CLoRA: A Contrastive Approach to Compose Multiple LoRA Models

• URL: http://arxiv.org/abs/2403.19776v1
• Date: Thu, 28 Mar 2024 18:58:43 GMT
• Title: CLoRA: A Contrastive Approach to Compose Multiple LoRA Models
• Authors: Tuna Han Salih Meral, Enis Simsar, Federico Tombari, Pinar Yanardag,
• Abstract summary: Low-Rank Adaptations (LoRAs) have emerged as a powerful and popular technique in the field of image generation. CLoRA addresses the problem of seamlessly blending multiple concept LoRAs to capture a variety of concepts in one image. Our method enables the creation of composite images that truly reflect the characteristics of each LoRA.
• Score: 44.037664077117945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-Rank Adaptations (LoRAs) have emerged as a powerful and popular technique in the field of image generation, offering a highly effective way to adapt and refine pre-trained deep learning models for specific tasks without the need for comprehensive retraining. By employing pre-trained LoRA models, such as those representing a specific cat and a particular dog, the objective is to generate an image that faithfully embodies both animals as defined by the LoRAs. However, the task of seamlessly blending multiple concept LoRAs to capture a variety of concepts in one image proves to be a significant challenge. Common approaches often fall short, primarily because the attention mechanisms within different LoRA models overlap, leading to scenarios where one concept may be completely ignored (e.g., omitting the dog) or where concepts are incorrectly combined (e.g., producing an image of two cats instead of one cat and one dog). To overcome these issues, CLoRA addresses them by updating the attention maps of multiple LoRA models and leveraging them to create semantic masks that facilitate the fusion of latent representations. Our method enables the creation of composite images that truly reflect the characteristics of each LoRA, successfully merging multiple concepts or styles. Our comprehensive evaluations, both qualitative and quantitative, demonstrate that our approach outperforms existing methodologies, marking a significant advancement in the field of image generation with LoRAs. Furthermore, we share our source code, benchmark dataset, and trained LoRA models to promote further research on this topic.

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