Image Upscaling

EfficientViT can speed up high-resolution diffusion models by compressing data with a ratio of up to 128 while keeping good image quality. It achieves a 19.1x speed increase for inference and a 17.9x speed increase for training on ImageNet 512x512 compared to other autoencoders.

Exploiting Diffusion Prior for Real-World Image Super-Resolution can restore high-quality images from low-resolution inputs using pre-trained text-to-image diffusion models. It allows users to balance image quality and fidelity through a controllable feature wrapping module and adapts to different image resolutions with a progressive aggregation sampling strategy.

FouriScale can generate high-resolution images from pre-trained diffusion models with various aspect ratios and achieve an astonishing capacity of arbitrary-size, high-resolution, and high-quality generation.

ResAdapter can generate images with any resolution and aspect ratio for diffusion models. It works with various personalized models and processes images efficiently, using only 0.5M parameters while keeping the original style.

ScaleCrafter can generate ultra-high-resolution images up to 4096x4096 and videos at 2048x1152 using pre-trained diffusion models. It reduces problems like object repetition and allows for custom aspect ratios, achieving excellent texture detail.

Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers can enhance low-resolution license plate images. It uses attention and transformer modules to improve details and a special loss function based on Optical Character Recognition to achieve better image quality.