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| import gradio as gr | |
| from transformers import pipeline | |
| import multiprocessing | |
| import torch | |
| import os | |
| from psutil import virtual_memory | |
| from pathlib import Path | |
| import random | |
| from datetime import datetime | |
| import PIL | |
| from PIL import Image | |
| import os | |
| import gc | |
| from translatepy import Translator, Language | |
| ts = Translator() | |
| from deep_translator import DeeplTranslator, GoogleTranslator | |
| from pathlib import Path | |
| import csv | |
| import logging | |
| import requests | |
| from rudalle.pipelines import generate_images, show, super_resolution, cherry_pick_by_ruclip | |
| from rudalle import get_rudalle_model, get_tokenizer, get_vae, get_realesrgan | |
| from rudalle.utils import seed_everything | |
| #from ruclip import load as get_ruclip | |
| import ruclip | |
| # prepare models: | |
| #device = 'cuda' | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| #device = "cuda" | |
| print('Using device:', device) | |
| dalle = get_rudalle_model('Malevich', pretrained=True, fp16=True, device=device) | |
| tokenizer = get_tokenizer() | |
| vae = get_vae(dwt=True).to(device) | |
| # pipeline utils: | |
| #TEST-------- | |
| realesrgan = get_realesrgan('x2', device=device) | |
| clip, processor = ruclip.load('ruclip-vit-base-patch32-384', device=device) | |
| clip_predictor = ruclip.Predictor(clip, processor, device, bs=8) | |
| text = 'радуга на фоне ночного города' | |
| #seed_everything(42) | |
| #pil_images = [] | |
| #scores = [] | |
| #for top_k, top_p, images_num in [ | |
| # (2048, 0.995, 6), | |
| #]: | |
| # _pil_images, _scores = generate_images(text, tokenizer, model, vae, top_k=top_k, images_num=images_num, #bs=8, top_p=top_p) | |
| # pil_images += _pil_images | |
| # scores += _scores | |
| def model(text, tokenizer=tokenizer, dalle=dalle, vae=vae, top_k=2048, images_num=1, bs=8, top_p=0.995): | |
| pil_images = generate_images(text, tokenizer, dalle, vae, top_k=top_k, images_num=images_num, bs=8, top_p=top_p) | |
| pil_images = pil_images[0] | |
| return pil_images[0] | |
| seed_everything(42) | |
| pil_images = [] | |
| iface = gr.Interface(fn=model, title="V1", | |
| inputs=[gr.inputs.Textbox(label="Text prompt")], | |
| outputs=[gr.outputs.Image(type="pil", label="Generated Image")]).launch(debug=True) | |