| --- |
| license: mit |
| tags: |
| - video-deblurring |
| - diffusion |
| - replica |
| --- |
| |
| # VD-Diff trained on Replica |
|
|
| Checkpoints from [VD-Diff](https://github.com/Chen-Rao/VD-Diff) (Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model, ECCV 2024) trained on the Replica scenes. |
|
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| Code that produced these checkpoints: <https://github.com/1zsw123/pretraineddatasetcode> |
|
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| ## Files |
|
|
| | File | Stage | Step | Size | |
| |---|---|---|---| |
| | `S1/net_g_200000.pth` | Stage 1 (KPN pre-train) | 200k | 38 MB | |
| | `S3/net_g_175000.pth` | Stage 3 (diffusion fine-tune) | 175k | 48 MB | |
|
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| ## How to use |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| s1 = hf_hub_download("zhaoshiwen/VD-Diff-Replica", "S1/net_g_200000.pth") |
| s3 = hf_hub_download("zhaoshiwen/VD-Diff-Replica", "S3/net_g_175000.pth") |
| ``` |
|
|
| Then point `pretrain_network_S1` / `pretrain_network_S2` in the VD-Diff training YAML at these paths. See the upstream README for the training pipeline. |
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|
