RRepoGEO

REPOGEO REPORT · LITE

IrisRainbowNeko/HCP-Diffusion

Default branch neko · commit 2fc9134a · scanned 6/10/2026, 6:57:28 PM

GitHub: 910 stars · 73 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
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 IrisRainbowNeko/HCP-Diffusion, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    stable-diffusion, diffusion-models, deep-learning, pytorch, lora, dreambooth, controlnet, textual-inversion, fine-tuning, prompt-tuning, ai-training-framework, generative-ai, machine-learning
  • highreadme#2
    Clarify the project's core purpose in the README introduction

    Why:

    CURRENT
    **HCP-Diffusion** is a Diffusion model toolbox built on top of the 🐱 RainbowNeko Engine. It features a clean code structure and a flexible **Python-based configuration file**, making it easier to conduct and manage complex experiments. It includes a wide variety of training components, and compared to existing frameworks, it's more extensible, flexible, and user-friendly. HCP-Diffusion allows you to use a single `.py` config file to unify training workflows across popular methods and model architectures, including Prompt-tuning (Textual Inversion), DreamArtist, Fine-tuning, DreamBooth, LoRA, ControlNet, ....
    COPY-PASTE FIX
    **HCP-Diffusion** is a **universal Stable Diffusion toolbox** built on top of the 🐱 RainbowNeko Engine. It provides a flexible, Python-based configuration framework for **training and fine-tuning Stable Diffusion models** using methods like Prompt-tuning (Textual Inversion), DreamArtist, Fine-tuning, DreamBooth, LoRA, and ControlNet. Designed for AI/ML researchers and developers, it offers a clean code structure and extensibility for managing complex generative AI experiments.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/7eu7d7/HCP-Diffusion

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 IrisRainbowNeko/HCP-Diffusion
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. AUTOMATIC1111/stable-diffusion-webui · recommended 2×
  3. Hugging Face Diffusers · recommended 1×
  4. PyTorch Lightning · recommended 1×
  5. Accelerate · recommended 1×
  • CATEGORY QUERY
    What are the best Python toolkits for fine-tuning and training stable diffusion models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Diffusers
    2. PyTorch Lightning
    3. Accelerate
    4. DeepSpeed
    5. Keras

    AI recommended 5 alternatives but never named IrisRainbowNeko/HCP-Diffusion. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I combine DreamBooth, LoRA, and ControlNet for advanced stable diffusion model training?
    you: not recommended
    AI recommended (in order):
    1. Diffusers (huggingface/diffusers)
    2. kohya_ss GUI
    3. peft library (huggingface/peft)
    4. Automatic1111 web UI (AUTOMATIC1111/stable-diffusion-webui)
    5. ControlNet training scripts within the `diffusers` library (huggingface/diffusers)
    6. `lllyasviel/ControlNet` repository (lllyasviel/ControlNet)
    7. ComfyUI (comfyanonymous/ComfyUI)
    8. Img2Img (Automatic1111) (AUTOMATIC1111/stable-diffusion-webui)
    9. Albumentations (albumentations-team/albumentations)

    AI recommended 9 alternatives but never named IrisRainbowNeko/HCP-Diffusion. 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 IrisRainbowNeko/HCP-Diffusion?
    pass
    AI named IrisRainbowNeko/HCP-Diffusion explicitly

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

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

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

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IrisRainbowNeko/HCP-Diffusion — 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