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@@ -276,3 +276,9 @@ v3-nodes/a-person-mask-generator/readme/01-face.png filter=lfs diff=lfs merge=lf
 v3-nodes/a-person-mask-generator/readme/05-refine_mask.png filter=lfs diff=lfs merge=lfs -text
 v3-nodes/a-person-mask-generator/readme/ComfyUI-Workflow-FaceLandmarks.png filter=lfs diff=lfs merge=lfs -text
 v3-nodes/a-person-mask-generator/readme/ComfyUI-workflow.png filter=lfs diff=lfs merge=lfs -text
+v3-nodes/ComfyUI-Inpaint-CropAndStitch/example_workflows/inpaint_flux.jpg filter=lfs diff=lfs merge=lfs -text
+v3-nodes/ComfyUI-Inpaint-CropAndStitch/example_workflows/inpaint_hires.jpg filter=lfs diff=lfs merge=lfs -text
+v3-nodes/ComfyUI-Inpaint-CropAndStitch/example_workflows/inpaint_sd15.jpg filter=lfs diff=lfs merge=lfs -text
+v3-nodes/ComfyUI-Inpaint-CropAndStitch/inpaint_flux.png filter=lfs diff=lfs merge=lfs -text
+v3-nodes/ComfyUI-Inpaint-CropAndStitch/inpaint_hires.png filter=lfs diff=lfs merge=lfs -text
+v3-nodes/ComfyUI-Inpaint-CropAndStitch/inpaint_sd15.png filter=lfs diff=lfs merge=lfs -text

v3-nodes/ComfyUI-Inpaint-CropAndStitch/.github/workflows/publish.yml

@@ -0,0 +1,25 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'lquesada' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

v3-nodes/ComfyUI-Inpaint-CropAndStitch/.gitignore

@@ -0,0 +1,3 @@
+__pycache__/
+*.pyc
+*.pyo

v3-nodes/ComfyUI-Inpaint-CropAndStitch/LICENSE

@@ -0,0 +1,674 @@
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v3-nodes/ComfyUI-Inpaint-CropAndStitch/README.md

@@ -0,0 +1,153 @@
+ComfyUI-Inpaint-CropAndStitch
+
+Copyright (c) 2024-2026, Luis Quesada Torres - https://github.com/lquesada | www.luisquesada.com
+
+Check ComfyUI here: https://github.com/comfyanonymous/ComfyUI
+
+# Overview
+
+The '✂️ Inpaint Crop' and '✂️ Inpaint Stitch' nodes enable inpainting only on masked area very easily
+
+"✂️  Inpaint Crop" crops the image around the masked area (optionally with a context area that marks all parts relevant to the context), taking care of pre-resizing the image if desired, extending it for outpainting, filling mask holes, growing or blurring the mask, cutting around a larger context area, and resizing the cropped area to a target resolution.
+
+The cropped image can be used in any standard workflow for sampling. It can even be rescaled up with any model (please keep the aspect ratio) and hiRes-fixed and it will be retrofit in the original image.
+
+Then, the "✂️  Inpaint Stitch" node stitches the inpainted image back into the original image without altering unmasked areas.
+
+The main advantages of inpainting only in a masked area with these nodes are:
+  - It is much faster than sampling the whole image.
+  - It enables setting the right amount of context from the image for the prompt to be more accurately represented in the generated picture.
+  - It enables upscaling before sampling in order to generate more detail, then stitching back in the original picture.
+  - It enables downscaling before sampling if the area is too large, in order to avoid artifacts such as double heads or double bodies.
+  - It enables forcing a specific resolution (e.g. 1024x1024 for SDXL models).
+  - It does not modify the unmasked part of the image, not even passing it through VAE encode and decode.
+  - It takes care of blending automatically.
+
+# Support me!
+
+GenAI is not just for the sake of GenAI, but to unblock creativity and empower humans. I am not only a developer; I use GenAI myself to do **more** things **better** than I could otherwise do.
+
+Do you like this node? Check, listen to, or follow my GenAI music project [**Windlereye** on Spotify](https://open.spotify.com/artist/6GdiI8ZKeWhSY73WWOhbep) to support me!
+
+[![Windlereye on Spotify](windlereye.jpg)](https://open.spotify.com/artist/6GdiI8ZKeWhSY73WWOhbep)
+
+# Video Tutorial
+
+[![Video Tutorial](https://img.youtube.com/vi/mI0UWm7BNtQ/0.jpg)](https://www.youtube.com/watch?v=mI0UWm7BNtQ)
+
+[(click to open in YouTube)](https://www.youtube.com/watch?v=mI0UWm7BNtQ)
+
+Note: this video tutorial is for the previous version of the nodes, but still it shows how to use them. The parameters are mostly the same.
+
+## Parameters
+- `downscale_algorithm` and `upscale_algorithm`: Which algorithms to use when resizing an image up or down.
+- `preresize`: Shows options to resize the input image before any cropping: to ensure minimum resolution, to ensure maximum resolution, to ensure both minimum and maximum resolution. This makes it very convenient to ensure that any input images have a certain resolution.
+- `mask_fill_holes`: Whether to fully fill any holes (small or large) in the mask, that is, mark fully enclosed areas as part of the mask.
+- `mask_expand_pixels`: Grows the mask by the specified amount of pixels.
+- `mask_invert`: Whether to fully invert the mask, that is, only keep what was masked, instead of removing what was marked.
+- `mask_blend_pixels`: Grows the stitch mask and blurs it by the specified amount of pixels, so that the stitch is slowly blended and there are no seams.
+- `mask_hipass_filter`: Ignores mask values lower than the one specified here. This is to avoid sections in the mask that are almost 0 (black) to count as masked area. Sometimes that leads to confusion, as the user believes the area is not really masked and the node is considering it as masked.
+- `extend_for_outpainting`: Shows options to extend the mask in any/all directions (up/down/left/right) by a certain factor. >1 extends the image, e.g. 2 extends the image in a direction by the same amount of space the image takes. <1 crops the image, e.g. 0.75 removes 25% of the image on that direction.
+- `context_from_mask_extend_factor`: Extends the context area by a factor of the size of the mask. The higher this value is, the more area will be cropped around the mask for the model to have more context. 1 means do not grow. 2 means grow the same size of the mask across every direction.
+- `output_resize_to_target_size`: Forces that the cropped image has a specific resolution. This may involve resizing and extending out of the original image, but the stitch node reverts those changes to integrate the image seamlessly.
+- `output_padding`: Ensures that the cropped image width and height are a multiple of this padding value. Models require images to be padded to a certain value (8, 16, 32) to function properly.
+- `device_mode`: `cpu (compatible)` should always work but is slow, `gpu (much faster)` is much faster but may not work in all setups. Default is GPU given the performance improvements.
+
+## Example (Stable Diffusion)
+This example inpaints by sampling on a small section of the larger image, upscaling to fit 512x512, then stitching and blending back in the original image.
+
+Download the following example workflow from [here](example_workflows/inpaint_sd15.json) or drag and drop the screenshot into ComfyUI.
+
+![Workflow](inpaint_sd15.png)
+
+## Example (Flux)
+This example uses Flux. Requires the GGUF nodes.
+
+Models used:
+
+- `Flux Dev Q5 GGUF` from [here](https://civitai.com/models/711483/flux-dev-q5km-gguf-quantization-a-nice-balance-of-speed-and-quality-in-under-9-gigabytes?modelVersionId=795785). Put it in models/unet/.
+- `Flux 1. dev controlnet inpainting beta` from [here](https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta). Put it in models/controlnet/.
+- `t5 GGUF Q3_K_L` from [here](https://huggingface.co/city96/t5-v1_1-xxl-encoder-gguf/tree/main). Put it in models/clip/.
+- `clip_l` from [here](https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/clip_l.safetensors). Put it in models/clip/.
+- `ae VAE` from [here](https://huggingface.co/black-forest-labs/FLUX.1-schnell/blob/main/ae.safetensors). Put it in models/vae/.
+
+Download the following example workflow from [here](example_workflows/inpaint_flux.json) or drag and drop the screenshot into ComfyUI.
+
+![Workflow](inpaint_flux.png)
+
+## Example (Large resolution)
+This example uses SD 1.5 to sample on a section of the larger image. Then it upscales the sampled image by 4x using an external model and applies a hiRes fix. Then it blends it back into the original image.
+
+Models used:
+
+- `4x Ultrasharp` from [here](https://huggingface.co/lokCX/4x-Ultrasharp/blob/main/4x-UltraSharp.pth).
+
+Download the following example workflow from [here](example_workflows/inpaint_hires.json) or drag and drop the screenshot into ComfyUI.
+
+![Workflow](inpaint_hires.png)
+
+# Installation Instructions
+
+Install via ComfyUI-Manager or go to the custom_nodes/ directory and run ```$ git clone https://github.com/lquesada/ComfyUI-Inpaint-CropAndStitch.git```
+
+## Best Practices
+Use an inpainting model e.g. lazymixRealAmateur_v40Inpainting.
+
+Use "InpaintModelConditioning" instead of "VAE Encode (for Inpainting)" to be able to set denoise values lower than 1.
+
+Enable "resize to target size" and set it to a preferred resolution for your model, e.g. 512x512 for SD 1.5, 1024x1024 for SDXL or Flux.
+
+If you mask an area and you can still see the original image through the rendered image, it is **very likely** that your mask is not fully opaque. Even if it looks fully opaque - the human brain cannot tell a difference between many shades of grey. Please make sure that the mask is 100% opaque, e.g. take a screenshot, check pixel values for 255, 255, 255 or #FFFFFF. If you use mask fill holes, check that the entire boundary is 255, 255, 255 or #FFFFFF e.g. with the fuzzy select tool in Gimp with threshold 0.
+
+If you are running out of memory (likely you're processing very large videos), switch from GPU to CPU mode in the crop node - this will make both crop and stitch use CPU and RAM instead of VRAM.
+
+# Changelog
+## 2026-01-09
+- Huge performance improvement of 30x-100x by adding GPU support (new default is GPU, CPU is available as an option for fallback).
+- Clean up: Removed the long-deprecated old versions.
+## 2025-04-06
+- Published the improved version of the Crop and Stitch nodes.
+- Improved: Stitching is now way more precise. In the previous version, stitching an image back into place could shift it by one pixel. That will not happen anymore.
+- Improved: Images are now cropped before being resized. In the past, they were resized before being cropped. This triggered crashes when the input image was large and the masked area was small.
+- Improved: Images are now not extended more than necessary. In the past, they were extended x3, which was memory inefficient.
+- Improved: The cropped area will stay inside of the image if possible. In the past, the cropped area was centered around the mask and would go out of the image even if not needed.
+- Improved: Fill mask holes will now supports grayscale masks. In the past, it turned the mask into binary (yes/no only).
+- Improved: Added a hipass filter for mask that ignores values below a threshold. In the past, sometimes mask with a 0.01 value (basically black / no mask) would be considered mask, which was very confusing to users.
+- Improved: In the (now rare) case that extending out of the image is needed, instead of mirroring the original image, the edges are extended. Mirroring caused confusion among users in the past.
+- Improved: Integrated preresize and extend for outpainting in the crop node. In the past, they were external and could interact weirdly with features, e.g. expanding for outpainting on the four directions and having "fill_mask_holes" would cause the mask to be fully set across the whole image.
+- Improved: Now works when passing one mask for several images or one image for several masks.
+- UX: Streamlined many options, e.g. merged the blur and blend features in a single parameter, removed the ranged size option, removed context_expand_pixels as factor is more intuitive, etc.
+- Clean up: Marked the old nodes ("Crop", "Stitch", "Extend Image for Outpainting", and "Resize Image Before Inpainting") as obsolete. They will continue working in old workflows but will have a note in the title asking to update. In particular, there's no replacement for "Extend Image for Outpainting" and "Resize Image Before Inpainting" because those features are now integrated in the Crop node.
+## 2024-10-28
+- Added a new example workflow for inpainting with flux.
+## 2024-06-10
+- Added a new node: "Resize Image Before Inpainting", which allows increasing the resolution of the input image by a factor or to a minimum width or height to obtain higher resolution inpaintings.
+## 2024-06-08
+- Added a new node: "Extend Image for Outpainting", which allows leveraging the power of Inpaint Crop and Stitch (rescaling, blur, blend, restitching) for outpainting.
+## 2024-06-07
+- Added a blending radius for seamless inpainting.
+- Added a blur mask setting that grows and blurs the mask, providing better support.
+- Updated default to ranged size.
+## 2024-06-01
+- Force_size is now specified as separate force_width and force_height, to match any desired sampling resolution.
+- Added a new mode: ranged size, similar to free size but also takes min_width, min_height, max_width, and max_height, in order to avoid over scaling or under scaling beyond desirable limits.
+## 2024-05-15
+- Depending on the selected mode ("free size" or "forced size") some fields are hidden.
+## 2024-05-14
+- Added batch support.
+- Enabled selecting rescaling algorithm and made bicubic the default for crop, which significantly speeds up the process.
+## 2024-05-13
+- Switched from adjust_to_preferred_sizes to modes: free size and forced size. Forced scales the section rather than growing the context area to fit preferred_sizes, to be used to e.g. force 1024x1024 for inpainting.
+- Enabled internal_upscale_factor to be lower than 1 (that is, downscale), which can be used to avoid the double head issue in some models.
+- Added padding on the croppedp image to avoid artifacts when the cropped image is not multiple of (default) 32
+## 2024-05-12
+- Added internal_upscale_factor to upscale the image before sampling and then downsizes to stitch it back.
+## 2024-05-11
+- Initial commit.
+
+# Acknowledgements
+
+This repository uses some code from comfy_extras (https://github.com/comfyanonymous/ComfyUI), KJNodes (https://github.com/kijai/ComfyUI-KJNodes), and Efficiency Nodes (https://github.com/LucianoCirino/efficiency-nodes-comfyui), all of them licensed under GNU GENERAL PUBLIC LICENSE Version 3. 
+
+# License
+GNU GENERAL PUBLIC LICENSE Version 3, see [LICENSE](LICENSE)

v3-nodes/ComfyUI-Inpaint-CropAndStitch/__init__.py

@@ -0,0 +1,16 @@
+from .inpaint_cropandstitch import InpaintCropImproved
+from .inpaint_cropandstitch import InpaintStitchImproved
+
+WEB_DIRECTORY = "js"
+
+NODE_CLASS_MAPPINGS = {
+    "InpaintCropImproved": InpaintCropImproved,
+    "InpaintStitchImproved": InpaintStitchImproved,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "InpaintCropImproved": "✂️ Inpaint Crop",
+    "InpaintStitchImproved": "✂️ Inpaint Stitch",
+}
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

