project
Diff-Instruct - A general framework for transferring knowledge from pre-trained diffusion models
Diff-Instruct is an advanced knowledge transfer method used to extract knowledge from pre-trained diffusion models to guide the training of other generative models. It is based on a novel divergence metric—Integral Kullback-Leibler (IKL) divergence...
What is Diff-Instruct?
Diff-Instruct is an advanced knowledge transfer method used to extract knowledge from pre-trained diffusion models to guide the training of other generative models. It is based on a novel divergence metric—integral Kullback-Leibler (IKL) divergence—designed specifically for diffusion models, comparing distributions by calculating the integral of the KL divergence along the diffusion process. This method can guide the training of any generative model without requiring additional data by minimizing the IKL divergence. The generality, effectiveness, and ability to significantly improve the performance of generative models have garnered attention in academia.
The main functions of Diff-Instruct
- Knowledge transferDiff-Instruct can learn from pre-trained diffusion models (DMs) and transfer that knowledge to other generative models in a way that requires no additional data.
- Guided Generative Model TrainingAs a general framework, Diff-Instruct can guide the training of any generative model, as long as the generated samples are differentiable with respect to the model parameters.
- Minimize new divergenceDiff-Instruct is based on rigorous mathematics, and its guiding process directly corresponds to minimizing a new type of divergence called integral Kullback-Leibler (IKL) divergence.
- Enhance robustnessIKL divergence is designed specifically for DMs and has been shown to be more robust when comparing distributions with misaligned support by calculating the KL divergence integral over the diffusion process.
- Improve generative model performanceDiff-Instruct demonstrates its effectiveness and universality in two scenarios: extracting pre-trained diffusion models and improving existing GAN models. Experiments show that Diff-Instruct can generate state-of-the-art single-step diffusion models and consistently improve pre-trained generators for GAN models.
The technical principle of Diff-Instruct
- General frameworkDiff-Instruct proposes a general framework that can guide the training of any generative model, as long as the generated samples are differentiable with respect to the model parameters.
- Integral Kullback-Leibler (IKL) divergenceDiff-Instruct is built on a rigorous mathematical foundation, where the instruction process directly corresponds to minimizing a new type of divergence called IKL divergence. IKL divergence is designed specifically for DMs and has been shown to be more robust when comparing distributions with misaligned supports by calculating the KL divergence integral along the diffusion process.
- Data Free LearningThe Diff-Instruct data-free learning scheme supports the use of pre-trained DMs as instructors to guide various generative models.
- flexibilityDiff-Instruct offers generators a high degree of flexibility, unlike traditional diff model distillation methods which impose strict limitations on generator selection. This means that generators can be based on convolutional neural networks (CNNs) or Transformer-based image generators, such as StyleGAN, or UNet-based generators adapted from pre-trained diff models.
Diff-Instruct project address
- Github repository:https://github.com/pkulwj1994/diff_instruct
- Technical Papers:https://openreview.net/pdf?id=MLIs5iRq4w
Application scenarios of Diff-Instruct
- Distillation of pre-trained diffusion modelsDiff-Instruct can learn from pre-trained diffusion models (DMs) and transfer that knowledge to other generative models in a data-free manner, thus achieving distillation of pre-trained diffusion models.
- Improve existing generative adversarial network (GAN) modelsDiff-Instruct can be used to distill pre-trained diffusion models to improve existing GAN models and enhance the performance of their generators.
- Video generationIn the field of multimodal generation, the technical principle of Diff-Instruct has also been applied to video generation, such as the MarDini model, which integrates the advantages of Mask Autoregression (MAR) into a unified diffusion model (DM) framework for large-scale video generation.
- Emoji fashion show video generationPika 2.0, based on the Diff-Instruct technology, supports generating videos by referencing images of backgrounds, characters, and clothing, demonstrating its application in the field of emoji fashion show video generation.