RRepoGEO

REPOGEO REPORT · LITE

ali-vilab/TeaCache

Default branch main · commit 7c10efc4 · scanned 5/23/2026, 1:48:00 PM

GitHub: 1,326 stars · 55 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 ali-vilab/TeaCache, 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 TeaCache's specific domain and differentiate from general caching/LLM KV cache in README introduction

    Why:

    COPY-PASTE FIX
    Add the following sentence directly after the H1: `TeaCache introduces a novel caching strategy specifically designed to accelerate inference for *video diffusion models*, distinguishing itself from general data loading caches or solutions for Large Language Model (LLM) Key-Value (KV) caches.`
  • mediumtopics#2
    Refine and expand topics for better categorization

    Why:

    CURRENT
    cogvideox, diffusion-models, hunyuan-video, inference-acceleration, latte, open-sora, open-sora-plan, video-generation
    COPY-PASTE FIX
    video-diffusion-models, video-generation-inference, diffusion-model-caching, inference-acceleration-video, kv-cache-video-diffusion, open-sora, latte, hunyuan-video, cogvideox
  • lowcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add the following section to your README: `## Comparison with Alternatives

TeaCache uniquely optimizes caching for video diffusion models, setting it apart from general ML acceleration frameworks like DeepSpeed or TensorRT, and generic caching solutions such as Redis or mmap.`

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 ali-vilab/TeaCache
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 1×
  2. PyTorch FSDP · recommended 1×
  3. TensorRT · recommended 1×
  4. OpenVINO · recommended 1×
  5. ONNX Runtime · recommended 1×
  • CATEGORY QUERY
    How to speed up video generation inference for large diffusion models?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. PyTorch FSDP
    3. TensorRT
    4. OpenVINO
    5. ONNX Runtime
    6. vLLM
    7. FlashAttention
    8. xFormers

    AI recommended 8 alternatives but never named ali-vilab/TeaCache. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to improve video diffusion model performance using caching strategies.
    you: not recommended
    AI recommended (in order):
    1. Redis (redis/redis)
    2. mmap
    3. NumPy (numpy/numpy)
    4. Amazon CloudFront
    5. Google Cloud CDN
    6. Cloudflare
    7. NVMe SSDs
    8. AWS i3/i4 instances
    9. GCP Local SSDs
    10. Varnish Cache (varnishcache/varnish-cache)
    11. Dask (dask/dask)
    12. functools.lru_cache

    AI recommended 12 alternatives but never named ali-vilab/TeaCache. 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 ali-vilab/TeaCache?
    pass
    AI named ali-vilab/TeaCache explicitly

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

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

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

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ali-vilab/TeaCache — 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