Image-to-Video
Diffusers
Safetensors
WanImageToVideoPipeline
text-image-to-video
wan
leoma
bittensor
Instructions to use Realfencer/IT2V_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Realfencer/IT2V_model 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("Realfencer/IT2V_model", 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
Download tokenizer/tokenizer.json from Realfencer/IT2V_model: direct link, hf CLI and curl.
- Browser
- Download file 16.8 MB
-
https://huggingface.co/Realfencer/IT2V_model/resolve/main/tokenizer/tokenizer.json
- Command line
-
hf download hf://Realfencer/IT2V_model/tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Realfencer/IT2V_model/resolve/main/tokenizer/tokenizer.json
16.8 MB
- Xet hash:
- 09c2a2b09f657cacbe7391ea619e41fc60b9e8be0f6e5f4ec116e14c2430801f
- Size of remote file:
- 16.8 MB
- SHA256:
- e87c960c36d5fbf4e7e76c2469b7eab877be7f8c5992efbf97e44d3123cc6521
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