RRepoGEO

REPOGEO REPORT · LITE

HelloVision/HelloMeme

Default branch main · commit e0c7fe12 · scanned 6/5/2026, 11:07:42 PM

GitHub: 628 stars · 39 forks

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 HelloVision/HelloMeme, 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
    Update the repository description to clarify its purpose

    Why:

    CURRENT
    The official HelloMeme GitHub site
    COPY-PASTE FIX
    Official repository for HelloMeme, a research project on integrating Spatial Knitting Attentions to enhance high-level and fidelity-rich conditions in diffusion models for advanced image generation.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    diffusion-models, generative-ai, spatial-attention, image-generation, deep-learning, computer-vision, research, attention-mechanisms
  • mediumreadme#3
    Add a concise introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    HelloMeme is a novel research framework designed to advance diffusion models by integrating Spatial Knitting Attentions. This approach enables the embedding of high-level and fidelity-rich conditions, significantly improving the quality and control of generated images. Our work focuses on pushing the boundaries of generative AI for complex visual synthesis.

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 HelloVision/HelloMeme
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion · recommended 2×
  2. BigGAN · recommended 2×
  3. ControlNet · recommended 1×
  4. IP-Adapter · recommended 1×
  5. GLIGEN · recommended 1×
  • CATEGORY QUERY
    How to integrate high-level conditions into diffusion models for better image generation?
    you: not recommended
    AI recommended (in order):
    1. ControlNet
    2. IP-Adapter
    3. GLIGEN
    4. T2I-Adapter
    5. Classifier-Free Guidance (CFG)
    6. Prompt-to-Prompt (P2P)
    7. DreamBooth
    8. LoRA (Low-Rank Adaptation)

    AI recommended 8 alternatives but never named HelloVision/HelloMeme. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What methods use spatial attention to improve fidelity in generative AI models?
    you: not recommended
    AI recommended (in order):
    1. Vision Transformer (ViT)
    2. StyleGAN3
    3. Stable Diffusion
    4. Stable Diffusion
    5. DALL-E 2
    6. Imagen
    7. SENet block
    8. DCGAN
    9. BigGAN
    10. CBAM blocks
    11. StyleGAN
    12. ProGAN
    13. Non-local block
    14. BigGAN
    15. VQ-VAE
    16. Axial-DeepLab
    17. Axial-ResNet

    AI recommended 17 alternatives but never named HelloVision/HelloMeme. 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 HelloVision/HelloMeme?
    pass
    AI named HelloVision/HelloMeme explicitly

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

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

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MARKDOWN (README)
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HelloVision/HelloMeme — 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