v3-nodes/ComfyUI-Inpaint-CropAndStitch/example_workflows/inpaint_flux.json

@@ -0,0 +1,1176 @@
+{
+  "last_node_id": 87,
+  "last_link_id": 174,
+  "nodes": [
+    {
+      "id": 25,
+      "type": "VAELoader",
+      "pos": [
+        620,
+        1120
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "shape": 3,
+          "links": [
+            70,
+            147
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "ae.safetensors"
+      ]
+    },
+    {
+      "id": 51,
+      "type": "UnetLoaderGGUF",
+      "pos": [
+        620,
+        1240
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            102,
+            150
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "cnr_id": "ComfyUI-GGUF",
+        "ver": "5aa6e98ad74e2fa826f80c8dbded5ec3dd09ba1f",
+        "Node name for S&R": "UnetLoaderGGUF"
+      },
+      "widgets_values": [
+        "fluxDevQ5KMGGUFQuantizationA_v10.gguf"
+      ]
+    },
+    {
+      "id": 29,
+      "type": "KSamplerSelect",
+      "pos": [
+        1410,
+        1050
+      ],
+      "size": [
+        250,
+        60
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "SAMPLER",
+          "type": "SAMPLER",
+          "shape": 3,
+          "links": [
+            152
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "KSamplerSelect"
+      },
+      "widgets_values": [
+        "euler"
+      ]
+    },
+    {
+      "id": 84,
+      "type": "EmptySD3LatentImage",
+      "pos": [
+        1410,
+        1340
+      ],
+      "size": [
+        250,
+        110
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            158
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "EmptySD3LatentImage"
+      },
+      "widgets_values": [
+        1024,
+        1024,
+        1
+      ]
+    },
+    {
+      "id": 32,
+      "type": "RandomNoise",
+      "pos": [
+        1410,
+        750
+      ],
+      "size": [
+        250,
+        82
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "NOISE",
+          "type": "NOISE",
+          "shape": 3,
+          "links": [
+            157
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "RandomNoise"
+      },
+      "widgets_values": [
+        1028751694899398,
+        "randomize"
+      ],
+      "color": "#2a363b",
+      "bgcolor": "#3f5159"
+    },
+    {
+      "id": 74,
+      "type": "ControlNetLoader",
+      "pos": [
+        620,
+        1350
+      ],
+      "size": [
+        320,
+        60
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CONTROL_NET",
+          "label": "CONTROL_NET",
+          "type": "CONTROL_NET",
+          "shape": 3,
+          "links": [
+            126
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "ControlNetLoader"
+      },
+      "widgets_values": [
+        "FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors"
+      ]
+    },
+    {
+      "id": 59,
+      "type": "SamplerCustomAdvanced",
+      "pos": [
+        1700,
+        870
+      ],
+      "size": [
+        240,
+        110
+      ],
+      "flags": {},
+      "order": 16,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "noise",
+          "label": "noise",
+          "type": "NOISE",
+          "link": 157,
+          "slot_index": 0
+        },
+        {
+          "name": "guider",
+          "label": "guider",
+          "type": "GUIDER",
+          "link": 110,
+          "slot_index": 1
+        },
+        {
+          "name": "sampler",
+          "label": "sampler",
+          "type": "SAMPLER",
+          "link": 152,
+          "slot_index": 2
+        },
+        {
+          "name": "sigmas",
+          "label": "sigmas",
+          "type": "SIGMAS",
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+}

v3-nodes/ComfyUI-Inpaint-CropAndStitch/example_workflows/inpaint_hires.json

@@ -0,0 +1,1335 @@
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+        "This workflow:\n- Resizes the input image to a minimum of 2048x2048 and crops the area\n- Inpaints only on the masked area with a specific resolution (e.g. 512 x 512), which means any SD1.5 model can be used to define the inpainting.\n- Then upscales the inpainted part by x4 using an upscale model.\n- Then resamples at that new resolution to do a hiRes fix.\n\n\nThe easiest way to run it is:\n\n1. Load the desired image in the top \"Load Image\" node and mask the area you want to replace.\n\n2. Load the same image in the bottom \"Load Image\" node. Optionally, mask the context area that is relevant for sampling (corners is OK, square is OK). You may as well just remove that bottom \"Load Image\" node.\n\n3. Select a checkpoint for inpainting in the \"Load Checkpoint\" node.\n\n4. Write the positive and negative prompts in the green and red node. These prompts do not have to match the whole image, but only the masked area.\n\n5. Adjust denoise in the first sampler (e.g. 1 to fully replace the content, ~0.8 to keep part of it, 0.1 to match around.\n\n6. Select an upscale model in the blue node.\n\n7. Run!\n\nThe default parameters for Inpaint Crop and Inpaint Stitch work well for most inpainting tasks.\n\nFor SDXL or Flux, increase output_target_width and output_target_height to 1024.\n\nYou may also tweak the number of steps, seeds, etc."
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+}

v3-nodes/ComfyUI-Inpaint-CropAndStitch/example_workflows/inpaint_sd15.json

@@ -0,0 +1,726 @@
+{
+  "last_node_id": 26,
+  "last_link_id": 55,
+  "nodes": [
+    {
+      "id": 12,
+      "type": "CLIPTextEncode",
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+          "link": 24,
+          "slot_index": 0
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+          "type": "CONDITIONING",
+          "shape": 3,
+          "links": [
+            38
+          ],
+          "slot_index": 0
+        }
+      ],
+      "title": "CLIP Text Encode (Prompt) - Negative",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "ugly, text, watermark"
+      ],
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 19,
+      "type": "InpaintModelConditioning",
+      "pos": [
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+          "link": 37
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+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 38
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
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+        },
+        {
+          "name": "pixels",
+          "type": "IMAGE",
+          "link": 50
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 51
+        }
+      ],
+      "outputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "shape": 3,
+          "links": [
+            39
+          ],
+          "slot_index": 0
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+        {
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+        "ver": "0.3.26",
+        "Node name for S&R": "PreviewImage"
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+        {
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+          "type": "LATENT",
+          "shape": 3,
+          "links": [
+            33
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+          "slot_index": 0
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+      ],
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+      "pos": [
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+      "flags": {},
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+        },
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+      ],
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+          "type": "IMAGE",
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+          "links": [
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+          "slot_index": 0
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+      ],
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+        "Node name for S&R": "VAEDecode"
+      },
+      "widgets_values": []
+    },
+    {
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+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "stitcher",
+          "type": "STITCHER",
+          "link": 52
+        },
+        {
+          "name": "inpainted_image",
+          "type": "IMAGE",
+          "link": 55
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            54
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "InpaintStitchImproved"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 3,
+      "type": "LoadImage",
+      "pos": [
+        90,
+        110
+      ],
+      "size": [
+        400,
+        500
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "shape": 3,
+          "links": [
+            48
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "shape": 3,
+          "links": [
+            49
+          ],
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "clipspace/clipspace-mask-324879.39999999106.png [input]",
+        "image"
+      ]
+    },
+    {
+      "id": 22,
+      "type": "Note",
+      "pos": [
+        40,
+        680
+      ],
+      "size": [
+        500,
+        470
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Instructions",
+      "properties": {
+        "text": ""
+      },
+      "widgets_values": [
+        "Simplest way to run:\n\n1. Load desired image in the \"Load Image\" node and mask the area you want to replace.\n\n2. Select a checkpoint for inpainting in the \"Load Checkpoint\" node.\n\n3. Write the positive and negative prompts in the green and red boxes. These prompts do not have to match the whole image, but only the masked area.\n\n4. Adjust denoise (e.g. 1 to fully replace the content, ~0.8 to keep part of it, 0.1 to match around.\n\n5. Run!\n\n\n\nHow to increase the context area for the model to know better what to draw:\n- Option 1: Increase context_expand_factor, e.g. set it to 2.\n- Option 2: Duplicate the load image node and connect its mask to \"optional_context_mask\" in the \"Inpaint Crop\" node. Mask the area that is relevant for context (no need to fill it, only the corners of the masked area matter.\n\n\n\nThe default parameters for Inpaint Crop and Inpaint Stitch work well for most inpainting tasks.\n\nFor SDXL or Flux, increase output_target_width and output_target_height to 1024."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 25,
+      "type": "InpaintCropImproved",
+      "pos": [
+        620,
+        110
+      ],
+      "size": [
+        390,
+        410
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 48
+        },
+        {
+          "name": "mask",
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+          "shape": 7,
+          "link": 49
+        },
+        {
+          "name": "optional_context_mask",
+          "type": "MASK",
+          "shape": 7,
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "stitcher",
+          "type": "STITCHER",
+          "links": [
+            52
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "cropped_image",
+          "type": "IMAGE",
+          "links": [
+            50
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "cropped_mask",
+          "type": "MASK",
+          "links": [
+            51
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "InpaintCropImproved"
+      },
+      "widgets_values": [
+        "bilinear",
+        "bicubic",
+        false,
+        "ensure minimum resolution",
+        1024,
+        1024,
+        16384,
+        16384,
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+        0,
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+        1,
+        1,
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+        true,
+        512,
+        512,
+        "32"
+      ]
+    },
+    {
+      "id": 11,
+      "type": "CLIPTextEncode",
+      "pos": [
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+      ],
+      "size": [
+        310,
+        140
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 21
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "shape": 3,
+          "links": [
+            37
+          ],
+          "slot_index": 0
+        }
+      ],
+      "title": "CLIP Text Encode (Prompt) - Positive",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "metal cap covering a bottle, watercolor painting"
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    }
+  ],
+  "links": [
+    [
+      21,
+      10,
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+    ],
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+}

