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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection
Lichen Bai, Shitong Shao, Zikai Zhou +4
Diffusion models, the most popular generative paradigm so far, can inject conditional information into the generation path to guide the latent towards desired directions. However,…
Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer
Shitong Shao, Zikai Zhou, Tian Ye +3
Text-to-image diffusion models (DMs) develop at an unprecedented pace, supported by thorough theoretical exploration and empirical analysis. Unfortunately, the discrepancy between…
Golden Noise for Diffusion Models: A Learning Framework
Zikai Zhou, Shitong Shao, Lichen Bai +4
Text-to-image diffusion model is a popular paradigm that synthesizes personalized images by providing a text prompt and a random Gaussian noise. While people observe that some nois…
IV-Mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis
Shitong Shao, Zikai Zhou, Lichen Bai +2
The multi-step sampling mechanism, a key feature of visual diffusion models, has significant potential to replicate the success of OpenAI's Strawberry in enhancing performance by i…
Alignment of Diffusion Models: Fundamentals, Challenges, and Future
Buhua Liu, Shitong Shao, Bao Li +6
Diffusion models have emerged as the leading paradigm in generative modeling, excelling in various applications. Despite their success, these models often misalign with human inten…
Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization
Zipeng Qi, Lichen Bai, Haoyi Xiong +1
Diffusion models that can generate high-quality data from randomly sampled Gaussian noises have become the mainstream generative method in both academia and industry. Are randomly…