Researchers Streamline Image Generation with Shortcut Models, Slashing Costs and Improving Efficiency

By Soham NandiReviewed by Joel ScanlonOct 23 2024

Researchers at UC Berkeley unveil shortcut models, a breakthrough in generative modeling that accelerates image creation without sacrificing quality—cutting through the complexities of traditional methods.

Research: One Step Diffusion via Shortcut Models. Image Credit: Chaosamran_Studio / Shutterstock

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.

In a research paper recently submitted to the arXiv preprint* server, researchers at UC Berkeley introduced shortcut models, a new family of generative models designed to significantly accelerate the image generation process in diffusion and flow-matching models. These models simplified sampling by using a single neural network and training phase, allowing for faster generation with fewer steps.

The introduction of shortcut models represents a substantial shift from traditional methods, as they eliminate the need for multi-phase training processes. Shortcut models outperformed existing methods by producing high-quality images across different step budgets, improving efficiency and flexibility in image generation tasks.

Background

Generative models such as diffusion and flow-matching have shown significant success in producing diverse outputs in images, videos, and other domains. These models rely on iterative denoising techniques that require numerous neural network passes, making the generation process slow and computationally expensive. However, this iterative nature has been a primary limitation, as it demands substantial computational resources.

Prior approaches to address this issue, like distillation and consistency models, involve complex multi-stage training processes, synthetic dataset generation, or carefully controlled learning schedules. While effective, these methods introduce additional complexity and computational overhead.

The main gap in prior work lies in their reliance on these multi-phase or bootstrapping processes to accelerate sampling. In contrast, this paper's novel solution, shortcut models, streamlines the generative modeling process. Shortcut models enabled efficient, high-quality generation using a single or few steps by training a model that can handle various sampling budgets.

By conditioning the model on noise and step size, these models skipped ahead in the generation process without needing separate distillation phases or complex scheduling. This significantly reduced the computational cost while maintaining or improving the output quality compared to prior approaches.

Shortcut Models for Efficient Few-Step Generation

Shortcut models introduced a novel approach to addressing the inefficiencies in traditional diffusion and flow-matching models. These models required numerous sampling steps and incurred high computational costs. By conditioning the model on both the timestep and a desired step size, shortcut models enabled large-step sampling while avoiding the errors typically introduced by large steps.

This innovation allowed the model to jump ahead in the denoising process, efficiently moving from noise to data without requiring iterative passes.

Training shortcut models involved learning shortcuts for all combinations of noise, timestep, and step size. A key aspect of this training process is the self-consistency mechanism, which allows the model to accurately predict larger jumps by learning from smaller steps.

Instead of relying on computationally expensive simulations, the model used a self-consistency mechanism where two smaller steps were combined to form a larger shortcut. This self-consistency ensured accurate large-step predictions, propagating generation capability from multi-step to one-step processes.

The training process was designed to balance empirical flow-matching, used for small steps, with self-consistency targets for larger steps. As a result, shortcut models managed to deliver high-quality outputs with fewer computational demands.

Shortcut models achieved efficient training with only a modest increase in computing, approximately 16% more than diffusion models. Additionally, techniques like weight decay, exponential moving average weights, and discrete-time sampling ensured stable and accurate training, allowing the model to bypass complex scheduling or multi-stage procedures. This streamlined approach not only reduced computational cost but also simplified the training pipeline.

Experiments and Discussion

The experiments evaluated the performance, scalability, and robustness of shortcut models across several tasks. Shortcut models demonstrated competitive few- and one-step generation quality, surpassing alternative end-to-end methods and maintaining the performance of diffusion models for multi-step generation.

The models also scaled well with increased model size and exhibited robust latent space interpolation. They provided a reliable alternative to existing approaches without compromising quality.

Shortcut models outperformed prior approaches across benchmarks, including CelebA-high quality (HQ) and Imagenet-256, compared to previous one-step generation methods, using the diffusion transformer (DiT)-B diffusion transformer architecture. In particular, the experiments highlighted the model's capacity to avoid common artifacts like blurriness and mode collapse, which have posed challenges to other generative techniques.

Shortcut models also performed well with few- and one-step sampling, avoiding artifacts like blurriness and mode collapse, which were more common in flow-matching models. Notably, the loss of self-consistency helped regularize model performance, potentially improving both few- and many-step generations.

The models scaled effectively, avoiding the rank-collapse issues typically seen in bootstrap-based methods. Furthermore, shortcut models offered an interpolatable latent noise space with smooth transitions between generated samples. Beyond image generation, shortcut models were successfully applied to non-image domains like robotic control, achieving strong performance while reducing inference to a single step.

This capability demonstrated the versatility of the approach, extending its applicability to a broader range of tasks.

Overall, shortcut models provided a flexible, scalable approach to few-step and one-step generative modeling, eliminating the need for multi-stage training and scheduling while demonstrating competitive performance across multiple domains.

Conclusion

In conclusion, the researchers introduced shortcut models, a novel generative modeling approach that accelerated image generation in diffusion and flow-matching frameworks. By simplifying the process to a single neural network and training phase, these models enabled efficient few-step or one-step generation while maintaining high output quality.

Shortcut models outperformed existing methods across various benchmarks, significantly reducing computational costs without sacrificing performance. They also avoided common issues like blurriness and mode collapse and could be applied beyond image generation, including robotic control tasks. This advancement enhances the flexibility and scalability of generative modeling, offering a streamlined and efficient alternative to more complex methods.

This innovation enhanced flexibility and scalability in generative modeling, streamlining the generation process.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.