REPOGEO REPORT · LITE

ai-forever/ru-dalle

Default branch master · commit 0255cec5 · scanned 6/28/2026, 8:32:22 PM

GitHub: 1,646 stars · 242 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 ai-forever/ru-dalle, 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 README H2 to emphasize Russian language focus
Why:
CURRENT
```
### Generate images from texts
```
COPY-PASTE FIX
```
### State-of-the-art text-to-image generation for Russian language prompts.
```

mediumreadme#2

Add a 'Key Features' section to the README

Why:

COPY-PASTE FIX

## Key Features
*   **Russian Language Specialization:** Specifically trained and optimized for generating high-quality images from Russian text prompts.
*   **Python Library:** Easily integrate ruDALL-E into your projects with a straightforward `pip install` and Python API.
*   **Diverse Models:** Access to various ruDALL-E models (Malevich, Emojich, Kandinsky) for different generation needs.
*   **Resource Efficient:** Examples demonstrate usage with as little as 3.5GB vRAM.

lowcomparison#3

Add a 'Why Choose ruDALL-E?' section

Why:

COPY-PASTE FIX

## Why Choose ruDALL-E?
While powerful general-purpose models like Stable Diffusion and DALL-E 3 excel with English prompts, ruDALL-E is uniquely designed and optimized for the Russian language. This specialization ensures:
*   **Higher Relevance:** Generations are more contextually accurate and culturally relevant for Russian text.
*   **Superior Quality:** Avoids common translation artifacts or misinterpretations that can occur when using non-Russian models with translated prompts.
*   **Community Focus:** Developed by AI-Forever for the Russian-speaking AI community.

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 ai-forever/ru-dalle

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Kandinsky 2.x

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Kandinsky 2.x · recommended 1×
Stable Diffusion · recommended 1×
DALL-E 3 · recommended 1×
Midjourney · recommended 1×
RuDALL-E · recommended 1×

CATEGORY QUERY
I need a text-to-image generation model that works effectively with Russian language prompts.
you: not recommended
AI recommended (in order):
1. Kandinsky 2.x
2. Stable Diffusion
3. DALL-E 3
4. Midjourney
5. RuDALL-E
AI recommended 5 alternatives but never named ai-forever/ru-dalle. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a Python library for creating images from Russian text using transformer architectures.
you: not recommended
AI recommended (in order):
1. Hugging Face Diffusers (huggingface/diffusers)
2. DALL-E mini / Craiyon
3. OpenAI API
4. Kandinsky
5. DeepPavlov (deepmipt/DeepPavlov)
AI recommended 5 alternatives but never named ai-forever/ru-dalle. 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 ai-forever/ru-dalle?
pass
AI named ai-forever/ru-dalle explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts ai-forever/ru-dalle in production, what risks or prerequisites should they evaluate first?
pass
AI named ai-forever/ru-dalle 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 ai-forever/ru-dalle solve, and who is the primary audience?
pass
AI named ai-forever/ru-dalle explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/ai-forever/ru-dalle"><img src="https://repogeo.com/badge/ai-forever/ru-dalle.svg" alt="RepoGEO" /></a>

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ai-forever/ru-dalle — 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