AdjointDEIS: Using Adjoint Sensitivity Analysis for Efficient Guided Generation in Diffusion Models

Główne pojęcia

This research introduces AdjointDEIS, a novel family of efficient ODE solvers for calculating gradients in diffusion models, enabling guided generation tasks like face morphing by optimizing latent codes, conditional inputs, and even model parameters.

Streszczenie

• Bibliographic Information: Blasingame, Z. W., & Liu, C. (2024). AdjointDEIS: Efficient Gradients for Diffusion Models. Advances in Neural Information Processing Systems, 38.

• Research Objective: This paper introduces a novel method called AdjointDEIS to efficiently compute gradients in diffusion models for guided generation tasks, addressing the limitations of traditional backpropagation techniques in these models.

• Methodology: The authors leverage the continuous adjoint equations from neural ODE literature and adapt them to the specific structure of diffusion models. They propose a family of ODE solvers, AdjointDEIS, which exploit exponential integrators to efficiently compute gradients for both diffusion ODEs and SDEs. The authors further extend their approach to handle time-dependent conditional information, a common practice in guided generation.

• Key Findings: The research demonstrates that AdjointDEIS solvers can efficiently compute gradients for latent representations, conditional inputs, and model parameters in diffusion models. They prove the convergence order of their solvers and show that the continuous adjoint equations for diffusion SDEs simplify to an ODE. Experiments on face morphing attacks show that AdjointDEIS significantly outperforms existing methods in generating successful morphed images.

• Main Conclusions: AdjointDEIS provides a powerful and efficient framework for guided generation in diffusion models. The proposed solvers enable the optimization of latent codes, conditional inputs, and model parameters, opening up new possibilities for controlling and manipulating the output of these models.

• Significance: This work significantly contributes to the field of diffusion models by providing an efficient and generalizable method for guided generation. The ability to efficiently compute gradients for various model components has significant implications for applications like image editing, style transfer, and adversarial attacks.

• Limitations and Future Research: The authors acknowledge that their experiments primarily focus on face morphing and suggest exploring the potential of AdjointDEIS in other guided generation tasks. Further research could investigate higher-order AdjointDEIS solvers and their applicability in different scenarios.

Statystyki

The authors use a learning rate of 0.01 for their experiments.
The sampling process utilizes N = 20 sampling steps.
AdjointDEIS employs M = 20 steps for gradient calculation.
The gradient descent optimization is performed for 50 steps.

Cytaty

"Naïve backpropagation techniques are memory intensive, requiring the storage of all intermediate states, and face additional complexity in handling the injected noise from the diffusion term of the diffusion SDE."
"To the best of our knowledge, this is the first general backpropagation technique designed for diffusion SDEs."
"Moreover, we show that the continuous adjoint equations for diffusion SDEs simplify to a mere ODE."

Kluczowe wnioski z

AdjointDEIS: Efficient Gradients for Diffusion Models

by Zander W. Bl... o arxiv.org 11-05-2024

https://arxiv.org/pdf/2405.15020.pdf