├── .github └── workflows │ └── publish.yml ├── .gitignore ├── LICENSE ├── README.md ├── __init__.py ├── files ├── batch_layers.json ├── bojnice-castle-1603142898.jpg ├── example.json ├── workflow.png └── workflow_img.png ├── install.py ├── layers_creation.py ├── pyproject.toml └── test_layers.py /.github/workflows/publish.yml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - main 7 | - master 8 | paths: 9 | - "pyproject.toml" 10 | 11 | jobs: 12 | publish-node: 13 | name: Publish Custom Node to registry 14 | runs-on: ubuntu-latest 15 | steps: 16 | - name: Check out code 17 | uses: actions/checkout@v4 18 | - name: Publish Custom Node 19 | uses: Comfy-Org/publish-node-action@main 20 | with: 21 | ## Add your own personal access token to your Github Repository secrets and reference it here. 22 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} 23 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # LayersSaver - Save Layers Node for ComfyUI 2 | These two custom nodes for ComfyUI allows you to save images into a PSD file as layers. 3 | This can be particularly useful for graphic design, image processing, and tasks requiring manipulation of image layers. 4 | 5 | ## LayersSaver - Save Layer 6 | This custom node for ComfyUI allows you to create layers of an image based on input masks and save them into a PSD file. The node provides flexibility to include the original image and creates separate layers for each masked region. 7 | 8 | ### Example 9 | ![Example Workflow](files/workflow.png) 10 | You can find the example workflow [here](files/example.json) 11 | 12 | ### Features 13 | 14 | - **Input Image Handling**: Processes a single input image with batch dimension removed. 15 | - **Mask Application**: Applies multiple masks to the input image, creating separate layers for each masked region. 16 | - **Original Image Inclusion**: Optionally includes the original image as a separate layer. 17 | - **Alpha Channel Management**: Ensures the input image includes an alpha channel for transparency. 18 | - **Dynamic PSD Naming**: Generates a unique PSD filename based on the current timestamp if not specified. 19 | - **PSD File Creation**: Saves the resulting layers into a PSD file with specified or dynamically generated filename. 20 | 21 | ## LayersSaver - Save Layer From Images 22 | This custom node for ComfyUI allows you to create layers of an image based on all the input images. Every image in the input batch will be saved as a separate layer in the output PSD. 23 | 24 | ### Example 25 | ![Example Workflow](files/workflow_img.png) 26 | You can find the example workflow [here](files/batch_layers.json) 27 | 28 | ### Features 29 | 30 | - **Input Image Handling**: Processes a infinite bach dimension input image. 31 | - **Alpha Channel Management**: Ensures the input image includes an alpha channel for transparency. 32 | - **Dynamic PSD Naming**: Generates a unique PSD filename based on the current timestamp if not specified. 33 | - **PSD File Creation**: Saves the resulting layers into a PSD file with specified or dynamically generated filename. 34 | 35 | ## Installation 36 | 37 | To use this custom node in ComfyUI, follow these steps: 38 | 39 | 1. Clone the repository into your ComfyUI custom_nodes directory: 40 | ```sh 41 | git clone https://github.com/yourusername/comfyui-layerssaver.git 42 | ``` 43 | 2. Install the following libraries or run `install.py` 44 | - `pip install pytoshop -I --no-cache-dir` 45 | - `pip install psd-tools --no-deps` 46 | 3. Restart ComfyUI to load the new node. 47 | 48 | ## Acknowledgements 49 | 50 | Special thanks to [jtydhr88/ComfyUI-LayerDivider](jtydhr88/ComfyUI-LayerDivider) for the inspiration. Their work provided a foundation and motivation for developing this custom node. 51 | 52 | 53 | ## Contributing 54 | 55 | We welcome contributions! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request. 56 | 57 | ## License 58 | 59 | This project is licensed under the GPL-3.0 License. See the [LICENSE](LICENSE) file for details. 60 | 61 | 62 | 63 | --- 64 | --- 65 | 66 | Thank you for using the LayersSaver node for ComfyUI. We hope it enhances your image processing workflows! -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | 2 | from .layers_creation import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 3 | __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] -------------------------------------------------------------------------------- /files/batch_layers.