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Author SHA1 Message Date
Feiteng
46f10faaee
Merge 57843a2111 into 7c81b2f27d 2025-07-17 20:37:56 +08:00
Shiwei Zhang
7c81b2f27d
Update README.md 2025-07-17 15:31:19 +08:00
Feiteng
57843a2111 Fix msk frame_num 2025-05-09 07:41:18 +00:00
2 changed files with 2 additions and 1 deletions

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@ -36,6 +36,7 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
## Community Works
If your work has improved **Wan2.1** and you would like more people to see it, please inform us.
- [EchoShot](https://github.com/JoHnneyWang/EchoShot), a native multi-shot portrait video generation model based on **Wan2.1-T2V-1.3B**, allows generation of multiple video clips featuring the same character as well as highly flexible content controllability. Refer to [their project page](https://johnneywang.github.io/EchoShot-webpage/) for more information.
- [AniCrafter](https://github.com/MyNiuuu/AniCrafter), a human-centric animation model based on **Wan2.1-14B-I2V**, controls the Video Diffusion Models with 3DGS Avatars to insert and animate anyone into any scene following given motion sequences. Refer to the [project page](https://myniuuu.github.io/AniCrafter) for more examples.
- [HyperMotion](https://vivocameraresearch.github.io/hypermotion/), a human image animation framework based on **Wan2.1**, addresses the challenge of generating complex human body motions in pose-guided animation. Refer to [their website](https://vivocameraresearch.github.io/magictryon/) for more examples.
- [MagicTryOn](https://vivocameraresearch.github.io/magictryon/), a video virtual try-on framework built upon **Wan2.1-14B-I2V**, addresses the limitations of existing models in expressing garment details and maintaining dynamic stability during human motion. Refer to [their website](https://vivocameraresearch.github.io/magictryon/) for more examples.

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@ -207,7 +207,7 @@ class WanI2V:
generator=seed_g,
device=self.device)
msk = torch.ones(1, 81, lat_h, lat_w, device=self.device)
msk = torch.ones(1, frame_num, lat_h, lat_w, device=self.device)
msk[:, 1:] = 0
msk = torch.concat([
torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]