RRepoGEO

REPOGEO REPORT · LITE

radames/Real-Time-Latent-Consistency-Model

Default branch main · commit c6b124f8 · scanned 5/16/2026, 10:28:07 PM

GitHub: 916 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
27 /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
1 / 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 clarify it's a real-time application/demo

    Why:

    CURRENT
    This demo showcases Latent Consistency Model (LCM) using Diffusers with a MJPEG stream server.
    COPY-PASTE FIX
    This repository provides a **ready-to-run real-time application** showcasing Latent Consistency Model (LCM) pipelines with Diffusers, specifically designed for live image-to-image transformations via a webcam and MJPEG stream server.
  • mediumtopics#2
    Add topics that highlight the application/demo nature and specific use case

    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, real-time-app, live-demo, webcam-inference, interactive-ai
  • lowcomparison#3
    Add a 'Comparison' or 'What This Is Not' section to clarify its role

    Why:

    COPY-PASTE FIX
    ## What is this, and what is it not?

This repository is a **complete, runnable application** demonstrating real-time AI inference with Latent Consistency Models. It is built *using* libraries like Hugging Face Diffusers, but it is **not** a standalone AI library, a model checkpoint, or an optimization framework like ONNX Runtime or TensorRT. Instead, it provides a practical example of how to integrate these technologies into a live, interactive system.

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
huggingface/diffusers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/diffusers · recommended 2×
  2. microsoft/onnxruntime · recommended 2×
  3. NVIDIA/TensorRT · recommended 2×
  4. Stable Diffusion · recommended 1×
  5. lllyasviel/ControlNet · recommended 1×
  • CATEGORY QUERY
    How to implement real-time image generation using diffusion models with a webcam feed?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. Diffusers Library (huggingface/diffusers)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. TensorRT (NVIDIA/TensorRT)
    5. ControlNet (lllyasviel/ControlNet)
    6. LoRAs
    7. Textual Inversion
    8. Optimum (huggingface/optimum)
    9. StreamDiffusion (ashawkey/StreamDiffusion)
    10. LCM-LoRA
    11. SDXL Turbo
    12. Mini-DALL-E (borisdayma/dalle-mini)
    13. OpenCV (opencv/opencv)
    14. PyQt
    15. Tkinter
    16. asyncio

    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 tools enable low-latency image-to-image transformations with latent consistency models?
    you: not recommended
    AI recommended (in order):
    1. Diffusers (huggingface/diffusers)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. TensorRT (NVIDIA/TensorRT)
    4. OpenVINO (openvinotoolkit/openvino)
    5. TorchScript (pytorch/pytorch)
    6. Triton Inference Server (triton-inference-server/server)

    AI recommended 6 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 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?

  • 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