0.0",
+]
+
+[project.urls]
+Repository = "https://github.com/ssitu/ComfyUI_UltimateSDUpscale"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "ssit"
+DisplayName = "ComfyUI_UltimateSDUpscale"
+Icon = ""
+includes = ["/repositories/ultimate_sd_upscale/"]
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["ComfyUI_UltimateSDUpscale"]

custom_nodes/comfyui_ultimatesdupscale/repositories/__init__.py

@@ -0,0 +1,14 @@
+import os
+import sys
+import importlib.util
+
+repositories_path = os.path.dirname(os.path.realpath(__file__))
+
+# Import the script
+script_name = os.path.join("scripts", "ultimate-upscale")
+repo_name = "ultimate_sd_upscale"
+script_path = os.path.join(repositories_path, repo_name, f"{script_name}.py")
+spec = importlib.util.spec_from_file_location(script_name, script_path)
+ultimate_upscale = importlib.util.module_from_spec(spec)
+sys.modules[script_name] = ultimate_upscale
+spec.loader.exec_module(ultimate_upscale)

custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/.gitignore

@@ -0,0 +1 @@
+.vscode

custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/LICENSE

@@ -0,0 +1,674 @@
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+in the business of distributing software, under which you make payment
+to the third party based on the extent of your activity of conveying
+the work, and under which the third party grants, to any of the
+parties who would receive the covered work from you, a discriminatory
+patent license (a) in connection with copies of the covered work
+conveyed by you (or copies made from those copies), or (b) primarily
+for and in connection with specific products or compilations that
+contain the covered work, unless you entered into that arrangement,
+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
+covered work so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
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+License will continue to apply to the part which is the covered work,
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+section 13, concerning interaction through a network will apply to the
+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    ultimate-upscale-for-automatic1111
+    Copyright (C) 2023  Mirzam
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) 2023  Mirzam
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/README.md

@@ -0,0 +1,119 @@
+# Ultimate SD Upscale extension for [AUTOMATIC1111 Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
+Now you have the opportunity to use a large denoise (0.3-0.5) and not spawn many artifacts. Works on any video card, since you can use a 512x512 tile size and the image will converge.
+
+News channel: https://t.me/usdunews
+
+# Instructions
+All instructions can be found on the project's [wiki](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki).
+
+# Refs
+
+https://github.com/ssitu/ComfyUI_UltimateSDUpscale - Implementation for ComfyUI
+
+# Examples
+More on [wiki page](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki/Examples)
+
+<details> 
+  <summary>E1</summary>
+  Original image
+
+  ![Original](https://i.imgur.com/J8mRYOD.png)
+
+  2k upscaled. **Tile size**: 512, **Padding**: 32, **Mask blur**: 16, **Denoise**: 0.4
+  ![2k upscale](https://i.imgur.com/0aKua4r.png)
+</details>
+
+<details> 
+  <summary>E2</summary>
+  Original image
+
+  ![Original](https://i.imgur.com/aALNI2w.png)
+
+  2k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.35
+  ![2k upscale](https://i.imgur.com/B5PHz0J.png)
+
+  4k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.35
+  ![4k upscale](https://i.imgur.com/tIUQ7TJ.jpg)
+</details>
+
+<details> 
+  <summary>E3</summary>
+  Original image
+
+  ![Original](https://i.imgur.com/AGtszA8.png)
+
+  4k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.4
+  ![4k upscale](https://i.imgur.com/LCYLfCs.jpg)
+</details>
+
+# API Usage
+
+```javascript
+{
+"script_name" : "ultimate sd upscale",
+"script_args" : [
+	null, // _ (not used)
+	512, // tile_width
+	512, // tile_height
+	8, // mask_blur
+	32, // padding
+	64, // seams_fix_width
+	0.35, // seams_fix_denoise
+	32, // seams_fix_padding
+	0, // upscaler_index
+	true, // save_upscaled_image a.k.a Upscaled
+	0, // redraw_mode
+	false, // save_seams_fix_image a.k.a Seams fix
+	8, // seams_fix_mask_blur
+	0, // seams_fix_type
+	0, // target_size_type
+	2048, // custom_width
+	2048, // custom_height
+	2 // custom_scale
+]
+}
+```
+upscaler_index
+| Value         |  |
+|:-------------:| -----:|
+| 0 | None |
+| 1 | Lanczos |
+| 2 | Nearest |
+| 3 | ESRGAN_4x |
+| 4 | LDSR |
+| 5 | R-ESRGAN_4x+ |
+| 6 | R-ESRGAN 4x+ Anime6B |
+| 7 | ScuNET GAN |
+| 8 | ScuNET PSNR |
+| 9 | SwinIR 4x |
+
+redraw_mode
+| Value         |  |
+|:-------------:| -----:|
+| 0 | Linear |
+| 1 | Chess |
+| 2 | None |
+
+seams_fix_mask_blur
+| Value         |  |
+|:-------------:| -----:|
+| 0 | None |
+| 1 | BAND_PASS |
+| 2 | HALF_TILE |
+| 3 | HALF_TILE_PLUS_INTERSECTIONS |
+
+seams_fix_type
+| Value         |  |
+|:-------------:| -----:|
+| 0 | None |
+| 1 | Band pass |
+| 2 | Half tile offset pass |
+| 3 | Half tile offset pass + intersections |
+
+seams_fix_type
+| Value         |  |
+|:-------------:| -----:|
+| 0 | From img2img2 settings |
+| 1 | Custom size |
+| 2 | Scale from image size |
+

custom_nodes/comfyui_ultimatesdupscale/repositories/ultimate_sd_upscale/scripts/ultimate-upscale.py

