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import os
import sys
import base64
from io import BytesIO
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
from fastapi import FastAPI
import numpy as np
from PIL import Image
import clip
from dalle.models import Dalle
from dalle.utils.utils import clip_score, download
print("Loading models...")
app = FastAPI()
url = "https://arena.kakaocdn.net/brainrepo/models/minDALL-E/57b008f02ceaa02b779c8b7463143315/1.3B.tar.gz"
root = os.path.expanduser("~/.cache/minDALLE")
filename = os.path.basename(url)
pathname = filename[: -len(".tar.gz")]
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 = "cuda" if torch.cuda.is_available() else "cpu"
model = Dalle.from_pretrained(result_path) # 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)
print("Models loaded !")
@app.get("/")
def read_root():
return {"minDALL-E!"}
@app.get("/{generate}")
def generate(prompt):
images = sample(prompt)
images = [to_base64(image) for image in images]
return {"images": images}
def sample(prompt):
# Sampling
images = (
model.sampling(prompt=prompt, top_k=96, top_p=None, softmax_temperature=1.0, num_candidates=9, 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
)
images = images[rank]
pil_images = []
for i in range(len(images)):
im = Image.fromarray((images[i] * 255).astype(np.uint8))
pil_images.append(im)
return pil_images
def to_base64(pil_image):
buffered = BytesIO()
pil_image.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()) |