New Breakthrough in Image Super-Resolution! InvSR Tool Transforms Blurry Photos to HD with One Click

AIbase基地

Published inAI News · 3 min read · Dec 23, 2024

Are you still troubled by blurry photos? A new image super-resolution tool called InvSR has emerged, allowing images to become clear and sharp with minimal processing steps. The strength of this tool lies in its utilization of the rich prior knowledge embedded in large pre-trained diffusion models, achieving efficient and high-quality image resolution enhancement.

InvSR's core technology is based on its innovative partial noise prediction strategy. It cleverly constructs the intermediate states of the diffusion model as starting points for the sampling process, using a deep noise predictor to estimate the optimal noise map during the forward diffusion process. Once trained, this noise predictor can accurately initialize the sampling process along the diffusion trajectory, generating high-resolution images.

One of the highlights of InvSR is its flexible and efficient sampling mechanism. Users can choose the number of sampling steps from 1 to 5 based on their needs. Even with just one processing step, the results can rival those of other methods that require multiple steps. This flexibility allows users to find the best balance between efficiency and effectiveness, easily handling tasks such as restoring blurry old photos or optimizing AI-generated images.

The tool is also very user-friendly. Users can quickly get started with simple command-line instructions and optimize memory usage for processing large images through various options. Additionally, users can customize pre-downloaded models and noise predictors for better processing results. To enhance user experience, InvSR also provides an online demo, allowing users to intuitively experience its powerful features.

For researchers with higher demands, InvSR offers a complete training process. Users only need to download a specific LPIPS model and prepare a configuration file to start training. The tool supports multi-GPU parallel computing and features interruption recovery, ensuring stability and continuity during training.

Project address: https://github.com/zsyOAOA/InvSR

Diffusion-Vas: Tracking Video Objects with Occlusion Completion

In the field of video analysis, the persistence of objects is an important cue for humans to understand that objects still exist even when completely occluded. However, current object segmentation methods mainly focus on visible (modal) objects, lacking the capability to handle non-modal (visible + invisible) objects. To address this issue, researchers proposed a two-stage method based on diffusion priors, Diffusion-Vas, aimed at improving the effectiveness of non-modal segmentation and content completion in videos, enabling the tracking of specified targets within the video and using diffusion models to complete the occluded parts.

Can Diffusion Models Play Games? DIAMOND Excels in Atari, Visual Details are Key!

Reinforcement learning has seen significant success in recent years, but its low sample efficiency limits its application in the real world. World models, as a type of environment generation model, offer hope for addressing this issue. They can serve as simulated environments to train reinforcement learning agents with higher sample efficiency. Currently, most world models simulate environment dynamics through discrete latent variable sequences. However, this method of compressing into a compact discrete representation may overlook visual details that are critical for reinforcement learning. Meanwhile, diffusion models have become prominent in the field of image generation.

Can Diffusion Models 'Learn by Analogy'? Alibaba's IC-LoRA Enhances Image Generation Models with Contextual Memory

Recent research from Alibaba's Tongyi Laboratory indicates that existing text-to-image Diffusion Transformer models already possess the ability to generate multiple images with specific relational contexts. With just a little 'guidance', they can achieve a synthesis of understanding and produce high-quality multi-image collections. Traditional Diffusion models are more like 'rote learning' students, requiring massive datasets to generate high-quality images. However, with the support of IC-LoRA, they become 'high-achieving students', capable of learning through analogy.

Improving AI-Generated Images Using Human Attention

A recent study proposed a new method that leverages human attention to enhance the quality of AI-generated images. This method uses a saliency detector to identify the most important areas in the image and prioritizes the generation of these areas. Traditional image generation methods optimize the entire image uniformly, whereas the new method identifies and prioritizes more 'important' regions, similar to human attention. This approach can improve image quality and fidelity to textual prompts. Researchers utilized stable diffusion models and saliency detectors to generate images, and