├── .github └── workflows │ └── publish.yml ├── README.md ├── __init__.py ├── animationAssistantNodes.py ├── animationWorkflows ├── animatediff │ ├── animateDiff_Vid_2_Vid(race and dress colour change).json │ ├── animateDiff_vid_2_cartoon.json │ └── vid2CartoonThicklines.json ├── generate_512x512_video_for_LivePortrait.json ├── img2img │ ├── ExtremelyFastImg2Img.json │ ├── ExtremelyFastImg2ImgSDXL.json │ └── img2cartoonFast.json ├── joinVideos │ ├── joinVideos_3no_15fps_&_2no__30fps.json │ ├── joinVideos_5no_15fps.json │ ├── joinVideos_5no_30fps.json │ ├── joinVideos_5no_30fps_2NoAudio.json │ └── joinVideos_9no_30fps.json └── move_fg_in_bg │ ├── 2 videos_with_2_subjects_in_moving_bg.mp4 │ ├── bike_moving_left.mp4 │ ├── car_speeding_out.mp4 │ ├── object_go_up_or_down.json │ ├── object_move_in_or_out.json │ ├── portrait_walk_in_or_out.json │ ├── portrait_walk_left_or_right.json │ ├── speeding car.mp4 │ └── video_with_movingOut_bg.mp4 ├── pyproject.toml ├── readMeAssets ├── joinVideos2.gif ├── joinVids.gif ├── livePortrait.gif ├── makeWalk.gif ├── moveLeft.gif ├── moveOut.gif ├── moveUpDown.gif └── replaceBg.gif ├── requirements.txt ├── styles.json └── utils.py /.github/workflows/publish.yml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - main 7 | paths: 8 | - "pyproject.toml" 9 | 10 | jobs: 11 | publish-node: 12 | name: Publish Custom Node to registry 13 | runs-on: ubuntu-latest 14 | # if this is a forked repository. Skipping the workflow. 15 | if: github.event.repository.fork == false 16 | steps: 17 | - name: Check out code 18 | uses: actions/checkout@v4 19 | - name: Publish Custom Node 20 | uses: Comfy-Org/publish-node-action@main 21 | with: 22 | ## Add your own personal access token to your Github Repository secrets and reference it here. 23 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} 24 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 3 | 4 | ## Join Videos 5 | 6 | The `Join Videos` node is for videos to video compilation. A node can take in up to 5 videos, and a combination of nodes can handle any number of videos with VRAM being the main limitation. 7 | 8 |
9 | 10 |

11 | 12 |
13 | 14 | You can watch a video about this node [here](https://youtu.be/WiLun8E9T-E) 15 | 16 | ## Replace Img_or_Vid_Bg 17 | 18 | The `Replace Img_or_Vid_Bg` node is for replacing the background of images and videos. It is to be used with any background removal node. 19 | 20 |
21 | 22 |

23 |
24 | 25 | ## Make Portrait Walk 26 | 27 | The `Make Portrait Walk` node receives a single portrait of a person and generates a batch of images that create the illusion of walking. 28 | 29 |
30 | 31 |

32 |
33 | 34 | ## Move In_Or_Out 35 | 36 | The `Move In_Or_Out` node can be used to create the illusion of moving into or out of a place. 37 | 38 |
39 | 40 |

41 |
42 | 43 | ## Move Left_Or_Right 44 | 45 | The `Move Left_Or_Right` node can be used to animate a background image to move right or left so that the foreground image looks like it's moving left or right. 46 | 47 |
48 | 49 |

50 | 51 |
52 | 53 | ## Move Up_Or_Down 54 | 55 | The `Move Up_Or_Down` node can be used to animate a background image to move down or up so that the foreground image looks like it's moving up or down. 56 | 57 |
58 | 59 |

60 |
61 | 62 | ## Video for LivePortrait 63 | 64 | The `Video for LivePortrait` node takes in a video of any size which contains a person and generates a 512x512 video of the person's face which can then be used as a driving video in Liveportrait (for face animation). 65 | 66 |
67 | 68 |

69 |
70 | 71 | You can watch a video about this node [here](https://youtu.be/uEicKGu89mY) 72 | 73 | 74 | ## Installation 75 | 76 | You can install these nodes via the ComfyUI Manager by searching for: 77 | 78 | 79 | ``` 80 | ComfyUI-Animation_Nodes_and_Workflows 81 | 82 | ``` 83 | 84 | Alternatively, 85 | You can get these nodes by downloading this repository, extracting, and placing it in the custom_nodes folder of your ComfyUI directory. 86 | You might already have all the requirements installed in your comfyUI environment, but if not, then with your comfyUI environment opened in your CLI, you can navigate to custom_nodes/ComfyUI-Animation_Nodes_and_Workflows folder and install requirements as shown below. 