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 9, 3 | "last_link_id": 12, 4 | "nodes": [ 5 | { 6 | "id": 5, 7 | "type": "PreviewImage", 8 | "pos": [ 9 | 1966, 10 | 618 11 | ], 12 | "size": { 13 | "0": 210, 14 | "1": 246 15 | }, 16 | "flags": {}, 17 | "order": 3, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "images", 22 | "type": "IMAGE", 23 | "link": 11 24 | } 25 | ], 26 | "properties": { 27 | "Node name for S&R": "PreviewImage" 28 | } 29 | }, 30 | { 31 | "id": 7, 32 | "type": "PreviewImage", 33 | "pos": [ 34 | 2251, 35 | 805 36 | ], 37 | "size": { 38 | "0": 210, 39 | "1": 246 40 | }, 41 | "flags": {}, 42 | "order": 5, 43 | "mode": 0, 44 | "inputs": [ 45 | { 46 | "name": "images", 47 | "type": "IMAGE", 48 | "link": 7 49 | } 50 | ], 51 | "properties": { 52 | "Node name for S&R": "PreviewImage" 53 | } 54 | }, 55 | { 56 | "id": 6, 57 | "type": "LayersSaver - Save Layer From Images", 58 | "pos": [ 59 | 1815.6581468174595, 60 | 965.0792603598245 61 | ], 62 | "size": { 63 | "0": 315, 64 | "1": 58 65 | }, 66 | "flags": {}, 67 | "order": 4, 68 | "mode": 0, 69 | "inputs": [ 70 | { 71 | "name": "input_image", 72 | "type": "IMAGE", 73 | "link": 12 74 | } 75 | ], 76 | "outputs": [ 77 | { 78 | "name": "layers", 79 | "type": "IMAGE", 80 | "links": [ 81 | 7 82 | ], 83 | "shape": 3, 84 | "slot_index": 0 85 | } 86 | ], 87 | "properties": { 88 | "Node name for S&R": "LayersSaver - Save Layer From Images" 89 | }, 90 | "widgets_values": [ 91 | "/home/server/Documents/ComfyUI/output/new_test" 92 | ] 93 | }, 94 | { 95 | "id": 8, 96 | "type": "LoadImage", 97 | "pos": [ 98 | 818, 99 | 775 100 | ], 101 | "size": { 102 | "0": 315, 103 | "1": 314 104 | }, 105 | "flags": {}, 106 | "order": 0, 107 | "mode": 0, 108 | "outputs": [ 109 | { 110 | "name": "IMAGE", 111 | "type": "IMAGE", 112 | "links": [ 113 | 10 114 | ], 115 | "shape": 3, 116 | "slot_index": 0 117 | }, 118 | { 119 | "name": "MASK", 120 | "type": "MASK", 121 | "links": null, 122 | "shape": 3 123 | } 124 | ], 125 | "properties": { 126 | "Node name for S&R": "LoadImage" 127 | }, 128 | "widgets_values": [ 129 | "AnimateDiff_00007.png", 130 | "image" 131 | ] 132 | }, 133 | { 134 | "id": 9, 135 | "type": "ImageBatch", 136 | "pos": [ 137 | 1404.5090661612314, 138 | 714.0125550876398 139 | ], 140 | "size": { 141 | "0": 210, 142 | "1": 46 143 | }, 144 | "flags": {}, 145 | "order": 2, 146 | "mode": 0, 147 | "inputs": [ 148 | { 149 | "name": "image1", 150 | "type": "IMAGE", 151 | "link": 9 152 | }, 153 | { 154 | "name": "image2", 155 | "type": "IMAGE", 156 | "link": 10 157 | } 158 | ], 159 | "outputs": [ 160 | { 161 | "name": "IMAGE", 162 | "type": "IMAGE", 163 | "links": [ 164 | 11, 165 | 12 166 | ], 167 | "shape": 3, 168 | "slot_index": 0 169 | } 170 | ], 171 | "properties": { 172 | "Node name for S&R": "ImageBatch" 173 | } 174 | }, 175 | { 176 | "id": 2, 177 | "type": "LoadImage", 178 | "pos": [ 179 | 817, 180 | 410 181 | ], 182 | "size": { 183 | "0": 315, 184 | "1": 314 185 | }, 186 | "flags": {}, 187 | "order": 1, 188 | "mode": 0, 189 | "outputs": [ 190 | { 191 | "name": "IMAGE", 192 | "type": "IMAGE", 193 | "links": [ 194 | 9 195 | ], 196 | "shape": 3, 197 | "slot_index": 0 198 | }, 199 | { 200 | "name": "MASK", 201 | "type": "MASK", 202 | "links": null, 203 | "shape": 3 204 | } 205 | ], 206 | "properties": { 207 | "Node name for S&R": "LoadImage" 208 | }, 209 | "widgets_values": [ 210 | "bojnice-castle-1603142898.jpg", 211 | "image" 212 | ] 213 | } 214 | ], 215 | "links": [ 216 | [ 217 | 7, 218 | 6, 219 | 0, 220 | 7, 221 | 0, 222 | "IMAGE" 223 | ], 224 | [ 225 | 9, 226 | 2, 227 | 0, 228 | 9, 229 | 0, 230 | "IMAGE" 231 | ], 232 | [ 233 | 10, 234 | 8, 235 | 0, 236 | 9, 237 | 1, 238 | "IMAGE" 239 | ], 240 | [ 241 | 11, 242 | 9, 243 | 0, 244 | 5, 245 | 0, 246 | "IMAGE" 247 | ], 248 | [ 249 | 12, 250 | 9, 251 | 0, 252 | 6, 253 | 0, 254 | "IMAGE" 255 | ] 256 | ], 257 | "groups": [], 258 | "config": {}, 259 | "extra": { 260 | "ds": { 261 | "scale": 1.2839025177495014, 262 | "offset": [ 263 | -258.30799487042407, 264 | -106.68023941616853 265 | ] 266 | } 267 | }, 268 | "version": 0.4 269 | } -------------------------------------------------------------------------------- /files/bojnice-castle-1603142898.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alessandrozonta/ComfyUI-Layers/70b63801867229f98a975176309c2c2ad085a2d1/files/bojnice-castle-1603142898.jpg -------------------------------------------------------------------------------- /files/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 19, 3 | "last_link_id": 27, 4 | "nodes": [ 5 | { 6 | "id": 3, 7 | "type": "GroundingDinoModelLoader (segment anything)", 8 | "pos": [ 9 | 493, 10 | 121 11 | ], 12 | "size": { 13 | "0": 361.20001220703125, 14 | "1": 58 15 | }, 16 | "flags": {}, 17 | "order": 0, 18 | "mode": 0, 19 | "outputs": [ 20 | { 21 | "name": "GROUNDING_DINO_MODEL", 22 | "type": "GROUNDING_DINO_MODEL", 23 | "links": [ 24 | 1, 25 | 4, 26 | 5 