RRepoGEO

REPOGEO REPORT · LITE

rohitgandikota/sliders

Default branch main · commit 710c995c · scanned 5/24/2026, 12:37:28 PM

GitHub: 1,134 stars · 88 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 rohitgandikota/sliders, 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
    Add a clear, domain-specific tagline to the README's H1

    Why:

    CURRENT
    # Concept Sliders
    COPY-PASTE FIX
    # Concept Sliders
A powerful method for precise, semantic control over diffusion model outputs.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    diffusion-models, lora, generative-ai, image-generation, computer-vision, ai-art, fine-tuning, stable-diffusion
  • mediumreadme#3
    Add a concise problem statement to the README's introduction

    Why:

    CURRENT
    Official code implementation of "Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models", European Conference on Computer Vision (ECCV 2024).
    COPY-PASTE FIX
    Concept Sliders offer a novel way to achieve granular, semantic control over diffusion model outputs, enabling users to fine-tune generations with intuitive 'sliders' for concepts like 'happiness' or 'style'.

Official code implementation of "Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models", European Conference on Computer Vision (ECCV 2024).

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/sliders
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ControlNet
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ControlNet · recommended 1×
  2. IP-Adapter · recommended 1×
  3. GLIGEN · recommended 1×
  4. InstructPix2Pix · recommended 1×
  5. ComfyUI · recommended 1×
  • CATEGORY QUERY
    How can I achieve more granular and precise control over my diffusion model generations?
    you: not recommended
    AI recommended (in order):
    1. ControlNet
    2. IP-Adapter
    3. GLIGEN
    4. InstructPix2Pix
    5. ComfyUI
    6. Automatic1111
    7. Stable Diffusion
    8. SDXL Refiner

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

    Show full AI answer
  • CATEGORY QUERY
    What tools allow fine-tuning diffusion model outputs using low-rank adaptors or similar techniques?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Diffusers
    2. Kohya's LoRA Trainer (kohya_ss)
    3. PEFT (Parameter-Efficient Fine-Tuning) Library by Hugging Face
    4. A1111 web UI (AUTOMATIC1111/stable-diffusion-webui)
    5. PyTorch Lightning
    6. Hugging Face Accelerate
    7. Diffusers-LoRA-Training-Scripts

    AI recommended 7 alternatives but never named rohitgandikota/sliders. 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/sliders?
    pass
    AI named rohitgandikota/sliders 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/sliders in production, what risks or prerequisites should they evaluate first?
    pass
    AI named rohitgandikota/sliders 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/sliders solve, and who is the primary audience?
    pass
    AI did not name rohitgandikota/sliders — 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 rohitgandikota/sliders. 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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HTML
<a href="https://repogeo.com/en/r/rohitgandikota/sliders"><img src="https://repogeo.com/badge/rohitgandikota/sliders.svg" alt="RepoGEO" /></a>
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rohitgandikota/sliders — 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