RRepoGEO

REPOGEO REPORT · LITE

dbolya/tomesd

Default branch main · commit 27a14a37 · scanned 5/9/2026, 2:07:14 AM

GitHub: 1,403 stars · 84 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 dbolya/tomesd, 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
    stable-diffusion, diffusion-models, image-generation, deep-learning, pytorch, acceleration, token-merging, computer-vision
  • highreadme#2
    Refine the README's opening sentence to clarify its role as an algorithmic method

    Why:

    CURRENT
    # Token Merging for Stable Diffusion

Using nothing but pure python and pytorch, ToMe for SD speeds up diffusion by merging _redundant_ tokens.
    COPY-PASTE FIX
    # Token Merging for Stable Diffusion

ToMe for SD is a pure Python and PyTorch *algorithmic method* that significantly speeds up Stable Diffusion inference by merging _redundant_ tokens, offering a direct acceleration technique without requiring extensive retraining.
  • mediumcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

ToMe for SD offers a unique approach to accelerating Stable Diffusion by directly merging redundant tokens. Unlike general frameworks or UIs like Automatic1111 WebUI or ComfyUI, ToMe for SD is a specific algorithmic optimization that can often be integrated *within* such environments. It also differs from low-level optimization libraries like xFormers or bitsandbytes, which focus on efficient attention mechanisms or quantization; ToMe operates at a higher semantic level by reducing the number of tokens processed, complementing these other optimizations rather than replacing them.

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 dbolya/tomesd
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ComfyUI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ComfyUI · recommended 2×
  2. Diffusers Library · recommended 2×
  3. Automatic1111 Stable Diffusion WebUI · recommended 1×
  4. xformers · recommended 1×
  5. bitsandbytes · recommended 1×
  • CATEGORY QUERY
    How can I accelerate Stable Diffusion inference without extensive model retraining?
    you: not recommended
    AI recommended (in order):
    1. Automatic1111 Stable Diffusion WebUI
    2. xformers
    3. bitsandbytes
    4. ComfyUI
    5. ONNX Runtime
    6. TensorRT
    7. Diffusers Library
    8. bettertransformer
    9. torch.compile
    10. OpenVINO

    AI recommended 10 alternatives but never named dbolya/tomesd. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are methods to reduce VRAM usage and speed up image generation in Stable Diffusion?
    you: not recommended
    AI recommended (in order):
    1. Automatic1111 Web UI
    2. xFormers
    3. Tiled Diffusion
    4. Tiled VAE
    5. ComfyUI
    6. Diffusers Library
    7. PyTorch
    8. InvokeAI
    9. RunPod
    10. Vast.ai
    11. Google Colab

    AI recommended 11 alternatives but never named dbolya/tomesd. 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 dbolya/tomesd?
    pass
    AI named dbolya/tomesd explicitly

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

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