27 | ], 28 | "shape": 3, 29 | "slot_index": 0 30 | } 31 | ], 32 | "properties": { 33 | "Node name for S&R": "GroundingDinoModelLoader (segment anything)" 34 | }, 35 | "widgets_values": [ 36 | "GroundingDINO_SwinT_OGC (694MB)" 37 | ] 38 | }, 39 | { 40 | "id": 5, 41 | "type": "SAMModelLoader (segment anything)", 42 | "pos": [ 43 | 479, 44 | 223 45 | ], 46 | "size": { 47 | "0": 315, 48 | "1": 58 49 | }, 50 | "flags": {}, 51 | "order": 1, 52 | "mode": 0, 53 | "outputs": [ 54 | { 55 | "name": "SAM_MODEL", 56 | "type": "SAM_MODEL", 57 | "links": [ 58 | 2, 59 | 6, 60 | 7 61 | ], 62 | "shape": 3, 63 | "slot_index": 0 64 | } 65 | ], 66 | "properties": { 67 | "Node name for S&R": "SAMModelLoader (segment anything)" 68 | }, 69 | "widgets_values": [ 70 | "sam_vit_h (2.56GB)" 71 | ] 72 | }, 73 | { 74 | "id": 1, 75 | "type": "LoadImage", 76 | "pos": [ 77 | 495, 78 | 341 79 | ], 80 | "size": { 81 | "0": 315, 82 | "1": 314 83 | }, 84 | "flags": {}, 85 | "order": 2, 86 | "mode": 0, 87 | "outputs": [ 88 | { 89 | "name": "IMAGE", 90 | "type": "IMAGE", 91 | "links": [ 92 | 3, 93 | 8, 94 | 9, 95 | 24 96 | ], 97 | "shape": 3, 98 | "slot_index": 0 99 | }, 100 | { 101 | "name": "MASK", 102 | "type": "MASK", 103 | "links": null, 104 | "shape": 3 105 | } 106 | ], 107 | "properties": { 108 | "Node name for S&R": "LoadImage" 109 | }, 110 | "widgets_values": [ 111 | "bojnice-castle-1603142898.jpg", 112 | "image" 113 | ] 114 | }, 115 | { 116 | "id": 4, 117 | "type": "GroundingDinoSAMSegment (segment anything)", 118 | "pos": [ 119 | 942, 120 | 134 121 | ], 122 | "size": { 123 | "0": 352.79998779296875, 124 | "1": 122 125 | }, 126 | "flags": {}, 127 | "order": 3, 128 | "mode": 0, 129 | "inputs": [ 130 | { 131 | "name": "sam_model", 132 | "type": "SAM_MODEL", 133 | "link": 2 134 | }, 135 | { 136 | "name": "grounding_dino_model", 137 | "type": "GROUNDING_DINO_MODEL", 138 | "link": 1 139 | }, 140 | { 141 | "name": "image", 142 | "type": "IMAGE", 143 | "link": 3 144 | } 145 | ], 146 | "outputs": [ 147 | { 148 | "name": "IMAGE", 149 | "type": "IMAGE", 150 | "links": [ 151 | 10 152 | ], 153 | "shape": 3, 154 | "slot_index": 0 155 | }, 156 | { 157 | "name": "MASK", 158 | "type": "MASK", 159 | "links": [ 160 | 13, 161 | 16 162 | ], 163 | "shape": 3, 164 | "slot_index": 1 165 | } 166 | ], 167 | "properties": { 168 | "Node name for S&R": "GroundingDinoSAMSegment (segment anything)" 169 | }, 170 | "widgets_values": [ 171 | "castle", 172 | 0.3 173 | ] 174 | }, 175 | { 176 | "id": 6, 177 | "type": "GroundingDinoSAMSegment (segment anything)", 178 | "pos": [ 179 | 948, 180 | 309 181 | ], 182 | "size": { 183 | "0": 352.79998779296875, 184 | "1": 122 185 | }, 186 | "flags": {}, 187 | "order": 4, 188 | "mode": 0, 189 | "inputs": [ 190 | { 191 | "name": "sam_model", 192 | "type": "SAM_MODEL", 193 | "link": 6 194 | }, 195 | { 196 | "name": "grounding_dino_model", 197 | "type": "GROUNDING_DINO_MODEL", 198 | "link": 4 199 | }, 200 | { 201 | "name": "image", 202 | "type": "IMAGE", 203 | "link": 8 204 | } 205 | ], 206 | "outputs": [ 207 | { 208 | "name": "IMAGE", 209 | "type": "IMAGE", 210 | "links": [ 211 | 11 212 | ], 213 | "shape": 3, 214 | "slot_index": 0 215 | }, 216 | { 217 | "name": "MASK", 218 | "type": "MASK", 219 | "links": [ 220 | 14, 221 | 17 222 | ], 223 | "shape": 3, 224 | "slot_index": 1 225 | } 226 | ], 227 | "properties": { 228 | "Node name for S&R": "GroundingDinoSAMSegment (segment anything)" 229 | }, 230 | "widgets_values": [ 231 | "trees", 232 | 0.3 233 | ] 234 | }, 235 | { 236 | "id": 7, 237 | "type": "GroundingDinoSAMSegment (segment anything)", 238 | "pos": [ 239 | 948, 240 | 484 241 | ], 242 | "size": { 243 | "0": 352.79998779296875, 244 | "1": 122 245 | }, 246 | "flags": {}, 247 | "order": 5, 248 | "mode": 0, 249 | "inputs": [ 250 | { 251 | "name": "sam_model", 252 | "type": "SAM_MODEL", 253 | "link": 7 254 | }, 255 | { 256 | "name": "grounding_dino_model", 257 | "type": "GROUNDING_DINO_MODEL", 258 | "link": 5 259 | }, 260 | { 261 | "name": "image", 262 | "type": "IMAGE", 263 | "link": 9 264 | } 265 | ], 266 | "outputs": [ 267 | { 268 | "name": "IMAGE", 269 | "type": "IMAGE", 270 | "links": [ 271 | 12 272 | ], 273 | "shape": 3, 274 | "slot_index": 0 275 | }, 276 | { 277 | "name": "MASK", 278 | "type": "MASK", 279 | "links": [ 280 | 15, 281 | 21 282 | ], 283 | "shape": 3, 284 | "slot_index": 1 285 | } 286 | ], 287 | "properties": { 288 | "Node name for S&R": "GroundingDinoSAMSegment (segment anything)" 289 | }, 290 | "widgets_values": [ 291 | "sky", 292 | 0.3 293 | ] 294 | }, 295 | { 296 | "id": 8, 297 | "type": "PreviewImage", 298 | "pos": [ 299 | 1362, 300 | -33 301 | ], 302 | "size": { 303 | "0": 210, 304 | "1": 246 305 | }, 306 | "flags": {}, 