Will LinandClaude Fable 5
9df19be719
basic_fast_minimax_h3.py runs FastVideo/FastVideo-Minimax-H3-Preview-v0.1, the data-free-DMD2 distillation of MiniMax-H3: 4 denoising steps on the release sampler's shift-12 schedule (vs the base model's 50), synchronized video+audio in one pipeline call, guidance_scale 1.0. The script always requests the VSA-H3 attention backend through the typed boot-time route (pipeline.experimental -> FastVideoArgs.attention_backend): the student checkpoint carries trained to_gate_compress gates, which only exist under that backend. --vsa-sparsity defaults to 0.0 (every tile selected — exactly dense attention); --vsa-tile-size defaults to 64, the geometry the student was trained with, and is forwarded even at sparsity 0 because the gate-compress branch pools per tile. The HF repo is private while the MiniMax H3 Community License review completes; --model-path accepts a local snapshot meanwhile (noted in the script and README). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Basic Video Generation Tutorial
The VideoGenerator class provides the primary Python interface for doing offline video generation, which is interacting with a diffusion pipeline without using a separate inference api server.
Requirements
- At least a single NVIDIA GPU with CUDA 12.4.
- Python 3.10-3.12
Installation
If you have not installed FastVideo, please following these instructions first.
Usage
The first script in this example shows the most basic usage of FastVideo. If you are new to Python and FastVideo, you should start here.
# if you have not cloned the directory:
git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
python examples/inference/basic/basic.py
For an example on Apple silicon:
python examples/inference/basic/basic_mps.py
For an example running DMD+VSA inference:
python examples/inference/basic/basic_dmd.py
For the typed config/request path added during the inference API refactor:
python examples/inference/basic/basic_dmd_new_api.py
For the few-step (4-step, DMD2-distilled) MiniMax-H3 preview, generating synchronized video and audio, optionally with block-sparse VSA attention:
python examples/inference/basic/basic_fast_minimax_h3.py --prompt "your prompt" [--vsa-sparsity 0.9]
The default checkpoint FastVideo/FastVideo-Minimax-H3-Preview-v0.1 is private on the Hub while its license review completes; until it flips public, pass --model-path with a local snapshot of the release.
Basic Walkthrough
All you need to generate videos using multi-gpus from state-of-the-art diffusion pipelines is the following few lines!
from fastvideo import VideoGenerator
def main():
generator = VideoGenerator.from_pretrained(
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
num_gpus=1,
)
prompt = ("A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
"wide with interest. The playful yet serene atmosphere is complemented by soft "
"natural light filtering through the petals. Mid-shot, warm and cheerful tones.")
video = generator.generate_video(prompt)
if __name__ == "__main__":
main()