Text-to-Video
Diffusers
Safetensors
MiniMaxH3ModularPipeline
video-generation
audio
fastvideo
bf16
pruned
Instructions to use FastVideo/FastVideo-FastH3-Trim-8-Step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FastVideo/FastVideo-FastH3-Trim-8-Step with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FastVideo/FastVideo-FastH3-Trim-8-Step", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Add FastH3 inference contract (8-step DMD schedule)
Browse files- fastvideo_inference.json +15 -0
fastvideo_inference.json
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{
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"attention_backend": "VIDEO_SPARSE_ATTN_H3",
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"audio_scheduler_shift": 3.0,
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"checkpoint_step": 300,
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"dmd_denoising_steps": [999, 874, 749, 624, 500, 375, 250, 125],
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"guidance_scale": 1.0,
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"num_inference_steps": 9,
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"schema_version": "fasth3-inference-contract-v1",
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"task": "t2av",
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"training_parent_run_id": "s42-r16-dmd8-vsa80-20260929-v1",
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"transformer_forwards": 8,
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"video_scheduler_shift": 10.0,
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"vsa_sparsity": 0.8,
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"vsa_tile_size": 64
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}
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