zsxkib/diffbir 🔢🖼️✓❓ → 🖼️
About
✨DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

Example Output
Output



Performance Metrics
73.05s
Prediction Time
75.05s
Total Time
All Input Parameters
{ "seed": 231, "input": "https://replicate.delivery/pbxt/JgdLVwRXXl4oaGqmF4Wdl7vOapnTlay32dE7B3UNgxSwylvQ/Audrey_Hepburn.jpg", "steps": 50, "tiled": false, "tile_size": 512, "has_aligned": false, "tile_stride": 256, "repeat_times": 1, "use_guidance": false, "color_fix_type": "wavelet", "guidance_scale": 0, "guidance_space": "latent", "guidance_repeat": 5, "only_center_face": false, "guidance_time_stop": -1, "guidance_time_start": 1001, "background_upsampler": "DiffBIR", "face_detection_model": "retinaface_resnet50", "upscaling_model_type": "faces", "restoration_model_type": "general_scenes", "super_resolution_factor": 2, "disable_preprocess_model": false, "reload_restoration_model": false, "background_upsampler_tile": 400, "background_upsampler_tile_stride": 400 }
Input Parameters
- seed
- Random seed to ensure reproducibility. Setting this ensures that multiple runs with the same input produce the same output.
- input (required)
- Path to the input image you want to enhance.
- steps
- The number of enhancement iterations to perform. More steps might result in a clearer image but can also introduce artifacts.
- tiled
- Whether to use patch-based sampling. This can be useful for very large images to enhance them in smaller chunks rather than all at once.
- tile_size
- Size of each tile (or patch) when 'tiled' option is enabled. Determines how the image is divided during patch-based enhancement.
- has_aligned
- For 'faces' mode: Indicates if the input images are already cropped and aligned to faces. If not, the model will attempt to do this.
- tile_stride
- Distance between the start of each tile when the image is divided for patch-based enhancement. A smaller stride means more overlap between tiles.
- repeat_times
- Number of times the enhancement process is repeated by feeding the output back as input. This can refine the result but might also introduce over-enhancement issues.
- use_guidance
- Use latent image guidance for enhancement. This can help in achieving more accurate and contextually relevant enhancements.
- color_fix_type
- Method used for color correction post enhancement. 'wavelet' and 'adain' offer different styles of color correction, while 'none' skips this step.
- guidance_scale
- For 'general_scenes': Scale factor for the guidance mechanism. Adjusts the influence of guidance on the enhancement process.
- guidance_space
- For 'general_scenes': Determines in which space (RGB or latent) the guidance operates. 'latent' can often provide more subtle and context-aware enhancements.
- guidance_repeat
- For 'general_scenes': Number of times the guidance process is repeated during enhancement.
- only_center_face
- For 'faces' mode: If multiple faces are detected, only enhance the center-most face in the image.
- guidance_time_stop
- For 'general_scenes': Specifies when (at which step) the guidance mechanism stops influencing the enhancement.
- guidance_time_start
- For 'general_scenes': Specifies when (at which step) the guidance mechanism starts influencing the enhancement.
- background_upsampler
- For 'faces' mode: Model used to upscale the background in images where the primary subject is a face.
- face_detection_model
- For 'faces' mode: Model used for detecting faces in the image. Choose based on accuracy and speed preferences.
- upscaling_model_type
- Choose the type of model best suited for the primary content of the image: 'faces' for portraits and 'general_scenes' for everything else.
- restoration_model_type
- Select the restoration model that aligns with the content of your image. This model is responsible for image restoration which removes degradations.
- super_resolution_factor
- Factor by which the input image resolution should be increased. For instance, a factor of 4 will make the resolution 4 times greater in both height and width.
- disable_preprocess_model
- Disables the initial preprocessing step using SwinIR. Turn this off if your input image is already of high quality and doesn't require restoration.
- reload_restoration_model
- Reload the image restoration model (SwinIR) if set to True. This can be useful if you've updated or changed the underlying SwinIR model.
- background_upsampler_tile
- For 'faces' mode: Size of each tile used by the background upsampler when dividing the image into patches.
- background_upsampler_tile_stride
- For 'faces' mode: Distance between the start of each tile when the background is divided for upscaling. A smaller stride means more overlap between tiles.
