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src/backend/models/device.py

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+from pydantic import BaseModel
+
+
+class DeviceInfo(BaseModel):
+    device_type: str
+    device_name: str
+    os: str
+    platform: str
+    processor: str

src/backend/models/gen_images.py

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+from pydantic import BaseModel
+from enum import Enum
+from paths import FastStableDiffusionPaths
+
+
+class ImageFormat(str, Enum):
+    """Image format"""
+
+    JPEG = "jpeg"
+    PNG = "png"
+
+
+class GeneratedImages(BaseModel):
+    path: str = FastStableDiffusionPaths.get_results_path()
+    format: str = ImageFormat.PNG.value.upper()
+    save_image: bool = True
+    save_image_quality: int = 90

src/backend/models/lcmdiffusion_setting.py

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

src/backend/models/upscale.py

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+from enum import Enum
+
+
+class UpscaleMode(str, Enum):
+    """Diffusion task types"""
+
+    normal = "normal"
+    sd_upscale = "sd_upscale"
+    aura_sr = "aura_sr"