RRepoGEO

REPOGEO REPORT · LITE

QuantFunc/ComfyUI-QuantFunc

Default branch main · commit e41176c0 · scanned 6/16/2026, 12:03:12 PM

GitHub: 910 stars · 21 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 QuantFunc/ComfyUI-QuantFunc, 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
    Add a concise description to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    ComfyUI plugin for QuantFunc, the fastest diffusion model inference engine. Accelerate text-to-image and image editing with native C++/CUDA quantization, offering 2x–11x speedup and zero Python model dependencies.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    comfyui, comfyui-custom-nodes, diffusion-models, image-generation, quantization, model-optimization, cuda, c-plus-plus, ai-acceleration, text-to-image
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) to clarify usage terms.

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 QuantFunc/ComfyUI-QuantFunc
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA RTX 40 Series GPUs
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA RTX 40 Series GPUs · recommended 1×
  2. PyTorch 2.x · recommended 1×
  3. CUDA 12.x · recommended 1×
  4. xFormers · recommended 1×
  5. SDXL Turbo · recommended 1×
  • CATEGORY QUERY
    How to accelerate diffusion model inference within ComfyUI for faster image generation?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA RTX 40 Series GPUs
    2. PyTorch 2.x
    3. CUDA 12.x
    4. xFormers
    5. SDXL Turbo
    6. LCM LoRAs
    7. SD 3 Turbo
    8. TensorRT
    9. ComfyUI-TensorRT
    10. AMD Radeon RX 7000 Series GPUs
    11. ROCm
    12. Intel Arc A770 / A750
    13. IPEX

    AI recommended 13 alternatives but never named QuantFunc/ComfyUI-QuantFunc. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for optimizing text-to-image diffusion models with quantization and LoRA stacking?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum
    2. ONNX Runtime
    3. Hugging Face Transformers
    4. Diffusers
    5. AutoGPTQ
    6. GPTQ-for-LLaMa
    7. bitsandbytes
    8. NVIDIA TensorRT
    9. Intel OpenVINO

    AI recommended 9 alternatives but never named QuantFunc/ComfyUI-QuantFunc. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 QuantFunc/ComfyUI-QuantFunc?
    pass
    AI named QuantFunc/ComfyUI-QuantFunc explicitly

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

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

Embed your GEO score

Drop this badge into the README of QuantFunc/ComfyUI-QuantFunc. 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)
[![RepoGEO](https://repogeo.com/badge/QuantFunc/ComfyUI-QuantFunc.svg)](https://repogeo.com/en/r/QuantFunc/ComfyUI-QuantFunc)
HTML
<a href="https://repogeo.com/en/r/QuantFunc/ComfyUI-QuantFunc"><img src="https://repogeo.com/badge/QuantFunc/ComfyUI-QuantFunc.svg" alt="RepoGEO" /></a>
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QuantFunc/ComfyUI-QuantFunc — 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