Instructions to use Alixn/OmniGen2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Alixn/OmniGen2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Alixn/OmniGen2", 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
Download mllm_processor/tokenizer.json from Alixn/OmniGen2: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Alixn/OmniGen2/resolve/main/mllm_processor/tokenizer.json
- Command line
-
hf download hf://Alixn/OmniGen2/mllm_processor/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Alixn/OmniGen2/resolve/main/mllm_processor/tokenizer.json
11.4 MB
- Xet hash:
- 2166a48cf3a5d2a5aeb07e19477669175d45cb1ecfc7387f3be5121d65cb6bf8
- Size of remote file:
- 11.4 MB
- SHA256:
- 69f12bcc978e3d112e092478b218f0161ef3d4bec08792866c99d29830772f08
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.