Model-based reinforcement learning methods often use learning only for the
purpose of estimating an approximate dynamics model, offloading the rest of the
decision-making work to classical trajectory optimizers. While conceptually
simple, this combination has a number of empirical shortcomings, suggesting
that learned models may not be well-suited to standard trajectory optimization.
In this paper, we consider what it would look like to fold as much of the
trajectory optimization pipeline as possible into the modeling problem, such
that sampling from the model and planning with it become nearly identical. The
core of our technical approach lies in a diffusion probabilistic model that
plans by iteratively denoising trajectories. We show how classifier-guided
sampling and image inpainting can be reinterpreted as coherent planning
strategies, explore the unusual and useful properties of diffusion-based
planning methods, and demonstrate the effectiveness of our framework in control
settings that emphasize long-horizon decision-making and test-time flexibility.

本文通过扩展动力学模型，利用扩散概率模型去掉了传统轨迹优化方法的瓶颈，将采样和计划步骤近乎完全融合，通过分类器和图像插值获得了在线规划策略，并在长期决策和测试时间灵活性强的控制环境中成功应用了该框架。

利用扩散计划实现灵活的行为合成

Planning with Diffusion for Flexible Behavior Synthesis

Free-form inpainting is the task of adding new content to an image in the
regions specified by an arbitrary binary mask. Most existing approaches train
for a certain distribution of masks, which limits their generalization
capabilities to unseen mask types. Furthermore, training with pixel-wise and
perceptual losses often leads to simple textural extensions towards the missing
areas instead of semantically meaningful generation. In this work, we propose
RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting
approach that is applicable to even extreme masks. We employ a pretrained
unconditional DDPM as the generative prior. To condition the generation
process, we only alter the reverse diffusion iterations by sampling the
unmasked regions using the given image information. Since this technique does
not modify or condition the original DDPM network itself, the model produces
high-quality and diverse output images for any inpainting form. We validate our
method for both faces and general-purpose image inpainting using standard and
extreme masks.
RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for
at least five out of six mask distributions.
Github Repository: git.io/RePaint

本文提出了 RePaint，一种基于 DDPM 的修复方法，针对自由形式的抠图效果更佳，能够进行信息补充，并对面部和通用图像修复进行了测试。

RePaint: 使用去噪扩散概率模型进行修复

RePaint: Inpainting using Denoising Diffusion Probabilistic Models

We propose Guided-TTS, a high-quality text-to-speech (TTS) model that does
not require any transcript of target speaker using classifier guidance.
Guided-TTS combines an unconditional diffusion probabilistic model with a
separately trained phoneme classifier for classifier guidance. Our
unconditional diffusion model learns to generate speech without any context
from untranscribed speech data. For TTS synthesis, we guide the generative
process of the diffusion model with a phoneme classifier trained on a
large-scale speech recognition dataset. We present a norm-based scaling method
that reduces the pronunciation errors of classifier guidance in Guided-TTS. We
show that Guided-TTS achieves a performance comparable to that of the
state-of-the-art TTS model, Grad-TTS, without any transcript for LJSpeech. We
further demonstrate that Guided-TTS performs well on diverse datasets including
a long-form untranscribed dataset.

本文介绍了一种名为 Guided-TTS 的高质量文本到语音 (TTS) 模型，使用分类器指导，不需要目标说话者的任何转录。具体来说，无条件扩散模型通过大规模语音识别数据集上训练的音素分类器指导生成 Diffusion 网络参数，实现了单批次推理下的 TTS 合成。实验结果表明，与最先进的 TTS 模型 Grad-TTS 相比，在 LJSpeech 数据集上，可以在没有转录的情况下达到性能可比的水平。同时，该模型能较好地适应多样化的数据集。

Guided-TTS: 基于分类器指导的文本语音合成扩散模型

Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance

Singing voice synthesis (SVS) systems are built to synthesize high-quality
and expressive singing voice, in which the acoustic model generates the
acoustic features (e.g., mel-spectrogram) given a music score. Previous singing
acoustic models adopt a simple loss (e.g., L1 and L2) or generative adversarial
network (GAN) to reconstruct the acoustic features, while they suffer from
over-smoothing and unstable training issues respectively, which hinder the
naturalness of synthesized singing. In this work, we propose DiffSinger, an
acoustic model for SVS based on the diffusion probabilistic model. DiffSinger
is a parameterized Markov chain that iteratively converts the noise into
mel-spectrogram conditioned on the music score. By implicitly optimizing
variational bound, DiffSinger can be stably trained and generate realistic
outputs. To further improve the voice quality and speed up inference, we
introduce a shallow diffusion mechanism to make better use of the prior
knowledge learned by the simple loss. Specifically, DiffSinger starts
generation at a shallow step smaller than the total number of diffusion steps,
according to the intersection of the diffusion trajectories of the ground-truth
mel-spectrogram and the one predicted by a simple mel-spectrogram decoder.
Besides, we propose boundary prediction methods to locate the intersection and
determine the shallow step adaptively. The evaluations conducted on a Chinese
singing dataset demonstrate that DiffSinger outperforms state-of-the-art SVS
work. Extensional experiments also prove the generalization of our methods on
text-to-speech task (DiffSpeech). Audio samples: this https URL
Codes: this https URL The old title of this
work: "Diffsinger: Diffusion acoustic model for singing voice synthesis".

本文提出了一种基于扩散概率模型的声学模型 DiffSinger，具有较好的稳定性和生成能力，较传统算法在歌唱合成上表现更好。