@@ -0,0 +1,569 @@
+import math
+import gradio as gr
+from PIL import Image, ImageDraw, ImageOps
+from modules import processing, shared, images, devices, scripts
+from modules.processing import StableDiffusionProcessing
+from modules.processing import Processed
+from modules.shared import opts, state
+from enum import Enum
+
+elem_id_prefix = "ultimateupscale"
+
+class USDUMode(Enum):
+    LINEAR = 0
+    CHESS = 1
+    NONE = 2
+
+class USDUSFMode(Enum):
+    NONE = 0
+    BAND_PASS = 1
+    HALF_TILE = 2
+    HALF_TILE_PLUS_INTERSECTIONS = 3
+
+class USDUpscaler():
+
+    def __init__(self, p, image, upscaler_index:int, save_redraw, save_seams_fix, tile_width, tile_height) -> None:
+        self.p:StableDiffusionProcessing = p
+        self.image:Image = image
+        self.scale_factor = math.ceil(max(p.width, p.height) / max(image.width, image.height))
+        self.upscaler = shared.sd_upscalers[upscaler_index]
+        self.redraw = USDURedraw()
+        self.redraw.save = save_redraw
+        self.redraw.tile_width = tile_width if tile_width > 0 else tile_height
+        self.redraw.tile_height = tile_height if tile_height > 0 else tile_width
+        self.seams_fix = USDUSeamsFix()
+        self.seams_fix.save = save_seams_fix
+        self.seams_fix.tile_width = tile_width if tile_width > 0 else tile_height
+        self.seams_fix.tile_height = tile_height if tile_height > 0 else tile_width
+        self.initial_info = None
+        self.rows = math.ceil(self.p.height / self.redraw.tile_height)
+        self.cols = math.ceil(self.p.width / self.redraw.tile_width)
+
+    def get_factor(self, num):
+        # Its just return, don't need elif
+        if num == 1:
+            return 2
+        if num % 4 == 0:
+            return 4
+        if num % 3 == 0:
+            return 3
+        if num % 2 == 0:
+            return 2
+        return 0
+
+    def get_factors(self):
+        scales = []
+        current_scale = 1
+        current_scale_factor = self.get_factor(self.scale_factor)
+        while current_scale_factor == 0:
+            self.scale_factor += 1
+            current_scale_factor = self.get_factor(self.scale_factor)
+        while current_scale < self.scale_factor:
+            current_scale_factor = self.get_factor(self.scale_factor // current_scale)
+            scales.append(current_scale_factor)
+            current_scale = current_scale * current_scale_factor
+            if current_scale_factor == 0:
+                break
+        self.scales = enumerate(scales)
+
+    def upscale(self):
+        # Log info
+        print(f"Canva size: {self.p.width}x{self.p.height}")
+        print(f"Image size: {self.image.width}x{self.image.height}")
+        print(f"Scale factor: {self.scale_factor}")
+        # Check upscaler is not empty
+        if self.upscaler.name == "None":
+            self.image = self.image.resize((self.p.width, self.p.height), resample=Image.LANCZOS)
+            return
+        # Get list with scale factors
+        self.get_factors()
+        # Upscaling image over all factors
+        for index, value in self.scales:
+            print(f"Upscaling iteration {index+1} with scale factor {value}")
+            self.image = self.upscaler.scaler.upscale(self.image, value, self.upscaler.data_path)
+        # Resize image to set values
+        self.image = self.image.resize((self.p.width, self.p.height), resample=Image.LANCZOS)
+
+    def setup_redraw(self, redraw_mode, padding, mask_blur):
+        self.redraw.mode = USDUMode(redraw_mode)
+        self.redraw.enabled = self.redraw.mode != USDUMode.NONE
+        self.redraw.padding = padding
+        self.p.mask_blur = mask_blur
+
+    def setup_seams_fix(self, padding, denoise, mask_blur, width, mode):
+        self.seams_fix.padding = padding
+        self.seams_fix.denoise = denoise
+        self.seams_fix.mask_blur = mask_blur
+        self.seams_fix.width = width
+        self.seams_fix.mode = USDUSFMode(mode)
+        self.seams_fix.enabled = self.seams_fix.mode != USDUSFMode.NONE
+
+    def save_image(self):
+        if type(self.p.prompt) != list:
+            images.save_image(self.image, self.p.outpath_samples, "", self.p.seed, self.p.prompt, opts.samples_format, info=self.initial_info, p=self.p)
+        else:
+            images.save_image(self.image, self.p.outpath_samples, "", self.p.seed, self.p.prompt[0], opts.samples_format, info=self.initial_info, p=self.p)
+
+    def calc_jobs_count(self):
+        redraw_job_count = (self.rows * self.cols) if self.redraw.enabled else 0
+        seams_job_count = 0
+        if self.seams_fix.mode == USDUSFMode.BAND_PASS:
+            seams_job_count = self.rows + self.cols - 2
+        elif self.seams_fix.mode == USDUSFMode.HALF_TILE:
+            seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols
+        elif self.seams_fix.mode == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS:
+            seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols + (self.rows - 1) * (self.cols - 1)
+
+        state.job_count = redraw_job_count + seams_job_count
+
+    def print_info(self):
+        print(f"Tile size: {self.redraw.tile_width}x{self.redraw.tile_height}")
+        print(f"Tiles amount: {self.rows * self.cols}")
+        print(f"Grid: {self.rows}x{self.cols}")
+        print(f"Redraw enabled: {self.redraw.enabled}")
+        print(f"Seams fix mode: {self.seams_fix.mode.name}")
+
+    def add_extra_info(self):
+        self.p.extra_generation_params["Ultimate SD upscale upscaler"] = self.upscaler.name
+        self.p.extra_generation_params["Ultimate SD upscale tile_width"] = self.redraw.tile_width
+        self.p.extra_generation_params["Ultimate SD upscale tile_height"] = self.redraw.tile_height
+        self.p.extra_generation_params["Ultimate SD upscale mask_blur"] = self.p.mask_blur
+        self.p.extra_generation_params["Ultimate SD upscale padding"] = self.redraw.padding
+
+    def process(self):
+        state.begin()
+        self.calc_jobs_count()
+        self.result_images = []
+        if self.redraw.enabled:
+            self.image = self.redraw.start(self.p, self.image, self.rows, self.cols)
+            self.initial_info = self.redraw.initial_info
+        self.result_images.append(self.image)
+        if self.redraw.save:
+            self.save_image()
+
+        if self.seams_fix.enabled:
+            self.image = self.seams_fix.start(self.p, self.image, self.rows, self.cols)
+            self.initial_info = self.seams_fix.initial_info
+            self.result_images.append(self.image)
+            if self.seams_fix.save:
+                self.save_image()
+        state.end()
+
+class USDURedraw():
+
+    def init_draw(self, p, width, height):
+        p.inpaint_full_res = True
+        p.inpaint_full_res_padding = self.padding
+        p.width = math.ceil((self.tile_width+self.padding) / 64) * 64
+        p.height = math.ceil((self.tile_height+self.padding) / 64) * 64
+        mask = Image.new("L", (width, height), "black")
+        draw = ImageDraw.Draw(mask)
+        return mask, draw
+
+    def calc_rectangle(self, xi, yi):
+        x1 = xi * self.tile_width
+        y1 = yi * self.tile_height
+        x2 = xi * self.tile_width + self.tile_width
+        y2 = yi * self.tile_height + self.tile_height
+
+        return x1, y1, x2, y2
+
+    def linear_process(self, p, image, rows, cols):
+        mask, draw = self.init_draw(p, image.width, image.height)
+        for yi in range(rows):
+            for xi in range(cols):
+                if state.interrupted:
+                    break
+                draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
+                p.init_images = [image]
+                p.image_mask = mask
+                processed = processing.process_images(p)
+                draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
+                if (len(processed.images) > 0):
+                    image = processed.images[0]
+
+        p.width = image.width
+        p.height = image.height
+        self.initial_info = processed.infotext(p, 0)
+
+        return image
+
+    def chess_process(self, p, image, rows, cols):
+        mask, draw = self.init_draw(p, image.width, image.height)
+        tiles = []
+        # calc tiles colors
+        for yi in range(rows):
+            for xi in range(cols):
+                if state.interrupted:
+                    break
+                if xi == 0:
+                    tiles.append([])
+                color = xi % 2 == 0
+                if yi > 0 and yi % 2 != 0:
+                    color = not color
+                tiles[yi].append(color)
+
+        for yi in range(len(tiles)):
+            for xi in range(len(tiles[yi])):
+                if state.interrupted:
+                    break
+                if not tiles[yi][xi]:
+                    tiles[yi][xi] = not tiles[yi][xi]
+                    continue
+                tiles[yi][xi] = not tiles[yi][xi]
+                draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
+                p.init_images = [image]
+                p.image_mask = mask
+                processed = processing.process_images(p)
+                draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
+                if (len(processed.images) > 0):
+                    image = processed.images[0]
+
+        for yi in range(len(tiles)):
+            for xi in range(len(tiles[yi])):
+                if state.interrupted:
+                    break
+                if not tiles[yi][xi]:
+                    continue
+                draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
+                p.init_images = [image]
+                p.image_mask = mask
+                processed = processing.process_images(p)
+                draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
+                if (len(processed.images) > 0):
+                    image = processed.images[0]
+
+        p.width = image.width
+        p.height = image.height
+        self.initial_info = processed.infotext(p, 0)
+
+        return image
+
+    def start(self, p, image, rows, cols):
+        self.initial_info = None
+        if self.mode == USDUMode.LINEAR:
+            return self.linear_process(p, image, rows, cols)
+        if self.mode == USDUMode.CHESS:
+            return self.chess_process(p, image, rows, cols)
+
+class USDUSeamsFix():
+
+    def init_draw(self, p):
+        self.initial_info = None
+        p.width = math.ceil((self.tile_width+self.padding) / 64) * 64
+        p.height = math.ceil((self.tile_height+self.padding) / 64) * 64
+
+    def half_tile_process(self, p, image, rows, cols):
+
+        self.init_draw(p)
+        processed = None
+
+        gradient = Image.linear_gradient("L")
+        row_gradient = Image.new("L", (self.tile_width, self.tile_height), "black")
+        row_gradient.paste(gradient.resize(
+            (self.tile_width, self.tile_height//2), resample=Image.BICUBIC), (0, 0))
+        row_gradient.paste(gradient.rotate(180).resize(
+                (self.tile_width, self.tile_height//2), resample=Image.BICUBIC),
+                (0, self.tile_height//2))
+        col_gradient = Image.new("L", (self.tile_width, self.tile_height), "black")
+        col_gradient.paste(gradient.rotate(90).resize(
+            (self.tile_width//2, self.tile_height), resample=Image.BICUBIC), (0, 0))
+        col_gradient.paste(gradient.rotate(270).resize(
+            (self.tile_width//2, self.tile_height), resample=Image.BICUBIC), (self.tile_width//2, 0))
+
+        p.denoising_strength = self.denoise
+        p.mask_blur = self.mask_blur
+
+        for yi in range(rows-1):
+            for xi in range(cols):
+                if state.interrupted:
+                    break
+                p.width = self.tile_width
+                p.height = self.tile_height
+                p.inpaint_full_res = True
+                p.inpaint_full_res_padding = self.padding
+                mask = Image.new("L", (image.width, image.height), "black")
+                mask.paste(row_gradient, (xi*self.tile_width, yi*self.tile_height + self.tile_height//2))
+
+                p.init_images = [image]
+                p.image_mask = mask
+                processed = processing.process_images(p)
+                if (len(processed.images) > 0):
+                    image = processed.images[0]
+
+        for yi in range(rows):
+            for xi in range(cols-1):
+                if state.interrupted:
+                    break
+                p.width = self.tile_width
+                p.height = self.tile_height
+                p.inpaint_full_res = True
+                p.inpaint_full_res_padding = self.padding
+                mask = Image.new("L", (image.width, image.height), "black")
+                mask.paste(col_gradient, (xi*self.tile_width+self.tile_width//2, yi*self.tile_height))
+
+                p.init_images = [image]
+                p.image_mask = mask
+                processed = processing.process_images(p)
+                if (len(processed.images) > 0):
+                    image = processed.images[0]
+
+        p.width = image.width
+        p.height = image.height
+        if processed is not None:
+            self.initial_info = processed.infotext(p, 0)
+
+        return image
+
+    def half_tile_process_corners(self, p, image, rows, cols):
+        fixed_image = self.half_tile_process(p, image, rows, cols)
+        processed = None
+        self.init_draw(p)
+        gradient = Image.radial_gradient("L").resize(
+            (self.tile_width, self.tile_height), resample=Image.BICUBIC)
+        gradient = ImageOps.invert(gradient)
+        p.denoising_strength = self.denoise
+        #p.mask_blur = 0
+        p.mask_blur = self.mask_blur
+
+        for yi in range(rows-1):
+            for xi in range(cols-1):
+                if state.interrupted:
+                    break
+                p.width = self.tile_width
+                p.height = self.tile_height
+                p.inpaint_full_res = True
+                p.inpaint_full_res_padding = 0
+                mask = Image.new("L", (fixed_image.width, fixed_image.height), "black")
+                mask.paste(gradient, (xi*self.tile_width + self.tile_width//2,
+                                      yi*self.tile_height + self.tile_height//2))
+
+                p.init_images = [fixed_image]
+                p.image_mask = mask
+                processed = processing.process_images(p)
+                if (len(processed.images) > 0):
+                    fixed_image = processed.images[0]
+
+        p.width = fixed_image.width
+        p.height = fixed_image.height
+        if processed is not None:
+            self.initial_info = processed.infotext(p, 0)
+
+        return fixed_image
+
+    def band_pass_process(self, p, image, cols, rows):
+
+        self.init_draw(p)
+        processed = None
+
+        p.denoising_strength = self.denoise
+        p.mask_blur = 0
+
+        gradient = Image.linear_gradient("L")
+        mirror_gradient = Image.new("L", (256, 256), "black")
+        mirror_gradient.paste(gradient.resize((256, 128), resample=Image.BICUBIC), (0, 0))
+        mirror_gradient.paste(gradient.rotate(180).resize((256, 128), resample=Image.BICUBIC), (0, 128))
+
+        row_gradient = mirror_gradient.resize((image.width, self.width), resample=Image.BICUBIC)
+        col_gradient = mirror_gradient.rotate(90).resize((self.width, image.height), resample=Image.BICUBIC)
+
+        for xi in range(1, rows):
+            if state.interrupted:
+                    break
+            p.width = self.width + self.padding * 2
+            p.height = image.height
+            p.inpaint_full_res = True
+            p.inpaint_full_res_padding = self.padding
+            mask = Image.new("L", (image.width, image.height), "black")
+            mask.paste(col_gradient, (xi * self.tile_width - self.width // 2, 0))
+
+            p.init_images = [image]
+            p.image_mask = mask
+            processed = processing.process_images(p)
+            if (len(processed.images) > 0):
+                image = processed.images[0]
+        for yi in range(1, cols):
+            if state.interrupted:
+                    break
+            p.width = image.width
+            p.height = self.width + self.padding * 2
+            p.inpaint_full_res = True
+            p.inpaint_full_res_padding = self.padding
+            mask = Image.new("L", (image.width, image.height), "black")
+            mask.paste(row_gradient, (0, yi * self.tile_height - self.width // 2))
+
+            p.init_images = [image]
+            p.image_mask = mask
+            processed = processing.process_images(p)
+            if (len(processed.images) > 0):
+                image = processed.images[0]
+
+        p.width = image.width
+        p.height = image.height
+        if processed is not None:
+            self.initial_info = processed.infotext(p, 0)
+
+        return image
+
+    def start(self, p, image, rows, cols):
+        if USDUSFMode(self.mode) == USDUSFMode.BAND_PASS:
+            return self.band_pass_process(p, image, rows, cols)
+        elif USDUSFMode(self.mode) == USDUSFMode.HALF_TILE:
+            return self.half_tile_process(p, image, rows, cols)
+        elif USDUSFMode(self.mode) == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS:
+            return self.half_tile_process_corners(p, image, rows, cols)
+        else:
+            return image
+
+class Script(scripts.Script):
+    def title(self):
+        return "Ultimate SD upscale"
+
+    def show(self, is_img2img):
+        return is_img2img
+
+    def ui(self, is_img2img):
+
+        target_size_types = [
+            "From img2img2 settings",
+            "Custom size",
+            "Scale from image size"
+        ]
+
+        seams_fix_types = [
+            "None",
+            "Band pass",
+            "Half tile offset pass",
+            "Half tile offset pass + intersections"
+        ]
+
+        redrow_modes = [
+            "Linear",
+            "Chess",
+            "None"
+        ]
+
+        info = gr.HTML(
+            "<p style=\"margin-bottom:0.75em\">Will upscale the image depending on the selected target size type</p>")
+
+        with gr.Row():
+            target_size_type = gr.Dropdown(label="Target size type", elem_id=f"{elem_id_prefix}_target_size_type", choices=[k for k in target_size_types], type="index",
+                                  value=next(iter(target_size_types)))
+
+            custom_width = gr.Slider(label='Custom width', elem_id=f"{elem_id_prefix}_custom_width", minimum=64, maximum=8192, step=64, value=2048, visible=False, interactive=True)
+            custom_height = gr.Slider(label='Custom height', elem_id=f"{elem_id_prefix}_custom_height", minimum=64, maximum=8192, step=64, value=2048, visible=False, interactive=True)
+            custom_scale = gr.Slider(label='Scale', elem_id=f"{elem_id_prefix}_custom_scale", minimum=1, maximum=16, step=0.01, value=2, visible=False, interactive=True)
+
+        gr.HTML("<p style=\"margin-bottom:0.75em\">Redraw options:</p>")
+        with gr.Row():
+            upscaler_index = gr.Radio(label='Upscaler', elem_id=f"{elem_id_prefix}_upscaler_index", choices=[x.name for x in shared.sd_upscalers],
+                                value=shared.sd_upscalers[0].name, type="index")
+        with gr.Row():
+            redraw_mode = gr.Dropdown(label="Type", elem_id=f"{elem_id_prefix}_redraw_mode", choices=[k for k in redrow_modes], type="index", value=next(iter(redrow_modes)))
+            tile_width = gr.Slider(elem_id=f"{elem_id_prefix}_tile_width", minimum=0, maximum=2048, step=64, label='Tile width', value=512)
+            tile_height = gr.Slider(elem_id=f"{elem_id_prefix}_tile_height", minimum=0, maximum=2048, step=64, label='Tile height', value=0)
+            mask_blur = gr.Slider(elem_id=f"{elem_id_prefix}_mask_blur", label='Mask blur', minimum=0, maximum=64, step=1, value=8)
+            padding = gr.Slider(elem_id=f"{elem_id_prefix}_padding", label='Padding', minimum=0, maximum=512, step=1, value=32)
+        gr.HTML("<p style=\"margin-bottom:0.75em\">Seams fix:</p>")
+        with gr.Row():
+            seams_fix_type = gr.Dropdown(label="Type", elem_id=f"{elem_id_prefix}_seams_fix_type", choices=[k for k in seams_fix_types], type="index", value=next(iter(seams_fix_types)))
+            seams_fix_denoise = gr.Slider(label='Denoise', elem_id=f"{elem_id_prefix}_seams_fix_denoise", minimum=0, maximum=1, step=0.01, value=0.35, visible=False, interactive=True)
+            seams_fix_width = gr.Slider(label='Width', elem_id=f"{elem_id_prefix}_seams_fix_width", minimum=0, maximum=128, step=1, value=64, visible=False, interactive=True)
+            seams_fix_mask_blur = gr.Slider(label='Mask blur', elem_id=f"{elem_id_prefix}_seams_fix_mask_blur", minimum=0, maximum=64, step=1, value=4, visible=False, interactive=True)
+            seams_fix_padding = gr.Slider(label='Padding', elem_id=f"{elem_id_prefix}_seams_fix_padding", minimum=0, maximum=128, step=1, value=16, visible=False, interactive=True)
+        gr.HTML("<p style=\"margin-bottom:0.75em\">Save options:</p>")
+        with gr.Row():
+            save_upscaled_image = gr.Checkbox(label="Upscaled", elem_id=f"{elem_id_prefix}_save_upscaled_image", value=True)
+            save_seams_fix_image = gr.Checkbox(label="Seams fix", elem_id=f"{elem_id_prefix}_save_seams_fix_image", value=False)
+
+        def select_fix_type(fix_index):
+            all_visible = fix_index != 0
+            mask_blur_visible = fix_index == 2 or fix_index == 3
+            width_visible = fix_index == 1
+
+            return [gr.update(visible=all_visible),
+                    gr.update(visible=width_visible),
+                    gr.update(visible=mask_blur_visible),
+                    gr.update(visible=all_visible)]
+
+        seams_fix_type.change(
+            fn=select_fix_type,
+            inputs=seams_fix_type,
+            outputs=[seams_fix_denoise, seams_fix_width, seams_fix_mask_blur, seams_fix_padding]
+        )
+
+        def select_scale_type(scale_index):
+            is_custom_size = scale_index == 1
+            is_custom_scale = scale_index == 2
+
+            return [gr.update(visible=is_custom_size),
+                    gr.update(visible=is_custom_size),
+                    gr.update(visible=is_custom_scale),
+                    ]
+
+        target_size_type.change(
+            fn=select_scale_type,
+            inputs=target_size_type,
+            outputs=[custom_width, custom_height, custom_scale]
+        )
+
+        def init_field(scale_name):
+            try:
+                scale_index = target_size_types.index(scale_name)
+                custom_width.visible = custom_height.visible = scale_index == 1
+                custom_scale.visible = scale_index == 2
+            except:
+                pass
+
+        target_size_type.init_field = init_field
+
+        return [info, tile_width, tile_height, mask_blur, padding, seams_fix_width, seams_fix_denoise, seams_fix_padding,
+                upscaler_index, save_upscaled_image, redraw_mode, save_seams_fix_image, seams_fix_mask_blur,
+                seams_fix_type, target_size_type, custom_width, custom_height, custom_scale]
+
+    def run(self, p, _, tile_width, tile_height, mask_blur, padding, seams_fix_width, seams_fix_denoise, seams_fix_padding,
+            upscaler_index, save_upscaled_image, redraw_mode, save_seams_fix_image, seams_fix_mask_blur,
+            seams_fix_type, target_size_type, custom_width, custom_height, custom_scale):
+
+        # Init
+        processing.fix_seed(p)
+        devices.torch_gc()
+
+        p.do_not_save_grid = True
+        p.do_not_save_samples = True
+        p.inpaint_full_res = False
+
+        p.inpainting_fill = 1
+        p.n_iter = 1
+        p.batch_size = 1
+
+        seed = p.seed
+
+        # Init image
+        init_img = p.init_images[0]
+        if init_img == None:
+            return Processed(p, [], seed, "Empty image")
+        init_img = images.flatten(init_img, opts.img2img_background_color)
+
+        #override size
+        if target_size_type == 1:
+            p.width = custom_width
+            p.height = custom_height
+        if target_size_type == 2:
+            p.width = math.ceil((init_img.width * custom_scale) / 64) * 64
+            p.height = math.ceil((init_img.height * custom_scale) / 64) * 64
+
+        # Upscaling
+        upscaler = USDUpscaler(p, init_img, upscaler_index, save_upscaled_image, save_seams_fix_image, tile_width, tile_height)
+        upscaler.upscale()
+        
+        # Drawing
+        upscaler.setup_redraw(redraw_mode, padding, mask_blur)
+        upscaler.setup_seams_fix(seams_fix_padding, seams_fix_denoise, seams_fix_mask_blur, seams_fix_width, seams_fix_type)
+        upscaler.print_info()
+        upscaler.add_extra_info()
+        upscaler.process()
+        result_images = upscaler.result_images
+
+        return Processed(p, result_images, seed, upscaler.initial_info if upscaler.initial_info is not None else "")
+

