RRepoGEO

REPOGEO REPORT · LITE

radames/Real-Time-Latent-Consistency-Model

Default branch main · commit c6b124f8 · scanned 6/27/2026, 8:22:52 PM

GitHub: 917 stars · 116 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
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 radames/Real-Time-Latent-Consistency-Model, 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 the README's opening to clearly state its application purpose

    Why:

    CURRENT
    This demo showcases Latent Consistency Model (LCM) using Diffusers with a MJPEG stream server.
    COPY-PASTE FIX
    This application provides a real-time image generation experience directly from webcam or streaming video input, leveraging Latent Consistency Models (LCM) and Diffusers with an MJPEG stream server.
  • mediumabout#2
    Enhance the repository description to include specific input sources

    Why:

    CURRENT
    App showcasing multiple real-time diffusion models pipelines with Diffusers
    COPY-PASTE FIX
    Real-time application for AI image generation from webcam and streaming video input, powered by Latent Consistency Models and Diffusers.
  • mediumtopics#3
    Add more specific topics related to webcam and video stream processing

    Why:

    CURRENT
    diffusers, diffusion-models, latent-consistency-model, machine-learning, mjpeg, mjpeg-stream, real-time, stable-diffusion
    COPY-PASTE FIX
    diffusers, diffusion-models, latent-consistency-model, machine-learning, mjpeg, mjpeg-stream, real-time, stable-diffusion, webcam-ai, video-stream-processing

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 radames/Real-Time-Latent-Consistency-Model
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenCV
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenCV · recommended 2×
  2. PyTorch · recommended 2×
  3. Stable Diffusion · recommended 1×
  4. Diffusers Library · recommended 1×
  5. xformers · recommended 1×
  • CATEGORY QUERY
    How to implement real-time image generation from webcam input using diffusion models?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. Diffusers Library
    3. xformers
    4. ControlNet
    5. ONNX Runtime
    6. TensorRT
    7. LCM-LoRA
    8. SDXL-Turbo
    9. StreamDiffusion
    10. InstantID
    11. IP-Adapter
    12. OpenCV
    13. PyTorch
    14. TensorFlow
    15. PyTorch Lightning
    16. Keras

    AI recommended 16 alternatives but never named radames/Real-Time-Latent-Consistency-Model. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries enable real-time AI image processing with streaming video input?
    you: not recommended
    AI recommended (in order):
    1. OpenCV
    2. TensorFlow Lite
    3. OpenVINO
    4. PyTorch
    5. TorchVision
    6. MediaPipe
    7. Ultralytics YOLO
    8. DeepStream

    AI recommended 8 alternatives but never named radames/Real-Time-Latent-Consistency-Model. 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 radames/Real-Time-Latent-Consistency-Model?
    pass
    AI named radames/Real-Time-Latent-Consistency-Model explicitly

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

  • If a team adopts radames/Real-Time-Latent-Consistency-Model in production, what risks or prerequisites should they evaluate first?
    pass
    AI named radames/Real-Time-Latent-Consistency-Model 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 radames/Real-Time-Latent-Consistency-Model solve, and who is the primary audience?
    pass
    AI did not name radames/Real-Time-Latent-Consistency-Model — 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?

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radames/Real-Time-Latent-Consistency-Model — 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