RRepoGEO

REPOGEO REPORT · LITE

hao-ai-lab/FastVideo

Default branch main · commit 1d15d974 · scanned 6/28/2026, 4:21:56 AM

GitHub: 3,777 stars · 369 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
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 hao-ai-lab/FastVideo, 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
    Clarify FastVideo's unique value proposition against generic inference engines in the README

    Why:

    COPY-PASTE FIX
    Add the following sentence immediately after the bolded opening statement in the README: "Unlike generic inference engines, FastVideo offers a complete, end-to-end framework tailored for diffusion-based video models, integrating post-training optimization with real-time inference specifically for video generation tasks."
  • mediumtopics#2
    Add more specific topics to emphasize FastVideo's role as a comprehensive framework

    Why:

    CURRENT
    diffusers, diffusion-models, distillation, inference, post-training, video-generation
    COPY-PASTE FIX
    diffusers, diffusion-models, distillation, inference, post-training, video-generation, video-generation-framework, end-to-end-video-ai
  • lowcomparison#3
    Add a 'Comparison' section to the README to explicitly differentiate from generic tools

    Why:

    COPY-PASTE FIX
    Add a new section to the README, perhaps titled 'Why FastVideo? Beyond Generic Inference Engines', that outlines how FastVideo provides a complete, higher-level framework for video generation, integrating features that generic tools like TensorRT or OpenVINO do not offer for this specific application.

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 hao-ai-lab/FastVideo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorRT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorRT · recommended 2×
  2. ONNX Runtime · recommended 2×
  3. OpenVINO · recommended 2×
  4. huggingface/diffusers · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    How to accelerate video generation from diffusion models for real-time applications?
    you: not recommended
    AI recommended (in order):
    1. Diffusers (huggingface/diffusers)
    2. TensorRT
    3. ONNX Runtime
    4. OpenVINO
    5. PyTorch 2.0 (pytorch/pytorch)
    6. DeepSpeed (microsoft/DeepSpeed)

    AI recommended 6 alternatives but never named hao-ai-lab/FastVideo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for optimizing and deploying diffusion-based video models with fast inference.
    you: not recommended
    AI recommended (in order):
    1. OpenVINO
    2. TensorRT
    3. ONNX Runtime
    4. DeepSpeed
    5. Hugging Face Accelerate
    6. PyTorch Compile
    7. Triton Inference Server

    AI recommended 7 alternatives but never named hao-ai-lab/FastVideo. 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 hao-ai-lab/FastVideo?
    pass
    AI named hao-ai-lab/FastVideo explicitly

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

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

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

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hao-ai-lab/FastVideo — 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