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 # Warning: Pipeline type "image-to-image" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-image", model="SeerRay-Lab/Unitranslator")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SeerRay-Lab/Unitranslator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scheduler.bin from SeerRay-Lab/Unitranslator: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/SeerRay-Lab/Unitranslator/resolve/main/scheduler.bin
- Command line
-
hf download hf://SeerRay-Lab/Unitranslator/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/SeerRay-Lab/Unitranslator/resolve/main/scheduler.bin
1.47 kB
- Xet hash:
- 5b91f4f783da057afedabc3de1be181db55acd189b3335beffed0d0b3a571b94
- Size of remote file:
- 1.47 kB
- SHA256:
- f7444ff6f98dd2724261acb55a555120cb54a286844da7ddd4bb4f803d82927a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.