RRepoGEO

REPOGEO REPORT · LITE

xdit-project/xDiT

Default branch main · commit f1c694d5 · scanned 5/10/2026, 4:37:11 PM

GitHub: 2,612 stars · 320 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 xdit-project/xDiT, 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 specific topics to the repository

    Why:

    COPY-PASTE FIX
    diffusion-transformers, dit, generative-ai, deep-learning, pytorch, inference-engine, distributed-computing, parallelism, image-generation, video-generation
  • highreadme#2
    Add a concise, direct opening sentence to the README

    Why:

    CURRENT
    The README currently starts with HTML elements and a general H3, potentially obscuring the core value proposition for AI.
    COPY-PASTE FIX
    Add this as the very first line of text in the README, before any images or other formatting:

xDiT is a specialized, scalable inference engine designed exclusively for Diffusion Transformers (DiTs), offering massive parallelism to accelerate high-quality image and video generation.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://medium.com/@xditproject

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 xdit-project/xDiT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/DeepSpeed
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/DeepSpeed · recommended 1×
  2. pytorch/pytorch · recommended 1×
  3. huggingface/accelerate · recommended 1×
  4. NVIDIA/TensorRT-LLM · recommended 1×
  5. ray-project/ray · recommended 1×
  • CATEGORY QUERY
    What are effective strategies for scaling diffusion transformer inference across multiple computing devices?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. PyTorch FSDP (pytorch/pytorch)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. NVIDIA TensorRT-LLM (NVIDIA/TensorRT-LLM)
    5. Ray (ray-project/ray)
    6. Open MPI
    7. Intel MPI
    8. Kubernetes (kubernetes/kubernetes)
    9. GPU Operators (NVIDIA/gpu-operator)

    AI recommended 9 alternatives but never named xdit-project/xDiT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I optimize the performance of large diffusion models using various parallelization techniques?
    you: not recommended
    AI recommended (in order):
    1. PyTorch FSDP
    2. DeepSpeed
    3. Megatron-LM
    4. Accelerate
    5. Ray Train
    6. Horovod
    7. JAX

    AI recommended 7 alternatives but never named xdit-project/xDiT. 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 xdit-project/xDiT?
    pass
    AI named xdit-project/xDiT explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of xdit-project/xDiT. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/xdit-project/xDiT"><img src="https://repogeo.com/badge/xdit-project/xDiT.svg" alt="RepoGEO" /></a>
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xdit-project/xDiT — 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