2d50679ec0
- FastAPI with job polling: POST /generate, GET /jobs/{id}, /download, /health
- SQLite job persistence (aiosqlite), background task processing
- LTX-2.3 DistilledPipeline via native ltx-pipelines (editable install from submodule)
- Configurable model paths, LoRA support, FP8 quantization via env vars
- Single-concurrency GPU lock for safe inference
- LTX-2 as git submodule under libs/
178 lines
6.2 KiB
Python
178 lines
6.2 KiB
Python
from __future__ import annotations
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import asyncio
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import os
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import uuid
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from datetime import datetime, timezone
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from typing import Optional
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import torch
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from ltx_pipelines import DistilledPipeline
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from PIL import Image
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from app.config import settings
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from app.database import JobDB, JobRecord, VALID_FRAME_COUNTS
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class LtxService:
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def __init__(self, db: JobDB) -> None:
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self.db = db
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self.pipeline: Optional[DistilledPipeline] = None
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self._load_lock = asyncio.Lock()
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self._job_semaphore = asyncio.Semaphore(settings.max_concurrent_jobs)
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self._video_dir = settings.video_output_dir
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async def load_pipeline(self) -> None:
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async with self._load_lock:
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if self.pipeline is not None:
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return
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lora_config = self._parse_lora_paths(settings.ltx_loras)
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self.pipeline = await asyncio.to_thread(
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DistilledPipeline,
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distilled_checkpoint_path=settings.ltx_distilled_checkpoint,
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gemma_root=settings.ltx_gemma_root,
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spatial_upsampler_path=settings.ltx_spatial_upsampler,
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loras=lora_config,
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device=settings.ltx_device,
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quantization=settings.ltx_quantization,
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)
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@staticmethod
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def _parse_lora_paths(lora_specs: list[str]) -> list[dict]:
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result = []
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for spec in lora_specs:
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if ":" in spec:
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path, scale = spec.rsplit(":", 1)
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result.append({"path": path.strip(), "scale": float(scale.strip())})
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else:
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result.append({"path": spec.strip(), "scale": 1.0})
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return result
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async def submit_job(self, image: Image.Image, request_params: dict) -> JobRecord:
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job_id = uuid.uuid4().hex[:12]
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now = datetime.now(timezone.utc).isoformat()
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prompt = request_params.get("prompt")
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width = request_params.get("width", settings.default_width)
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height = request_params.get("height", settings.default_height)
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num_frames = request_params.get("num_frames", settings.default_frames)
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fps = request_params.get("fps", settings.default_fps)
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seed = request_params.get("seed")
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if num_frames not in VALID_FRAME_COUNTS:
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candidates = sorted(
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f for f in VALID_FRAME_COUNTS if abs(f - num_frames) <= 8
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)
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raise ValueError(
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f"num_frames must be 8n+1. Got {num_frames}. "
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f"Closest valid: {candidates[:4]}"
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)
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job = JobRecord(
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id=job_id,
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status="pending",
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prompt=prompt,
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params={
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"width": width,
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"height": height,
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"num_frames": num_frames,
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"fps": fps,
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"seed": seed,
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},
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created_at=now,
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)
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await self.db.create_job(job)
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asyncio.create_task(self._process_job(job_id, image, job.params))
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return job
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async def _process_job(
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self,
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job_id: str,
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image: Image.Image,
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params: dict,
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) -> None:
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async with self._job_semaphore:
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try:
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await self.db.update_status(job_id, "processing")
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await self.load_pipeline()
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prompt = params.get("prompt", "") or ""
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width = params.get("width", settings.default_width)
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height = params.get("height", settings.default_height)
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num_frames = params.get("num_frames", settings.default_frames)
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fps = params.get("fps", settings.default_fps)
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seed = params.get("seed") if params.get("seed") is not None else 42
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if image.mode != "RGB":
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image = image.convert("RGB")
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image = image.resize((width, height), Image.LANCZOS)
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output = await asyncio.to_thread(
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self.pipeline,
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prompt=prompt,
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images=image,
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width=width,
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height=height,
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num_frames=num_frames,
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frame_rate=fps,
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seed=seed,
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)
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video_path = os.path.join(self._video_dir, f"{job_id}.mp4")
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os.makedirs(self._video_dir, exist_ok=True)
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await asyncio.to_thread(self._save_video, output, video_path, fps)
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await self.db.update_status(job_id, "completed")
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except Exception as e:
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await self.db.update_status(job_id, "failed", error=str(e))
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@staticmethod
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def _save_video(output: any, path: str, fps: float) -> None:
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import imageio.v3 as iio
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if isinstance(output, torch.Tensor):
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frames = output.cpu().numpy()
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elif isinstance(output, dict) and "video" in output:
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v = output["video"]
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frames = v.cpu().numpy() if isinstance(v, torch.Tensor) else v
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else:
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frames = output
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if isinstance(frames, torch.Tensor):
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frames = frames.cpu().numpy()
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if frames.ndim == 4:
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frames = frames[0]
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if frames.max() <= 1.0:
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frames = (frames * 255).astype("uint8")
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elif frames.dtype != "uint8":
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frames = frames.clip(0, 255).astype("uint8")
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iio.imwrite(path, frames, fps=float(fps), codec="libx264")
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async def get_job(self, job_id: str) -> JobRecord | None:
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return await self.db.get_job(job_id)
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async def delete_job(self, job_id: str) -> tuple[bool, str | None]:
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video_path = os.path.join(self._video_dir, f"{job_id}.mp4")
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deleted = await self.db.delete_job(job_id)
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if os.path.exists(video_path):
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os.remove(video_path)
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return deleted, job_id
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async def get_video_path(self, job_id: str) -> str | None:
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video_path = os.path.join(self._video_dir, f"{job_id}.mp4")
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if os.path.exists(video_path):
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return video_path
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return None
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_service: Optional[LtxService] = None
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async def get_service() -> LtxService:
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global _service
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if _service is None:
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db = JobDB(settings.db_path)
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await db.init()
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_service = LtxService(db)
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return _service
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