OpenAI-compatible serving examples

The REST serving engine is model-agnostic. Any model supported by VideoGenerator can use the same /v1/models, /v1/videos, and /v1/images surface; the two configs here are FastH3 validation profiles.

Launch the full FastH3 checkpoint:

fastvideo serve --config examples/serving/openai_fasth3.yaml

Launch the dense FastH3 LoRA on the base MiniMax-H3 checkpoint:

adapter_path="$(hf download \
  FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA \
  dense-datafree/adapter_model.safetensors)"

fastvideo serve --config examples/serving/openai_fasth3_lora.yaml \
  --generator.pipeline.components.lora_path "$adapter_path"

FastH3 adapters are hybrid startup patches: alongside low-rank factors they may contain dense deltas and a VSA compression-gate replacement. They must be selected when the server starts. A request may carry the vLLM-Omni lora selector, but its name, path, and scale must match that startup adapter. A VSA adapter also needs attention_backend: VIDEO_SPARSE_ATTN_H3, VSA_sparsity, and VSA_tile_size like the full-checkpoint config.

Submit and poll an asynchronous job:

job_id="$(curl -sS http://localhost:8000/v1/videos \
  -H 'content-type: application/json' \
  -d '{"model":"fasth3","prompt":"A fox runs through fresh snow."}' \
  | jq -r .id)"

curl -sS "http://localhost:8000/v1/videos/$job_id"
curl -o result.mp4 "http://localhost:8000/v1/videos/$job_id/content"

For a blocking call, POST /v1/videos/sync returns the MP4 body directly.

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OpenAI-compatible serving examples

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