87 | 88 | ``` 89 | pip install -r requirements.txt 90 | 91 | ``` 92 | 93 | 94 | If using a python-embedded environment, you can open your CLI in your '/custom_nodes/ComfyUI-Animation_Nodes_and_Workflows' folder and install the requirements as shown below. 95 | 96 | 97 | ``` 98 | ..\..\..\python_embeded\python.exe -s -m pip install -r requirements.txt 99 | 100 | ``` 101 | 102 | You can restart comfyUI after the installation and start using the nodes. If you get any red node from the associated workflows, try installing the missing custom nodes with the comfyUI manager. If the nodes cannot be found, then check your CLI for any error. Also ensure you did not miss any step in the installation process. 103 | 104 | 105 | ** Note **: 106 | 107 | 108 | - >You can get the workflows from the animationWorkflows folder. The folder also contains animation workflows related to animatediff. 109 | 110 | 119 | 120 | 121 | 122 | ## Disclaimer 123 | 124 | I am not responsible for any user-generated content. Users are fully responsible for their actions when using these nodes and workflows. I don't have any legal affiliation with or accountability for users' behaviors. It is crucial to use these nodes and workflows responsibly, following both ethical and legal standards. 125 | 126 | 127 | 128 | 129 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .animationAssistantNodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 2 | 3 | __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] -------------------------------------------------------------------------------- /animationWorkflows/generate_512x512_video_for_LivePortrait.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 49, 3 | "last_link_id": 107, 4 | "nodes": [ 5 | { 6 | "id": 35, 7 | "type": "VHS_VideoCombine", 8 | "pos": { 9 | "0": 660, 10 | "1": -21 11 | }, 12 | "size": [ 13 | 427.8516540527344, 14 | 731.8516540527344 15 | ], 16 | "flags": {}, 17 | "order": 2, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "images", 22 | "type": "IMAGE", 23 | "link": 107 24 | }, 25 | { 26 | "name": "audio", 27 | "type": "AUDIO", 28 | "link": null 29 | }, 30 | { 31 | "name": "meta_batch", 32 | "type": "VHS_BatchManager", 33 | "link": null 34 | }, 35 | { 36 | "name": "vae", 37 | "type": "VAE", 38 | "link": null 39 | } 40 | ], 41 | "outputs": [ 42 | { 43 | "name": "Filenames", 44 | "type": "VHS_FILENAMES", 45 | "links": null, 46 | "shape": 3 47 | } 48 | ], 49 | "properties": { 50 | "Node name for S&R": "VHS_VideoCombine" 51 | }, 52 | "widgets_values": { 53 | "frame_rate": 30, 54 | "loop_count": 0, 55 | "filename_prefix": "bgRemovedVid", 56 | "format": "video/h264-mp4", 57 | "pix_fmt": "yuv420p", 58 | "crf": 19, 59 | "save_metadata": true, 60 | "pingpong": false, 61 | "save_output": false, 62 | "videopreview": { 63 | "hidden": false, 64 | "paused": false, 65 | "params": { 66 | "filename": "bgRemovedVid_00025.mp4", 67 | "subfolder": "", 68 | "type": "temp", 69 | "format": "video/h264-mp4", 70 | "frame_rate": 30 71 | }, 72 | "muted": false 73 | } 74 | } 75 | }, 76 | { 77 | "id": 49, 78 | "type": "MakeDrivingVideoForLivePortrait", 79 | "pos": { 80 | "0": 350, 81 | "1": -21 82 | }, 83 | "size": { 84 | "0": 248.30335998535156, 85 | "1": 126 86 | }, 87 | "flags": {}, 88 | "order": 1, 89 | "mode": 0, 90 | "inputs": [ 91 | { 92 | "name": "video", 93 | "type": "IMAGE", 94 | "link": 106 95 | } 96 | ], 97 | "outputs": [ 98 | { 99 | "name": "video", 100 | "type": "IMAGE", 101 | "links": [ 102 | 107 103 | ], 104 | "slot_index": 0, 105 | "shape": 3 106 | }, 107 | { 108 | "name": "video2", 109 | "type": "IMAGE", 110 | "links": null, 111 | "slot_index": 1, 112 | "shape": 3 113 | } 114 | ], 115 | "properties": { 116 | "Node name for S&R": "MakeDrivingVideoForLivePortrait" 117 | }, 118 | "widgets_values": [ 119 | false, 120 | false, 121 | 256 122 | ] 123 | }, 124 | { 125 | "id": 48, 126 | "type": "VHS_LoadVideo", 127 | "pos": { 128 | "0": 41, 129 | "1": -17 130 | }, 131 | "size": [ 132 | 247.455078125, 133 | 503.455078125 134 | ], 135 | "flags": {}, 136 | "order": 0, 137 | "mode": 0, 138 | "inputs": [ 139 | { 140 | "name": "meta_batch", 141 | "type": "VHS_BatchManager", 142 | "link": null 143 | }, 144 | { 145 | "name": "vae", 146 | "type": "VAE", 147 | "link": null 148 | } 149 | ], 150 | "outputs": [ 