Instructions to use Video-Reason/VBVR-Wan2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Video-Reason/VBVR-Wan2.2 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-Wan2.2", 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
Upload folder using huggingface_hub
Browse files
scheduler/scheduler_config.json
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{
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"_class_name": "UniPCMultistepScheduler",
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"_diffusers_version": "0.35.0.dev0",
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"beta_end": 0.02,
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"beta_schedule": "linear",
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"beta_start": 0.0001,
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"disable_corrector": [],
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"dynamic_thresholding_ratio": 0.995,
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"final_sigmas_type": "zero",
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"flow_shift": 3.0,
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"lower_order_final": true,
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"num_train_timesteps": 1000,
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"predict_x0": true,
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"prediction_type": "flow_prediction",
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"rescale_betas_zero_snr": false,
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"sample_max_value": 1.0,
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"solver_order": 2,
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"solver_p": null,
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"solver_type": "bh2",
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"steps_offset": 0,
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"thresholding": false,
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"time_shift_type": "exponential",
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"timestep_spacing": "linspace",
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"trained_betas": null,
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"use_beta_sigmas": false,
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"use_dynamic_shifting": false,
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"use_exponential_sigmas": false,
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"use_flow_sigmas": true,
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"use_karras_sigmas": false
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}
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