LaDe: Unified Multi-Layered Graphic Media Generation and Decomposition

• URL: http://arxiv.org/abs/2603.17965v1
• Date: Wed, 18 Mar 2026 17:34:07 GMT
• Title: LaDe: Unified Multi-Layered Graphic Media Generation and Decomposition
• Abstract summary: We propose LaDe, a latent diffusion framework that generates a flexible number of semantically meaningful layers.<n>LaDe combines three components: an LLM-based prompt expander that transforms a short user intent into structured per-layer descriptions.<n>By conditioning on layer samples during training, our unified framework supports three tasks: text-to-image generation, text-to-layers media design generation, and media design decomposition.
• Score: 4.794064830289192
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Media design layer generation enables the creation of fully editable, layered design documents such as posters, flyers, and logos using only natural language prompts. Existing methods either restrict outputs to a fixed number of layers or require each layer to contain only spatially continuous regions, causing the layer count to scale linearly with design complexity. We propose LaDe (Layered Media Design), a latent diffusion framework that generates a flexible number of semantically meaningful layers. LaDe combines three components: an LLM-based prompt expander that transforms a short user intent into structured per-layer descriptions that guide the generation, a Latent Diffusion Transformer with a 4D RoPE positional encoding mechanism that jointly generates the full media design and its constituent RGBA layers, and an RGBA VAE that decodes each layer with full alpha-channel support. By conditioning on layer samples during training, our unified framework supports three tasks: text-to-image generation, text-to-layers media design generation, and media design decomposition. We compare LaDe to Qwen-Image-Layered on text-to-layers and image-to-layers tasks on the Crello test set. LaDe outperforms Qwen-Image-Layered in text-to-layers generation by improving text-to-layer alignment, as validated by two VLM-as-a-judge evaluators (GPT-4o mini and Qwen3-VL).

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