Instruct-Imagen: Image Generation with Multi-modal Instruction
This addresses the need for more flexible and generalizable image generation models for AI and creative applications, though it is incremental as it builds on pre-trained diffusion models.
The paper tackles the problem of heterogeneous image generation tasks by introducing a multi-modal instruction framework that standardizes generation intents, and results show that instruct-imagen matches or surpasses prior task-specific models in-domain while generalizing to unseen tasks.
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.