Interpolating between Images with Diffusion Models (2307.12560v1)

Abstract: One little-explored frontier of image generation and editing is the task of interpolating between two input images, a feature missing from all currently deployed image generation pipelines. We argue that such a feature can expand the creative applications of such models, and propose a method for zero-shot interpolation using latent diffusion models. We apply interpolation in the latent space at a sequence of decreasing noise levels, then perform denoising conditioned on interpolated text embeddings derived from textual inversion and (optionally) subject poses. For greater consistency, or to specify additional criteria, we can generate several candidates and use CLIP to select the highest quality image. We obtain convincing interpolations across diverse subject poses, image styles, and image content, and show that standard quantitative metrics such as FID are insufficient to measure the quality of an interpolation. Code and data are available at https://clintonjwang.github.io/interpolation.

• The paper introduces a zero-shot interpolation method using latent diffusion models that integrates text inversion and pose guidance for high-quality image transitions.
• It employs CLIP ranking to select the most coherent interpolation from multiple candidates, outperforming baseline methods in visual quality.
• Experimental results show robust performance across various styles and layouts while exposing the limitations of traditional evaluation metrics.

Interpolating Between Images with Diffusion Models: A Study

The research paper titled "Interpolating between Images with Diffusion Models" by Clinton J. Wang and Polina Golland introduces an innovative approach to image interpolation utilizing latent diffusion models. This analysis explores the techniques and findings presented in the paper, which expands the capabilities of diffusion models to interpolate between real images with varied styles, layouts, and subjects.

Methodology and Approach

The authors propose a zero-shot interpolation method using pre-trained latent diffusion models. The process involves interpolating in the latent space at multiple descending noise levels, followed by a denoising operation that is conditioned on interpolated text embeddings derived from textual inversion, and optionally incorporates subject poses. To maintain consistency or introduce additional criteria, multiple candidates can be generated, and the highest quality image is selected using CLIP scoring.

Key Components

1. Latent Diffusion Models (LDMs): The authors leverage the power of LDMs, characterized by their ability to produce photorealistic outputs and the customizable nature of text prompts, for interpolating between real images—an area not thoroughly explored previously.
2. Textual Inversion: To adapt and refine the conditions under which images are generated, the authors employ textual inversion. This technique optimizes the text embedding by minimizing the denoising error when conditioned on the text prompt. This step is crucial for generating high-quality interpolations.
3. Pose Guidance: When dealing with subjects in differing poses, pose guidance—via ControlNet—is instrumental. It helps in producing plausible interpolations by mitigating abrupt changes in poses, thus enhancing the realism of the generated outputs.
4. CLIP Ranking: The variability in LDM outputs can affect sequence quality. Therefore, the authors propose generating multiple candidates for each interpolation and using CLIP to rank them. This approach helps in selecting the most coherent image in terms of prescribed characteristics.

Experimental Results

The paper provides qualitative analysis across a diverse set of image pairs spanning various domains such as photography, artwork, and cartoons. The proposed pipeline successfully generates high-quality interpolations that maintain the semantic content and style transitions of the input images. Furthermore, the authors compare their method with several baseline interpolation schemes, demonstrating its superiority in generating visually convincing transformations.

Quantitative Analysis

Despite qualitative success, the authors note deficiencies in traditional metrics like Fréchet Inception Distance (FID) and Perceptual Path Length (PPL) in capturing interpolation effectiveness. Although these metrics favor simpler approaches resembling alpha blending, they fail to recognize the creative and semantic value offered by more complex interpolations. This highlights a gap in existing evaluation metrics, indicating a direction for future research.

Implications and Future Directions

This research presents significant implications for creative industries, including art, design, and media, by providing a new tool for seamless transitions between diverse images. The method's integration with existing generative pipelines, particularly in video generation, suggests potential for broader applications. However, challenges remain in handling extreme variations in style and layout, as noted in the paper's failure cases. Moving forward, refining evaluation metrics and broadening the model's applicability across more diverse input scenarios are viable research paths.

In conclusion, Wang and Golland's work represents a substantial contribution to the field of image generation. It opens avenues for deploying diffusion models in novel, creative contexts, while also prompting a reevaluation of how researchers assess the efficacy of image interpolation methodologies.