RRepoGEO

REPOGEO REPORT · LITE

CompVis/adaptive-style-transfer

Default branch master · commit 51b4c90d · scanned 6/2/2026, 8:58:04 AM

GitHub: 742 stars · 140 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 CompVis/adaptive-style-transfer, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumreadme#1
    Add a concise introductory sentence to the README

    Why:

    CURRENT
    # A Style-Aware Content Loss for Real-time HD Style Transfer
Artsiom Sanakoyeu\*, Dmytro Kotovenko\*, Sabine Lang, Björn Ommer*, In ECCV 2018 (Oral)Website**: https://compvis.github.io/adaptive-style-transfer   
**Paper**: https://arxiv.org/abs/1807.10201
    COPY-PASTE FIX
    # A Style-Aware Content Loss for Real-time HD Style Transfer

This repository provides the official source code for our ECCV 2018 paper, "A Style-Aware Content Loss for Real-time HD Style Transfer," enabling high-definition artistic style transfer with a focus on real-time performance and a novel style-aware content loss.

Artsiom Sanakoyeu\*, Dmytro Kotovenko\*, Sabine Lang, Björn Ommer*, In ECCV 2018 (Oral)Website**: https://compvis.github.io/adaptive-style-transfer   
**Paper**: https://arxiv.org/abs/1807.10201
  • mediumreadme#2
    Highlight key differentiators in the README

    Why:

    COPY-PASTE FIX
    ## Key Features

- **Real-time HD Style Transfer:** Achieve high-resolution artistic style transfer efficiently.
- **Style-Aware Content Loss:** Utilizes a novel content loss function for improved stylistic fidelity.
- **Optimal Transport:** Employs relaxed optimal transport for robust content-style alignment.

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 CompVis/adaptive-style-transfer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepMotion
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepMotion · recommended 1×
  2. RunwayML · recommended 1×
  3. StyleGAN2 / StyleGAN3 · recommended 1×
  4. Pytorch-Style-Transfer · recommended 1×
  5. TensorFlow Hub / Keras Applications · recommended 1×
  • CATEGORY QUERY
    How can I apply artistic styles from one image to another in high definition?
    you: not recommended
    AI recommended (in order):
    1. DeepMotion
    2. RunwayML
    3. StyleGAN2 / StyleGAN3
    4. Pytorch-Style-Transfer
    5. TensorFlow Hub / Keras Applications
    6. Artbreeder
    7. Deep Dream Generator

    AI recommended 7 alternatives but never named CompVis/adaptive-style-transfer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a real-time neural style transfer solution for high-resolution images using Python.
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. ONNX Runtime
    3. PyTorch
    4. TorchScript
    5. NVIDIA TensorRT
    6. OpenCV
    7. Keras
    8. TensorFlow
    9. tf.function
    10. torch.jit.script

    AI recommended 10 alternatives but never named CompVis/adaptive-style-transfer. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 CompVis/adaptive-style-transfer?
    pass
    AI named CompVis/adaptive-style-transfer explicitly

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

  • If a team adopts CompVis/adaptive-style-transfer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named CompVis/adaptive-style-transfer 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 CompVis/adaptive-style-transfer solve, and who is the primary audience?
    pass
    AI named CompVis/adaptive-style-transfer explicitly

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

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CompVis/adaptive-style-transfer — 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