import os import yaml import torch import argparse import numpy as np import gradio as gr from PIL import Image from copy import deepcopy from torch.nn.parallel import DataParallel, DistributedDataParallel from huggingface_hub import hf_hub_download from gradio_imageslider import ImageSlider ## local code from models import seemore 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 def load_img (filename, norm=True,): img = np.array(Image.open(filename).convert("RGB")) h, w = img.shape[:2] if w > 1920 or h > 1080: new_h, new_w = h // 4, w // 4 img = np.array(Image.fromarray(img).resize((new_w, new_h), Image.BICUBIC)) if norm: img = img / 255. img = img.astype(np.float32) return img def process_img (image): img = np.array(image) img = img / 255. img = img.astype(np.float32) y = torch.tensor(img).permute(2,0,1).unsqueeze(0).to(device) with torch.no_grad(): x_hat = model(y) 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) # return (image, Image.fromarray(restored_img)) def load_network(net, load_path, strict=True, param_key='params'): if isinstance(net, (DataParallel, DistributedDataParallel)): net = net.module load_net = torch.load(load_path, map_location=lambda storage, loc: storage) if param_key is not None: if param_key not in load_net and 'params' in load_net: param_key = 'params' load_net = load_net[param_key] # remove unnecessary 'module.' for k, v in deepcopy(load_net).items(): if k.startswith('module.'): load_net[k[7:]] = v load_net.pop(k) net.load_state_dict(load_net, strict=strict) CONFIG = "configs/eval_seemore_t_x4.yml" hf_hub_download(repo_id="eduardzamfir/SeemoRe-T", filename="SeemoRe_T_X4.pth", local_dir="./") MODEL_NAME = "SeemoRe_T_X4.pth" # parse config file with open(os.path.join(CONFIG), "r") as f: config = yaml.safe_load(f) cfg = dict2namespace(config) device = torch.device("cpu") model = seemore.SeemoRe(scale=cfg.model.scale, in_chans=cfg.model.in_chans, num_experts=cfg.model.num_experts, num_layers=cfg.model.num_layers, embedding_dim=cfg.model.embedding_dim, img_range=cfg.model.img_range, use_shuffle=cfg.model.use_shuffle, global_kernel_size=cfg.model.global_kernel_size, recursive=cfg.model.recursive, lr_space=cfg.model.lr_space, topk=cfg.model.topk) model = model.to(device) print ("IMAGE MODEL CKPT:", MODEL_NAME) load_network(model, MODEL_NAME, strict=True, param_key='params') title = "See More Details" description = ''' ### See More Details: Efficient Image Super-Resolution by Experts Mining - ICML 2024, Vienna, Austria #### [Eduard Zamfir1](https://eduardzamfir.github.io), [Zongwei Wu1*](https://sites.google.com/view/zwwu/accueil), [Nancy Mehta1](https://scholar.google.com/citations?user=WwdYdlUAAAAJ&hl=en&oi=ao), [Yulun Zhang2,3*](http://yulunzhang.com/) and [Radu Timofte1](https://www.informatik.uni-wuerzburg.de/computervision/) #### **1 University of Würzburg, Germany - 2 Shanghai Jiao Tong University, China - 3 ETH Zürich, Switzerland** #### *** Corresponding authors**
Abstract (click me to read)

Reconstructing high-resolution (HR) images from low-resolution (LR) inputs poses a significant challenge in image super-resolution (SR). While recent approaches have demonstrated the efficacy of intricate operations customized for various objectives, the straightforward stacking of these disparate operations can result in a substantial computational burden, hampering their practical utility. In response, we introduce **S**eemo**R**e, an efficient SR model employing expert mining. Our approach strategically incorporates experts at different levels, adopting a collaborative methodology. At the macro scale, our experts address rank-wise and spatial-wise informative features, providing a holistic understanding. Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts. By tapping into experts specialized in distinct key factors crucial for accurate SR, our model excels in uncovering intricate intra-feature details. This collaborative approach is reminiscent of the concept of **see more**, allowing our model to achieve an optimal performance with minimal computational costs in efficient settings

#### Drag the slider on the super-resolution image left and right to see the changes in the image details.
@inproceedings{zamfir2024details, title={See More Details: Efficient Image Super-Resolution by Experts Mining}, author={Eduard Zamfir and Zongwei Wu and Nancy Mehta and Yulun Zhang and Radu Timofte}, booktitle={International Conference on Machine Learning}, year={2024}, organization={PMLR} }
''' article = "

See More Details: Efficient Image Super-Resolution by Experts Mining

" #### Image,Prompts examples examples = [['images/img002x4.png'], ['images/img003x4.png'], ['images/img004x4.png'], ['images/img035x4.png'], ['images/img053x4.png'], ['images/img064x4.png'], ['images/img083x4.png'], ['images/img092x4.png'], ] 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", value="images/img002x4.png"),], outputs=ImageSlider(label="Super-Resolved Image", type="pil", show_download_button=True, ), #[gr.Image(type="pil", label="Ouput", min_width=500)], title=title, description=description, article=article, examples=examples, css=css, ) if __name__ == "__main__": demo.launch()