Spaces:
Paused
Paused
| import spaces | |
| import os | |
| import requests | |
| import time | |
| import torch | |
| from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler | |
| from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker | |
| from diffusers.models import AutoencoderKL | |
| from diffusers.models.attention_processor import AttnProcessor2_0 | |
| from PIL import Image | |
| import cv2 | |
| import numpy as np | |
| from RealESRGAN import RealESRGAN | |
| import gradio as gr | |
| from gradio_imageslider import ImageSlider | |
| USE_TORCH_COMPILE = False | |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| def download_file(url, folder_path, filename): | |
| if not os.path.exists(folder_path): | |
| os.makedirs(folder_path) | |
| file_path = os.path.join(folder_path, filename) | |
| if os.path.isfile(file_path): | |
| print(f"File already exists: {file_path}") | |
| else: | |
| response = requests.get(url, stream=True) | |
| if response.status_code == 200: | |
| with open(file_path, 'wb') as file: | |
| for chunk in response.iter_content(chunk_size=1024): | |
| file.write(chunk) | |
| print(f"File successfully downloaded and saved: {file_path}") | |
| else: | |
| print(f"Error downloading the file. Status code: {response.status_code}") | |
| def download_models(): | |
| models = { | |
| "MODEL": ("https://huggingface.co/dantea1118/juggernaut_reborn/resolve/main/juggernaut_reborn.safetensors?download=true", "models/models/Stable-diffusion", "juggernaut_reborn.safetensors"), | |
| "UPSCALER_X2": ("https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth?download=true", "models/upscalers/", "RealESRGAN_x2.pth"), | |
| "UPSCALER_X4": ("https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth?download=true", "models/upscalers/", "RealESRGAN_x4.pth"), | |
| "NEGATIVE_1": ("https://huggingface.co/philz1337x/embeddings/resolve/main/verybadimagenegative_v1.3.pt?download=true", "models/embeddings", "verybadimagenegative_v1.3.pt"), | |
| "NEGATIVE_2": ("https://huggingface.co/datasets/AddictiveFuture/sd-negative-embeddings/resolve/main/JuggernautNegative-neg.pt?download=true", "models/embeddings", "JuggernautNegative-neg.pt"), | |
| "LORA_1": ("https://huggingface.co/philz1337x/loras/resolve/main/SDXLrender_v2.0.safetensors?download=true", "models/Lora", "SDXLrender_v2.0.safetensors"), | |
| "LORA_2": ("https://huggingface.co/philz1337x/loras/resolve/main/more_details.safetensors?download=true", "models/Lora", "more_details.safetensors"), | |
| "CONTROLNET": ("https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth?download=true", "models/ControlNet", "control_v11f1e_sd15_tile.pth"), | |
| "VAE": ("https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors?download=true", "models/VAE", "vae-ft-mse-840000-ema-pruned.safetensors"), | |
| } | |
| for model, (url, folder, filename) in models.items(): | |
| download_file(url, folder, filename) | |
| download_models() | |
| def timer_func(func): | |
| def wrapper(*args, **kwargs): | |
| start_time = time.time() | |
| result = func(*args, **kwargs) | |
| end_time = time.time() | |
| print(f"{func.__name__} took {end_time - start_time:.2f} seconds") | |
| return result | |
| return wrapper | |
| class LazyLoadPipeline: | |
| def __init__(self): | |
| self.pipe = None | |
| def load(self): | |
| if self.pipe is None: | |
| print("Starting to load the pipeline...") | |
| self.pipe = self.setup_pipeline() | |
| print(f"Moving pipeline to device: {device}") | |
| self.pipe.to(device) | |
| if USE_TORCH_COMPILE: | |
| print("Compiling the model...") | |
| self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| def setup_pipeline(self): | |
| print("Setting up the pipeline...") | |
| controlnet = ControlNetModel.from_single_file( | |
| "models/ControlNet/control_v11f1e_sd15_tile.pth", torch_dtype=torch.float16 | |
| ) | |
| safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker") | |
| model_path = "models/models/Stable-diffusion/juggernaut_reborn.safetensors" | |
| pipe = StableDiffusionControlNetImg2ImgPipeline.from_single_file( | |
| model_path, | |
| controlnet=controlnet, | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| safety_checker=safety_checker | |
| ) | |
| vae = AutoencoderKL.from_single_file( | |
| "models/VAE/vae-ft-mse-840000-ema-pruned.safetensors", | |
| torch_dtype=torch.float16 | |
| ) | |
| pipe.vae = vae | |
| pipe.load_textual_inversion("models/embeddings/verybadimagenegative_v1.3.pt") | |
| pipe.load_textual_inversion("models/embeddings/JuggernautNegative-neg.pt") | |
| pipe.load_lora_weights("models/Lora/SDXLrender_v2.0.safetensors") | |
| pipe.fuse_lora(lora_scale=0.5) | |
| pipe.load_lora_weights("models/Lora/more_details.safetensors") | |
| pipe.fuse_lora(lora_scale=1.) | |
| pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) | |
| pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.3, b2=1.4) | |
| return pipe | |
| def __call__(self, *args, **kwargs): | |
| return self.pipe(*args, **kwargs) | |
| class LazyRealESRGAN: | |
| def __init__(self, device, scale): | |
| self.device = device | |
| self.scale = scale | |
| self.model = None | |
| def load_model(self): | |
| if self.model is None: | |
| self.model = RealESRGAN(self.device, scale=self.scale) | |
| self.model.load_weights(f'models/upscalers/RealESRGAN_x{self.scale}.pth', download=False) | |
| def predict(self, img): | |
| self.load_model() | |
| return self.model.predict(img) | |
| lazy_realesrgan_x2 = LazyRealESRGAN(device, scale=2) | |
| lazy_realesrgan_x4 = LazyRealESRGAN(device, scale=4) | |
| def resize_and_upscale(input_image, resolution): | |
| scale = 2 if resolution <= 2048 else 4 | |
| input_image = input_image.convert("RGB") | |
| W, H = input_image.size | |
| k = float(resolution) / min(H, W) | |
| H = int(round(H * k / 64.0)) * 64 | |
| W = int(round(W * k / 64.0)) * 64 | |
| img = input_image.resize((W, H), resample=Image.LANCZOS) | |
| if scale == 2: | |
| img = lazy_realesrgan_x2.predict(img) | |
| else: | |
| img = lazy_realesrgan_x4.predict(img) | |
| return img | |
| def create_hdr_effect(original_image, hdr): | |
| if hdr == 0: | |
| return original_image | |
| cv_original = cv2.cvtColor(np.array(original_image), cv2.COLOR_RGB2BGR) | |
| factors = [1.0 - 0.9 * hdr, 1.0 - 0.7 * hdr, 1.0 - 0.45 * hdr, | |
| 1.0 - 0.25 * hdr, 1.0, 1.0 + 0.2 * hdr, | |
| 1.0 + 0.4 * hdr, 1.0 + 0.6 * hdr, 1.0 + 0.8 * hdr] | |
| images = [cv2.convertScaleAbs(cv_original, alpha=factor) for factor in factors] | |
| merge_mertens = cv2.createMergeMertens() | |
| hdr_image = merge_mertens.process(images) | |
| hdr_image_8bit = np.clip(hdr_image * 255, 0, 255).astype('uint8') | |
| return Image.fromarray(cv2.cvtColor(hdr_image_8bit, cv2.COLOR_BGR2RGB)) | |
| lazy_pipe = LazyLoadPipeline() | |
| lazy_pipe.load() | |
| def prepare_image(input_image, resolution, hdr): | |
| condition_image = resize_and_upscale(input_image, resolution) | |
| condition_image = create_hdr_effect(condition_image, hdr) | |
| return condition_image | |
| def gradio_process_image(input_image, resolution, num_inference_steps, strength, hdr, guidance_scale): | |
| print("Starting image processing...") | |
| torch.cuda.empty_cache() | |
| condition_image = prepare_image(input_image, resolution, hdr) | |
| prompt = "masterpiece, best quality, highres" | |
| negative_prompt = "low quality, normal quality, ugly, blurry, blur, lowres, bad anatomy, bad hands, cropped, worst quality, verybadimagenegative_v1.3, JuggernautNegative-neg" | |
| options = { | |
| "prompt": prompt, | |
| "negative_prompt": negative_prompt, | |
| "image": condition_image, | |
| "control_image": condition_image, | |
| "width": condition_image.size[0], | |
| "height": condition_image.size[1], | |
| "strength": strength, | |
| "num_inference_steps": num_inference_steps, | |
| "guidance_scale": guidance_scale, | |
| "generator": torch.Generator(device=device).manual_seed(0), | |
| } | |
| print("Running inference...") | |
| result = lazy_pipe(**options).images[0] | |
| print("Image processing completed successfully") | |
| # Convert input_image and result to numpy arrays | |
| input_array = np.array(input_image) | |
| result_array = np.array(result) | |
| return [input_array, result_array] | |
| title = """<h1 align="center">Image Upscaler with Tile Controlnet</h1> | |
| <p align="center">The main ideas come from</p> | |
| <p><center> | |
| <a href="https://github.com/philz1337x/clarity-upscaler" target="_blank">[philz1337x]</a> | |
| <a href="https://github.com/BatouResearch/controlnet-tile-upscale" target="_blank">[Pau-Lozano]</a> | |
| </center></p> | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.HTML(title) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(type="pil", label="Input Image") | |
| run_button = gr.Button("Enhance Image") | |
| with gr.Column(): | |
| output_slider = ImageSlider(label="Before / After", type="numpy") | |
| with gr.Accordion("Advanced Options", open=False): | |
| resolution = gr.Slider(minimum=256, maximum=2048, value=512, step=256, label="Resolution") | |
| num_inference_steps = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Number of Inference Steps") | |
| strength = gr.Slider(minimum=0, maximum=1, value=0.4, step=0.01, label="Strength") | |
| hdr = gr.Slider(minimum=0, maximum=1, value=0, step=0.1, label="HDR Effect") | |
| guidance_scale = gr.Slider(minimum=0, maximum=20, value=3, step=0.5, label="Guidance Scale") | |
| run_button.click(fn=gradio_process_image, | |
| inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale], | |
| outputs=output_slider) | |
| # Add examples with all required inputs | |
| gr.Examples( | |
| examples=[ | |
| ["image1.jpg", 512, 20, 0.4, 0, 3], | |
| ["image2.png", 512, 20, 0.4, 0, 3], | |
| ["image3.png", 512, 20, 0.4, 0, 3], | |
| ], | |
| inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale], | |
| outputs=output_slider, | |
| fn=gradio_process_image, | |
| cache_examples=True, | |
| ) | |
| demo.launch(share=True) |