RRepoGEO

REPOGEO REPORT · LITE

bytedance/Bernini

Default branch main · commit ed504e3a · scanned 6/14/2026, 9:37:55 AM

GitHub: 765 stars · 58 forks

AI VISIBILITY SCORE
40 /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
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 bytedance/Bernini, 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
    Add a clear, disambiguating introductory sentence to the README

    Why:

    CURRENT
    The README currently starts with a centered div and a title 'Latent Semantic Planning for Video Diffusion'.
    COPY-PASTE FIX
    Add the following sentence immediately after the main title in the README: "This repository presents Bernini, a unified framework specifically designed for **video generation and editing** using MLLM-based semantic planning and DiT-based rendering."
  • mediumabout#2
    Enhance the repository description with more specific keywords

    Why:

    CURRENT
    Bernini is a unified framework for video generation and editing that combines an MLLM-based semantic planner with a DiT-based renderer.
    COPY-PASTE FIX
    Bernini is a unified framework for **state-of-the-art video generation and editing**, combining an **MLLM-based semantic planner** with a **DiT-based diffusion renderer**. It enables advanced capabilities for video creation and manipulation.
  • lowreadme#3
    Make external links in README descriptive

    Why:

    CURRENT
    [](https://arxiv.org/abs/2605.22344)
[](https://bernini-ai.github.io/)
[](https://huggingface.co/collections/ByteDance/bernini)
    COPY-PASTE FIX
    [Paper (arXiv)](https://arxiv.org/abs/2605.22344)
[Project Page](https://bernini-ai.github.io/)
[Hugging Face Models](https://huggingface.co/collections/ByteDance/bernini)

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 bytedance/Bernini
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/diffusers · recommended 1×
  2. SORA · recommended 1×
  3. ModelScope Text-to-Video Synthesis · recommended 1×
  4. lllyasviel/ControlNet · recommended 1×
  5. Stability-AI/generative-models · recommended 1×
  • CATEGORY QUERY
    What framework helps generate and edit videos using semantic planning and diffusion models?
    you: not recommended
    AI recommended (in order):
    1. Diffusers (huggingface/diffusers)
    2. SORA
    3. ModelScope Text-to-Video Synthesis
    4. ControlNet (lllyasviel/ControlNet)
    5. Stable Video Diffusion (Stability-AI/generative-models)
    6. DeepMotion
    7. RunwayML Gen-1/Gen-2

    AI recommended 7 alternatives but never named bytedance/Bernini. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I combine multimodal large language models with diffusion for video and image creation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Diffusers Library
    2. PyTorch
    3. TensorFlow
    4. DALL-E 3
    5. Sora
    6. ComfyUI
    7. Automatic1111's Stable Diffusion WebUI
    8. RunwayML Gen-2
    9. Midjourney

    AI recommended 9 alternatives but never named bytedance/Bernini. 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 bytedance/Bernini?
    pass
    AI named bytedance/Bernini explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite