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
File size: 468 Bytes
dc4d366 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"attention_backend": "VIDEO_SPARSE_ATTN_H3",
"audio_scheduler_shift": 3.0,
"checkpoint_step": 300,
"dmd_denoising_steps": [999, 874, 749, 624, 500, 375, 250, 125],
"guidance_scale": 1.0,
"num_inference_steps": 9,
"schema_version": "fasth3-inference-contract-v1",
"task": "t2av",
"training_parent_run_id": "s42-r16-dmd8-vsa80-20260929-v1",
"transformer_forwards": 8,
"video_scheduler_shift": 10.0,
"vsa_sparsity": 0.8,
"vsa_tile_size": 64
}
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