RRepoGEO

REPOGEO REPORT · LITE

continue-revolution/sd-webui-segment-anything

Default branch master · commit 982138cf · scanned 5/19/2026, 11:27:59 AM

GitHub: 3,510 stars · 221 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 continue-revolution/sd-webui-segment-anything, 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
  • highlicense#1
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, GPL-3.0) in the repository root. If a specific license is already intended, use that one. Otherwise, consider a permissive license like MIT.
  • highhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL in the GitHub repository settings to a relevant page, such as the project's main documentation, a demo, or the GitHub Pages site if applicable. For example, `https://github.com/continue-revolution/sd-webui-segment-anything/wiki`.
  • highreadme#3
    Clarify the README's opening sentence to emphasize its role as a WebUI extension

    Why:

    CURRENT
    This extension aim for connecting AUTOMATIC1111 Stable Diffusion WebUI and Mikubill ControlNet Extension with segment anything and GroundingDINO to enhance Stable Diffusion/ControlNet inpainting, enhance ControlNet semantic segmentation, automate image matting and create LoRA/LyCORIS training set.
    COPY-PASTE FIX
    This is an essential extension for AUTOMATIC1111 Stable Diffusion WebUI, integrating Segment Anything and GroundingDINO to significantly enhance inpainting, semantic segmentation, image matting, and dataset creation for Stable Diffusion and ControlNet.

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 continue-revolution/sd-webui-segment-anything
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Diffusion
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion · recommended 1×
  2. ControlNet · recommended 1×
  3. Automatic1111's Stable Diffusion WebUI · recommended 1×
  4. SAM (Segment Anything Model) · recommended 1×
  5. Focal loss · recommended 1×
  • CATEGORY QUERY
    How to improve inpainting and semantic segmentation within my AI image generation workflow?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. ControlNet
    3. Automatic1111's Stable Diffusion WebUI
    4. SAM (Segment Anything Model)
    5. Focal loss
    6. Dice loss
    7. U-Net
    8. Mask R-CNN

    AI recommended 8 alternatives but never named continue-revolution/sd-webui-segment-anything. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tool can automate mask generation for image matting or creating custom AI model datasets?
    you: not recommended
    AI recommended (in order):
    1. Segment Anything Model (SAM) (facebookresearch/segment-anything)
    2. Label Studio (heartexlabs/label-studio)
    3. Roboflow
    4. CVAT (Computer Vision Annotation Tool) (opencv/cvat)
    5. SuperAnnotate
    6. VGG Image Annotator (VIA) (vfane/vgg-image-annotator)

    AI recommended 6 alternatives but never named continue-revolution/sd-webui-segment-anything. 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 continue-revolution/sd-webui-segment-anything?
    pass
    AI named continue-revolution/sd-webui-segment-anything explicitly

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

  • If a team adopts continue-revolution/sd-webui-segment-anything in production, what risks or prerequisites should they evaluate first?
    pass
    AI named continue-revolution/sd-webui-segment-anything 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 continue-revolution/sd-webui-segment-anything solve, and who is the primary audience?
    pass
    AI did not name continue-revolution/sd-webui-segment-anything — 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?

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continue-revolution/sd-webui-segment-anything — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite