Text conditioning is achieved with the LLM's tokenizer and embedder. The model learns to map token sequences (visual or textual) to SVG code. The symbol ⊕ denotes mutually exclusive …

LLM4SVG facilitates a deeper understanding of SVG components through learnable semantic tokens, which precisely encode these tokens and their corresponding properties to generate …

Our LLM4SVG is designed to: (a) Understand the semantics of SVG (Scalable Vector Graphics) source code and directly extract the meanings conveyed by vector images; (b) Generate …

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Introducing LLM4SVG: Advancing LLM Capabilities for SVG Understanding and Generation. We present LLM4SVG, a pioneering model designed to bridge the gap in SVG generation within …

Our approach first uses an LLM to generate semantically meaningful SVG templates from basic geometric primitives. Guided by image diffusion models, a dual-stage optimization pipeline …

Jun 9, 2023 · To enable the LLM to process images, we convert them into a representation given by Scalable Vector Graphics (SVG). To study what the LLM can do with this XML-based …

Large Language Models (LLMs) often struggle to generate precise image-rendering code. This project aims to bridge that gap by generating SVG, a vector image format that uses XML to …

SVGCraft employs an LLM as a layout generator to create layouts with "background prompt", "grounding object" and their corresponding bounding boxes, generating masked latents for …

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