RRepoGEO

REPOGEO REPORT · LITE

GaParmar/img2img-turbo

Default branch main · commit 86f54146 · scanned 5/12/2026, 4:53:04 AM

GitHub: 2,437 stars · 283 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 GaParmar/img2img-turbo, 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 the project's core identity as a fast, one-step diffusion model in the README's opening

    Why:

    CURRENT
    We propose a general method for adapting a single-step diffusion model, such as SD-Turbo, to new tasks and domains through adversarial learning.
    COPY-PASTE FIX
    img2img-turbo introduces **CycleGAN-Turbo** and **pix2pix-turbo**, a general method for adapting **single-step Stable Diffusion turbo models** to new image-to-image translation tasks through adversarial learning. This enables **lightning-fast, one-step inference** for various tasks like sketch2image and day2night, outperforming existing GANs and matching diffusion models like ControlNet in speed.
  • hightopics#2
    Update topics to accurately reflect 'diffusion models' and 'fast inference'

    Why:

    CURRENT
    computer-vision, deep-learning, generative-adversarial-network, generative-art, stable-diffusion
    COPY-PASTE FIX
    computer-vision, deep-learning, generative-art, stable-diffusion, diffusion-models, image-to-image-translation, real-time-inference
  • mediumhomepage#3
    Add the paper link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2403.12036

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 GaParmar/img2img-turbo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pix2PixHD
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pix2PixHD · recommended 2×
  2. CycleGAN · recommended 2×
  3. StyleGAN · recommended 2×
  4. StarGAN v2 · recommended 1×
  5. U-Net based models · recommended 1×
  • CATEGORY QUERY
    Looking for a fast, one-step image-to-image translation model for various generative tasks.
    you: not recommended
    AI recommended (in order):
    1. Pix2PixHD
    2. CycleGAN
    3. StarGAN v2
    4. StyleGAN
    5. U-Net based models

    AI recommended 5 alternatives but never named GaParmar/img2img-turbo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What deep learning tools convert sketches to realistic images with single-step inference?
    you: not recommended
    AI recommended (in order):
    1. Pix2Pix
    2. Pix2PixHD
    3. CycleGAN
    4. SPADE
    5. GauGAN
    6. StyleGAN
    7. StyleGAN2
    8. StyleGAN3
    9. Stable Diffusion
    10. DALL-E 2
    11. ControlNet

    AI recommended 11 alternatives but never named GaParmar/img2img-turbo. 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 GaParmar/img2img-turbo?
    pass
    AI did not name GaParmar/img2img-turbo — 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 GaParmar/img2img-turbo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named GaParmar/img2img-turbo 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 GaParmar/img2img-turbo solve, and who is the primary audience?
    pass
    AI named GaParmar/img2img-turbo 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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GaParmar/img2img-turbo — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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