In-Context LoRA is a fine-tuning technique for Diffusion Transformers (DiTs) that combines images rather than relying solely on text, allowing for fine-tuning on specific tasks while retaining task independence. The main advantage of this technique is its ability to effectively fine-tune on small datasets without any modifications to the original DiT model, solely by altering the training data. By jointly describing multiple images and applying task-specific LoRA fine-tuning, In-Context LoRA generates high-fidelity image sets that closely align with prompt requirements. This technique holds significant importance in the field of image generation as it provides a powerful tool for generating high-quality images for specific tasks without sacrificing task independence.