Home AI News Unlocking Innovative Applications: Interpolating Images with Latent Diffusion Models

Unlocking Innovative Applications: Interpolating Images with Latent Diffusion Models

Unlocking Innovative Applications: Interpolating Images with Latent Diffusion Models

Artificial Intelligence (AI) is currently a hot topic among developers and researchers. It is making advancements in various domains, such as Natural Language Processing, Natural Language Understanding, and Computer Vision. One area that still requires more research is the interpolation between two input images. Current image-generating pipelines are unable to perform this task.

However, a team of researchers from MIT CSAIL has recently released a research paper that addresses this issue. They propose a strategy that utilizes pre-trained latent diffusion models to produce high-quality interpolations across various domains and layouts. This strategy involves working in the latent space of the generative model and applying interpolation between the latent representations of the input images.

The interpolation process occurs at different levels of noise, which refers to random perturbations applied to the latent vectors that affect the appearance of the resulting image. The researchers also focus on denoising the interpolated representations to enhance the quality of the interpolated images.

Textual Inversion and Subject Poses

To denoise the interpolated representations, textual inversion is used to obtain interpolated text embeddings. Written descriptions are converted into visual features, allowing the model to understand the desired interpolation properties. Additionally, subject poses are incorporated to guide the interpolation process and produce more consistent and realistic interpolations that convey information about the positioning and orientation of objects or people in the photos.

Selection and Evaluation of Interpolations

The approach presented by the researchers generates multiple candidate interpolations to ensure high-quality results and flexibility. These candidates can be compared using CLIP, a neural network that can understand the content of images and text. Based on specific requirements or user preferences, the best interpolation can be selected. The researchers have demonstrated the effectiveness of this method in various settings, including subject poses, image styles, and image content.

Traditional quantitative metrics like Fr├ęchet Inception Distance (FID), which are commonly used to evaluate the quality of generated images, are not suitable for assessing the quality of interpolations. The researchers emphasize that their proposed pipeline provides flexibility through text conditioning, noise scheduling, and manual selection from the generated candidates.


This research addresses the previously neglected issue of interpolation in image editing. By utilizing latent diffusion models, the proposed strategy outperforms other interpolation methods in terms of qualitative outcomes. The research paper, Github repository, and project page for this work can be found in the provided links.

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