custom_nodes/comfyui_ultimatesdupscale/test/.gitignore

@@ -0,0 +1 @@
+sample_images/

custom_nodes/comfyui_ultimatesdupscale/test/README.md

@@ -0,0 +1,49 @@
+# Running Tests
+
+This directory contains tests for ComfyUI_UltimateSDUpscale.
+
+## Prerequisites
+
+- These tests assume that ComfyUI is installed using a virtual environment
+- Activate the ComfyUI virtual environment before running tests
+- The checkpoint `v1-5-pruned-emaonly-fp16.safetensors` is available
+- The upscale model `4x-UltraSharp.pth` is available
+
+## Running Tests
+
+### Using the convenience scripts (works from repo root or test directory):
+
+**Linux/Mac (Bash):**
+```bash
+./test/run_tests.sh       # From repo root
+./run_tests.sh            # From test directory
+```
+run_tests.sh will forward all arguments into pytest.
+
+### Using pytest directly (must be in test directory):
+
+```bash
+cd test
+pytest          # Run all tests
+pytest -v       # Verbose
+```
+
+### Common pytest options:
+
+- `-v` - Verbose output
+- `-s` - Show print statements
+- `--log-cli-level=INFO` - Show info-level logs
+- `-k PATTERN` - Run tests matching pattern
+- `--lf` - Run last failed tests
+
+## Test Structure
+
+- `conftest.py` - Pytest configuration, fixtures, and path setup
+- `sample_images/` - Generated test images for visual inspection
+- `test_images/` - Reference images used as inputs or expected outputs
+
+## Troubleshooting
+
+If you encounter import errors:
+1. Make sure you're running from the `test/` directory
+2. Verify the virtual environment is activated

custom_nodes/comfyui_ultimatesdupscale/test/configs.py

@@ -0,0 +1,9 @@
+import pathlib
+
+
+class DirectoryConfig:
+    """Helper class for test directories."""
+
+    def __init__(self, test_images: pathlib.Path, sample_images: pathlib.Path):
+        self.test_images = test_images
+        self.sample_images = sample_images

custom_nodes/comfyui_ultimatesdupscale/test/conftest.py

@@ -0,0 +1,174 @@
+"""
+Setup for the ComfyUI engine and shared test fixtures.
+"""
+
+import os
+import sys
+from pathlib import Path
+import pytest
+import asyncio
+import logging
+
+from setup_utils import SilenceLogs, execute
+from hf_downloader import download_test_images
+from configs import DirectoryConfig
+# Because of manipulations to sys.path, non-packaged imports should be delayed to avoid import issues
+
+
+#
+# # Configuration
+#
+TEST_CHECKPOINT = "v1-5-pruned-emaonly-fp16.safetensors"
+TEST_UPSCALE_MODEL = "4x-UltraSharp.pth"
+SAMPLE_IMAGE_SUBDIR = "sample_images"
+TEST_IMAGE_SUBDIR = "test_images"
+# conftest.py is in repo_root/test/ directory
+REPO_ROOT = Path(__file__).parent.parent.resolve()
+COMFYUI_ROOT = REPO_ROOT.parent.parent.resolve()
+
+# Make sure the repo root is in sys.path for imports
+# Ensure submodule root is in path for test imports
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+# Ensure ComfyUI path is set up
+if str(COMFYUI_ROOT) not in sys.path:
+    sys.path.insert(0, str(COMFYUI_ROOT))
+
+
+# Session scoped fixtures
+from fixtures_images import base_image
+
+
+def pytest_configure(config):
+    """Called before test collection begins."""
+    # Download test images
+    download_test_images(
+        repo_id="ssitu/ultimatesdupscale_test",
+        save_dir=(REPO_ROOT / "test" / "test_images").resolve(),
+        repo_folder="test_images",
+    )
+
+    from comfy.cli_args import args
+
+    # args.cpu = True  # Force CPU mode for tests
+    # args.force_fp16 = True  # Force float16 mode for tests
+    args.disable_all_custom_nodes = True
+    # Assumes the name of the custom node folder is ComfyUI_UltimateSDUpscale
+    args.whitelist_custom_nodes = ["ComfyUI_UltimateSDUpscale"]
+
+
+#
+# # Path Setup
+#
+def _setup_comfyui_paths():
+    """Configure ComfyUI folder paths for testing."""
+    # Ensure modules containing a utils.py are NOT in sys.path
+    # The comfy directory must be removed to prevent comfy/utils.py from shadowing
+    # ComfyUI's utils/ package directory when we import utils.extra_config
+    to_remove = [
+        str(COMFYUI_ROOT / "comfy"),
+    ]
+    for path_to_remove in to_remove:
+        while path_to_remove in sys.path:
+            sys.path.remove(path_to_remove)
+
+    # Ensure ComfyUI is in path
+    if str(COMFYUI_ROOT) not in sys.path:
+        sys.path.insert(0, str(COMFYUI_ROOT))
+
+    # Apply custom paths
+    # main.py will trigger a warning that torch was already imported, probably by pytest. Shouldn't be a problem as far as I know.
+    from main import apply_custom_paths
+
+    apply_custom_paths()
+
+
+#
+# # Fixtures
+#
+@pytest.fixture(scope="session")
+def comfyui_initialized():
+    """Initialize ComfyUI nodes once per test session."""
+    from nodes import init_extra_nodes
+
+    _setup_comfyui_paths()
+
+    async def _init():
+        with SilenceLogs():
+            await init_extra_nodes(init_api_nodes=False)
+
+    asyncio.run(_init())
+
+    yield True
+
+
+@pytest.fixture(scope="session")
+def node_classes(comfyui_initialized):
+    """Get ComfyUI node class mappings."""
+    from nodes import NODE_CLASS_MAPPINGS
+
+    return NODE_CLASS_MAPPINGS
+
+
+@pytest.fixture(scope="session")
+def test_checkpoint():
+    """Find and return a valid test checkpoint."""
+    import folder_paths
+
+    checkpoints = folder_paths.get_filename_list("checkpoints")
+    # TODO: Should probably use a hash instead of matching the filename
+    if TEST_CHECKPOINT not in checkpoints:
+        pytest.skip(f"No test checkpoint found. Please add {TEST_CHECKPOINT}")
+
+    return TEST_CHECKPOINT
+
+
+@pytest.fixture(scope="session")
+def loaded_checkpoint(comfyui_initialized, test_checkpoint, node_classes):
+    """Load checkpoint and return (model, clip, vae) tuple."""
+    import torch
+
+    with torch.inference_mode():
+        CheckpointLoaderSimple = node_classes["CheckpointLoaderSimple"]
+        model, clip, vae = execute(CheckpointLoaderSimple, test_checkpoint)
+
+    return model, clip, vae
+
+
+@pytest.fixture(scope="session")
+def upscale_model(comfyui_initialized, node_classes):
+    """Load the first available upscale model."""
+    import torch
+    import folder_paths
+
+    UpscaleModelLoader = node_classes["UpscaleModelLoader"]
+
+    upscale_models = folder_paths.get_filename_list("upscale_models")
+    # TODO: Should probably use a hash instead of matching the filename
+    if TEST_UPSCALE_MODEL not in upscale_models:
+        pytest.skip("No upscale models found")
+
+    model_name = upscale_models[0]
+    with torch.inference_mode():
+        (model,) = execute(UpscaleModelLoader, model_name)
+
+    return model
+
+
+@pytest.fixture(scope="session")
+def test_dirs():
+    """Return paths to test and sample image directories."""
+    test_dir = REPO_ROOT / "test"
+    test_image_dir = test_dir / TEST_IMAGE_SUBDIR
+    sample_image_dir = test_dir / SAMPLE_IMAGE_SUBDIR
+    sample_image_dir.mkdir(exist_ok=True)
+    return DirectoryConfig(
+        test_images=test_image_dir,
+        sample_images=sample_image_dir,
+    )
+
+
+@pytest.fixture(scope="session")
+def seed():
+    """Default seed for reproducible tests."""
+    return 1

custom_nodes/comfyui_ultimatesdupscale/test/fixtures_images.py

@@ -0,0 +1,70 @@
+"""
+Fixtures for base images.
+"""
+
+import pathlib
+import pytest
+import torch
+
+from setup_utils import execute
+from io_utils import save_image, load_image
+from configs import DirectoryConfig
+
+# Image file names
+EXT = ".jpg"
+CATEGORY = pathlib.Path("base_images")
+BASE_IMAGE_1_NAME = "main1_sd15" + EXT
+BASE_IMAGE_2_NAME = "main2_sd15" + EXT
+
+# Prepend category path
+BASE_IMAGE_1 = CATEGORY / BASE_IMAGE_1_NAME
+BASE_IMAGE_2 = CATEGORY / BASE_IMAGE_2_NAME
+
+
+@pytest.fixture(scope="session")
+def base_image(loaded_checkpoint, seed, test_dirs: DirectoryConfig, node_classes):
+    """Generate a base image for upscaling tests."""
+    EmptyLatentImage = node_classes["EmptyLatentImage"]
+    CLIPTextEncode = node_classes["CLIPTextEncode"]
+    KSampler = node_classes["KSampler"]
+    VAEDecode = node_classes["VAEDecode"]
+
+    model, clip, vae = loaded_checkpoint
+
+    with torch.inference_mode():
+        (empty_latent,) = execute(EmptyLatentImage, width=512, height=512, batch_size=2)
+
+        (positive,) = execute(
+            CLIPTextEncode,
+            text="beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
+            clip=clip,
+        )
+
+        (negative,) = execute(CLIPTextEncode, text="text, watermark", clip=clip)
+
+        (samples,) = execute(
+            KSampler,
+            model=model,
+            positive=positive,
+            negative=negative,
+            latent_image=empty_latent,
+            seed=seed,
+            steps=10,
+            cfg=8,
+            sampler_name="dpmpp_2m",
+            scheduler="karras",
+            denoise=1.0,
+        )
+
+        (image,) = execute(VAEDecode, samples=samples, vae=vae)
+
+    # Save base images
+    sample_dir = test_dirs.sample_images
+    base_img1_path = sample_dir / BASE_IMAGE_1
+    base_img2_path = sample_dir / BASE_IMAGE_2
+    save_image(image[0:1], base_img1_path)
+    save_image(image[1:2], base_img2_path)
+
+    # Load images back as tensors to account for compression
+    image = torch.cat([load_image(base_img1_path), load_image(base_img2_path)])
+    return image, positive, negative

