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Will LinandClaude Fable 5 3f00983287 [feat] attention: opt-in sm_100a CUDA forward route for VSA-H3 tile-64

FASTVIDEO_VSA_SM100A=1 (default off) sends no-grad tile-64 forwards through
fastvideo_kernel.block_sparse_attn_sm100a (the Blackwell block-sparse
forward merged in #1719, which handles per-q-tile NON-uniform q2k_num rows
and zero-count rows). Route preconditions: module importable,
is_supported() (sm_100 device, bf16, head_dim 128, even tile count), no
grad tracking; any failure with the env set logs one warning and falls
back to the Triton-64 kernels, which also keep the entire grad path
unchanged.

The bool block map is compacted with the same map_to_index the Triton bool
entry uses, so the sm_100a kernel sees H3's true NON-uniform per-row counts
(prefix query tiles dense, video tiles prefix+top-k).

The FastH3 example surfaces the route as --vsa-kernel {triton,sm100a}
(default triton), which sets the env before pipeline boot so spawned GPU
workers inherit it; documented in the basic README.

CPU route-selection tests: default-off, engage-on-env, grad fallback,
warn-once fallback for missing module/unsupported geometry, no-grad-context
detection.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

2026-08-21 09:27:38 +00:00

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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_fasth3.py --prompt "your prompt" [--vsa-sparsity 0.9]

The default checkpoint FastVideo/FastH3-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.

On Blackwell (sm_100) GPUs with a fastvideo-kernel build that carries the sm_100a block-sparse extension, --vsa-kernel sm100a routes the tile-64 attention forwards through the CUDA kernel instead of Triton (it sets FASTVIDEO_VSA_SM100A=1 before the pipeline boots); if the extension or the arch is missing, the run warns once and falls back to Triton.

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()

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