151 | { 152 | "name": "IMAGE", 153 | "type": "IMAGE", 154 | "links": [ 155 | 106 156 | ], 157 | "slot_index": 0, 158 | "shape": 3 159 | }, 160 | { 161 | "name": "frame_count", 162 | "type": "INT", 163 | "links": null, 164 | "shape": 3 165 | }, 166 | { 167 | "name": "audio", 168 | "type": "AUDIO", 169 | "links": null, 170 | "shape": 3 171 | }, 172 | { 173 | "name": "video_info", 174 | "type": "VHS_VIDEOINFO", 175 | "links": null, 176 | "shape": 3 177 | } 178 | ], 179 | "properties": { 180 | "Node name for S&R": "VHS_LoadVideo" 181 | }, 182 | "widgets_values": { 183 | "video": "moveForewardGif_00004.mp4", 184 | "force_rate": 0, 185 | "force_size": "Disabled", 186 | "custom_width": 512, 187 | "custom_height": 512, 188 | "frame_load_cap": 20, 189 | "skip_first_frames": 10, 190 | "select_every_nth": 1, 191 | "choose video to upload": "image", 192 | "videopreview": { 193 | "hidden": false, 194 | "paused": false, 195 | "params": { 196 | "frame_load_cap": 20, 197 | "skip_first_frames": 10, 198 | "force_rate": 0, 199 | "filename": "moveForewardGif_00004.mp4", 200 | "type": "input", 201 | "format": "video/mp4", 202 | "select_every_nth": 1 203 | }, 204 | "muted": false 205 | } 206 | } 207 | } 208 | ], 209 | "links": [ 210 | [ 211 | 106, 212 | 48, 213 | 0, 214 | 49, 215 | 0, 216 | "IMAGE" 217 | ], 218 | [ 219 | 107, 220 | 49, 221 | 0, 222 | 35, 223 | 0, 224 | "IMAGE" 225 | ] 226 | ], 227 | "groups": [], 228 | "config": {}, 229 | "extra": { 230 | "ds": { 231 | "scale": 1.2284597357367426, 232 | "offset": [ 233 | 411.5665916776225, 234 | 205.7806150869727 235 | ] 236 | } 237 | }, 238 | "version": 0.4 239 | } -------------------------------------------------------------------------------- /animationWorkflows/img2img/ExtremelyFastImg2Img.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 24, 3 | "last_link_id": 39, 4 | "nodes": [ 5 | { 6 | "id": 10, 7 | "type": "PreviewImage", 8 | "pos": { 9 | "0": 261.917724609375, 10 | "1": -760.8536987304688 11 | }, 12 | "size": { 13 | "0": 335.5668640136719, 14 | "1": 693.993408203125 15 | }, 16 | "flags": {}, 17 | "order": 10, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "images", 22 | "type": "IMAGE", 23 | "link": 10 24 | } 25 | ], 26 | "outputs": [], 27 | "properties": { 28 | "Node name for S&R": "PreviewImage" 29 | }, 30 | "widgets_values": [] 31 | }, 32 | { 33 | "id": 18, 34 | "type": "PreviewImage", 35 | "pos": { 36 | "0": 622.9177856445312, 37 | "1": -750.8536987304688 38 | }, 39 | "size": { 40 | "0": 330.10015869140625, 41 | "1": 675.7266845703125 42 | }, 43 | "flags": {}, 44 | "order": 12, 45 | "mode": 0, 46 | "inputs": [ 47 | { 48 | "name": "images", 49 | "type": "IMAGE", 50 | "link": 21 51 | } 52 | ], 53 | "outputs": [], 54 | "properties": { 55 | "Node name for S&R": "PreviewImage" 56 | }, 57 | "widgets_values": [] 58 | }, 59 | { 60 | "id": 4, 61 | "type": "CheckpointLoaderSimple", 62 | "pos": { 63 | "0": -635.210693359375, 64 | "1": 27.455110549926758 65 | }, 66 | "size": { 67 | "0": 315, 68 | "1": 98 69 | }, 70 | "flags": {}, 71 | "order": 0, 72 | "mode": 0, 73 | "inputs": [], 74 | "outputs": [ 75 | { 76 | "name": "MODEL", 77 | "type": "MODEL", 78 | "links": [ 79 | 22 80 | ], 81 | "slot_index": 0 82 | }, 83 | { 84 | "name": "CLIP", 85 | "type": "CLIP", 86 | "links": [ 87 | 11 88 | ], 89 | "slot_index": 1 90 | }, 91 | { 92 | "name": "VAE", 93 | "type": "VAE", 94 | "links": [ 95 | 8, 96 | 24 97 | ], 98 | "slot_index": 2 99 | } 100 | ], 101 | "properties": { 102 | "Node name for S&R": "CheckpointLoaderSimple" 103 | }, 104 | "widgets_values": [ 105 | "realisticVisionV60B1_v20Novae.safetensors" 106 | ] 107 | }, 108 | { 109 | "id": 19, 110 | "type": "LoraLoaderModelOnly", 111 | "pos": { 112 | "0": -246.21099853515625, 113 | "1": 27.455110549926758 114 | }, 115 | "size": { 116 | "0": 315, 117 | "1": 82 118 | }, 119 | "flags": {}, 120 | "order": 3, 121 | "mode": 0, 122 | "inputs": [ 123 | { 124 | "name": "model", 125 | "type": "MODEL", 126 | "link": 22 127 | } 128 | ], 129 | "outputs": [ 130 | { 131 | "name": "MODEL", 132 | "type": "MODEL", 133 | "links": [ 134 | 38 135 | ], 136 | "slot_index": 0, 137 | "shape": 3 138 | } 139 | ], 140 | "properties": { 141 | "Node name for S&R": "LoraLoaderModelOnly" 142 | }, 143 | "widgets_values": [ 144 | "lcm_lora_sd15.safetensors", 