← 研踪 PaperTrailarXiv:2606.14700 · v2
RepFusion: Leveraging Multimodal Priors for Denoising in Representation Space
首次提交 2026-06-13 01:59
版本更新 2026-11-11 02:58
雷达观测 2026-11-11 13:07
摘要
Large language models (LLMs) are widely used in text-to-image (T2I) systems, but they are typically limited to text encoding, while denoising is handled by newly trained generative backbones. The emergence of representation autoencoders (RAEs) shifts the generation target toward semantically structured visual representations, creating a latent space that is more compatible with pretrained LLM priors. Inspired by multimodal LLMs (MLLMs), where an MLP projector is sufficient to align clean visual representations with a pretrained LLM, we repurpose the MLLM itself as a noisy representation encoder, extending this mechanism from clean to noisy inputs. We present RepFusion, which uses the resulting MLLM outputs as the conditioning signal for a diffusion transformer. In controlled comparisons at similar inference budgets, RepFusion outperforms baselines that devote comparable capacity to newly initialized denoisers. Reusing MLLM outputs across denoising steps further accelerates inference while retaining performance comparable to per-step MLLM recomputation. These results demonstrate that MLLMs provide strong priors for denoising visual representations.
作者备注：NeurIPS 2026; Project page: https://xichenpan.com/repfusion
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v2 · 2026-11-11 02:58 · 最新观测版本
RepFusion: Leveraging Multimodal Priors for Denoising in Representation Space
Large language models (LLMs) are widely used in text-to-image (T2I) systems, but they are typically limited to text encoding, while denoising is handled by newly trained generative backbones. The emergence of representation autoencoders (RAEs) shifts the generation target toward semantically structured visual representations, creating a latent space that is more compatible with pretrained LLM priors. Inspired by multimodal LLMs (MLLMs), where an MLP projector is sufficient to align clean visual representations with a pretrained LLM, we repurpose the MLLM itself as a noisy representation encoder, extending this mechanism from clean to noisy inputs. We present RepFusion, which uses the resulting MLLM outputs as the conditioning signal for a diffusion transformer. In controlled comparisons at similar inference budgets, RepFusion outperforms baselines that devote comparable capacity to newly initialized denoisers. Reusing MLLM outputs across denoising steps further accelerates inference while retaining performance comparable to per-step MLLM recomputation. These results demonstrate that MLLMs provide strong priors for denoising visual representations.
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