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from enum import Enum
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from PIL import Image
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from typing import Any, Optional, Union
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from constants import LCM_DEFAULT_MODEL, LCM_DEFAULT_MODEL_OPENVINO
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from paths import FastStableDiffusionPaths
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from pydantic import BaseModel
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class LCMLora(BaseModel):
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base_model_id: str = "Lykon/dreamshaper-8"
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lcm_lora_id: str = "latent-consistency/lcm-lora-sdv1-5"
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class DiffusionTask(str, Enum):
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"""Diffusion task types"""
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text_to_image = "text_to_image"
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image_to_image = "image_to_image"
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class Lora(BaseModel):
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models_dir: str = FastStableDiffusionPaths.get_lora_models_path()
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path: Optional[Any] = None
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weight: Optional[float] = 0.5
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fuse: bool = True
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enabled: bool = False
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class ControlNetSetting(BaseModel):
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adapter_path: Optional[str] = None
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conditioning_scale: float = 0.5
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enabled: bool = False
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_control_image: Image = None
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class GGUFModel(BaseModel):
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gguf_models: str = FastStableDiffusionPaths.get_gguf_models_path()
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diffusion_path: Optional[str] = None
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clip_path: Optional[str] = None
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t5xxl_path: Optional[str] = None
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vae_path: Optional[str] = None
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class LCMDiffusionSetting(BaseModel):
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lcm_model_id: str = LCM_DEFAULT_MODEL
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openvino_lcm_model_id: str = LCM_DEFAULT_MODEL_OPENVINO
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use_offline_model: bool = False
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use_lcm_lora: bool = False
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lcm_lora: Optional[LCMLora] = LCMLora()
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use_tiny_auto_encoder: bool = False
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use_openvino: bool = False
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prompt: str = ""
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negative_prompt: str = ""
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init_image: Any = None
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strength: Optional[float] = 0.6
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image_height: Optional[int] = 512
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image_width: Optional[int] = 512
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inference_steps: Optional[int] = 1
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guidance_scale: Optional[float] = 1
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clip_skip: Optional[int] = 1
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token_merging: Optional[float] = 0
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number_of_images: Optional[int] = 1
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seed: Optional[int] = 123123
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use_seed: bool = False
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use_safety_checker: bool = False
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diffusion_task: str = DiffusionTask.text_to_image.value
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lora: Optional[Lora] = Lora()
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controlnet: Optional[Union[ControlNetSetting, list[ControlNetSetting]]] = None
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dirs: dict = {
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"controlnet": FastStableDiffusionPaths.get_controlnet_models_path(),
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"lora": FastStableDiffusionPaths.get_lora_models_path(),
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}
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rebuild_pipeline: bool = False
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use_gguf_model: bool = False
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gguf_model: Optional[GGUFModel] = GGUFModel()
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