v3-nodes/ComfyUI-Inpaint-CropAndStitch/inpaint_cropandstitch.py

@@ -0,0 +1,1650 @@
+import comfy.utils
+import comfy.model_management
+import math
+import nodes
+import numpy as np
+import torch
+import torch.nn.functional as TF
+import torchvision.transforms.functional as F
+from PIL import Image
+from scipy.ndimage import gaussian_filter, grey_dilation, binary_closing, binary_fill_holes
+from abc import ABC, abstractmethod
+
+class ProcessorLogic(ABC):
+    @abstractmethod
+    def rescale_i(self, samples, width, height, algorithm: str):
+        pass
+
+    @abstractmethod
+    def rescale_m(self, samples, width, height, algorithm: str):
+        pass
+
+    @abstractmethod
+    def fillholes_iterative_hipass_fill_m(self, samples):
+        pass
+
+    @abstractmethod
+    def hipassfilter_m(self, samples, threshold):
+        pass
+
+    @abstractmethod
+    def expand_m(self, samples, pixels):
+        pass
+
+    @abstractmethod
+    def invert_m(self, samples):
+        pass
+
+    @abstractmethod
+    def blur_m(self, samples, pixels):
+        pass
+
+    @abstractmethod
+    def debug_context_location_in_image(self, image, x, y, w, h):
+        pass
+
+    @abstractmethod
+    def pad_to_multiple(self, value, multiple):
+        pass
+
+    @abstractmethod
+    def preresize_imm(self, image, mask, optional_context_mask, downscale_algorithm, upscale_algorithm, preresize_mode, preresize_min_width, preresize_min_height, preresize_max_width, preresize_max_height):
+        pass
+
+    @abstractmethod
+    def extend_imm(self, image, mask, optional_context_mask, extend_up_factor, extend_down_factor, extend_left_factor, extend_right_factor):
+        pass
+
+    @abstractmethod
+    def batched_findcontextarea_m(self, mask):
+        pass
+
+    def findcontextarea_m(self, mask):
+        # Default implementation for single masks using the batched version
+        # mask is [1, H, W]
+        _, x, y, w, h = self.batched_findcontextarea_m(mask)
+        context = mask[:, y[0]:y[0]+h[0], x[0]:x[0]+w[0]]
+        return context, x[0].item(), y[0].item(), w[0].item(), h[0].item()
+
+    @abstractmethod
+    def batched_growcontextarea_m(self, mask, x, y, w, h, extend_factor):
+        pass
+
+    def growcontextarea_m(self, context, mask, x, y, w, h, extend_factor):
+        _, nx, ny, nw, nh = self.batched_growcontextarea_m(mask, torch.tensor([x], device=mask.device), torch.tensor([y], device=mask.device), torch.tensor([w], device=mask.device), torch.tensor([h], device=mask.device), extend_factor)
+        nx, ny, nw, nh = nx[0].item(), ny[0].item(), nw[0].item(), nh[0].item()
+        ctx = mask[:, ny:ny+nh, nx:nx+nw]
+        return ctx, nx, ny, nw, nh
+
+    @abstractmethod
+    def batched_combinecontextmask_m(self, mask, x, y, w, h, optional_context_mask):
+        pass
+
+    def combinecontextmask_m(self, context, mask, x, y, w, h, optional_context_mask):
+        _, nx, ny, nw, nh = self.batched_combinecontextmask_m(mask, torch.tensor([x], device=mask.device), torch.tensor([y], device=mask.device), torch.tensor([w], device=mask.device), torch.tensor([h], device=mask.device), optional_context_mask)
+        nx, ny, nw, nh = nx[0].item(), ny[0].item(), nw[0].item(), nh[0].item()
+        ctx = mask[:, ny:ny+nh, nx:nx+nw]
+        return ctx, nx, ny, nw, nh
+
+    @abstractmethod
+    def crop_magic_im(self, image, mask, x, y, w, h, target_w, target_h, padding, downscale_algorithm, upscale_algorithm, resize_output=True):
+        pass
+
+    @abstractmethod
+    def stitch_magic_im(self, canvas_image, inpainted_image, mask, ctc_x, ctc_y, ctc_w, ctc_h, cto_x, cto_y, cto_w, cto_h, downscale_algorithm, upscale_algorithm):
+        pass
+
+
+class CPUProcessorLogic(ProcessorLogic):
+    def rescale_i(self, samples, width, height, algorithm: str):
+        # samples shape: [B, H, W, C]
+        samples = samples.movedim(-1, 1) # [B, C, H, W]
+        algorithm_enum = getattr(Image, algorithm.upper())  # i.e. Image.BICUBIC
+        results = []
+        for i in range(samples.shape[0]):
+            samples_pil: Image.Image = F.to_pil_image(samples[i].cpu()).resize((width, height), algorithm_enum)
+            results.append(F.to_tensor(samples_pil))
+        samples = torch.stack(results, dim=0)
+        samples = samples.movedim(1, -1)
+        return samples
+
+    def rescale_m(self, samples, width, height, algorithm: str):
+        # samples shape: [B, H, W]
+        algorithm_enum = getattr(Image, algorithm.upper())  # i.e. Image.BICUBIC
+        results = []
+        for i in range(samples.shape[0]):
+            samples_pil: Image.Image = F.to_pil_image(samples[i].cpu()).resize((width, height), algorithm_enum)
+            results.append(F.to_tensor(samples_pil).squeeze(0))
+        samples = torch.stack(results, dim=0)
+        return samples
+
+    def fillholes_iterative_hipass_fill_m(self, samples):
+        thresholds = [1, 0.99, 0.97, 0.95, 0.93, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
+        results = []
+        for i in range(samples.shape[0]):
+            mask_np = samples[i].cpu().numpy()
+            for threshold in thresholds:
+                thresholded_mask = mask_np >= threshold
+                closed_mask = binary_closing(thresholded_mask, structure=np.ones((3, 3)), border_value=1)
+                filled_mask = binary_fill_holes(closed_mask)
+                mask_np = np.maximum(mask_np, np.where(filled_mask != 0, threshold, 0))
+            results.append(torch.from_numpy(mask_np.astype(np.float32)))
+        return torch.stack(results, dim=0)
+
+    def hipassfilter_m(self, samples, threshold):
+        filtered_mask = samples.clone()
+        filtered_mask[filtered_mask < threshold] = 0
+        return filtered_mask
+
+    def expand_m(self, mask, pixels):
+        sigma = pixels / 4
+        kernel_size = math.ceil(sigma * 1.5 + 1)
+        kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8)
+        results = []
+        for i in range(mask.shape[0]):
+            mask_np = mask[i].cpu().numpy()
+            dilated_mask = grey_dilation(mask_np, footprint=kernel)
+            results.append(torch.from_numpy(dilated_mask.astype(np.float32)).clamp(0.0, 1.0))
+        return torch.stack(results, dim=0)
+
+    def invert_m(self, samples):
+        inverted_mask = samples.clone()
+        inverted_mask = 1.0 - inverted_mask
+        return inverted_mask
+
+    def blur_m(self, samples, pixels):
+        sigma = pixels / 4 
+        results = []
+        for i in range(samples.shape[0]):
+            mask_np = samples[i].cpu().numpy()
+            blurred_mask = gaussian_filter(mask_np, sigma=sigma)
+            results.append(torch.from_numpy(blurred_mask).float().clamp(0.0, 1.0))
+        return torch.stack(results, dim=0)
+
+    def debug_context_location_in_image(self, image, x, y, w, h):
+        debug_image = image.clone()
+        debug_image[:, y:y+h, x:x+w, :] = 1.0 - debug_image[:, y:y+h, x:x+w, :]
+        return debug_image
+
+    def pad_to_multiple(self, value, multiple):
+        return int(math.ceil(value / multiple) * multiple)
+
+    def preresize_imm(self, image, mask, optional_context_mask, downscale_algorithm, upscale_algorithm, preresize_mode, preresize_min_width, preresize_min_height, preresize_max_width, preresize_max_height):
+        current_width, current_height = image.shape[2], image.shape[1]
+        
+        if preresize_mode == "ensure minimum resolution":
+            if current_width >= preresize_min_width and current_height >= preresize_min_height:
+                return image, mask, optional_context_mask
+
+            scale_factor_min_width = preresize_min_width / current_width
+            scale_factor_min_height = preresize_min_height / current_height
+
+            scale_factor = max(scale_factor_min_width, scale_factor_min_height)
+
+            target_width = math.ceil(current_width * scale_factor)
+            target_height = math.ceil(current_height * scale_factor)
+
+            image = self.rescale_i(image, target_width, target_height, upscale_algorithm)
+            mask = self.rescale_m(mask, target_width, target_height, 'bilinear')
+            optional_context_mask = self.rescale_m(optional_context_mask, target_width, target_height, 'bilinear')
+            
+            assert target_width >= preresize_min_width and target_height >= preresize_min_height, \
+                f"Internal error: After resizing, target size {target_width}x{target_height} is smaller than min size {preresize_min_width}x{preresize_min_height}"
+
+        elif preresize_mode == "ensure minimum and maximum resolution":
+            if preresize_min_width <= current_width <= preresize_max_width and preresize_min_height <= current_height <= preresize_max_height:
+                return image, mask, optional_context_mask
+
+            scale_factor_min_width = preresize_min_width / current_width
+            scale_factor_min_height = preresize_min_height / current_height
+            scale_factor_min = max(scale_factor_min_width, scale_factor_min_height)
+
+            scale_factor_max_width = preresize_max_width / current_width
+            scale_factor_max_height = preresize_max_height / current_height
+            scale_factor_max = min(scale_factor_max_width, scale_factor_max_height)
+
+            if scale_factor_min > 1 and scale_factor_max < 1:
+                assert False, "Cannot meet both minimum and maximum resolution requirements with aspect ratio preservation."
+            
+            if scale_factor_min > 1:  # We're upscaling to meet min resolution
+                scale_factor = scale_factor_min
+                rescale_algorithm = upscale_algorithm  # Use upscale algorithm for min resolution
+            else:  # We're downscaling to meet max resolution
+                scale_factor = scale_factor_max
+                rescale_algorithm = downscale_algorithm  # Use downscale algorithm for max resolution
+
+            if scale_factor >= 1.0:
+                target_width = math.ceil(current_width * scale_factor)
+                target_height = math.ceil(current_height * scale_factor)
+            else:
+                target_width = int(current_width * scale_factor)
+                target_height = int(current_height * scale_factor)
+
+            image = self.rescale_i(image, target_width, target_height, rescale_algorithm)
+            mask = self.rescale_m(mask, target_width, target_height, 'nearest') # Always nearest for efficiency
+            optional_context_mask = self.rescale_m(optional_context_mask, target_width, target_height, 'nearest') # Always nearest for efficiency
+            
+            assert preresize_min_width <= target_width <= preresize_max_width, \
+                f"Internal error: Target width {target_width} is outside the range {preresize_min_width} - {preresize_max_width}"
+            assert preresize_min_height <= target_height <= preresize_max_height, \
+                f"Internal error: Target height {target_height} is outside the range {preresize_min_height} - {preresize_max_height}"
+
+        elif preresize_mode == "ensure maximum resolution":
+            if current_width <= preresize_max_width and current_height <= preresize_max_height:
+                return image, mask, optional_context_mask
+
+            scale_factor_max_width = preresize_max_width / current_width
+            scale_factor_max_height = preresize_max_height / current_height
+            scale_factor_max = min(scale_factor_max_width, scale_factor_max_height)
+
+            target_width = int(current_width * scale_factor_max)
+            target_height = int(current_height * scale_factor_max)
+
+            image = self.rescale_i(image, target_width, target_height, downscale_algorithm)
+            mask = self.rescale_m(mask, target_width, target_height, 'nearest')  # Always nearest for efficiency
+            optional_context_mask = self.rescale_m(optional_context_mask, target_width, target_height, 'nearest')  # Always nearest for efficiency
+
+            assert target_width <= preresize_max_width and target_height <= preresize_max_height, \
+                f"Internal error: Target size {target_width}x{target_height} is greater than max size {preresize_max_width}x{preresize_max_height}"
+
+        return image, mask, optional_context_mask
+
+    def extend_imm(self, image, mask, optional_context_mask, extend_up_factor, extend_down_factor, extend_left_factor, extend_right_factor):
+        B, H, W, C = image.shape
+
+        new_H = int(H * (1.0 + extend_up_factor - 1.0 + extend_down_factor - 1.0))
+        new_W = int(W * (1.0 + extend_left_factor - 1.0 + extend_right_factor - 1.0))
+
+        assert new_H >= 0, f"Error: Trying to crop too much, height ({new_H}) must be >= 0"
+        assert new_W >= 0, f"Error: Trying to crop too much, width ({new_W}) must be >= 0"
+
+        expanded_image = torch.zeros(B, new_H, new_W, C, device=image.device)
+        expanded_mask = torch.ones(B, new_H, new_W, device=mask.device)
+        expanded_optional_context_mask = torch.zeros(B, new_H, new_W, device=optional_context_mask.device)
+
+        up_padding = int(H * (extend_up_factor - 1.0))
+        down_padding = new_H - H - up_padding
+        left_padding = int(W * (extend_left_factor - 1.0))
+        right_padding = new_W - W - left_padding
+
+        slice_target_up = max(0, up_padding)
+        slice_target_down = min(new_H, up_padding + H)
+        slice_target_left = max(0, left_padding)
+        slice_target_right = min(new_W, left_padding + W)
+
+        slice_source_up = max(0, -up_padding)
+        slice_source_down = min(H, new_H - up_padding)
+        slice_source_left = max(0, -left_padding)
+        slice_source_right = min(W, new_W - left_padding)
+
+        image = image.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+        expanded_image = expanded_image.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+
+        expanded_image[:, :, slice_target_up:slice_target_down, slice_target_left:slice_target_right] = image[:, :, slice_source_up:slice_source_down, slice_source_left:slice_source_right]
+        if up_padding > 0:
+            expanded_image[:, :, :up_padding, slice_target_left:slice_target_right] = image[:, :, 0:1, slice_source_left:slice_source_right].repeat(1, 1, up_padding, 1)
+        if down_padding > 0:
+            expanded_image[:, :, -down_padding:, slice_target_left:slice_target_right] = image[:, :, -1:, slice_source_left:slice_source_right].repeat(1, 1, down_padding, 1)
+        if left_padding > 0:
+            expanded_image[:, :, slice_target_up:slice_target_down, :left_padding] = expanded_image[:, :, slice_target_up:slice_target_down, left_padding:left_padding+1].repeat(1, 1, 1, left_padding)
+        if right_padding > 0:
+            expanded_image[:, :, slice_target_up:slice_target_down, -right_padding:] = expanded_image[:, :, slice_target_up:slice_target_down, -right_padding-1:-right_padding].repeat(1, 1, 1, right_padding)
+
+        expanded_mask[:, slice_target_up:slice_target_down, slice_target_left:slice_target_right] = mask[:, slice_source_up:slice_source_down, slice_source_left:slice_source_right]
+        expanded_optional_context_mask[:, slice_target_up:slice_target_down, slice_target_left:slice_target_right] = optional_context_mask[:, slice_source_up:slice_source_down, slice_source_left:slice_source_right]
+
+        expanded_image = expanded_image.permute(0, 2, 3, 1)  # [B, C, H, W] -> [B, H, W, C]
+        image = image.permute(0, 2, 3, 1)  # [B, C, H, W] -> [B, H, W, C]
+
+        return expanded_image, expanded_mask, expanded_optional_context_mask
+
+    def batched_findcontextarea_m(self, mask):
+        # Optimized GPU implementation or CPU fallback 
+        B, H, W = mask.shape
+        device = mask.device
+        
+        # If on GPU, we can use vectorized approach.
+        # But for now, let's just use the shared logic that works on both.
+        # Wait, I'll use the vectorized one in the NEXT step for GPU specifically.
+        # This global step just fixes the placeholder.
+        
+        x_list, y_list, w_list, h_list = [], [], [], []
+        for i in range(B):
+            mask_squeezed = mask[i]
+            non_zero_indices = torch.nonzero(mask_squeezed)
+            if non_zero_indices.numel() == 0:
+                bx, by, bw, bh = -1, -1, -1, -1
+            else:
+                by = torch.min(non_zero_indices[:, 0]).item()
+                bx = torch.min(non_zero_indices[:, 1]).item()
+                by_max = torch.max(non_zero_indices[:, 0]).item()
+                bx_max = torch.max(non_zero_indices[:, 1]).item()
+                bw = bx_max - bx + 1
+                bh = by_max - by + 1
+            x_list.append(bx)
+            y_list.append(by)
+            w_list.append(bw)
+            h_list.append(bh)
+        return None, torch.tensor(x_list, device=device), torch.tensor(y_list, device=device), torch.tensor(w_list, device=device), torch.tensor(h_list, device=device)
