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The CLIP Model is Secretly an Image-to-Prompt Converter

Yuxuan Ding, Chunna Tian, Haoxuan Ding, Lingqiao Liu

TL;DR

This paper demonstrates that the CLIP model, as utilized in Stable Diffusion, inherently possesses the ability to instantaneously convert images into text prompts, and can be achieved by utilizing a linear projection matrix that is calculated in a closed form.

Abstract

The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Training (CLIP). However, text prompts have limitations when it comes to incorporating implicit information from reference images. Existing methods have attempted to address this limitation by employing expensive training procedures involving millions of training samples for image-to-image generation. In contrast, this paper demonstrates that the CLIP model, as utilized in Stable Diffusion, inherently possesses the ability to instantaneously convert images into text prompts. Such an image-to-prompt conversion can be achieved by utilizing a linear projection matrix that is calculated in a closed form. Moreover, the paper showcases that this capability can be further enhanced by either utilizing a small amount of similar-domain training data (approximately 100 images) or incorporating several online training steps (around 30 iterations) on the reference images. By leveraging these approaches, the proposed method offers a simple and flexible solution to bridge the gap between images and text prompts. This methodology can be applied to various tasks such as image variation and image editing, facilitating more effective and seamless interaction between images and textual prompts.

The CLIP Model is Secretly an Image-to-Prompt Converter

TL;DR

This paper demonstrates that the CLIP model, as utilized in Stable Diffusion, inherently possesses the ability to instantaneously convert images into text prompts, and can be achieved by utilizing a linear projection matrix that is calculated in a closed form.

Abstract

The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Training (CLIP). However, text prompts have limitations when it comes to incorporating implicit information from reference images. Existing methods have attempted to address this limitation by employing expensive training procedures involving millions of training samples for image-to-image generation. In contrast, this paper demonstrates that the CLIP model, as utilized in Stable Diffusion, inherently possesses the ability to instantaneously convert images into text prompts. Such an image-to-prompt conversion can be achieved by utilizing a linear projection matrix that is calculated in a closed form. Moreover, the paper showcases that this capability can be further enhanced by either utilizing a small amount of similar-domain training data (approximately 100 images) or incorporating several online training steps (around 30 iterations) on the reference images. By leveraging these approaches, the proposed method offers a simple and flexible solution to bridge the gap between images and text prompts. This methodology can be applied to various tasks such as image variation and image editing, facilitating more effective and seamless interaction between images and textual prompts.
Paper Structure (31 sections, 8 equations, 28 figures, 7 tables)

This paper contains 31 sections, 8 equations, 28 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Background and Related Works
  3. Diffusion Model
  4. CLIP Model
  5. Image Variation & Customized Generation
  6. Methodology
  7. Image-to-Prompt Conversion via Projecting CLIP embedding
  8. Fine-tuning with Image-to-Prompt Conversion
  9. Fast Update for Customized Generation
  10. Experiments
  11. Training Details
  12. Image Variation Results
  13. Customized Generation Results
  14. Ablation Study
  15. Limitations & Feature Directions
  16. ...and 16 more sections

Figures (28)

  • Figure 1: Demonstration of image variation. The image on the left is a real reference image, while the four on the right are generated from our method.
  • Figure 2: Attention map of Stable Diffusion rombach2022high. The bottom row sets attention weights of caption words to zero, only keeping the start-/end-token, so the caption maps of the bottom are black. Also notice the start-token has strong weights so the map is all white.
  • Figure 3: Image variation results on MSCOCO lin2014microsoft. SD w/ Text rombach2022high is generation from the ground-truth text prompts that are not available for variation methods such as SD-R and SD-IPC. SD-IPC is our method, notice that SD-IPC does not need any training compared to SD-R rombach2022high.
  • Figure 4: Fine-tuned SD-IPC, denoted as SD-IPC-FT, can enhance the image-to-prompt conversion quality.
  • Figure 5: Image editing result with SD-IPC-FT trained with 100 images sampled from ImageNet deng2009imagenet. SD-IPC-FT shows better editing performance than that of SD-R rombach2022high.
  • ...and 23 more figures