RRepoGEO

REPOGEO REPORT · LITE

showlab/Tune-A-Video

Default branch main · commit 7d4a89a2 · scanned 5/21/2026, 4:37:51 AM

GitHub: 4,366 stars · 391 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 showlab/Tune-A-Video, 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
    text-to-video, video-generation, diffusion-models, generative-ai, deep-learning, computer-vision, image-to-video, iccv-2023, machine-learning
  • highreadme#2
    Reposition the README's initial sentence to clarify project type

    Why:

    CURRENT
    This repository is the official implementation of Tune-A-Video.
    COPY-PASTE FIX
    Tune-A-Video is an official research implementation for one-shot tuning of image diffusion models, enabling high-quality text-to-video generation.
  • mediumreadme#3
    Add a 'Key Features' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Features

- **One-Shot Tuning:** Efficiently adapt pre-trained text-to-image diffusion models for text-to-video generation using a single video-text pair.
- **High-Quality Video Synthesis:** Generate dynamic video content from text descriptions with improved consistency (DDIM inversion).
- **Personalization:** Tune videos on personalized DreamBooth models.
- **Research Implementation:** Official code for the ICCV 2023 paper, providing a robust foundation for further research and development in video AI.

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 showlab/Tune-A-Video
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RunwayML Gen-2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. RunwayML Gen-2 · recommended 1×
  2. Pika Labs · recommended 1×
  3. Synthesys AI Studio · recommended 1×
  4. HeyGen · recommended 1×
  5. Descript · recommended 1×
  • CATEGORY QUERY
    How can I convert text descriptions into dynamic video content using AI?
    you: not recommended
    AI recommended (in order):
    1. RunwayML Gen-2
    2. Pika Labs
    3. Synthesys AI Studio
    4. HeyGen
    5. Descript
    6. DeepMotion
    7. Google Imagen Video
    8. Phenaki

    AI recommended 8 alternatives but never named showlab/Tune-A-Video. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to adapt pre-trained image diffusion models for video synthesis.
    you: not recommended
    AI recommended (in order):
    1. Diffusers (huggingface/diffusers)
    2. Open-Sora (hpca-lab/Open-Sora)
    3. Kandinsky (sberbank-ai/Kandinsky)
    4. PyTorch Video (facebookresearch/pytorchvideo)
    5. TensorFlow Video

    AI recommended 5 alternatives but never named showlab/Tune-A-Video. 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 showlab/Tune-A-Video?
    pass
    AI named showlab/Tune-A-Video explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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showlab/Tune-A-Video — 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