REPOGEO REPORT · LITE

andreas128/RePaint

Default branch main · commit 8e2a0552 · scanned 6/25/2026, 1:13:20 PM

GitHub: 2,256 stars · 199 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)

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

AI VISIBILITY SCORE

35 /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

3 / 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 andreas128/RePaint, 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

highlicense#1

Add a LICENSE file to the repository

Why:

CURRENT

(no LICENSE file detected — the repo has no recognizable license)

COPY-PASTE FIX

Create a LICENSE file in the repository root. Consider adding a standard open-source license such as MIT, Apache-2.0, or GPL-3.0, or clearly state the intended licensing terms if it's a custom license.

highreadme#2

Reposition README opening to emphasize PyTorch implementation for research

Why:

CURRENT

# RePaint
**Inpainting using Denoising Diffusion Probabilistic Models**
CVPR 2022 [[Paper]](https://bit.ly/3b1ABEb)

COPY-PASTE FIX

# RePaint: Official PyTorch Implementation for Image Inpainting
This repository contains the official PyTorch code and models for "RePaint: Inpainting using Denoising Diffusion Probabilistic Models", presented at CVPR 2022 [[Paper]](https://bit.ly/3b1ABEb). RePaint leverages Denoising Diffusion Probabilistic Models (DDPMs) to generate diverse and high-quality completions for missing parts of images.

mediumtopics#3
Add 'pytorch' to repository topics
Why:
CURRENT
```
cvpr2022, diffusion-models, inpainting
```
COPY-PASTE FIX
```
cvpr2022, diffusion-models, inpainting, pytorch
```

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 andreas128/RePaint

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Adobe Photoshop (Generative Fill)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Adobe Photoshop (Generative Fill) · recommended 1×
Stable Diffusion · recommended 1×
Automatic1111 Web UI · recommended 1×
ComfyUI · recommended 1×
Midjourney · recommended 1×

CATEGORY QUERY
How can I fill in missing parts of an image using advanced generative AI models?
you: not recommended
AI recommended (in order):
1. Adobe Photoshop (Generative Fill)
2. Stable Diffusion
3. Automatic1111 Web UI
4. ComfyUI
5. Midjourney
6. DALL-E 2
7. RunwayML
8. LaMa (Large Mask Inpainting)
9. BigGAN
10. StyleGAN
AI recommended 10 alternatives but never named andreas128/RePaint. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best PyTorch libraries for image completion with diffusion models?
you: not recommended
AI recommended (in order):
1. Hugging Face Diffusers
2. CompVis Latent Diffusion Models (LDM)
3. OpenAI GLIDE
4. KerasCV
5. PyTorch-Image-Models (timm)
AI recommended 5 alternatives but never named andreas128/RePaint. 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 andreas128/RePaint?
pass
AI named andreas128/RePaint explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts andreas128/RePaint in production, what risks or prerequisites should they evaluate first?
pass
AI named andreas128/RePaint 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 andreas128/RePaint solve, and who is the primary audience?
pass
AI named andreas128/RePaint explicitly
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 andreas128/RePaint. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/andreas128/RePaint.svg)](https://repogeo.com/en/r/andreas128/RePaint)

HTML

<a href="https://repogeo.com/en/r/andreas128/RePaint"><img src="https://repogeo.com/badge/andreas128/RePaint.svg" alt="RepoGEO" /></a>

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andreas128/RePaint — 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