RRepoGEO

REPOGEO REPORT · LITE

vipshop/cache-dit

Default branch main · commit 929041ee · scanned 5/24/2026, 6:37:30 AM

GitHub: 1,179 stars · 70 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 vipshop/cache-dit, 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
    Update repository topics with relevant keywords

    Why:

    CURRENT
    flux2-klein, parallelism, svdquant
    COPY-PASTE FIX
    pytorch, diffusion-models, transformers, inference-engine, deep-learning, gpu-acceleration, quantization, parallelism, cache
  • highreadme#2
    Reinforce core identity in README's introductory paragraph

    Why:

    CURRENT
    **🤗Why Cache-DiT❓❓**Cache-DiT is built on top of the 🤗Diffusers library and now supports nearly ALL DiTs from Diffusers.
    COPY-PASTE FIX
    **Cache-DiT is a high-performance, PyTorch-native inference engine specifically designed for Diffusion Transformers (DiTs).** Built on top of the 🤗Diffusers library, Cache-DiT provides advanced optimizations like hybrid cache acceleration, comprehensive parallelism (Context, Tensor, 2D/3D), and full compatibility with quantization and compilation for efficient deployment on NVIDIA, Ascend, and AMD GPUs.
  • mediumcomparison#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Cache-DiT vs. Alternatives' or 'Why Cache-DiT?' that briefly compares its features (e.g., PyTorch-native, specific DiT optimizations, hybrid cache, comprehensive parallelism) against general inference engines like TensorRT, DeepSpeed, or Optimum, highlighting its unique focus on Diffusion Transformers.

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 vipshop/cache-dit
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. NVIDIA TensorRT · recommended 1×
  3. Hugging Face Optimum · recommended 1×
  4. ONNX Runtime · recommended 1×
  5. Intel OpenVINO · recommended 1×
  • CATEGORY QUERY
    How to accelerate PyTorch diffusion transformer inference with caching and parallelism?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. NVIDIA TensorRT
    3. Hugging Face Optimum
    4. ONNX Runtime
    5. Intel OpenVINO
    6. torch.compile
    7. FlashAttention
    8. xFormers
    9. NVIDIA Triton Inference Server

    AI recommended 9 alternatives but never named vipshop/cache-dit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective PyTorch inference engines for diffusion models supporting quantization and GPUs?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT (NVIDIA/TensorRT)
    2. OpenVINO (openvinotoolkit/openvino)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. PyTorch `torch.compile` (pytorch/pytorch)
    5. DeepSpeed-MII (microsoft/DeepSpeed)

    AI recommended 5 alternatives but never named vipshop/cache-dit. 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 vipshop/cache-dit?
    pass
    AI named vipshop/cache-dit explicitly

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

  • If a team adopts vipshop/cache-dit in production, what risks or prerequisites should they evaluate first?
    pass
    AI named vipshop/cache-dit 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 vipshop/cache-dit solve, and who is the primary audience?
    pass
    AI named vipshop/cache-dit 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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vipshop/cache-dit — 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