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CSGO: Content-Style Composition in Text-to-Image Generation

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arxiv 2408.16766 v2 pith:WPR3Y4KA submitted 2024-08-29 cs.CV

Peng Xing , Haofan Wang , Yanpeng Sun , Qixun Wang , Xu Bai , Hao Ai , Renyuan Huang , Zechao Li This is my paper
classification cs.CV
keywords imagestyletransfercsgodatagenerationstylizedtriplets
verification ladder T0 review T1 audit T2 compute T3 formal
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The diffusion model has shown exceptional capabilities in controlled image generation, which has further fueled interest in image style transfer. Existing works mainly focus on training free-based methods (e.g., image inversion) due to the scarcity of specific data. In this study, we present a data construction pipeline for content-style-stylized image triplets that generates and automatically cleanses stylized data triplets. Based on this pipeline, we construct a dataset IMAGStyle, the first large-scale style transfer dataset containing 210k image triplets, available for the community to explore and research. Equipped with IMAGStyle, we propose CSGO, a style transfer model based on end-to-end training, which explicitly decouples content and style features employing independent feature injection. The unified CSGO implements image-driven style transfer, text-driven stylized synthesis, and text editing-driven stylized synthesis. Extensive experiments demonstrate the effectiveness of our approach in enhancing style control capabilities in image generation. Additional visualization and access to the source code can be located on the project page: \url{https://csgo-gen.github.io/}.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DreamStyle3D: Efficient 3D Stylized Asset Generation via Dual-Attention Disentanglement

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Decoupled dual cross-attention plus style/content augmentations let a TRELLIS-based model inject image style into 3D assets in ~10s while better preserving geometry than prior 2D-to-3D pipelines.

  2. PoseAlign: Sculpting Pose-Consistent Meshes via Text-Guided Deformation

    cs.GR 2026-07 conditional novelty 6.0 of 10

    Two-stage text-guided mesh deformation (Laplacian CLIP scaling + attention-shared SDS Jacobian sculpting) better preserves source pose while aligning to text than TextDeformer or MeshUp.

  3. iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation

    cs.CV 2025-11 conditional novelty 6.0 of 10

    iMontage repurposes a pretrained video diffusion model to generate coherent yet highly dynamic image sets from arbitrary numbers of input images.

  4. USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

    cs.CV 2025-08 conditional novelty 6.0 of 10

    USO trains one DiT model for subject-driven, style-driven, and joint generation by disentangling content and style from triplet data and adding a style-reward objective, claiming SOTA on USO-Bench.

  5. AIComposer: Any Style and Content Image Composition via Feature Integration

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A nearly training-free SDXL pipeline composes foreground content with background style using a small MLP that merges CLIP image features, removing the need for text prompts.

  6. OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    OneIG-Bench introduces a 2,440-prompt, six-dimension benchmark with automated metrics for text-to-image models, covering alignment, text, reasoning, style, and diversity in English and Chinese.

  7. CDST: Color Disentangled Style Transfer for Universal Style Reference Customization

    cs.CV 2025-05 conditional novelty 6.0 of 10

    CDST disentangles color from style via greyscale style input and a color histogram stream, enabling zero-shot style transfer with separate color control and a new characteristics-preserved mode.

  8. OmniStyle: Filtering High Quality Style Transfer Data at Scale

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A new million-triplet dataset and a diffusion transformer model that performs text-guided and image-guided style transfer, with a filtering pipeline used to curate high-quality training examples.

  9. DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models

    cs.CV 2026-07 conditional novelty 5.5 of 10

    Noise-perturbed condition injection plus contrastive trajectory refinement improves training-free conditional diffusion sampling across style transfer, super-resolution and deblurring.

  10. Training Free Stylized Abstraction

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A training-free framework coupling VLLM-based identity distillation with cross-domain rectified flow inversion generates identity-preserving stylized abstractions from a single reference image, evaluated by a new GPT-...

  11. Style Transfer: A Decade Survey

    cs.GR 2025-06 reject novelty 2.0 of 10

    A broad survey of deep-learning style transfer methods organized by generative model family, with an unvalidated evaluation framework.

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