mteehan
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/
43 lines
1.1 KiB
Python
43 lines
1.1 KiB
Python
from __future__ import annotations
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import os
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from typing import Optional
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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app_name: str = "Revids"
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host: str = "0.0.0.0"
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port: int = 8000
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reload: bool = True
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# LTX-2.3 model paths (update after downloading models)
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ltx_distilled_checkpoint: str = "models/ltx-2.3-22b-distilled-1.1.safetensors"
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ltx_gemma_root: str = "models/gpt-4o-5805-ava-gguf-model"
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ltx_spatial_upsampler: str | None = None
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ltx_device: str = "cuda"
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ltx_quantization: str = "fp8_cast"
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# LoRA paths (list of "path:scale" strings, env: REVIDS_LTX_LORAS="models/my-lora.safetensors:1.0")
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ltx_loras: list[str] = []
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video_output_dir: str = os.path.join(os.path.dirname(os.path.dirname(__file__)), "videos")
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# DB
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db_path: str = os.path.join(os.path.dirname(__file__), "jobs.db")
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# Generation defaults
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default_frames: int = 65
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default_fps: float = 24.0
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default_width: int = 768
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default_height: int = 512
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# Concurrency
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max_concurrent_jobs: int = 1
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model_config = {"env_prefix": "REVIDS_", "env_file": ".env"}
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settings = Settings()
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