REPOGEO REPORT · LITE

rinongal/StyleGAN-nada

Default branch main · commit dc8406ae · scanned 5/16/2026, 7:23:32 PM

GitHub: 1,195 stars · 144 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 rinongal/StyleGAN-nada, 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

highabout#1

Add a concise 'About' description

Why:

COPY-PASTE FIX

CLIP-guided domain adaptation for StyleGANs, enabling text-prompt-driven transfer to new visual domains without requiring any target domain images.

hightopics#2

Add more specific topics to improve categorization

Why:

CURRENT

domain-adaptation, generative-adversarial-network, stylegan

COPY-PASTE FIX

domain-adaptation, generative-adversarial-network, stylegan, text-guided-generation, zero-shot-learning, gan-transfer, clip-guidance

mediumreadme#3

Add a concise problem statement immediately after the H1

Why:

CURRENT

# StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators (SIGGRAPH 2022)

[](http://colab.research.google.com/github/rinongal/stylegan-nada/blob/main/stylegan_nada.ipynb) 
[](https://www.kaggle.com/ratthachat/stylegan-nada-playground) [](https://arxiv.org/abs/2108.00946)

COPY-PASTE FIX

# StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators (SIGGRAPH 2022)

This repository presents a method to adapt existing image generators to new domains using only text prompts, *without requiring any target domain images*.

[](http://colab.research.google.com/github/rinongal/stylegan-nada/blob/main/stylegan_nada.ipynb) 
[](https://www.kaggle.com/ratthachat/stylegan-nada-playground) [](https://arxiv.org/abs/2108.00946)

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 rinongal/StyleGAN-nada

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

openai/CLIP

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

openai/CLIP · recommended 2×
Midjourney · recommended 1×
Stable Diffusion · recommended 1×
DALL-E 3 · recommended 1×
kohya-ss/sd-scripts · recommended 1×

CATEGORY QUERY
How to perform domain adaptation for image generators using only text prompts and no datasets?
you: not recommended
AI recommended (in order):
1. Midjourney
2. Stable Diffusion
3. DALL-E 3
4. Kohya's GUI (kohya-ss/sd-scripts)
5. PyTorch (pytorch/pytorch)
6. Hugging Face Transformers (huggingface/transformers)
7. CLIP (openai/CLIP)
8. StyleGAN-XL (NVlabs/stylegan-xl)
AI recommended 8 alternatives but never named rinongal/StyleGAN-nada. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective techniques for text-guided generative model transfer to new visual domains?
you: not recommended
AI recommended (in order):
1. DreamBooth
2. LoRA
3. Textual Inversion
4. ControlNet (lllyasviel/ControlNet)
5. Adapter (huggingface/diffusers)
6. CLIP (openai/CLIP)
AI recommended 6 alternatives but never named rinongal/StyleGAN-nada. 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 rinongal/StyleGAN-nada?
pass
AI named rinongal/StyleGAN-nada explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts rinongal/StyleGAN-nada in production, what risks or prerequisites should they evaluate first?
pass
AI named rinongal/StyleGAN-nada 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 rinongal/StyleGAN-nada solve, and who is the primary audience?
pass
AI did not name rinongal/StyleGAN-nada — 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?

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MARKDOWN (README)

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HTML

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rinongal/StyleGAN-nada — 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