RRepoGEO

REPOGEO REPORT · LITE

aigc-apps/VideoX-Fun

Default branch main · commit 8fb0bb16 · scanned 5/8/2026, 4:07:56 PM

GitHub: 2,065 stars · 161 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 aigc-apps/VideoX-Fun, 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 improve categorization

    Why:

    COPY-PASTE FIX
    video-generation, ai-video, diffusion-models, image-to-video, text-to-video, lora, diffusion-transformer, machine-learning, deep-learning
  • highreadme#2
    Strengthen README introduction with key differentiators

    Why:

    CURRENT
    VideoX-Fun is a video generation pipeline that can be used to generate AI images and videos, as well as to train baseline and Lora models for Diffusion Transformer. We support direct prediction from pre-trained baseline models to generate videos with different resolutions, durations, and FPS. Additionally, we also support users in training their own baseline and Lora models to perform specific style transformations.
    COPY-PASTE FIX
    VideoX-Fun is a flexible, open-source video generation framework designed for creating AI images and videos at **any resolution**, including **image-to-video** conversion. It uniquely supports training custom **Diffusion Transformer baseline and LoRA models** for specific style transformations, offering unparalleled control over video generation, duration, and FPS.
  • mediumabout#3
    Enhance the repository description

    Why:

    CURRENT
    📹 A more flexible framework that can generate videos at any resolution and creates videos from images.
    COPY-PASTE FIX
    📹 A flexible framework for AI video generation, enabling creation of videos from images at any resolution, and supporting training of custom Diffusion Transformer baseline and LoRA models.

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 aigc-apps/VideoX-Fun
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Diffusion
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion · recommended 2×
  2. ControlNet · recommended 2×
  3. RunwayML Gen-1/Gen-2 · recommended 1×
  4. Deforum · recommended 1×
  5. Pika Labs · recommended 1×
  • CATEGORY QUERY
    What are some flexible tools for generating AI videos from images at custom resolutions?
    you: not recommended
    AI recommended (in order):
    1. RunwayML Gen-1/Gen-2
    2. Stable Diffusion
    3. Deforum
    4. ControlNet
    5. Pika Labs
    6. Kaiber
    7. Domino

    AI recommended 7 alternatives but never named aigc-apps/VideoX-Fun. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I train custom AI models for video generation with specific style transformations?
    you: not recommended
    AI recommended (in order):
    1. RunwayML Gen-1 / Gen-2
    2. Stable Diffusion
    3. ControlNet
    4. AnimateDiff
    5. PyTorch
    6. Generative Adversarial Networks (GANs)
    7. StyleGAN
    8. Pix2PixHD
    9. CycleGAN
    10. Diffusion Models
    11. DDPMs (Denoising Diffusion Probabilistic Models)
    12. Latent Diffusion Models
    13. TensorFlow
    14. Keras
    15. OpenAI Jukebox
    16. DALL-E 2

    AI recommended 16 alternatives but never named aigc-apps/VideoX-Fun. 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 aigc-apps/VideoX-Fun?
    pass
    AI named aigc-apps/VideoX-Fun explicitly

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

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

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

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aigc-apps/VideoX-Fun — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite