RRepoGEO

REPOGEO REPORT · LITE

xdit-project/xDiT

Default branch main · commit c1635e65 · scanned 6/20/2026, 3:47:06 PM

GitHub: 2,637 stars · 321 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
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 improve categorization

    Why:

    COPY-PASTE FIX
    diffusion-transformers, dit, inference-engine, gpu-acceleration, deep-learning, image-generation, video-generation, parallelism, pytorch
  • highreadme#2
    Add a direct, concise opening statement to the README

    Why:

    CURRENT
    The README currently starts with visual elements and then an H3: `<div align="center"> ... <h3>A Scalable Inference Engine for Diffusion Transformers (DiTs) on Multiple Computing Devices</h3>`.
    COPY-PASTE FIX
    Add this as the very first line of the README, before any visual elements or headings: `xDiT is a high-performance inference engine specifically designed to accelerate Diffusion Transformers (DiTs) with massive parallelism across multiple computing devices, enabling faster and higher-quality image and video generation.`
  • mediumhomepage#3
    Add the project's blog as the homepage link

    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
pytorch/pytorch
Recommended in 5 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 5×
  2. microsoft/DeepSpeed · recommended 2×
  3. huggingface/accelerate · recommended 2×
  4. Dao-AILab/flash-attention · recommended 2×
  5. NVIDIA/TensorRT-LLM · recommended 1×
  • CATEGORY QUERY
    How can I accelerate large diffusion transformer model inference across multiple GPUs efficiently?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. NVIDIA TensorRT-LLM (NVIDIA/TensorRT-LLM)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. PyTorch FSDP (pytorch/pytorch)
    5. vLLM (vllm-project/vllm)
    6. Ray Core / Ray Serve (ray-project/ray)
    7. OpenVINO (openvinotoolkit/openvino)

    AI recommended 7 alternatives but never named xdit-project/xDiT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best methods to optimize diffusion transformer inference performance on single or multiple GPUs?
    you: not recommended
    AI recommended (in order):
    1. PyTorch FSDP (pytorch/pytorch)
    2. torch.quantization (pytorch/pytorch)
    3. NVIDIA TensorRT (NVIDIA/TensorRT)
    4. ONNX Runtime (microsoft/onnxruntime)
    5. TorchDynamo (pytorch/pytorch)
    6. TorchInductor (pytorch/pytorch)
    7. XLA (openxla/xla)
    8. JAX (google/jax)
    9. TensorFlow (tensorflow/tensorflow)
    10. FlashAttention (Dao-AILab/flash-attention)
    11. xFormers (facebookresearch/xformers)
    12. FlashAttention-2 (Dao-AILab/flash-attention)
    13. DeepSpeed (microsoft/DeepSpeed)
    14. Hugging Face Accelerate (huggingface/accelerate)
    15. NVIDIA DALI (NVIDIA/DALI)

    AI recommended 15 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?

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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