RRepoGEO

REPOGEO REPORT · LITE

finegrain-ai/refiners

Default branch main · commit 505dbdcf · scanned 6/12/2026, 3:57:06 AM

GitHub: 834 stars · 65 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 finegrain-ai/refiners, 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
  • highreadme#1
    Reposition README's main tagline to specify generative AI/diffusion

    Why:

    CURRENT
    **The simplest way to train and run adapters on top of foundation modelsManifesto** |
    COPY-PASTE FIX
    **The simplest way to train and run adapters on top of generative AI and diffusion models** | **Manifesto** |
  • mediumabout#2
    Refine the repository description for clarity on model type

    Why:

    CURRENT
    A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation
    COPY-PASTE FIX
    A microframework on top of PyTorch with first-class citizen APIs for generative AI and diffusion model adaptation.
  • lowtopics#3
    Add broader category topics for generative AI and PyTorch frameworks

    Why:

    CURRENT
    background-generation, background-removal, controlnet, diffusion-models, dinov2, image-generation, ip-adapter, lcm, lcm-lora, lora, sam, sdxl, segment-anything, shadow-generation, stable-diffusion, t2i-adapter, text-to-image, textual-inversion, upscaler
    COPY-PASTE FIX
    generative-ai, pytorch-framework, background-generation, background-removal, controlnet, diffusion-models, dinov2, image-generation, ip-adapter, lcm, lcm-lora, lora, sam, sdxl, segment-anything, shadow-generation, stable-diffusion, t2i-adapter, text-to-image, textual-inversion, upscaler

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 finegrain-ai/refiners
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. PEFT · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. DeepSpeed · recommended 1×
  5. Accelerate · recommended 1×
  • CATEGORY QUERY
    What are good PyTorch frameworks for adapting and fine-tuning large foundation models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. PyTorch Lightning
    4. DeepSpeed
    5. Accelerate

    AI recommended 5 alternatives but never named finegrain-ai/refiners. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I easily integrate various adapters like LoRA or ControlNet for custom image generation?
    you: not recommended
    AI recommended (in order):
    1. Automatic1111 Stable Diffusion web UI
    2. ComfyUI
    3. Diffusers
    4. InvokeAI
    5. Fooocus

    AI recommended 5 alternatives but never named finegrain-ai/refiners. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 finegrain-ai/refiners?
    pass
    AI named finegrain-ai/refiners explicitly

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

  • If a team adopts finegrain-ai/refiners in production, what risks or prerequisites should they evaluate first?
    pass
    AI named finegrain-ai/refiners 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 finegrain-ai/refiners solve, and who is the primary audience?
    pass
    AI named finegrain-ai/refiners explicitly

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

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finegrain-ai/refiners — 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