Instructions to use svjack/vit-gpt-diffusion-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svjack/vit-gpt-diffusion-zh with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" 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-text", model="svjack/vit-gpt-diffusion-zh")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("svjack/vit-gpt-diffusion-zh") model = AutoModelForImageTextToText.from_pretrained("svjack/vit-gpt-diffusion-zh") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4326ddffe0929d1c82adf3c0725e32d53da25b5e40453c6956595ad2e50f54d3
- Size of remote file:
- 982 MB
- SHA256:
- b59f9ae93dab9e1bce18e64f769d1f86bc77f7a2128cf3891a83316408a264af
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