Text-to-Image (T2I) models have made significant advancements in recent
years, but they still struggle to accurately capture intricate details
specified in complex compositional prompts. While fine-tuning T2I models with
reward objectives has shown promise, it suffers from "reward hacking" and may
not generalize well to unseen prompt distributions. In this work, we propose
Reward-based Noise Optimization (ReNO), a novel approach that enhances T2I
models at inference by optimizing the initial noise based on the signal from
one or multiple human preference reward models. Remarkably, solving this
optimization problem with gradient ascent for 50 iterations yields impressive
results on four different one-step models across two competitive benchmarks,
T2I-CompBench and GenEval. Within a computational budget of 20-50 seconds,
ReNO-enhanced one-step models consistently surpass the performance of all
current open-source Text-to-Image models. Extensive user studies demonstrate
that our model is preferred nearly twice as often compared to the popular SDXL
model and is on par with the proprietary Stable Diffusion 3 with 8B parameters.
Moreover, given the same computational resources, a ReNO-optimized one-step
model outperforms widely-used open-source models such as SDXL and
PixArt-$\alpha$, highlighting the efficiency and effectiveness of ReNO in
enhancing T2I model performance at inference time. Code is available at
this https URL

基于奖励的噪音优化 (ReNO) 是一种新颖方法，通过基于来自一个或多个人类偏好奖励模型的信号优化初始噪音，以提高文本到图像 (T2I) 模型推理过程中的性能表现。在计算资源相同的情况下，ReNO 优化的单步模型表现出超越目前所有开源文本到图像模型的性能，同时也凸显了 ReNO 在提高 T2I 模型推理性能中的效率和有效性。

ReNO: 基于奖励的噪声优化提升一步完成的文本到图像模型

ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise  Optimization

Recent breakthroughs in diffusion models, multimodal pretraining, and
efficient finetuning have led to an explosion of text-to-image generative
models. Given human evaluation is expensive and difficult to scale, automated
methods are critical for evaluating the increasingly large number of new
models. However, most current automated evaluation metrics like FID or
CLIPScore only offer a holistic measure of image quality or image-text
alignment, and are unsuited for fine-grained or instance-level analysis. In
this paper, we introduce GenEval, an object-focused framework to evaluate
compositional image properties such as object co-occurrence, position, count,
and color. We show that current object detection models can be leveraged to
evaluate text-to-image models on a variety of generation tasks with strong
human agreement, and that other discriminative vision models can be linked to
this pipeline to further verify properties like object color. We then evaluate
several open-source text-to-image models and analyze their relative generative
capabilities on our benchmark. We find that recent models demonstrate
significant improvement on these tasks, though they are still lacking in
complex capabilities such as spatial relations and attribute binding. Finally,
we demonstrate how GenEval might be used to help discover existing failure
modes, in order to inform development of the next generation of text-to-image
models. Our code to run the GenEval framework is publicly available at
this https URL

通过 GenEval 评估框架，我们展示了当前目标检测模型可用于评估各种生成任务的文本到图像模型，并分析其生成能力。我们发现最近的模型在这些任务上有显著改进，但在空间关系和属性绑定等复杂能力方面仍有缺失。最后，我们展示了如何使用 GenEval 来发现现有的失败模式，以促进下一代文本到图像模型的发展。