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This paper introduces Giraffe, a novel architecture that efficiently maps hidden text representations to visual embeddings using a single [IMG] token per image, overcoming the limitations of previous methods that required multiple specialized tokens. By employing two shallow MLP blocks with distinct compression and shared expansion modules, Giraffe achieves significant improvements in graphic design generation tasks, demonstrating strong performance in both image-to-design and text-to-design scenarios. The architecture's lightweight design and innovative training approach allow for seamless integration of multimedia content, addressing a critical gap in multimodal large language models' capabilities.
Giraffe achieves graphic design generation with unprecedented efficiency by using a single token per image, drastically reducing input length without sacrificing quality.
Multimodal large language models (MLLMs) have made significant progress in understanding and interpreting mul- timedia content. However, their ability to generate me- dia remains limited. Recent approaches have attempted to bridge this gap by translating the hidden representations of token sequences into the embedding space of visual models or directly into raw image data. However, these methods often represent each image using multiple specialised to- kens which significantly increases the input length. This be- comes a major limitation for tasks such as graphic design generation where the output typically involves a seamless blend of thousands of tokens across text, multiple images, and layout information. To address this challenge, a novel architecture is proposed that maps hidden token represen- tations to the embedding space of visual models, such as CLIP ViT-L/14, using a single [IMG] token per image. The architecture employs two shallow MLP blocks, each with a separate compression module followed by a shared expan- sion module, trained with six distinct loss functions. One block aids the other during training and is omitted during inference, resulting in a lightweight solution. Strong perfor- mance is demonstrated in both image-to-design and text-to- design generation tasks.