RRepoGEO

REPOGEO REPORT · LITE

kijai/ComfyUI-Florence2

Default branch main · commit 9ece3de9 · scanned 5/13/2026, 2:02:44 AM

GitHub: 1,684 stars · 140 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 kijai/ComfyUI-Florence2, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    comfyui, custom-node, florence2, vision-language-model, vlm, image-processing, object-detection, segmentation, docvqa
  • highreadme#2
    Reposition the README's opening to emphasize ComfyUI integration

    Why:

    CURRENT
    # Florence2 in ComfyUI

> Florence-2 is an advanced vision foundation model...
    COPY-PASTE FIX
    # ComfyUI-Florence2: Florence-2 Vision Model Integration for ComfyUI

This custom node integrates Microsoft's Florence-2, an advanced vision foundation model, directly into ComfyUI. It enables users to leverage Florence-2's prompt-based approach for a wide range of vision and vision-language tasks within their ComfyUI workflows, including captioning, object detection, segmentation, and Document Visual Question Answering (DocVQA).
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/kijai/ComfyUI-Florence2

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 kijai/ComfyUI-Florence2
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Vision AI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Vision AI · recommended 1×
  2. Microsoft Azure Form Recognizer · recommended 1×
  3. Amazon Textract · recommended 1×
  4. microsoft/unilm · recommended 1×
  5. naver-ai/donut · recommended 1×
  • CATEGORY QUERY
    How to implement visual question answering for scanned documents using an AI model?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Vision AI
    2. Microsoft Azure Form Recognizer
    3. Amazon Textract
    4. LayoutLM (microsoft/unilm)
    5. Donut (naver-ai/donut)
    6. VisualBERT (uclanlp/visualbert)
    7. Hugging Face Transformers Library (huggingface/transformers)
    8. PyTorch (pytorch/pytorch)
    9. TensorFlow (tensorflow/tensorflow)
    10. DocVQA Dataset
    11. SQuAD

    AI recommended 11 alternatives but never named kijai/ComfyUI-Florence2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a flexible visual foundation model for diverse tasks like captioning and detection.
    you: not recommended
    AI recommended (in order):
    1. OWL-ViT
    2. CLIP
    3. DINOv2
    4. SAM
    5. BLIP-2

    AI recommended 5 alternatives but never named kijai/ComfyUI-Florence2. 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 kijai/ComfyUI-Florence2?
    pass
    AI named kijai/ComfyUI-Florence2 explicitly

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

  • If a team adopts kijai/ComfyUI-Florence2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kijai/ComfyUI-Florence2 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 kijai/ComfyUI-Florence2 solve, and who is the primary audience?
    pass
    AI named kijai/ComfyUI-Florence2 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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kijai/ComfyUI-Florence2 — 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