REPOGEO REPORT · LITE

FoundationVision/LlamaGen

Default branch main · commit ce98ec41 · scanned 6/28/2026, 2:23:10 PM

GitHub: 1,959 stars · 95 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

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 FoundationVision/LlamaGen, 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

Add a clarifying sentence to the README's introduction

Why:

COPY-PASTE FIX

Add this sentence immediately after the first introductory paragraph in the README: "Crucially, LlamaGen is specifically designed for high-quality, scalable image generation using an autoregressive Llama architecture, distinguishing it from general Multimodal Large Language Model (MLLM) frameworks."

mediumtopics#2

Expand repository topics with more specific image generation terms

Why:

CURRENT

auto-regressive-model, diffusion, diffusion-models, image-generation, llama, llm, text2image

COPY-PASTE FIX

auto-regressive-model, diffusion, diffusion-models, image-generation, llama, llm, text2image, image-synthesis, generative-ai, visual-generation, next-token-prediction

lowreadme#3

Introduce a 'Comparison' section in the README

Why:

COPY-PASTE FIX

Add a new section titled "## 💡 Why LlamaGen? (Comparison to X)" or "## 🚀 LlamaGen vs. Diffusion/GANs" to the README, explicitly outlining its advantages or unique approach compared to common alternatives like diffusion models (e.g., Stable Diffusion) and GANs (e.g., StyleGAN).

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 FoundationVision/LlamaGen

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

NVlabs/stylegan3

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

NVlabs/stylegan3 · recommended 1×
VQ-VAE-2 · recommended 1×
CompVis/taming-transformers · recommended 1×
NVlabs/eDiff-I · recommended 1×
MAE-GAN · recommended 1×

CATEGORY QUERY
What are efficient and scalable image generation models beyond traditional diffusion techniques?
you: not recommended
AI recommended (in order):
1. StyleGAN (NVlabs/stylegan3)
2. VQ-VAE-2
3. VQGAN (CompVis/taming-transformers)
4. eDiff-I (NVlabs/eDiff-I)
5. MAE-GAN
6. MADE
7. PixelCNN
8. ImageGPT (openai/image-gpt)
AI recommended 8 alternatives but never named FoundationVision/LlamaGen. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I generate images using large language model architectures or autoregressive token prediction?
you: not recommended
AI recommended (in order):
1. Stable Diffusion
2. Hugging Face Diffusers
3. Automatic1111's Stable Diffusion WebUI (AUTOMATIC1111/stable-diffusion-webui)
4. ComfyUI
5. DALL-E 3
6. Midjourney
7. Imagen
8. VQGAN + CLIP
9. Parti
AI recommended 9 alternatives but never named FoundationVision/LlamaGen. 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 FoundationVision/LlamaGen?
pass
AI named FoundationVision/LlamaGen explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts FoundationVision/LlamaGen in production, what risks or prerequisites should they evaluate first?
pass
AI named FoundationVision/LlamaGen 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 FoundationVision/LlamaGen solve, and who is the primary audience?
pass
AI named FoundationVision/LlamaGen 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 FoundationVision/LlamaGen. 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)

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

HTML

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

Pro

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FoundationVision/LlamaGen — 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