from ast import parse from distutils.log import error from msilib.schema import Directory import os import time from IPython.display import display, update_display import torch from tqdm.auto import tqdm from min_dalle import MinDalle t = torch.cuda.get_device_properties(0).total_memory if t <=8500000000: print("Not enough GPU memory to generate pictures") else: amount = int(input("Amount: ")) directory = "./data/input/" if not os.path.exists(directory): os.makedirs(directory) for root, subdirectories, files in os.walk(directory): for filename in files: if filename.endswith(".png"): path_img = os.path.join(root, filename) os.remove(path_img) dtype = "float32" if t <= 10500000000: dtype = "float16" model = MinDalle( dtype=getattr(torch, dtype), device='cuda', is_mega=True, is_reusable=True ) file1 = open("prompt.txt","r+") text = file1.read() print(text) ##text = "Brave cat knight in closeface helmet with big cat eyes and cat fur, open neck, full face, rainy background, raytraced, digital art , 4k , highly detailed , trending on artstation, close to life" #@param {type:"string"} progressive_outputs = True seamless = True grid_size = 1 temperature = 2 supercondition_factor = 16 top_k = 128 start_time = time.time() for counterr in range(amount): print("Making pic "+str(counterr)) image_stream = model.generate_image_stream( text=text, seed=-1, grid_size=grid_size, progressive_outputs=progressive_outputs, is_seamless=seamless, temperature=temperature, top_k=int(top_k), supercondition_factor=float(supercondition_factor) ) for image in image_stream: image.save(directory+str(counterr)+".png") print("Made " + str(amount) + " pictures in " + str(time.time()-start_time) + " seconds")