ImageRAGTurbo: 向一步文本到图像生成迈进的检索增强扩散模型
ImageRAGTurbo: Towards One-step Text-to-Image Generation with Retrieval-Augmented Diffusion Models
AI总结：
ImageRAGTurbo通过检索增强扩散模型实现高效一步文本到图像生成，提升图像质量并减少延迟。
摘要：
扩散模型已成为文本到图像生成的领先方法。然而，其迭代采样过程，即逐渐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。尽管最近的少步扩散模型将采样步骤减少到一到四步，但它们往往在图像质量和提示对齐方面做出妥协，尤其是在一步生成中。此外，这些模型需要计算上昂贵的训练过程。为了解决这些限制，我们提出了ImageRAGTurbo，一种通过检索增强高效微调少步扩散模型的新方法。给定一个文本提示，我们从数据库中检索相关文本图像对，并利用它们来条件生成过程。我们认为，这些检索到的例子为UNet去噪器提供了丰富的上下文信息，有助于在不牺牲图像质量的情况下减少去噪步骤。事实上，我们的初步研究显示，使用检索内容编辑去噪器的潜在空间（H空间）而不进行额外微调，已经提高了提示保真度。为了进一步提高生成图像的质量，我们将在H空间中增强UNet去噪器，添加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融合。在快速文本到图像生成的实验结果中，我们的方法生成高质量图像，而无需牺牲延迟，相比现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