+
+    def batched_growcontextarea_m(self, mask, x, y, w, h, extend_factor):
+        img_h, img_w = mask.shape[1], mask.shape[2]
+        device = mask.device
+        
+        grow_x = (w.float() * (extend_factor - 1.0) / 2.0).round().long()
+        grow_y = (h.float() * (extend_factor - 1.0) / 2.0).round().long()
+        
+        new_x = torch.clamp(x - grow_x, min=0)
+        new_y = torch.clamp(y - grow_y, min=0)
+        new_x2 = torch.clamp(x + w + grow_x, max=img_w)
+        new_y2 = torch.clamp(y + h + grow_y, max=img_h)
+        
+        new_w = new_x2 - new_x
+        new_h = new_y2 - new_y
+        
+        empty = (w == -1)
+        new_x[empty] = 0
+        new_y[empty] = 0
+        new_w[empty] = img_w
+        new_h[empty] = img_h
+        
+        return None, new_x, new_y, new_w, new_h
+
+    def batched_combinecontextmask_m(self, mask, x, y, w, h, optional_context_mask):
+        _, ox, oy, ow, oh = self.batched_findcontextarea_m(optional_context_mask)
+        
+        mask_x_neg1 = (x == -1)
+        x_1 = torch.where(mask_x_neg1, ox, x)
+        y_1 = torch.where(mask_x_neg1, oy, y)
+        w_1 = torch.where(mask_x_neg1, ow, w)
+        h_1 = torch.where(mask_x_neg1, oh, h)
+        
+        mask_ox_neg1 = (ox == -1)
+        ox_2 = torch.where(mask_ox_neg1, x_1, ox)
+        oy_2 = torch.where(mask_ox_neg1, y_1, oy)
+        ow_2 = torch.where(mask_ox_neg1, w_1, ow)
+        oh_2 = torch.where(mask_ox_neg1, h_1, oh)
+        
+        new_x = torch.min(x_1, ox_2)
+        new_y = torch.min(y_1, oy_2)
+        new_x_max = torch.max(x_1 + w_1, ox_2 + ow_2)
+        new_y_max = torch.max(y_1 + h_1, oy_2 + oh_2)
+        new_w = new_x_max - new_x
+        new_h = new_y_max - new_y
+        
+        both_empty = (x_1 == -1)
+        new_x[both_empty] = -1
+        new_y[both_empty] = -1
+        new_w[both_empty] = -1
+        new_h[both_empty] = -1
+        
+        return None, new_x, new_y, new_w, new_h
+
+    def crop_magic_im(self, image, mask, x, y, w, h, target_w, target_h, padding, downscale_algorithm, upscale_algorithm, resize_output=True):
+        image = image.clone()
+        mask = mask.clone()
+        
+        # Check for invalid inputs
+        if target_w <= 0 or target_h <= 0 or w == 0 or h == 0:
+            return image, 0, 0, image.shape[2], image.shape[1], image, mask, 0, 0, image.shape[2], image.shape[1]
+
+        # Step 1: Pad target dimensions to be multiples of padding
+        if padding != 0:
+            target_w = self.pad_to_multiple(target_w, padding)
+            target_h = self.pad_to_multiple(target_h, padding)
+
+        # Step 2: Calculate target aspect ratio
+        target_aspect_ratio = target_w / target_h
+
+        # Step 3: Grow current context area to meet the target aspect ratio
+        B, image_h, image_w, C = image.shape
+        context_aspect_ratio = w / h
+        if context_aspect_ratio < target_aspect_ratio:
+            # Grow width to meet aspect ratio
+            new_w = int(h * target_aspect_ratio)
+            new_h = h
+            new_x = x - (new_w - w) // 2
+            new_y = y
+
+            # Adjust new_x to keep within bounds
+            if new_x < 0:
+                shift = -new_x
+                if new_x + new_w + shift <= image_w:
+                    new_x += shift
+                else:
+                    overflow = (new_w - image_w) // 2
+                    new_x = -overflow
+            elif new_x + new_w > image_w:
+                overflow = new_x + new_w - image_w
+                if new_x - overflow >= 0:
+                    new_x -= overflow
+                else:
+                    overflow = (new_w - image_w) // 2
+                    new_x = -overflow
+
+        else:
+            # Grow height to meet aspect ratio
+            new_w = w
+            new_h = int(w / target_aspect_ratio)
+            new_x = x
+            new_y = y - (new_h - h) // 2
+
+            # Adjust new_y to keep within bounds
+            if new_y < 0:
+                shift = -new_y
+                if new_y + new_h + shift <= image_h:
+                    new_y += shift
+                else:
+                    overflow = (new_h - image_h) // 2
+                    new_y = -overflow
+            elif new_y + new_h > image_h:
+                overflow = new_y + new_h - image_h
+                if new_y - overflow >= 0:
+                    new_y -= overflow
+                else:
+                    overflow = (new_h - image_h) // 2
+                    new_y = -overflow
+
+        # Step 3b: When not resizing output, ensure dimensions are at least target dimensions
+        # This ensures output_padding works correctly even without resize (Option A: expand context, keep centered)
+        if not resize_output:
+            if new_w < target_w:
+                grow_w = target_w - new_w
+                new_x -= grow_w // 2
+                new_w = target_w
+                # Recalculate bounds
+                if new_x < 0:
+                    shift = -new_x
+                    if new_x + new_w + shift <= image_w:
+                        new_x += shift
+                    else:
+                        new_x = -((new_w - image_w) // 2)
+                elif new_x + new_w > image_w:
+                    overflow = new_x + new_w - image_w
+                    if new_x - overflow >= 0:
+                        new_x -= overflow
+                    else:
+                        new_x = -((new_w - image_w) // 2)
+            if new_h < target_h:
+                grow_h = target_h - new_h
+                new_y -= grow_h // 2
+                new_h = target_h
+                # Recalculate bounds
+                if new_y < 0:
+                    shift = -new_y
+                    if new_y + new_h + shift <= image_h:
+                        new_y += shift
+                    else:
+                        new_y = -((new_h - image_h) // 2)
+                elif new_y + new_h > image_h:
+                    overflow = new_y + new_h - image_h
+                    if new_y - overflow >= 0:
+                        new_y -= overflow
+                    else:
+                        new_y = -((new_h - image_h) // 2)
+
+        # Step 4: Grow the image to accommodate the new context area
+        up_padding, down_padding, left_padding, right_padding = 0, 0, 0, 0
+
+        expanded_image_w = image_w
+        expanded_image_h = image_h
+
+        # Adjust width for left overflow (x < 0) and right overflow (x + w > image_w)
+        if new_x < 0:
+            left_padding = -new_x
+            expanded_image_w += left_padding
+        if new_x + new_w > image_w:
+            right_padding = (new_x + new_w - image_w)
+            expanded_image_w += right_padding
+        # Adjust height for top overflow (y < 0) and bottom overflow (y + h > image_h)
+        if new_y < 0:
+            up_padding = -new_y
+            expanded_image_h += up_padding 
+        if new_y + new_h > image_h:
+            down_padding = (new_y + new_h - image_h)
+            expanded_image_h += down_padding
+
+        # Step 5: Create the new image and mask
+        expanded_image = torch.zeros((image.shape[0], expanded_image_h, expanded_image_w, image.shape[3]), device=image.device)
+        expanded_mask = torch.ones((mask.shape[0], expanded_image_h, expanded_image_w), device=mask.device)
+
+        # Reorder the tensors to match the required dimension format for padding
+        image = image.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+        expanded_image = expanded_image.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+
+        # Ensure the expanded image has enough room to hold the padded version of the original image
+        expanded_image[:, :, up_padding:up_padding + image_h, left_padding:left_padding + image_w] = image
+
+        # Fill the new extended areas with the edge values of the image
+        if up_padding > 0:
+            expanded_image[:, :, :up_padding, left_padding:left_padding + image_w] = expanded_image[:, :, up_padding:up_padding + 1, left_padding:left_padding + image_w].repeat(1, 1, up_padding, 1)
+        if down_padding > 0:
+            expanded_image[:, :, -down_padding:, left_padding:left_padding + image_w] = expanded_image[:, :, up_padding + image_h - 1:up_padding + image_h, left_padding:left_padding + image_w].repeat(1, 1, down_padding, 1)
+        if left_padding > 0:
+            expanded_image[:, :, up_padding:up_padding + image_h, :left_padding] = expanded_image[:, :, up_padding:up_padding + image_h, left_padding:left_padding+1].repeat(1, 1, 1, left_padding)
+        if right_padding > 0:
+            expanded_image[:, :, up_padding:up_padding + image_h, -right_padding:] = expanded_image[:, :, up_padding:up_padding + image_h, -right_padding-1:-right_padding].repeat(1, 1, 1, right_padding)
+
+        # Reorder the tensors back to [B, H, W, C] format
+        expanded_image = expanded_image.permute(0, 2, 3, 1)  # [B, C, H, W] -> [B, H, W, C]
+        image = image.permute(0, 2, 3, 1)  # [B, C, H, W] -> [B, H, W, C]
+
+        # Same for the mask
+        expanded_mask[:, up_padding:up_padding + image_h, left_padding:left_padding + image_w] = mask
+
+        # Record the cto values (canvas to original)
+        cto_x = left_padding
+        cto_y = up_padding
+        cto_w = image_w
+        cto_h = image_h
+
+        # The final expanded image and mask
+        canvas_image = expanded_image
+        canvas_mask = expanded_mask
+
+        # Step 6: Crop the image and mask around x, y, w, h
+        ctc_x = new_x+left_padding
+        ctc_y = new_y+up_padding
+        ctc_w = new_w
+        ctc_h = new_h
+
+        # Crop the image and mask
+        cropped_image = canvas_image[:, ctc_y:ctc_y + ctc_h, ctc_x:ctc_x + ctc_w]
+        cropped_mask = canvas_mask[:, ctc_y:ctc_y + ctc_h, ctc_x:ctc_x + ctc_w]
+
+        # Step 7: Resize image and mask to the target width and height
+        if resize_output:
+            # Decide which algorithm to use based on the scaling direction
+            if target_w > ctc_w or target_h > ctc_h:  # Upscaling
+                cropped_image = self.rescale_i(cropped_image, target_w, target_h, upscale_algorithm)
+                cropped_mask = self.rescale_m(cropped_mask, target_w, target_h, upscale_algorithm)
+            else:  # Downscaling
+                cropped_image = self.rescale_i(cropped_image, target_w, target_h, downscale_algorithm)
+                cropped_mask = self.rescale_m(cropped_mask, target_w, target_h, downscale_algorithm)
+
+        return canvas_image, cto_x, cto_y, cto_w, cto_h, cropped_image, cropped_mask, ctc_x, ctc_y, ctc_w, ctc_h
+
+    def stitch_magic_im(self, canvas_image, inpainted_image, mask, ctc_x, ctc_y, ctc_w, ctc_h, cto_x, cto_y, cto_w, cto_h, downscale_algorithm, upscale_algorithm):
+        canvas_image = canvas_image.clone()
+        inpainted_image = inpainted_image.clone()
+        mask = mask.clone()
+
+        # Resize inpainted image and mask to match the context size
+        B, h, w, _ = inpainted_image.shape
+        if ctc_w > w or ctc_h > h:  # Upscaling
+            resized_image = self.rescale_i(inpainted_image, ctc_w, ctc_h, upscale_algorithm)
+            resized_mask = self.rescale_m(mask, ctc_w, ctc_h, upscale_algorithm)
+        else:  # Downscaling
+            resized_image = self.rescale_i(inpainted_image, ctc_w, ctc_h, downscale_algorithm)
+            resized_mask = self.rescale_m(mask, ctc_w, ctc_h, downscale_algorithm)
+
+        # Clamp mask to [0, 1] and expand to match image channels
+        resized_mask = resized_mask.clamp(0, 1).unsqueeze(-1)  # shape: [B, H, W, 1]
+
+        # Extract the canvas region we're about to overwrite
+        canvas_crop = canvas_image[:, ctc_y:ctc_y + ctc_h, ctc_x:ctc_x + ctc_w]
+
+        # Blend: new = mask * inpainted + (1 - mask) * canvas
+        blended = resized_mask * resized_image + (1.0 - resized_mask) * canvas_crop
+
+        # Paste the blended region back onto the canvas
+        canvas_image[:, ctc_y:ctc_y + ctc_h, ctc_x:ctc_x + ctc_w] = blended
+
+        # Final crop to get back the original image area
+        output_image = canvas_image[:, cto_y:cto_y + cto_h, cto_x:cto_x + cto_w]
+
+        return output_image
+
+
+class GPUProcessorLogic(ProcessorLogic):
+    def rescale_i(self, samples, width, height, algorithm: str):
+        # samples shape: [B, H, W, C]
+        mode = algorithm.lower()
+        
+        # CPU works better, fallback to CPU for rescaling
+        original_device = samples.device
+        samples = samples.movedim(-1, 1)  # [B, C, H, W]
+        algorithm_enum = getattr(Image, algorithm.upper())
+        results = []
+        for i in range(samples.shape[0]):
+            samples_pil: Image.Image = F.to_pil_image(samples[i].float().cpu()).resize((width, height), algorithm_enum)
+            results.append(F.to_tensor(samples_pil))
+        samples = torch.stack(results, dim=0).to(original_device)
+        samples = samples.movedim(1, -1)
+        return samples
+        
+        #samples = samples.movedim(-1, 1)  # [B, C, H, W]
+        #samples = TF.interpolate(samples, size=(height, width), mode=mode, align_corners=False if mode not in ['nearest', 'area'] else None)
+        #samples = samples.movedim(1, -1)
+        #return samples
+
+    def rescale_m(self, samples, width, height, algorithm: str):
+        # samples shape: [B, H, W]
+        mode = algorithm.lower()
+        
+        # CPU works better, fallback to CPU for rescaling
+        original_device = samples.device
+        algorithm_enum = getattr(Image, algorithm.upper())
+        results = []
+        for i in range(samples.shape[0]):
+            samples_pil: Image.Image = F.to_pil_image(samples[i].float().cpu()).resize((width, height), algorithm_enum)
+            results.append(F.to_tensor(samples_pil).squeeze(0))
+        samples = torch.stack(results, dim=0).to(original_device)
+        return samples
+        
+        #samples = samples.unsqueeze(1)  # [B, H, W] -> [B, 1, H, W]
+        #samples = TF.interpolate(samples, size=(height, width), mode=mode, align_corners=False if mode not in ['nearest', 'area'] else None)
+        #samples = samples.squeeze(1)
+        #return samples
+
+    def fillholes_iterative_hipass_fill_m(self, samples):
+        # We want this to always run in CPU for simplicity of implementation.
+        # Just convert whatever inputs from GPU to CPU at the beginning of the function,
+        # then convert them back to GPU at the end of the function.
+        # The implementation is verbatim from CPUProcessorLogic.
+
+        thresholds = [1, 0.99, 0.97, 0.95, 0.93, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
+        results = []
+        original_device = samples.device
+        for i in range(samples.shape[0]):
+            mask_np = samples[i].cpu().numpy()
+            for threshold in thresholds:
+                thresholded_mask = mask_np >= threshold
+                closed_mask = binary_closing(thresholded_mask, structure=np.ones((3, 3)), border_value=1)
+                filled_mask = binary_fill_holes(closed_mask)
+                mask_np = np.maximum(mask_np, np.where(filled_mask != 0, threshold, 0))
+            results.append(torch.from_numpy(mask_np.astype(np.float32)))
+        return torch.stack(results, dim=0).to(original_device)
+
+    def hipassfilter_m(self, samples, threshold):
+        filtered_mask = samples.clone()
+        filtered_mask[filtered_mask < threshold] = 0
+        return filtered_mask
+
+    def expand_m(self, mask, pixels):
+        # Dilation can be approximated with max pooling
+        sigma = pixels / 4
+        kernel_size = math.ceil(sigma * 1.5 + 1)
+        if kernel_size % 2 == 0:
+            kernel_size += 1
+            
+        padding = kernel_size // 2
+        
+        # mask is [B, H, W] -> [B, 1, H, W]
+        mask_in = mask.unsqueeze(1)
+        
+        # MaxPool2d is equivalent to dilation with a square kernel of 1s
+        dilated = TF.max_pool2d(mask_in, kernel_size=kernel_size, stride=1, padding=padding)
+        
+        return dilated.squeeze(1)
+
+    def invert_m(self, samples):
+        inverted_mask = samples.clone()
+        inverted_mask = 1.0 - inverted_mask
+        return inverted_mask
+
+    def blur_m(self, samples, pixels):
+        sigma = pixels / 4
+        # Gaussian blur implementation on GPU
+        kernel_size = 2 * int(4.0 * sigma + 0.5) + 1
+        
+        # Create gaussian kernel
+        x = torch.arange(kernel_size, device=samples.device, dtype=samples.dtype) - (kernel_size - 1) / 2
+        kernel_1d = torch.exp(-0.5 * (x / sigma).pow(2))
+        kernel_1d = kernel_1d / kernel_1d.sum()
+        
+        kernel_2d = kernel_1d.unsqueeze(1) * kernel_1d.unsqueeze(0)
+        kernel_2d = kernel_2d.expand(1, 1, kernel_size, kernel_size)
+        
+        mask_in = samples.unsqueeze(1)
