Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis (2306.08645v2)

Abstract: Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis. Abiding by the tradition in deep learning, DMs are trained and evaluated on the images with fixed sizes. However, users are demanding for various images with specific sizes and various aspect ratio. This paper focuses on adapting text-to-image diffusion models to handle such variety while maintaining visual fidelity. First we observe that, during the synthesis, lower resolution images suffer from incomplete object portrayal, while higher resolution images exhibit repetitively disordered presentation. Next, we establish a statistical relationship indicating that attention entropy changes with token quantity, suggesting that models aggregate spatial information in proportion to image resolution. The subsequent interpretation on our observations is that objects are incompletely depicted due to limited spatial information for low resolutions, while repetitively disorganized presentation arises from redundant spatial information for high resolutions. From this perspective, we propose a scaling factor to alleviate the change of attention entropy and mitigate the defective pattern observed. Extensive experimental results validate the efficacy of the proposed scaling factor, enabling models to achieve better visual effects, image quality, and text alignment. Notably, these improvements are achieved without additional training or fine-tuning techniques.

Analyzing a Novel Training-free Adaptation Technique for Diffusion Models in Text-to-Image Synthesis

This paper introduces an innovative method to adapt text-to-image diffusion models to generate images of various sizes and aspect ratios without additional training. The authors begin by examining the limitations of current diffusion models, which are traditionally confined to fixed image resolutions during training and evaluation. This restriction becomes a drawback in real-world scenarios where images of diverse sizes and aspect ratios are desired.

Key Observations and Methodology

The researchers identify two distinct patterns that result from changing image resolutions - low-resolution images often suffer from incomplete object portrayal, while high-resolution images tend to exhibit repetitive disordered presentations. Through a statistical analysis, the authors establish a relationship between attention entropy and token quantity, suggesting that these models aggregate spatial information proportionally to the image resolution. This leads them to propose a novel scaling factor aimed at stabilizing attention entropy variations, addressing the issues in both low and high-resolution synthesis.

Crucially, the proposed scaling factor alters the attention calculation in diffusion models in a training-free manner. By modifying the scaling factor to account for token variations proportional to resolution, the adapted models align text prompts with synthesized images more accurately. Such modifications enable the generation of visually consistent and high-quality images across different resolutions without additional training or fine-tuning.

Experimental Results and Implications

Comprehensive experiments are conducted using subsets of LAION-400M and LAION-5B datasets to evaluate the efficacy of the proposed method. The scaling factor significantly improves Fréchet Inception Distance (FID) and CLIP scores across multiple resolutions for two prominent diffusion models: Stable Diffusion and Latent Diffusion. These quantitative improvements are also supported by a user paper, which emphasizes better textual alignment and image naturalness with the adapted scaling technique.

Qualitative assessments further reveal that the scaling factor effectively manages to mitigate the depicted flaws. In lower resolutions, it prevents incomplete object portrayals by enhancing the focus on relevant contextual details. Conversely, for higher resolutions, it counteracts repetitive disordered presentations by regulating the extent of contextual information integration.

The proposed approach highlights a valuable pathway to utilize diffusion models effectively across different image resolutions. It suggests a method to reduce training complexity and cost, making it feasible to leverage existing pretrained models for varied use cases efficiently.

Theoretical and Practical Implications

Theoretically, this paper uncovers a noteworthy link between attention entropy and image resolution, offering a deeper insight into how spatial information is processed in diffusion models. By doing so, it also contributes to the ongoing discussion about efficient model adaptation techniques, especially relevant as model sizes and associated training costs continue to rise significantly.

Practically, the method presents a simpler yet effective way for designers and developers to generate versatile and high-quality image outputs using pretrained models. This potentially lowers the entry barrier for small-scale operators venturing into model manipulation and image synthesis, as they can now adapt existing models to suit specific demands without incurring the high costs of training specialized models.

Future Prospects

This paper opens several avenues for future exploration in the adaptation of generative models. Research could extend beyond basic text-to-image synthesis to other domains where generative adversarial networks (GANs) or other deep learning models are employed. Additionally, this method’s efficacy in synthesizing large-scale models with lower computational resource requirements signifies its applicability in a wider array of applications.

Overall, this paper provides a substantial contribution towards adaptive model techniques in text-to-image synthesis. By addressing practical challenges associated with model adaptation without introducing significant training overhead, it offers a compelling approach for efficient diffusion model deployment in dynamic environments.