custom_nodes/comfyui_ultimatesdupscale/test/hf_downloader.py

@@ -0,0 +1,85 @@
+import logging
+import re
+import urllib.parse
+import urllib.request
+from pathlib import Path
+
+logging.basicConfig(level=logging.INFO)
+
+
+def _fetch_hf_html(repo_id: str, folder_path: str) -> str:
+    """Fetch HTML from HuggingFace tree page."""
+    url = f"https://huggingface.co/datasets/{repo_id}/tree/main/{folder_path}"
+    with urllib.request.urlopen(url) as response:
+        return response.read().decode("utf-8")
+
+
+def list_hf_subfolders(repo_id: str, folder_path: str) -> list[str]:
+    """List subfolders in a HuggingFace dataset folder."""
+    try:
+        html = _fetch_hf_html(repo_id, folder_path)
+        pattern = rf'/datasets/{repo_id}/tree/main/({folder_path}/[^"/?]+)'
+        return sorted(set(re.findall(pattern, html)))
+    except Exception as e:
+        logging.error(f"Failed to list subfolders in {folder_path}: {e}")
+        return []
+
+
+def list_hf_files(
+    repo_id: str,
+    folder_path: str,
+    extensions: tuple = (".jpg", ".jpeg", ".png", ".webp"),
+) -> list[str]:
+    """List image files in a HuggingFace dataset folder."""
+    try:
+        html = _fetch_hf_html(repo_id, folder_path)
+        pattern = rf'/datasets/{repo_id}/blob/main/({folder_path}/[^"]+?({"|".join(e for e in extensions)}))'
+        return [urllib.parse.unquote(match[0]) for match in re.findall(pattern, html)]
+    except Exception as e:
+        logging.error(f"Failed to list files in {folder_path}: {e}")
+        return []
+
+
+def download_test_images(save_dir: Path, repo_folder: str, repo_id: str) -> Path:
+    """Download the test_images/ folder from the HF test dataset repo"""
+    # Discover all subfolders and collect files
+    subfolders = list_hf_subfolders(repo_id, repo_folder)
+    if not subfolders:
+        logging.warning(f"No subfolders found in {repo_folder}")
+        return save_dir
+
+    all_files = [f for folder in subfolders for f in list_hf_files(repo_id, folder)]
+    if not all_files:
+        logging.warning(f"No image files found in {repo_folder}")
+        return save_dir
+
+    logging.info(f"Found {len(all_files)} files from {len(subfolders)} folders")
+    # Download files, preserving folder structure
+    save_dir_path = Path(save_dir)
+    downloaded = 0
+    skipped = 0
+    for file_path in all_files:
+        relative_path = Path(file_path).relative_to(repo_folder)
+        save_path = save_dir_path / relative_path
+        if save_path.exists():
+            logging.info(f"Skipping {relative_path} (already exists)")
+            skipped += 1
+            continue
+
+        save_path.parent.mkdir(parents=True, exist_ok=True)
+        url = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{file_path}"
+        logging.info(f"Downloading {relative_path}...")
+        urllib.request.urlretrieve(url, save_path)
+        downloaded += 1
+
+    logging.info(f"Downloaded {downloaded} files, skipped {skipped} existing files")
+    return save_dir_path
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    download_test_images(
+        repo_id="ssitu/ultimatesdupscale_test",
+        save_dir=Path("./test/test_images/"),
+        repo_folder="test_images",
+    )

custom_nodes/comfyui_ultimatesdupscale/test/io_utils.py

@@ -0,0 +1,20 @@
+import pathlib
+from PIL import Image
+import usdu_utils
+
+
+def save_image(tensor, path: pathlib.Path):
+    """The goto function to save a tensor image to the sampled images directory."""
+    assert tensor.ndim == 3 or (tensor.ndim == 4 and tensor.shape[0] == 1), (
+        f"Expected a 3D tensor (H, W, C) or (1, H, W, C), got {tensor.ndim=}"
+    )
+    if tensor.ndim == 3:
+        tensor = tensor.unsqueeze(0)
+    image = usdu_utils.tensor_to_pil(tensor.cpu())
+    path.parent.mkdir(parents=True, exist_ok=True)
+    image.save(path, quality=75, optimize=True)
+
+
+def load_image(path: pathlib.Path, device=None):
+    """Load an image from disk and convert it to a tensor."""
+    return usdu_utils.pil_to_tensor(Image.open(path)).to(device=device)

custom_nodes/comfyui_ultimatesdupscale/test/pytest.ini

@@ -0,0 +1,9 @@
+[pytest]
+
+# Filter out warnings that are unavoidable or from external libraries
+filterwarnings =
+    # Ignore CUDA compatibility warnings (hardware limitation)
+    ignore::UserWarning:torch.cuda
+    # Ignore Swig type warnings from importlib by pytest
+    ignore:builtin type.*has no __module__ attribute:DeprecationWarning:importlib._bootstrap:488
+    ignore:builtin type.*has no __module__ attribute:DeprecationWarning:sys:0

custom_nodes/comfyui_ultimatesdupscale/test/run_tests.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+# Can be run from either the repo root or the test directory
+# Example usage: sh ./run_tests.sh [additional pytest arguments]
+
+# Get the script directory
+SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+
+# Determine the test directory
+if [[ "$(basename "$SCRIPT_DIR")" == "test" ]]; then
+    # Script is in test directory
+    TEST_DIR="$SCRIPT_DIR"
+else
+    # Script is in repo root
+    TEST_DIR="$SCRIPT_DIR/test"
+fi
+
+cd "$TEST_DIR"
+python -m pytest "$@"

custom_nodes/comfyui_ultimatesdupscale/test/setup_utils.py

@@ -0,0 +1,20 @@
+import logging
+
+
+class SilenceLogs:
+    """Context manager to temporarily silence logging."""
+
+    def __enter__(self):
+        logging.disable(logging.CRITICAL)
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        logging.disable(logging.NOTSET)
+
+
+def execute(node, *args, **kwargs):
+    """Execute a ComfyUI node, handling both V3 and legacy schemas."""
+    if hasattr(node, "execute"):
+        return node.execute(*args, **kwargs)
+    else:
+        return getattr(node(), node.FUNCTION)(*args, **kwargs)

custom_nodes/comfyui_ultimatesdupscale/test/tensor_utils.py

@@ -0,0 +1,27 @@
+import torchvision.transforms.functional as TF
+
+
+def img_tensor_mae(tensor1, tensor2):
+    """Calculate the mean absolute difference between two image tensors."""
+    # Remove batch dimensions if present
+    tensor1 = tensor1.squeeze(0).cpu()
+    tensor2 = tensor2.squeeze(0).cpu()
+    if tensor1.shape != tensor2.shape:
+        raise ValueError(
+            f"Tensors must have the same shape for comparison. Got {tensor1.shape=} and {tensor2.shape=}."
+        )
+    return (tensor1 - tensor2).abs().mean().item()
+
+
+def blur(tensor, kernel_size=9, sigma=None):
+    """Apply Gaussian blur to an image tensor."""
+    # [1, H, W, C] -> [1, C, H, W]
+    if tensor.ndim == 4:
+        tensor = tensor.permute(0, 3, 1, 2)
+    elif tensor.ndim == 3:
+        tensor = tensor.permute(2, 0, 1).unsqueeze(0)
+    else:
+        raise ValueError(f"Expected a 3D or 4D tensor, got {tensor.ndim=}")
+    return TF.gaussian_blur(tensor, kernel_size=kernel_size, sigma=sigma).permute(  # type: ignore
+        0, 2, 3, 1
+    )

custom_nodes/comfyui_ultimatesdupscale/test/test_base_images.py

@@ -0,0 +1,31 @@
+"""
+Tests for base image generation.
+"""
+
+import logging
+from configs import DirectoryConfig
+from tensor_utils import img_tensor_mae, blur
+from io_utils import load_image
+from fixtures_images import BASE_IMAGE_1, BASE_IMAGE_2
+
+
+def test_base_image_matches_reference(base_image, test_dirs: DirectoryConfig):
+    """
+    Verify generated base images match reference images.
+    This is just to check if the checkpoint and generation pipeline are as expected for the tests dependent on their behavior.
+    """
+    logger = logging.getLogger("test_base_image_matches_reference")
+    image, _, _ = base_image
+    test_image_dir = test_dirs.test_images
+    im1 = image[0:1]
+    im2 = image[1:2]
+
+    test_im1 = load_image(test_image_dir / BASE_IMAGE_1)
+    test_im2 = load_image(test_image_dir / BASE_IMAGE_2)
+
+    # Reduce high-frequency noise differences with gaussian blur. Using perceptual metrics are probably overkill.
+    diff1 = img_tensor_mae(blur(im1), blur(test_im1))
+    diff2 = img_tensor_mae(blur(im2), blur(test_im2))
+    logger.info(f"Base Image Diff1: {diff1}, Diff2: {diff2}")
+    assert diff1 < 0.05, "Image 1 does not match its test image."
+    assert diff2 < 0.05, "Image 2 does not match its test image."

custom_nodes/comfyui_ultimatesdupscale/test/test_controlnet.py

@@ -0,0 +1,96 @@
+"""
+Test using controlnet in the upscaling workflow.
+"""
+
+import logging
+import pathlib
+import pytest
+import torch
+
+from setup_utils import execute
+from tensor_utils import img_tensor_mae, blur
+from io_utils import save_image, load_image
+from configs import DirectoryConfig
+from fixtures_images import EXT
+
+CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_"))
+CONTROLNET_TILE_OUTPUT_IMAGE = "controlnet_tile" + EXT
+TEST_CONTROLNET_TILE_MODEL = "control_v11f1e_sd15_tile.pth"
+
+
+class TestControlNet:
+    """Integration tests for the upscaling workflow with ControlNet."""
+
+    @pytest.fixture(scope="class")
+    def controlnet_upscaled_image(
+        self,
+        base_image,
+        loaded_checkpoint,
+        upscale_model,
+        node_classes,
+        seed,
+        test_dirs,
+    ):
+        """Generate upscaled images using ControlNet."""
+        image, positive, negative = base_image
+        model, clip, vae = loaded_checkpoint
+        image = image[0:1]
+
+        (controlnet_tile_model,) = execute(
+            node_classes["ControlNetLoader"], TEST_CONTROLNET_TILE_MODEL
+        )
+        (positive,) = execute(
+            node_classes["ControlNetApply"], positive, controlnet_tile_model, image, 1.0
+        )
+
+        with torch.inference_mode():
+            # Run upscale with ControlNet
+            usdu = node_classes["UltimateSDUpscale"]
+            (upscaled,) = usdu().upscale(
+                image=image,
+                model=model,
+                positive=positive,
+                negative=negative,
+                vae=vae,
+                upscale_by=2.0,
+                seed=seed,
+                steps=5,
+                cfg=8,
+                sampler_name="euler",
+                scheduler="normal",
+                denoise=1.0,
+                upscale_model=None,
+                mode_type="Chess",
+                tile_width=512,
+                tile_height=512,
+                mask_blur=8,
+                tile_padding=32,
+                seam_fix_mode="None",
+                seam_fix_denoise=1.0,
+                seam_fix_width=64,
+                seam_fix_mask_blur=8,
+                seam_fix_padding=16,
+                force_uniform_tiles=True,
+                tiled_decode=False,
+            )
+        # Save and reload sample image
+        sample_dir = test_dirs.sample_images
+        filename = CATEGORY / CONTROLNET_TILE_OUTPUT_IMAGE
+        save_image(upscaled[0], sample_dir / filename)
+        upscaled = load_image(sample_dir / filename)
+        return upscaled
+
+    def test_controlnet_upscaled_image_matches_reference(
+        self, controlnet_upscaled_image, test_dirs: DirectoryConfig
+    ):
+        """
+        Verify ControlNet upscaled images match reference images.
+        """
+        logger = logging.getLogger("test_controlnet_upscaled_image_matches_reference")
+        test_img_dir = test_dirs.test_images
+        test_img = load_image(test_img_dir / CATEGORY / CONTROLNET_TILE_OUTPUT_IMAGE)
+
+        # Reduce high-frequency noise differences with gaussian blur
+        diff = img_tensor_mae(blur(controlnet_upscaled_image), blur(test_img))
+        logger.info(f"ControlNet Upscaled Image Diff: {diff}")
+        assert diff < 0.05, "ControlNet upscaled image does not match its test image."

