Conditional Diffusion on Web-Scale Image Pairs leads to Diverse Image Variations

Abstract: Generating image variations, where a model produces variations of an input image while preserving the semantic context has gained increasing attention. Current image variation techniques involve adapting a text-to-image model to reconstruct an input image conditioned on the same image. We first demonstrate that a diffusion model trained to reconstruct an input image from frozen embeddings, can reconstruct the image with minor variations. Second, inspired by how text-to-image models learn from web-scale text-image pairs, we explore a new pretraining strategy to generate image variations using a large collection of image pairs. Our diffusion model \textit{Semantica} receives a random (encoded) image from a webpage as conditional input and denoises another noisy random image from the same webpage. We carefully examine various design choices for the image encoder, given its crucial role in extracting relevant context from the input image. Once trained, \textit{Semantica} can adaptively generate new images from a dataset by simply using images from that dataset as input. Finally, we identify limitations in standard image consistency metrics for evaluating image variations and propose alternative metrics based on few-shot generation.

• The paper introduces Semantica, a novel diffusion model that generates diverse image variations conditioned on web-scale image pairs.
• It leverages semantic consistency from paired images to surpass traditional models relying on frozen embeddings.
• The study proposes few-shot evaluation metrics to better assess the quality and diversity of the generated images.

Overview of the Paper: "Conditional Diffusion on Web-Scale Image Pairs leads to Diverse Image Variations"

The paper "Conditional Diffusion on Web-Scale Image Pairs leads to Diverse Image Variations" explores the task of generating image variations using diffusion models conditioned on web-scale image pairs, as opposed to the more common approach of using text-image pairs. The main focus of the research is to demonstrate the efficacy of this new strategy, using a diffusion model named Semantica, which generates image variations while maintaining semantic consistency with the input image.

Key Contributions

1. Diffusion Models for Image Variations: The paper introduces a diffusion model architecture that, unlike traditional diffusion models, is optimized for generating variations of a given image. The approach contrasts with models that reconstruct the image based solely on frozen embeddings, such as those derived from CLIP.
2. Web-Scale Image Pair Data: The authors utilize a unique pretraining strategy leveraging web-scale image pairs from the same semantic context (e.g., images from the same webpage) to train their diffusion model. This approach posits that images within the same webpage share semantic information and can thus be used to guide the generation of contextually coherent image variations.
3. Evaluation Constraints and New Metrics: The paper highlights limitations in standard image consistency metrics (e.g., LPIPS and FID) when applied to image variation tasks. The authors propose the use of few-shot metrics to better capture the diversity and quality of image variation outputs.

Methodology

The methodology revolves around training a conditional diffusion model (Semantica) that processes two images from the same webpage to achieve a meaningful image variation. The innovation lies in using image pairings instead of just text or single images, which improves semantic coherence in the generated image sets.

The study thoroughly evaluates the choice between different architectures for image encoding and explores alternatives like DINOv2 and CLIP, demonstrating that DINOv2 provides superior performance in producing image variations.

Experimental Results

• Comparative Performance: Semantica is evaluated against established baseline methods like Versatile Diffusion, IP-Adapter, and Stable Diffusion v2. Semantica demonstrates a favorable balance of precision and recall, indicating higher diversity while maintaining quality in the generated images.
• Scalability and Generalization: By scaling both the encoder and the diffusion decoder, the model exhibits improved performance in generating quality image variations, extending its applicability across different datasets like ImageNet and SUN-397.

Implications and Future Work

The results present evidence that conditional diffusion on image pairs can serve as an effective approach for generating image variations without requiring vast text annotations. A notable implication is the potential for this method to be extended and refined for applications where semantic preservation is critical, such as content-creation and design.

The authors also bring attention to the need for improved metrics in capturing the diversity of generated images, advocating for the further exploration of few-shot evaluation approaches which more accurately reflect the scope of image variations.

Looking forward, integrating more sophisticated conditioning techniques, possibly leveraging textual data or additional semantic indicators, could expand the model's utility and strengthen semantic coherence even further. The insights derived from utilizing web-scale data hint at further potential advantages in applying this model to other domains where large-scale unstructured image sets are available.

In summary, the paper provides substantial contributions to the field of image generation, particularly in introducing a novel pretraining strategy and evaluation metrics, thereby paving a path for future explorations and developments in AI-generated content.