The recent emergence of latent diffusion models such as SDXL and SD 1.5 has
shown significant capability in generating highly detailed and realistic
images. Despite their remarkable ability to produce images, generating accurate
text within images still remains a challenging task. In this paper, we examine
the validity of fine-tuning approaches in generating legible text within the
image. We propose a low-cost approach by leveraging SDXL without any
time-consuming training on large-scale datasets. The proposed strategy employs
a fine-tuning technique that examines the effects of data refinement levels and
synthetic captions. Moreover, our results demonstrate how our small scale
fine-tuning approach can improve the accuracy of text generation in different
scenarios without the need of additional multimodal encoders. Our experiments
show that with the addition of random letters to our raw dataset, our model's
performance improves in producing well-formed visual text.

通过使用 SDXL，我们提出一种低成本的微调技术来改善不同情况下文本生成的准确性，并通过将随机字符添加到原始数据集中来提高模型在生成良好形式视觉文本方面的性能。

利用合成标题改进图像文本生成

Improving Text Generation on Images with Synthetic Captions

We propose a diffusion distillation method that achieves new state-of-the-art
in one-step/few-step 1024px text-to-image generation based on SDXL. Our method
combines progressive and adversarial distillation to achieve a balance between
quality and mode coverage. In this paper, we discuss the theoretical analysis,
discriminator design, model formulation, and training techniques. We
open-source our distilled SDXL-Lightning models both as LoRA and full UNet
weights.

我们提出了一种扩散蒸馏方法，基于 SDXL，在一步 / 几步 1024 像素的文本到图像生成任务上达到了新的最先进水平。我们的方法结合了渐进式和对抗性蒸馏来在质量和模态覆盖之间找到平衡。在本文中，我们讨论了理论分析、判别器设计、模型公式和训练技术。我们以 LoRA 和完整的 UNet 权重形式开源了我们蒸馏的 SDXL-Lightning 模型。

SDXL-Lightning：渐进对抗扩散蒸馏

SDXL-Lightning: Progressive Adversarial Diffusion Distillation

We present SDXL, a latent diffusion model for text-to-image synthesis.
Compared to previous versions of Stable Diffusion, SDXL leverages a three times
larger UNet backbone: The increase of model parameters is mainly due to more
attention blocks and a larger cross-attention context as SDXL uses a second
text encoder. We design multiple novel conditioning schemes and train SDXL on
multiple aspect ratios. We also introduce a refinement model which is used to
improve the visual fidelity of samples generated by SDXL using a post-hoc
image-to-image technique. We demonstrate that SDXL shows drastically improved
performance compared the previous versions of Stable Diffusion and achieves
results competitive with those of black-box state-of-the-art image generators.
In the spirit of promoting open research and fostering transparency in large
model training and evaluation, we provide access to code and model weights at
this https URL

SDXL 是一种潜在扩散模型，用于文本到图像的合成，其使用更大的 UNet 骨干网络以及多重新颖的调制方式和改进模型，且其结果竞争力与黑箱最先进的图像生成器相比具有显著提高。