Papers with T2I
Evaluation of Text-to-Image Generation from a Creativity Perspective (2025.findings-emnlp)
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| Challenge: | Recent studies have assessed the creativity of T2I models, but little has been done on the quality of generated images and image-text alignment. |
| Approach: | They define the creativity of T2I models and propose metrics to test reliability . they also develop a pipeline capable of transforming existing image-text datasets into benchmarks . |
| Outcome: | The proposed method tests the reliability of the metric and a fully automated pipeline capable of transforming image-text datasets into benchmarks tailored for evaluating creativity. |
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback (2025.naacl-long)
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Jiao Sun, Deqing Fu, Yushi Hu, Su Wang, Royi Rassin, Da-Cheng Juan, Dana Alon, Charles Herrmann, Sjoerd Van Steenkiste, Ranjay Krishna, Cyrus Rashtchian
| Challenge: | Text-to-Image models (T2I) still struggle to produce images that are both aesthetically pleasing and faithful to the user’s input text. |
| Approach: | They propose a training algorithm that trains T2I models to be faithful to the input text. |
| Outcome: | The proposed model improves both the semantic alignment and aesthetic appeal of two diffusion-based T2I models, evidenced by multiple benchmarks (+1.7% on TIFA, +2.9% on DSG1K, +3.4% on VILA aesthetic). |
Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models (2025.naacl-long)
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| Challenge: | Text-to-image (T2I) diffusion models rely on encoded prompts to guide the image generation process. |
| Approach: | They conduct the first in-depth analysis of the role padding tokens play in T2I diffusion models by using two causal techniques to analyze how information is encoded in the representation of tokens across different components of the pipeline. |
| Outcome: | The proposed techniques reveal that padding tokens may affect the model’s output during text encoding, during the diffusion process, or be effectively ignored. |
T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation (2026.findings-acl)
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| Challenge: | Text-to-image (T2I) generative models have demonstrated exceptional capability in synthesizing high-quality images from textual prompts. |
| Approach: | They propose a benchmark to explore the knowledge-driven reasoning capabilities of T2I models. |
| Outcome: | The proposed benchmark examines the knowledge-driven reasoning capabilities of T2I models. |
Red-Teaming NSFW Image Classifiers as Text-to-Image Safeguards (2026.findings-acl)
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Tinghao Xie, Yueqi Xie, Alireza Zareian, Shuming Hu, Felix Juefei-Xu, Xiaowen Lin, Ankit Jain, Prateek Mittal, Li Chen
| Challenge: | Not Safe for Work (NSFW) image classifiers play a critical role in safeguarding text-to-image systems. |
| Approach: | They propose an automated red-teaming framework that leverages a set of generative AI tools to uncover NSFW image failures. |
| Outcome: | The proposed framework uncovers and interprets failure modes and enables it to be applied to real-world T2I and T2V systems. |
Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines (2024.acl-long)
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| Challenge: | Text-to-image diffusion models use a latent text prompt to guide image generation . however, the process by which the encoder produces the text representation is unknown . |
| Approach: | They propose a method for analyzing the text encoder of T2I models by generating images from its intermediate representations. |
| Outcome: | The proposed method provides valuable insights into the text encoder component in T2I pipelines. |