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Analyzing Diffusion Models: Transformer Integration and Prompt Optimization for Text-To-Image Generation
- 1 Courant institute of Mathematical Sciences, New York University, New York, USA
* Author to whom correspondence should be addressed.
Abstract
This paper delves into the analysis of various diffusion models, focusing on their utilization of text-conditional sampling and assessment using the contrastive language-image pre-training (CLIP) score as a metric. Leveraging Bidirectional Transformers (BERT) and Vision Transformers (ViT) within the CLIP model framework, it crucially compares models incorporating transformer blocks with those that do not. Specifically, it examines the structures of latent diffusion models and diffusion transformer models, emphasizing the use of Unet and Transformer block decoders, respectively. Additionally, the study explores different prompts to generate similar images, aiming to optimize conditioning training for text-to-image generation. Furthermore, the paper investigates the performance of the Latent Diffusion Model (LDM) and the DiT model in object generation, analyzing their interaction with text embeddings and prompt structures. The findings underscore the significance of prompt structures on model performance, with implications for future research into multimodal models and prompt optimization.
Keywords
Diffusion models, Transformer integration, Prompt optimization, Text-to-image generation
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Cite this article
Tang,Q. (2025). Analyzing Diffusion Models: Transformer Integration and Prompt Optimization for Text-To-Image Generation. Applied and Computational Engineering,108,175-181.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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