145 | 1 146 | ] 147 | }, 148 | { 149 | "id": 14, 150 | "type": "ControlNetLoader", 151 | "pos": { 152 | "0": -422.26751708984375, 153 | "1": 465.09454345703125 154 | }, 155 | "size": { 156 | "0": 315, 157 | "1": 58 158 | }, 159 | "flags": {}, 160 | "order": 1, 161 | "mode": 0, 162 | "inputs": [], 163 | "outputs": [ 164 | { 165 | "name": "CONTROL_NET", 166 | "type": "CONTROL_NET", 167 | "links": [ 168 | 13 169 | ], 170 | "slot_index": 0, 171 | "shape": 3 172 | } 173 | ], 174 | "properties": { 175 | "Node name for S&R": "ControlNetLoader" 176 | }, 177 | "widgets_values": [ 178 | "control_v11f1e_sd15_tile.pth" 179 | ] 180 | }, 181 | { 182 | "id": 13, 183 | "type": "ControlNetApplyAdvanced", 184 | "pos": { 185 | "0": -9.267619132995605, 186 | "1": 219.09457397460938 187 | }, 188 | "size": { 189 | "0": 315, 190 | "1": 186 191 | }, 192 | "flags": {}, 193 | "order": 7, 194 | "mode": 0, 195 | "inputs": [ 196 | { 197 | "name": "positive", 198 | "type": "CONDITIONING", 199 | "link": 14 200 | }, 201 | { 202 | "name": "negative", 203 | "type": "CONDITIONING", 204 | "link": 15 205 | }, 206 | { 207 | "name": "control_net", 208 | "type": "CONTROL_NET", 209 | "link": 13 210 | }, 211 | { 212 | "name": "image", 213 | "type": "IMAGE", 214 | "link": 12 215 | }, 216 | { 217 | "name": "vae", 218 | "type": "VAE", 219 | "link": null 220 | } 221 | ], 222 | "outputs": [ 223 | { 224 | "name": "positive", 225 | "type": "CONDITIONING", 226 | "links": [ 227 | 35 228 | ], 229 | "slot_index": 0, 230 | "shape": 3 231 | }, 232 | { 233 | "name": "negative", 234 | "type": "CONDITIONING", 235 | "links": [ 236 | 37 237 | ], 238 | "slot_index": 1, 239 | "shape": 3 240 | } 241 | ], 242 | "properties": { 243 | "Node name for S&R": "ControlNetApplyAdvanced" 244 | }, 245 | "widgets_values": [ 246 | 0.7000000000000001, 247 | 0, 248 | 0.8 249 | ] 250 | }, 251 | { 252 | "id": 16, 253 | "type": "VAEEncode", 254 | "pos": { 255 | "0": 475.1601257324219, 256 | "1": 315.832763671875 257 | }, 258 | "size": { 259 | "0": 210, 260 | "1": 46 261 | }, 262 | "flags": {}, 263 | "order": 5, 264 | "mode": 0, 265 | "inputs": [ 266 | { 267 | "name": "pixels", 268 | "type": "IMAGE", 269 | "link": 18 270 | }, 271 | { 272 | "name": "vae", 273 | "type": "VAE", 274 | "link": 24 275 | } 276 | ], 277 | "outputs": [ 278 | { 279 | "name": "LATENT", 280 | "type": "LATENT", 281 | "links": [ 282 | 19 283 | ], 284 | "slot_index": 0, 285 | "shape": 3 286 | } 287 | ], 288 | "properties": { 289 | "Node name for S&R": "VAEEncode" 290 | }, 291 | "widgets_values": [] 292 | }, 293 | { 294 | "id": 8, 295 | "type": "VAEDecode", 296 | "pos": { 297 | "0": 1069.15966796875, 298 | "1": 53.832767486572266 299 | }, 300 | "size": { 301 | "0": 210, 302 | "1": 46 303 | }, 304 | "flags": {}, 305 | "order": 9, 306 | "mode": 0, 307 | "inputs": [ 308 | { 309 | "name": "samples", 310 | "type": "LATENT", 311 | "link": 7 312 | }, 313 | { 314 | "name": "vae", 315 | "type": "VAE", 316 | "link": 8 317 | } 318 | ], 319 | "outputs": [ 320 | { 321 | "name": "IMAGE", 322 | "type": "IMAGE", 323 | "links": [ 324 | 10, 325 | 20 326 | ], 327 | "slot_index": 0 328 | } 329 | ], 330 | "properties": { 331 | "Node name for S&R": "VAEDecode" 332 | }, 333 | "widgets_values": [] 334 | }, 335 | { 336 | "id": 24, 337 | "type": "LoraLoaderModelOnly", 338 | "pos": { 339 | "0": 133.79466247558594, 340 | "1": 26.727554321289062 341 | }, 342 | "size": { 343 | "0": 315, 344 | "1": 82 345 | }, 346 | "flags": {}, 347 | "order": 6, 348 | "mode": 0, 349 | "inputs": [ 350 | { 351 | "name": "model", 352 | "type": "MODEL", 353 | "link": 38 354 | } 355 | ], 356 | "outputs": [ 357 | { 358 | "name": "MODEL", 359 | "type": "MODEL", 360 | "links": [ 361 | 39 362 | ], 363 | "slot_index": 0, 364 | "shape": 3 365 | } 366 | ], 367 | "properties": { 368 | "Node name for S&R": "LoraLoaderModelOnly" 369 | }, 370 | "widgets_values": [ 371 | "thickline_fp16.safetensors", 372 | 1 373 | ] 374 | }, 375 | { 376 | "id": 17, 377 | "type": "Painting", 378 | "pos": { 379 | "0": 1041.15966796875, 380 | "1": 171.832763671875 381 | }, 382 | "size": { 383 | "0": 315, 384 | "1": 270 385 | }, 386 | "flags": {}, 387 | "order": 11, 388 | "mode": 0, 389 | "inputs": [ 390 | { 391 | "name": "image", 392 | "type": "IMAGE", 393 | "link": 20 394 | }, 395 | { 396 | "name": "lineArt", 397 | "type": "IMAGE", 398 | "link": null 399 | } 400 | ], 401 | "outputs": [ 402 | { 403 | "name": "painting", 404 | "type": "IMAGE", 405 | "links": null, 406 | "shape": 3 407 | }, 408 | { 409 | "name": "sharpImage", 410 | "type": "IMAGE", 411 | "links": [ 412 | 21 413 | ], 414 | "slot_index": 1, 415 | "shape": 3 416 | } 417 | ], 418 | "properties": { 419 | "Node name for S&R": "Painting" 420 | }, 421 | "widgets_values": [ 422 | 26, 423 | 1, 424 | 7, 425 | 1, 426 | 0, 427 | 0.5, 428 | 1.4, 429 | 1, 430 | false 431 | ] 432 | }, 433 | { 434 | "id": 3, 435 | "type": "KSampler", 436 | "pos": { 437 | "0": 710.8602905273438, 438 | "1": 27.345535278320312 439 | }, 440 | "size": { 441 | "0": 315, 442 | "1": 474 443 | }, 444 | "flags": {}, 445 | "order": 8, 446 | "mode": 0, 447 | "inputs": [ 448 | { 449 | "name": "model", 450 | "type": "MODEL", 451 | "link": 39 452 | }, 453 | { 454 | "name": "positive", 455 | "type": "CONDITIONING", 456 | "link": 35 457 | }, 458 | { 459 | "name": "negative", 460 | "type": "CONDITIONING", 461 | "link": 37 462 | }, 463 | { 464 | "name": "latent_image", 465 | "type": "LATENT", 466 | "link": 19 467 | } 468 | ], 469 | "outputs": [ 470 | { 471 | "name": "LATENT", 472 | "type": "LATENT", 473 | "links": [ 474 | 7 475 | ], 476 | "slot_index": 0 477 | } 478 | ], 479 | "properties": { 480 | "Node name for S&R": "KSampler" 481 | }, 482 | "widgets_values": [ 483 | 1000, 484 | "fixed", 485 | 6, 486 | 1.5, 487 | "lcm", 488 | "normal", 489 | 0.25 490 | ] 491 | }, 492 | { 493 | "id": 11, 494 | "type": "CLIPTextEncodeStyles", 495 | "pos": { 496 | "0": -489, 497 | "1": 217 498 | }, 499 | "size": { 500 | "0": 400, 501 | "1": 200 502 | }, 503 | "flags": {}, 504 | "order": 4, 505 | "mode": 0, 506 | "inputs": [ 507 | { 508 | "name": "clip", 509 | "type": "CLIP", 510 | "link": 11 511 | } 512 | ], 513 | "outputs": [ 514 | { 515 | "name": "+VE", 516 | "type": "CONDITIONING", 517 | "links": [ 518 | 14 519 | ], 520 | "slot_index": 0, 521 | "shape": 3 522 | }, 523 | { 524 | "name": "-VE", 525 | "type": "CONDITIONING", 526 | "links": [ 527 | 15 528 | ], 529 | "slot_index": 1, 530 | "shape": 3 531 | } 532 | ], 533 | "properties": { 534 | "Node name for S&R": "CLIPTextEncodeStyles" 535 | }, 536 | "widgets_values": [ 537 | "", 538 | "", 539 | "Anime" 540 | ] 541 | }, 542 | { 543 | "id": 15, 544 | "type": "LoadImage", 545 | "pos": { 546 | "0": -94, 547 | "1": -793 548 | }, 549 | "size": [ 550 | 338.56568023767215, 551 | 687.0202185163913 552 | ], 553 | "flags": {}, 554 | "order": 2, 555 | "mode": 0, 556 | "inputs": [], 557 | "outputs": [ 558 | { 559 | "name": "IMAGE", 560 | "type": "IMAGE", 561 | "links": [ 562 | 12, 563 | 18 564 | ], 565 | "slot_index": 0, 566 | "shape": 3 567 | }, 568 | { 569 | "name": "MASK", 570 | "type": "MASK", 571 | "links": null, 572 | "shape": 3 573 | } 574 | ], 575 | "properties": { 576 | "Node name for S&R": "LoadImage" 577 | }, 578 | "widgets_values": [ 579 | "officeGirl512.png", 580 | "image" 581 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/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | torchaudio 3 | numpy 4 | opencv-python 5 | mediapipe -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torchaudio 3 | import numpy as np 4 | import cv2 5 | import json 6 | import os 7 | 8 | 9 | 10 | def convert_to_uint8(image): 11 | if image.dtype == np.uint8: 12 | return image 13 | if image.dtype != np.float32 and image.dtype != np.float64: 14 | image = image.astype(np.float32) 15 | if image.max() <= 1.0: 16 | image = image * 255.0 17 | image = np.clip(image, 0, 255) 18 | return image.astype(np.uint8) 19 | 20 | def convert_to_unit8_v2(image): 21 | image = (image - image.min()) / (image.max() - image.min())*255 22 | 23 | return image.astype(np.uint8) 24 | 25 | 26 | 27 | 28 | def resize_image(imageTensor, target_height, target_width): 29 | 30 | images = [] 31 | for image in imageTensor: 32 | print("Resizing Image...") 33 | image = image.squeeze(0).cpu().numpy() 34 | image = convert_to_uint8(image) 35 | image = cv2.resize(image, (int(target_width), int(target_height)), interpolation=cv2.INTER_AREA) 36 | image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).float()/255 37 | image = image.permute(0, 2, 3, 1) 38 | images.append(image) 39 | 40 | print ("Images resized!") 