bad anatomy, distorted face, disproportionate, ugly, deformed eyes, extra limbs, cartoon
๐ Model Overview
BAAR4-IMG-335M-Realistic4
is an ultra-lightweight,
native multilingual text-to-image generation model
engineered with approximately 335M parameters. Built from the ground up for extreme efficiency, this model understands direct prompts across
20 major languages
(Korean, English, German, Spanish, French, Japanese, Chinese, Italian, Dutch, Polish, Portuguese, Russian, Arabic, Bulgarian, Greek, Hindi, Swahili, Thai, Turkish, and Vietnamese) without requiring external translation pipelines.
Specifically architected to run directly inside client-side web browsers (WebGPU / ONNX Runtime Web / WebAssembly) and edge hardware without relying on expensive server-side GPU instances, it delivers crisp, high-fidelity photorealistic visual generation while maintaining a compact footprint and INT8 quantization friendliness.
Non-Commercial Use Only (๋น์์ ์ ์ฐ๊ตฌยท๊ฐ์ธ ์ด์ฉ ํ์ )
:
This model is strictly available for
non-commercial, educational, and research purposes only
. Any form of monetization (direct or indirect) without an explicit commercial license is strictly prohibited.
๋ณธ ๋ชจ๋ธ์
์์ ์ฐ๊ตฌ, ๊ต์ก ๋ฐ ๊ฐ์ธ ๋น์๋ฆฌ ๋ชฉ์ ์ ํํด ๋ฌด๋ฃ๋ก ์ฌ์ฉ
ํ ์ ์์ต๋๋ค. ์ฌ์ ํ๊ฐ ์๋ ์ผ์ฒด์ ์์ต ์ฐฝ์ถ ํ์(์งยท๊ฐ์ ์ ์์ ์ ์ด์ฉ)๋ ์๊ฒฉํ ๊ธ์ง๋ฉ๋๋ค.
Monetization & Commercial Licensing Inquiries (์์ต ์ฐฝ์ถ ๋ฐ ์์ฉ ๋ผ์ด์ ์ค ๋ฌธ์)
:
If you wish to
monetize, integrate this model into commercial products, provide paid web apps/APIs, or require customized enterprise deployments
, you must obtain a separate commercial license. Please reach out via email for licensing terms and enterprise adoption.
๋ณธ ๋ชจ๋ธ์ ํ์ฉํ์ฌ
์์ต์ ์ฐฝ์ถํ๊ณ ์ ํ๊ฑฐ๋, ์์ฉ ์น ์๋น์ค/์ ๋ฃ ์ฑ์ ํ์ฌ, ๋๋ ๊ธฐ์ ์ฉ ์จ๋๋ฐ์ด์ค ์๋ฃจ์ ๋์ ๋ฐ ๊ธฐ์ ์ง์์ด ํ์ํ ๊ฒฝ์ฐ
๋ฐ๋์ ๋ณ๋์ ์์ฉ ๋ผ์ด์ ์ค๋ฅผ ์ทจ๋ํ์ ์ผ ํฉ๋๋ค. ๋์ ๋ฐ ๋ผ์ด์ ์ค ๋ฐ๊ธ์ ์๋ ์ด๋ฉ์ผ๋ก ๋ฌธ์ํด ์ฃผ์๊ธฐ ๋ฐ๋๋๋ค.
Multilingual Native Prompting
: Generates images directly from native language prompts (ko, en, de, es, fr, ja, zh, it, nl, pl, pt, ru, ar, bg, el, hi, sw, th, tr, vi) without translation latency.
Client-Side Optimization
: Designed specifically for WebGPU, WebAssembly (Wasm), and INT8 quantization pipelines (
diffusers
, ONNX Runtime).
Recommended Inference Setup
: For optimal image fidelity and prompt comprehension, Euler A / DPM++ 2M Karras schedulers with 15โ25 inference steps at 512ร512 resolution are recommended.
This project demonstrates practical competency in custom generative model architecture design, multilingual cross-attention integration, model compression/quantization (INT8/Edge), and zero-server-cost in-browser inference deployment under compute constraints.
I am actively seeking
AI Engineering / Research opportunities, team recruitment offers, and project investment/partnerships
.
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