REPOGEO REPORT · LITE
leejet/stable-diffusion.cpp
Default branch master · commit 90e87bc8 · scanned 5/11/2026, 9:32:06 PM
GitHub: 5,981 stars · 613 forks
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 leejet/stable-diffusion.cpp, 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.
- highreadme#1Reposition README's opening to clarify project type and scope
Why:
CURRENTDiffusion model(SD,Flux,Wan,...) inference in pure C/C++
COPY-PASTE FIXA **lightweight, pure C/C++ inference engine** for state-of-the-art diffusion models (SD, Flux, Wan, Qwen Image, Z-Image, etc.), designed for **efficient local execution on CPUs** without external dependencies. Think of it as `llama.cpp` for image generation.
- mediumhomepage#2Add a project homepage URL
Why:
COPY-PASTE FIXhttps://github.com/leejet/stable-diffusion.cpp
- mediumreadme#3Add a clear statement about its library utility
Why:
COPY-PASTE FIXAdd a new section or bullet point under 'Features': - **Developer-friendly library:** Easily integrate diffusion model inference into your C/C++ applications.
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.
- TensorRT · recommended 2×
- OpenVINO Toolkit · recommended 2×
- ONNX Runtime · recommended 1×
- DirectML · recommended 1×
- CUDA · recommended 1×
- CATEGORY QUERYHow can I run image generation diffusion models efficiently using C++ on local hardware?you: #12AI recommended (in order):
- ONNX Runtime
- DirectML
- CUDA
- cuDNN
- TensorRT
- OpenVINO Toolkit
- LibTorch
- TensorFlow Lite
- XNNPACK
- GGML
- llama.cpp
- stable-diffusion.cpp ← you
- OpenCL
Show full AI answer
- CATEGORY QUERYSeeking a performant C/C++ library for local text-to-image and image-to-image inference.you: not recommendedAI recommended (in order):
- TensorRT
- OpenVINO Toolkit
- ONNX Runtime (microsoft/onnxruntime)
- GGML (ggerganov/llama.cpp)
- LibTorch (pytorch/pytorch)
- Apache TVM (apache/tvm)
- MNN (alibaba/MNN)
AI recommended 7 alternatives but never named leejet/stable-diffusion.cpp. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 leejet/stable-diffusion.cpp?passAI did not name leejet/stable-diffusion.cpp — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts leejet/stable-diffusion.cpp in production, what risks or prerequisites should they evaluate first?passAI named leejet/stable-diffusion.cpp 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 leejet/stable-diffusion.cpp solve, and who is the primary audience?passAI did not name leejet/stable-diffusion.cpp — likely talking about a different project
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 leejet/stable-diffusion.cpp. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
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leejet/stable-diffusion.cpp — 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