41 | return torch.cat(images, dim=0) 42 | 43 | 44 | 45 | 46 | def generate_silent_audio(duration, sample_rate, num_channels=2, batch_size=1): 47 | """ 48 | Generates a silent audio tensor of the given duration. 49 | 50 | Parameters: 51 | duration (float): Duration of the silence in seconds (can be fractional). 52 | sample_rate (int): The audio sample rate (e.g., 44100 Hz). 53 | num_channels (int): Number of audio channels (1 for mono, 2 for stereo, etc.). 54 | batch_size (int): Number of audio batches to generate (default is 1). 55 | 56 | Returns: 57 | torch.Tensor: A 3D tensor filled with zeros, representing silence with shape (batch_size, num_channels, num_samples). 58 | """ 59 | # Compute the total number of samples based on duration and sample rate 60 | num_samples = int(round(duration * sample_rate)) 61 | 62 | # Return a 3D tensor of zeros (representing silence) 63 | return torch.zeros((batch_size, 2, num_samples), dtype=torch.float32) 64 | 65 | 66 | 67 | def resample_audio(audio_tensor, target_sample_rate): 68 | """ 69 | Resamples the audio to the target sample rate if necessary, and ensures a 3D tensor output. 70 | 71 | Parameters: 72 | audio_tensor (dict): Dictionary containing 'waveform' (audio tensor) and 'sample_rate'. 73 | target_sample_rate (int): The target sample rate to resample the audio. 74 | 75 | Returns: 76 | dict: Dictionary containing the resampled 'waveform' (3D tensor) and 'sample_rate'. 77 | """ 78 | orig_sample_rate = audio_tensor['sample_rate'] 79 | waveform = audio_tensor['waveform'] 80 | 81 | # Ensure the waveform has a batch dimension (convert 2D to 3D if necessary) 82 | if waveform.dim() == 2: # Shape is (num_channels, num_samples) 83 | waveform = waveform.unsqueeze(0) # Add a batch dimension -> (1, num_channels, num_samples) 84 | 85 | # Resample if necessary 86 | if orig_sample_rate != target_sample_rate: 87 | resampler = torchaudio.transforms.Resample(orig_freq=orig_sample_rate, new_freq=target_sample_rate) 88 | resampled_waveform = resampler(waveform) 89 | return {'waveform': resampled_waveform, 'sample_rate': target_sample_rate} 90 | else: 91 | return {'waveform': waveform, 'sample_rate': orig_sample_rate} 92 | 93 | 94 | 95 | def get_first_available_sample_rate(audios_list, default_sample_rate=44100): 96 | """ 97 | Get the sample rate of the first available (non-None) audio from the list. 98 | 99 | Parameters: 100 | audios_list (list of dict): List of dictionaries containing 'waveform' and 'sample_rate', 101 | where None indicates no audio is present. 102 | default_sample_rate (int): Default sample rate to return if no valid audio is found. 103 | 104 | Returns: 105 | int: The sample rate of the first available audio or the default sample rate if none is found. 106 | """ 107 | for audio_tensor in audios_list: 108 | if audio_tensor is not None: 109 | # Extract the sample rate from the dictionary 110 | print (audio_tensor['sample_rate']) 111 | return audio_tensor['sample_rate'] # Return the sample rate of the first valid audio 112 | 113 | # Return the default sample rate if no valid audio is found 114 | print("No valid audio sample rate could be determined from the audio list. Using default sample rate.") 115 | 116 | return default_sample_rate 117 | 118 | 119 | 120 | 121 | def create_frame_and_audio_lists(frames_set1=None, frames_set2=None, frames_set3=None, frames_set4=None, frames_set5=None, audio_set1=None, audio_set2=None, audio_set3=None, audio_set4=None, audio_set5=None): 122 | 123 | frames_list = [frames_set1, frames_set2, frames_set3, frames_set4, frames_set5] 124 | audios_list = [audio_set1, audio_set2, audio_set3, audio_set4, audio_set5] 125 | filtered_frames_list = [frames for frames in frames_list if frames is not None] 126 | 127 | return filtered_frames_list, audios_list 128 | 129 | 130 | 131 | 132 | def combine_frames_and_audios(frames_list, audios_list=None, frame_rate=30): 133 | """ 134 | Combines multiple sets of image frames (as tensors) and audios (as tensors) into a single set. 135 | Inserts silence for image sets that have no corresponding audio. Resizes and crops images. 136 | 137 | Parameters: 138 | frames_list (list of torch.Tensor): A list of 4D tensors of image frames (batch_size x channels x height x width). 