Researchers have developed ScaleCrafter, a method that enables the generation of ultra-high-resolution images using pre-trained diffusion models. By dynamically adjusting the convolutional receptive field, ScaleCrafter addresses issues like object repetition and incorrect object topologies. It also introduces innovative strategies like dispersed convolution and noise-damped classifier-free guidance. The method has been successfully applied to a text-to-video model and extensively evaluated, demonstrating its effectiveness in improving high-resolution image synthesis.
Unlocking Ultra-High-Resolution Image Synthesis with Pre-trained Diffusion Models
The development of image synthesis techniques has made significant progress in recent years, attracting attention from academia and industry. However, current models are limited in generating high-resolution images for applications like advertising.
Generating larger images than the training resolutions poses challenges such as object repetition and deformed object architectures. Existing methods struggle to address these issues effectively. Researchers have identified a crucial element causing these problems: convolutional kernels’ limited perceptual fields.
To overcome these challenges, a team of researchers has proposed ScaleCrafter. It utilizes a simple yet powerful solution called re-dilation, which dynamically adjusts the convolutional perceptual field during image generation. This approach enhances the coherence and quality of generated images, allowing for ultra-high-resolution photographs up to 4096 x 4096 pixels. ScaleCrafter does not require additional training or optimization stages, making it a practical solution for high-resolution image synthesis.
Comprehensive tests have demonstrated that ScaleCrafter successfully addresses object repetition and produces cutting-edge results, especially in displaying complex texture details. This research also opens up possibilities for using pre-trained diffusion models to generate high-resolution visuals without extensive retraining.
Key contributions of this research include:
1. Identifying that object repetition is caused by the constrained receptive field of convolutional procedures, rather than the number of attention tokens.
2. Introducing the re-dilation approach, which dynamically increases the convolutional receptive field during inference, tackling the root of the problem.
3. Presenting innovative strategies like dispersed convolution and noise-damped classifier-free guidance for creating ultra-high-resolution images.
4. Evaluating the method across various diffusion models, including Stable Diffusion, and demonstrating its effectiveness in addressing object recurrence and improving high-resolution image synthesis.
To learn more about this research, you can read the full paper and access the GitHub repository.
If you are interested in leveraging AI to transform your company and stay competitive, consider exploring the potential of ScaleCrafter: Unlocking Ultra-High-Resolution Image Synthesis with Pre-trained Diffusion Models.
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