import spaces import gradio as gr import torch from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, ControlNetModel, AutoencoderKL from PIL import Image import os import time from utils.utils import load_cn_model, load_cn_config, load_tagger_model, load_lora_model, resize_image_aspect_ratio, base_generation from utils.prompt_utils import execute_prompt, remove_color, remove_duplicates from utils.tagger import modelLoad, analysis path = os.getcwd() cn_dir = f"{path}/controlnet" tagger_dir = f"{path}/tagger" lora_dir = f"{path}/lora" os.makedirs(cn_dir, exist_ok=True) os.makedirs(tagger_dir, exist_ok=True) os.makedirs(lora_dir, exist_ok=True) load_cn_model(cn_dir) load_cn_config(cn_dir) load_tagger_model(tagger_dir) load_lora_model(lora_dir) def load_model(lora_dir, cn_dir): device = "cuda" if torch.cuda.is_available() else "cpu" dtype = torch.float16 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) controlnet = ControlNetModel.from_pretrained(cn_dir, torch_dtype=dtype, use_safetensors=True) pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained( "cagliostrolab/animagine-xl-3.1", controlnet=controlnet, vae=vae, torch_dtype=torch.float16 ) pipe.load_lora_weights(lora_dir, weight_name="sdxl_BW_bold_Line.safetensors", adapter_name="sdxl_BW_bold_Line") pipe.set_adapters(["sdxl_BW_bold_Line"], adapter_weights=[1.2]) pipe.fuse_lora() pipe = pipe.to(device) return pipe @spaces.GPU def predict(input_image_path, prompt, negative_prompt, controlnet_scale): pipe = load_model(lora_dir, cn_dir) input_image_pil = Image.open(input_image_path) base_size = input_image_pil.size resize_image = resize_image_aspect_ratio(input_image_pil) white_base_pil = base_generation(resize_image.size, (255, 255, 255, 255)).convert("RGB") generator = torch.manual_seed(0) last_time = time.time() prompt = "masterpiece, best quality, monochrome, lineart, white background, " + prompt execute_tags = ["sketch", "transparent background"] prompt = execute_prompt(execute_tags, prompt) prompt = remove_duplicates(prompt) prompt = remove_color(prompt) print(prompt) output_image = pipe( image=white_base_pil, control_image=resize_image, strength=1.0, prompt=prompt, negative_prompt = negative_prompt, controlnet_conditioning_scale=float(controlnet_scale), generator=generator, num_inference_steps=30, eta=1.0, ).images[0] print(f"Time taken: {time.time() - last_time}") output_image = output_image.resize(base_size, Image.LANCZOS) return output_image class Img2Img: def __init__(self): self.demo = self.layout() self.post_filter = True self.tagger_model = None self.input_image_path = None def process_prompt_analysis(self, input_image_path): if self.tagger_model is None: self.tagger_model = modelLoad(tagger_dir) tags = analysis(input_image_path, tagger_dir, self.tagger_model) tags_list = tags if self.post_filter: tags_list = remove_color(tags) return tags_list def layout(self): css = """ #intro{ max-width: 32rem; text-align: center; margin: 0 auto; } """ with gr.Blocks(css=css) as demo: with gr.Row(): with gr.Column(): self.input_image_path = gr.Image(label="input_image", type='filepath') self.prompt = gr.Textbox(label="prompt", lines=3) self.negative_prompt = gr.Textbox(label="negative_prompt", lines=3, value="lowres, error, extra digit, fewer digits, cropped, worst quality,low quality, normal quality, jpeg artifacts, blurry") prompt_analysis_button = gr.Button("prompt_analysis") self.controlnet_scale = gr.Slider(minimum=0.5, maximum=1.25, value=1.0, step=0.01, label="controlnet_scale") generate_button = gr.Button("generate") with gr.Column(): self.output_image = gr.Image(type="pil", label="output_image") prompt_analysis_button.click( self.process_prompt_analysis, inputs=[self.input_image_path], outputs=self.prompt ) generate_button.click( fn=predict, inputs=[self.input_image_path, self.prompt, self.negative_prompt, self.controlnet_scale], outputs=self.output_image ) return demo img2img = Img2Img() img2img.demo.launch(share=True)