Improving Diffusion-Based Image Synthesis with Context Prediction
(2401.02015)Abstract
Diffusion models are a new class of generative models, and have dramatically promoted image generation with unprecedented quality and diversity. Existing diffusion models mainly try to reconstruct input image from a corrupted one with a pixel-wise or feature-wise constraint along spatial axes. However, such point-based reconstruction may fail to make each predicted pixel/feature fully preserve its neighborhood context, impairing diffusion-based image synthesis. As a powerful source of automatic supervisory signal, context has been well studied for learning representations. Inspired by this, we for the first time propose ConPreDiff to improve diffusion-based image synthesis with context prediction. We explicitly reinforce each point to predict its neighborhood context (i.e., multi-stride features/tokens/pixels) with a context decoder at the end of diffusion denoising blocks in training stage, and remove the decoder for inference. In this way, each point can better reconstruct itself by preserving its semantic connections with neighborhood context. This new paradigm of ConPreDiff can generalize to arbitrary discrete and continuous diffusion backbones without introducing extra parameters in sampling procedure. Extensive experiments are conducted on unconditional image generation, text-to-image generation and image inpainting tasks. Our ConPreDiff consistently outperforms previous methods and achieves a new SOTA text-to-image generation results on MS-COCO, with a zero-shot FID score of 6.21.
Overview
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Diffusion models are advanced generative tools for image creation, but commonly fail to consider the context of each pixel or feature.
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ConPreDiff framework introduces context prediction to diffusion models, significantly enhancing image synthesis quality.
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Utilizes a context decoder in denoising steps and employs Wasserstein distance for contextual fidelity in reconstruction.
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Experimentally outperforms previous methods on tasks like image generation and text-to-image translation without extra inference parameters.
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Demonstrates the flexibility of ConPreDiff, showing its benefits across different diffusion model types without computational overhead.
Introduction to Context Prediction in Image Synthesis
Diffusion models represent a transformative shift in the field of generative modeling, yielding remarkable advancements in image generation. These models operate by gradually adding noise to an image and learning to revert this noisy data back to its original form. Distinctions among these models can be made based on whether they manipulate pixels directly or work within a latent space that encapsulates semantic information more succinctly. Notably, despite the impressive progress made, a common limitation persists: the reconstruction process tends to focus on individual points in isolation, often neglecting the rich contextual fabric that surrounds each pixel or feature. Failing to capture these semantic linkages can compromise the quality and fidelity of synthesized images.
Context Prediction: An Innovation in Diffusion Models
Addressing this gap, the innovative ConPreDiff framework emerges. The approach introduces a mechanism for context prediction in diffusion-based image synthesis. By incorporating a context decoder into the architecture of denoising blocks during training, ConPreDiff ensures that during the reconstruction phase, each pixel, feature, or token is not just restored, but also imbued with an awareness of its contextual neighborhood. Specifically, this is achieved by predicting a range of multi-stride neighborhood elements and then employing a loss function based on the Wasserstein distance—a metric well-suited for gauging structural similarities between distributions.
Extensive Experimental Verification
Through rigorous experimental validation across three tasks—unconditional image generation, text-to-image translation, and image inpainting—ConPreDiff demonstrated superior performance relative to previous methods. It not only established new state-of-the-art results on the well-known MS-COCO dataset for text-to-image generation but also excelled in the other tasks. Crucially, it achieved these results without requiring additional parameters in the inference process, maintaining computational efficiency.
Toward a Broader Application
A compelling feature of ConPreDiff is its ability to enhance both discrete and continuous diffusion models without adding computational overhead during inference. This contextual prediction strategy can be integrated into existing models, consistently boosting their performance. The research presented confirms that ConPreDiff is a highly versatile and effective upgrade for diffusion-based generative models, pushing the envelope in image synthesis quality and preserving neighborhood contexts more effectively.
Conclusion
In summary, ConPreDiff represents a pivotal step forward in diffusion-based image synthesis. By introducing context prediction into the generative process, it enables a more semantically rich reconstruction of each point within an image. The results of this study highlight the framework's potential to uplift a variety of diffusion models, opening new horizons for high-quality image generation across diverse applications.
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