RRepoGEO

REPOGEO REPORT · LITE

rohitgandikota/erasing

Default branch main · commit 2ff19a10 · scanned 6/11/2026, 5:42:48 AM

GitHub: 662 stars · 43 forks

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 rohitgandikota/erasing, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    diffusion-models, concept-erasing, unlearning, generative-ai, stable-diffusion, machine-unlearning, ai-safety, model-editing, bias-removal
  • highreadme#2
    Add a concise, differentiating introductory paragraph to the README

    Why:

    CURRENT
    The README currently starts with '# Erasing Concepts from Diffusion Models' followed by links and 'Updated code 🚀'.
    COPY-PASTE FIX
    After the H1 '# Erasing Concepts from Diffusion Models', add: 'This repository provides efficient methods and code for **erasing specific concepts, styles, or undesirable biases from pre-trained text-to-image diffusion models** like Stable Diffusion, SDXL, and FLUX. It focuses on machine unlearning techniques to remove unwanted knowledge without full model retraining, offering a faster and more memory-efficient approach.'
  • mediumcomparison#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a section titled 'Why Erasing Concepts? (vs. Fine-tuning / General Diffusion Tools)' or similar, explaining how this repo's approach to concept unlearning differs from general fine-tuning, DreamBooth, LoRA, or broader tools like Diffusers/Automatic1111 which focus on adding or generating content.

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 rohitgandikota/erasing
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Diffusers Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Diffusers Library · recommended 1×
  2. Kohya's SS GUI · recommended 1×
  3. Automatic1111 Stable Diffusion WebUI · recommended 1×
  4. Eraser · recommended 1×
  5. LAION-5B · recommended 1×
  • CATEGORY QUERY
    How can I remove specific concepts or styles from a trained diffusion model?
    you: not recommended
    AI recommended (in order):
    1. Diffusers Library
    2. Kohya's SS GUI
    3. Automatic1111 Stable Diffusion WebUI
    4. Eraser
    5. LAION-5B
    6. Pillow
    7. NLTK
    8. spaCy

    AI recommended 8 alternatives but never named rohitgandikota/erasing. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient methods for unlearning undesirable concepts from generative image models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Diffusers
    2. PyTorch
    3. TensorFlow
    4. LoRA
    5. DreamBooth
    6. Cleanlab
    7. MEMIT

    AI recommended 7 alternatives but never named rohitgandikota/erasing. 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 rohitgandikota/erasing?
    pass
    AI named rohitgandikota/erasing explicitly

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

  • If a team adopts rohitgandikota/erasing in production, what risks or prerequisites should they evaluate first?
    pass
    AI named rohitgandikota/erasing 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 rohitgandikota/erasing solve, and who is the primary audience?
    pass
    AI named rohitgandikota/erasing explicitly

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

Embed your GEO score

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rohitgandikota/erasing — 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