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In this work, we empirically study Diffusion Transformers (DiTs) for text-to-image generation, focusing on architectural choices, text-conditioning strategies, and training protocols.
Microsoft coco: Common objects in context, 2015
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Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2018
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2018
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Cogview: Mastering text-to-image generation via transformers
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On Neural Differential Equations
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Clipscore: A reference-free evaluation metric for image captioning, 2022
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2022
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Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis, 2023
Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li · 2023
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Imagereward: Learning and evaluating human preferences for text-to-image generation
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Directly fine-tuning diffusion models on differentiable rewards, 2024
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Geneval: An object-focused framework for evaluating text-to-image alignment, 2023
Dhruba Ghosh, Hanna Hajishirzi, and Ludwig Schmidt · 2023
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T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation, 2023
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Pick-a-pic: An open dataset of user preferences for text-to-image generation, 2023
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Kevin Clark, Paul Vicol, Kevin Swersky, and David J Fleet · 2024
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Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024
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T2i-compbench++: An enhanced and comprehensive benchmark for compositional text-to-image generation
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