Licence

license inherited from Salesforce/blip-image-captioning-base

Overview

ifmain/blip-image2promt-stable-diffusion-base is a model based on Salesforce/blip-image-captioning-base, trained on the 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.

I used my Blip training code: BLIP-Easy-Trainer

Example Usage

import torch
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
import re

def prepare(text):
    text = text.replace('. ','.').replace(' .','.')
    text = text.replace('( ','(').replace(' (','(')
    text = text.replace(') ',')').replace(' )',')')
    text = text.replace(': ',':').replace(' :',':')
    text = text.replace('_ ','_').replace(' _','_')
    text = text.replace(',(())','').replace('(()),','')
    for i in range(10):
        text = text.replace(')))','))').replace('(((','((')
    text = re.sub(r'<[^>]*>', '', text)
    return text

path_to_model = "ifmain/blip-image2promt-stable-diffusion-base"

processor = BlipProcessor.from_pretrained(path_to_model)
model = BlipForConditionalGeneration.from_pretrained(path_to_model, torch_dtype=torch.float16).to("cuda")

img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)

out = model.generate(**inputs, max_new_tokens=100)

out_txt = processor.decode(out[0], skip_special_tokens=True)

print(prepare(out_txt)) # woman sitting on the beach at sunset, rear view,((happy)),((happy)),((dog)),((mixed)),(()),((

Addition

This model support SFW and NSFW content