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In-Context LoRA for Diffusion Transformers (2410.23775v3)

Published 31 Oct 2024 in cs.CV and cs.GR

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 https://github.com/ali-vilab/In-Context-LoRA

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Authors (9)
  1. Lianghua Huang (19 papers)
  2. Wei Wang (1793 papers)
  3. Zhi-Fan Wu (8 papers)
  4. Yupeng Shi (11 papers)
  5. Huanzhang Dou (16 papers)
  6. Chen Liang (140 papers)
  7. Yutong Feng (33 papers)
  8. Yu Liu (786 papers)
  9. Jingren Zhou (198 papers)
Citations (1)
View on Semantic Scholar

Summary

In-Context LoRA for Diffusion Transformers: Enhancing Text-to-Image Generation

The paper "In-Context LoRA for Diffusion Transformers" by Huang et al. addresses the challenge of improving the fidelity and applicability of text-to-image diffusion models in generating coherent image sets from textual prompts. The authors focus on Diffusion Transformers (DiTs) and propose an enhancement technique termed In-Context LoRA (IC-LoRA), which leverages the intrinsic in-context learning abilities of text-to-image models without requiring architectural modifications.

Overview of Contributions

This research proceeds from the hypothesis that Diffusion Transformers inherently possess in-context generation capabilities across diverse tasks, and that these capabilities can be activated and enhanced with minimal changes and computational resources, specifically through strategic data modification. Major contributions include:

  1. Simplified Pipeline for In-Context Learning: The authors present a minimalistic yet effective approach by concatenating multiple images and addressing them within a single textual prompt. This enables the original DiT models to generate high-fidelity image sets efficiently.
  2. Low-Rank Adaptation (LoRA) Tuning: Instead of comprehensive parameter tuning using extensive datasets, the paper employs task-specific LoRA tuning on small datasets consisting of 20 to 100 samples. This significantly reduces computational demands while maintaining or improving image output quality.
  3. Task-Agnostic yet Task-Specific Tuning: By keeping the DiT architecture unmodified and adapting only the training data, the method achieves task-agnostic adaptation while relying on task-specific tuning data, enriching the model’s applicability across different domains like portrait illustration and visual identity design.

Numerical Strengths and Claims

The paper presents strong qualitative results across various tasks, demonstrating versatility. The method’s capability is exemplified in generating consistent image sets for characterized applications such as storyboard creation and font design. A notable result includes the ability to interpret and convert varied relational prompts into coherent image sets with relative consistency in style, lighting, and thematic attributes.

Relational Insights

The introduction of In-Context LoRA aligns with contemporary pursuits in developing flexible, task-agnostic generation models in AI research. By validating their hypothesis of inherent in-context generation capabilities within text-to-image models, the authors provide a valuable pivot from task-specific architectures towards reusable, pre-trained frameworks. This could suggest a shift in research focus towards optimizing data and manual tuning processes vs. pushing for architectural changes in pursuit of fidelity and accuracy enhancements.

Implications and Future Directions

Practically, IC-LoRA introduces an appealing model optimization approach, potentially influencing industrial applications in content creation, where rapid adaptation to new tasks without extensive retraining can yield significant benefits in terms of creativity and efficiency. Additionally, the strategy of data concatenation and prompt formulation might inspire similar methods in different generative modalities, including video and audio synthesis.

Theoretically, the dissemination of LoRA tuning insights could stimulate further exploration in minimizing the inefficiencies present in large-scale model training, with implications extending to other domains like reinforcement learning or natural language generation.

Future research can focus on addressing identified inconsistencies in image-conditional generation, enhancing visual fidelity and coherence between input-output image pairs. Moreover, extending LoRA approaches to multimodal generation frameworks, potentially integrating audio and video, presents a novel yet challenging avenue for exploration.

Overall, Huang et al. contribute a strategically minimalistic yet effective framework to the field of text-to-image generation, promising to recalibrate the implementation and utility of existing diffusion models. The paper’s insights beckon further innovation towards harmonizing architectural simplicity with expansive generative competencies.

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  1. GitHub - ali-vilab/In-Context-LoRA: Official repository of In-Context LoRA for Diffusion Transformers (100 stars)
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