133 lines
4.2 KiB
Python
133 lines
4.2 KiB
Python
import json
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import os
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import shutil
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from argparse import ArgumentParser
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import time
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from time import gmtime, strftime
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from PIL import Image
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import torch
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from torch import autocast
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from tqdm.auto import tqdm
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import numpy as np
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import piexif
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import piexif.helper
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from diffusers import (
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DDIMScheduler,
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#PNDMScheduler,
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)
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from diffusers import StableDiffusionImg2ImgPipeline
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t = torch.cuda.get_device_properties(0).total_memory
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if t<=6400000000:
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print("Running with less than 6gb memory. Working is not garanted")
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file1 = open("prompt.txt","r+")
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text = file1.read()
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print(text)
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##Generating with stable diffusion
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device = "cuda"
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model_path = "CompVis/stable-diffusion-v1-4"
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# Using DDIMScheduler as an example,this also works with PNDMScheduler
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# uncomment this line if you want to use it.
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#scheduler = PNDMScheduler.from_config(model_path, subfolder="scheduler", use_auth_token=True)
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scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
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model_path,
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scheduler=scheduler,
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revision="fp16",
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torch_dtype=torch.float16,
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use_auth_token=True
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).to(device)
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if t <= 11000000000:
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print("Less then 11gb video memory. Running with attention slicing.")
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pipe.enable_attention_slicing()
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def dummy_checker(images, **kwargs):
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return images, False
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pipe.safety_checker = dummy_checker
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def preprocess(image):
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), Image.Resampling.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return 2.*image - 1.
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path_img = []
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startStrength = 0.86
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deltaStrength = 0.02
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endStrength = 0.941
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startScale = 7.5
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deltaScale = 2.5
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endScale = 15.0
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startSeed = 1022
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endSeed = 1024
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directory_in = "./data/input"
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if not os.path.exists(directory_in):
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os.makedirs(directory_in)
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for root, subdirectories, files in os.walk(directory_in):
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for filename in files:
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if filename.endswith(".png"):
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path_img.append(os.path.join(root, filename))
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print("Found " +str(len(path_img)) +" pictures")
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start_time = time.time()
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counterr=0
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allwork=0
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directory="./data/out/" + strftime("%Y-%m-%d_%H-%M-%S") + "/"
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if not os.path.exists(directory):
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os.makedirs(directory)
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with open(directory+"prompt.txt", 'w') as f:
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f.write(text)
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shutil.copytree(directory_in, directory+"input")
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for i in path_img:
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print(i)
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if not os.path.exists(directory+str(counterr)):
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os.makedirs(directory+str(counterr))
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init_img = Image.open(i)
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init_img = init_img.resize((768, 512))
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init_image = preprocess(init_img)
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prompt = text
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for seed in range(endSeed-startSeed+1):
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generator = torch.Generator(device=device).manual_seed(startSeed+seed)
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guidance_scale = startScale
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while guidance_scale <= endScale:
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strenght = startStrength
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while strenght <= endStrength:
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with autocast("cuda"):
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image = pipe(prompt=prompt, init_image=init_image, strength=strenght, guidance_scale=guidance_scale, generator=generator)["sample"][0]
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image.save(directory+str(counterr)+"/" + str(allwork) +".jpg")
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exif_dict = piexif.load(directory+str(counterr)+"/" + str(allwork) +".jpg")
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userCommentAsDict = {}
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userCommentAsDict['Seed']=str(seed+startSeed)
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userCommentAsDict['Strength']=str(strenght)
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userCommentAsDict['Guidance_scale']=str(guidance_scale)
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user_comment = piexif.helper.UserComment.dump(json.dumps(userCommentAsDict))
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exif_dict["Exif"][piexif.ExifIFD.UserComment] = user_comment
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exif_bytes = piexif.dump(exif_dict)
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piexif.insert(exif_bytes, directory+str(counterr)+"/" + str(allwork) +".jpg")
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allwork+=1
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strenght+=deltaStrength
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guidance_scale+=deltaScale
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counterr+=1
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print("Made " + str(allwork) + " pictures in " + str(time.time()-start_time) + " seconds") |