REPOGEO REPORT · LITE

chengzeyi/stable-fast

Default branch main · commit 2b9b84e9 · scanned 5/12/2026, 2:47:36 PM

GitHub: 1,304 stars · 92 forks

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

Reposition README's opening to state core value before project status

Why:

CURRENT

The current README starts with specific model mentions and then immediately announces paused development and a new project.

COPY-PASTE FIX

Start the README with a clear, concise statement of `stable-fast`'s primary purpose and benefits, similar to the repository description, before discussing future plans or new projects. For example, 'Stable-Fast is the leading inference performance optimization framework for HuggingFace Diffusers on NVIDIA GPUs, achieving SOTA inference performance on all kinds of diffuser models...'

mediumabout#2
Add homepage URL to repository metadata
Why:
COPY-PASTE FIX
```
https://wavespeed.ai/
```

mediumreadme#3

Add a dedicated comparison section in the README

Why:

CURRENT

The comparison is a single sentence buried in the README.

COPY-PASTE FIX

Add a new section, e.g., 'Why Stable-Fast? (vs. TensorRT, Optimum, etc.)' or 'Comparison with Alternatives,' detailing its advantages (e.g., faster compilation, dynamic shape, LoRA/ControlNet support) over common alternatives.

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

Of all named tools, what % are you?

Top rival

huggingface/optimum

Recommended in 3 of 2 queries

COMPETITOR LEADERBOARD

huggingface/optimum · recommended 3×
microsoft/onnxruntime · recommended 2×
microsoft/DeepSpeed · recommended 2×
facebookresearch/xformers · recommended 2×
Dao-AILab/flash-attention · recommended 2×

CATEGORY QUERY
What tools can significantly improve inference performance for HuggingFace Diffusers on NVIDIA GPUs?
you: not recommended
AI recommended (in order):
1. PyTorch 2.0 `torch.compile`
2. NVIDIA TensorRT
3. optimum-nvidia (huggingface/optimum)
4. ONNX Runtime (microsoft/onnxruntime)
5. DeepSpeed (microsoft/DeepSpeed)
6. xFormers (facebookresearch/xformers)
7. FlashAttention (Dao-AILab/flash-attention)
8. BetterTransformer (huggingface/optimum)
9. bitsandbytes (TimDettmers/bitsandbytes)
AI recommended 9 alternatives but never named chengzeyi/stable-fast. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a Python library to optimize stable diffusion and video diffusion model inference speed.
you: not recommended
AI recommended (in order):
1. Diffusers (HuggingFace/diffusers)
2. xFormers (facebookresearch/xformers)
3. FlashAttention 2 (Dao-AILab/flash-attention)
4. ONNX Runtime (microsoft/onnxruntime)
5. Optimum (huggingface/optimum)
6. TensorRT (NVIDIA/TensorRT)
7. OpenVINO (openvinotoolkit/openvino)
8. DeepSpeed (microsoft/DeepSpeed)
9. TRT-LLM (NVIDIA/TRT-LLM)
AI recommended 9 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?

Embed your GEO score

Drop this badge into the README of chengzeyi/stable-fast. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/chengzeyi/stable-fast.svg)](https://repogeo.com/en/r/chengzeyi/stable-fast)

HTML

<a href="https://repogeo.com/en/r/chengzeyi/stable-fast"><img src="https://repogeo.com/badge/chengzeyi/stable-fast.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

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