RRepoGEO

REPOGEO REPORT · LITE

city96/ComfyUI-GGUF

Default branch main · commit 6ea2651e · scanned 6/22/2026, 5:43:04 AM

GitHub: 3,767 stars · 315 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
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 city96/ComfyUI-GGUF, 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, comfyui-custom-nodes, gguf, quantization, diffusion-models, transformer-models, vram-optimization
  • highreadme#2
    Clarify the README's opening sentence to emphasize ComfyUI custom node for GGUF diffusion/transformer models

    Why:

    CURRENT
    # ComfyUI-GGUF
GGUF Quantization support for native ComfyUI models

This is currently very much WIP. These custom nodes provide support for model files stored in the GGUF format popularized by llama.cpp.
    COPY-PASTE FIX
    # ComfyUI-GGUF
Custom Nodes for GGUF Quantization of Diffusion and Transformer Models in ComfyUI

This repository provides custom nodes for ComfyUI, enabling direct support for GGUF-formatted diffusion and transformer models (like DiT/Flux) and quantized T5 text encoders. This allows for significant VRAM savings and efficient inference on GPUs with limited memory.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/city96/ComfyUI-GGUF

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 city96/ComfyUI-GGUF
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
sd-webui-comfyui extension
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. sd-webui-comfyui extension · recommended 1×
  2. Automatic1111's WebUI · recommended 1×
  3. xformers · recommended 1×
  4. GGUF · recommended 1×
  5. llama.cpp · recommended 1×
  • CATEGORY QUERY
    How can I optimize ComfyUI model inference to run on GPUs with very limited VRAM?
    you: not recommended
    AI recommended (in order):
    1. sd-webui-comfyui extension
    2. Automatic1111's WebUI
    3. xformers
    4. GGUF
    5. llama.cpp
    6. ONNX
    7. SDXL Turbo
    8. SD 1.5 Turbo
    9. LCM LoRAs
    10. Efficient KSampler
    11. ComfyUI-Impact-Pack
    12. FreeU
    13. ControlNet-Lite
    14. T2I-Adapter
    15. ZRAM

    AI recommended 15 alternatives but never named city96/ComfyUI-GGUF. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for ways to apply GGUF quantization to diffusion models for reduced GPU memory usage.
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. Hugging Face optimum (huggingface/optimum)
    3. diffusers-gguf
    4. mlc-llm (mlc-ai/mlc-llm)
    5. ggml (ggerganov/ggml)

    AI recommended 5 alternatives but never named city96/ComfyUI-GGUF. 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 city96/ComfyUI-GGUF?
    pass
    AI named city96/ComfyUI-GGUF explicitly

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

  • If a team adopts city96/ComfyUI-GGUF in production, what risks or prerequisites should they evaluate first?
    pass
    AI named city96/ComfyUI-GGUF 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 city96/ComfyUI-GGUF solve, and who is the primary audience?
    pass
    AI named city96/ComfyUI-GGUF 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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MARKDOWN (README)
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city96/ComfyUI-GGUF — 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