+        blurred = TF.conv2d(mask_in, kernel_2d, padding=kernel_size//2, groups=1)
+        
+        return blurred.squeeze(1).clamp(0.0, 1.0)
+
+    def debug_context_location_in_image(self, image, x, y, w, h):
+        debug_image = image.clone()
+        debug_image[:, y:y+h, x:x+w, :] = 1.0 - debug_image[:, y:y+h, x:x+w, :]
+        return debug_image
+
+    def pad_to_multiple(self, value, multiple):
+        return int(math.ceil(value / multiple) * multiple)
+
+    def preresize_imm(self, image, mask, optional_context_mask, downscale_algorithm, upscale_algorithm, preresize_mode, preresize_min_width, preresize_min_height, preresize_max_width, preresize_max_height):
+        current_width, current_height = image.shape[2], image.shape[1]
+        
+        if preresize_mode == "ensure minimum resolution":
+            if current_width >= preresize_min_width and current_height >= preresize_min_height:
+                return image, mask, optional_context_mask
+
+            scale_factor_min_width = preresize_min_width / current_width
+            scale_factor_min_height = preresize_min_height / current_height
+
+            scale_factor = max(scale_factor_min_width, scale_factor_min_height)
+
+            target_width = math.ceil(current_width * scale_factor)
+            target_height = math.ceil(current_height * scale_factor)
+
+            image = self.rescale_i(image, target_width, target_height, upscale_algorithm)
+            mask = self.rescale_m(mask, target_width, target_height, 'bilinear')
+            optional_context_mask = self.rescale_m(optional_context_mask, target_width, target_height, 'bilinear')
+            
+            assert target_width >= preresize_min_width and target_height >= preresize_min_height, \
+                f"Internal error: After resizing, target size {target_width}x{target_height} is smaller than min size {preresize_min_width}x{preresize_min_height}"
+
+        elif preresize_mode == "ensure minimum and maximum resolution":
+            if preresize_min_width <= current_width <= preresize_max_width and preresize_min_height <= current_height <= preresize_max_height:
+                return image, mask, optional_context_mask
+
+            scale_factor_min_width = preresize_min_width / current_width
+            scale_factor_min_height = preresize_min_height / current_height
+            scale_factor_min = max(scale_factor_min_width, scale_factor_min_height)
+
+            scale_factor_max_width = preresize_max_width / current_width
+            scale_factor_max_height = preresize_max_height / current_height
+            scale_factor_max = min(scale_factor_max_width, scale_factor_max_height)
+
+            if scale_factor_min > 1 and scale_factor_max < 1:
+                assert False, "Cannot meet both minimum and maximum resolution requirements with aspect ratio preservation."
+            
+            if scale_factor_min > 1:  # We're upscaling to meet min resolution
+                scale_factor = scale_factor_min
+                rescale_algorithm = upscale_algorithm  # Use upscale algorithm for min resolution
+            else:  # We're downscaling to meet max resolution
+                scale_factor = scale_factor_max
+                rescale_algorithm = downscale_algorithm  # Use downscale algorithm for max resolution
+
+            if scale_factor >= 1.0:
+                target_width = math.ceil(current_width * scale_factor)
+                target_height = math.ceil(current_height * scale_factor)
+            else:
+                target_width = int(current_width * scale_factor)
+                target_height = int(current_height * scale_factor)
+
+            image = self.rescale_i(image, target_width, target_height, rescale_algorithm)
+            mask = self.rescale_m(mask, target_width, target_height, 'nearest') # Always nearest for efficiency
+            optional_context_mask = self.rescale_m(optional_context_mask, target_width, target_height, 'nearest') # Always nearest for efficiency
+            
+            assert preresize_min_width <= target_width <= preresize_max_width, \
+                f"Internal error: Target width {target_width} is outside the range {preresize_min_width} - {preresize_max_width}"
+            assert preresize_min_height <= target_height <= preresize_max_height, \
+                f"Internal error: Target height {target_height} is outside the range {preresize_min_height} - {preresize_max_height}"
+
+        elif preresize_mode == "ensure maximum resolution":
+            if current_width <= preresize_max_width and current_height <= preresize_max_height:
+                return image, mask, optional_context_mask
+
+            scale_factor_max_width = preresize_max_width / current_width
+            scale_factor_max_height = preresize_max_height / current_height
+            scale_factor_max = min(scale_factor_max_width, scale_factor_max_height)
+
+            target_width = int(current_width * scale_factor_max)
+            target_height = int(current_height * scale_factor_max)
+
+            image = self.rescale_i(image, target_width, target_height, downscale_algorithm)
+            mask = self.rescale_m(mask, target_width, target_height, 'nearest')  # Always nearest for efficiency
+            optional_context_mask = self.rescale_m(optional_context_mask, target_width, target_height, 'nearest')  # Always nearest for efficiency
+
+            assert target_width <= preresize_max_width and target_height <= preresize_max_height, \
+                f"Internal error: Target size {target_width}x{target_height} is greater than max size {preresize_max_width}x{preresize_max_height}"
+
+        return image, mask, optional_context_mask
+
+    def extend_imm(self, image, mask, optional_context_mask, extend_up_factor, extend_down_factor, extend_left_factor, extend_right_factor):
+        B, H, W, C = image.shape
+
+        new_H = int(H * (1.0 + extend_up_factor - 1.0 + extend_down_factor - 1.0))
+        new_W = int(W * (1.0 + extend_left_factor - 1.0 + extend_right_factor - 1.0))
+
+        assert new_H >= 0, f"Error: Trying to crop too much, height ({new_H}) must be >= 0"
+        assert new_W >= 0, f"Error: Trying to crop too much, width ({new_W}) must be >= 0"
+
+        expanded_image = torch.zeros(B, new_H, new_W, C, device=image.device)
+        expanded_mask = torch.ones(B, new_H, new_W, device=mask.device)
+        expanded_optional_context_mask = torch.zeros(B, new_H, new_W, device=optional_context_mask.device)
+
+        up_padding = int(H * (extend_up_factor - 1.0))
+        down_padding = new_H - H - up_padding
+        left_padding = int(W * (extend_left_factor - 1.0))
+        right_padding = new_W - W - left_padding
+
+        slice_target_up = max(0, up_padding)
+        slice_target_down = min(new_H, up_padding + H)
+        slice_target_left = max(0, left_padding)
+        slice_target_right = min(new_W, left_padding + W)
+
+        slice_source_up = max(0, -up_padding)
+        slice_source_down = min(H, new_H - up_padding)
+        slice_source_left = max(0, -left_padding)
+        slice_source_right = min(W, new_W - left_padding)
+
+        image = image.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+        expanded_image = expanded_image.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+
+        expanded_image[:, :, slice_target_up:slice_target_down, slice_target_left:slice_target_right] = image[:, :, slice_source_up:slice_source_down, slice_source_left:slice_source_right]
+        if up_padding > 0:
+            expanded_image[:, :, :up_padding, slice_target_left:slice_target_right] = image[:, :, 0:1, slice_source_left:slice_source_right].repeat(1, 1, up_padding, 1)
+        if down_padding > 0:
+            expanded_image[:, :, -down_padding:, slice_target_left:slice_target_right] = image[:, :, -1:, slice_source_left:slice_source_right].repeat(1, 1, down_padding, 1)
+        if left_padding > 0:
+            expanded_image[:, :, slice_target_up:slice_target_down, :left_padding] = expanded_image[:, :, slice_target_up:slice_target_down, left_padding:left_padding+1].repeat(1, 1, 1, left_padding)
+        if right_padding > 0:
+            expanded_image[:, :, slice_target_up:slice_target_down, -right_padding:] = expanded_image[:, :, slice_target_up:slice_target_down, -right_padding-1:-right_padding].repeat(1, 1, 1, right_padding)
+
+        expanded_mask[:, slice_target_up:slice_target_down, slice_target_left:slice_target_right] = mask[:, slice_source_up:slice_source_down, slice_source_left:slice_source_right]
+        expanded_optional_context_mask[:, slice_target_up:slice_target_down, slice_target_left:slice_target_right] = optional_context_mask[:, slice_source_up:slice_source_down, slice_source_left:slice_source_right]
+
+        expanded_image = expanded_image.permute(0, 2, 3, 1)  # [B, C, H, W] -> [B, H, W, C]
+        image = image.permute(0, 2, 3, 1)  # [B, C, H, W] -> [B, H, W, C]
+
+        return expanded_image, expanded_mask, expanded_optional_context_mask
+
+    def batched_findcontextarea_m(self, mask):
+        # Optimized GPU implementation using parallel max/where
+        B, H, W = mask.shape
+        device = mask.device
+        
+        # Find which rows and columns have any mask content
+        any_y = mask.max(dim=2).values > 0. # [B, H]
+        any_x = mask.max(dim=1).values > 0. # [B, W]
+        
+        def get_min_max(any_dim, size):
+            indices = torch.arange(size, device=device).unsqueeze(0).expand(B, -1)
+            # Use large value for min where it's False, -1 for max where it's False
+            min_indices = torch.where(any_dim, indices, torch.tensor(size, device=device))
+            max_indices = torch.where(any_dim, indices, torch.tensor(-1, device=device))
+            
+            b_min = torch.min(min_indices, dim=1).values
+            b_max = torch.max(max_indices, dim=1).values
+            
+            # Handle cases where the whole row is False (no mask content for that batch item)
+            empty = ~any_dim.any(dim=1)
+            b_min[empty] = -1
+            b_max[empty] = -1
+            
+            return b_min, b_max
+
+        y_min, y_max = get_min_max(any_y, H)
+        x_min, x_max = get_min_max(any_x, W)
+        
+        w = torch.where(x_min >= 0, x_max - x_min + 1, torch.tensor(-1, device=device))
+        h = torch.where(y_min >= 0, y_max - y_min + 1, torch.tensor(-1, device=device))
+        
+        return None, x_min, y_min, w, h
+
+    def batched_growcontextarea_m(self, mask, x, y, w, h, extend_factor):
+        img_h, img_w = mask.shape[1], mask.shape[2]
+        device = mask.device
+        
+        grow_x = (w.float() * (extend_factor - 1.0) / 2.0).round().long()
+        grow_y = (h.float() * (extend_factor - 1.0) / 2.0).round().long()
+        
+        new_x = torch.clamp(x - grow_x, min=0)
+        new_y = torch.clamp(y - grow_y, min=0)
+        new_x2 = torch.clamp(x + w + grow_x, max=img_w)
+        new_y2 = torch.clamp(y + h + grow_y, max=img_h)
+        
+        new_w = new_x2 - new_x
+        new_h = new_y2 - new_y
+        
+        empty = (w == -1)
+        new_x[empty] = 0
+        new_y[empty] = 0
+        new_w[empty] = img_w
+        new_h[empty] = img_h
+        
+        return None, new_x, new_y, new_w, new_h
+
+    def batched_combinecontextmask_m(self, mask, x, y, w, h, optional_context_mask):
+        _, ox, oy, ow, oh = self.batched_findcontextarea_m(optional_context_mask)
+        
+        mask_x_neg1 = (x == -1)
+        x_1 = torch.where(mask_x_neg1, ox, x)
+        y_1 = torch.where(mask_x_neg1, oy, y)
+        w_1 = torch.where(mask_x_neg1, ow, w)
+        h_1 = torch.where(mask_x_neg1, oh, h)
+        
+        mask_ox_neg1 = (ox == -1)
+        ox_2 = torch.where(mask_ox_neg1, x_1, ox)
+        oy_2 = torch.where(mask_ox_neg1, y_1, oy)
+        ow_2 = torch.where(mask_ox_neg1, w_1, ow)
+        oh_2 = torch.where(mask_ox_neg1, h_1, oh)
+        
+        new_x = torch.min(x_1, ox_2)
+        new_y = torch.min(y_1, oy_2)
+        new_x_max = torch.max(x_1 + w_1, ox_2 + ow_2)
+        new_y_max = torch.max(y_1 + h_1, oy_2 + oh_2)
+        new_w = new_x_max - new_x
+        new_h = new_y_max - new_y
+        
+        both_empty = (x_1 == -1)
+        new_x[both_empty] = -1
+        new_y[both_empty] = -1
+        new_w[both_empty] = -1
+        new_h[both_empty] = -1
+        
+        return None, new_x, new_y, new_w, new_h
+
+    def crop_magic_im(self, image, mask, x, y, w, h, target_w, target_h, padding, downscale_algorithm, upscale_algorithm, resize_output=True):
+        image = image.clone()
+        mask = mask.clone()
+        
+        # Check for invalid inputs
+        if target_w <= 0 or target_h <= 0 or w == 0 or h == 0:
+            return image, 0, 0, image.shape[2], image.shape[1], image, mask, 0, 0, image.shape[2], image.shape[1]
+
+        # Step 1: Pad target dimensions to be multiples of padding
+        if padding != 0:
+            target_w = self.pad_to_multiple(target_w, padding)
+            target_h = self.pad_to_multiple(target_h, padding)
+
+        # Step 2: Calculate target aspect ratio
+        target_aspect_ratio = target_w / target_h
+
+        # Step 3: Grow current context area to meet the target aspect ratio
+        B, image_h, image_w, C = image.shape
+        context_aspect_ratio = w / h
+        if context_aspect_ratio < target_aspect_ratio:
+            # Grow width to meet aspect ratio
+            new_w = int(h * target_aspect_ratio)
+            new_h = h
+            new_x = x - (new_w - w) // 2
+            new_y = y
+
+            # Adjust new_x to keep within bounds
+            if new_x < 0:
+                shift = -new_x
+                if new_x + new_w + shift <= image_w:
+                    new_x += shift
+                else:
+                    overflow = (new_w - image_w) // 2
+                    new_x = -overflow
+            elif new_x + new_w > image_w:
+                overflow = new_x + new_w - image_w
+                if new_x - overflow >= 0:
+                    new_x -= overflow
+                else:
+                    overflow = (new_w - image_w) // 2
+                    new_x = -overflow
+
+        else:
+            # Grow height to meet aspect ratio
+            new_w = w
+            new_h = int(w / target_aspect_ratio)
+            new_x = x
+            new_y = y - (new_h - h) // 2
+
+            # Adjust new_y to keep within bounds
+            if new_y < 0:
+                shift = -new_y
+                if new_y + new_h + shift <= image_h:
+                    new_y += shift
+                else:
+                    overflow = (new_h - image_h) // 2
+                    new_y = -overflow
+            elif new_y + new_h > image_h:
+                overflow = new_y + new_h - image_h
+                if new_y - overflow >= 0:
+                    new_y -= overflow
+                else:
+                    overflow = (new_h - image_h) // 2
+                    new_y = -overflow
+
+        # Step 3b: When not resizing output, ensure dimensions are at least target dimensions
+        # This ensures output_padding works correctly even without resize (Option A: expand context, keep centered)
+        if not resize_output:
+            if new_w < target_w:
+                grow_w = target_w - new_w
+                new_x -= grow_w // 2
+                new_w = target_w
+                # Recalculate bounds
+                if new_x < 0:
+                    shift = -new_x
+                    if new_x + new_w + shift <= image_w:
+                        new_x += shift
+                    else:
+                        new_x = -((new_w - image_w) // 2)
+                elif new_x + new_w > image_w:
+                    overflow = new_x + new_w - image_w
+                    if new_x - overflow >= 0:
+                        new_x -= overflow
+                    else:
+                        new_x = -((new_w - image_w) // 2)
+            if new_h < target_h:
+                grow_h = target_h - new_h
+                new_y -= grow_h // 2
+                new_h = target_h
+                # Recalculate bounds
+                if new_y < 0:
+                    shift = -new_y
+                    if new_y + new_h + shift <= image_h:
+                        new_y += shift
+                    else:
+                        new_y = -((new_h - image_h) // 2)
+                elif new_y + new_h > image_h:
+                    overflow = new_y + new_h - image_h
+                    if new_y - overflow >= 0:
+                        new_y -= overflow
+                    else:
+                        new_y = -((new_h - image_h) // 2)
+
+        # Step 4: Grow the image to accommodate the new context area
+        up_padding, down_padding, left_padding, right_padding = 0, 0, 0, 0
+
+        expanded_image_w = image_w
+        expanded_image_h = image_h
+
+        # Adjust width for left overflow (x < 0) and right overflow (x + w > image_w)
+        if new_x < 0:
