  This paper presents instruct-imagen, a model that tackles heterogeneous image
generation tasks and generalizes across unseen tasks. We introduce *multi-modal
instruction* for image generation, a task representation articulating a range
of generation intents with precision. It uses natural language to amalgamate
disparate modalities (e.g., text, edge, style, subject, etc.), such that
abundant generation intents can be standardized in a uniform format.
  We then build instruct-imagen by fine-tuning a pre-trained text-to-image
diffusion model with a two-stage framework. First, we adapt the model using the
retrieval-augmented training, to enhance model's capabilities to ground its
generation on external multimodal context. Subsequently, we fine-tune the
adapted model on diverse image generation tasks that requires vision-language
understanding (e.g., subject-driven generation, etc.), each paired with a
multi-modal instruction encapsulating the task's essence. Human evaluation on
various image generation datasets reveals that instruct-imagen matches or
surpasses prior task-specific models in-domain and demonstrates promising
generalization to unseen and more complex tasks.

Computer Vision and Pattern Recognition;Artificial Intelligence;Computation and Language