REPOGEO REPORT · LITE

hustvl/LightningDiT

Default branch main · commit f315f25b · scanned 6/27/2026, 11:58:23 AM

GitHub: 1,500 stars · 57 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

28 /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

2 / 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 hustvl/LightningDiT, 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 relevant topics to the repository

Why:

COPY-PASTE FIX

diffusion-models, latent-diffusion, diffusion-transformers, image-generation, deep-learning, pytorch, computer-vision, cvpr-2025, generative-ai, efficient-training

highreadme#2

Reposition the README's opening statement

Why:

CURRENT

<h2>⚡Reconstruction <i>vs.</i> Generation: Taming Optimization Dilemma in Latent Diffusion Models</h2>

COPY-PASTE FIX

This repository provides the official PyTorch implementation of **LightningDiT**, a novel framework for significantly faster and more affordable training of Diffusion Transformers (DiT) in Latent Diffusion Models, achieving state-of-the-art image generation quality. Our work, "Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models," was accepted as an Oral Presentation at CVPR 2025.

mediumhomepage#3
Add the arXiv paper link as the repository homepage
Why:
COPY-PASTE FIX
```
https://arxiv.org/abs/2501.01423
```

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 hustvl/LightningDiT

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Stable Diffusion XL (SDXL)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Stable Diffusion XL (SDXL) · recommended 1×
Kandinsky 2.2 · recommended 1×
Midjourney · recommended 1×
Imagen / Parti · recommended 1×
Gen-1 / Gen-2 · recommended 1×

CATEGORY QUERY
How to achieve faster training and better image generation quality with latent diffusion models?
you: not recommended
AI recommended (in order):
1. Stable Diffusion XL (SDXL)
2. Kandinsky 2.2
3. Midjourney
4. Imagen / Parti
5. Gen-1 / Gen-2
6. LoRAs (Low-Rank Adaptation)
7. vae-ft-mse-840000-ema-pruned
AI recommended 7 alternatives but never named hustvl/LightningDiT. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking efficient methods to make diffusion transformer research more affordable and computationally accessible.
you: not recommended
AI recommended (in order):
1. Hugging Face Optimum
2. bitsandbytes
3. NVIDIA TensorRT
4. OpenVINO
5. PyTorch
6. TensorFlow
7. Hugging Face transformers
8. PaddlePaddle PaddleSlim
9. AWS EC2 Spot Instances
10. Google Cloud Preemptible VMs
11. Azure Spot Virtual Machines
12. NVIDIA Jetson Series
13. Google Coral
14. Raspberry Pi
15. Intel Neural Compute Stick
AI recommended 15 alternatives but never named hustvl/LightningDiT. 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 hustvl/LightningDiT?
pass
AI did not name hustvl/LightningDiT — 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 hustvl/LightningDiT in production, what risks or prerequisites should they evaluate first?
pass
AI named hustvl/LightningDiT 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 hustvl/LightningDiT solve, and who is the primary audience?
pass
AI named hustvl/LightningDiT 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 hustvl/LightningDiT. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/hustvl/LightningDiT.svg)](https://repogeo.com/en/r/hustvl/LightningDiT)

HTML

<a href="https://repogeo.com/en/r/hustvl/LightningDiT"><img src="https://repogeo.com/badge/hustvl/LightningDiT.svg" alt="RepoGEO" /></a>

Pro

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hustvl/LightningDiT — 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