custom_nodes/comfyui_ultimatesdupscale/test/test_main_workflow.py

@@ -0,0 +1,246 @@
+"""
+Tests a common workflow for UltimateSDUpscale.
+"""
+
+import logging
+import pathlib
+import torch
+
+from setup_utils import execute
+from tensor_utils import img_tensor_mae, blur
+from io_utils import save_image, load_image
+from configs import DirectoryConfig
+from fixtures_images import EXT
+
+# Image file names
+CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_"))
+IMAGE_1 = CATEGORY / ("main1_sd15_upscaled" + EXT)
+IMAGE_2 = CATEGORY / ("main2_sd15_upscaled" + EXT)
+NO_UPSCALE_IMAGE_1 = CATEGORY / ("main1_sd15_upscaled_no_upscale" + EXT)
+NO_UPSCALE_IMAGE_2 = CATEGORY / ("main2_sd15_upscaled_no_upscale" + EXT)
+CUSTOM_SAMPLER_IMAGE_1 = CATEGORY / ("main1_sd15_upscaled_custom_sampler" + EXT)
+CUSTOM_SAMPLER_IMAGE_2 = CATEGORY / ("main2_sd15_upscaled_custom_sampler" + EXT)
+
+
+class TestMainWorkflow:
+    """Integration tests for the main upscaling workflow."""
+
+    def test_upscale(
+        self,
+        base_image,
+        loaded_checkpoint,
+        upscale_model,
+        node_classes,
+        seed,
+        test_dirs: DirectoryConfig,
+    ):
+        """Generate upscaled images using standard workflow."""
+        image, positive, negative = base_image
+        model, clip, vae = loaded_checkpoint
+
+        with torch.inference_mode():
+            usdu = node_classes["UltimateSDUpscale"]
+            (upscaled,) = usdu().upscale(
+                image=image,
+                model=model,
+                positive=positive,
+                negative=negative,
+                vae=vae,
+                upscale_by=2.00000004,  # Test small float difference doesn't add extra tiles
+                seed=seed,
+                steps=10,
+                cfg=8,
+                sampler_name="euler",
+                scheduler="normal",
+                denoise=0.2,
+                upscale_model=upscale_model,
+                mode_type="Chess",
+                tile_width=512,
+                tile_height=512,
+                mask_blur=8,
+                tile_padding=32,
+                seam_fix_mode="None",
+                seam_fix_denoise=1.0,
+                seam_fix_width=64,
+                seam_fix_mask_blur=8,
+                seam_fix_padding=16,
+                force_uniform_tiles=True,
+                tiled_decode=False,
+            )
+        # Save images
+        sample_dir = test_dirs.sample_images
+        upscaled_img1_path = sample_dir / IMAGE_1
+        upscaled_img2_path = sample_dir / IMAGE_2
+        save_image(upscaled[0], upscaled_img1_path)
+        save_image(upscaled[1], upscaled_img2_path)
+        # Load to account for compression
+        upscaled = torch.cat(
+            [load_image(upscaled_img1_path), load_image(upscaled_img2_path)]
+        )
+        # Verify results
+        logger = logging.getLogger("test_upscale")
+        test_image_dir = test_dirs.test_images
+        im1_upscaled = upscaled[0]
+        im2_upscaled = upscaled[1]
+
+        test_im1_upscaled = load_image(test_image_dir / IMAGE_1)
+        test_im2_upscaled = load_image(test_image_dir / IMAGE_2)
+
+        diff1 = img_tensor_mae(blur(im1_upscaled), blur(test_im1_upscaled))
+        diff2 = img_tensor_mae(blur(im2_upscaled), blur(test_im2_upscaled))
+
+        # This tolerance is enough to handle both cpu and gpu as the device, as well as jpg compression differences.
+        logger.info(f"Diff1: {diff1}, Diff2: {diff2}")
+        assert diff1 < 0.05, "Upscaled Image 1 doesn't match its test image."
+        assert diff2 < 0.05, "Upscaled Image 2 doesn't match its test image."
+
+    def test_upscale_no_upscale(
+        self,
+        base_image,
+        loaded_checkpoint,
+        node_classes,
+        seed,
+        test_dirs: DirectoryConfig,
+    ):
+        """Generate upscaled images using standard workflow using the no upscale node."""
+        image, positive, negative = base_image
+        model, clip, vae = loaded_checkpoint
+        (image,) = execute(
+            node_classes["ImageScaleBy"],
+            image=image,
+            upscale_method="lanczos",
+            scale_by=2.0,
+        )
+
+        with torch.inference_mode():
+            usdu = node_classes["UltimateSDUpscaleNoUpscale"]
+            (upscaled,) = usdu().upscale(
+                upscaled_image=image,
+                model=model,
+                positive=positive,
+                negative=negative,
+                vae=vae,
+                seed=seed,
+                steps=10,
+                cfg=8,
+                sampler_name="euler",
+                scheduler="normal",
+                denoise=0.2,
+                mode_type="Chess",
+                tile_width=512,
+                tile_height=512,
+                mask_blur=8,
+                tile_padding=32,
+                seam_fix_mode="None",
+                seam_fix_denoise=1.0,
+                seam_fix_width=64,
+                seam_fix_mask_blur=8,
+                seam_fix_padding=16,
+                force_uniform_tiles=True,
+                tiled_decode=False,
+            )
+        # Save images
+        sample_dir = test_dirs.sample_images
+        upscaled_img1_path = sample_dir / NO_UPSCALE_IMAGE_1
+        upscaled_img2_path = sample_dir / NO_UPSCALE_IMAGE_2
+        save_image(upscaled[0], upscaled_img1_path)
+        save_image(upscaled[1], upscaled_img2_path)
+        # Load to account for compression
+        upscaled = torch.cat(
+            [load_image(upscaled_img1_path), load_image(upscaled_img2_path)]
+        )
+        # Verify results
+        logger = logging.getLogger("test_upscale_no_upscale")
+        test_image_dir = test_dirs.test_images
+        im1_upscaled = upscaled[0]
+        im2_upscaled = upscaled[1]
+
+        test_im1_upscaled = load_image(test_image_dir / NO_UPSCALE_IMAGE_1)
+        test_im2_upscaled = load_image(test_image_dir / NO_UPSCALE_IMAGE_2)
+
+        diff1 = img_tensor_mae(blur(im1_upscaled), blur(test_im1_upscaled))
+        diff2 = img_tensor_mae(blur(im2_upscaled), blur(test_im2_upscaled))
+        # This tolerance is enough to handle both cpu and gpu as the device, as well as jpg compression differences.
+        logger.info(f"Diff1: {diff1}, Diff2: {diff2}")
+        assert diff1 < 0.05, "No Upscale Image 1 doesn't match its test image."
+        assert diff2 < 0.05, "No Upscale Image 2 doesn't match its test image."
+
+    def test_upscale_with_custom_sampler(
+        self,
+        base_image,
+        loaded_checkpoint,
+        upscale_model,
+        node_classes,
+        seed,
+        test_dirs: DirectoryConfig,
+    ):
+        """Generate upscaled images using standard workflow using the custom sampler node."""
+        image, positive, negative = base_image
+        model, clip, vae = loaded_checkpoint
+
+        with torch.inference_mode():
+            # Setup custom scheduler and sampler
+            custom_scheduler = node_classes["KarrasScheduler"]
+            (sigmas,) = execute(custom_scheduler, 20, 14.614642, 0.0291675, 7.0)
+            (_, sigmas) = execute(node_classes["SplitSigmasDenoise"], sigmas, 0.15)
+
+            custom_sampler = node_classes["KSamplerSelect"]
+            (sampler,) = execute(custom_sampler, "dpmpp_2m")
+
+            # Run upscale
+            usdu = node_classes["UltimateSDUpscaleCustomSample"]
+            (upscaled,) = usdu().upscale(
+                image=image,
+                model=model,
+                positive=positive,
+                negative=negative,
+                vae=vae,
+                upscale_by=2.0,
+                seed=seed,
+                steps=10,
+                cfg=8,
+                sampler_name="euler",
+                scheduler="normal",
+                denoise=0.2,
+                upscale_model=upscale_model,
+                mode_type="Chess",
+                tile_width=512,
+                tile_height=512,
+                mask_blur=8,
+                tile_padding=32,
+                seam_fix_mode="None",
+                seam_fix_denoise=1.0,
+                seam_fix_width=64,
+                seam_fix_mask_blur=8,
+                seam_fix_padding=16,
+                force_uniform_tiles=True,
+                tiled_decode=False,
+                custom_sampler=sampler,
+                custom_sigmas=sigmas,
+            )
+        # Save images
+        sample_dir = test_dirs.sample_images
+        upscaled_img1_path = sample_dir / CUSTOM_SAMPLER_IMAGE_1
+        upscaled_img2_path = sample_dir / CUSTOM_SAMPLER_IMAGE_2
+        save_image(upscaled[0], upscaled_img1_path)
+        save_image(upscaled[1], upscaled_img2_path)
+        # Load to account for compression
+        upscaled = torch.cat(
+            [load_image(upscaled_img1_path), load_image(upscaled_img2_path)]
+        )
+        # Verify results
+        logger = logging.getLogger("test_upscale_with_custom_sampler")
+        test_image_dir = test_dirs.test_images
+        im1_upscaled = upscaled[0]
+        im2_upscaled = upscaled[1]
+
+        test_im1_upscaled = load_image(test_image_dir / CUSTOM_SAMPLER_IMAGE_1)
+        test_im2_upscaled = load_image(test_image_dir / CUSTOM_SAMPLER_IMAGE_2)
+
+        diff1 = img_tensor_mae(blur(im1_upscaled), blur(test_im1_upscaled))
+        diff2 = img_tensor_mae(blur(im2_upscaled), blur(test_im2_upscaled))
+
+        # This tolerance is enough to handle both cpu and gpu as the device, as well as jpg compression differences.
+        logger.info(f"Diff1: {diff1}, Diff2: {diff2}")
+        assert diff1 < 0.05, "Upscaled Image 1 doesn't match its test image."
+        assert diff2 < 0.05, "Upscaled Image 2 doesn't match its test image."

custom_nodes/comfyui_ultimatesdupscale/test/test_settings.py

@@ -0,0 +1,69 @@
+"""
+Test for other settings included in the upscaling nodes.
+"""
+
+import logging
+import pathlib
+import pytest
+import torch
+
+from tensor_utils import img_tensor_mae, blur
+from io_utils import save_image, load_image
+from configs import DirectoryConfig
+from fixtures_images import EXT
+
+# Image file names
+CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_"))
+
+
+def test_minimal_tile_sizes(
+    base_image, loaded_checkpoint, node_classes, seed, test_dirs: DirectoryConfig
+):
+    """Test upscaling with minimal tile sizes."""
+    filename = "non_uniform_tiles"
+    image, positive, negative = base_image
+    model, clip, vae = loaded_checkpoint
+
+    with torch.inference_mode():
+        usdu = node_classes["UltimateSDUpscale"]
+        (upscaled,) = usdu().upscale(
+            image=image[0:1],
+            model=model,
+            positive=positive,
+            negative=negative,
+            vae=vae,
+            upscale_by=1.5,
+            seed=seed,
+            steps=5,
+            cfg=8,
+            sampler_name="euler",
+            scheduler="normal",
+            denoise=0.15,
+            upscale_model=None,
+            mode_type="Chess",
+            tile_width=512,
+            tile_height=512,
+            mask_blur=8,
+            tile_padding=8,
+            seam_fix_mode="None",
+            seam_fix_denoise=1.0,
+            seam_fix_width=16,
+            seam_fix_mask_blur=8,
+            seam_fix_padding=4,
+            force_uniform_tiles=False,
+            tiled_decode=False,
+        )
+
+    # Save and reload sample image
+    sample_dir = test_dirs.sample_images
+    filename_path = CATEGORY / (filename + EXT)
+    save_image(upscaled[0], sample_dir / filename_path)
+    upscaled = load_image(sample_dir / filename_path)
+
+    # Compare with reference
+    test_image_dir = test_dirs.test_images
+    test_image = load_image(test_image_dir / filename_path)
+    diff = img_tensor_mae(blur(upscaled), blur(test_image))
+    logger = logging.getLogger(__name__)
+    logger.info(f"{filename} MAE: {diff}")
+    assert diff < 0.05, f"{filename} output doesn't match reference"

