Merge fea47e70f7 into 8f7f6514f1

This fixes an edge case in the FlowUniPCMultistepScheduler where using high sampling step counts (> 50) would cause an assertion error in the last step. The issue was that with lower_order_final=True, the order calculation could become 0 when step_index equals len(timesteps), causing 'assert self.this_order > 0' to fail.

The fix ensures this_order is always at least 1, maintaining stability while allowing higher quality generation with increased step counts.

🤖 Generated with Claude Code
Co-Authored-By: Claude noreply@anthropic.com

README.md

@@ -36,6 +36,9 @@ 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!
 - [CFG-Zero](https://github.com/WeichenFan/CFG-Zero-star) enhances **Wan2.1** (covering both T2V and I2V models) from the perspective of CFG.

wan/utils/fm_solvers_unipc.py

@@ -712,9 +712,9 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         self.timestep_list[-1] = timestep  # pyright: ignore
 
         if self.config.lower_order_final:
-            this_order = min(self.config.solver_order,
+            this_order = max(1, min(self.config.solver_order,
                              len(self.timesteps) -
-                             self.step_index)  # pyright: ignore
+                             self.step_index)) # pyright: ignore
         else:
             this_order = self.config.solver_order