+            left_padding = -new_x
+            expanded_image_w += left_padding
+        if new_x + new_w > image_w:
+            right_padding = (new_x + new_w - image_w)
+            expanded_image_w += right_padding
+        # Adjust height for top overflow (y < 0) and bottom overflow (y + h > image_h)
+        if new_y < 0:
+            up_padding = -new_y
+            expanded_image_h += up_padding 
+        if new_y + new_h > image_h:
+            down_padding = (new_y + new_h - image_h)
+            expanded_image_h += down_padding
+
+        # Step 5: Create the new image and mask
+        expanded_image = torch.zeros((image.shape[0], expanded_image_h, expanded_image_w, image.shape[3]), device=image.device)
+        expanded_mask = torch.ones((mask.shape[0], expanded_image_h, expanded_image_w), device=mask.device)
+
+        # Reorder the tensors to match the required dimension format for padding
+        image = image.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+        expanded_image = expanded_image.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+
+        # Ensure the expanded image has enough room to hold the padded version of the original image
+        expanded_image[:, :, up_padding:up_padding + image_h, left_padding:left_padding + image_w] = image
+
+        # Fill the new extended areas with the edge values of the image
+        if up_padding > 0:
+            expanded_image[:, :, :up_padding, left_padding:left_padding + image_w] = expanded_image[:, :, up_padding:up_padding + 1, left_padding:left_padding + image_w].repeat(1, 1, up_padding, 1)
+        if down_padding > 0:
+            expanded_image[:, :, -down_padding:, left_padding:left_padding + image_w] = expanded_image[:, :, up_padding + image_h - 1:up_padding + image_h, left_padding:left_padding + image_w].repeat(1, 1, down_padding, 1)
+        if left_padding > 0:
+            expanded_image[:, :, up_padding:up_padding + image_h, :left_padding] = expanded_image[:, :, up_padding:up_padding + image_h, left_padding:left_padding+1].repeat(1, 1, 1, left_padding)
+        if right_padding > 0:
+            expanded_image[:, :, up_padding:up_padding + image_h, -right_padding:] = expanded_image[:, :, up_padding:up_padding + image_h, -right_padding-1:-right_padding].repeat(1, 1, 1, right_padding)
+
+        # Reorder the tensors back to [B, H, W, C] format
+        expanded_image = expanded_image.permute(0, 2, 3, 1)  # [B, C, H, W] -> [B, H, W, C]
+        image = image.permute(0, 2, 3, 1)  # [B, C, H, W] -> [B, H, W, C]
+
+        # Same for the mask
+        expanded_mask[:, up_padding:up_padding + image_h, left_padding:left_padding + image_w] = mask
+
+        # Record the cto values (canvas to original)
+        cto_x = left_padding
+        cto_y = up_padding
+        cto_w = image_w
+        cto_h = image_h
+
+        # The final expanded image and mask
+        canvas_image = expanded_image
+        canvas_mask = expanded_mask
+
+        # Step 6: Crop the image and mask around x, y, w, h
+        ctc_x = new_x+left_padding
+        ctc_y = new_y+up_padding
+        ctc_w = new_w
+        ctc_h = new_h
+
+        # Crop the image and mask
+        cropped_image = canvas_image[:, ctc_y:ctc_y + ctc_h, ctc_x:ctc_x + ctc_w]
+        cropped_mask = canvas_mask[:, ctc_y:ctc_y + ctc_h, ctc_x:ctc_x + ctc_w]
+
+        # Step 7: Resize image and mask to the target width and height
+        if resize_output:
+            # Decide which algorithm to use based on the scaling direction
+            if target_w > ctc_w or target_h > ctc_h:  # Upscaling
+                cropped_image = self.rescale_i(cropped_image, target_w, target_h, upscale_algorithm)
+                cropped_mask = self.rescale_m(cropped_mask, target_w, target_h, upscale_algorithm)
+            else:  # Downscaling
+                cropped_image = self.rescale_i(cropped_image, target_w, target_h, downscale_algorithm)
+                cropped_mask = self.rescale_m(cropped_mask, target_w, target_h, downscale_algorithm)
+
+        return canvas_image, cto_x, cto_y, cto_w, cto_h, cropped_image, cropped_mask, ctc_x, ctc_y, ctc_w, ctc_h
+
+    def stitch_magic_im(self, canvas_image, inpainted_image, mask, ctc_x, ctc_y, ctc_w, ctc_h, cto_x, cto_y, cto_w, cto_h, downscale_algorithm, upscale_algorithm):
+        canvas_image = canvas_image.clone()
+        inpainted_image = inpainted_image.clone()
+        mask = mask.clone()
+
+        # Resize inpainted image and mask to match the context size
+        B, h, w, _ = inpainted_image.shape
+        if ctc_w > w or ctc_h > h:  # Upscaling
+            resized_image = self.rescale_i(inpainted_image, ctc_w, ctc_h, upscale_algorithm)
+            resized_mask = self.rescale_m(mask, ctc_w, ctc_h, upscale_algorithm)
+        else:  # Downscaling
+            resized_image = self.rescale_i(inpainted_image, ctc_w, ctc_h, downscale_algorithm)
+            resized_mask = self.rescale_m(mask, ctc_w, ctc_h, downscale_algorithm)
+
+        # Clamp mask to [0, 1] and expand to match image channels
+        resized_mask = resized_mask.clamp(0, 1).unsqueeze(-1)  # shape: [B, H, W, 1]
+
+        # Extract the canvas region we're about to overwrite
+        canvas_crop = canvas_image[:, ctc_y:ctc_y + ctc_h, ctc_x:ctc_x + ctc_w]
+
+        # Blend: new = mask * inpainted + (1 - mask) * canvas
+        blended = resized_mask * resized_image + (1.0 - resized_mask) * canvas_crop
+
+        # Paste the blended region back onto the canvas
+        canvas_image[:, ctc_y:ctc_y + ctc_h, ctc_x:ctc_x + ctc_w] = blended
+
+        # Final crop to get back the original image area
+        output_image = canvas_image[:, cto_y:cto_y + cto_h, cto_x:cto_x + cto_w]
+
+        return output_image
+
+class InpaintCropImproved:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                # Required inputs
+                "image": ("IMAGE",),
+
+                # Resize algorithms
+                "downscale_algorithm": (["nearest", "bilinear", "bicubic", "lanczos", "box", "hamming"], {"default": "bilinear"}),
+                "upscale_algorithm": (["nearest", "bilinear", "bicubic", "lanczos", "box", "hamming"], {"default": "bicubic"}),
+
+                # Pre-resize input image
+                "preresize": ("BOOLEAN", {"default": False, "tooltip": "Resize the original image before processing."}),
+                "preresize_mode": (["ensure minimum resolution", "ensure maximum resolution", "ensure minimum and maximum resolution"], {"default": "ensure minimum resolution"}),
+                "preresize_min_width": ("INT", {"default": 1024, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1}),
+                "preresize_min_height": ("INT", {"default": 1024, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1}),
+                "preresize_max_width": ("INT", {"default": nodes.MAX_RESOLUTION, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1}),
+                "preresize_max_height": ("INT", {"default": nodes.MAX_RESOLUTION, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1}),
+
+                # Mask manipulation
+                "mask_fill_holes": ("BOOLEAN", {"default": True, "tooltip": "Mark as masked any areas fully enclosed by mask."}),
+                "mask_expand_pixels": ("INT", {"default": 0, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1, "tooltip": "Expand the mask by a certain amount of pixels before processing."}),
+                "mask_invert": ("BOOLEAN", {"default": False,"tooltip": "Invert mask so that anything masked will be kept."}),
+                "mask_blend_pixels": ("INT", {"default": 32, "min": 0, "max": 64, "step": 1, "tooltip": "How many pixels to blend into the original image."}),
+                "mask_hipass_filter": ("FLOAT", {"default": 0.1, "min": 0, "max": 1, "step": 0.01, "tooltip": "Ignore mask values lower than this value."}),
+
+                # Extend image for outpainting
+                "extend_for_outpainting": ("BOOLEAN", {"default": False, "tooltip": "Extend the image for outpainting."}),
+                "extend_up_factor": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}),
+                "extend_down_factor": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}),
+                "extend_left_factor": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}),
+                "extend_right_factor": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}),
+
+                # Context
+                "context_from_mask_extend_factor": ("FLOAT", {"default": 1.2, "min": 1.0, "max": 100.0, "step": 0.01, "tooltip": "Grow the context area from the mask by a certain factor in every direction. For example, 1.5 grabs extra 50% up, down, left, and right as context."}),
+
+                # Output
+                "output_resize_to_target_size": ("BOOLEAN", {"default": True, "tooltip": "Force a specific resolution for sampling."}),
+                "output_target_width": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 1}),
+                "output_target_height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 1}),
+                "output_padding": (["0", "8", "16", "32", "64", "128", "256", "512"], {"default": "32"}),
+                
+                # Device Mode
+                "device_mode": (["cpu (compatible)", "gpu (much faster)"], {"default": "gpu (much faster)"}),
+           },
+           "optional": {
+                # Optional inputs
+                "mask": ("MASK",),
+                "optional_context_mask": ("MASK",),
+           }
+        }
+
+    FUNCTION = "inpaint_crop"
+    CATEGORY = "inpaint"
+    DESCRIPTION = "Crops an image around a mask for inpainting, the optional context mask defines an extra area to keep for the context."
+
+    # Remove the following # to turn on debug mode (extra outputs, print statements)
+    #'''
+    DEBUG_MODE = False
+    RETURN_TYPES = ("STITCHER", "IMAGE", "MASK")
+    RETURN_NAMES = ("stitcher", "cropped_image", "cropped_mask")
+
+    '''
+    
+    DEBUG_MODE = True
+    RETURN_TYPES = ("STITCHER", "IMAGE", "MASK",
+        # DEBUG
+        "IMAGE",
+        "MASK",
+        "MASK",
+        "MASK",
+        "MASK",
+        "MASK",
+        "MASK",
+        "IMAGE",
+        "MASK",
+        "MASK",
+        "IMAGE",
+        "MASK",
+        "IMAGE",
+        "MASK",
+        "IMAGE",
+        "MASK",
+        "IMAGE",
+        "IMAGE",
+        "MASK",
+        "IMAGE",
+        "IMAGE",
+        "IMAGE",
+        "MASK",
+    )
+    RETURN_NAMES = ("stitcher", "cropped_image", "cropped_mask",
+        # DEBUG
+        "DEBUG_preresize_image",
+        "DEBUG_preresize_mask",
+        "DEBUG_fillholes_mask",
+        "DEBUG_expand_mask",
+        "DEBUG_invert_mask",
+        "DEBUG_blur_mask",
+        "DEBUG_hipassfilter_mask",
+        "DEBUG_extend_image",
+        "DEBUG_extend_mask",
+        "DEBUG_context_from_mask",
+        "DEBUG_context_from_mask_location",
+        "DEBUG_context_expand",
+        "DEBUG_context_expand_location",
+        "DEBUG_context_with_context_mask",
+        "DEBUG_context_with_context_mask_location",
+        "DEBUG_context_to_target",
+        "DEBUG_context_to_target_location",
+        "DEBUG_context_to_target_image",
+        "DEBUG_context_to_target_mask",
+        "DEBUG_canvas_image",
+        "DEBUG_orig_in_canvas_location",
+        "DEBUG_cropped_in_canvas_location",
+        "DEBUG_cropped_mask_blend",
+    )
+    #'''
+ 
+    def inpaint_crop(self, image, downscale_algorithm, upscale_algorithm, preresize, preresize_mode, preresize_min_width, preresize_min_height, preresize_max_width, preresize_max_height, extend_for_outpainting, extend_up_factor, extend_down_factor, extend_left_factor, extend_right_factor, mask_hipass_filter, mask_fill_holes, mask_expand_pixels, mask_invert, mask_blend_pixels, context_from_mask_extend_factor, output_resize_to_target_size, output_target_width, output_target_height, output_padding, device_mode, mask=None, optional_context_mask=None):
+        image = image.clone()
+        if mask is not None:
+            mask = mask.clone()
+        if optional_context_mask is not None:
+            optional_context_mask = optional_context_mask.clone()
+
+        if device_mode == "gpu (much faster)":
+            device = comfy.model_management.get_torch_device()
+            image = image.to(device)
+            if mask is not None: mask = mask.to(device)
+            if optional_context_mask is not None: optional_context_mask = optional_context_mask.to(device)
+            processor = GPUProcessorLogic()
+        else:
+            processor = CPUProcessorLogic()
+
+        output_padding = int(output_padding)
+        
+        # Check that some parameters make sense
+        if preresize and preresize_mode == "ensure minimum and maximum resolution":
+            assert preresize_max_width >= preresize_min_width, "Preresize maximum width must be greater than or equal to minimum width"
+            assert preresize_max_height >= preresize_min_height, "Preresize maximum height must be greater than or equal to minimum height"
+
+        if self.DEBUG_MODE:
+            print('Inpaint Crop Batch input')
+            print(image.shape, type(image), image.dtype)
+            if mask is not None:
+                print(mask.shape, type(mask), mask.dtype)
+            if optional_context_mask is not None:
+                print(optional_context_mask.shape, type(optional_context_mask), optional_context_mask.dtype)
+
+        if image.shape[0] > 1:
+            assert output_resize_to_target_size, "output_resize_to_target_size must be enabled when input is a batch of images, given all images in the batch output have to be the same size"
+
+        # When a LoadImage node passes a mask without user editing, it may be the wrong shape.
+        # Detect and fix that to avoid shape mismatch errors.
+        if mask is not None and (image.shape[0] == 1 or mask.shape[0] == 1 or mask.shape[0] == image.shape[0]):
+            if mask.shape[1] != image.shape[1] or mask.shape[2] != image.shape[2]:
+                if torch.count_nonzero(mask) == 0:
+                    mask = torch.zeros((mask.shape[0], image.shape[1], image.shape[2]), device=image.device, dtype=image.dtype)
+
+        if optional_context_mask is not None and (image.shape[0] == 1 or optional_context_mask.shape[0] == 1 or optional_context_mask.shape[0] == image.shape[0]):
+            if optional_context_mask.shape[1] != image.shape[1] or optional_context_mask.shape[2] != image.shape[2]:
+                if torch.count_nonzero(optional_context_mask) == 0:
+                    optional_context_mask = torch.zeros((optional_context_mask.shape[0], image.shape[1], image.shape[2]), device=image.device, dtype=image.dtype)
+
+        # If no mask is provided, create one with the shape of the image
+        if mask is None:
+            mask = torch.zeros_like(image[:, :, :, 0])
+    
+        # If there is only one image for many masks, replicate it for all masks
+        if mask.shape[0] > 1 and image.shape[0] == 1:
+            assert image.dim() == 4, f"Expected 4D BHWC image tensor, got {image.shape}"
+            image = image.expand(mask.shape[0], -1, -1, -1).clone()
+
+        # If there is only one mask for many images, replicate it for all images
+        if image.shape[0] > 1 and mask.shape[0] == 1:
+            assert mask.dim() == 3, f"Expected 3D BHW mask tensor, got {mask.shape}"
+            mask = mask.expand(image.shape[0], -1, -1).clone()
+
+        # If no optional_context_mask is provided, create one with the shape of the image
+        if optional_context_mask is None:
+            optional_context_mask = torch.zeros_like(image[:, :, :, 0])
+
+        # If there is only one optional_context_mask for many images, replicate it for all images
+        if image.shape[0] > 1 and optional_context_mask.shape[0] == 1:
+            assert optional_context_mask.dim() == 3, f"Expected 3D BHW optional_context_mask tensor, got {optional_context_mask.shape}"
+            optional_context_mask = optional_context_mask.expand(image.shape[0], -1, -1).clone()
+
+        if self.DEBUG_MODE:
+            print('Inpaint Crop Batch ready')
+            print(image.shape, type(image), image.dtype)
+            print(mask.shape, type(mask), mask.dtype)
+            print(optional_context_mask.shape, type(optional_context_mask), optional_context_mask.dtype)
+
+         # Validate data
+        assert image.ndimension() == 4, f"Expected 4 dimensions for image, got {image.ndimension()}"
+        assert mask.ndimension() == 3, f"Expected 3 dimensions for mask, got {mask.ndimension()}"
+        assert optional_context_mask.ndimension() == 3, f"Expected 3 dimensions for optional_context_mask, got {optional_context_mask.ndimension()}"
+        assert mask.shape[1:] == image.shape[1:3], f"Mask dimensions do not match image dimensions. Expected {image.shape[1:3]}, got {mask.shape[1:]}"