139 | audios_list (list of dict, optional): A list of dictionaries, each containing 'waveform' (audio tensor) and 'sample_rate', or None if no audio is present. 140 | 141 | Returns: 142 | combined_frames (torch.Tensor): A combined 4D tensor of image frames. 143 | combined_audio (torch.Tensor or None): A combined 3D tensor of audio data, with silence where necessary. 144 | """ 145 | # Get the target width and height from the first set of frames 146 | target_height, target_width = frames_list[0].size(1), frames_list[0].size(2) 147 | 148 | # print(f"target_height, target_width = {frames_list[0].size(1), frames_list[0].size(2)}") 149 | 150 | # Resize and crop each set of frames to match the first set 151 | resized_frames_list = [resize_image(frames, target_height, target_width) for frames in frames_list] 152 | 153 | # Combine the frames from all videos 154 | combined_frames = torch.cat(resized_frames_list, dim=0) # Concatenate along the batch dimension 155 | 156 | # print(video_info) 157 | # if video_info is not None: 158 | # first_key = next(iter(video_info)) 159 | # first_value = video_info[first_key] 160 | 161 | fps = frame_rate 162 | 163 | # If audios_list is provided, resample and combine audios 164 | if audios_list is not None: 165 | # Get the sample rate of the first available audio tensor 166 | first_audio_sample_rate = get_first_available_sample_rate(audios_list) 167 | 168 | # Prepare a list to store resampled or silent audio tensors 169 | combined_audios = [] 170 | combined_audios_dict = [] 171 | 172 | for i, frames in enumerate(frames_list): 173 | 174 | num_frames = frames.size(0) # Number of frames in this set 175 | print(f"There are {num_frames} frames in this video.") 176 | duration = num_frames / fps # Duration in seconds based on the frame rate 177 | 178 | # print(f"The duration of this frame is : {duration}") 179 | 180 | audio_dict = audios_list[i] # The audio dictionary for this set 181 | 182 | if audio_dict is None: 183 | # Extract the number of channels from the first available audio or default to 1 184 | num_channels = audios_list[0]['waveform'].size(0) if audios_list[0] is not None else 2 185 | silent_audio = generate_silent_audio(duration, first_audio_sample_rate, num_channels) 186 | combined_audios.append(silent_audio) 187 | 188 | print("No audio provided, so silent audio added!") 189 | # print(f"Silent Audio: {silent_audio.shape}") 190 | else: 191 | # Extract the waveform and sample_rate from the audio dictionary 192 | waveform = audio_dict['waveform'] 193 | sample_rate = audio_dict['sample_rate'] 194 | print(f"Audio provided! Sample rate: {sample_rate}") 195 | # Resample the audio to match the sample rate of the first audio set 196 | resampled_audio = resample_audio({'waveform': waveform, 'sample_rate': sample_rate}, first_audio_sample_rate) 197 | print(f"New Sample rate: {resampled_audio['sample_rate']}") 198 | # print(f"Audio: {resampled_audio['waveform'].shape}") 199 | combined_audios.append(resampled_audio['waveform']) # Append the resampled waveform 200 | # combined_audios.append(audio_dict['waveform']) 201 | 202 | # Concatenate all audios (real and silent) 203 | combined_audio = torch.cat(combined_audios, dim=2) # Concatenate along the sample dimension 204 | 205 | combined_audios_dict = { 206 | 'waveform': combined_audio, 207 | 'sample_rate': first_audio_sample_rate 208 | } 209 | 210 | else: 211 | combined_audios_dict = None 212 | print("No audio provided!") 213 | 214 | return combined_frames, combined_audios_dict 215 | 216 | 217 | 218 | def get_script_dir(): 219 | """Get the directory where the script is located.""" 220 | return os.path.dirname(os.path.abspath(__file__)) 221 | 222 | def load_styles(): 223 | """Load the styles.json file from the script's directory.""" 224 | script_dir = get_script_dir() 225 | styles_path = os.path.join(script_dir, "styles.json") 226 | 227 | try: 228 | with open(styles_path, "r") as f: 229 | return json.load(f) 230 | except FileNotFoundError: 231 | print(f"Error: The styles.json file was not found in {styles_path}.") 