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Computer Science > Computer Vision and Pattern Recognition

arXiv:2410.23775 (cs)
[Submitted on 31 Oct 2024 (v1), last revised 5 Nov 2024 (this version, v3)]

Title:In-Context LoRA for Diffusion Transformers

Authors:Lianghua Huang, Wei Wang, Zhi-Fan Wu, Yupeng Shi, Huanzhang Dou, Chen Liang, Yutong Feng, Yu Liu, Jingren Zhou
View a PDF of the paper titled In-Context LoRA for Diffusion Transformers, by Lianghua Huang and 8 other authors
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Abstract:Recent research arXiv:2410.15027 has explored the use of diffusion transformers (DiTs) for task-agnostic image generation by simply concatenating attention tokens across images. However, despite substantial computational resources, the fidelity of the generated images remains suboptimal. In this study, we reevaluate and streamline this framework by hypothesizing that text-to-image DiTs inherently possess in-context generation capabilities, requiring only minimal tuning to activate them. Through diverse task experiments, we qualitatively demonstrate that existing text-to-image DiTs can effectively perform in-context generation without any tuning. Building on this insight, we propose a remarkably simple pipeline to leverage the in-context abilities of DiTs: (1) concatenate images instead of tokens, (2) perform joint captioning of multiple images, and (3) apply task-specific LoRA tuning using small datasets (e.g., 20~100 samples) instead of full-parameter tuning with large datasets. We name our models In-Context LoRA (IC-LoRA). This approach requires no modifications to the original DiT models, only changes to the training data. Remarkably, our pipeline generates high-fidelity image sets that better adhere to prompts. While task-specific in terms of tuning data, our framework remains task-agnostic in architecture and pipeline, offering a powerful tool for the community and providing valuable insights for further research on product-level task-agnostic generation systems. We release our code, data, and models at this https URL
Comments: Tech report. Project page: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
Cite as: arXiv:2410.23775 [cs.CV]
  (or arXiv:2410.23775v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2410.23775
arXiv-issued DOI via DataCite

Submission history

From: Lianghua Huang Dr. [view email]
[v1] Thu, 31 Oct 2024 09:45:00 UTC (23,218 KB)
[v2] Fri, 1 Nov 2024 03:15:02 UTC (23,563 KB)
[v3] Tue, 5 Nov 2024 06:41:27 UTC (23,500 KB)
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