@inproceedings{chen-etal-2024-learning-mistakes,
title = "Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training",
author = "Chen, Xinyan and
Ge, Jiaxin and
Zhang, Tianjun and
Liu, Jiaming and
Zhang, Shanghang",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.165",
pages = "2937--2952",
abstract = "Diffusion models have shown impressive performance in many domains. However, the model{'}s capability to follow natural language instructions (e.g., spatial relationships between objects, generating complex scenes) is still unsatisfactory. In this work, we propose Iterative Prompt Relabeling (IPR), a novel algorithm that aligns images to text through iterative image sampling and prompt relabeling with feedback. IPR first samples a batch of images conditioned on the text, then relabels the text prompts of unmatched text-image pairs with classifier feedback. We conduct thorough experiments on SDv2 and SDXL, testing their capability to follow instructions on spatial relations. With IPR, we improved up to 15.22{\%} (absolute improvement) on the challenging spatial relation VISOR benchmark, demonstrating superior performance compared to previous RL methods. Our code is publicly available at https://github.com/cxy000000/IPR-RLDF.",
}
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<abstract>Diffusion models have shown impressive performance in many domains. However, the model’s capability to follow natural language instructions (e.g., spatial relationships between objects, generating complex scenes) is still unsatisfactory. In this work, we propose Iterative Prompt Relabeling (IPR), a novel algorithm that aligns images to text through iterative image sampling and prompt relabeling with feedback. IPR first samples a batch of images conditioned on the text, then relabels the text prompts of unmatched text-image pairs with classifier feedback. We conduct thorough experiments on SDv2 and SDXL, testing their capability to follow instructions on spatial relations. With IPR, we improved up to 15.22% (absolute improvement) on the challenging spatial relation VISOR benchmark, demonstrating superior performance compared to previous RL methods. Our code is publicly available at https://github.com/cxy000000/IPR-RLDF.</abstract>
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%0 Conference Proceedings
%T Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training
%A Chen, Xinyan
%A Ge, Jiaxin
%A Zhang, Tianjun
%A Liu, Jiaming
%A Zhang, Shanghang
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chen-etal-2024-learning-mistakes
%X Diffusion models have shown impressive performance in many domains. However, the model’s capability to follow natural language instructions (e.g., spatial relationships between objects, generating complex scenes) is still unsatisfactory. In this work, we propose Iterative Prompt Relabeling (IPR), a novel algorithm that aligns images to text through iterative image sampling and prompt relabeling with feedback. IPR first samples a batch of images conditioned on the text, then relabels the text prompts of unmatched text-image pairs with classifier feedback. We conduct thorough experiments on SDv2 and SDXL, testing their capability to follow instructions on spatial relations. With IPR, we improved up to 15.22% (absolute improvement) on the challenging spatial relation VISOR benchmark, demonstrating superior performance compared to previous RL methods. Our code is publicly available at https://github.com/cxy000000/IPR-RLDF.
%U https://aclanthology.org/2024.findings-emnlp.165
%P 2937-2952
Markdown (Informal)
[Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training](https://aclanthology.org/2024.findings-emnlp.165) (Chen et al., Findings 2024)
ACL