CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion
This addresses a specific challenge in text-to-image synthesis for users needing precise object counts, but it is incremental as it builds on existing diffusion models.
The paper tackles the problem of generating images with accurate object quantities from text descriptions in diffusion models, proposing CountDiffusion, a training-free framework that improves accurate object quantity generation by a large margin.
Stable Diffusion has advanced text-to-image synthesis, but training models to generate images with accurate object quantity is still difficult due to the high computational cost and the challenge of teaching models the abstract concept of quantity. In this paper, we propose CountDiffusion, a training-free framework aiming at generating images with correct object quantity from textual descriptions. CountDiffusion consists of two stages. In the first stage, an intermediate denoising result is generated by the diffusion model to predict the final synthesized image with one-step denoising, and a counting model is used to count the number of objects in this image. In the second stage, a correction module is used to correct the object quantity by changing the attention map of the object with universal guidance. The proposed CountDiffusion can be plugged into any diffusion-based text-to-image (T2I) generation models without further training. Experiment results demonstrate the superiority of our proposed CountDiffusion, which improves the accurate object quantity generation ability of T2I models by a large margin.