custom_nodes/comfyui_ultimatesdupscale/test/test_tiling_modes.py

@@ -0,0 +1,134 @@
+"""
+Tests for different upscaling modes and seam fix modes.
+"""
+
+import logging
+import pathlib
+import pytest
+import torch
+
+from tensor_utils import img_tensor_mae, blur
+from io_utils import save_image, load_image
+from configs import DirectoryConfig
+from fixtures_images import EXT
+
+# Image file names
+CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_"))
+
+
+def image_name_format(prefix: str, mode: str) -> str:
+    """Helper for the image name format for the tests below."""
+    return f"{prefix}_{mode.lower().replace(' ', '_')}{EXT}"
+
+
+class TestTilingModes:
+    def _test_upscale_variant(
+        self,
+        base_image,
+        loaded_checkpoint,
+        node_classes,
+        seed,
+        test_dirs: DirectoryConfig,
+        mode_type,
+        seam_fix_mode,
+        seam_fix_denoise,
+        filename_prefix,
+    ):
+        """Helper method to test upscale variants with different parameters."""
+        logger = logging.getLogger(f"test_{filename_prefix}")
+        image, positive, negative = base_image
+        model, clip, vae = loaded_checkpoint
+
+        with torch.inference_mode():
+            usdu = node_classes["UltimateSDUpscale"]
+            (upscaled,) = usdu().upscale(
+                image=image[0:1],
+                model=model,
+                positive=positive,
+                negative=negative,
+                vae=vae,
+                upscale_by=2.0,
+                seed=seed,
+                steps=3,
+                cfg=8,
+                sampler_name="euler",
+                scheduler="normal",
+                denoise=0.2,
+                upscale_model=None,
+                mode_type=mode_type,
+                tile_width=512,
+                tile_height=512,
+                mask_blur=8,
+                tile_padding=32,
+                seam_fix_mode=seam_fix_mode,
+                seam_fix_denoise=seam_fix_denoise,
+                seam_fix_width=64,
+                seam_fix_mask_blur=8,
+                seam_fix_padding=16,
+                force_uniform_tiles=True,
+                tiled_decode=False,
+            )
+
+        # Save and reload sample image
+        sample_dir = test_dirs.sample_images
+        filename = CATEGORY / filename_prefix
+        save_image(upscaled[0], sample_dir / filename)
+        upscaled = load_image(sample_dir / filename)
+
+        # Compare with reference
+        test_image_dir = test_dirs.test_images
+        test_image = load_image(test_image_dir / filename)
+        diff = img_tensor_mae(blur(upscaled), blur(test_image))
+        logger.info(f"{filename_prefix} MAE: {diff}")
+        assert diff < 0.05, f"{filename_prefix} output doesn't match reference"
+
+    # "Chess" is tested in the main workflow test
+    @pytest.mark.parametrize("mode_type", ["Linear", "None"])
+    def test_mode_types(
+        self,
+        base_image,
+        loaded_checkpoint,
+        node_classes,
+        seed,
+        mode_type,
+        test_dirs: DirectoryConfig,
+    ):
+        """Test different tiling mode types."""
+        filename = image_name_format("mode", mode_type)
+        self._test_upscale_variant(
+            base_image,
+            loaded_checkpoint,
+            node_classes,
+            seed,
+            test_dirs,
+            mode_type=mode_type,
+            seam_fix_mode="None",
+            seam_fix_denoise=1.0,
+            filename_prefix=filename,
+        )
+
+    @pytest.mark.parametrize(
+        "seam_fix_mode", ["None", "Band Pass", "Half Tile", "Half Tile + Intersections"]
+    )
+    def test_seam_fix_modes(
+        self,
+        base_image,
+        loaded_checkpoint,
+        node_classes,
+        seed,
+        seam_fix_mode,
+        test_dirs: DirectoryConfig,
+    ):
+        """Test different seam fix modes."""
+        filename = image_name_format("seamfix", seam_fix_mode)
+        self._test_upscale_variant(
+            base_image,
+            loaded_checkpoint,
+            node_classes,
+            seed,
+            test_dirs,
+            mode_type="None",
+            seam_fix_mode=seam_fix_mode,
+            seam_fix_denoise=0.5,
+            filename_prefix=filename,
+        )

custom_nodes/comfyui_ultimatesdupscale/usdu_nodes.py

@@ -0,0 +1,243 @@
+# ComfyUI Node for Ultimate SD Upscale by Coyote-A: https://github.com/Coyote-A/ultimate-upscale-for-automatic1111
+
+import logging
+import torch
+import comfy
+from usdu_patch import usdu
+from usdu_utils import tensor_to_pil, pil_to_tensor
+from modules.processing import StableDiffusionProcessing
+import modules.shared as shared
+from modules.upscaler import UpscalerData
+
+MAX_RESOLUTION = 8192
+# The modes available for Ultimate SD Upscale
+MODES = {
+    "Linear": usdu.USDUMode.LINEAR,
+    "Chess": usdu.USDUMode.CHESS,
+    "None": usdu.USDUMode.NONE,
+}
+# The seam fix modes
+SEAM_FIX_MODES = {
+    "None": usdu.USDUSFMode.NONE,
+    "Band Pass": usdu.USDUSFMode.BAND_PASS,
+    "Half Tile": usdu.USDUSFMode.HALF_TILE,
+    "Half Tile + Intersections": usdu.USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS,
+}
+
+
+def USDU_base_inputs():
+    required = [
+        ("image", ("IMAGE", {"tooltip": "The image to upscale."})),
+        # Sampling Params
+        ("model", ("MODEL", {"tooltip": "The model to use for image-to-image."})),
+        ("positive", ("CONDITIONING", {"tooltip": "The positive conditioning for each tile."})),
+        ("negative", ("CONDITIONING", {"tooltip": "The negative conditioning for each tile."})),
+        ("vae", ("VAE", {"tooltip": "The VAE model to use for tiles."})),
+        ("upscale_by", ("FLOAT", {"default": 2, "min": 0.05, "max": 4, "step": 0.05, "tooltip": "The factor to upscale the image by."})),
+        ("seed", ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "The seed to use for image-to-image."})),
+        ("steps", ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1, "tooltip": "The number of steps to use for each tile."})),
+        ("cfg", ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "The CFG scale to use for each tile."})),
+        ("sampler_name", (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The sampler to use for each tile."})),
+        ("scheduler", (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler to use for each tile."})),
+        ("denoise", ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The denoising strength to use for each tile."})),
+        # Upscale Params
+        ("upscale_model", ("UPSCALE_MODEL", {"tooltip": "The upscaler model for upscaling the image."})),
+        ("mode_type", (list(MODES.keys()), {"tooltip": "The tiling order to use for the redraw step."})),
+        ("tile_width", ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The width of each tile."})),
+        ("tile_height", ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The height of each tile."})),
+        ("mask_blur", ("INT", {"default": 8, "min": 0, "max": 64, "step": 1, "tooltip": "The blur radius for the mask."})),
+        ("tile_padding", ("INT", {"default": 32, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The padding to apply between tiles."})),
+        # Seam fix params
+        ("seam_fix_mode", (list(SEAM_FIX_MODES.keys()), {"tooltip": "The seam fix mode to use."})),
+        ("seam_fix_denoise", ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The denoising strength to use for the seam fix."})),
+        ("seam_fix_width", ("INT", {"default": 64, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The width of the bands used for the Band Pass seam fix mode."})),
+        ("seam_fix_mask_blur", ("INT", {"default": 8, "min": 0, "max": 64, "step": 1, "tooltip": "The blur radius for the seam fix mask."})),
+        ("seam_fix_padding", ("INT", {"default": 16, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The padding to apply for the seam fix tiles."})),
+        # Misc
+        ("force_uniform_tiles", ("BOOLEAN", {"default": True, "tooltip": "Force all tiles to be the same as the set tile size, even when tiles could be smaller. This can help prevent the model from working with irregular tile sizes."})),
+        ("tiled_decode", ("BOOLEAN", {"default": False, "tooltip": "Whether to use tiled decoding when decoding tiles."})),
+    ]
+
+    optional = []
+
+    return required, optional
+
+
+def prepare_inputs(required: list, optional: list = None):
+    inputs = {}
+    if required:
+        inputs["required"] = {}
+        for name, type in required:
+            inputs["required"][name] = type
+    if optional:
+        inputs["optional"] = {}
+        for name, type in optional:
+            inputs["optional"][name] = type
+    return inputs
+
+
+def remove_input(inputs: list, input_name: str):
+    for i, (n, _) in enumerate(inputs):
+        if n == input_name:
+            del inputs[i]
+            break
+
+
+def rename_input(inputs: list, old_name: str, new_name: str):
+    for i, (n, t) in enumerate(inputs):
+        if n == old_name:
+            inputs[i] = (new_name, t)
+            break
+
+
+class UltimateSDUpscale:
+    @classmethod
+    def INPUT_TYPES(s):
+        required, optional = USDU_base_inputs()
+        return prepare_inputs(required, optional)
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "upscale"
+
+    CATEGORY = "image/upscaling"
+    OUTPUT_TOOLTIPS = ("The final upscaled image.",)
+    DESCRIPTION = "Upscales an image and runs image-to-image on tiles from the input image."
+
+    def upscale(self, image, model, positive, negative, vae, upscale_by, seed,
+                steps, cfg, sampler_name, scheduler, denoise, upscale_model,
+                mode_type, tile_width, tile_height, mask_blur, tile_padding,
+                seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
+                seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode, 
+                custom_sampler=None, custom_sigmas=None):
+        # Store params
+        self.tile_width = tile_width
+        self.tile_height = tile_height
+        self.mask_blur = mask_blur
+        self.tile_padding = tile_padding
+        self.seam_fix_width = seam_fix_width
+        self.seam_fix_denoise = seam_fix_denoise
+        self.seam_fix_padding = seam_fix_padding
+        self.seam_fix_mode = seam_fix_mode
+        self.mode_type = mode_type
+        self.upscale_by = upscale_by
+        self.seam_fix_mask_blur = seam_fix_mask_blur
+
+        #
+        # Set up A1111 patches
+        #
+
+        # Upscaler
+        # An object that the script works with
+        shared.sd_upscalers[0] = UpscalerData()
+        # Where the actual upscaler is stored, will be used when the script upscales using the Upscaler in UpscalerData
+        shared.actual_upscaler = upscale_model
+
+        # Set the batch of images
+        shared.batch = [tensor_to_pil(image, i) for i in range(len(image))]
+        shared.batch_as_tensor = image
+
+        # Processing
+        sdprocessing = StableDiffusionProcessing(
+            shared.batch[0], model, positive, negative, vae,
+            seed, steps, cfg, sampler_name, scheduler, denoise, upscale_by, force_uniform_tiles, tiled_decode,
+            tile_width, tile_height, MODES[self.mode_type], SEAM_FIX_MODES[self.seam_fix_mode],
+            custom_sampler, custom_sigmas,
+        )
+
+        # Disable logging
+        logger = logging.getLogger()
+        old_level = logger.getEffectiveLevel()
+        logger.setLevel(logging.CRITICAL + 1)
+        try:
+            #
+            # Running the script
+            #
+            script = usdu.Script()
+            processed = script.run(p=sdprocessing, _=None, tile_width=self.tile_width, tile_height=self.tile_height,
+                               mask_blur=self.mask_blur, padding=self.tile_padding, seams_fix_width=self.seam_fix_width,
+                               seams_fix_denoise=self.seam_fix_denoise, seams_fix_padding=self.seam_fix_padding,
+                               upscaler_index=0, save_upscaled_image=False, redraw_mode=MODES[self.mode_type],
+                               save_seams_fix_image=False, seams_fix_mask_blur=self.seam_fix_mask_blur,
+                               seams_fix_type=SEAM_FIX_MODES[self.seam_fix_mode], target_size_type=2,
+                               custom_width=None, custom_height=None, custom_scale=self.upscale_by)
+
+            # Return the resulting images
+            images = [pil_to_tensor(img) for img in shared.batch]
+            tensor = torch.cat(images, dim=0)
+            return (tensor,)
+        finally:
+            # Restore the original logging level
+            logger.setLevel(old_level)
+
+class UltimateSDUpscaleNoUpscale(UltimateSDUpscale):
+    @classmethod
+    def INPUT_TYPES(s):
+        required, optional = USDU_base_inputs()
+        remove_input(required, "upscale_model")
+        remove_input(required, "upscale_by")
+        rename_input(required, "image", "upscaled_image")
+        return prepare_inputs(required, optional)
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "upscale"
+    CATEGORY = "image/upscaling"
+    OUTPUT_TOOLTIPS = ("The final refined image.",)
+    DESCRIPTION = "Runs image-to-image on tiles from the input image."
+
+    def upscale(self, upscaled_image, model, positive, negative, vae, seed,
+                steps, cfg, sampler_name, scheduler, denoise,
+                mode_type, tile_width, tile_height, mask_blur, tile_padding,
+                seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
+                seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode):
+        upscale_by = 1.0
+        return super().upscale(upscaled_image, model, positive, negative, vae, upscale_by, seed,
+                               steps, cfg, sampler_name, scheduler, denoise, None,
+                               mode_type, tile_width, tile_height, mask_blur, tile_padding,
+                               seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
+                               seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode)
+    
+class UltimateSDUpscaleCustomSample(UltimateSDUpscale):
+    @classmethod
+    def INPUT_TYPES(s):
+        required, optional = USDU_base_inputs()
+        remove_input(required, "upscale_model")
+        optional.append(("upscale_model", ("UPSCALE_MODEL", {"tooltip": "The model to use for upscaling the image. If not provided, a simple Lanczos scaling will be used instead."})))
+        optional.append(("custom_sampler", ("SAMPLER", {"tooltip": "A custom sampler to use instead of the built-in ComfyUI sampler specified by sampler_name. Only used if both custom_sampler and custom_sigmas are provided."})))
+        optional.append(("custom_sigmas", ("SIGMAS", {"tooltip": "A custom noise schedule to use during sampling. Only used if both custom_sampler and custom_sigmas are provided."})))
+        return prepare_inputs(required, optional)
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "upscale"
+    CATEGORY = "image/upscaling"
+    OUTPUT_TOOLTIPS = ("The final upscaled image.",)
+    DESCRIPTION = "Runs image-to-image on tiles from the input image."
+
+    def upscale(self, image, model, positive, negative, vae, upscale_by, seed,
+                steps, cfg, sampler_name, scheduler, denoise,
+                mode_type, tile_width, tile_height, mask_blur, tile_padding,
+                seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
+                seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode,
+                upscale_model=None,
+                custom_sampler=None, custom_sigmas=None):
+        return super().upscale(image, model, positive, negative, vae, upscale_by, seed,
+                steps, cfg, sampler_name, scheduler, denoise, upscale_model,
+                mode_type, tile_width, tile_height, mask_blur, tile_padding,
+                seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
+                seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode,
+                custom_sampler, custom_sigmas)
+
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+    "UltimateSDUpscale": UltimateSDUpscale,
+    "UltimateSDUpscaleNoUpscale": UltimateSDUpscaleNoUpscale,
+    "UltimateSDUpscaleCustomSample": UltimateSDUpscaleCustomSample
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "UltimateSDUpscale": "Ultimate SD Upscale",
+    "UltimateSDUpscaleNoUpscale": "Ultimate SD Upscale (No Upscale)",
+    "UltimateSDUpscaleCustomSample": "Ultimate SD Upscale (Custom Sample)"
+}

