Diffusion Model Training Has Been Wrong All Along: Saining Xie on the Importance of Representation
New York University’s renowned researcher Saining Xie has declared, Representation matters, three times in a row, highlighting a critical oversight in diffusion model training. He believes that we may have been training diffusion models using the wrong methods all along.Even for generative models, representation remains crucial. Based on this insight, Xie and his team have proposed REPA, a representation alignment technique that makes training diffusiontransformers simpler than you might think.
Yann LeCun, a prominent figure in the field, has also endorsed this research, stating: We know that when training visual encoders with self-supervised learning, using a decoder withreconstruction loss is far less effective than using a joint embedding architecture with feature prediction loss and collapse prevention mechanisms. This paper from NYU’s @sainingxie shows that even if you are only interested in generating pixels (e.g., using adiffusion transformer to generate beautiful images), you should include a feature prediction loss so that the internal representation of the decoder can predict features from a pretrained visual encoder (e.g., DINOv2).
Diffusion models, known for their prowess in generating high-dimensional visual data, have gained widespread adoption. These models, including diffusion and flow-based models, rely on denoising principles. Recently, research has explored using diffusion models as representation learners, recognizing their potential in this domain.
Xie’s research, however, sheds light on a fundamental flaw in the conventional approach to diffusion model training. He argues thatfocusing solely on pixel generation neglects the importance of representation. By incorporating feature prediction loss, REPA aligns the internal representations of the decoder with those of a pre-trained visual encoder, leading to significant improvements in model performance.
This discovery has the potential to revolutionize diffusion model training. By prioritizing representation, researchers can unlock new possibilities for generating high-quality images and other visual data. The implications extend beyond image generation, suggesting a paradigm shift in how we approach generative modeling across various domains.
As Xie emphasizes, Representation matters. This simple yet profound statement underscores the need for a more nuancedunderstanding of representation in generative modeling. By embracing this principle, we can unlock the full potential of diffusion models and advance the field of artificial intelligence.
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