232 | # Optional: return default styles as a fallback 233 | return { 234 | "none": { 235 | "positive": "", 236 | "negative": "" 237 | }, 238 | "anime1": { 239 | "positive": "Japanese comic book anime style, samurai x, exaggerated features, sharp dark lines, anime, anime style, colorful, vibrant, detailed, intricate, large expressive eyes", 240 | "negative": "text, logo, signature, watermark, soft edges, low quality, blurry, realistic style, distorted faces, bad anatomy, poor proportions" 241 | }, 242 | "anime2": { 243 | "positive": "a beautiful anime-style illustration, expressive eyes, exaggerated features, vibrant colors, detailed, expressive characters, studio ghibli influence", 244 | "negative": "low quality, blurry, realistic style, distorted faces, bad anatomy, poor proportions" 245 | }, 246 | "beauArt": { 247 | "positive": "hard edges, black and white book illustration, bold lines, black lines, white background", 248 | "negative": "color, poor contrast, realistic, soft edges" 249 | }, 250 | "black Line art":{ 251 | "positive": "Black Line art, black and white, sharp lines, clear, minimalist, graphic, precision, ink, monochrome", 252 | "negative": "photograph, stock photo, realistic, deformed, glitch, color, vague, blurry, noisy, low contrast, photorealistic, realism, impressionism, expressionism, oil, acrylic, watercolor, pastel, textured, gradient, shaded" 253 | }, 254 | "comic1": { 255 | "positive": "vivid comic book illustration, bold lines, halftone shading, dramatic action", 256 | "negative": "washed-out colors, lack of contrast, soft edges, overly detailed backgrounds" 257 | }, 258 | "comic2": { 259 | "positive": "comic book style, dynamic pose, exaggerated features, bold lines, vibrant colors, ink wash, retro style, comic book panel", 260 | "negative": "blurry, low quality, poorly drawn, deformed, disfigured, extra limbs, missing limbs, extra fingers, missing fingers, unrealistic anatomy, poorly proportioned, monochrome, grayscale, black and white" 261 | }, 262 | "disney": { 263 | "positive": "classic Disney-style art, colorful, detailed characters, expressive emotions, smooth animations, magical and whimsical setting", 264 | "negative": "dull colors, lack of detail, distorted features, dark themes" 265 | }, 266 | "manga": { 267 | "positive": "black and white manga illustration, bold lines, dynamic compositions, detailed linework, expressive characters, action-packed panels", 268 | "negative": "color, poor contrast, lack of detail, blurry, distorted anatomy, flat expressions" 269 | }, 270 | "ps1": { 271 | "positive": "low-poly 3D graphics in PS1 style, retro gaming aesthetics, simple geometric shapes, nostalgic vibe", 272 | "negative": "modern high-resolution graphics, realistic textures, excessive detail, smooth rendering" 273 | }, 274 | "pixar": { 275 | "positive": "a 3D animation in Pixar style, highly detailed, vibrant, whimsical, expressive characters, cinematic lighting, storybook atmosphere", 276 | "negative": "grainy textures, flat colors, dull, uninspired composition, poor rendering" 277 | }, 278 | "toy": { 279 | "positive": "toy-like miniature characters, bright colors, soft plastic textures, whimsical design, playful atmosphere, detailed craftsmanship", 280 | "negative": "realistic details, dull colors, rough textures, lack of charm" 281 | }, 282 | "watercolor": { 283 | "positive": "soft and vibrant watercolor painting, gentle gradients, flowing textures, light and airy feel, pastel tones", 284 | "negative": "harsh lines, digital appearance, over-saturated colors, poor blending" 285 | } 286 | 287 | } 288 | 289 | 290 | def get_available_styles(): 291 | styles = load_styles() 292 | return list(styles.keys()) 293 | 294 | 295 | def generate_prompts(art_style, user_positive_prompt="sharp image", user_negative_prompt="blurry"): 296 | styles = load_styles() 297 | 298 | if art_style not in styles: 299 | return { 300 | "positive_prompt": f"Sorry, no predefined prompts for the '{art_style}' style. Please specify another style.", 301 | "negative_prompt": "None" 302 | } 303 | 304 | style = styles[art_style] 305 | combined_positive = f"{style['positive']}, {user_positive_prompt}".strip(", ") 306 | combined_negative = f"{style['negative']}, {user_negative_prompt}".strip(", ") 307 | 308 | return { 309 | "positive_prompt": combined_positive, 310 | "negative_prompt": combined_negative 311 | } 312 | 313 | 314 | --------------------------------------------------------------------------------