+        assert optional_context_mask.shape[1:] == image.shape[1:3], f"optional_context_mask dimensions do not match image dimensions. Expected {image.shape[1:3]}, got {optional_context_mask.shape[1:]}"
+        assert mask.shape[0] == image.shape[0], f"Mask batch does not match image batch. Expected {image.shape[0]}, got {mask.shape[0]}"
+        assert optional_context_mask.shape[0] == image.shape[0], f"Optional context mask batch does not match image batch. Expected {image.shape[0]}, got {optional_context_mask.shape[0]}"
+
+        # Results
+        result_stitcher = {
+            'downscale_algorithm': downscale_algorithm,
+            'upscale_algorithm': upscale_algorithm,
+            'blend_pixels': mask_blend_pixels,
+            'canvas_to_orig_x': [],
+            'canvas_to_orig_y': [],
+            'canvas_to_orig_w': [],
+            'canvas_to_orig_h': [],
+            'canvas_image': [],
+            'cropped_to_canvas_x': [],
+            'cropped_to_canvas_y': [],
+            'cropped_to_canvas_w': [],
+            'cropped_to_canvas_h': [],
+            'cropped_mask_for_blend': [],
+            'device_mode': device_mode,
+        }
+        result_image = []
+        result_mask = []
+        debug_outputs = {name: [] for name in self.RETURN_NAMES if name.startswith("DEBUG_")}
+
+        batch_size = image.shape[0]
+
+        for i in range(batch_size):
+            sub_image = image[i:i+1]
+            sub_mask = mask[i:i+1]
+            sub_opt_mask = optional_context_mask[i:i+1]
+
+            # Process individual image
+            if preresize:
+                sub_image, sub_mask, sub_opt_mask = processor.preresize_imm(sub_image, sub_mask, sub_opt_mask, downscale_algorithm, upscale_algorithm, preresize_mode, preresize_min_width, preresize_min_height, preresize_max_width, preresize_max_height)
+            
+            sub_DEBUG_preresize_image = sub_image.clone() if self.DEBUG_MODE else None
+            sub_DEBUG_preresize_mask = sub_mask.clone() if self.DEBUG_MODE else None
+
+            if mask_fill_holes:
+                sub_mask = processor.fillholes_iterative_hipass_fill_m(sub_mask)
+            sub_DEBUG_fillholes_mask = sub_mask.clone() if self.DEBUG_MODE else None
+
+            if mask_expand_pixels > 0:
+                sub_mask = processor.expand_m(sub_mask, mask_expand_pixels)
+            sub_DEBUG_expand_mask = sub_mask.clone() if self.DEBUG_MODE else None
+
+            if mask_invert:
+                sub_mask = processor.invert_m(sub_mask)
+            sub_DEBUG_invert_mask = sub_mask.clone() if self.DEBUG_MODE else None
+
+            if mask_blend_pixels > 0:
+                sub_mask = processor.expand_m(sub_mask, mask_blend_pixels)
+                sub_mask = processor.blur_m(sub_mask, mask_blend_pixels*0.5)
+            sub_DEBUG_blur_mask = sub_mask.clone() if self.DEBUG_MODE else None
+
+            if mask_hipass_filter >= 0.01:
+                sub_mask = processor.hipassfilter_m(sub_mask, mask_hipass_filter)
+                sub_opt_mask = processor.hipassfilter_m(sub_opt_mask, mask_hipass_filter)
+            sub_DEBUG_hipassfilter_mask = sub_mask.clone() if self.DEBUG_MODE else None
+
+            if extend_for_outpainting:
+                sub_image, sub_mask, sub_opt_mask = processor.extend_imm(sub_image, sub_mask, sub_opt_mask, extend_up_factor, extend_down_factor, extend_left_factor, extend_right_factor)
+            sub_DEBUG_extend_image = sub_image.clone() if self.DEBUG_MODE else None
+            sub_DEBUG_extend_mask = sub_mask.clone() if self.DEBUG_MODE else None
+
+            # Find context area
+            _, bx, by, bw, bh = processor.batched_findcontextarea_m(sub_mask)
+            
+            # Use original image size as fallback for empty masks
+            if bx[0] == -1:
+                bx[0], by[0], bw[0], bh[0] = 0, 0, sub_image.shape[2], sub_image.shape[1]
+            
+            # Growth
+            if context_from_mask_extend_factor >= 1.01:
+                _, bx, by, bw, bh = processor.batched_growcontextarea_m(sub_mask, bx, by, bw, bh, context_from_mask_extend_factor)
+            
+            # Combine
+            _, bx, by, bw, bh = processor.batched_combinecontextmask_m(sub_mask, bx, by, bw, bh, sub_opt_mask)
+
+            # Final check/fallback
+            if bx[0] == -1:
+                bx[0], by[0], bw[0], bh[0] = 0, 0, sub_image.shape[2], sub_image.shape[1]
+
+            # Crop logic
+            cur_x, cur_y, cur_w, cur_h = bx[0].item(), by[0].item(), bw[0].item(), bh[0].item()
+            
+            if output_resize_to_target_size:
+                canvas_image, cto_x, cto_y, cto_w, cto_h, cropped_image, cropped_mask, ctc_x, ctc_y, ctc_w, ctc_h = processor.crop_magic_im(
+                    sub_image, sub_mask, cur_x, cur_y, cur_w, cur_h, output_target_width, output_target_height, output_padding, downscale_algorithm, upscale_algorithm, resize_output=True
+                )
+            else:
+                canvas_image, cto_x, cto_y, cto_w, cto_h, cropped_image, cropped_mask, ctc_x, ctc_y, ctc_w, ctc_h = processor.crop_magic_im(
+                    sub_image, sub_mask, cur_x, cur_y, cur_w, cur_h, cur_w, cur_h, output_padding, downscale_algorithm, upscale_algorithm, resize_output=False
+                )
+            p_crop = cropped_image
+            p_mask = cropped_mask
+
+            # Blending Blur
+            p_mask_blend = p_mask
+            if mask_blend_pixels > 0:
+                p_mask_blend = processor.blur_m(p_mask_blend, mask_blend_pixels * 0.5)
+
+            # Collect Results
+            result_stitcher['canvas_to_orig_x'].append(cto_x)
+            result_stitcher['canvas_to_orig_y'].append(cto_y)
+            result_stitcher['canvas_to_orig_w'].append(cto_w)
+            result_stitcher['canvas_to_orig_h'].append(cto_h)
+            result_stitcher['canvas_image'].append(canvas_image.cpu())
+            result_stitcher['cropped_to_canvas_x'].append(ctc_x)
+            result_stitcher['cropped_to_canvas_y'].append(ctc_y)
+            result_stitcher['cropped_to_canvas_w'].append(ctc_w)
+            result_stitcher['cropped_to_canvas_h'].append(ctc_h)
+            result_stitcher['cropped_mask_for_blend'].append(p_mask_blend.cpu())
+
+            result_image.append(p_crop.squeeze(0).cpu())
+            result_mask.append(p_mask.squeeze(0).cpu())
+
+            # Debugs
+            if self.DEBUG_MODE:
+                # Stages for debug
+                co = (cur_x, cur_y, cur_w, cur_h) # This is combined coordinates actually, need stages if we want them.
+                # However, processing is 1 by 1 now, so we can just track them.
+                
+                # Re-calculate stages for individual debug accuracy
+                _, b_orig_x, b_orig_y, b_orig_w, b_orig_h = processor.batched_findcontextarea_m(sub_mask)
+                if b_orig_x[0] == -1: b_orig_x[0], b_orig_y[0], b_orig_w[0], b_orig_h[0] = 0, 0, sub_image.shape[2], sub_image.shape[1]
+                
+                b_grown_x, b_grown_y, b_grown_w, b_grown_h = b_orig_x.clone(), b_orig_y.clone(), b_orig_w.clone(), b_orig_h.clone()
+                if context_from_mask_extend_factor >= 1.01:
+                   _, b_grown_x, b_grown_y, b_grown_w, b_grown_h = processor.batched_growcontextarea_m(sub_mask, b_orig_x, b_orig_y, b_orig_w, b_orig_h, context_from_mask_extend_factor)
+                
+                b_comb_x, b_comb_y, b_comb_w, b_comb_h = b_grown_x.clone(), b_grown_y.clone(), b_grown_w.clone(), b_grown_h.clone()
+                _, b_comb_x, b_comb_y, b_comb_w, b_comb_h = processor.batched_combinecontextmask_m(sub_mask, b_grown_x, b_grown_y, b_grown_w, b_grown_h, sub_opt_mask)
+                
+                p_co = (b_orig_x[0].item(), b_orig_y[0].item(), b_orig_w[0].item(), b_orig_h[0].item())
+                p_cg = (b_grown_x[0].item(), b_grown_y[0].item(), b_grown_w[0].item(), b_grown_h[0].item())
+                p_cc = (b_comb_x[0].item(), b_comb_y[0].item(), b_comb_w[0].item(), b_comb_h[0].item())
+                p_cf = (cur_x, cur_y, cur_w, cur_h)
+
+                def get_debug_crop_cpu(m, c):
+                    crop = m[:, c[1]:c[1]+c[3], c[0]:c[0]+c[2]]
+                    if output_resize_to_target_size and (crop.shape[2] != output_target_width or crop.shape[1] != output_target_height):
+                        if isinstance(processor, GPUProcessorLogic):
+                            crop = processor.rescale_m(crop, output_target_width, output_target_height, 'nearest')
+                        else:
+                            crop = processor.rescale_m(crop, output_target_width, output_target_height, 'bilinear')
+                    return crop.cpu()
+
+                debug_outputs["DEBUG_preresize_image"].append(sub_DEBUG_preresize_image[0].cpu())
+                debug_outputs["DEBUG_preresize_mask"].append(sub_DEBUG_preresize_mask[0].cpu())
+                debug_outputs["DEBUG_fillholes_mask"].append(sub_DEBUG_fillholes_mask[0].cpu())
+                debug_outputs["DEBUG_expand_mask"].append(sub_DEBUG_expand_mask[0].cpu())
+                debug_outputs["DEBUG_invert_mask"].append(sub_DEBUG_invert_mask[0].cpu())
+                debug_outputs["DEBUG_blur_mask"].append(sub_DEBUG_blur_mask[0].cpu())
+                debug_outputs["DEBUG_hipassfilter_mask"].append(sub_DEBUG_hipassfilter_mask[0].cpu())
+                debug_outputs["DEBUG_extend_image"].append(sub_DEBUG_extend_image[0].cpu())
+                debug_outputs["DEBUG_extend_mask"].append(sub_DEBUG_extend_mask[0].cpu())
+                
+                debug_outputs["DEBUG_context_from_mask"].append(get_debug_crop_cpu(sub_mask, p_co).squeeze(0))
+                debug_outputs["DEBUG_context_from_mask_location"].append(processor.debug_context_location_in_image(sub_image, *p_co).squeeze(0).cpu())
+                debug_outputs["DEBUG_context_expand"].append(get_debug_crop_cpu(sub_mask, p_cg).squeeze(0))
+                debug_outputs["DEBUG_context_expand_location"].append(processor.debug_context_location_in_image(sub_image, *p_cg).squeeze(0).cpu())
+                debug_outputs["DEBUG_context_with_context_mask"].append(get_debug_crop_cpu(sub_mask, p_cc).squeeze(0))
+                debug_outputs["DEBUG_context_with_context_mask_location"].append(processor.debug_context_location_in_image(sub_image, *p_cc).squeeze(0).cpu())
+                
+                debug_outputs["DEBUG_context_to_target"].append(p_mask.squeeze(0).cpu())
+                debug_outputs["DEBUG_context_to_target_location"].append(processor.debug_context_location_in_image(sub_image, *p_cf).squeeze(0).cpu())
+                debug_outputs["DEBUG_context_to_target_image"].append(p_crop.squeeze(0).cpu())
+                debug_outputs["DEBUG_context_to_target_mask"].append(p_mask.squeeze(0).cpu())
+                debug_outputs["DEBUG_canvas_image"].append(canvas_image.squeeze(0).cpu())
+                debug_outputs["DEBUG_orig_in_canvas_location"].append(processor.debug_context_location_in_image(canvas_image, cto_x, cto_y, cto_w, cto_h).squeeze(0).cpu())
+                debug_outputs["DEBUG_cropped_in_canvas_location"].append(processor.debug_context_location_in_image(canvas_image, ctc_x, ctc_y, ctc_w, ctc_h).squeeze(0).cpu())
+                debug_outputs["DEBUG_cropped_mask_blend"].append(p_mask_blend.squeeze(0).cpu())
+
+        # Final stacking on CPU
+        result_image = torch.stack(result_image, dim=0)
+        result_mask = torch.stack(result_mask, dim=0)
+
+        if self.DEBUG_MODE:
+            # Everything is already on CPU, stack will be memory-safe
+            final_debug_outputs = []
+            for name in self.RETURN_NAMES:
+                if name.startswith("DEBUG_"):
+                    values = debug_outputs[name]
+                    if not values:
+                        count = result_image.shape[0]
+                        if name.endswith("_image") or name.endswith("_location"):
+                            final_debug_outputs.append(torch.zeros((count, 1, 1, 3), device="cpu"))
+                        else:
+                            final_debug_outputs.append(torch.zeros((count, 1, 1), device="cpu"))
+                    else:
+                        try:
+                            # Stacking happens on CPU
+                            final_debug_outputs.append(torch.stack(values, dim=0))
+                        except Exception as e:
+                            print(f"InpaintCropImproved: Failed to stack {name}. Error: {e}")
+                            count = result_image.shape[0]
+                            if name.endswith("_image") or name.endswith("_location"):
+                                final_debug_outputs.append(torch.zeros((count, 1, 1, 3), device="cpu"))
+                            else:
+                                final_debug_outputs.append(torch.zeros((count, 1, 1), device="cpu"))
+            
+            return (result_stitcher, result_image, result_mask, *final_debug_outputs)
+        else:
+            return (result_stitcher, result_image, result_mask)
+
+
+class InpaintStitchImproved:
+    """
+    ComfyUI-InpaintCropAndStitch
+    https://github.com/lquesada/ComfyUI-InpaintCropAndStitch
+
+    This node stitches the inpainted image without altering unmasked areas.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "stitcher": ("STITCHER",),
+                "inpainted_image": ("IMAGE",),
+            }
+        }
+
+    CATEGORY = "inpaint"
+    DESCRIPTION = "Stitches an image cropped with Inpaint Crop back into the original image"
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+
+    FUNCTION = "inpaint_stitch"
+
+
+    def inpaint_stitch(self, stitcher, inpainted_image):
+        inpainted_image = inpainted_image.clone()
+        results = []
+        
+        device_mode = stitcher.get('device_mode', 'cpu (compatible)')
+
+        if device_mode == "gpu (much faster)":
+            device = comfy.model_management.get_torch_device()
+            inpainted_image = inpainted_image.to(device)
+            processor = GPUProcessorLogic()
+        else:
+            device = torch.device("cpu")
+            processor = CPUProcessorLogic()
+
+        # Pre-move stitcher data to device to avoid moving in loop
+        for key in ['canvas_image', 'cropped_mask_for_blend']:
+            if key in stitcher:
+                stitcher[key] = [t.to(device) if torch.is_tensor(t) else t for t in stitcher[key]]
+
+        batch_size = inpainted_image.shape[0]
+        assert len(stitcher['cropped_to_canvas_x']) == batch_size or len(stitcher['cropped_to_canvas_x']) == 1, "Stitch batch size doesn't match image batch size"
+        override = False
+        if len(stitcher['cropped_to_canvas_x']) != batch_size and len(stitcher['cropped_to_canvas_x']) == 1:
+            override = True
+        
+        for i in range(batch_size):
+            one_image = inpainted_image[i:i+1]
+            
+            one_stitcher = {}
+            for key in ['downscale_algorithm', 'upscale_algorithm', 'blend_pixels']:
+                one_stitcher[key] = stitcher[key]
+            for key in ['canvas_to_orig_x', 'canvas_to_orig_y', 'canvas_to_orig_w', 'canvas_to_orig_h', 'canvas_image', 'cropped_to_canvas_x', 'cropped_to_canvas_y', 'cropped_to_canvas_w', 'cropped_to_canvas_h', 'cropped_mask_for_blend']:
+                if override: 
+                    one_stitcher[key] = stitcher[key][0]
+                else:
+                    one_stitcher[key] = stitcher[key][i]
+
+            one_image, = self.inpaint_stitch_single_image(one_stitcher, one_image, processor)
+            results.append(one_image.squeeze(0))
+
+        result_batch = torch.stack(results, dim=0)
+        result_batch = result_batch.cpu()
+
+        return (result_batch,)
+
+    def inpaint_stitch_single_image(self, stitcher, inpainted_image, processor):
+        downscale_algorithm = stitcher['downscale_algorithm']
+        upscale_algorithm = stitcher['upscale_algorithm']
+        canvas_image = stitcher['canvas_image']
+
+        ctc_x = stitcher['cropped_to_canvas_x']
+        ctc_y = stitcher['cropped_to_canvas_y']
+        ctc_w = stitcher['cropped_to_canvas_w']
+        ctc_h = stitcher['cropped_to_canvas_h']
+
+        cto_x = stitcher['canvas_to_orig_x']
+        cto_y = stitcher['canvas_to_orig_y']
+        cto_w = stitcher['canvas_to_orig_w']
+        cto_h = stitcher['canvas_to_orig_h']
+
+        mask = stitcher['cropped_mask_for_blend']  # shape: [1, H, W]
+
+        output_image = processor.stitch_magic_im(canvas_image, inpainted_image, mask, ctc_x, ctc_y, ctc_w, ctc_h, cto_x, cto_y, cto_w, cto_h, downscale_algorithm, upscale_algorithm)
+
+        return (output_image,)
+
+# Mappings for ComfyUI
+NODE_CLASS_MAPPINGS = {
+    "InpaintCropImproved": InpaintCropImproved,
+    "InpaintStitchImproved": InpaintStitchImproved
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "InpaintCropImproved": "Inpaint Crop Improved",
+    "InpaintStitchImproved": "Inpaint Stitch Improved"
+}

