Delete ui_model.py

ui_model.py

@@ -1,70 +0,0 @@
-import torch
-import cv2
-import numpy as np
-
-from torchvision.transforms.functional import to_tensor, center_crop, resize
-from PIL import Image
-from EngageEngine.pipeline import EngagePipeline
-
-from diffusers import (
-    EulerAncestralDiscreteScheduler,
-    AutoencoderKL, ControlNetModel,
-)
-
-
-def process_sketch(x, im_size=(1024, 1024), sketch_detail=0.5, sketch_softness=0.5):
-    x_b = Image.new("RGBA", x.size, "WHITE")
-    x_b.paste(x, mask=x)
-    x = to_tensor(x_b.convert('RGB')).unsqueeze(0)
-    x = center_crop(x, x.shape[-1])
-    x = resize(x, im_size)
-
-    u_th = (1 - sketch_detail) * 190 + 10
-    l_th = (1 - sketch_detail) ** (sketch_softness * 8 + 1) * 185 + 5
-
-    edges = [cv2.Canny(x[i].mul(255).permute(1, 2, 0).numpy().astype(np.uint8),
-                       u_th, l_th, L2gradient=True) for i in range(len(x))]
-    edges = torch.stack([torch.tensor(e).div(255).unsqueeze(0) for e in edges], dim=0)
-    edges = torch.concatenate([edges, edges, edges], dim=1)
-    return edges
-
-
-def process_mask(x, mask, im_size=(1024, 1024)):
-    x = to_tensor(x.convert('RGB')).unsqueeze(0)
-    x = center_crop(x, x.shape[-1])
-    x = resize(x, im_size)
-
-    mask = to_tensor(mask.convert('L')).unsqueeze(0)
-    mask = center_crop(mask, mask.shape[-1])
-    mask = resize(mask, im_size)
-
-    return x, mask
-
-
-def fetch_model():
-    # Load VAE component
-    vae = AutoencoderKL.from_pretrained(
-        "madebyollin/sdxl-vae-fp16-fix",
-        torch_dtype=torch.float16
-    )
-
-    controlnet = ControlNetModel.from_pretrained(
-        "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
-    )
-
-    # Configure the pipeline
-    pipe = EngagePipeline.from_pretrained(
-        "dataautogpt3/ProteusV0.4-Lightning",
-        vae=vae,
-        controlnet=controlnet,
-        torch_dtype=torch.float16
-    )
-    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-    pipe.load_lora_weights("EngageEngine/ENGAGE_LORA.safetensors", adapter_name="ENGAGE_LORA")
-    pipe.load_lora_weights("EngageEngine/FILM_LORA.safetensors", adapter_name="FILM_LORA")
-    pipe.load_lora_weights("EngageEngine/MJ_LORA.safetensors", adapter_name="MJ_LORA")
-    pipe.load_lora_weights("EngageEngine/MORE_ART_LORA.safetensors", adapter_name="MORE_ART_LORA")
-    pipe.set_adapters(["ENGAGE_LORA", "FILM_LORA", "MJ_LORA", "MORE_ART_LORA"], adapter_weights=[0.0, 0.0, 0.0, 0.0])
-    pipe.to('cuda')
-
-    return pipe