Report on Current Developments in Diffusion Models
General Direction of the Field
The field of diffusion models is currently witnessing a surge of innovative approaches aimed at enhancing the efficiency, quality, and applicability of these models. A common theme across recent developments is the optimization of diffusion models to reduce computational overhead while maintaining or improving the quality of generated images. This is particularly important for real-time applications and deployment on edge devices with limited computational resources.
One of the key areas of focus is the refinement of guidance mechanisms within diffusion models. Researchers are exploring ways to mitigate the oversaturation and artifacts that often accompany high guidance scales, which are typically necessary for high-quality image generation. This involves rethinking the update rules and introducing adaptive methods that can maintain the benefits of high guidance without the drawbacks.
Another significant trend is the acceleration of diffusion models through knowledge distillation and novel sampling techniques. These methods aim to reduce the number of sampling steps required, thereby lowering inference time and computational cost. The introduction of one-to-many knowledge distillation and distillation-free approaches is particularly noteworthy, as they offer substantial speedups without compromising on the quality of the generated images.
Additionally, there is a growing interest in fine-tuning and adapting pre-trained diffusion models for specific tasks, such as style transfer and concept customization, without the need for extensive retraining. This is achieved through pairwise sample optimization and other novel fine-tuning strategies that leverage the strengths of distilled models while allowing for flexible adaptation.
Finally, the pursuit of high-resolution image generation is a prominent area of research. Efforts are being made to enhance the resolution of generated images without incurring significant computational costs. This includes the development of attentive and progressive latent diffusion models that can generate high-resolution images more efficiently.
Noteworthy Papers
Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models: Introduces adaptive projected guidance (APG) to maintain high-quality generations without oversaturation, making it a superior alternative to standard classifier-free guidance.
Accelerating Diffusion Models with One-to-Many Knowledge Distillation: Proposes one-to-many knowledge distillation (O2MKD) to significantly accelerate diffusion models by distilling a single teacher model into multiple student models, each trained for a subset of timesteps.
Distillation-Free One-Step Diffusion for Real-World Image Super-Resolution: Presents DFOSD, a distillation-free one-step diffusion model that achieves comparable or superior results to multi-step methods, with enhanced authenticity and fine details.
AP-LDM: Attentive and Progressive Latent Diffusion Model for Training-Free High-Resolution Image Generation: Introduces AP-LDM, a training-free framework that significantly enhances high-resolution image generation efficiency and quality, delivering up to a 5x speedup.
Relational Diffusion Distillation for Efficient Image Generation: Proposes Relational Diffusion Distillation (RDD) to enhance the effectiveness of progressive distillation within diffusion models, leading to significant improvements in image quality and speed.
