[docs] streaming: OpenAI HTTP contract reference
docs/design/server_contracts/ lands the contract reference set that PR 8 locks down ahead of the streaming + Dynamo upstreams. This commit adds the index page plus the OpenAI HTTP reference: * endpoint catalogue (/v1/videos/generations, list, status, content, images, models, health) * VideoGenerationsRequest shape with SGLang-compatible extensions * merge precedence (body > default_request > hardcoded fallback) with a pointer to the explicit-path tracking mechanism that makes it work * continuation-state roundtrip on the stateless surface * HTTP error code table * explicit non-goals: flat legacy kwargs, private Dreamverse fields, raw tensor payloads — all excluded from this boundary by design Co-authored-by: Junda (David) Su <90978028+Davids048@users.noreply.github.com> Co-authored-by: Matthew Noto <99706358+RandNMR73@users.noreply.github.com> Co-authored-by: XOR-op <17672363+XOR-op@users.noreply.github.com> Co-authored-by: Zhang Peiyuan <42993249+jzhang38@users.noreply.github.com>
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co-authored by
Junda Su
Matthew Noto
XOR-op
Zhang Peiyuan
parent
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commit
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# Server Contracts
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FastVideo's typed public API (`fastvideo.api`) is consumed by three
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server-class integrations that must share one execution substrate so we
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don't grow three near-duplicate progress loops:
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| Consumer | Transport | Request shape | State model |
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| --- | --- | --- | --- |
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| [Stateless OpenAI](openai.md) | HTTP POST `/v1/videos` | `VideoGenerationsRequest` → `GenerationRequest` merged onto `ServeConfig.default_request` | Stateless; optional `ContinuationState` round-trip |
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| [Streaming WebSocket](streaming.md) | WebSocket JSON + binary fMP4 | `GenerationRequest` per segment | Server-held `SessionStore`, snapshot-on-demand |
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| [Dynamo native backend](dynamo.md) | Dynamo RPC | `NvCreateVideoRequest` → adapter → `GenerationRequest` | Aggregated today; disaggregated via `ContinuationState` later |
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All three consume the same underlying surface:
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```python
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from fastvideo import VideoGenerator
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from fastvideo.api import (
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ContinuationState,
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GenerationRequest,
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InputConfig,
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OutputConfig,
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SamplingConfig,
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ServeConfig,
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)
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# Sync today, async after PR 7.10 lands VideoGenerator.generate_async.
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result = generator.generate(request)
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```
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These docs lock down the request/response shapes so drift between
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FastVideo, the internal UI, and Dynamo can be caught at review time.
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PR 8 does not ship runtime code; it ships the contract reference and
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the contract tests that guard it.
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## Related
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- [API refactor design](../overview.md)
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- Parity inventory: [`inference_schema_parity_inventory.yaml`](../inference_schema_parity_inventory.yaml)
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- [Streaming server upstream plan](../../../.agents/exploration/streaming-server-upstream-plan.md)
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- Draft PR (closed) that establishes the Dynamo shape:
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https://github.com/ai-dynamo/dynamo/pull/7544
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# OpenAI-compatible HTTP Contract
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The stateless FastVideo HTTP server lives at
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[`fastvideo/entrypoints/openai/`](https://github.com/hao-ai-lab/FastVideo/tree/main/fastvideo/entrypoints/openai).
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Launch: `fastvideo serve --config serve.yaml`.
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## Endpoints
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| Method | Path | Description |
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| --- | --- | --- |
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| `POST` | `/v1/videos/generations` | Synchronous video generation |
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| `GET` | `/v1/videos` | List prior jobs held in the in-memory store |
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| `GET` | `/v1/videos/{id}` | Job status / result |
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| `GET` | `/v1/videos/{id}/content` | Download the MP4 once ready |
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| `POST` | `/v1/images/generations` | Synchronous image generation |
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| `GET` | `/v1/models` | Enumerate registered models |
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| `GET` | `/health` | Liveness probe |
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## `VideoGenerationsRequest` shape
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Mirrors the OpenAI `POST /v1/videos/generations` shape:
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```json
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{
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"prompt": "a fox running through snow",
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"size": "1024x1536",
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"seconds": 5,
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"fps": 24,
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"num_frames": 121,
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"seed": 42,
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"num_inference_steps": 8,
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"guidance_scale": 1.0,
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"negative_prompt": "blurry, low quality",
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"input_reference": "/path/to/init.png"
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}
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```
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SGLang-compatible extensions carried today:
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`num_inference_steps`, `guidance_scale`, `guidance_scale_2`,
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`true_cfg_scale`, `negative_prompt`, `enable_teacache`, `output_path`.
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## Merge precedence
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The server builds a `GenerationRequest` each call using three layers,
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highest first:
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1. **Request body (client-explicit)** — only fields carried in
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`request.model_fields_set` (Pydantic v2). Unset fields do not count,
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even if the Pydantic model has a schema default for them.
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2. **`ServeConfig.default_request` (operator-explicit)** — projected via
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[`explicit_request_updates()`](../../../fastvideo/api/compat.py);
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only fields the operator actually wrote into the YAML count as
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defaults. Every other field inherits the schema default rather than
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being pinned.
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3. **Hardcoded fallback** — e.g. `fps = 24`.
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The gate matters: both surfaces carry schema defaults. Without
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`model_fields_set` / explicit-path tracking, schema defaults would
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masquerade as intent and silently shadow the other side.
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See [`video_api.py::_build_generation_kwargs`](../../../fastvideo/entrypoints/openai/video_api.py)
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for the canonical implementation; the per-request assembly lives there,
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not in pipeline code.
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## Continuation state
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The stateless surface accepts an opaque `ContinuationState` round-trip.
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Clients that want continuation pass the prior `state` blob back on the
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next request, and receive a new one on the response when
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`request.output.return_state = true`.
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Shape:
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```json
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{
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"state": {
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"kind": "ltx2.v1",
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"payload": { "schema_version": 1, "segment_index": 3, ... }
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}
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}
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```
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Payload is always JSON-serializable. Large tensors may live in an
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opaque blob-store reference the client simply round-trips; see
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[`LTX2ContinuationState`](../../../fastvideo/pipelines/basic/ltx2/continuation.py).
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Continuation is not yet wired all the way through to
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`generator.generate_video(...)` — PR 7.6 (GPU pool upstream) is the
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pipeline-level consumer. PR 7 locked the envelope so this surface is
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stable ahead of that plumbing.
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## Error codes
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| HTTP | Condition |
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| --- | --- |
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| `400 Bad Request` | Parse/validation failure (unknown field, type mismatch, incompatible preset/state) |
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| `404 Not Found` | `GET /v1/videos/{id}` for an unknown job |
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| `409 Conflict` | Job id already exists |
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| `500 Internal Server Error` | Pipeline raised; body mirrors upstream OpenAI error envelope |
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| `503 Service Unavailable` | No generator loaded, or shutdown in progress |
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Errors include a JSON body with
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`{"error": {"type": "...", "message": "..."}}` matching the OpenAI
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Python SDK's expectation.
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## What does not cross this boundary
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* Flat legacy kwargs (`ltx2_refine_enabled`, `torch_compile_kwargs`,
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etc.) — these are init-time, configured via `ServeConfig.generator`,
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never per-request.
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* Private Dreamverse-only fields — those live in a private adapter on
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the Dreamverse side; the public FastVideo surface never promises
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backward compatibility for them.
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* Raw tensor payloads (`ltx2_audio_clean_latent` et al.) — these are
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derived by the pipeline from `ContinuationState`, never shipped as
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request fields.
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