OmniPSD: Layered PSD Generation with Diffusion Transformer

• URL: http://arxiv.org/abs/2512.09247v1
• Date: Wed, 10 Dec 2025 02:09:59 GMT
• Title: OmniPSD: Layered PSD Generation with Diffusion Transformer
• Authors: Cheng Liu, Yiren Song, Haofan Wang, Mike Zheng Shou,
• Abstract summary: We propose OmniPSD, a unified diffusion framework built upon the Flux ecosystem.<n>It enables text-to-PSD generation and image-to-PSD decomposition through in-context learning.<n>Experiments on our new RGBA-layered dataset demonstrate that OmniPSD achieves high-fidelity generation.
• Score: 59.20320950128599
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent advances in diffusion models have greatly improved image generation and editing, yet generating or reconstructing layered PSD files with transparent alpha channels remains highly challenging. We propose OmniPSD, a unified diffusion framework built upon the Flux ecosystem that enables both text-to-PSD generation and image-to-PSD decomposition through in-context learning. For text-to-PSD generation, OmniPSD arranges multiple target layers spatially into a single canvas and learns their compositional relationships through spatial attention, producing semantically coherent and hierarchically structured layers. For image-to-PSD decomposition, it performs iterative in-context editing, progressively extracting and erasing textual and foreground components to reconstruct editable PSD layers from a single flattened image. An RGBA-VAE is employed as an auxiliary representation module to preserve transparency without affecting structure learning. Extensive experiments on our new RGBA-layered dataset demonstrate that OmniPSD achieves high-fidelity generation, structural consistency, and transparency awareness, offering a new paradigm for layered design generation and decomposition with diffusion transformers.

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