Davids048andClaude Opus 5.5
6c2a250fd0
- ComposedPipelineBase owns a ComponentState: it records each component's path after loading and attaches the state to every stage it registers. from_pretrained(model_path, *, resolved_config, ...) takes a resolved config instead of building FastVideoArgs from keywords or argparse; the training_args alias and TrainingArgs checks are gone, and the training DiT offload is a recorded override. Compile settings come from engine.compile and torch_compile_kwargs(); LoRA settings from pipeline.components.lora_* and training.lora.*. - The worker and executors read engine.num_gpus, engine.parallelism.*, engine.execution_backend, and pipeline.output_type; the Ray executor owns its placement group and runtime env. - VideoGenerator builds from a resolved config only (_from_resolved_config); the prompt_txt fallback is gone (requests carry inputs.prompt_path). The OpenAI server state holds the resolved config (get_resolved_config()). - Tests use resolved configs; the SSIM and performance helpers map their settings to dotted typed paths. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
# Adapted from SGLang
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# (https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/runtime/entrypoints/openai/common_api.py)
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import time
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from fastapi import APIRouter
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from fastapi.responses import ORJSONResponse
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from pydantic import BaseModel, Field
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from fastvideo.entrypoints.openai.state import get_resolved_config, get_served_model_name
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from fastvideo.logger import init_logger
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router = APIRouter(prefix="/v1")
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logger = init_logger(__name__)
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class ModelCard(BaseModel):
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"""OpenAI-compatible model card"""
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id: str
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object: str = "model"
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created: int = Field(default_factory=lambda: int(time.time()))
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owned_by: str = "fastvideo"
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root: str | None = None
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@router.get("/models", response_class=ORJSONResponse)
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async def available_models():
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"""Show available models"""
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resolved_config = get_resolved_config()
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cards = [ModelCard(id=get_served_model_name(), root=resolved_config.model_path)]
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return {"object": "list", "data": [card.model_dump() for card in cards]}
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@router.get("/models/{model:path}", response_class=ORJSONResponse)
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async def retrieve_model(model: str):
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"""Retrieve a model by name"""
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resolved_config = get_resolved_config()
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served_model_name = get_served_model_name()
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available = {served_model_name}
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if model not in available:
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return ORJSONResponse(
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status_code=404,
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content={
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"error": {
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"message": f"The model '{model}' does not exist",
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"type": "invalid_request_error",
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"param": "model",
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"code": "model_not_found",
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}
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},
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)
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card = ModelCard(id=model, root=resolved_config.model_path)
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return card.model_dump()
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@router.get("/model_info")
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async def model_info():
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"""Get basic model information"""
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resolved_config = get_resolved_config()
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components = resolved_config.pipeline.components
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return {
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"model_path":
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resolved_config.model_path,
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"served_model_name":
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get_served_model_name(),
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"lora": ({
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"name": components.lora_nickname,
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"path": components.lora_path,
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"scale": components.lora_strength,
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} if components.lora_path else None),
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}
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