Diffusion models, a powerful and universal generative AI technology, have achieved
tremendous success in computer vision, audio, reinforcement learning, and computational …

This tutorial provides a comprehensive survey of methods for fine-tuning diffusion models to
optimize downstream reward functions. While diffusion models are widely known to provide …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Large-scale generative models are capable of producing high-quality images from detailed
prompts. However, many aspects of an image are difficult or impossible to convey through …

Recently, diffusion models have achieved remarkable success in text-to-image generation,
enabling the creation of high-quality images from text prompts and various conditions …

Personalization has emerged as a prominent aspect within the field of generative AI
enabling the synthesis of individuals in diverse contexts and styles while retaining high …

Recent text-to-image diffusion models have demonstrated an astonishing capacity to
generate high-quality images. However, researchers mainly studied the way of synthesizing …

Diffusion models are a class of flexible generative models trained with an approximation to
the log-likelihood objective. However, most use cases of diffusion models are not concerned …

Accurate and controllable image editing is a challenging task that has attracted significant
attention recently. Notably DragGAN developed by Pan et al.(2023) is an interactive point …

Existing text-to-image diffusion models struggle to synthesize realistic images given dense
captions, where each text prompt provides a detailed description for a specific image region …