Instructions to use jiuhai/BLIP3o-Model-4B-Pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jiuhai/BLIP3o-Model-4B-Pretrain with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jiuhai/BLIP3o-Model-4B-Pretrain", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Download README.md from jiuhai/BLIP3o-Model-4B-Pretrain: direct link, hf CLI and curl.
- Browser
- Download file 534 Bytes
-
https://huggingface.co/jiuhai/BLIP3o-Model-4B-Pretrain/resolve/main/README.md
- Command line
-
hf download hf://jiuhai/BLIP3o-Model-4B-Pretrain/README.md
-
curl -L -o README.md https://huggingface.co/jiuhai/BLIP3o-Model-4B-Pretrain/resolve/main/README.md
534 Bytes
metadata
language:
- en
license: apache-2.0
This is BLIP3o-4B Pretrain checkpoint trained on the open source data.
Download
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="jiuhai/BLIP3o-Model-4B-Pretrain",
repo_type="model"
)
Clone the repo (if you haven’t already) and install the environment:
git clone https://github.com/JiuhaiChen/BLIP3o.git
Change to the demo folder:
cd gradio
Launch with your model path:
python app.py /path/to/your/model