YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy

• highlicense#1

  Add a LICENSE file to clarify usage terms

  原因：

  复制粘贴的修复

  复制

  Add a LICENSE file (e.g., MIT License) to the repository root.

  待办完成忽略
• highreadme#2

  Reposition the README's opening to highlight the unique taxonomy

  原因：

  当前

  This repo is constructed for collecting and categorizing papers about diffusion models according to our survey paper——_**Diffusion Models: A Comprehensive Survey of Methods and Applications**_, which has been accepted by the journal **ACM Computing Surveys**. Considering the fast development of this field, we will continue to update **both arxiv paper and this repo**.

  复制粘贴的修复

  复制

  This repository provides a comprehensive, continuously updated taxonomy and curated collection of diffusion model papers, structured according to our ACM Computing Surveys paper, "Diffusion Models: A Comprehensive Survey of Methods and Applications." It serves as a living, organized guide for researchers to navigate the rapidly evolving landscape of diffusion models, offering a unique hierarchical classification of algorithms and applications.

  待办完成忽略
• mediumtopics#3

  Expand repository topics for broader and more specific categorization

  原因：

  当前

  diffusion-models, stable-diffusion, survey, text-to-3d, text-to-image, text-to-video

  复制粘贴的修复

  复制

  diffusion-models, stable-diffusion, survey, taxonomy, generative-ai, machine-learning-research, paper-collection, literature-review, deep-learning, text-to-image, text-to-video, text-to-3d

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Where can I find a comprehensive overview of recent advancements in diffusion models research?

  你：未被推荐
  AI 推荐顺序：

  1. Papers With Code - Diffusion Models
  2. arXiv.org
  3. Hugging Face Blog/Diffusion Models Course
  4. The Batch (DeepLearning.AI Newsletter)
  5. Google AI Blog
  6. Meta AI Blog

  AI 推荐了 6 个替代方案，却始终没点名 YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Looking for a structured guide to different sampling acceleration techniques in generative AI.

  你：未被推荐
  AI 推荐顺序：

  1. Google's Speculative Decoding
  2. Medusa
  3. bitsandbytes
  4. AWQ
  5. GPTQ
  6. ONNX Runtime
  7. NVIDIA TensorRT
  8. Hugging Face's DistilBERT
  9. TinyLlama
  10. Top-K Sampling
  11. Nucleus Sampling
  12. Temperature Scaling
  13. vLLM

  AI 推荐了 13 个替代方案，却始终没点名 YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy?

  pass
  ⚠AI 未点名 YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy solve, and who is the primary audience?

  pass
  ⚠AI 未点名 YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？