Instructions to use ifmain/blip-image2promt-stable-diffusion-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ifmain/blip-image2promt-stable-diffusion-base 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="ifmain/blip-image2promt-stable-diffusion-base")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ifmain/blip-image2promt-stable-diffusion-base") model = AutoModelForMultimodalLM.from_pretrained("ifmain/blip-image2promt-stable-diffusion-base") - Notebooks
- Google Colab
- Kaggle
File size: 686 Bytes
63288d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"_name_or_path": "Salesforce/blip-image-captioning-base",
"architectures": [
"BlipForConditionalGeneration"
],
"image_text_hidden_size": 256,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"label_smoothing": 0.0,
"logit_scale_init_value": 2.6592,
"model_type": "blip",
"projection_dim": 512,
"text_config": {
"initializer_factor": 1.0,
"model_type": "blip_text_model",
"num_attention_heads": 12
},
"torch_dtype": "float32",
"transformers_version": "4.43.0.dev0",
"vision_config": {
"dropout": 0.0,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"model_type": "blip_vision_model",
"num_channels": 3
}
}
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