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Diffusion models generate high-quality images but require hundreds of forward passes. MIT CSAIL and Adobe Research introduce Distribution Matching Distillation (DMD), a distillation approach that converts costly multi-step diffusion models into fast one-step generators. A thread 🧵

MIT CSAIL

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34,383 Aufrufe • vor 2 Jahren •via X (Twitter)

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9 Kommentare

Profilbild von MIT CSAIL

MIT CSAILvor 2 Jahren

DMD trains a one-step generator that maps random noise into realistic images, consisting of two key components. First up: it uses a regression loss to anchor the mapping process, ensuring a coarse organization of the image space, enhancing the stability of the training phase.

Profilbild von MIT CSAIL

MIT CSAILvor 2 Jahren

Additionally, it employs a distribution matching loss to guarantee that the likelihood of generating a specific image w/the student model aligns w/its actual frequency of occurrence in the real world.

Profilbild von MIT CSAIL

MIT CSAILvor 2 Jahren

The gradient of this loss is formulated as the difference between two diffusion models’ output, trained on real and fake samples respectively.

Profilbild von MIT CSAIL

MIT CSAILvor 2 Jahren

DMD achieves a strong 11.49 FID on zero-shot COCO-30K, comparable to Stable Diffusion v1.5 while being 30X faster. Compared to previous approaches, it notably balances image quality with sample diversity.

Profilbild von MIT CSAIL

MIT CSAILvor 2 Jahren

DMD paves the way for real-time visual generation. This same approach could improve diffusion-based generative models across various fields, from design, to scientific discovery and beyond, by significantly enhancing speed and effectiveness.

Profilbild von MIT CSAIL

MIT CSAILvor 2 Jahren

Paper: Authors: @TianweiY, @m_gharbi, @rzhang88, @elishechtman, @fredodurand, Bill Freeman, and Taesung Park. Project page: MIT News:

Profilbild von menguzat

menguzatvor 2 Jahren

@AdobeResearch will you release the code / model for this?

Profilbild von Prashant

Prashantvor 2 Jahren

@AdobeResearch Could this approach of distribution matching loss be applied to other generative AI tasks besides image generation? For example, text generation or music composition?

Profilbild von 𝗦𝗼𝘂𝗹𝘀𝗳𝗲𝗻𝗴 𝗡𝗲𝘄 𝗬𝗼𝗿𝗸

𝗦𝗼𝘂𝗹𝘀𝗳𝗲𝗻𝗴 𝗡𝗲𝘄 𝗬𝗼𝗿𝗸vor 2 Jahren

@rzhang88 @AdobeResearch Good for you my friend, we are try use your model colorization(which is 4 years ago) for sneakers now, lol, thank you for everything.

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