Image-to-Video
Diffusers
Safetensors
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WanImageToVideoPipeline
wan
wan2.1
video-generation
fp16
Instructions to use wavespeed/Wan2.1-I2V-14B-720P-Diffusers-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wavespeed/Wan2.1-I2V-14B-720P-Diffusers-fp16 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wavespeed/Wan2.1-I2V-14B-720P-Diffusers-fp16", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Wan2.1-I2V-14B-720P-Diffusers-fp16
An fp16 cast of Wan2.1-I2V-14B-720P-Diffusers.
What was changed
Upstream ships the transformer in fp32. Here every component — transformer, UMT5 text encoder, CLIP image encoder and VAE — is stored in fp16. The transformer goes from ~65.6 GB to ~32.8 GB. Tensor names and shapes are unchanged and nothing is quantized; this is a straight dtype cast.
Usage
import torch
from diffusers import WanImageToVideoPipeline
from diffusers.utils import load_image
pipe = WanImageToVideoPipeline.from_pretrained(
"wavespeed/Wan2.1-I2V-14B-720P-Diffusers-fp16", torch_dtype=torch.float16
).to("cuda")
frames = pipe(
image=load_image("still.png"),
prompt="the camera pushes in slowly",
height=720, width=1280, num_frames=81,
).frames[0]
License
Apache-2.0, inherited from Wan2.1.
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Model tree for wavespeed/Wan2.1-I2V-14B-720P-Diffusers-fp16
Base model
Wan-AI/Wan2.1-I2V-14B-720P-Diffusers