UNIMO-G: Unified Image Generation through Multimodal Conditional
  Diffusion
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.
65b1cf4cfcead433ff15b9a4
Wei Li
claimed_verified
2024-01-26T09:52:51.136Z
63044e025c70c21d0eaf08bc
/avatars/a2d39973d7fbcbe9d4cce5648b3149c2.svg
Wei Li
user
Wiley085
65b1cf4cfcead433ff15b9a5
Xue Xu
65b1cf4cfcead433ff15b9a6
Jiachen Liu
65b1cf4cfcead433ff15b9a7
Xinyan Xiao
UNIMO-G: Unified Image Generation through Multimodal Conditional
  Diffusion
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.
A multimodal conditional diffusion framework named UNIMO-G generates images from both text and visual prompts, capable of high-fidelity synthesis of intricate details and diverse image entities.
Multimodal Large Language Model conditional denoising diffusion network text-driven subject-driven image generation large-scale text-image pairs language grounding image segmentation multimodal prompts zero-shot synthesis high-fidelity images