Kosmos-G: Generating Images in Context with Multimodal Large Language Models

1Microsoft Research, 2New York University, 3University of Waterloo
ICLR 2024

Zero-shot image generation examples with multimodal prompts. Kosmos-G regards all image inputs as a “foreign language”. It can perceive generalized vision-language inputs that span multiple images and faithfully generate images.

Abstract

Recent advancements in text-to-image (T2I) and vision-language-to-image (VL2I) generation have made significant strides. However, the generation from generalized vision-language inputs, especially involving multiple images, remains under-explored. This paper presents Kosmos-G, a model that leverages the advanced perception capabilities of Multimodal Large Language Models (MLLMs) to tackle the aforementioned challenge. Our approach aligns the output space of MLLM with CLIP using the textual modality as an anchor and performs compositional instruction tuning on curated data. Kosmos-G demonstrates a unique capability of zero-shot multi-entity subject-driven generation. Notably, the score distillation instruction tuning requires no modifications to the image decoder. This allows for a seamless substitution of CLIP and effortless integration with a myriad of U-Net techniques ranging from fine-grained controls to personalized image decoder variants. We posit Kosmos-G as an initial attempt towards the goal of "image as a foreign language in image generation."

Approch

Kosmos-G is a model that can perceive general modalities, follow instructions, and generate image conditions. It comprises an MLLM for multimodal perception, coupled with an AlignerNet that bridges the MLLM to the diffusion U-Net image decoder. Kosmos-G can pass the fine concept-level guidance from interleaved input to image decoder, and offer a seamless alternative to CLIP. Specifically, the backbone of Kosmos-G MLLM is a Transformer-based causal language model, serving as a general-purpose interface to multimodal input. We train Kosmos-G following an "align before instruct" manner, the entire training pipeline can be divided into 3 stages:
  1. Multimodal Language Modeling: We pre-train the MLLM on multimodal corpora, including monomodal data, cross-modal paired data, and interleaved multimodal data with language modeling loss following Kosmos-1.
  2. Image Decoder Aligning: We use the U-Net of Stable Diffusion v1.5 as our image decoder. We trained an AlignerNet on only textual data to align the output space of Kosmos-G to U-Net's input space through CLIP supervision. Here, the language acts as the anchoring modality, ensuring image input is also compatible with the image decoder.
  3. Instruction Tuning: We further fine-tune Kosmos-G through a compositional generation task on curated data, with the differentiable gradient passed from the frozen U-Net.
We construct a large-scale dataset based on OpenImage V7 for instruction tuning, which contains around 9 million images.

Results

Kosmos-G demonstrates a unique capability of zero-shot multi-entity subject-driven generation.

Kosmos-G can seamlessly substitute CLIP and effortlessly integrate with a myriad of U-Net techniques such as fine-grained controls by ControlNet.

Kosmos-G can also work perfectly with customized image decoder variants. Left: with standard U-Net. Right: with LoRA fine-tuned U-Net.

BibTeX

@article{kosmos-g,
  author = {Pan, Xichen and Dong, Li and Huang, Shaohan and Peng, Zhiliang and Chen, Wenhu and Wei, Furu},
  journal = {ArXiv preprint},
  title = {Kosmos-G: Generating Images in Context with Multimodal Large Language Models},
  url = {https://arxiv.org/abs/2310.02992},
  volume = {abs/2310.02992},
  year = {2023}
}