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
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base_model: Salesforce/blip-image-captioning-base
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# Overview
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`ifmain/blip-image2prompt-stable-diffusion` is a model based on
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I used my Blip training code: [BLIP-Easy-Trainer](https://github.com/ifmain/BLIP-Easy-Trainer)
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base_model: Salesforce/blip-image-captioning-base
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# Licence
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license inherited from [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base)
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# Overview
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`ifmain/blip-image2prompt-stable-diffusion` is a model based on [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), trained on the [Ar4ikov/civitai-sd-337k](https://huggingface.co/datasets/Ar4ikov/civitai-sd-337k) dataset (2K images). This model is designed to generate text descriptions of images in the style of prompts for use with Stable Diffusion models.
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I used my Blip training code: [BLIP-Easy-Trainer](https://github.com/ifmain/BLIP-Easy-Trainer)
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