307 | "order": 6, 308 | "mode": 0, 309 | "inputs": [ 310 | { 311 | "name": "images", 312 | "type": "IMAGE", 313 | "link": 10 314 | } 315 | ], 316 | "properties": { 317 | "Node name for S&R": "PreviewImage" 318 | } 319 | }, 320 | { 321 | "id": 11, 322 | "type": "MaskPreview+", 323 | "pos": [ 324 | 1594, 325 | -41 326 | ], 327 | "size": { 328 | "0": 210, 329 | "1": 246 330 | }, 331 | "flags": {}, 332 | "order": 7, 333 | "mode": 0, 334 | "inputs": [ 335 | { 336 | "name": "mask", 337 | "type": "MASK", 338 | "link": 13 339 | } 340 | ], 341 | "properties": { 342 | "Node name for S&R": "MaskPreview+" 343 | } 344 | }, 345 | { 346 | "id": 9, 347 | "type": "PreviewImage", 348 | "pos": [ 349 | 1363, 350 | 253 351 | ], 352 | "size": { 353 | "0": 210, 354 | "1": 246 355 | }, 356 | "flags": {}, 357 | "order": 8, 358 | "mode": 0, 359 | "inputs": [ 360 | { 361 | "name": "images", 362 | "type": "IMAGE", 363 | "link": 11 364 | } 365 | ], 366 | "properties": { 367 | "Node name for S&R": "PreviewImage" 368 | } 369 | }, 370 | { 371 | "id": 12, 372 | "type": "MaskPreview+", 373 | "pos": [ 374 | 1597, 375 | 249 376 | ], 377 | "size": { 378 | "0": 210, 379 | "1": 246 380 | }, 381 | "flags": {}, 382 | "order": 9, 383 | "mode": 0, 384 | "inputs": [ 385 | { 386 | "name": "mask", 387 | "type": "MASK", 388 | "link": 14 389 | } 390 | ], 391 | "properties": { 392 | "Node name for S&R": "MaskPreview+" 393 | } 394 | }, 395 | { 396 | "id": 10, 397 | "type": "PreviewImage", 398 | "pos": [ 399 | 1365, 400 | 546 401 | ], 402 | "size": { 403 | "0": 210, 404 | "1": 246 405 | }, 406 | "flags": {}, 407 | "order": 11, 408 | "mode": 0, 409 | "inputs": [ 410 | { 411 | "name": "images", 412 | "type": "IMAGE", 413 | "link": 12 414 | } 415 | ], 416 | "properties": { 417 | "Node name for S&R": "PreviewImage" 418 | } 419 | }, 420 | { 421 | "id": 13, 422 | "type": "MaskPreview+", 423 | "pos": [ 424 | 1610, 425 | 545 426 | ], 427 | "size": { 428 | "0": 210, 429 | "1": 246 430 | }, 431 | "flags": { 432 | "collapsed": false 433 | }, 434 | "order": 12, 435 | "mode": 0, 436 | "inputs": [ 437 | { 438 | "name": "mask", 439 | "type": "MASK", 440 | "link": 15 441 | } 442 | ], 443 | "properties": { 444 | "Node name for S&R": "MaskPreview+" 445 | } 446 | }, 447 | { 448 | "id": 14, 449 | "type": "MaskBatch+", 450 | "pos": [ 451 | 1875, 452 | 281 453 | ], 454 | "size": { 455 | "0": 210, 456 | "1": 46 457 | }, 458 | "flags": {}, 459 | "order": 10, 460 | "mode": 0, 461 | "inputs": [ 462 | { 463 | "name": "mask1", 464 | "type": "MASK", 465 | "link": 16 466 | }, 467 | { 468 | "name": "mask2", 469 | "type": "MASK", 470 | "link": 17 471 | } 472 | ], 473 | "outputs": [ 474 | { 475 | "name": "MASK", 476 | "type": "MASK", 477 | "links": [ 478 | 22 479 | ], 480 | "shape": 3, 481 | "slot_index": 0 482 | } 483 | ], 484 | "properties": { 485 | "Node name for S&R": "MaskBatch+" 486 | } 487 | }, 488 | { 489 | "id": 15, 490 | "type": "MaskBatch+", 491 | "pos": [ 492 | 1882, 493 | 397 494 | ], 495 | "size": { 496 | "0": 210, 497 | "1": 46 498 | }, 499 | "flags": {}, 500 | "order": 13, 501 | "mode": 0, 502 | "inputs": [ 503 | { 504 | "name": "mask1", 505 | "type": "MASK", 506 | "link": 22 507 | }, 508 | { 509 | "name": "mask2", 510 | "type": "MASK", 511 | "link": 21 512 | } 513 | ], 514 | "outputs": [ 515 | { 516 | "name": "MASK", 517 | "type": "MASK", 518 | "links": [ 519 | 23, 520 | 26 521 | ], 522 | "shape": 3, 523 | "slot_index": 0 524 | } 525 | ], 526 | "properties": { 527 | "Node name for S&R": "MaskBatch+" 528 | } 529 | }, 530 | { 531 | "id": 16, 532 | "type": "MaskPreview+", 533 | "pos": [ 534 | 2132, 535 | 512 536 | ], 537 | "size": { 538 | "0": 210, 539 | "1": 246 540 | }, 541 | "flags": {}, 542 | "order": 14, 543 | "mode": 0, 544 | "inputs": [ 545 | { 546 | "name": "mask", 547 | "type": "MASK", 548 | "link": 23 549 | } 550 | ], 551 | "properties": { 552 | "Node name for S&R": "MaskPreview+" 553 | } 554 | }, 555 | { 556 | "id": 18, 557 | "type": "LayersSaver - Save Layer", 558 | "pos": [ 559 | 2155, 560 | 200 561 | ], 562 | "size": { 563 | "0": 315, 564 | "1": 102 565 | }, 566 | "flags": {}, 567 | "order": 15, 568 | "mode": 0, 569 | "inputs": [ 570 | { 571 | "name": "input_image", 572 | "type": "IMAGE", 573 | "link": 24 574 | }, 575 | { 576 | "name": "masks", 577 | "type": "MASK", 578 | "link": 26 579 | } 580 | ], 581 | "outputs": [ 582 | { 583 | "name": "layers", 584 | "type": "IMAGE", 585 | "links": [ 586 | 27 587 | ], 588 | "shape": 3, 589 | "slot_index": 0 590 | } 591 | ], 592 | "properties": { 593 | "Node name for S&R": "LayersSaver - Save Layer" 594 | }, 595 | "widgets_values": [ 596 | "/home/server/Documents/ComfyUI/output/layers.psd", 597 | true 598 | ] 599 | }, 600 | { 601 | "id": 