import argparse import gradio as gr from PIL import Image import os import torch import numpy as np import yaml from huggingface_hub import hf_hub_download #from gradio_imageslider import ImageSlider ## local code from models import instructir from text.models import LanguageModel, LMHead def dict2namespace(config): namespace = argparse.Namespace() for key, value in config.items(): if isinstance(value, dict): new_value = dict2namespace(value) else: new_value = value setattr(namespace, key, new_value) return namespace hf_hub_download(repo_id="marcosv/InstructIR", filename="im_instructir-7d.pt", local_dir="./") hf_hub_download(repo_id="marcosv/InstructIR", filename="lm_instructir-7d.pt", local_dir="./") CONFIG = "configs/eval5d.yml" LM_MODEL = "lm_instructir-7d.pt" MODEL_NAME = "im_instructir-7d.pt" # parse config file with open(os.path.join(CONFIG), "r") as f: config = yaml.safe_load(f) cfg = dict2namespace(config) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = instructir.create_model(input_channels =cfg.model.in_ch, width=cfg.model.width, enc_blks = cfg.model.enc_blks, middle_blk_num = cfg.model.middle_blk_num, dec_blks = cfg.model.dec_blks, txtdim=cfg.model.textdim) model = model.to(device) print ("IMAGE MODEL CKPT:", MODEL_NAME) model.load_state_dict(torch.load(MODEL_NAME, map_location="cpu"), strict=True) os.environ["TOKENIZERS_PARALLELISM"] = "false" LMODEL = cfg.llm.model language_model = LanguageModel(model=LMODEL) lm_head = LMHead(embedding_dim=cfg.llm.model_dim, hidden_dim=cfg.llm.embd_dim, num_classes=cfg.llm.nclasses) lm_head = lm_head.to(device) print("LMHEAD MODEL CKPT:", LM_MODEL) lm_head.load_state_dict(torch.load(LM_MODEL, map_location="cpu"), strict=True) def load_img (filename, norm=True,): img = np.array(Image.open(filename).convert("RGB")) if norm: img = img / 255. img = img.astype(np.float32) return img def process_img (image, prompt): img = np.array(image) img = img / 255. img = img.astype(np.float32) y = torch.tensor(img).permute(2,0,1).unsqueeze(0).to(device) lm_embd = language_model(prompt) lm_embd = lm_embd.to(device) with torch.no_grad(): text_embd, deg_pred = lm_head (lm_embd) x_hat = model(y, text_embd) restored_img = x_hat.squeeze().permute(1,2,0).clamp_(0, 1).cpu().detach().numpy() restored_img = np.clip(restored_img, 0. , 1.) restored_img = (restored_img * 255.0).round().astype(np.uint8) # float32 to uint8 return Image.fromarray(restored_img) #(image, Image.fromarray(restored_img)) title = "InstructIR ✏️🖼️ 🤗" description = ''' ## [High-Quality Image Restoration Following Human Instructions](https://github.com/mv-lab/InstructIR) [Marcos V. Conde](https://scholar.google.com/citations?user=NtB1kjYAAAAJ&hl=en), [Gregor Geigle](https://scholar.google.com/citations?user=uIlyqRwAAAAJ&hl=en), [Radu Timofte](https://scholar.google.com/citations?user=u3MwH5kAAAAJ&hl=en) Computer Vision Lab, University of Wuerzburg | Sony PlayStation, FTG ### TL;DR: quickstart ***InstructIR takes as input an image and a human-written instruction for how to improve that image.*** The (single) neural model performs all-in-one image restoration. InstructIR achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement. **🚀 You can start with the [demo tutorial.](https://github.com/mv-lab/InstructIR/blob/main/demo.ipynb)** Check [our github](https://github.com/mv-lab/InstructIR) for more information
Abstract (click me to read)

Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model. In this work, we present the first approach that uses human-written instructions to guide the image restoration model. Given natural language prompts, our model can recover high-quality images from their degraded counterparts, considering multiple degradation types. Our method, InstructIR, achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement. InstructIR improves +1dB over previous all-in-one restoration methods. Moreover, our dataset and results represent a novel benchmark for new research on text-guided image restoration and enhancement.

> **Disclaimer:** please remember this is not a product, thus, you will notice some limitations. **This demo expects an image with some degradations (blur, noise, rain, low-light, haze) and a prompt requesting what should be done.** Due to the GPU memory limitations, the app might crash if you feed a high-resolution image (2K, 4K).
The model was trained using mostly synthetic data, thus it might not work great on real-world complex images. However, it works surprisingly well on real-world foggy and low-light images. You can also try general image enhancement prompts (e.g., "retouch this image", "enhance the colors") and see how it improves the colors.
''' article = "

High-Quality Image Restoration Following Human Instructions

" #### Image,Prompts examples examples = [['images/rain-020.png', "I love this photo, could you remove the raindrops? please keep the content intact"], ['images/gradio_demo_images/city.jpg', "I took this photo during a foggy day, can you improve it?"], ['images/gradio_demo_images/frog.png', "can you remove the tiny dots in the image? it is very unpleasant"], ["images/lol_748.png", "my image is too dark, I cannot see anything, can you fix it?"], ["images/gopro.png", "I took this photo while I was running, can you stabilize the image? it is too blurry"], ["images/a0010.jpg", "please I want this image for my photo album, can you edit it as a photographer"], ["images/real_fog.png", "How can I remove the fog and mist from this photo?"]] css = """ .image-frame img, .image-container img { width: auto; height: auto; max-width: none; } """ demo = gr.Interface( fn=process_img, inputs=[ gr.Image(type="pil", label="Input"), gr.Text(label="Prompt") ], outputs=[gr.Image(type="pil", label="Ouput")], title=title, description=description, article=article, examples=examples, css=css, ) if __name__ == "__main__": demo.launch()