YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy

• highlicense#1

  Add a LICENSE file to clarify usage terms

  Why:

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

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• highreadme#2

  Reposition the README's opening to highlight the unique taxonomy

  Why:

  CURRENT

  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**.

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  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.

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• mediumtopics#3

  Expand repository topics for broader and more specific categorization

  Why:

  CURRENT

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

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  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

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

  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 recommended 6 alternatives but never named YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy. This is the gap to close.

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• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

  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 recommended 13 alternatives but never named YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy. This is the gap to close.

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• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

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

  pass
  ⚠AI did not name YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy — likely talking about a different project

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

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  ✓AI named YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy explicitly

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

  pass
  ⚠AI did not name YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?