End-to-End Diffusion Latent Optimization Improves Classifier Guidance (2303.13703v2)

Abstract: Classifier guidance -- using the gradients of an image classifier to steer the generations of a diffusion model -- has the potential to dramatically expand the creative control over image generation and editing. However, currently classifier guidance requires either training new noise-aware models to obtain accurate gradients or using a one-step denoising approximation of the final generation, which leads to misaligned gradients and sub-optimal control. We highlight this approximation's shortcomings and propose a novel guidance method: Direct Optimization of Diffusion Latents (DOODL), which enables plug-and-play guidance by optimizing diffusion latents w.r.t. the gradients of a pre-trained classifier on the true generated pixels, using an invertible diffusion process to achieve memory-efficient backpropagation. Showcasing the potential of more precise guidance, DOODL outperforms one-step classifier guidance on computational and human evaluation metrics across different forms of guidance: using CLIP guidance to improve generations of complex prompts from DrawBench, using fine-grained visual classifiers to expand the vocabulary of Stable Diffusion, enabling image-conditioned generation with a CLIP visual encoder, and improving image aesthetics using an aesthetic scoring network. Code at https://github.com/salesforce/DOODL.

• The paper introduces DOODL, which directly optimizes diffusion latents to improve gradient alignment and memory efficiency compared to one-step approximation methods.
• It leverages an invertible diffusion process (EDICT framework) that enables efficient backpropagation without storing intermediate activations.
• Performance gains include enhanced aesthetics, expanded vocabulary, and personalized image generation, broadening the utility of text-to-image models.

End-to-End Diffusion Latent Optimization for Enhanced Classifier Guidance

The paper "End-to-End Diffusion Latent Optimization Improves Classifier Guidance" proposes an innovative method termed Direct Optimization of Diffusion Latents (DOODL) to enhance the utility of classifier guidance in diffusion models. The work addresses the limitations associated with existing classifier guidance methods in text-to-image models, particularly focusing on memory efficiency and gradient alignment.

Background and Motivation

Text-conditioned denoising diffusion models (DDMs) serve as the foundation for generating coherent images from textual descriptions. Traditional methods facilitate image generation through specified conditioning modalities, but they often face constraints when integrating different signal forms, such as image classifiers. Classifier guidance is a potential solution to integrate multiple modalities, but its current methods suffer from either high computational retraining costs or inaccuracy due to one-step noise approximation methods.

Methodological Innovation: DOODL

DOODL introduces a systematic approach to directly optimize the diffusion latents concerning the gradients derived from pre-trained classifier models. This process leverages a discretely invertible diffusion process, specifically the EDICT framework, enabling efficient backpropagation with constant memory requirements. Thus, it resolves the issue of misaligned gradients in one-step approximation methods.

The core innovation lies in optimizing diffusion latents w.r.t model-based loss functions on the final generated pixels. This is accomplished by iteratively applying transformations across all diffusion steps without storing intermediate activations, thanks to the invertibility property established by EDICT.

Performance and Evaluation

DOODL's performance is validated on multiple fronts, including aesthetics improvement, vocabulary expansion, and personalized image generation:

1. Aesthetics Improvement: The application of an aesthetic scoring network demonstrated DOODL's ability to enhance the aesthetic quality of images produced by diffusion models without retraining. This highlights the practical benefit of generating more visually appealing images from pre-existing datasets.
2. Vocabulary Expansion: Through the use of fine-grained classifiers, DOODL successfully expands the vocabulary capabilities of standard diffusion models. This is particularly noteworthy in rare vocabulary scenarios, where traditional models significantly underperform due to limited contextual exposure.
3. Visual Personalization: DOODL facilitates the generation of personalized images by aligning generated content with specific user-provided cues, demonstrating substantial advancements over existing classifier guidance methods.

Broader Implications and Future Directions

DOODL sets a precedent for developments in classifier-guided diffusion models, with potent implications for both practical applications and theoretical understanding. Practically, it streamlines memory consumption and computation time, making it feasible to incorporate sophisticated model-based losses into generative workflows. Theoretically, it expands the field of plug-and-play capabilities for diffusion models, which could be further explored across different modalities beyond text and image.

Future directions may include extending this framework to other generative models and exploring applications in real-world scenarios, such as dynamic content creation, video generation, and more intricate multi-modal integrations. Moreover, the interplay between optimization efficiency and image quality in various contexts warrants additional investigation, which could drive further refinement of the proposed methodology.

In sum, "End-to-End Diffusion Latent Optimization Improves Classifier Guidance" exemplifies a significant stride in improving the integration of classifier guidance within diffusion models, mitigating computational inefficiencies, and enhancing generative performance across diverse use cases.