OmniPrism: Learning Disentangled Visual Concept for Image Generation

• URL: http://arxiv.org/abs/2412.12242v1
• Date: Mon, 16 Dec 2024 18:59:52 GMT
• Title: OmniPrism: Learning Disentangled Visual Concept for Image Generation
• Authors: Yangyang Li, Daqing Liu, Wu Liu, Allen He, Xinchen Liu, Yongdong Zhang, Guoqing Jin,
• Abstract summary: Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. We propose OmniPrism, a visual concept disentangling approach for creative image generation. Our method learns disentangled concept representations guided by natural language and trains a diffusion model to incorporate these concepts.
• Score: 57.21097864811521
• License:
• Abstract: Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. However, existing methods are typically constrained to single-aspect concept generation or are easily disrupted by irrelevant concepts in multi-aspect concept scenarios, leading to concept confusion and hindering creative generation. To address this, we propose OmniPrism, a visual concept disentangling approach for creative image generation. Our method learns disentangled concept representations guided by natural language and trains a diffusion model to incorporate these concepts. We utilize the rich semantic space of a multimodal extractor to achieve concept disentanglement from given images and concept guidance. To disentangle concepts with different semantics, we construct a paired concept disentangled dataset (PCD-200K), where each pair shares the same concept such as content, style, and composition. We learn disentangled concept representations through our contrastive orthogonal disentangled (COD) training pipeline, which are then injected into additional diffusion cross-attention layers for generation. A set of block embeddings is designed to adapt each block's concept domain in the diffusion models. Extensive experiments demonstrate that our method can generate high-quality, concept-disentangled results with high fidelity to text prompts and desired concepts.

Related papers

• CusConcept: Customized Visual Concept Decomposition with Diffusion Models [13.95568624067449]
  We propose a two-stage framework, CusConcept, to extract customized visual concept embedding vectors. In the first stage, CusConcept employs a vocabularies-guided concept decomposition mechanism. In the second stage, joint concept refinement is performed to enhance the fidelity and quality of generated images.
  arXiv  Detail & Related papers  (2024-10-01T04:41:44Z)
• Concept Conductor: Orchestrating Multiple Personalized Concepts in Text-to-Image Synthesis [14.21719970175159]
  Concept Conductor is designed to ensure visual fidelity and correct layout in multi-concept customization. We present a concept injection technique that employs shape-aware masks to specify the generation area for each concept. Our method supports the combination of any number of concepts and maintains high fidelity even when dealing with visually similar concepts.
  arXiv  Detail & Related papers  (2024-08-07T08:43:58Z)
• ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction [20.43411883845885]
  We introduce a novel task named Unsupervised Concept Extraction (UCE) that considers an unsupervised setting without any human knowledge of the concepts. Given an image that contains multiple concepts, the task aims to extract and recreate individual concepts solely relying on the existing knowledge from pretrained diffusion models. We present ConceptExpress that tackles UCE by unleashing the inherent capabilities of pretrained diffusion models in two aspects.
  arXiv  Detail & Related papers  (2024-07-09T17:50:28Z)
• Non-confusing Generation of Customized Concepts in Diffusion Models [135.4385383284657]
  We tackle the common challenge of inter-concept visual confusion in compositional concept generation using text-guided diffusion models (TGDMs) Existing customized generation methods only focus on fine-tuning the second stage while overlooking the first one. We propose a simple yet effective solution called CLIF: contrastive image-language fine-tuning.
  arXiv  Detail & Related papers  (2024-05-11T05:01:53Z)
• Implicit Concept Removal of Diffusion Models [92.55152501707995]
  Text-to-image (T2I) diffusion models often inadvertently generate unwanted concepts such as watermarks and unsafe images. We present the Geom-Erasing, a novel concept removal method based on the geometric-driven control.
  arXiv  Detail & Related papers  (2023-10-09T17:13:10Z)
• The Hidden Language of Diffusion Models [70.03691458189604]
  We present Conceptor, a novel method to interpret the internal representation of a textual concept by a diffusion model. We find surprising visual connections between concepts, that transcend their textual semantics. We additionally discover concepts that rely on mixtures of exemplars, biases, renowned artistic styles, or a simultaneous fusion of multiple meanings.
  arXiv  Detail & Related papers  (2023-06-01T17:57:08Z)
• Concept Decomposition for Visual Exploration and Inspiration [53.06983340652571]
  We propose a method to decompose a visual concept into different visual aspects encoded in a hierarchical tree structure. We utilize large vision-language models and their rich latent space for concept decomposition and generation.
  arXiv  Detail & Related papers  (2023-05-29T16:56:56Z)
• Visual Concepts Tokenization [65.61987357146997]
  We propose an unsupervised transformer-based Visual Concepts Tokenization framework, dubbed VCT, to perceive an image into a set of disentangled visual concept tokens. To obtain these concept tokens, we only use cross-attention to extract visual information from the image tokens layer by layer without self-attention between concept tokens. We further propose a Concept Disentangling Loss to facilitate that different concept tokens represent independent visual concepts.
  arXiv  Detail & Related papers  (2022-05-20T11:25:31Z)

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.