Model reference · open weights
Boogu-Image-0.1-Edit is an open-weight image model from Boogu. Boogu-Image-0.1-Edit (BF16) weighs 38.5 GB; the smallest configuration that runs it is RTX 5090 32 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the Boogu/Boogu-Image-0.1-Edit model card, 2026-10-01
What it is
| Released by | Boogu |
|---|---|
| Released | 2026-06-16 |
| Parameters | 10.3B |
| VRAM | 38.5 GB for the weights |
What it runs on
| Card | One image | Memory |
|---|---|---|
| RTX 3060 12 GB … RTX 4090 24 GB | does not fit | |
| RTX 5090 32 GB | fits (encoders offloaded) | 31.0 GB |
| L40S 48 GB | fits (encoders offloaded) | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
Welcome to the official repository for Boogu-Image-0.1 !
English | 中文
⚠️ Important Notice
The Boogu team does NOT currently provide any paid API, subscription, or commercial service for Boogu-Image. Any paid product or service offered under the name "Boogu-Image" — or any similar / variant name such as
booguimage,Boogu Image,Boogu, etc. — is NOT affiliated with this project and is unofficial. Please verify carefully before making any payment, and stay vigilant to protect your personal privacy and financial safety.Boogu-Image-0.1 is a research project only, and not an official model release.
Boogu-Image-0.1 is a competitive Apache-2.0 open-source unified image generation and editing model family, including Base, Turbo, Edit, and Edit-Turbo, and other variants that provide stable, practical capabilities for high-quality text-to-image generation, fast generation, image editing, and Chinese-English text rendering. Closed-source multimodal understanding and generation systems like Nano Banana Pro and GPT-Image-2 achieve remarkable performance not because of a single model, but through a highly unified suite of system capabilities. However, under training compute that is extremely limited compared with closed-source systems, we find that systematically improving a model's understanding ability, data quality, and training pipeline can still significantly improve image generation and editing performance. Specifically, compared with some existing open-source models, our training data scale is roughly one order of magnitude smaller. We hope our empirical study and open-source release will help advance the open-source ecosystem for multimodal generation and understanding.
This repository provides checkpoints and inference code for Boogu-Image-0.1.
npu branch for initial NPU backend support and instructions. We welcome feedback and bug reports!Boogu-Image is built to grow with its users. Share what you create, report issues, exchange ideas, and help shape what comes next.
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Since we could not evaluate on LM Arena directly, we bui
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. How to run this model is on its model card.