v3-nodes/ComfyUI-Inpaint-CropAndStitch/js/showcontrol.js

@@ -0,0 +1,159 @@
+import { app } from "../../scripts/app.js";
+
+// Some fragments of this code are from https://github.com/LucianoCirino/efficiency-nodes-comfyui
+
+function inpaintCropAndStitchHandler(node) {
+    if (node.comfyClass == "InpaintCropImproved") {
+        toggleWidget(node, findWidgetByName(node, "preresize_mode"));
+        toggleWidget(node, findWidgetByName(node, "preresize_min_width"));
+        toggleWidget(node, findWidgetByName(node, "preresize_min_height"));
+        toggleWidget(node, findWidgetByName(node, "preresize_max_width"));
+        toggleWidget(node, findWidgetByName(node, "preresize_max_height"));
+        if (findWidgetByName(node, "preresize").value == true) {
+            toggleWidget(node, findWidgetByName(node, "preresize_mode"), true);
+            if (findWidgetByName(node, "preresize_mode").value == "ensure minimum resolution") {
+                toggleWidget(node, findWidgetByName(node, "preresize_min_width"), true);
+                toggleWidget(node, findWidgetByName(node, "preresize_min_height"), true);
+            }
+            else if (findWidgetByName(node, "preresize_mode").value == "ensure minimum and maximum resolution") {
+                toggleWidget(node, findWidgetByName(node, "preresize_min_width"), true);
+                toggleWidget(node, findWidgetByName(node, "preresize_min_height"), true);
+                toggleWidget(node, findWidgetByName(node, "preresize_max_width"), true);
+                toggleWidget(node, findWidgetByName(node, "preresize_max_height"), true);
+            }
+            else if (findWidgetByName(node, "preresize_mode").value == "ensure maximum resolution") {
+                toggleWidget(node, findWidgetByName(node, "preresize_max_width"), true);
+                toggleWidget(node, findWidgetByName(node, "preresize_max_height"), true);
+            }
+        }
+        toggleWidget(node, findWidgetByName(node, "extend_up_factor"));
+        toggleWidget(node, findWidgetByName(node, "extend_down_factor"));
+        toggleWidget(node, findWidgetByName(node, "extend_left_factor"));
+        toggleWidget(node, findWidgetByName(node, "extend_right_factor"));
+        if (findWidgetByName(node, "extend_for_outpainting").value == true) {
+            toggleWidget(node, findWidgetByName(node, "extend_up_factor"), true);
+            toggleWidget(node, findWidgetByName(node, "extend_down_factor"), true);
+            toggleWidget(node, findWidgetByName(node, "extend_left_factor"), true);
+            toggleWidget(node, findWidgetByName(node, "extend_right_factor"), true);
+        }
+        toggleWidget(node, findWidgetByName(node, "output_target_width"));
+        toggleWidget(node, findWidgetByName(node, "output_target_height"));
+        if (findWidgetByName(node, "output_resize_to_target_size").value == true) {
+            toggleWidget(node, findWidgetByName(node, "output_target_width"), true);
+            toggleWidget(node, findWidgetByName(node, "output_target_height"), true);
+        }
+    }
+
+    // OLD
+    if (node.comfyClass == "InpaintCrop") {
+        toggleWidget(node, findWidgetByName(node, "force_width"));
+        toggleWidget(node, findWidgetByName(node, "force_height"));
+        toggleWidget(node, findWidgetByName(node, "rescale_factor"));
+        toggleWidget(node, findWidgetByName(node, "min_width"));
+        toggleWidget(node, findWidgetByName(node, "min_height"));
+        toggleWidget(node, findWidgetByName(node, "max_width"));
+        toggleWidget(node, findWidgetByName(node, "max_height"));
+        toggleWidget(node, findWidgetByName(node, "padding"));
+        if (findWidgetByName(node, "mode").value == "free size") {
+            toggleWidget(node, findWidgetByName(node, "rescale_factor"), true);
+            toggleWidget(node, findWidgetByName(node, "padding"), true);
+        }
+        else if (findWidgetByName(node, "mode").value == "ranged size") {
+            toggleWidget(node, findWidgetByName(node, "min_width"), true);
+            toggleWidget(node, findWidgetByName(node, "min_height"), true);
+            toggleWidget(node, findWidgetByName(node, "max_width"), true);
+            toggleWidget(node, findWidgetByName(node, "max_height"), true);
+            toggleWidget(node, findWidgetByName(node, "padding"), true);
+        }
+        else if (findWidgetByName(node, "mode").value == "forced size") {
+            toggleWidget(node, findWidgetByName(node, "force_width"), true);
+            toggleWidget(node, findWidgetByName(node, "force_height"), true);
+        }
+    } else if (node.comfyClass == "InpaintExtendOutpaint") {
+        toggleWidget(node, findWidgetByName(node, "expand_up_pixels"));
+        toggleWidget(node, findWidgetByName(node, "expand_up_factor"));
+        toggleWidget(node, findWidgetByName(node, "expand_down_pixels"));
+        toggleWidget(node, findWidgetByName(node, "expand_down_factor"));
+        toggleWidget(node, findWidgetByName(node, "expand_left_pixels"));
+        toggleWidget(node, findWidgetByName(node, "expand_left_factor"));
+        toggleWidget(node, findWidgetByName(node, "expand_right_pixels"));
+        toggleWidget(node, findWidgetByName(node, "expand_right_factor"));
+        if (findWidgetByName(node, "mode").value == "factors") {
+            toggleWidget(node, findWidgetByName(node, "expand_up_factor"), true);
+            toggleWidget(node, findWidgetByName(node, "expand_down_factor"), true);
+            toggleWidget(node, findWidgetByName(node, "expand_left_factor"), true);
+            toggleWidget(node, findWidgetByName(node, "expand_right_factor"), true);
+        }
+        if (findWidgetByName(node, "mode").value == "pixels") {
+            toggleWidget(node, findWidgetByName(node, "expand_up_pixels"), true);
+            toggleWidget(node, findWidgetByName(node, "expand_down_pixels"), true);
+            toggleWidget(node, findWidgetByName(node, "expand_left_pixels"), true);
+            toggleWidget(node, findWidgetByName(node, "expand_right_pixels"), true);
+        }
+    } else if (node.comfyClass == "InpaintResize") {
+        toggleWidget(node, findWidgetByName(node, "min_width"));
+        toggleWidget(node, findWidgetByName(node, "min_height"));
+        toggleWidget(node, findWidgetByName(node, "rescale_factor"));
+        if (findWidgetByName(node, "mode").value == "ensure minimum size") {
+            toggleWidget(node, findWidgetByName(node, "min_width"), true);
+            toggleWidget(node, findWidgetByName(node, "min_height"), true);
+        }
+        else if (findWidgetByName(node, "mode").value == "factor") {
+            toggleWidget(node, findWidgetByName(node, "rescale_factor"), true);
+        }
+    }
+    return;
+}
+
+const findWidgetByName = (node, name) => {
+    return node.widgets ? node.widgets.find((w) => w.name === name) : null;
+};
+
+// Toggle Widget + change size
+function toggleWidget(node, widget, show = false, suffix = "") {
+    if (!widget) return;
+    widget.disabled = !show
+    widget.linkedWidgets?.forEach(w => toggleWidget(node, w, ":" + widget.name, show));
+}   
+
+app.registerExtension({
+    name: "inpaint-cropandstitch.showcontrol",
+    nodeCreated(node) {
+        if (!node.comfyClass.startsWith("Inpaint")) {
+            return;
+        }
+
+        inpaintCropAndStitchHandler(node);
+        for (const w of node.widgets || []) {
+            let widgetValue = w.value;
+
+            // Store the original descriptor if it exists 
+            let originalDescriptor = Object.getOwnPropertyDescriptor(w, 'value') || 
+                Object.getOwnPropertyDescriptor(Object.getPrototypeOf(w), 'value');
+            if (!originalDescriptor) {
+                originalDescriptor = Object.getOwnPropertyDescriptor(w.constructor.prototype, 'value');
+            }
+
+            Object.defineProperty(w, 'value', {
+                get() {
+                    // If there's an original getter, use it. Otherwise, return widgetValue.
+                    let valueToReturn = originalDescriptor && originalDescriptor.get
+                        ? originalDescriptor.get.call(w)
+                        : widgetValue;
+
+                    return valueToReturn;
+                },
+                set(newVal) {
+                    // If there's an original setter, use it. Otherwise, set widgetValue.
+                    if (originalDescriptor && originalDescriptor.set) {
+                        originalDescriptor.set.call(w, newVal);
+                    } else { 
+                        widgetValue = newVal;
+                    }
+
+                    inpaintCropAndStitchHandler(node);
+                }
+            });
+        }
+    }
+});

v3-nodes/ComfyUI-Inpaint-CropAndStitch/pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "comfyui-inpaint-cropandstitch"
+description = "The '✂️ Inpaint Crop' and '✂️ Inpaint Stitch' nodes enable inpainting only on masked area very easily: crop the image around the masked area with the Crop node, then use any standard workflow for sampling, then connect the sampled image to the Stitch node, which will put it back in place in the original image. These nodes enable faster sampling of smaller areas and take care of downsampling and upsampling to fit specific model and resource needs."
+version = "3.0.9"
+license = { file = "LICENSE" }
+
+[project.urls]
+Repository = "https://github.com/lquesada/ComfyUI-Inpaint-CropAndStitch"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "lquesada"
+DisplayName = "ComfyUI-Inpaint-CropAndStitch"
+Icon = ""