Merge e649b328e9 into 8f7f6514f1

README.md

@@ -36,6 +36,8 @@ 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.
+- [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.
 - [ATI](https://github.com/bytedance/ATI), built on **Wan2.1-I2V-14B**, is a trajectory-based motion-control framework that unifies object, local, and camera movements in video generation. Refer to [their website](https://anytraj.github.io/) for more examples.
 - [Phantom](https://github.com/Phantom-video/Phantom) has developed a unified video generation framework for single and multi-subject references based on both **Wan2.1-T2V-1.3B** and **Wan2.1-T2V-14B**. Please refer to [their examples](https://github.com/Phantom-video/Phantom).
 - [UniAnimate-DiT](https://github.com/ali-vilab/UniAnimate-DiT), based on **Wan2.1-14B-I2V**, has trained a Human image animation model and has open-sourced the inference and training code. Feel free to enjoy it!

wan/vace.py

@@ -352,7 +352,7 @@ class WanVace(WanT2V):
 
         if n_prompt == "":
             n_prompt = self.sample_neg_prompt
-        seed = seed if seed >= 0 else random.randint(0, sys.maxsize)
+        seed = seed if seed >= 0 else random.randint(0, 1e7)
         seed_g = torch.Generator(device=self.device)
         seed_g.manual_seed(seed)
 
@@ -649,7 +649,7 @@ class WanVaceMP(WanVace):
 
                 if n_prompt == "":
                     n_prompt = sample_neg_prompt
-                seed = seed if seed >= 0 else random.randint(0, sys.maxsize)
+                seed = seed if seed >= 0 else random.randint(0, 1e7)
                 seed_g = torch.Generator(device=gpu)
                 seed_g.manual_seed(seed)