  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 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.
