RRepoGEO

REPOGEO REPORT · LITE

FireRedTeam/FireRed-Image-Edit

Default branch main · commit 456d010b · scanned 5/27/2026, 5:23:00 PM

GitHub: 1,226 stars · 74 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 FireRedTeam/FireRed-Image-Edit, 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
  • highabout#1
    Reposition the repository description to clarify its red team focus

    Why:

    CURRENT
    FireRed-Image-Edit is a powerful image editing foundation model achieving open-source state-of-the-art performance with precise instruction following, high-fidelity generation, superior identity consistency, and seamless multi-element fusion.
    COPY-PASTE FIX
    FireRed-Image-Edit is a powerful image editing foundation model specifically designed for red team operations, enabling precise instruction following for tasks like payload embedding, metadata manipulation, and steganography with high-fidelity generation and identity consistency.
  • highreadme#2
    Add a clear opening sentence to the README emphasizing red team capabilities

    Why:

    COPY-PASTE FIX
    FireRed-Image-Edit is an advanced image editing foundation model tailored for red team operations, offering capabilities for payload embedding, metadata manipulation, and steganography.
  • mediumtopics#3
    Add specific topics related to red teaming and security

    Why:

    CURRENT
    aigc, deep-learning, diffusion-models, image-generation, image2image, pytorch
    COPY-PASTE FIX
    aigc, deep-learning, diffusion-models, image-generation, image2image, pytorch, red-team, offensive-security, steganography, payload-embedding, image-manipulation

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 FireRedTeam/FireRed-Image-Edit
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Diffusion XL (SDXL)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion XL (SDXL) · recommended 1×
  2. Stable Diffusion 1.5/2.1 · recommended 1×
  3. ControlNet · recommended 1×
  4. DeepFloyd IF · recommended 1×
  5. Kandinsky 2.2 · recommended 1×
  • CATEGORY QUERY
    What open-source image generation models offer precise instruction following and high-fidelity output?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion XL (SDXL)
    2. Stable Diffusion 1.5/2.1
    3. ControlNet
    4. DeepFloyd IF
    5. Kandinsky 2.2
    6. PixArt-α

    AI recommended 6 alternatives but never named FireRedTeam/FireRed-Image-Edit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a PyTorch-based diffusion model for image editing with strong identity consistency and multi-element fusion.
    you: not recommended
    AI recommended (in order):
    1. Diffusers (huggingface/diffusers)
    2. ControlNet (lllyasviel/ControlNet)
    3. IP-Adapter (tencent-ailab/IP-Adapter)
    4. InstantID (InstantID/InstantID)
    5. DreamBooth (google/dreambooth)
    6. GLIDE
    7. CompVis/latent-diffusion (CompVis/latent-diffusion)

    AI recommended 7 alternatives but never named FireRedTeam/FireRed-Image-Edit. 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 FireRedTeam/FireRed-Image-Edit?
    pass
    AI named FireRedTeam/FireRed-Image-Edit explicitly

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

  • If a team adopts FireRedTeam/FireRed-Image-Edit in production, what risks or prerequisites should they evaluate first?
    pass
    AI named FireRedTeam/FireRed-Image-Edit 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 FireRedTeam/FireRed-Image-Edit solve, and who is the primary audience?
    pass
    AI named FireRedTeam/FireRed-Image-Edit 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 FireRedTeam/FireRed-Image-Edit. 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/FireRedTeam/FireRed-Image-Edit"><img src="https://repogeo.com/badge/FireRedTeam/FireRed-Image-Edit.svg" alt="RepoGEO" /></a>
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FireRedTeam/FireRed-Image-Edit — 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