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Running on Zero
Apply for community grant: Academic project
SynLayers is a research project that decomposes a real-world image into editable, semantically meaningful transparent RGBA layers. Our public Space demonstrates a two-stage pipeline that combines vision-language understanding for caption and bounding-box prediction with diffusion-based layer decomposition, allowing users to upload an image and obtain structured layered results for editing, analysis, and content creation.
Project Page: https://yanghaolin0526.github.io/SynLayers/
GitHub Repository: https://github.com/YangHaolin0526/SynLayers
The associated research paper is currently under review. We hope to make the demo publicly accessible to researchers, creators, and the broader AIGC community.
For a public demo, CPU-only inference is unrunnable and leads to poor user experience because the full pipeline is computationally intensive. A community GPU grant would allow us to run the complete system reliably on Hugging Face Spaces, significantly reduce latency, and make SynLayers more accessible to researchers, creators, and the broader AIGC community.
Looking forward to your reply!