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: 1,754 Bytes
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"_class_name": "AutoencoderKLWan",
"_diffusers_version": "0.37.1",
"_name_or_path": "Wan-AI/Wan2.2-TI2V-5B-Diffusers/vae",
"attn_scales": [],
"base_dim": 160,
"clip_output": false,
"decoder_base_dim": 256,
"dim_mult": [
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"dropout": 0.0,
"in_channels": 12,
"is_residual": true,
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"num_res_blocks": 2,
"out_channels": 12,
"patch_size": 2,
"scale_factor_spatial": 16,
"scale_factor_temporal": 4,
"temperal_downsample": [
false,
true,
true
],
"z_dim": 48
}
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