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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 torch import nn
from fastapi import FastAPI
import numpy as np
from PIL import Image
#import clip
from dalle.models import Dalle
import logging
import streamlit as st
from dalle.utils.utils import clip_score, download
print("Loading models...")
app = FastAPI()
from huggingface_hub import hf_hub_download
logging.info("Start downloading")
full_dict_path = hf_hub_download(repo_id="MatthiasC/dall-e-logo", filename="full_dict_new.ckpt",
use_auth_token=st.secrets["model_hub"])
logging.info("End downloading")
logging.info(full_dict_path)
# 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("minDALL-E/1.3B") # This will automatically download the pretrained model.
#model.to(device=device)
# OLD CODE
# -----------------------------------------------------------
# state_dict_ = torch.load('last.ckpt', map_location='cpu')
# vqgan_stage_dict = model.stage1.state_dict()
#
# for name, param in state_dict_['state_dict'].items():
# if name not in model.stage1.state_dict().keys():
# continue
# if isinstance(param, nn.parameter.Parameter):
# param = param.data
# vqgan_stage_dict[name].copy_(param)
#
# model.stage1.load_state_dict(vqgan_stage_dict)
# #---------------------------------------------------------
# state_dict_dalle = torch.load('dalle_last.ckpt', map_location='cpu')
# dalle_stage_dict = model.stage2.state_dict()
#
# for name, param in state_dict_dalle['state_dict'].items():
# if name[6:] not in model.stage2.state_dict().keys():
# print(name)
# continue
# if isinstance(param, nn.parameter.Parameter):
# param = param.data
# dalle_stage_dict[name[6:]].copy_(param)
#
# model.stage2.load_state_dict(dalle_stage_dict)
# NEW METHOD
model.load_state_dict(torch.load(full_dict_path, map_location=torch.device('cpu')))
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
logging.info("starting sampling")
images = (
model.sampling(prompt=prompt, top_k=96, top_p=None, softmax_temperature=1.0, num_candidates=9, device=device)
.cpu()
.numpy()
)
logging.info("sampling succeeded")
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()) |