  We propose Context Diffusion, a diffusion-based framework that enables image
generation models to learn from visual examples presented in context. Recent
work tackles such in-context learning for image generation, where a query image
is provided alongside context examples and text prompts. However, the quality
and fidelity of the generated images deteriorate when the prompt is not
present, demonstrating that these models are unable to truly learn from the
visual context. To address this, we propose a novel framework that separates
the encoding of the visual context and preserving the structure of the query
images. This results in the ability to learn from the visual context and text
prompts, but also from either one of them. Furthermore, we enable our model to
handle few-shot settings, to effectively address diverse in-context learning
scenarios. Our experiments and user study demonstrate that Context Diffusion
excels in both in-domain and out-of-domain tasks, resulting in an overall
enhancement in image quality and fidelity compared to counterpart models.
