| base_model: Wan-AI/Wan2.1-T2V-14B-Diffusers | |
| language: | |
| - en | |
| - zh | |
| library_name: diffusers | |
| license: apache-2.0 | |
| pipeline_tag: text-to-video | |
| tags: | |
| - wan | |
| - wan2.1 | |
| - text-to-video | |
| - video-generation | |
| - fp16 | |
| - diffusers | |
| base_model_relation: quantized | |
| # Wan2.1-T2V-14B-Diffusers-fp16 | |
| An fp16 cast of [Wan2.1-T2V-14B-Diffusers](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B-Diffusers). | |
| ## What was changed | |
| Upstream ships the transformer in fp32. Here every component is stored in fp16, | |
| which halves the download and the resident weight footprint — the transformer | |
| goes from ~57.2 GB to ~28.6 GB — with no change to tensor names or shapes. | |
| Nothing is quantized; this is a straight dtype cast. | |
| fp16 rather than bf16 is deliberate: it is the dtype our fp8/nf4 variants are | |
| built on top of, so the whole family shares one baseline. | |
| ## Usage | |
| ```python | |
| import torch | |
| from diffusers import WanPipeline | |
| pipe = WanPipeline.from_pretrained( | |
| "wavespeed/Wan2.1-T2V-14B-Diffusers-fp16", torch_dtype=torch.float16 | |
| ).to("cuda") | |
| frames = pipe("a paper boat drifting down a gutter after rain", num_frames=81).frames[0] | |
| ``` | |
| ## Related | |
| - [`wavespeed/Wan2.1-T2V-14B-Diffusers-fp16-nf4`](https://huggingface.co/wavespeed/Wan2.1-T2V-14B-Diffusers-fp16-nf4) | |
| — the same baseline with NF4-quantized transformer blocks. | |
| ## License | |
| Apache-2.0, inherited from Wan2.1. | |