19, 602 | "type": "PreviewImage", 603 | "pos": [ 604 | 2539, 605 | 197 606 | ], 607 | "size": [ 608 | 210, 609 | 246 610 | ], 611 | "flags": {}, 612 | "order": 16, 613 | "mode": 0, 614 | "inputs": [ 615 | { 616 | "name": "images", 617 | "type": "IMAGE", 618 | "link": 27 619 | } 620 | ], 621 | "properties": { 622 | "Node name for S&R": "PreviewImage" 623 | } 624 | } 625 | ], 626 | "links": [ 627 | [ 628 | 1, 629 | 3, 630 | 0, 631 | 4, 632 | 1, 633 | "GROUNDING_DINO_MODEL" 634 | ], 635 | [ 636 | 2, 637 | 5, 638 | 0, 639 | 4, 640 | 0, 641 | "SAM_MODEL" 642 | ], 643 | [ 644 | 3, 645 | 1, 646 | 0, 647 | 4, 648 | 2, 649 | "IMAGE" 650 | ], 651 | [ 652 | 4, 653 | 3, 654 | 0, 655 | 6, 656 | 1, 657 | "GROUNDING_DINO_MODEL" 658 | ], 659 | [ 660 | 5, 661 | 3, 662 | 0, 663 | 7, 664 | 1, 665 | "GROUNDING_DINO_MODEL" 666 | ], 667 | [ 668 | 6, 669 | 5, 670 | 0, 671 | 6, 672 | 0, 673 | "SAM_MODEL" 674 | ], 675 | [ 676 | 7, 677 | 5, 678 | 0, 679 | 7, 680 | 0, 681 | "SAM_MODEL" 682 | ], 683 | [ 684 | 8, 685 | 1, 686 | 0, 687 | 6, 688 | 2, 689 | "IMAGE" 690 | ], 691 | [ 692 | 9, 693 | 1, 694 | 0, 695 | 7, 696 | 2, 697 | "IMAGE" 698 | ], 699 | [ 700 | 10, 701 | 4, 702 | 0, 703 | 8, 704 | 0, 705 | "IMAGE" 706 | ], 707 | [ 708 | 11, 709 | 6, 710 | 0, 711 | 9, 712 | 0, 713 | "IMAGE" 714 | ], 715 | [ 716 | 12, 717 | 7, 718 | 0, 719 | 10, 720 | 0, 721 | "IMAGE" 722 | ], 723 | [ 724 | 13, 725 | 4, 726 | 1, 727 | 11, 728 | 0, 729 | "MASK" 730 | ], 731 | [ 732 | 14, 733 | 6, 734 | 1, 735 | 12, 736 | 0, 737 | "MASK" 738 | ], 739 | [ 740 | 15, 741 | 7, 742 | 1, 743 | 13, 744 | 0, 745 | "MASK" 746 | ], 747 | [ 748 | 16, 749 | 4, 750 | 1, 751 | 14, 752 | 0, 753 | "MASK" 754 | ], 755 | [ 756 | 17, 757 | 6, 758 | 1, 759 | 14, 760 | 1, 761 | "MASK" 762 | ], 763 | [ 764 | 21, 765 | 7, 766 | 1, 767 | 15, 768 | 1, 769 | "MASK" 770 | ], 771 | [ 772 | 22, 773 | 14, 774 | 0, 775 | 15, 776 | 0, 777 | "MASK" 778 | ], 779 | [ 780 | 23, 781 | 15, 782 | 0, 783 | 16, 784 | 0, 785 | "MASK" 786 | ], 787 | [ 788 | 24, 789 | 1, 790 | 0, 791 | 18, 792 | 0, 793 | "IMAGE" 794 | ], 795 | [ 796 | 26, 797 | 15, 798 | 0, 799 | 18, 800 | 1, 801 | "MASK" 802 | ], 803 | [ 804 | 27, 805 | 18, 806 | 0, 807 | 19, 808 | 0, 809 | "IMAGE" 810 | ] 811 | ], 812 | "groups": [], 813 | "config": {}, 814 | "extra": { 815 | "ds": { 816 | "scale": 0.7247295000000004, 817 | "offset": [ 818 | -152.39241560701936, 819 | 278.7954206822446 820 | ] 821 | } 822 | }, 823 | "version": 0.4 824 | } -------------------------------------------------------------------------------- /files/workflow.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alessandrozonta/ComfyUI-Layers/70b63801867229f98a975176309c2c2ad085a2d1/files/workflow.png -------------------------------------------------------------------------------- /files/workflow_img.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alessandrozonta/ComfyUI-Layers/70b63801867229f98a975176309c2c2ad085a2d1/files/workflow_img.png -------------------------------------------------------------------------------- /install.py: -------------------------------------------------------------------------------- 1 | import subprocess 2 | import sys 3 | 4 | def install_package(package_name, options=""): 5 | command = [sys.executable, "-m", "pip", "install", package_name] + options.split() 6 | subprocess.check_call(command) 7 | 8 | def main(): 9 | try: 10 | print("Installing pytoshop...") 11 | install_package("pytoshop", "-I --no-cache-dir") 12 | print("pytoshop installed successfully.") 13 | 14 | print("Installing psd-tools...") 15 | install_package("psd-tools", "--no-deps") 16 | print("psd-tools installed successfully.") 17 | 18 | except subprocess.CalledProcessError as e: 19 | print(f"An error occurred while installing packages: {e}") 20 | sys.exit(1) 21 | 22 | if __name__ == "__main__": 23 | main() 24 | -------------------------------------------------------------------------------- /layers_creation.py: -------------------------------------------------------------------------------- 1 | import pytoshop 2 | from pytoshop import layers 3 | from pytoshop.enums import BlendMode 4 | import torch 5 | import numpy as np 6 | import os 7 | import datetime 8 | 9 | class PSDLayerCreator: 10 | def __init__(self): 11 | pass 12 | 13 | @classmethod 14 | def INPUT_TYPES(s): 15 | return { 16 | "required": { 17 | "input_image": ("IMAGE",), 18 | "masks": ("MASK",), 19 | "output_path": ("STRING", {}), 20 | "original_image": ("BOOLEAN", {"default": True}), 21 | } 22 | } 23 | 24 | RETURN_TYPES = ("IMAGE",) 25 | RETURN_NAMES = ("layers",) 26 | FUNCTION = "main" 27 | 28 | CATEGORY = "LayersDivider" 29 | 30 | def main(self, input_image, masks, output_path, original_image): 31 | if input_image.shape[0] > 1: 32 | raise ValueError("Batch input is not supported.") 