tabs/images/models.py

from typing import List, Optional, Dict, Any

import gradio as gr
from pydantic import BaseModel, field_validator
from PIL import Image

from config import Config as appConfig


class ControlNetReq(BaseModel):
    controlnets: List[str] # ["canny", "tile", "depth", "scribble"]
    control_images: List[Image.Image]
    controlnet_conditioning_scale: List[float]
    
    class Config:
        arbitrary_types_allowed=True


class BaseReq(BaseModel):
    model: str = ""
    prompt: str = ""
    negative_prompt: Optional[str] = None
    fast_generation: Optional[bool] = True
    loras: Optional[list] = []
    embeddings: Optional[list] = None
    resize_mode: Optional[str] = "resize_and_fill" # resize_only, crop_and_resize, resize_and_fill
    scheduler: Optional[str] = "euler_fl"
    height: int = 1024
    width: int = 1024
    num_images_per_prompt: int = 1
    num_inference_steps: int = 8
    clip_skip: Optional[int] = None
    guidance_scale: float = 3.5
    seed: Optional[int] = 0
    refiner: bool = False
    vae: bool = True
    controlnet_config: Optional[ControlNetReq] = None
    custom_addons: Optional[Dict[Any, Any]] = None
    
    class Config:
        arbitrary_types_allowed=True
    
    @field_validator('model', 'negative_prompt', 'embeddings', 'clip_skip', 'controlnet_config')
    def check_model(cls, values):
        for m in appConfig.IMAGES_MODELS:
            gr.Info(f"{m.get('repo_id')} {values.get('model')}")
            if m.get('repo_id') == values.get('model'):
                loader = m.get('loader')
            
        if loader == "flux" and values.get('negative_prompt'):
            raise ValueError("Negative prompt is not supported for Flux models.")
        if loader == "flux" and values.get('embeddings'):
            raise ValueError("Embeddings are not supported for Flux models.")
        if loader == "flux" and values.get('clip_skip'):
            raise ValueError("Clip skip is not supported for Flux models.")
        if loader == "flux" and values.get('controlnet_config'):
            if "scribble" in values.get('controlnet_config').controlnets:
                raise ValueError("Scribble is not supported for Flux models.")
        return values


class BaseImg2ImgReq(BaseReq):
    image: Image.Image
    strength: float = 1.0
    
    class Config:
        arbitrary_types_allowed=True


class BaseInpaintReq(BaseImg2ImgReq):
    mask_image: Image.Image
    
    class Config:
        arbitrary_types_allowed=True