"In-Context LoRA for Diffusion Transformers"

Generate multiple related images without changing model architecture - just concatenate and tune

https://arxiv.org/abs/2410.23775

🎯 Original Problem:

Text-to-image models struggle with generating coherent sets of related images. Current solutions either require task-specific architectures or extensive computational resources, limiting their practical applications.

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🔧 Solution in this Paper:

→ Built a simple pipeline called In-Context LoRA that leverages inherent capabilities of text-to-image Diffusion Transformers

→ Concatenates multiple images into one large image instead of combining attention tokens

→ Uses joint captioning for multiple images with single merged prompt

→ Applies task-specific LoRA tuning using very small datasets (20-100 samples)

→ Training done on single A100 GPU for 5000 steps with batch size of 4

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💡 Key Insights:

→ Text-to-image models already have built-in capabilities for in-context generation

→ No need to modify original model architecture - only changes to training data required

→ Small high-quality datasets sufficient instead of large-scale training

→ Single unified prompt better than separate prompts per image

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📊 Results:

→ Successfully handles diverse tasks: storyboard generation, font design, portrait photography, visual identity design

→ Maintains high visual quality and consistency across generated image sets

→ Works in both reference-free (text-only) and reference-based (text+image) generation

→ Requires minimal computational resources compared to previous approaches