REPOGEO REPORT · LITE

CleanDiffuserTeam/CleanDiffuser

Default branch main · commit 05f17fc9 · scanned 6/5/2026, 5:57:50 PM

GitHub: 718 stars · 78 forks

AI VISIBILITY SCORE

28 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface CleanDiffuserTeam/CleanDiffuser, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

hightopics#1

Add specific topics to improve categorization

Why:

CURRENT

(none)

COPY-PASTE FIX

diffusion-models, reinforcement-learning, decision-making, control, robotics, deep-learning, pytorch, modular-library, machine-learning

highreadme#2

Strengthen README's opening paragraph to highlight unique value for decision-making

Why:

CURRENT

CleanDiffuser is an easy-to-use modularized Diffusion Model library tailored for decision-making, which comprehensively integrates different types of diffusion algorithmic branches. CleanDiffuser offers a variety of advanced *diffusion models*, *network structures*, diverse *conditions*, and *algorithm pipelines* in a simple and user-friendly manner. Inheriting the design philosophy of CleanRL and Diffusers, CleanDiffuser emphasizes **usabi

COPY-PASTE FIX

CleanDiffuser is the premier easy-to-use modularized library specifically designed for applying Diffusion Models to **decision-making, reinforcement learning, and control tasks**. Unlike general diffusion frameworks, CleanDiffuser provides a comprehensive toolkit with advanced diffusion models, network structures, and algorithm pipelines *optimized for sequential decision problems*, inheriting the clarity of CleanRL and the modularity of Diffusers.

mediumhomepage#3
Add the documentation URL as the repository homepage
Why:
COPY-PASTE FIX
```
https://cleandiffuserteam.github.io/CleanDiffuserDocs/
```

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

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface CleanDiffuserTeam/CleanDiffuser

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/diffusers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

huggingface/diffusers · recommended 2×
google-research/diffuser · recommended 1×
lucidrains/denoising-diffusion-pytorch · recommended 1×
openai/improved-diffusion · recommended 1×
ray-project/ray · recommended 1×

CATEGORY QUERY
How can I easily implement diffusion models for reinforcement learning decision making tasks?
you: not recommended
AI recommended (in order):
1. Diffuser (google-research/diffuser)
2. Hugging Face `diffusers` (huggingface/diffusers)
3. denoising-diffusion-pytorch (lucidrains/denoising-diffusion-pytorch)
4. pytorch-diffusion (openai/improved-diffusion)
5. RLlib (ray-project/ray)
AI recommended 5 alternatives but never named CleanDiffuserTeam/CleanDiffuser. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What modular Python library helps with diffusion models for control, supporting multi-GPU training?
you: not recommended
AI recommended (in order):
1. Diffusers (huggingface/diffusers)
2. PyTorch-Lightning (PyTorchLightning/pytorch-lightning)
3. Accelerate (huggingface/accelerate)
4. Keras (keras-team/keras)
5. JAX/Flax
AI recommended 5 alternatives but never named CleanDiffuserTeam/CleanDiffuser. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of CleanDiffuserTeam/CleanDiffuser?
pass
AI did not name CleanDiffuserTeam/CleanDiffuser — 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?
If a team adopts CleanDiffuserTeam/CleanDiffuser in production, what risks or prerequisites should they evaluate first?
pass
AI named CleanDiffuserTeam/CleanDiffuser explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo CleanDiffuserTeam/CleanDiffuser solve, and who is the primary audience?
pass
AI named CleanDiffuserTeam/CleanDiffuser explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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HTML

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CleanDiffuserTeam/CleanDiffuser — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite