Instruct-Imagen: Image Generation with Multi-modal Instruction

• URL: http://arxiv.org/abs/2401.01952v1
• Date: Wed, 3 Jan 2024 19:31:58 GMT
• Title: Instruct-Imagen: Image Generation with Multi-modal Instruction
• Authors: Hexiang Hu, Kelvin C.K. Chan, Yu-Chuan Su, Wenhu Chen, Yandong Li, Kihyuk Sohn, Yang Zhao, Xue Ben, Boqing Gong, William Cohen, Ming-Wei Chang, Xuhui Jia
• Abstract summary: instruct-imagen is 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. Human evaluation on various image generation datasets reveals that instruct-imagen matches or surpasses prior task-specific models in-domain.
• Score: 90.04481955523514
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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