Output Schema
Output
Example Execution Logs
ckptckptckpt weights/face_full_v1.ckpt Switching from mode 'FULL' to 'FACE'... Building and loading 'FACE' mode model... ControlLDM: Running in eps-prediction mode Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. DiffusionWrapper has 865.91 M params. making attention of type 'vanilla-xformers' with 512 in_channels building MemoryEfficientAttnBlock with 512 in_channels... Working with z of shape (1, 4, 32, 32) = 4096 dimensions. making attention of type 'vanilla-xformers' with 512 in_channels building MemoryEfficientAttnBlock with 512 in_channels... Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off] Loading model from: /root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/lpips/weights/v0.1/alex.pth reload swinir model from weights/general_swinir_v1.ckpt ENABLE XFORMERS! Model successfully switched to 'FACE' mode. {'bg_tile': 400, 'bg_tile_stride': 400, 'bg_upsampler': 'DiffBIR', 'ckpt': 'weights/face_full_v1.ckpt', 'color_fix_type': 'wavelet', 'config': 'configs/model/cldm.yaml', 'detection_model': 'retinaface_resnet50', 'device': 'cuda', 'disable_preprocess_model': False, 'g_repeat': 5, 'g_scale': 0.0, 'g_space': 'latent', 'g_t_start': 1001, 'g_t_stop': -1, 'has_aligned': False, 'image_size': 512, 'input': '/tmp/tmpwg3l1z7wAudrey_Hepburn.jpg', 'only_center_face': False, 'output': '.', 'reload_swinir': False, 'repeat_times': 1, 'seed': 231, 'show_lq': False, 'skip_if_exist': False, 'sr_scale': 2, 'steps': 50, 'swinir_ckpt': 'weights/general_swinir_v1.ckpt', 'tile_size': 512, 'tile_stride': 256, 'tiled': False, 'use_guidance': False} Global seed set to 231 /root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`. warnings.warn(msg) Downloading: "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth" to /root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/facexlib/weights/detection_Resnet50_Final.pth 0%| | 0.00/104M [00:00<?, ?B/s] 37%|███▋ | 38.6M/104M [00:00<00:00, 405MB/s] 76%|███████▋ | 79.8M/104M [00:00<00:00, 421MB/s] 100%|██████████| 104M/104M [00:00<00:00, 423MB/s] Downloading: "https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth" to /root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/facexlib/weights/parsing_parsenet.pth 0%| | 0.00/81.4M [00:00<?, ?B/s] 37%|███▋ | 30.4M/81.4M [00:00<00:00, 319MB/s] 87%|████████▋ | 70.5M/81.4M [00:00<00:00, 378MB/s] 100%|██████████| 81.4M/81.4M [00:00<00:00, 378MB/s] ControlLDM: Running in eps-prediction mode Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. DiffusionWrapper has 865.91 M params. making attention of type 'vanilla-xformers' with 512 in_channels building MemoryEfficientAttnBlock with 512 in_channels... Working with z of shape (1, 4, 32, 32) = 4096 dimensions. making attention of type 'vanilla-xformers' with 512 in_channels building MemoryEfficientAttnBlock with 512 in_channels... Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off] Loading model from: /root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/lpips/weights/v0.1/alex.pth reload swinir model from weights/general_swinir_v1.ckpt timesteps used in spaced sampler: [0, 20, 41, 61, 82, 102, 122, 143, 163, 183, 204, 224, 245, 265, 285, 306, 326, 347, 367, 387, 408, 428, 449, 469, 489, 510, 530, 550, 571, 591, 612, 632, 652, 673, 693, 714, 734, 754, 775, 795, 816, 836, 856, 877, 897, 917, 938, 958, 979, 999] Spaced Sampler: 0%| | 0/50 [00:00<?, ?it/s] Spaced Sampler: 2%|▏ | 1/50 [00:00<00:10, 4.82it/s] Spaced Sampler: 6%|▌ | 3/50 [00:00<00:05, 8.76it/s] Spaced Sampler: 10%|█ | 5/50 [00:00<00:04, 10.31it/s] Spaced Sampler: 14%|█▍ | 7/50 [00:00<00:03, 11.08it/s] Spaced Sampler: 18%|█▊ | 9/50 [00:00<00:03, 11.51it/s] Spaced Sampler: 22%|██▏ | 11/50 [00:01<00:03, 11.78it/s] Spaced Sampler: 26%|██▌ | 13/50 [00:01<00:03, 11.95it/s] Spaced Sampler: 30%|███ | 15/50 [00:01<00:02, 12.06it/s] 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98%|█████████▊| 49/50 [00:04<00:00, 12.25it/s] Spaced Sampler: 100%|██████████| 50/50 [00:04<00:00, 11.91it/s] upsampling the background image using DiffBIR... timesteps used in spaced sampler: [0, 20, 41, 61, 82, 102, 122, 143, 163, 183, 204, 224, 245, 265, 285, 306, 326, 347, 367, 387, 408, 428, 449, 469, 489, 510, 530, 550, 571, 591, 612, 632, 652, 673, 693, 714, 734, 754, 775, 795, 816, 836, 856, 877, 897, 917, 938, 958, 979, 999] Spaced Sampler: 0%| | 0/50 [00:00<?, ?it/s] Spaced Sampler: 2%|▏ | 1/50 [00:00<00:44, 1.11it/s] Spaced Sampler: 4%|▍ | 2/50 [00:01<00:28, 1.67it/s] Spaced Sampler: 6%|▌ | 3/50 [00:01<00:23, 1.98it/s] Spaced Sampler: 8%|▊ | 4/50 [00:02<00:21, 2.18it/s] Spaced Sampler: 10%|█ | 5/50 [00:02<00:19, 2.30it/s] Spaced Sampler: 12%|█▏ | 6/50 [00:02<00:18, 2.38it/s] Spaced Sampler: 14%|█▍ | 7/50 [00:03<00:17, 2.44it/s] Spaced Sampler: 16%|█▌ | 8/50 [00:03<00:16, 2.48it/s] Spaced Sampler: 18%|█▊ | 9/50 [00:04<00:16, 2.51it/s] Spaced Sampler: 20%|██ | 10/50 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Version Details
- Version ID
51ed1464d8bbbaca811153b051d3b09ab42f0bdeb85804ae26ba323d7a66a4ac
- Version Created
- October 12, 2023