Despite the increasing use of deep learning in medical image segmentation,
the limited availability of annotated training data remains a major challenge
due to the time-consuming data acquisition and privacy regulations. In the
context of segmentation tasks, providing both medical images and their
corresponding target masks is essential. However, conventional data
augmentation approaches mainly focus on image synthesis. In this study, we
propose a novel slice-based latent diffusion architecture designed to address
the complexities of volumetric data generation in a slice-by-slice fashion.
This approach extends the joint distribution modeling of medical images and
their associated masks, allowing a simultaneous generation of both under
data-scarce regimes. Our approach mitigates the computational complexity and
memory expensiveness typically associated with diffusion models. Furthermore,
our architecture can be conditioned by tumor characteristics, including size,
shape, and relative position, thereby providing a diverse range of tumor
variations. Experiments on a segmentation task using the BRATS2022 confirm the
effectiveness of the synthesized volumes and masks for data augmentation.

利用切片扩散架构，从医学图像生成融合体数据和对应掩膜，以解决医学图像分割中数据获取不足的问题，并考虑肿瘤特征进行生成，通过对 BRATS2022 数据集的实验证明生成数据对数据增强的有效性。

基于切片隐式扩散模型的 3D MRI 合成：改善数据稀缺情况下肿瘤分割任务

3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving  Tumor Segmentation Tasks in Data-Scarce Regimes

We study the problem of symbolic music generation (e.g., generating piano
rolls), with a technical focus on non-differentiable rule guidance. Musical
rules are often expressed in symbolic form on note characteristics, such as
note density or chord progression, many of which are non-differentiable which
pose a challenge when using them for guided diffusion. We propose Stochastic
Control Guidance (SCG), a novel guidance method that only requires forward
evaluation of rule functions that can work with pre-trained diffusion models in
a plug-and-play way, thus achieving training-free guidance for
non-differentiable rules for the first time. Additionally, we introduce a
latent diffusion architecture for symbolic music generation with high time
resolution, which can be composed with SCG in a plug-and-play fashion. Compared
to standard strong baselines in symbolic music generation, this framework
demonstrates marked advancements in music quality and rule-based
controllability, outperforming current state-of-the-art generators in a variety
of settings. For detailed demonstrations, please visit our project site:
this https URL

我们研究了符号音乐生成中的非可微分规则引导问题，提出了一种名为 Stochastic Control Guidance (SCG) 的新型引导方法，以插拔方式与预训练的扩散模型结合使用，实现了对非可微分规则的无训练引导。我们还引入了具有高时间分辨率的潜在扩散架构，可与 SCG 以插拔方式相结合。与符号音乐生成的标准基线相比，该框架在音乐质量和基于规则的控制性能方面表现出明显的进步，优于当前各种场景下的最先进生成器。

用非可微分规则引导扩散进行符号音乐生成

Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion

Text-to-image generation is a significant domain in modern computer vision
and has achieved substantial improvements through the evolution of generative
architectures. Among these, there are diffusion-based models that have
demonstrated essential quality enhancements. These models are generally split
into two categories: pixel-level and latent-level approaches. We present
Kandinsky1, a novel exploration of latent diffusion architecture, combining the
principles of the image prior models with latent diffusion techniques. The
image prior model is trained separately to map text embeddings to image
embeddings of CLIP. Another distinct feature of the proposed model is the
modified MoVQ implementation, which serves as the image autoencoder component.
Overall, the designed model contains 3.3B parameters. We also deployed a
user-friendly demo system that supports diverse generative modes such as
text-to-image generation, image fusion, text and image fusion, image variations
generation, and text-guided inpainting/outpainting. Additionally, we released
the source code and checkpoints for the Kandinsky models. Experimental
evaluations demonstrate a FID score of 8.03 on the COCO-30K dataset, marking
our model as the top open-source performer in terms of measurable image
generation quality.

Kandinsky1 是一种使用潜在扩散架构的文本到图像生成模型，与图像先验模型的原则结合，并通过修改的 MoVQ 实现作为图像自编码器组件，支持多样化的生成模式，有 8.03 的 FID 得分和可测量的图像生成质量。