DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation

• URL: http://arxiv.org/abs/2412.07589v1
• Date: Tue, 10 Dec 2024 15:24:12 GMT
• Title: DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation
• Authors: Jianzong Wu, Chao Tang, Jingbo Wang, Yanhong Zeng, Xiangtai Li, Yunhai Tong,
• Abstract summary: textbfDiffSensei is a framework specifically designed for generating manga with dynamic multi-character control. DiffSensei integrates a diffusion-based image generator with a multimodal large language model (MLLM) that acts as a text-compatible identity adapter. Our approach employs masked cross-attention to seamlessly incorporate character features, enabling precise layout control without direct pixel transfer.
• Score: 32.24143157812589
• License:
• Abstract: Story visualization, the task of creating visual narratives from textual descriptions, has seen progress with text-to-image generation models. However, these models often lack effective control over character appearances and interactions, particularly in multi-character scenes. To address these limitations, we propose a new task: \textbf{customized manga generation} and introduce \textbf{DiffSensei}, an innovative framework specifically designed for generating manga with dynamic multi-character control. DiffSensei integrates a diffusion-based image generator with a multimodal large language model (MLLM) that acts as a text-compatible identity adapter. Our approach employs masked cross-attention to seamlessly incorporate character features, enabling precise layout control without direct pixel transfer. Additionally, the MLLM-based adapter adjusts character features to align with panel-specific text cues, allowing flexible adjustments in character expressions, poses, and actions. We also introduce \textbf{MangaZero}, a large-scale dataset tailored to this task, containing 43,264 manga pages and 427,147 annotated panels, supporting the visualization of varied character interactions and movements across sequential frames. Extensive experiments demonstrate that DiffSensei outperforms existing models, marking a significant advancement in manga generation by enabling text-adaptable character customization. The project page is https://jianzongwu.github.io/projects/diffsensei/.

Related papers

• Bringing Characters to New Stories: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting [71.29100512700064]
  We present T-Prompter, a training-free method for theme-specific image generation. T-Prompter integrates reference images into generative models, allowing users to seamlessly specify the target theme. Our approach enables consistent story generation, character design, realistic character generation, and style-guided image generation.
  arXiv  Detail & Related papers  (2025-01-26T19:01:19Z)
• Towards Visual Text Design Transfer Across Languages [49.78504488452978]
  We introduce a novel task of Multimodal Style Translation (MuST-Bench) MuST-Bench is a benchmark designed to evaluate the ability of visual text generation models to perform translation across different writing systems. In response, we introduce SIGIL, a framework for multimodal style translation that eliminates the need for style descriptions.
  arXiv  Detail & Related papers  (2024-10-24T15:15:01Z)
• LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation [30.897935761304034]
  We propose a novel framework called textbfLLM4GEN, which enhances the semantic understanding of text-to-image diffusion models. A specially designed Cross-Adapter Module (CAM) integrates the original text features of text-to-image models with LLM features. DensePrompts, which contains $7,000$ dense prompts, provides a comprehensive evaluation for the text-to-image generation task.
  arXiv  Detail & Related papers  (2024-06-30T15:50:32Z)
• TheaterGen: Character Management with LLM for Consistent Multi-turn Image Generation [44.740794326596664]
  TheaterGen is a training-free framework that integrates large language models (LLMs) and text-to-image (T2I) models. Within this framework, LLMs, acting as "Screenwriter", engage in multi-turn interaction, generating and managing a standardized prompt book. With the effective management of prompt books and character images, TheaterGen significantly improves semantic and contextual consistency in synthesized images.
  arXiv  Detail & Related papers  (2024-04-29T17:58:14Z)
• Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs [77.86214400258473]
  We propose a new training-free text-to-image generation/editing framework, namely Recaption, Plan and Generate (RPG) RPG harnesses the powerful chain-of-thought reasoning ability of multimodal LLMs to enhance the compositionality of text-to-image diffusion models. Our framework exhibits wide compatibility with various MLLM architectures.
  arXiv  Detail & Related papers  (2024-01-22T06:16:29Z)
• 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)
• Character-Centric Story Visualization via Visual Planning and Token Alignment [53.44760407148918]
  Story visualization advances the traditional text-to-image generation by enabling multiple image generation based on a complete story. Key challenge of consistent story visualization is to preserve characters that are essential in stories. We propose to adapt a recent work that augments Vector-Quantized Variational Autoencoders with a text-tovisual-token architecture.
  arXiv  Detail & Related papers  (2022-10-16T06:50:39Z)
• CM3: A Causal Masked Multimodal Model of the Internet [86.32652030161374]
  We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents. We train causally masked language-image models on large-scale web and Wikipedia articles. CM3 models can generate rich structured, multi-modal outputs while conditioning on arbitrary masked document contexts.
  arXiv  Detail & Related papers  (2022-01-19T10:45:38Z)

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.