custom_nodes/comfyui_ultimatesdupscale/usdu_patch.py

@@ -0,0 +1,71 @@
+# Make some patches to the script
+from repositories import ultimate_upscale as usdu
+import modules.shared as shared
+import math
+from PIL import Image
+
+
+if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
+    Image.Resampling = Image
+
+#
+# Instead of using multiples of 64, use multiples of 8
+#
+
+
+def round_length(length, multiple=8):
+    return round(length / multiple) * multiple
+
+
+# Upscaler
+old_init = usdu.USDUpscaler.__init__
+
+
+def new_init(self, p, image, upscaler_index, save_redraw, save_seams_fix, tile_width, tile_height):
+    p.width = round_length(image.width * p.upscale_by)
+    p.height = round_length(image.height * p.upscale_by)
+    old_init(self, p, image, upscaler_index, save_redraw, save_seams_fix, tile_width, tile_height)
+
+
+usdu.USDUpscaler.__init__ = new_init
+
+# Redraw
+old_setup_redraw = usdu.USDURedraw.init_draw
+
+
+def new_setup_redraw(self, p, width, height):
+    mask, draw = old_setup_redraw(self, p, width, height)
+    p.width = round_length(self.tile_width + self.padding)
+    p.height = round_length(self.tile_height + self.padding)
+    return mask, draw
+
+
+usdu.USDURedraw.init_draw = new_setup_redraw
+
+# Seams fix
+old_setup_seams_fix = usdu.USDUSeamsFix.init_draw
+
+
+def new_setup_seams_fix(self, p):
+    old_setup_seams_fix(self, p)
+    p.width = round_length(self.tile_width + self.padding)
+    p.height = round_length(self.tile_height + self.padding)
+
+
+usdu.USDUSeamsFix.init_draw = new_setup_seams_fix
+
+
+#
+# Make the script upscale on a batch of images instead of one image
+#
+
+old_upscale = usdu.USDUpscaler.upscale
+
+
+def new_upscale(self):
+    old_upscale(self)
+    shared.batch = [self.image] + \
+        [img.resize((self.p.width, self.p.height), resample=Image.LANCZOS) for img in shared.batch[1:]]
+
+
+usdu.USDUpscaler.upscale = new_upscale

