ImageRAGTurbo：面向一步文本到图像生成的检索增强扩散模型
ImageRAGTurbo: Towards One-step Text-to-Image Generation with Retrieval-Augmented Diffusion Models
提出在UNet去噪器的潜空间（H-space）中通过交叉注意力融合检索到的文本-图像对，以极低微调成本提升少步/单步扩散模型的提示词保真度，为快速文本到图像生成提供可复用的检索增强适配器方案。
原文依据 we augment the UNet denoiser with a trainable adapter in the \mathcal H -space, which efficiently blends the retrieved content with the target prompt using a cross-attention mechanism.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论扩散模型已成为文本到图像生成的主流方法。然而，其迭代采样过程逐步将随机噪声转化为连贯图像，引入了显著的延迟，限制了其应用。尽管近期的少步扩散模型将采样步数减少至一到四步，但它们往往牺牲图像质量和提示词对齐度，尤其是在单步生成中。此外，这些模型需要计算昂贵的训练过程。为解决这些局限，我们提出ImageRAGTurbo，一种通过检索增强来高效微调少步扩散模型的新方法。给定一个文本提示词，我们从数据库中检索相关的文本-图像对，并用它们来条件化生成过程。我们认为，这些检索到的示例为UNet去噪器提供了丰富的上下文信息，有助于在不损害图像质量的情况下减少去噪步数。事实上，我们的初步研究表明，利用检索到的内容编辑去噪器的潜空间（H空间）而无需额外微调，已经能提升提示词保真度。为进一步提升生成图像的质量，我们在H空间中为UNet去噪器增加了一个可训练的适配器，该适配器通过交叉注意力机制高效地将检索内容与目标提示词融合。快速文本到图像生成的实验结果表明，与现有方法相比，我们的方法能在不增加延迟的情况下生成高保真图像。
英文原文摘要 1592 字符
Diffusion models have emerged as the leading approach for text-to-image generation. However, their iterative sampling process, which gradually morphs random noise into coherent images, introduces significant latency that limits their applicability. While recent few-step diffusion models reduce the number of sampling steps to as few as one to four steps, they often compromise image quality and prompt alignment, especially in one-step generation. Additionally, these models require computationally expensive training procedures. To address these limitations, we propose ImageRAGTurbo, a novel approach to efficiently finetune few-step diffusion models via retrieval augmentation. Given a text prompt, we retrieve relevant text-image pairs from a database and use them to condition the generation process. We argue that such retrieved examples provide rich contextual information to the UNet denoiser that helps reduce the number of denoising steps without compromising image quality. Indeed, our initial investigations show that using the retrieved content to edit the denoiser's latent space (\mathcal H -space) without additional finetuning already improves prompt fidelity. To further improve the quality of the generated images, we augment the UNet denoiser with a trainable adapter in the \mathcal H -space, which efficiently blends the retrieved content with the target prompt using a cross-attention mechanism. Experimental results on fast text-to-image generation demonstrate that our approach produces high-fidelity images without compromising latency compared to existing methods.