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 new divergence measure—the Integral Kullback-Leibler (IKL) divergence—designed specifically for diffusion models, which compares distributions by computing the integral of KL divergence along the diffusion process. This method enables training guidance for arbitrary generative models by minimizing the IKL divergence without requiring additional data. The generality, effectiveness, and ability of Diff-Instruct to significantly improve generative model performance have attracted attention in academia.
Main Features of Diff-Instruct
- Knowledge Transfer: Diff-Instruct can learn from pre-trained diffusion models (DMs) and transfer this knowledge to other generative models without additional data.
- Guiding Generative Model Training: As a general framework, Diff-Instruct can guide the training of arbitrary generative models, as long as the generated samples are differentiable with respect to the model parameters.
- Minimizing a Novel Divergence: Diff-Instruct is built on a rigorous mathematical foundation, and its guidance process directly corresponds to minimizing a novel divergence called the Integral Kullback-Leibler (IKL) divergence.
- Enhanced Robustness: The IKL divergence is designed specifically for DMs and, by computing the integral of KL divergence along the diffusion process, has been shown to be more robust when comparing distributions with misaligned supports.
- Improving Generative Model Performance: Diff-Instruct demonstrated its effectiveness and generality in two scenarios: extracting pre-trained diffusion models and improving existing GAN models. Experiments show that Diff-Instruct can produce state-of-the-art one-step diffusion models and consistently improve the pre-trained generators of GAN models.
Technical Principles of Diff-Instruct
- General Framework: Diff-Instruct proposes a general framework that can guide the training of arbitrary generative models, as long as the generated samples are differentiable with respect to the model parameters.
- Integral Kullback-Leibler (IKL) Divergence: Diff-Instruct is built on a rigorous mathematical foundation, in which the guidance process directly corresponds to minimizing a novel divergence called the IKL divergence. The IKL divergence is designed specifically for DMs and, by computing the integral of KL divergence along the diffusion process, has been shown to be more robust when comparing distributions with misaligned supports.
- Data-Free Learning: The data-free learning scheme of Diff-Instruct supports using pre-trained DMs as teachers to guide various generative models.
- Flexibility: Diff-Instruct provides very high flexibility for generators, unlike traditional diffusion model distillation methods that strictly restrict the choice of generator. This means the generator can be a convolutional neural network (CNN)-based or Transformer-based image generator, such as StyleGAN, or a UNet-based generator adapted from a pre-trained diffusion model.
Project Address of Diff-Instruct
- Github Repository：https://github.com/pkulwj1994/diff_instruct
- Technical Paper：https://openreview.net/pdf?id=MLIs5iRq4w
Application Scenarios of Diff-Instruct
- Distillation of Pre-trained Diffusion Models: Diff-Instruct can learn from pre-trained diffusion models (DMs) and transfer this knowledge to other generative models in a data-free manner, achieving distillation of pre-trained diffusion models.
- Improving Existing Generative Adversarial Network (GAN) Models: Diff-Instruct can be used to distill pre-trained diffusion models to improve existing GAN models and enhance the performance of their generators.
- Video Generation: In the field of multimodal generation, the technical principles of Diff-Instruct have also been applied to video generation, such as the MarDini model, which integrates the advantages of masked autoregressive (MAR) into a unified diffusion model (DM) framework for large-scale video generation.
- Meme Costume Show Video Generation: Pika 2.0 is based on the technical principles of Diff-Instruct and supports generating videos from reference background, character, and clothing images, demonstrating its application in the field of meme costume show video generation.