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 FastH3 configs here are source-backed examples, not recorded
serving benchmarks. Both configure four CUDA GPUs without a GPU model or VRAM claim.
Use the H3 server cookbook for the guided install, server, and client workflow.
Launch the full FastH3 checkpoint:
fastvideo serve --config examples/serving/openai_fasth3.yaml --server.host 127.0.0.1
Launch the dense FastH3 LoRA on the base MiniMax-H3 checkpoint:
adapter_path="$(hf download \
FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA \
dense-datafree/adapter_model.safetensors)"
fastvideo serve --config examples/serving/openai_fasth3_lora.yaml \
--server.host 127.0.0.1 \
--generator.pipeline.components.lora_path "$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.
After startup, open http://127.0.0.1:8000/playground/ to generate and review
H3 videos in your browser. CUDA prompts reuse the loaded model. The playground
and API clients share the same server; both can run locally.
For native Apple Silicon MLX, use
python -m fastvideo.entrypoints.openai.mlx_server --config examples/serving/mlx_fasth3.yaml
after preparing the weights and editing their paths in that configuration.
The same playground and clients work with MLX. Its pipeline still loads and
releases components between phases to limit unified-memory use. See the
cookbook for setup and the supported text-to-video/audio request fields.
Or submit, poll, and download with the OpenAI Python client:
python -m pip install openai==3.6.0
python examples/serving/clients/video.py
The clients/ directory also contains cURL and OpenAI JavaScript examples.
They default to model fasth3; set FASTVIDEO_MODEL to the advertised alias
when using the LoRA config. Set FASTVIDEO_BASE_URL for another endpoint.
These clients call your FastVideo server, not OpenAI's cloud. The SDK key
local is a placeholder, not authentication. Keep the server on loopback or
use an authenticated proxy for remote access.
For a blocking call, POST /v1/videos/sync returns the MP4 body directly.