从看似合理到精确：图像生成的下一前沿

From Plausible to Precise: The Next Frontier in Image Generation

法提赫·波里克利 Fatih Porikli · TWIML AI 播客 · 2026-08-12 · 约 56 分钟 · 原视频 ↗

本期速览 · Overview

高通研究员讨论文本到图像生成中剩余的挑战，包括可控性、质量和效率。

A Qualcomm researcher discusses the remaining challenges in text-to-image generation, including controllability, quality, and efficiency.

文本到图像模型在可控性、质量和效率上仍有挑战，但新方法通过多样性优化、任务分解和潜在空间技术来解决。
Text-to-image models still struggle with controllability, quality, and efficiency, but new methods address these via diversity optimization, task decomposition, and latent space techniques.
在训练中显式优化多样性（如通过强化学习）可改善多人图像生成中的身份分离。
Explicitly optimizing for diversity in training, e.g., via reinforcement learning, improves identity separation in multi-person image generation.
将规划与渲染分离，并利用潜在空间分块，可在内存受限设备上实现高分辨率生成。
Separating planning from rendering and using latent space patchification enable high-resolution generation on limited memory devices.

文本到图像模型已显著改进，但可控性、质量和效率仍是关键挑战。
Text-to-image models have improved significantly, but controllability, quality, and efficiency remain key challenges.
在训练中显式优化多样性（例如通过强化学习）可改善生成图像中的身份区分。
Explicitly optimizing for diversity in training, e.g., via reinforcement learning, improves identity separation in generated images.
像R2C2框架那样将规划与渲染分离，可以简化复杂的图像生成任务。
Separating planning from rendering, as in the R2C2 framework, can simplify complex image generation tasks.
在潜在空间中使用分块处理运行扩散模型，可以在内存受限的设备上生成高分辨率图像。
Running diffusion models in latent space with patchification enables high-resolution image generation on memory-limited devices.
修复模型通过使用输入图像的反演噪声而非随机噪声，可以避免边界伪影。
Inpainting models can avoid boundary artifacts by using inverted noise from the input image instead of random noise.

多人图像生成的主要问题不是图像质量，而是训练中缺乏显式的多样性目标。
The main issue in multi-person image generation is not image quality but the lack of explicit diversity objectives in training.
与其扩大数据规模，不如在强化学习中将多样性作为奖励，这是一种数据高效的方法。
Instead of scaling data, adding diversity as a reward in reinforcement learning is a data-efficient way to improve models.
单个模型可能被要求同时解决太多问题；分解任务可能更有效。
A single model may be asked to solve too many problems at once; decomposing tasks can be more effective.
高分辨率图像生成可以通过在潜在空间中进行上采样和细化来实现，而不是增加潜在空间大小。
High-resolution image generation can be achieved by upsampling and refining in latent space, not by increasing latent space size.
使用反演噪声进行修复可以在保留背景的同时实现无缝协调，且无边界伪影。
Using inverted noise for inpainting allows background preservation and seamless harmonization without boundary artifacts.