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 random_states_5.pkl from SeerRay-Lab/Unitranslator: direct link, hf CLI and curl.
- Browser
- Download file 16.5 kB
-
https://huggingface.co/SeerRay-Lab/Unitranslator/resolve/main/random_states_5.pkl
- Command line
-
hf download hf://SeerRay-Lab/Unitranslator/random_states_5.pkl
-
curl -L -o random_states_5.pkl https://huggingface.co/SeerRay-Lab/Unitranslator/resolve/main/random_states_5.pkl
16.5 kB
- Xet hash:
- 11ebb1b9b0915d5bd204a92f84f6758414ce5607f31f0e67c0cdcf2b4249e625
- Size of remote file:
- 16.5 kB
- SHA256:
- 5dde50fb3bb42735c6127ce1d60e4594f0ec89f887fce0e041e6a6f31a608dcd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.