Image-to-Video
Diffusers
Safetensors
Wan2.2
English
Chinese
WanPipeline
video-generation
visual-reasoning
reinforcement-learning
rlvr
vbvr-pro
Instructions to use Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B-RLVR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B-RLVR 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("Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B-RLVR", 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") - Wan2.2
How to use Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B-RLVR with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 544 Bytes
c696161 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"_class_name": "WanPipeline",
"_diffusers_version": "0.37.1",
"_name_or_path": "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
"boundary_ratio": null,
"expand_timesteps": true,
"scheduler": [
"diffusers",
"UniPCMultistepScheduler"
],
"text_encoder": [
"transformers",
"UMT5EncoderModel"
],
"tokenizer": [
"transformers",
"T5Tokenizer"
],
"transformer": [
"diffusers",
"WanTransformer3DModel"
],
"transformer_2": [
null,
null
],
"vae": [
"diffusers",
"AutoencoderKLWan"
]
}
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