EliGen: Entity-Level Controlled Image Generation with Regional Attention

• URL: http://arxiv.org/abs/2501.01097v3
• Date: Thu, 30 Jan 2025 04:51:26 GMT
• Title: EliGen: Entity-Level Controlled Image Generation with Regional Attention
• Authors: Hong Zhang, Zhongjie Duan, Xingjun Wang, Yingda Chen, Yu Zhang,
• Abstract summary: We present EliGen, a novel framework for entity-level controlled image Generation. We train EliGen to achieve robust and accurate entity-level manipulation, surpassing existing methods in both spatial precision and image quality. We propose an inpainting fusion pipeline, extending its capabilities to multi-entity image inpainting tasks.
• Score: 7.7120747804211405
• License:
• Abstract: Recent advancements in diffusion models have significantly advanced text-to-image generation, yet global text prompts alone remain insufficient for achieving fine-grained control over individual entities within an image. To address this limitation, we present EliGen, a novel framework for Entity-level controlled image Generation. Firstly, we put forward regional attention, a mechanism for diffusion transformers that requires no additional parameters, seamlessly integrating entity prompts and arbitrary-shaped spatial masks. By contributing a high-quality dataset with fine-grained spatial and semantic entity-level annotations, we train EliGen to achieve robust and accurate entity-level manipulation, surpassing existing methods in both spatial precision and image quality. Additionally, we propose an inpainting fusion pipeline, extending its capabilities to multi-entity image inpainting tasks. We further demonstrate its flexibility by integrating it with other open-source models such as IP-Adapter, In-Context LoRA and MLLM, unlocking new creative possibilities. The source code, model, and dataset are published at https://github.com/modelscope/DiffSynth-Studio.git.

Related papers

• Generating Compositional Scenes via Text-to-image RGBA Instance Generation [82.63805151691024]
  Text-to-image diffusion generative models can generate high quality images at the cost of tedious prompt engineering. We propose a novel multi-stage generation paradigm that is designed for fine-grained control, flexibility and interactivity. Our experiments show that our RGBA diffusion model is capable of generating diverse and high quality instances with precise control over object attributes.
  arXiv  Detail & Related papers  (2024-11-16T23:44:14Z)
• Free-Mask: A Novel Paradigm of Integration Between the Segmentation Diffusion Model and Image Editing to Improve Segmentation Ability [5.767984430681467]
  We propose a framework textbfFree-Mask that combines a Diffusion Model for segmentation with advanced image editing capabilities. Results show that textbfFree-Mask achieves new state-of-the-art results on previously unseen classes in the VOC 2012 benchmark.
  arXiv  Detail & Related papers  (2024-11-04T05:39:01Z)
• MaxFusion: Plug&Play Multi-Modal Generation in Text-to-Image Diffusion Models [34.611309081801345]
  Large diffusion-based Text-to-Image (T2I) models have shown impressive generative powers for text-to-image generation. In this paper, we propose a novel strategy to scale a generative model across new tasks with minimal compute.
  arXiv  Detail & Related papers  (2024-04-15T17:55:56Z)
• 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)
• SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation [68.42476385214785]
  We propose a novel Spatial-Semantic Map Guided (SSMG) diffusion model that adopts the feature map, derived from the layout, as guidance. SSMG achieves superior generation quality with sufficient spatial and semantic controllability compared to previous works. We also propose the Relation-Sensitive Attention (RSA) and Location-Sensitive Attention (LSA) mechanisms.
  arXiv  Detail & Related papers  (2023-08-20T04:09:12Z)
• LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation [121.45667242282721]
  We propose a coarse-to-fine paradigm to achieve layout planning and image generation. Our proposed method outperforms the state-of-the-art models in terms of photorealistic layout and image generation.
  arXiv  Detail & Related papers  (2023-08-09T17:45:04Z)
• Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning [6.288699905490906]
  We propose Subject-Diffusion, a novel open-domain personalized image generation model. Our method outperforms other SOTA frameworks in single, multiple, and human customized image generation.
  arXiv  Detail & Related papers  (2023-07-21T08:09:47Z)
• BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing [73.74570290836152]
  BLIP-Diffusion is a new subject-driven image generation model that supports multimodal control. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation.
  arXiv  Detail & Related papers  (2023-05-24T04:51:04Z)
• Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation [10.39028769374367]
  We present a new framework that takes text-to-image synthesis to the realm of image-to-image translation. Our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text.
  arXiv  Detail & Related papers  (2022-11-22T20:39:18Z)
• InvGAN: Invertible GANs [88.58338626299837]
  InvGAN, short for Invertible GAN, successfully embeds real images to the latent space of a high quality generative model. This allows us to perform image inpainting, merging, and online data augmentation.
  arXiv  Detail & Related papers  (2021-12-08T21:39:00Z)

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.