custom_nodes/comfyui_ultimatesdupscale/usdu_utils.py

@@ -0,0 +1,517 @@
+import numpy as np
+from PIL import Image, ImageFilter
+import torch
+import torch.nn.functional as F
+from torchvision.transforms import GaussianBlur
+import math
+
+if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
+    Image.Resampling = Image
+
+BLUR_KERNEL_SIZE = 15
+
+
+def tensor_to_pil(img_tensor, batch_index=0):
+    # Takes a batch of images in the form of a tensor of shape [batch_size, height, width, channels]
+    # and returns an RGB PIL Image. Assumes channels=3
+    safe_tensor = torch.nan_to_num(img_tensor[batch_index])
+    return Image.fromarray((255 * safe_tensor.cpu().numpy()).astype(np.uint8))
+
+
+def pil_to_tensor(image):
+    # Takes a PIL image and returns a tensor of shape [1, height, width, channels]
+    image = np.array(image).astype(np.float32) / 255.0
+    image = torch.from_numpy(image).unsqueeze(0)
+    if len(image.shape) == 3:  # If the image is grayscale, add a channel dimension
+        image = image.unsqueeze(-1)
+    return image
+
+
+def controlnet_hint_to_pil(tensor, batch_index=0):
+    return tensor_to_pil(tensor.movedim(1, -1), batch_index)
+
+
+def pil_to_controlnet_hint(img):
+    return pil_to_tensor(img).movedim(-1, 1)
+
+
+def crop_tensor(tensor, region):
+    # Takes a tensor of shape [batch_size, height, width, channels] and crops it to the given region
+    x1, y1, x2, y2 = region
+    return tensor[:, y1:y2, x1:x2, :]
+
+
+def resize_tensor(tensor, size, mode="nearest-exact"):
+    # Takes a tensor of shape [B, C, H, W] and resizes
+    # it to a shape of [B, C, size[0], size[1]] using the given mode
+    return torch.nn.functional.interpolate(tensor, size=size, mode=mode)
+
+
+def get_crop_region(mask, pad=0):
+    # Takes a black and white PIL image in 'L' mode and returns the coordinates of the white rectangular mask region
+    # Should be equivalent to the get_crop_region function from https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/modules/masking.py
+    coordinates = mask.getbbox()
+    if coordinates is not None:
+        x1, y1, x2, y2 = coordinates
+    else:
+        x1, y1, x2, y2 = mask.width, mask.height, 0, 0
+    # Apply padding
+    x1 = max(x1 - pad, 0)
+    y1 = max(y1 - pad, 0)
+    x2 = min(x2 + pad, mask.width)
+    y2 = min(y2 + pad, mask.height)
+    return fix_crop_region((x1, y1, x2, y2), (mask.width, mask.height))
+
+
+def fix_crop_region(region, image_size):
+    # Remove the extra pixel added by the get_crop_region function
+    image_width, image_height = image_size
+    x1, y1, x2, y2 = region
+    if x2 < image_width:
+        x2 -= 1
+    if y2 < image_height:
+        y2 -= 1
+    return x1, y1, x2, y2
+
+
+def expand_crop(region, width, height, target_width, target_height):
+    '''
+    Expands a crop region to a specified target size.
+    :param region: A tuple of the form (x1, y1, x2, y2) denoting the upper left and the lower right points
+        of the rectangular region. Expected to have x2 > x1 and y2 > y1.
+    :param width: The width of the image the crop region is from.
+    :param height: The height of the image the crop region is from.
+    :param target_width: The desired width of the crop region.
+    :param target_height: The desired height of the crop region.
+    '''
+    x1, y1, x2, y2 = region
+    actual_width = x2 - x1
+    actual_height = y2 - y1
+    # target_width = math.ceil(actual_width / 8) * 8
+    # target_height = math.ceil(actual_height / 8) * 8
+
+    # Try to expand region to the right of half the difference
+    width_diff = target_width - actual_width
+    x2 = min(x2 + width_diff // 2, width)
+    # Expand region to the left of the difference including the pixels that could not be expanded to the right
+    width_diff = target_width - (x2 - x1)
+    x1 = max(x1 - width_diff, 0)
+    # Try the right again
+    width_diff = target_width - (x2 - x1)
+    x2 = min(x2 + width_diff, width)
+
+    # Try to expand region to the bottom of half the difference
+    height_diff = target_height - actual_height
+    y2 = min(y2 + height_diff // 2, height)
+    # Expand region to the top of the difference including the pixels that could not be expanded to the bottom
+    height_diff = target_height - (y2 - y1)
+    y1 = max(y1 - height_diff, 0)
+    # Try the bottom again
+    height_diff = target_height - (y2 - y1)
+    y2 = min(y2 + height_diff, height)
+
+    return (x1, y1, x2, y2), (target_width, target_height)
+
+
+def resize_region(region, init_size, resize_size):
+    # Resize a crop so that it fits an image that was resized to the given width and height
+    x1, y1, x2, y2 = region
+    init_width, init_height = init_size
+    resize_width, resize_height = resize_size
+    x1 = math.floor(x1 * resize_width / init_width)
+    x2 = math.ceil(x2 * resize_width / init_width)
+    y1 = math.floor(y1 * resize_height / init_height)
+    y2 = math.ceil(y2 * resize_height / init_height)
+    return (x1, y1, x2, y2)
+
+
+def pad_image(image, left_pad, right_pad, top_pad, bottom_pad, fill=False, blur=False):
+    '''
+    Pads an image with the given number of pixels on each side and fills the padding with data from the edges.
+    :param image: A PIL image
+    :param left_pad: The number of pixels to pad on the left side
+    :param right_pad: The number of pixels to pad on the right side
+    :param top_pad: The number of pixels to pad on the top side
+    :param bottom_pad: The number of pixels to pad on the bottom side
+    :param blur: Whether to blur the padded edges
+    :return: A PIL image with size (image.width + left_pad + right_pad, image.height + top_pad + bottom_pad)
+    '''
+    left_edge = image.crop((0, 1, 1, image.height - 1))
+    right_edge = image.crop((image.width - 1, 1, image.width, image.height - 1))
+    top_edge = image.crop((1, 0, image.width - 1, 1))
+    bottom_edge = image.crop((1, image.height - 1, image.width - 1, image.height))
+    new_width = image.width + left_pad + right_pad
+    new_height = image.height + top_pad + bottom_pad
+    padded_image = Image.new(image.mode, (new_width, new_height))
+    padded_image.paste(image, (left_pad, top_pad))
+    if fill:
+        for i in range(left_pad):
+            edge = left_edge.resize(
+                (1, new_height - i * (top_pad + bottom_pad) // left_pad), resample=Image.Resampling.NEAREST)
+            padded_image.paste(edge, (i, i * top_pad // left_pad))
+        for i in range(right_pad):
+            edge = right_edge.resize(
+                (1, new_height - i * (top_pad + bottom_pad) // right_pad), resample=Image.Resampling.NEAREST)
+            padded_image.paste(edge, (new_width - 1 - i, i * top_pad // right_pad))
+        for i in range(top_pad):
+            edge = top_edge.resize(
+                (new_width - i * (left_pad + right_pad) // top_pad, 1), resample=Image.Resampling.NEAREST)
+            padded_image.paste(edge, (i * left_pad // top_pad, i))
+        for i in range(bottom_pad):
+            edge = bottom_edge.resize(
+                (new_width - i * (left_pad + right_pad) // bottom_pad, 1), resample=Image.Resampling.NEAREST)
+            padded_image.paste(edge, (i * left_pad // bottom_pad, new_height - 1 - i))
+        if blur and not (left_pad == right_pad == top_pad == bottom_pad == 0):
+            padded_image = padded_image.filter(ImageFilter.GaussianBlur(BLUR_KERNEL_SIZE))
+            padded_image.paste(image, (left_pad, top_pad))
+    return padded_image
+
+
+def pad_image2(image, left_pad, right_pad, top_pad, bottom_pad, fill=False, blur=False):
+    '''
+    Pads an image with the given number of pixels on each side and fills the padding with data from the edges. 
+    Faster than pad_image, but only pads with edge data in straight lines.
+    :param image: A PIL image
+    :param left_pad: The number of pixels to pad on the left side
+    :param right_pad: The number of pixels to pad on the right side
+    :param top_pad: The number of pixels to pad on the top side
+    :param bottom_pad: The number of pixels to pad on the bottom side
+    :param blur: Whether to blur the padded edges
+    :return: A PIL image with size (image.width + left_pad + right_pad, image.height + top_pad + bottom_pad)
+    '''
+    left_edge = image.crop((0, 1, 1, image.height - 1))
+    right_edge = image.crop((image.width - 1, 1, image.width, image.height - 1))
+    top_edge = image.crop((1, 0, image.width - 1, 1))
+    bottom_edge = image.crop((1, image.height - 1, image.width - 1, image.height))
+    new_width = image.width + left_pad + right_pad
+    new_height = image.height + top_pad + bottom_pad
+    padded_image = Image.new(image.mode, (new_width, new_height))
+    padded_image.paste(image, (left_pad, top_pad))
+    if fill:
+        if left_pad > 0:
+            padded_image.paste(left_edge.resize((left_pad, new_height), resample=Image.Resampling.NEAREST), (0, 0))
+        if right_pad > 0:
+            padded_image.paste(right_edge.resize((right_pad, new_height),
+                               resample=Image.Resampling.NEAREST), (new_width - right_pad, 0))
+        if top_pad > 0:
+            padded_image.paste(top_edge.resize((new_width, top_pad), resample=Image.Resampling.NEAREST), (0, 0))
+        if bottom_pad > 0:
+            padded_image.paste(bottom_edge.resize((new_width, bottom_pad),
+                               resample=Image.Resampling.NEAREST), (0, new_height - bottom_pad))
+        if blur and not (left_pad == right_pad == top_pad == bottom_pad == 0):
+            padded_image = padded_image.filter(ImageFilter.GaussianBlur(BLUR_KERNEL_SIZE))
+            padded_image.paste(image, (left_pad, top_pad))
+    return padded_image
+
+
+def pad_tensor(tensor, left_pad, right_pad, top_pad, bottom_pad, fill=False, blur=False):
+    '''
+    Pads an image tensor with the given number of pixels on each side and fills the padding with data from the edges.
+    :param tensor: A tensor of shape [B, H, W, C]
+    :param left_pad: The number of pixels to pad on the left side
+    :param right_pad: The number of pixels to pad on the right side
+    :param top_pad: The number of pixels to pad on the top side
+    :param bottom_pad: The number of pixels to pad on the bottom side
+    :param blur: Whether to blur the padded edges
+    :return: A tensor of shape [B, H + top_pad + bottom_pad, W + left_pad + right_pad, C]
+    '''
+    batch_size, channels, height, width = tensor.shape
+    h_pad = left_pad + right_pad
+    v_pad = top_pad + bottom_pad
+    new_width = width + h_pad
+    new_height = height + v_pad
+
+    # Create empty image
+    padded = torch.zeros((batch_size, channels, new_height, new_width), dtype=tensor.dtype)
+
+    # Copy the original image into the centor of the padded tensor
+    padded[:, :, top_pad:top_pad + height, left_pad:left_pad + width] = tensor
+
+    # Duplicate the edges of the original image into the padding
+    if top_pad > 0:
+        padded[:, :, :top_pad, :] = padded[:, :, top_pad:top_pad + 1, :]  # Top edge
+    if bottom_pad > 0:
+        padded[:, :, -bottom_pad:, :] = padded[:, :, -bottom_pad - 1:-bottom_pad, :]  # Bottom edge
+    if left_pad > 0:
+        padded[:, :, :, :left_pad] = padded[:, :, :, left_pad:left_pad + 1]  # Left edge
+    if right_pad > 0:
+        padded[:, :, :, -right_pad:] = padded[:, :, :, -right_pad - 1:-right_pad]  # Right edge
+
+    return padded
+
+
+def resize_and_pad_image(image, width, height, fill=False, blur=False):
+    '''
+    Resizes an image to the given width and height and pads it to the given width and height.
+    :param image: A PIL image
+    :param width: The width of the resized image
+    :param height: The height of the resized image
+    :param fill: Whether to fill the padding with data from the edges
+    :param blur: Whether to blur the padded edges
+    :return: A PIL image of size (width, height)
+    '''
+    width_ratio = width / image.width
+    height_ratio = height / image.height
+    if height_ratio > width_ratio:
+        resize_ratio = width_ratio
+    else:
+        resize_ratio = height_ratio
+    resize_width = round(image.width * resize_ratio)
+    resize_height = round(image.height * resize_ratio)
+    resized = image.resize((resize_width, resize_height), resample=Image.Resampling.LANCZOS)
+    # Pad the sides of the image to get the image to the desired size that wasn't covered by the resize
+    horizontal_pad = (width - resize_width) // 2
+    vertical_pad = (height - resize_height) // 2
+    result = pad_image2(resized, horizontal_pad, horizontal_pad, vertical_pad, vertical_pad, fill, blur)
+    result = result.resize((width, height), resample=Image.Resampling.LANCZOS)
+    return result, (horizontal_pad, vertical_pad)
+
+
+def resize_and_pad_tensor(tensor, width, height, fill=False, blur=False):
+    '''
+    Resizes an image tensor to the given width and height and pads it to the given width and height.
+    :param tensor: A tensor of shape [B, H, W, C]
+    :param width: The width of the resized image
+    :param height: The height of the resized image
+    :param fill: Whether to fill the padding with data from the edges
+    :param blur: Whether to blur the padded edges
+    :return: A tensor of shape [B, height, width, C]
+    '''
+    # Resize the image to the closest size that maintains the aspect ratio
+    width_ratio = width / tensor.shape[3]
+    height_ratio = height / tensor.shape[2]
+    if height_ratio > width_ratio:
+        resize_ratio = width_ratio
+    else:
+        resize_ratio = height_ratio
+    resize_width = round(tensor.shape[3] * resize_ratio)
+    resize_height = round(tensor.shape[2] * resize_ratio)
+    resized = F.interpolate(tensor, size=(resize_height, resize_width), mode='nearest-exact')
+    # Pad the sides of the image to get the image to the desired size that wasn't covered by the resize
+    horizontal_pad = (width - resize_width) // 2
+    vertical_pad = (height - resize_height) // 2
+    result = pad_tensor(resized, horizontal_pad, horizontal_pad, vertical_pad, vertical_pad, fill, blur)
+    result = F.interpolate(result, size=(height, width), mode='nearest-exact')
+    return result
+
+
+def crop_controlnet(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad):
+    if "control" not in cond_dict:
+        return
+    c = cond_dict["control"]
+    controlnet = c.copy()
+    cond_dict["control"] = controlnet
+    while c is not None:
+        # hint is shape (B, C, H, W)
+        hint = controlnet.cond_hint_original
+        resized_crop = resize_region(region, canvas_size, hint.shape[:-3:-1])
+        hint = crop_tensor(hint.movedim(1, -1), resized_crop).movedim(-1, 1)
+        hint = resize_tensor(hint, tile_size[::-1])
+        controlnet.cond_hint_original = hint
+        c = c.previous_controlnet
+        controlnet.set_previous_controlnet(c.copy() if c is not None else None)
+        controlnet = controlnet.previous_controlnet
+
+
+def region_intersection(region1, region2):
+    """
+    Returns the coordinates of the intersection of two rectangular regions.
+    :param region1: A tuple of the form (x1, y1, x2, y2) denoting the upper left and the lower right points 
+        of the first rectangular region. Expected to have x2 > x1 and y2 > y1.
+    :param region2: The second rectangular region with the same format as the first.
+    :return: A tuple of the form (x1, y1, x2, y2) denoting the rectangular intersection. 
+        None if there is no intersection.
+    """
+    x1, y1, x2, y2 = region1
+    x1_, y1_, x2_, y2_ = region2
+    x1 = max(x1, x1_)
+    y1 = max(y1, y1_)
+    x2 = min(x2, x2_)
+    y2 = min(y2, y2_)
+    if x1 >= x2 or y1 >= y2:
+        return None
+    return (x1, y1, x2, y2)
+
+
+def crop_gligen(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad):
+    if "gligen" not in cond_dict:
+        return
+    type, model, cond = cond_dict["gligen"]
+    if type != "position":
+        from warnings import warn
+        warn(f"Unknown gligen type {type}")
+        return
+    cropped = []
+    for c in cond:
+        emb, h, w, y, x = c
+        # Get the coordinates of the box in the upscaled image
+        x1 = x * 8
+        y1 = y * 8
+        x2 = x1 + w * 8
+        y2 = y1 + h * 8
+        gligen_upscaled_box = resize_region((x1, y1, x2, y2), init_size, canvas_size)
+
+        # Calculate the intersection of the gligen box and the region
+        intersection = region_intersection(gligen_upscaled_box, region)
+        if intersection is None:
+            continue
+        x1, y1, x2, y2 = intersection
+
+        # Offset the gligen box so that the origin is at the top left of the tile region
+        x1 -= region[0]
+        y1 -= region[1]
+        x2 -= region[0]
+        y2 -= region[1]
+
+        # Add the padding
+        x1 += w_pad
+        y1 += h_pad
+        x2 += w_pad
+        y2 += h_pad
+
+        # Set the new position params
+        h = (y2 - y1) // 8
+        w = (x2 - x1) // 8
+        x = x1 // 8
+        y = y1 // 8
+        cropped.append((emb, h, w, y, x))
+
+    cond_dict["gligen"] = (type, model, cropped)
+
+
+def crop_area(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad):
+    if "area" not in cond_dict:
+        return
+
+    # Resize the area conditioning to the canvas size and confine it to the tile region
+    h, w, y, x = cond_dict["area"]
+    w, h, x, y = 8 * w, 8 * h, 8 * x, 8 * y
+    x1, y1, x2, y2 = resize_region((x, y, x + w, y + h), init_size, canvas_size)
+    intersection = region_intersection((x1, y1, x2, y2), region)
+    if intersection is None:
+        del cond_dict["area"]
+        del cond_dict["strength"]
+        return
+    x1, y1, x2, y2 = intersection
+
+    # Offset origin to the top left of the tile
+    x1 -= region[0]
+    y1 -= region[1]
+    x2 -= region[0]
+    y2 -= region[1]
+
+    # Add the padding
+    x1 += w_pad
+    y1 += h_pad
+    x2 += w_pad
+    y2 += h_pad
+
+    # Set the params for tile
+    w, h = (x2 - x1) // 8, (y2 - y1) // 8
+    x, y = x1 // 8, y1 // 8
+
+    cond_dict["area"] = (h, w, y, x)
+
+
+def crop_mask(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad):
+    if "mask" not in cond_dict:
+        return
+    mask_tensor = cond_dict["mask"]  # (B, H, W)
+    masks = []
+    for i in range(mask_tensor.shape[0]):
+        # Convert to PIL image
+        mask = tensor_to_pil(mask_tensor, i)  # W x H
+
+        # Resize the mask to the canvas size
+        mask = mask.resize(canvas_size, Image.Resampling.BICUBIC)
+
+        # Crop the mask to the region
+        mask = mask.crop(region)
+
+        # Add padding
+        mask, _ = resize_and_pad_image(mask, tile_size[0], tile_size[1], fill=True)
+
+        # Resize the mask to the tile size
+        if tile_size != mask.size:
+            mask = mask.resize(tile_size, Image.Resampling.BICUBIC)
+
+        # Convert back to tensor
+        mask = pil_to_tensor(mask)  # (1, H, W, 1)
+        mask = mask.squeeze(-1)  # (1, H, W)
+        masks.append(mask)
+
+    cond_dict["mask"] = torch.cat(masks, dim=0)  # (B, H, W)
+
+# Added Flux-Kontext Support crop_reference_latents by TBG ETUR
+def crop_reference_latents(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad):
+    """
+    1. Resize each latent to `canvas_size` in latent units.
+    2. Crop the rectangle `region` (pixel coordinates).
+    3. Down-sample the crop to latent-space `tile_size`.
+    Expects a list of BCHW tensors under "reference_latents".
+    """
+
+    latents = cond_dict.get("reference_latents")
+    if not isinstance(latents, list):
+        return  # nothing to do
+
+    k = 8  # down-sample factor from pixel space → latent space (SD-type models)
+
+    W_can_px, H_can_px = canvas_size
+    # canvas size expressed in latent units
+    W_can_lat, H_can_lat = W_can_px // k, H_can_px // k
+
+    W_tile_px, H_tile_px = tile_size
+    W_tile_lat, H_tile_lat = max(1, W_tile_px // k), max(1, H_tile_px // k)
+
+    x1_px, y1_px, x2_px, y2_px = region
+
+    new_latents = []
+    for t in latents:  # (B,C,H_lat_in,W_lat_in)
+        has_5d = False
+        if t.ndim == 5: # (B,C,1,H_lat_in,W_lat_in)
+            has_5d = True
+            t = t.squeeze(2)
+        if t.ndim != 4:
+            raise ValueError(f"expected BCHW, got {t.shape}")
+
+        # 1. Resize to canvas resolution in latent units only if needed
+        if t.shape[-2:] != (H_can_lat, W_can_lat):
+            t = F.interpolate(t,
+                              size=(H_can_lat, W_can_lat),
+                              mode="bilinear",
+                              align_corners=False)
+
+        # 2. Convert pixel crop → latent slice
+        w0_lat = int(round(x1_px / k))
+        w1_lat = int(round(x2_px / k))
+        h0_lat = int(round(y1_px / k))
+        h1_lat = int(round(y2_px / k))
+
+        cropped = t[:, :, h0_lat:h1_lat, w0_lat:w1_lat]  # view
+
+        # 3. Down-sample to latent-tile size
+        cropped = F.interpolate(cropped,
+                                size=(H_tile_lat, W_tile_lat),
+                                mode="bilinear",
+                                align_corners=False)
+        if has_5d:
+            cropped = cropped.unsqueeze(2)
+        new_latents.append(cropped)
+
+    cond_dict["reference_latents"] = new_latents
+
+
+
+def crop_cond(cond, region, init_size, canvas_size, tile_size, w_pad=0, h_pad=0):
+    cropped = []
+    for emb, x in cond:
+        cond_dict = x.copy()
+        n = [emb, cond_dict]
+        crop_controlnet(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
+        crop_gligen(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
+        crop_area(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
+        crop_mask(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
+        crop_reference_latents(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
+        cropped.append(n)
+    return cropped