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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("svjack/vit-gpt-diffusion-zh") model = AutoModelForMultimodalLM.from_pretrained("svjack/vit-gpt-diffusion-zh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cc2886d36848b82a0d131c3fb4202fe93d1f6267d13325e283408847b0b4033
- Size of remote file:
- 1.22 GB
- SHA256:
- 0284ae5f029dbf25775b7f347eb1c0b1fbace6571732e4e5a4338785d8dbfc12
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