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: 886 Bytes
3c1e400 | 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 29 30 31 32 33 34 35 36 37 | ---
license: mit
datasets:
- Ar4ikov/civitai-sd-337k
language:
- en
pipeline_tag: image-to-text
base_model: Salesforce/blip-image-captioning-base
---
# Overview
`ifmain/blip-image2prompt-stable-diffusion` is a model based on `Salesforce/blip-image-captioning-base`, trained on the `Ar4ikov/civitai-sd-337k` dataset. This model is designed to generate text descriptions of images in the style of prompts for use with Stable Diffusion models.
# Example Usage
```python
past_the_code
```
## Junk
This model contains references to lore, they can be removed as follows:
```python
past_the_code
```
## Examples
```note
paste there images table
```
| Original | Original prompt | Generated prompt by image | Generated image |
| -------- | --------------- | ------------------------- | --------------- |
| pass | pass | pass | pass |
|