RRepoGEO

REPOGEO REPORT · LITE

G-U-N/Phased-Consistency-Model

Default branch master · commit b127277f · scanned 6/13/2026, 7:57:56 AM

GitHub: 519 stars · 19 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 G-U-N/Phased-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 README's opening to clarify generative AI domain

    Why:

    CURRENT
    The README starts with a centered H1 and links.
    COPY-PASTE FIX
    Add a concise, explicit sentence immediately after the main title (or as a tagline) that clearly states its purpose in generative AI. For example, right after `## ⚡️Phased Consistency Models⚡️`, add: `A novel approach to accelerate and improve high-quality image generation with diffusion models.`
  • mediumtopics#2
    Expand GitHub topics with relevant generative AI keywords

    Why:

    CURRENT
    consistency-models, diffusion-models
    COPY-PASTE FIX
    consistency-models, diffusion-models, image-generation, generative-ai, stable-diffusion, model-acceleration, fast-inference
  • lowcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, `## Why Phased Consistency Models? (PCM) / Comparison to Alternatives`. In this section, briefly explain how PCM improves upon or differs from common diffusion model acceleration techniques like LCM-LoRA, SDXL Turbo, DPM-Solver++, DDIM, PLMS.

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 G-U-N/Phased-Consistency-Model
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LCM-LoRA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LCM-LoRA · recommended 1×
  2. SDXL Turbo · recommended 1×
  3. DPM-Solver++ · recommended 1×
  4. DDIM · recommended 1×
  5. PLMS · recommended 1×
  • CATEGORY QUERY
    How to accelerate image generation in diffusion models while maintaining quality?
    you: not recommended
    AI recommended (in order):
    1. LCM-LoRA
    2. SDXL Turbo
    3. DPM-Solver++
    4. DDIM
    5. PLMS
    6. Euler A
    7. Lightning Diffusion
    8. Progressive Distillation

    AI recommended 8 alternatives but never named G-U-N/Phased-Consistency-Model. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to improve efficiency and speed of consistency model training.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Distributed
    2. TensorFlow Distributed
    3. NVIDIA APEX
    4. PyTorch AMP
    5. AdamW
    6. LAMB
    7. torch.utils.checkpoint
    8. PyTorch DataLoader
    9. TensorFlow tf.data
    10. NVIDIA A100
    11. NVIDIA H100

    AI recommended 11 alternatives but never named G-U-N/Phased-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 G-U-N/Phased-Consistency-Model?
    pass
    AI did not name G-U-N/Phased-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 G-U-N/Phased-Consistency-Model in production, what risks or prerequisites should they evaluate first?
    pass
    AI named G-U-N/Phased-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 G-U-N/Phased-Consistency-Model solve, and who is the primary audience?
    pass
    AI named G-U-N/Phased-Consistency-Model explicitly

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

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G-U-N/Phased-Consistency-Model — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite