Key Points

1. Latent Diffusion Models (LDMs) have been successful in synthesizing high-resolution images and have gained significant attention in various domains.

2. Diffusion models suffer from slow generation speed due to the iterative reverse sampling process, leading researchers to develop methods to enhance sampling efficiency and speed up the generation process.

3. Consistency Models (CMs) have been proposed as a promising alternative for speeding up the generation process by learning consistency mappings that maintain point consistency on the ODE-trajectory, allowing for single-step or few-step generation.

4. The paper introduces Latent Consistency Models (LCMs) and proposes a simple and efficient one-stage guided consistency distillation method to distill pre-trained guided diffusion models into LCMs for few-step (2∼4) or even one-step sampling.

5. LCMs, trained on large-scale diffusion models like Stable Diffusion, offer efficient image generation quality and reduced computational load, enabling the generation of high-resolution images in few steps.

6. LCMs achieve state-of-the-art performance on the LAION-5B-Aesthetics dataset for text-to-image generation tasks, surpassing baseline methods, such as Guided-Distill, in low step regions (1∼4).

7. The paper also introduces Latent Consistency Fine-tuning (LCF), a fine-tuning method for pre-trained LCMs to support few-step inference on customized image datasets while preserving the ability for rapid inference.

8. Several ablation studies are conducted to demonstrate the effectiveness of different ODE solvers, skipping-step schedules, and guidance scales in accelerating the convergence and improving sample quality.

9. The results show that LCMs significantly outperform baseline methods in generating high-quality images in 2-4 steps, making them a promising alternative for efficient image generation.

Summary

The paper "SYNTHESIZING HIGH-RESOLUTION IMAGES WITH FEW-STEP INFERENCE" introduces Latent Consistency Models (LCMs) and discusses several novel methods for fast and high-resolution image generation. The Latent Diffusion models (LDMs) have shown remarkable results in high-resolution image synthesis tasks but suffer from slow generation speed due to the iterative reverse sampling process.

Improved Sampling and Distillation Techniques
The paper proposes Latent Consistency Models (LCMs) that efficiently distill pre-trained classifier-free guided diffusion models, allowing for rapid, high-fidelity sampling within 2-4 steps. The one-stage guided distillation method significantly accelerates convergence while preserving high image quality. The paper also introduces Latent Consistency Fine-tuning (LCF), enabling efficient adaptation of pre-trained LCMs to customized datasets while maintaining fast inference capabilities.

Exploring Consistency Models for Faster Sampling
Efforts to enhance sampling speed include accelerating the denoising process by enhancing ODE solvers and distilling a pre-trained diffusion model into models that enable few-step inference. Consistency models are explored as a promising alternative for faster sampling by learning consistency mappings that maintain point consistency on ODE trajectory.

Addressing Challenges and Overcoming Limitations
Challenges addressed include the computational intensity of the iterative sampling process and the limitations of current approaches in synthesizing high-resolution images. The proposed LCMs present significant advancements in overcoming these challenges, providing state-of-the-art text-to-image generation performance with few-step inference. The proposed methods for improving sampling efficiency and the newly introduced LCF method offer solutions to these challenges, further enhancing the capabilities of LDMs for image generation.

This paper provides valuable insights into the advancements, challenges, and proposed methods for improving latent diffusion models for high-resolution image generation and offers an in-depth discussion on the drawbacks, recent efforts to enhance sampling efficiency, and the limitations of current approaches in the field.

Reference: https://arxiv.org/abs/2310.04378