Existing text-to-image diffusion models primarily generate images from text
prompts. However, the inherent conciseness of textual descriptions poses
challenges in faithfully synthesizing images with intricate details, such as
specific entities or scenes. This paper presents \textbf{UNIMO-G}, a simple
multimodal conditional diffusion framework that operates on multimodal prompts
with interleaved textual and visual inputs, which demonstrates a unified
ability for both text-driven and subject-driven image generation. UNIMO-G
comprises two core components: a Multimodal Large Language Model (MLLM) for
encoding multimodal prompts, and a conditional denoising diffusion network for
generating images based on the encoded multimodal input. We leverage a
two-stage training strategy to effectively train the framework: firstly
pre-training on large-scale text-image pairs to develop conditional image
generation capabilities, and then instruction tuning with multimodal prompts to
achieve unified image generation proficiency. A well-designed data processing
pipeline involving language grounding and image segmentation is employed to
construct multi-modal prompts. UNIMO-G excels in both text-to-image generation
and zero-shot subject-driven synthesis, and is notably effective in generating
high-fidelity images from complex multimodal prompts involving multiple image
entities.

UNIMO-G 是一个简单的多模态条件扩散框架，能够对多模态提示进行操作，并展示了文本驱动和主体驱动图像生成的统一能力。该框架通过训练大规模文本 - 图像对，以及使用多模态提示进行指导微调，实现了高保真度的图像生成。