Multi-LoRA Composition for Image Generation

This project explores new methods for text-to-image generation, with a focus on the integration of multiple Low-Rank Adaptations (LoRAs) to create highly customized and detailed images. We present LoRA Switch and LoRA Composite, approaches that aim to surpass traditional techniques in terms of accuracy and image quality, especially in complex compositions.

Project Features:

• 🚀 Training-Free Methods
• LoRA Switch and LoRA Composite enable dynamic and precise integration of multiple LoRAs without fine-tuning.
• Unlike methods that merge LoRA weights, ours focuses on the decoding process, keeping all LoRA weights intact.
• 📊 ComposLoRA Testbed
• A new comprehensive platform, featuring 480 composition sets and 22 pre-trained LoRAs across six categories.
• ComposLoRA is designed for the quantitative evaluation of LoRA-based composable image generation tasks.
• 📝 GPT-4V-based Evaluator
• We propose using GPT-4V as an evaluator to assess the efficacy of compositions and the quality of images.
• This evaluator has demonstrated a better correlation with human judgments.
• 🏆 Superior Performance
• Both automated and human evaluations show that our approaches substantially outperform the prevalent LoRA Merge.
• Our methods exhibit a more significant advantage when generating complex compositions.
• 🕵️‍♂️ Detailed Analysis
• We delve deeply into the scenarios where each method excels.
• We explore the potential bias associated with using GPT-4V for evaluation.

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