REPOGEO REPORT · LITE

xlite-dev/Awesome-DiT-Inference

Default branch main · commit 507c45f3 · scanned 6/11/2026, 2:06:48 AM

GitHub: 564 stars · 26 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

22 /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

1 / 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 xlite-dev/Awesome-DiT-Inference, 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

Reposition the core value proposition in the README

Why:

CURRENT

<div align='center'>
  
</div>   
 
<div align='center'>
  
  
  
  
  
 </div>   
 
📒A curated list of Awesome **Diffusion** Inference Papers with codes. For Awesome LLM Inference, please check 📖Awesome-LLM-Inference   for more details.

COPY-PASTE FIX

📒A curated list of Awesome **Diffusion** Inference Papers with codes. For Awesome LLM Inference, please check 📖Awesome-LLM-Inference   for more details.

<div align='center'>
  
</div>   
 
<div align='center'>
  
  
  
  
  
 </div>

hightopics#2

Add topics to clarify 'awesome list' and 'research papers'

Why:

CURRENT

cogvideox, deepcache, diffusion, dit, flux, open-sora, open-sora-plan, sd15, sdxl, sora, stable-diffusion, wan

COPY-PASTE FIX

awesome-list, research-papers, diffusion-inference, generative-ai-optimization, ml-inference, deep-learning-optimization, cogvideox, deepcache, diffusion, dit, flux, open-sora, open-sora-plan, sd15, sdxl, sora, stable-diffusion, wan

mediumhomepage#3
Add the repository URL as the homepage
Why:
COPY-PASTE FIX
```
https://github.com/xlite-dev/Awesome-DiT-Inference
```

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 xlite-dev/Awesome-DiT-Inference

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ONNX Runtime

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

ONNX Runtime · recommended 1×
PyTorch 2.x · recommended 1×
TensorRT · recommended 1×
Hugging Face Optimum · recommended 1×
OpenVINO · recommended 1×

CATEGORY QUERY
Looking for techniques to improve inference performance of generative diffusion models.
you: not recommended
AI recommended (in order):
1. ONNX Runtime
2. PyTorch 2.x
3. TensorRT
4. Hugging Face Optimum
5. OpenVINO
6. Stable Diffusion
AI recommended 6 alternatives but never named xlite-dev/Awesome-DiT-Inference. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I reduce latency and memory usage for large scale diffusion model inference?
you: not recommended
AI recommended (in order):
1. NVIDIA TensorRT
2. OpenVINO (openvinotoolkit/openvino)
3. ONNX Runtime (microsoft/onnxruntime)
4. DeepSpeed (microsoft/DeepSpeed)
5. bitsandbytes (TimDettmers/bitsandbytes)
6. PyTorch 2.0 (pytorch/pytorch)
7. Optimum (huggingface/optimum)
AI recommended 7 alternatives but never named xlite-dev/Awesome-DiT-Inference. 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 xlite-dev/Awesome-DiT-Inference?
pass
AI did not name xlite-dev/Awesome-DiT-Inference — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts xlite-dev/Awesome-DiT-Inference in production, what risks or prerequisites should they evaluate first?
pass
AI named xlite-dev/Awesome-DiT-Inference 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 xlite-dev/Awesome-DiT-Inference solve, and who is the primary audience?
pass
AI did not name xlite-dev/Awesome-DiT-Inference — likely talking about a different project
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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Live preview

MARKDOWN (README)

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HTML

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Pro

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xlite-dev/Awesome-DiT-Inference — 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