一个统一的PyTorch生成模型工具包，让新手快速上手各类生成模型。
Venom: A PyTorch Generative Modeling Toolkit
- 统一接口整合扩散、自编码、生成对抗等主流生成模型
- 支持训练、采样、引导等完整流程，代码清晰可读
- 适合教学、原型验证和轻量级基准测试
现代生成建模已发展为多个相关但常独立实现的方法体系，包括去噪扩散模型、基于得分的随机微分方程、流匹配、变分自编码器、归一化流、对抗模型和能量模型。对初学者而言，这些方法分散在不同代码库中，难以在同一框架下比较训练目标、推断过程、采样算法和条件机制。我们提出V ENOM，一个以MNIST为起点的教育性PyTorch工具包，将代表性生成模型家族统一实现。该工具包强调广度、可读性、可复现的入门路径及一致的训练与采样API，而非大规模性能优化。目前包含扩散与基于得分的模型、流匹配与一步生成器、变分自编码器、归一化流、生成对抗网络和能量模型。提供独立训练与采样脚本、分类器与无分类器引导示例、双语教程笔记本，以及支持教学、原型开发和轻量级基准测试的模型族组织结构。
原文摘要 · Abstract (English)
Modern generative modeling has grown into a broad collection of related but often separately implemented paradigms, including denoising diffusion models, score-based stochastic differential equations, flow matching, variational autoencoders, normalizing flows, adversarial models, and energy-based models. For newcomers, this fragmentation makes it difficult to compare training objectives, inference procedures, sampling algorithms, and conditioning mechanisms within a single coherent codebase. We introduce V ENOM, an educational PyTorch toolkit that implements representative generative modeling families under a unified, MNIST-first interface. V ENOM emphasizes breadth, readability, reproducible entry points, and consistent training and sampling APIs rather than large-scale performance engineering. The package currently includes diffusion and score-based models, flow matching and one-step generators, variational autoencoders, normalizing flows, generative adversarial networks, and energy-based models. It provides separate training and sampling scripts, classifier and classifier-free guidance examples, bilingual tutorial notebooks, and a model-family organization that supports teaching, prototyping, and lightweight benchmarking.
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