RRepoGEO

REPOGEO REPORT · LITE

chengzeyi/stable-fast

Default branch main · commit 2b9b84e9 · scanned 6/22/2026, 9:32:03 PM

GitHub: 1,303 stars · 92 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 chengzeyi/stable-fast, 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
    Reconcile README's 'paused' status with its value proposition

    Why:

    CURRENT
    The 'NOTE' section starting with 'Active development on `stable-fast` has been paused.'
    COPY-PASTE FIX
    __NOTE__: While active *new feature* development on `stable-fast` has transitioned to next-generation projects like `Comfy-WaveSpeed`, `stable-fast` remains a robust and highly performant solution for existing HuggingFace Diffusers models. It continues to achieve SOTA inference performance on all kinds of diffuser models, including `StableVideoDiffusionPipeline`, offering a stable and efficient optimization framework.
  • mediumabout#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://wavespeed.ai/
  • lowtopics#3
    Add 'huggingface-diffusers' to repository topics

    Why:

    CURRENT
    cuda, deeplearnng, diffusers, inference-engines, openai-triton, performance-optimizations, pytorch, stable-diffusion, stable-video-diffusion, torch
    COPY-PASTE FIX
    cuda, deeplearnng, diffusers, huggingface-diffusers, inference-engines, openai-triton, performance-optimizations, pytorch, stable-diffusion, stable-video-diffusion, torch

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 chengzeyi/stable-fast
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 1×
  2. facebookresearch/xformers · recommended 1×
  3. Dao-AILab/flash-attention · recommended 1×
  4. microsoft/onnxruntime · recommended 1×
  5. NVIDIA/TensorRT · recommended 1×
  • CATEGORY QUERY
    How to accelerate HuggingFace Diffusers inference speed on NVIDIA GPUs?
    you: not recommended
    AI recommended (in order):
    1. PyTorch torch.compile (pytorch/pytorch)
    2. xformers (facebookresearch/xformers)
    3. FlashAttention (Dao-AILab/flash-attention)
    4. ONNX Runtime (microsoft/onnxruntime)
    5. TensorRT (NVIDIA/TensorRT)
    6. optimum (huggingface/optimum)

    AI recommended 6 alternatives but never named chengzeyi/stable-fast. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide blazing fast inference for PyTorch stable diffusion models?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO
    3. ONNX Runtime
    4. DeepSpeed
    5. TorchDynamo
    6. xFormers

    AI recommended 6 alternatives but never named chengzeyi/stable-fast. 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 chengzeyi/stable-fast?
    pass
    AI named chengzeyi/stable-fast explicitly

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

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

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

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chengzeyi/stable-fast — 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