33 | # remove the batch 34 | input_image = input_image.squeeze(0) 35 | 36 | # check if alfa channel is present, if not add it to the original image 37 | if input_image.shape[2] != 4: # Check if image already has 4 channels (RGBA) 38 | # Create an alpha channel with full opacity 39 | alpha_channel = torch.ones(input_image.shape[0], input_image.shape[1], 1) 40 | 41 | # Concatenate channels to create RGBA image 42 | input_image = torch.cat((input_image, alpha_channel), dim=2) 43 | 44 | # Validate mask and input image dimensions 45 | if masks.shape[1:] != input_image.shape[:2]: 46 | raise ValueError("Mask dimensions must match the input image dimensions.") 47 | 48 | # check if output_path contains the name of the file 49 | if not output_path.endswith(".psd"): 50 | # Generate random name using current time 51 | timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") 52 | random_name = f"layered_image_{timestamp}.psd" 53 | os.makedirs(output_path, exist_ok=True) 54 | output_path = os.path.join(output_path, random_name) # Join path with random name 55 | 56 | # Create a new empty PSD document (consider using desired color mode if needed) 57 | psd = pytoshop.core.PsdFile(num_channels=4, height=input_image.shape[0], width=input_image.shape[1]) 58 | 59 | if original_image: 60 | # Save the original image as the first layer (optional layer name) 61 | original_image_uint8 = (input_image.cpu().detach().numpy() * 255).astype(np.uint8) 62 | 63 | original_layer_data = [layers.ChannelImageData(image=original_image_uint8[:, :, channel], compression=1) for channel in range(input_image.shape[2])] 64 | original_layer_record = layers.LayerRecord( 65 | channels={-1: original_layer_data[3], 0: original_layer_data[0], 1: original_layer_data[1], 2: original_layer_data[2]}, 66 | top=0, bottom=input_image.shape[0], left=0, right=input_image.shape[1], 67 | blend_mode=BlendMode.normal, 68 | name="Original Image", # Optional layer name 69 | opacity=255, 70 | ) 71 | psd.layer_and_mask_info.layer_info.layer_records.append(original_layer_record) 72 | 73 | 74 | all_masked_images = [] 75 | # Create and add masked image layers 76 | for i in range(masks.shape[0]): # Iterate based on number of channels 77 | print(f"Processing mask {i}") 78 | 79 | mask = masks[i] # Select the i-th element (channel) as the mask 80 | 81 | # Ensure mask has a single leading dimension (batch dimension) 82 | if mask.ndim == 2: 83 | mask = mask.unsqueeze(2) 84 | 85 | masked_image = input_image * mask.float() 86 | 87 | all_masked_images.append(masked_image) 88 | 89 | # Convert masked image to numpy array (assuming CPU tensor) 90 | masked_image_numpy = masked_image.cpu().detach().numpy() 91 | 92 | # Convert masked image to a suitable unsigned integer format (e.g., uint8) 93 | masked_image_uint8 = (masked_image_numpy * 255).astype(np.uint8) 94 | 95 | # Create layer data objects from masked image channels (adjust based on mask count) 96 | layer_data = [] 97 | for channel in range(input_image.shape[2]): 98 | layer_data.append( 99 | layers.ChannelImageData(image=masked_image_uint8[:, :, channel], compression=1) 100 | ) 101 | 102 | # Create a new layer record with the layer data 103 | new_layer_record = layers.LayerRecord( 104 | channels={-1: layer_data[3], 0: layer_data[0], 1: layer_data[1], 2: layer_data[2]}, 105 | top=0, bottom=input_image.shape[0], left=0, right=input_image.shape[1], 106 | blend_mode=BlendMode.normal, 107 | name=f"Masked_{i+1}", 108 | opacity=255, 109 | ) 110 | psd.layer_and_mask_info.layer_info.layer_records.append(new_layer_record) 111 | 112 | 113 | 114 | # Save the PSD file at the specified output path 115 | try: 116 | with open(f"{output_path}", 'wb') as fd2: 117 | psd.write(fd2) 118 | print(f"Masked images saved as PSD with separate layers: {output_path}") 119 | except Exception as e: 120 | print(f"Error saving PSD: {e}") 121 | return (all_masked_images,) 122 | 123 | class PSDLayerCreatorFromImagesOnly: 124 | def __init__(self): 125 | pass 126 | 127 | @classmethod 128 | def INPUT_TYPES(s): 129 | return { 130 | "required": { 131 | "input_image": ("IMAGE",), 132 | "output_path": ("STRING", {}), 133 | } 134 | } 135 | 136 | RETURN_TYPES = ("IMAGE",) 137 | RETURN_NAMES = ("layers",) 138 | FUNCTION = "main" 139 | 140 | CATEGORY = "LayersDivider" 141 | 142 | def main(self, input_image, output_path): 143 | # Ensure the input is a batch 144 | print(input_image.shape) 145 | if input_image.ndim != 4 or input_image.shape[3] not in [1, 3, 4]: 146 | raise ValueError("Input image should have shape [batch_size, channels, height, width]") 147 | 148 | # Determine the number of images in the batch 149 | batch_size, height, width, channels = input_image.shape 150 | 151 | # Add alpha channel if not present 152 | if channels != 4: # Check if image has 4 channels (RGBA) 153 | # Create an alpha channel with full opacity 154 | alpha_channel = torch.ones(batch_size, height, width, 1, device=input_image.device) 155 | # Concatenate channels to create RGBA image 156 | input_image = torch.cat((input_image, alpha_channel), dim=3) 157 | 158 | 159 | # check if output_path contains the name of the file 160 | if not output_path.endswith(".psd"): 161 | # Generate random name using current time 162 | timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") 163 | random_name = f"layered_image_{timestamp}.psd" 164 | os.makedirs(output_path, exist_ok=True) 165 | output_path = os.path.join(output_path, random_name) # Join path with random name 166 | 167 | # Create a new empty PSD document (consider using desired color mode if needed) 168 | psd = pytoshop.core.PsdFile(num_channels=4, height=height, width=width) 169 | 170 | # Create and add image layers 171 | for i in range(batch_size): 172 | print(f"Processing image {i+1}/{batch_size}") 173 | 174 | image = input_image[i] # Select the i-th image 175 | 176 | # Convert image to numpy array 177 | image_numpy = image.cpu().detach().numpy() 178 | 179 | # Convert image to uint8 format 180 | image_uint8 = (image_numpy * 255).astype(np.uint8) 181 | 182 | # Create layer data objects from image channels 183 | layer_data = [layers.ChannelImageData(image=image_uint8[:, :, channel], compression=1) for channel in range(image.shape[2])] 184 | 185 | # Create a new layer record with the layer data 186 | new_layer_record = layers.LayerRecord( 187 | channels={-1: layer_data[3], 0: layer_data[0], 1: layer_data[1], 2: layer_data[2]}, 188 | top=0, bottom=height, left=0, right=width, 189 | blend_mode=BlendMode.normal, 190 | name=f"Image_{i+1}", 191 | opacity=255, 192 | ) 193 | psd.layer_and_mask_info.layer_info.layer_records.append(new_layer_record) 194 | 195 | 196 | # Save the PSD file at the specified output path 197 | try: 198 | with open(f"{output_path}", 'wb') as fd2: 199 | psd.write(fd2) 200 | print(f"Masked images saved as PSD with separate layers: {output_path}") 201 | except Exception as e: 202 | print(f"Error saving PSD: {e}") 203 | return (input_image,) 204 | 205 | NODE_CLASS_MAPPINGS = { 206 | "LayersSaver - Save Layer": PSDLayerCreator, 207 | "LayersSaver - Save Layer From Images": PSDLayerCreatorFromImagesOnly 208 | } 209 | 210 | NODE_DISPLAY_NAME_MAPPINGS = { 211 | "LayerDividerDivideLayer": "LayersSaver - Save Layer", 212 | "PSDLayerCreatorFromImagesOnly": "LayersSaver - Save Layer From Images" 213 | } -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "comfyui-layers" 3 | description = "This custom node for ComfyUI allows you to create layers of an image based on input masks and save them into a PSD file." 4 | version = "1.0.0" 5 | license = "LICENSE" 6 | 7 | [project.urls] 8 | Repository = "https://github.com/alessandrozonta/ComfyUI-Layers" 9 | # Used by Comfy Registry https://comfyregistry.org 10 | 11 | [tool.comfy] 12 | PublisherId = "alezonta" 13 | DisplayName = "ComfyUI-Layers" 14 | Icon = "" 15 | -------------------------------------------------------------------------------- /test_layers.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | import torch 3 | from layers_creation import PSDLayerCreator, PSDLayerCreatorFromImagesOnly 4 | import os 5 | import glob 6 | from PIL import Image 7 | from torchvision import transforms 8 | 9 | class LayerDividerTest(unittest.TestCase): 10 | 11 | def test_multiple_batch(self): 12 | """Tests the function with multiple batches.""" 13 | input_image = torch.rand(4, 256, 256, 3) 14 | output_path = "./test.psd" 15 | 16 | layer_divide = PSDLayerCreatorFromImagesOnly() 17 | try: 18 | layer_divide.main(input_image, output_path) 19 | # Since we're not saving the PSD, success means no exceptions 20 | print("Test successful: Layer creation logic seems functional for multiple images.") 21 | except Exception as e: 22 | self.fail(f"Unexpected error: {e}") 23 | psd_files = glob.glob(os.path.join(os.path.dirname(output_path), "*.psd")) 24 | # Be sure the file is saved 25 | self.assertEqual(len(psd_files), 1) 26 | os.remove(psd_files[0]) 27 | 28 | def test_one_batch(self): 29 | """Tests the function with multiple batches.""" 30 | input_image = torch.rand(1, 256, 256, 3) 31 | output_path = "./test.psd" 32 | 33 | layer_divide = PSDLayerCreatorFromImagesOnly() 34 | try: 35 | layer_divide.main(input_image, output_path) 36 | # Since we're not saving the PSD, success means no exceptions 37 | print("Test successful: Layer creation logic seems functional for multiple images.") 