# Wan [Wan 2.1](https://github.com/Wan-Video/Wan2.1) by the Alibaba Wan Team. Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. Recommendations for inference: - VAE in `torch.float32` for better decoding quality. - `num_frames` should be of the form `4 * k + 1`, for example `49` or `81`. - For smaller resolution videos, try lower values of `shift` (between `2.0` to `5.0`) in the [Scheduler](https://huggingface.co/docs/diffusers/main/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler.shift). For larger resolution videos, try higher values (between `7.0` and `12.0`). The default value is `3.0` for Wan. ### Using a custom scheduler Wan can be used with many different schedulers, each with their own benefits regarding speed and generation quality. By default, Wan uses the `UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0)` scheduler. You can use a different scheduler as follows: ```python from diffusers import FlowMatchEulerDiscreteScheduler, UniPCMultistepScheduler, WanPipeline scheduler_a = FlowMatchEulerDiscreteScheduler(shift=5.0) scheduler_b = UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=4.0) pipe = WanPipeline.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", scheduler=) # or, pipe.scheduler = ``` ## WanPipeline [[autodoc]] WanPipeline - all - __call__ ## WanImageToVideoPipeline [[autodoc]] WanImageToVideoPipeline - all - __call__ ## WanPipelineOutput [[autodoc]] pipelines.wan.pipeline_output.WanPipelineOutput