RRepoGEO

REPOGEO REPORT · LITE

segmind/distill-sd

Default branch master · commit c1e97a70 · scanned 6/3/2026, 9:18:12 PM

GitHub: 620 stars · 39 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 segmind/distill-sd, 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 opening to clearly state offering and audience

    Why:

    CURRENT
    Knowledge-distilled, smaller versions of Stable Diffusion. Unofficial implementation as described in BK-SDM.<br>These distillation-trained models produce images of similar quality to the full-sized Stable-Diffusion model while being significantly faster and smaller.
    COPY-PASTE FIX
    This repository provides **knowledge-distilled, smaller, and faster versions of Stable Diffusion models**, along with the tools to create them. Designed for AI developers and researchers, `segmind/distill-sd` enables efficient deployment of high-quality image generation by significantly reducing model size and inference time compared to full-sized Stable Diffusion models.
  • hightopics#2
    Add more specific application-oriented topics

    Why:

    CURRENT
    distillation, inference, knowledge-distillation, stable-diffusion
    COPY-PASTE FIX
    distillation, inference, knowledge-distillation, stable-diffusion, generative-ai, image-generation, efficient-ai, ai-models, diffusion-models
  • mediumlicense#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is released under [insert specific license name(s) here, e.g., 'a custom license based on Apache 2.0 and MIT']. Please refer to the [LICENSE](LICENSE) file for full details.

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 segmind/distill-sd
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 2×
  2. NVIDIA TensorRT · recommended 1×
  3. OpenVINO · recommended 1×
  4. ONNX Runtime · recommended 1×
  5. DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How to achieve faster inference with large generative AI image models?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO
    3. ONNX Runtime
    4. DeepSpeed
    5. PyTorch 2.0
    6. bitsandbytes
    7. Hugging Face Optimum
    8. Triton Inference Server

    AI recommended 8 alternatives but never named segmind/distill-sd. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for techniques to significantly reduce the size of diffusion models for efficient deployment.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Quantization (pytorch/pytorch)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. TensorRT
    4. Hugging Face Transformers/Diffusers (huggingface/transformers)
    5. Distiller (Intel) (IntelAI/distiller)
    6. PyTorch Pruning (pytorch/pytorch)
    7. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    8. Diffusers Library (Hugging Face) (huggingface/diffusers)

    AI recommended 8 alternatives but never named segmind/distill-sd. 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 segmind/distill-sd?
    pass
    AI named segmind/distill-sd explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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