38 | except Exception as e: 39 | self.fail(f"Unexpected error: {e}") 40 | psd_files = glob.glob(os.path.join(os.path.dirname(output_path), "*.psd")) 41 | # Be sure the file is saved 42 | self.assertEqual(len(psd_files), 1) 43 | os.remove(psd_files[0]) 44 | 45 | def test_invalid_batch_input_dimensions(self): 46 | """Tests the function with mismatched mask dimensions.""" 47 | input_image = torch.rand(4, 256, 256, 3) 48 | masks = torch.rand(3, 256, 256) # Mismatched dimensions 49 | output_path = "test.psd" 50 | original_image = True 51 | 52 | layer_divide = PSDLayerCreator() 53 | with self.assertRaises(ValueError) as error: 54 | layer_divide.main(input_image, masks, output_path, original_image) 55 | 56 | self.assertEqual(str(error.exception), "Batch input is not supported.") 57 | 58 | 59 | def test_invalid_mask_dimensions(self): 60 | """Tests the function with mismatched mask dimensions.""" 61 | input_image = torch.rand(1, 256, 256, 3) 62 | masks = torch.rand(3, 128, 128) # Mismatched dimensions 63 | output_path = "test.psd" 64 | original_image = True 65 | 66 | layer_divide = PSDLayerCreator() 67 | with self.assertRaises(ValueError) as error: 68 | layer_divide.main(input_image, masks, output_path, original_image) 69 | 70 | self.assertEqual(str(error.exception), "Mask dimensions must match the input image dimensions.") 71 | 72 | 73 | def test_single_mask(self): 74 | """Tests the function with a single mask.""" 75 | input_image = torch.rand(1, 256, 256, 3) 76 | masks = torch.rand(1, 256, 256) 77 | output_path = "test.psd" # Replace with a writable location for testing 78 | original_image = True 79 | 80 | layer_divide = PSDLayerCreator() 81 | # This test verifies basic layer creation logic without external libraries 82 | try: 83 | layer_divide.main(input_image, masks, output_path, original_image) 84 | # Since we're not saving the PSD, success means no exceptions 85 | print("Test successful: Layer creation logic seems functional for a single mask.") 86 | except Exception as e: 87 | self.fail(f"Unexpected error: {e}") 88 | os.remove(output_path) 89 | 90 | 91 | def test_multiple_masks(self): 92 | """Tests the function with multiple masks.""" 93 | input_image = torch.rand(1, 256, 256, 3) 94 | masks = torch.rand(10, 256, 256) 95 | output_path = "test.psd" # Replace with a writable location for testing 96 | original_image = True 97 | 98 | layer_divide = PSDLayerCreator() 99 | # This test verifies basic layer creation logic without external libraries 100 | try: 101 | layer_divide.main(input_image, masks, output_path, original_image) 102 | # Since we're not saving the PSD, success means no exceptions 103 | print("Test successful: Layer creation logic seems functional for multiple masks.") 104 | except Exception as e: 105 | self.fail(f"Unexpected error: {e}") 106 | os.remove(output_path) 107 | 108 | def test_already_with_alpha_channel(self): 109 | """Tests the function with input image already with alpha channel.""" 110 | input_image = torch.rand(1, 256, 256, 4) 111 | masks = torch.rand(10, 256, 256) 112 | output_path = "test.psd" # Replace with a writable location for testing 113 | original_image = True 114 | 115 | layer_divide = PSDLayerCreator() 116 | # This test verifies basic layer creation logic without external libraries 117 | try: 118 | layer_divide.main(input_image, masks, output_path, original_image) 119 | # Since we're not saving the PSD, success means no exceptions 120 | print("Test successful: Layer creation logic seems functional for multiple masks.") 121 | except Exception as e: 122 | self.fail(f"Unexpected error: {e}") 123 | os.remove(output_path) 124 | 125 | def test_output_path_no_name(self): 126 | """Tests the function with without giving the name of the file as output""" 127 | input_image = torch.rand(1, 256, 256, 4) 128 | masks = torch.rand(10, 256, 256) 129 | output_path = "./" # Replace with a writable location for testing 130 | original_image = True 131 | 132 | layer_divide = PSDLayerCreator() 133 | # This test verifies basic layer creation logic without external libraries 134 | try: 135 | layer_divide.main(input_image, masks, output_path, original_image) 136 | # Since we're not saving the PSD, success means no exceptions 137 | print("Test successful: Layer creation logic seems functional for multiple masks.") 138 | except Exception as e: 139 | self.fail(f"Unexpected error: {e}") 140 | psd_files = glob.glob(os.path.join(os.path.dirname(output_path), "*.psd")) 141 | # Be sure the file is saved 142 | self.assertEqual(len(psd_files), 1) 143 | os.remove(psd_files[0]) 144 | 145 | if __name__ == '__main__': 146 | unittest.main() 147 | --------------------------------------------------------------------------------