RRepoGEO

REPOGEO REPORT · LITE

stochasticai/x-stable-diffusion

Default branch main · commit 56c8fc81 · scanned 6/8/2026, 12:33:05 AM

GitHub: 558 stars · 34 forks

AI VISIBILITY SCORE
33 /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
2 / 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 stochasticai/x-stable-diffusion, 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 emphasize integrated solution/platform

    Why:

    CURRENT
    Welcome to `x-stable-diffusion` by Stochastic!

This project is a compilation of acceleration techniques for the Stable Diffusion model to help you generate images faster and more efficiently, saving you both time and money.
    COPY-PASTE FIX
    Welcome to `x-stable-diffusion` by Stochastic!

This project provides a unified, real-time inference platform for Stable Diffusion, integrating leading acceleration techniques like AITemplate, nvFuser, TensorRT, and FlashAttention to help you generate images faster and more efficiently, saving you both time and money.
  • mediumtopics#2
    Add more solution-oriented and framework-related topics

    Why:

    CURRENT
    aitemplate, automl, cuda, docker, inference, notebook, nvfuser, onnx, onnxruntime, pytorch, stable-diffusion, tensorrt
    COPY-PASTE FIX
    aitemplate, automl, cuda, docker, inference, notebook, nvfuser, onnx, onnxruntime, pytorch, stable-diffusion, tensorrt, real-time-inference, ai-acceleration, deep-learning-optimization, inference-engine, machine-learning-platform
  • lowabout#3
    Refine description to align with README's emphasis on unified platform

    Why:

    CURRENT
    Real-time inference for Stable Diffusion - 0.88s latency. Covers AITemplate, nvFuser, TensorRT, FlashAttention. Join our Discord communty: https://discord.com/invite/TgHXuSJEk6
    COPY-PASTE FIX
    A unified platform for real-time Stable Diffusion inference, achieving 0.88s latency by integrating AITemplate, nvFuser, TensorRT, and FlashAttention. Join our Discord community: https://discord.com/invite/TgHXuSJEk6

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 stochasticai/x-stable-diffusion
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
openvinotoolkit/openvino
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. openvinotoolkit/openvino · recommended 2×
  2. microsoft/onnxruntime · recommended 2×
  3. pytorch/pytorch · recommended 2×
  4. NVIDIA TensorRT · recommended 1×
  5. microsoft/DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How to accelerate Stable Diffusion image generation for real-time applications?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO (openvinotoolkit/openvino)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. Hugging Face Accelerate (huggingface/accelerate)
    6. torch.compile (pytorch/pytorch)
    7. DirectML (microsoft/DirectML)
    8. bitsandbytes (TimDettmers/bitsandbytes)
    9. AWQ (mit-han-lab/awq)
    10. GPTQ (IST-DASLab/gptq)

    AI recommended 10 alternatives but never named stochasticai/x-stable-diffusion. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best techniques for optimizing Stable Diffusion inference performance and cost?
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime (microsoft/onnxruntime)
    2. TensorRT
    3. OpenVINO (openvinotoolkit/openvino)
    4. Hugging Face Optimum (huggingface/optimum)
    5. DPM-Solver++ (2M) Karras
    6. Euler A
    7. LCM (Latent Consistency Models) Sampler
    8. NVIDIA A100/H100 GPUs
    9. NVIDIA RTX 4090/4080 GPUs
    10. AWS Inferentia2
    11. Google Cloud TPUs
    12. PyTorch 2.0 (pytorch/pytorch)
    13. Diffusers Library (Hugging Face) (huggingface/diffusers)
    14. xFormers (facebookresearch/xformers)
    15. SDXL-Turbo
    16. LCM-LoRAs

    AI recommended 16 alternatives but never named stochasticai/x-stable-diffusion. 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 stochasticai/x-stable-diffusion?
    pass
    AI did not name stochasticai/x-stable-diffusion — 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 stochasticai/x-stable-diffusion in production, what risks or prerequisites should they evaluate first?
    pass
    AI named stochasticai/x-stable-diffusion 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 stochasticai/x-stable-diffusion solve, and who is the primary audience?
    pass
    AI named stochasticai/x-stable-diffusion explicitly

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

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
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