UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion

• URL: http://arxiv.org/abs/2401.13388v3
• Date: Thu, 6 Jun 2024 13:25:09 GMT
• Title: UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion
• Authors: Wei Li, Xue Xu, Jiachen Liu, Xinyan Xiao,
• Abstract summary: UNIMO-G is a conditional diffusion framework that operates on multimodal prompts with interleaved textual and visual inputs. It excels in both text-to-image generation and zero-shot subject-driven synthesis.
• Score: 36.06457895469353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks [62.758680527838436]
  Leopard is a vision-language model for handling vision-language tasks involving multiple text-rich images. First, we curated about one million high-quality multimodal instruction-tuning data, tailored to text-rich, multi-image scenarios. Second, we developed an adaptive high-resolution multi-image encoding module to dynamically optimize the allocation of visual sequence length.
  arXiv  Detail & Related papers  (2024-10-02T16:55:01Z)
• SEED-Story: Multimodal Long Story Generation with Large Language Model [66.37077224696242]
  SEED-Story is a novel method that leverages a Multimodal Large Language Model (MLLM) to generate extended multimodal stories. We propose multimodal attention sink mechanism to enable the generation of stories with up to 25 sequences (only 10 for training) in a highly efficient autoregressive manner. We present a large-scale and high-resolution dataset named StoryStream for training our model and quantitatively evaluating the task of multimodal story generation in various aspects.
  arXiv  Detail & Related papers  (2024-07-11T17:21:03Z)
• MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer [106.79844459065828]
  This paper presents MM-Interleaved, an end-to-end generative model for interleaved image-text data. It introduces a multi-scale and multi-image feature synchronizer module, allowing direct access to fine-grained image features in the previous context. Experiments demonstrate the versatility of MM-Interleaved in recognizing visual details following multi-modal instructions and generating consistent images following both textual and visual conditions.
  arXiv  Detail & Related papers  (2024-01-18T18:50:16Z)
• Instruct-Imagen: Image Generation with Multi-modal Instruction [90.04481955523514]
  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.
  arXiv  Detail & Related papers  (2024-01-03T19:31:58Z)
• De-Diffusion Makes Text a Strong Cross-Modal Interface [33.90004746543745]
  We employ an autoencoder that uses a pre-trained text-to-image diffusion model for decoding. Experiments validate the precision and comprehensiveness of De-Diffusion text representing images. A single De-Diffusion model can generalize to provide transferable prompts for different text-to-image tools.
  arXiv  Detail & Related papers  (2023-11-01T16:12:40Z)
• Emu: Generative Pretraining in Multimodality [43.759593451544546]
  Transformer-based multimodal foundation model can seamlessly generate images and texts in multimodal context. Emu can serve as a generalist multimodal interface for both image-to-text and text-to-image tasks. Emu demonstrates superb performance compared to state-of-the-art large multimodal models.
  arXiv  Detail & Related papers  (2023-07-11T12:45:39Z)
• Generating Images with Multimodal Language Models [78.6660334861137]
  We propose a method to fuse frozen text-only large language models with pre-trained image encoder and decoder models. Our model demonstrates a wide suite of multimodal capabilities: image retrieval, novel image generation, and multimodal dialogue.
  arXiv  Detail & Related papers  (2023-05-26T19:22:03Z)
• Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation [63.061871048769596]
  We present a novel Unified Multi-Modal Latent Diffusion (UMM-Diffusion) which takes joint texts and images containing specified subjects as input sequences. To be more specific, both input texts and images are encoded into one unified multi-modal latent space. Our method is able to generate high-quality images with complex semantics from both aspects of input texts and images.
  arXiv  Detail & Related papers  (2023-03-16T13:50:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.