Instructions to use UCSC-VLAA/gpt-image-edit-training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/gpt-image-edit-training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="UCSC-VLAA/gpt-image-edit-training")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/gpt-image-edit-training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0fbe0708751b2ff0960c49aff0a8ddd9f13d74f8e7309678af1f2fc7480bfaa6
- Size of remote file:
- 145 MB
- SHA256:
- 9530027d07b1124b6ee7665ef4609a344ae4fbea43cf8e376854d7a00606e6a2
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