How do diffusion models perform on high-resolution image generation tasks?

Diffusion models perform well on high-resolution image generation tasks due to their ability to generate fine details and maintain coherence across large images. These models work by starting with pure noise and gradually refining it through multiple denoising steps. This iterative process allows them to produce high-quality images with sharp edges, realistic textures, and smooth color transitions, which are essential for high-resolution outputs. Compared to traditional generative models like GANs, diffusion models are more stable during training and less prone to artifacts such as mode collapse, where a model generates limited variations of an image instead of diverse outputs.

One of the reasons diffusion models excel at high-resolution image generation is their scalability. They can be trained on progressively larger resolutions and use techniques such as classifier-free guidance to balance realism and diversity in generated images. For instance, Stable Diffusion and Imagen have demonstrated strong performance by generating detailed images at resolutions like 1024×1024 or higher. Additionally, techniques such as latent diffusion reduce the computational cost by applying the denoising process in a lower-dimensional latent space rather than directly on pixel data, making high-resolution generation more efficient.

Despite their strengths, diffusion models still have challenges when generating extremely high-resolution images. The process is computationally expensive, requiring powerful GPUs or TPUs to handle long denoising chains effectively. Additionally, as image resolution increases, ensuring global consistency across different parts of the image becomes more difficult, sometimes leading to inconsistencies or unnatural transitions. Researchers are addressing these issues with methods like hierarchical diffusion, where models generate lower-resolution versions first and then refine details at higher resolutions. As these techniques improve, diffusion models are expected to become even more effective for high-resolution image generation.