RRepoGEO

REPOGEO REPORT · LITE

NVlabs/FastGen

Default branch main · commit c40fbea1 · scanned 6/14/2026, 11:23:17 AM

GitHub: 807 stars · 63 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 NVlabs/FastGen, 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
    Clarify FastGen's focus on diffusion models, not LLMs, in the README intro

    Why:

    CURRENT
    FastGen is a PyTorch-based framework for building fast generative models using various distillation and acceleration techniques.
    COPY-PASTE FIX
    FastGen is a PyTorch-based framework for building fast generative *diffusion models* using various distillation and acceleration techniques. This framework is dedicated to diffusion models, not large language models (LLMs).
  • hightopics#2
    Expand GitHub topics for better category visibility

    Why:

    CURRENT
    diffusion-models, distillation
    COPY-PASTE FIX
    diffusion-models, distillation, generative-ai, pytorch, model-acceleration, inference-optimization, deep-learning-framework
  • mediumreadme#3
    Add a section differentiating FastGen from generic acceleration tools

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why FastGen?' or 'Key Differentiators' after the initial description, stating: 'FastGen is a comprehensive PyTorch-based framework for diffusion model acceleration and distillation. Unlike generic inference engines or low-level optimization libraries such as TensorRT or ONNX Runtime, FastGen provides integrated methods and workflows specifically tailored for generative diffusion models, enabling rapid development and deployment of high-performance solutions.'

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 NVlabs/FastGen
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorRT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorRT · recommended 2×
  2. PyTorch built-in quantization · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. TorchDynamo · recommended 1×
  5. TorchInductor · recommended 1×
  • CATEGORY QUERY
    How can I accelerate inference speed for large-scale diffusion models in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch built-in quantization
    2. ONNX Runtime
    3. TensorRT
    4. TorchDynamo
    5. TorchInductor
    6. TensorRT
    7. OpenVINO
    8. Diffusers library
    9. k-diffusion library
    10. xFormers
    11. FlashAttention
    12. PyTorch DDP
    13. PyTorch FSDP
    14. DeepSpeed

    AI recommended 14 alternatives but never named NVlabs/FastGen. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks help distill large diffusion models for faster, efficient generative inference?
    you: not recommended
    AI recommended (in order):
    1. Diffusers (huggingface/diffusers)
    2. PyTorch-Lightning (Lightning-AI/lightning)
    3. Accelerate (huggingface/accelerate)
    4. OpenVINO (openvinotoolkit/openvino)
    5. TensorRT (NVIDIA/TensorRT)
    6. ONNX Runtime (microsoft/onnxruntime)
    7. DeepSpeed (microsoft/DeepSpeed)

    AI recommended 7 alternatives but never named NVlabs/FastGen. 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 NVlabs/FastGen?
    pass
    AI named NVlabs/FastGen explicitly

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

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

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

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NVlabs/FastGen — 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