Create config.py

config.py

@@ -0,0 +1,50 @@
+from dataclasses import dataclass, field
+from typing import List, Literal
+
+import torch
+import os
+
+SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "False") == "True"
+
+
+@dataclass
+class Config:
+    """
+    The configuration for the API.
+    """
+
+    ####################################################################
+    # Server
+    ####################################################################
+    # In most cases, you should leave this as it is.
+    host: str = "127.0.0.1"
+    port: int = 9090
+    workers: int = 1
+
+    ####################################################################
+    # Model configuration
+    ####################################################################
+    mode: Literal["txt2img", "img2img"] = "txt2img"
+    # SD1.x variant model
+    model_id_or_path: str = os.environ.get("MODEL", "KBlueLeaf/kohaku-v2.1")
+    # LoRA dictionary write like    field(default_factory=lambda: {'E:/stable-diffusion-webui/models/Lora_1.safetensors' : 1.0 , 'E:/stable-diffusion-webui/models/Lora_2.safetensors' : 0.2})
+    lora_dict: dict = None
+    # LCM-LORA model
+    lcm_lora_id: str = os.environ.get("LORA", "latent-consistency/lcm-lora-sdv1-5")
+    # TinyVAE model
+    vae_id: str = os.environ.get("VAE", "madebyollin/taesd")
+    # Device to use
+    device: torch.device = torch.device("cuda")
+    # Data type
+    dtype: torch.dtype = torch.float16
+    # acceleration
+    acceleration: Literal["none", "xformers", "tensorrt"] = "xformers"
+
+    ####################################################################
+    # Inference configuration
+    ####################################################################
+    # Number of inference steps
+    t_index_list: List[int] = field(default_factory=lambda: [0, 16, 32, 45])
+    # Number of warmup steps
+    warmup: int = 10
+    use_safety_checker: bool = SAFETY_CHECKER