Image-to-Image
Transformers
Safetensors
in-image-machine-translation
image-translation
image-editing
multimodal
qwen2.5-vl
flux
Instructions to use SeerRay-Lab/Unitranslator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SeerRay-Lab/Unitranslator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="SeerRay-Lab/Unitranslator")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SeerRay-Lab/Unitranslator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download univa/tokenizer.json from SeerRay-Lab/Unitranslator: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/SeerRay-Lab/Unitranslator/resolve/main/univa/tokenizer.json
- Command line
-
hf download hf://SeerRay-Lab/Unitranslator/univa/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SeerRay-Lab/Unitranslator/resolve/main/univa/tokenizer.json
11.4 MB
- Xet hash:
- 4bd2f092eeec244c0448f13e31edd4b25e625568713e4155993a3dea90c7437a
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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