mteehan
a33a3a593d
- Use DistilledPipeline constructor (not from_config) - Use ImageConditioningInput namedtuple for image conditioning - Use LoraPathStrengthAndSDOps for LoRA config - Use QuantizationPolicy with fp8_cast sd_ops directly - Update spatial_upsampler to required x2-1.1 model - Update gemma_root to google/gemma-3-12b-it-qat-q4_0-unquantized - Handle video output as Iterator[torch.Tensor] from pipeline - Flesh out README with all model download commands, endpoint docs, env vars, frame/resolution tables
42 lines
1.1 KiB
Python
42 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 from HuggingFace)
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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/gemma-3-12b-it-qat-q4_0-unquantized"
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ltx_spatial_upsampler: str = "models/ltx-2.3-spatial-upscaler-x2-1.1.safetensors"
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ltx_device: str = "cuda"
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ltx_quantization: str = "fp8_cast"
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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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