RRepoGEO

REPOGEO REPORT · LITE

Janspiry/Palette-Image-to-Image-Diffusion-Models

Default branch main · commit 136b29f5 · scanned 6/26/2026, 3:12:46 PM

GitHub: 1,826 stars · 240 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
22 /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
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 Janspiry/Palette-Image-to-Image-Diffusion-Models, 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
  • highhomepage#1
    Add the official project page as the repository homepage

    Why:

    COPY-PASTE FIX
    https://[YOUR_PALETTE_PROJECT_PAGE_URL_HERE]
  • mediumtopics#2
    Add specific image-to-image task topics

    Why:

    CURRENT
    ddp, ddpm, diffusion-model, image-restoration, implementation, pytorch
    COPY-PASTE FIX
    ddp, ddpm, diffusion-model, image-restoration, implementation, pytorch, inpainting, uncropping, colorization

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 Janspiry/Palette-Image-to-Image-Diffusion-Models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Diffusers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Diffusers · recommended 1×
  2. KerasCV · recommended 1×
  3. PyTorch-Image-Models (timm) · recommended 1×
  4. OpenAI's Diffusion · recommended 1×
  5. CompVis/latent-diffusion · recommended 1×
  • CATEGORY QUERY
    How to implement image-to-image translation using diffusion models in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Diffusers
    2. KerasCV
    3. PyTorch-Image-Models (timm)
    4. OpenAI's Diffusion
    5. latent-diffusion (CompVis/latent-diffusion)

    AI recommended 5 alternatives but never named Janspiry/Palette-Image-to-Image-Diffusion-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are robust PyTorch libraries for image restoration, inpainting, and colorization?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Image-Models (timm) (rwightman/pytorch-image-models)
    2. TorchVision (pytorch/vision)
    3. MMEditing (OpenMMLab) (open-mmlab/mmediting)
    4. BasicSR (xinntao/BasicSR)
    5. Hugging Face Diffusers (huggingface/diffusers)
    6. DeepFillv2
    7. Colorful Image Colorization

    AI recommended 7 alternatives but never named Janspiry/Palette-Image-to-Image-Diffusion-Models. 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 Janspiry/Palette-Image-to-Image-Diffusion-Models?
    pass
    AI did not name Janspiry/Palette-Image-to-Image-Diffusion-Models — 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 Janspiry/Palette-Image-to-Image-Diffusion-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Janspiry/Palette-Image-to-Image-Diffusion-Models 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 Janspiry/Palette-Image-to-Image-Diffusion-Models solve, and who is the primary audience?
    pass
    AI did not name Janspiry/Palette-Image-to-Image-Diffusion-Models — 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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  • Brand-free category queries5 vs 2 in Lite
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