Palette: Image-to-Image Diffusion Models (2111.05826v2)

Abstract: This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpainting, uncropping, and JPEG restoration. Our simple implementation of image-to-image diffusion models outperforms strong GAN and regression baselines on all tasks, without task-specific hyper-parameter tuning, architecture customization, or any auxiliary loss or sophisticated new techniques needed. We uncover the impact of an L2 vs. L1 loss in the denoising diffusion objective on sample diversity, and demonstrate the importance of self-attention in the neural architecture through empirical studies. Importantly, we advocate a unified evaluation protocol based on ImageNet, with human evaluation and sample quality scores (FID, Inception Score, Classification Accuracy of a pre-trained ResNet-50, and Perceptual Distance against original images). We expect this standardized evaluation protocol to play a role in advancing image-to-image translation research. Finally, we show that a generalist, multi-task diffusion model performs as well or better than task-specific specialist counterparts. Check out https://diffusion-palette.github.io for an overview of the results.

• The paper introduces a unified diffusion framework that outperforms GAN-based methods in colorization, inpainting, uncropping, and JPEG restoration.
• It employs a standardized evaluation protocol using metrics like FID, Inception Score, and human assessments to validate superior image quality.
• The model’s architecture, including key self-attention layers, enables robust multi-task learning without task-specific tuning.

An Overview of Palette: Image-to-Image Diffusion Models

The paper "Palette: Image-to-Image Diffusion Models" by Chitwan Saharia et al. introduces a unified framework for tackling the image-to-image translation problem using conditional diffusion models. This framework is evaluated across four challenging tasks: colorization, inpainting, uncropping, and JPEG restoration. Remarkably, the proposed implementation, known as Palette, outstrips traditional GAN-based methods and regression-based baselines in performance on all tasks. This is achieved without necessitating task-specific hyperparameter adjustments or architectural changes.

Key Contributions

• Unified Framework: The paper presents a cohesive approach for image-to-image translation tasks using diffusion models, which are traditionally used for generating samples by gradually denoising a variable.
• Performance Across Tasks: Palette surpasses several well-established baselines in tasks such as colorization, inpainting, uncropping, and JPEG restoration. This was achieved without the need for task-specific optimizations.
• Evaluation Protocol: The authors propose a standardized evaluation protocol involving human evaluations alongside automated quality metrics like FID, Inception Score, Classification Accuracy of a pre-trained ResNet-50, and Perceptual Distance from original images. They hope this will streamline future research comparison in image-to-image translation.
• Self-Attention Analysis: An interesting finding is the importance of self-attention layers within the U-Net architecture, which add to the model's performance by facilitating long-range dependencies in the image.
• Multitask Learning: The paper demonstrates the proficiency of a generalist, multi-task diffusion model which performs comparably to or better than task-specific models, suggesting the general robustness and flexibility of the Palette approach.

Numerical Results and Empirical Validations

The empirical evaluations show Palette's efficacy clearly:

• Colorization: Palette achieves an FID score of 15.78 compared to ColTran’s 19.37 and PixColor’s 24.32. It also obtained a Classification Accuracy of 72.5% and a nearly 48% fool rate in human evaluations, close to the ideal 50%.
• Inpainting: The Palette model achieves FID scores significantly lower than DeepFillv2 and HiFill, indicating superior image consistency and realism in the filled regions. For example, with 20-30% free-form masks, Palette records an FID score of 5.2 on ImageNet, compared to Co-ModGAN's 12.4 on Places2.
• Uncropping: In this particularly challenging task of extending images along one or more borders, Palette achieves an FID score of 5.8 on ImageNet compared to Boundless’s 18.7. Human evaluations also reported a fool rate of 40% for Palette.
• JPEG Restoration: Examined at low-quality factors, Palette outperformed a strong regression model baseline, especially at QF=5 with an FID score of 8.3 compared to 29.0 for the regression model.

Theoretical and Practical Implications

The transition from GAN-based methods to diffusion models for image-to-image translation presents a meaningful shift. GANs, while powerful, often encounter difficulties such as mode collapse and training instability. Palette's diffusion framework not only mitigates these issues but also enhances performance across different translation tasks. The inherent capability of diffusion models to capture diverse sample distributions, as demonstrated by Palette's $L_2$ loss function, reinforces their suitability for dealing with high-dimensional data like images.

Practically, Palette achieves robustness and superior performance without needing handcrafted task-specific interventions. This flexibility can significantly ease the deployment of image manipulation models across varied applications, from photo editing to automated image restoration and enhancement in consumer gadgets.

Future Directions

Future research might explore optimizing the sampling efficiency of conditional diffusion models, potentially through methods that reduce the number of required diffusion steps. Additionally, examining the utility of multi-task learning and further assessing the application of Palette across even more varied image-to-image translation tasks could be beneficial. Understanding the biases inherent within diffusion models and devising strategies to address them are also critical areas for future exploration, particularly for broader real-world applications.

In conclusion, this paper expands the scope of conditional diffusion models, highlighting their versatility and robustness in addressing complex vision tasks. Palette's standardized evaluation protocol and multi-task proficiency push the frontier of image-to-image translation research, setting a benchmark for future innovations.