OmniPSD: Layered PSD Generation with Diffusion Transformer
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.
693a372474fced5bf9c323fd
Cheng Liu
693a372474fced5bf9c323fe
Yiren Song
admin_assigned
2025-12-11T10:48:03.757Z
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/avatars/b6dc5ca373bedbaa368208517954c375.svg
Yiren Song
yiren98
user
yiren98
693a372474fced5bf9c323ff
Haofan Wang
admin_assigned
2025-12-11T10:47:57.012Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
Haofan Wang
wanghaofan
user
wanghaofan
693a372474fced5bf9c32400
Mike Zheng Shou
admin_assigned
2025-12-11T10:47:38.881Z
661ab3da2b14565c7acccf5c
/avatars/fa4fc03664803e02aede4d4c3d50b393.svg
Mike Zheng Shou
AnalMom
user
AnalMom
OmniPSD: Layered PSD Generation with Diffusion Transformer
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.
OmniPSD, a diffusion framework within the Flux ecosystem, enables text-to-PSD generation and image-to-PSD decomposition, achieving high-fidelity results with transparency awareness.
diffusion models OmniPSD Flux ecosystem in-context learning spatial attention iterative in-context editing RGBA-VAE RGBA-layered dataset diffusion transformers