RRepoGEO

REPOGEO REPORT · LITE

Shilin-LU/TF-ICON

Default branch main · commit efc69451 · scanned 6/11/2026, 11:32:56 AM

GitHub: 819 stars · 101 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 Shilin-LU/TF-ICON, 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
    Reposition the README's opening statement to clarify the project's core purpose

    Why:

    CURRENT
    Official implementation of TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition.
    COPY-PASTE FIX
    TF-ICON is a novel Training-Free Image COmpositioN framework that leverages off-the-shelf text-driven diffusion models for seamless cross-domain image-guided composition, without costly instance-based optimization or finetuning.
  • mediumtopics#2
    Add more specific topics to reinforce the project's unique approach

    Why:

    CURRENT
    diffusion-model, generative-ai, image-composition, image-inversion, stable-diffusion, text-to-image
    COPY-PASTE FIX
    diffusion-model, generative-ai, image-composition, image-inversion, stable-diffusion, text-to-image, training-free, cross-domain, image-editing
  • lowreadme#3
    Add a 'What TF-ICON is NOT' section to explicitly counter AI's miscategorization

    Why:

    COPY-PASTE FIX
    ## What TF-ICON is NOT

TF-ICON is *not* a project for 3D human body pose and shape estimation, nor does it utilize Neural Radiance Fields (NeRFs) for reconstruction. Our focus is solely on 2D image composition using diffusion models.

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 Shilin-LU/TF-ICON
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ControlNet
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ControlNet · recommended 2×
  2. Adobe Photoshop · recommended 1×
  3. Midjourney · recommended 1×
  4. Stable Diffusion · recommended 1×
  5. DALL-E 3 · recommended 1×
  • CATEGORY QUERY
    How can I integrate objects from one image into a new visual context using generative AI?
    you: not recommended
    AI recommended (in order):
    1. Adobe Photoshop
    2. Midjourney
    3. Stable Diffusion
    4. ControlNet
    5. DALL-E 3
    6. RunwayML
    7. Fooocus

    AI recommended 7 alternatives but never named Shilin-LU/TF-ICON. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective training-free methods for cross-domain image composition with diffusion models?
    you: not recommended
    AI recommended (in order):
    1. Prompt-to-Prompt (P2P)
    2. Plug-and-Play (PnP) Diffusion Features
    3. InstructPix2Pix
    4. ControlNet
    5. GLIGEN (Grounded-Language-to-Image Generation)

    AI recommended 5 alternatives but never named Shilin-LU/TF-ICON. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 Shilin-LU/TF-ICON?
    pass
    AI named Shilin-LU/TF-ICON explicitly

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

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

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

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Shilin-LU/TF-ICON — 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