Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents

TL;DR

Bifrost-1 introduces a unified framework that efficiently combines pretrained multimodal LLMs and diffusion models using patch-level CLIP image embeddings, enabling high-quality controllable image generation without extensive retraining.

Contribution

The paper proposes a novel method that bridges pretrained multimodal LLMs and diffusion models via patch-level CLIP latents, reducing training costs and preserving reasoning abilities.

Findings

Achieves high-fidelity controllable image generation.

Performs comparably or better than previous methods.

Requires substantially less training compute.

Abstract

There is growing interest in integrating high-fidelity visual synthesis capabilities into large language models (LLMs) without compromising their strong reasoning capabilities. Existing methods that directly train LLMs or bridge LLMs and diffusion models usually suffer from costly training since the backbone LLMs have not seen image representations during pretraining. We present Bifrost-1, a unified framework that bridges pretrained multimodal LLMs (MLLMs) and diffusion models using patch-level CLIP image embeddings as latent variables, which are natively aligned with the MLLM's CLIP visual encoder. These patch-level image embeddings are integrated into the diffusion model with a lightweight adaptation of its ControlNet. To retain the original multimodal reasoning capabilities of MLLMs, we equip the MLLM with a visual generation branch initialized from the original MLLM parameters when predicting the patch-level image embeddings. By seamlessly integrating pretrained MLLMs and diffusion models with patch-level CLIP latents, our framework enables high-fidelity controllable image generation with significant training efficiency. Our experiments demonstrate that Bifrost-1 achieves comparable or better performance than previous methods in terms of visual fidelity and multimodal understanding, with substantially lower compute during training. We also provide comprehensive ablation studies showing the effectiveness of our design choices.