REPOGEO REPORT · LITE

PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models

Default branch main · commit 756fb34d · scanned 5/29/2026, 1:18:27 AM

GitHub: 1,113 stars · 32 forks

AI VISIBILITY SCORE

22 /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

1 / 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 PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models, 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

highreadme#1

Clarify repo's identity as a resource collection in the README's opening

Why:

CURRENT

We are focusing on how to **Control** text-to-image diffusion models with **Novel Conditions**.

COPY-PASTE FIX

This repository is a curated collection of resources focusing on how to **Control** text-to-image diffusion models with **Novel Conditions**.

highhomepage#2
Add the survey paper's arXiv link as the repository homepage
Why:
COPY-PASTE FIX
```
https://arxiv.org/abs/2403.04279
```

mediumtopics#3

Add a topic to emphasize the repository's role as a research collection

Why:

CURRENT

awesome, awesome-list, controllable-generation, diffusion-models, multi-concept, personalization, spatial-controls, text-to-image

COPY-PASTE FIX

awesome, awesome-list, controllable-generation, diffusion-models, multi-concept, personalization, spatial-controls, text-to-image, research-collection

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 PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ControlNet

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

ControlNet · recommended 2×
Textual Inversion · recommended 2×
DreamBooth · recommended 2×
Automatic1111's Stable Diffusion web UI · recommended 1×
ComfyUI · recommended 1×

CATEGORY QUERY
How to achieve fine-grained control over text-to-image diffusion model outputs?
you: not recommended
AI recommended (in order):
1. ControlNet
2. Automatic1111's Stable Diffusion web UI
3. ComfyUI
4. IP-Adapter
5. LoRA
6. Textual Inversion
7. DreamBooth
AI recommended 7 alternatives but never named PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What resources exist for personalizing or adding multiple concepts to image diffusion models?
you: not recommended
AI recommended (in order):
1. DreamBooth
2. LoRA (Low-Rank Adaptation)
3. Textual Inversion
4. Hypernetworks
5. ControlNet
6. GLIGEN (Grounded Language-Image Generation)
7. Custom Diffusion
8. Compose
9. Cones
AI recommended 9 alternatives but never named PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models. 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 PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models?
pass
AI did not name PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models — 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 PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models in production, what risks or prerequisites should they evaluate first?
pass
AI named PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models 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 PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models solve, and who is the primary audience?
pass
AI did not name PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models — 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?

Embed your GEO score

Drop this badge into the README of PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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