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from htbuilder import HtmlElement, div, ul, li, br, hr, a, p, img, styles, classes, fonts | |
from htbuilder.units import percent, px | |
from htbuilder.funcs import rgba, rgb | |
import streamlit as st | |
import os | |
import sys | |
import argparse | |
import clip | |
import numpy as np | |
from PIL import Image | |
from dalle.models import Dalle | |
from dalle.utils.utils import set_seed, clip_score | |
import cv2 | |
import subprocess | |
import signal | |
def signal_handler(sig, frame): | |
print('You pressed Ctrl+C!') | |
sys.exit(0) | |
def generate(prompt,crazy): | |
print("-------------------") | |
signal.signal(signal.SIGINT, signal_handler) | |
device = 'cpu' | |
model = Dalle.from_pretrained('minDALL-E/1.3B') # This will automatically download the pretrained model. | |
model.to(device=device) | |
num_candidates = 3 | |
images = [] | |
set_seed(np.random.randint(0,10000)) | |
# Sampling | |
images = model.sampling(prompt=prompt, | |
top_k=2048, | |
top_p=None, | |
softmax_temperature=crazy, | |
num_candidates=num_candidates, | |
device=device).cpu().numpy() | |
images = np.transpose(images, (0, 2, 3, 1)) | |
# CLIP Re-ranking | |
model_clip, preprocess_clip = clip.load("ViT-B/32", device=device) | |
model_clip.to(device=device) | |
rank = clip_score(prompt=prompt, | |
images=images, | |
model_clip=model_clip, | |
preprocess_clip=preprocess_clip, | |
device=device) | |
# Save images | |
#return images[rank] | |
for image in images: | |
cv2.imwrite('temp/'+str(np.random.randint(0,10000))+'.jpeg', image) | |
generate("a pink house",0.75) |