app.py

import os
import sys

sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import numpy as np
import streamlit as st
from PIL import Image

import clip
from dalle.models import Dalle
from dalle.utils.utils import clip_score, download

url = "https://arena.kakaocdn.net/brainrepo/models/minDALL-E/57b008f02ceaa02b779c8b7463143315/1.3B.tar.gz"
root = os.path.expanduser("~/.cache/minDALL-E")
filename = os.path.basename(url)
pathname = filename[:-len('.tar.gz')]

expected_md5 = url.split("/")[-2]
download_target = os.path.join(root, filename)
result_path = os.path.join(root, pathname)

if not os.path.exists(result_path):
    result_path = download(url, root)



device = "cpu"
model = Dalle.from_pretrained("minDALL-E/1.3B")  # This will automatically download the pretrained model.
model.to(device=device)

model_clip, preprocess_clip = clip.load("ViT-B/32", device=device)
model_clip.to(device=device)


def sample(prompt):
    # Sampling
    images = (
        model.sampling(prompt=prompt, top_k=256, top_p=None, softmax_temperature=1.0, num_candidates=3, device=device)
        .cpu()
        .numpy()
    )
    images = np.transpose(images, (0, 2, 3, 1))

    # CLIP Re-ranking
    rank = clip_score(
        prompt=prompt, images=images, model_clip=model_clip, preprocess_clip=preprocess_clip, device=device
    )

    # Save images
    images = images[rank]
    # print(rank, images.shape)
    pil_images = []
    for i in range(len(images)):
        im = Image.fromarray((images[i] * 255).astype(np.uint8))
        pil_images.append(im)

    # im = Image.fromarray((images[0] * 255).astype(np.uint8))
    return pil_images


st.header("minDALL-E")
st.subheader("Generate images from text")

prompt = st.text_input("What do you want to see?")

DEBUG = False
if prompt != "":
    container = st.empty()
    container.markdown(
        f"""
        <style> p {{ margin:0 }} div {{ margin:0 }} </style>
        <div data-stale="false" class="element-container css-1e5imcs e1tzin5v1">
        <div class="stAlert">
        <div role="alert" data-baseweb="notification" class="st-ae st-af st-ag st-ah st-ai st-aj st-ak st-g3 st-am st-b8 st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-b9 st-b1 st-b2 st-b3 st-b4 st-b5 st-b6">
        <div class="st-b7">
        <div class="css-whx05o e13vu3m50">
        <div data-testid="stMarkdownContainer" class="css-1ekf893 e16nr0p30">
                <img src="https://raw.githubusercontent.com/borisdayma/dalle-mini/main/app/streamlit/img/loading.gif" width="30"/>
                Generating predictions for: <b>{prompt}</b>
        </div>
        </div>
        </div>
        </div>
        </div>
        </div>
        <small><i>Predictions may take up to 40s under high load. Please stand by.</i></small>
    """,
        unsafe_allow_html=True,
    )

    print(f"Getting selections: {prompt}")
    selected = sample(prompt)

    margin = 0.1  #for better position of zoom in arrow
    n_columns = 3
    cols = st.columns([1] + [margin, 1] * (n_columns - 1))
    for i, img in enumerate(selected):
        cols[(i % n_columns) * 2].image(img)
    container.markdown(f"**{prompt}**")

    st.button("Again!", key="again_button")