LCM-LoRA: A Universal Stable-Diffusion Acceleration Module (2311.05556v1)

Abstract: Latent Consistency Models (LCMs) have achieved impressive performance in accelerating text-to-image generative tasks, producing high-quality images with minimal inference steps. LCMs are distilled from pre-trained latent diffusion models (LDMs), requiring only ~32 A100 GPU training hours. This report further extends LCMs' potential in two aspects: First, by applying LoRA distillation to Stable-Diffusion models including SD-V1.5, SSD-1B, and SDXL, we have expanded LCM's scope to larger models with significantly less memory consumption, achieving superior image generation quality. Second, we identify the LoRA parameters obtained through LCM distillation as a universal Stable-Diffusion acceleration module, named LCM-LoRA. LCM-LoRA can be directly plugged into various Stable-Diffusion fine-tuned models or LoRAs without training, thus representing a universally applicable accelerator for diverse image generation tasks. Compared with previous numerical PF-ODE solvers such as DDIM, DPM-Solver, LCM-LoRA can be viewed as a plug-in neural PF-ODE solver that possesses strong generalization abilities. Project page: https://github.com/luosiallen/latent-consistency-model.

• The paper introduces LCM-LoRA to accelerate image generation by combining Latent Consistency Distillation and Low-Rank Adaptation, significantly reducing inference steps and memory usage.
• The paper demonstrates a drastic parameter reduction, with models like SDXL dropping from 3.5B to 197M parameters, achieving high-fidelity images in just 1 to 4 sampling steps.
• The paper shows that LCM-LoRA can universally integrate with fine-tuned Stable-Diffusion and LoRA models, paving the way for efficient and accessible real-time image generation.

Overview of the Paper "LCM-LoRA: A Universal Stable-Diffusion Acceleration Module"

The paper presents a novel approach to accelerating text-to-image generative tasks through the introduction of a module termed LCM-LoRA. This module leverages Latent Consistency Models (LCMs) and Low-Rank Adaptation (LoRA) within the scope of Stable Diffusion models to achieve superior image generation quality with minimal inference steps and reduced memory consumption.

Summary of Contributions

The paper extends the capabilities of LCMs, originally designed to expedite the reverse sampling process in latent diffusion models (LDMs), in two significant ways. First, the application of LoRA distillation to various Stable-Diffusion models—specifically SD-V1.5, SSD-1B, and SDXL—enables the handling of larger models without extensive computational resources. Second, the authors introduce LCM-LoRA, a universal module that can plug directly into fine-tuned Stable-Diffusion models or other LoRAs, facilitating rapid, high-quality image generation without requiring additional training.

Technical Innovations

The cornerstone of this work is the combination of Latent Consistency Distillation (LCD) and LoRA techniques. Specific innovations include:

1. Efficient Memory Usage: Leveraging LoRA to reduce the total number of trainable parameters during LCM distillation, hence significantly lowering memory overhead and enabling the training of large models like SDXL and SSD-1B.
2. Universal Approach: Introducing LCM-LoRA parameters that act as "acceleration vectors" which can be linearly combined with "style vectors" from LoRA parameters fine-tuned on specific datasets, allowing for the generation of customized images with minimal steps.

Numerical Results and Claims

LCM-LoRA demonstrates its effectiveness through several numerical evaluations, revealing notable outcomes:

1. Parameter Reduction: The use of LoRA techniques leads to a substantial decrease in the number of trainable parameters. For instance, the SDXL model's parameters are reduced from 3.5 billion to 197 million.
2. Image Generation Quality: Generated images showcase high fidelity, with experiments indicating that LCM-LoRA achieves impressive generalization capabilities across different fine-tuned models and datasets.
3. Sampling Efficiency: The combination of LCM-LoRA with style-specific LoRAs results in high-quality images within 1 to 4 sampling steps, proving the module's efficiency.

The authors also display visual comparisons to elucidate the qualitative benefits of incorporating LCM-LoRA.

Implications and Speculations on Future Developments

The implications of this research are multifaceted, impacting both practical applications and theoretical advancements in AI:

1. Practical Applications: The LCM-LoRA module can be universally applied to various Stable-Diffusion models, offering a robust solution for real-time image generation tasks in consumer-grade hardware scenarios. This makes large-scale, high-resolution image generation accessible for broader applications, from media and entertainment to education and beyond.
2. Theoretical Insights: The ability to combine acceleration and style vectors introduces a new dimension in model adaptation and customization, paving the way for further exploration in parameter-efficient fine-tuning methods.

Conclusion

This paper presents a pragmatic approach to overcoming the computational limitations inherent in high-quality image generation using LDMs and Stable-Diffusion models. The LCM-LoRA module imbues the process with both efficiency and flexibility, opening new avenues for practical deployment of state-of-the-art generative models. Future work could explore optimizing and extending the arithmetic properties of these vectors, broadening the scope of their application.