mirror of
https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-14 22:13:41 +03:00
1612 lines
71 KiB
Python
1612 lines
71 KiB
Python
# base webui import and utils.
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from webui_streamlit import st
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# streamlit imports
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#other imports
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import warnings
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import json
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import base64
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import os, sys, re, random, datetime, time, math
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from PIL import Image, ImageFont, ImageDraw, ImageFilter
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from PIL.PngImagePlugin import PngInfo
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from scipy import integrate
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import torch
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from torchdiffeq import odeint
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import k_diffusion as K
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import math
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import mimetypes
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import numpy as np
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import pynvml
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import threading
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import torch
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from torch import autocast
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from torchvision import transforms
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import torch.nn as nn
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from omegaconf import OmegaConf
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import yaml
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from pathlib import Path
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from contextlib import nullcontext
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from einops import rearrange
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from ldm.util import instantiate_from_config
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from retry import retry
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from slugify import slugify
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import skimage
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import piexif
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import piexif.helper
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from tqdm import trange
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# Temp imports
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# end of imports
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#---------------------------------------------------------------------------------------------------------------
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try:
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging
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logging.set_verbosity_error()
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except:
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pass
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# remove some annoying deprecation warnings that show every now and then.
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warnings.filterwarnings("ignore", category=DeprecationWarning)
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# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
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mimetypes.init()
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mimetypes.add_type('application/javascript', '.js')
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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opt_C = 4
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opt_f = 8
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# should and will be moved to a settings menu in the UI at some point
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grid_format = [s.lower() for s in st.session_state["defaults"].general.grid_format.split(':')]
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grid_lossless = False
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grid_quality = 100
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if grid_format[0] == 'png':
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grid_ext = 'png'
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grid_format = 'png'
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elif grid_format[0] in ['jpg', 'jpeg']:
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grid_quality = int(grid_format[1]) if len(grid_format) > 1 else 100
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grid_ext = 'jpg'
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grid_format = 'jpeg'
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elif grid_format[0] == 'webp':
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grid_quality = int(grid_format[1]) if len(grid_format) > 1 else 100
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grid_ext = 'webp'
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grid_format = 'webp'
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if grid_quality < 0: # e.g. webp:-100 for lossless mode
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grid_lossless = True
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grid_quality = abs(grid_quality)
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# should and will be moved to a settings menu in the UI at some point
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save_format = [s.lower() for s in st.session_state["defaults"].general.save_format.split(':')]
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save_lossless = False
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save_quality = 100
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if save_format[0] == 'png':
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save_ext = 'png'
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save_format = 'png'
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elif save_format[0] in ['jpg', 'jpeg']:
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save_quality = int(save_format[1]) if len(save_format) > 1 else 100
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save_ext = 'jpg'
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save_format = 'jpeg'
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elif save_format[0] == 'webp':
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save_quality = int(save_format[1]) if len(save_format) > 1 else 100
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save_ext = 'webp'
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save_format = 'webp'
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if save_quality < 0: # e.g. webp:-100 for lossless mode
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save_lossless = True
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save_quality = abs(save_quality)
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# this should force GFPGAN and RealESRGAN onto the selected gpu as well
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os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
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os.environ["CUDA_VISIBLE_DEVICES"] = str(st.session_state["defaults"].general.gpu)
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@retry(tries=5)
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def load_models(continue_prev_run = False, use_GFPGAN=False, use_RealESRGAN=False, RealESRGAN_model="RealESRGAN_x4plus",
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CustomModel_available=False, custom_model="Stable Diffusion v1.4"):
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"""Load the different models. We also reuse the models that are already in memory to speed things up instead of loading them again. """
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print ("Loading models.")
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st.session_state["progress_bar_text"].text("Loading models...")
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# Generate random run ID
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# Used to link runs linked w/ continue_prev_run which is not yet implemented
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# Use URL and filesystem safe version just in case.
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st.session_state["run_id"] = base64.urlsafe_b64encode(
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os.urandom(6)
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).decode("ascii")
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# check what models we want to use and if the they are already loaded.
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if use_GFPGAN:
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if "GFPGAN" in st.session_state:
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print("GFPGAN already loaded")
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else:
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# Load GFPGAN
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if os.path.exists(st.session_state["defaults"].general.GFPGAN_dir):
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try:
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st.session_state["GFPGAN"] = load_GFPGAN()
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print("Loaded GFPGAN")
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except Exception:
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import traceback
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print("Error loading GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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else:
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if "GFPGAN" in st.session_state:
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del st.session_state["GFPGAN"]
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if use_RealESRGAN:
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if "RealESRGAN" in st.session_state and st.session_state["RealESRGAN"].model.name == RealESRGAN_model:
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print("RealESRGAN already loaded")
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else:
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#Load RealESRGAN
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try:
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# We first remove the variable in case it has something there,
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# some errors can load the model incorrectly and leave things in memory.
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del st.session_state["RealESRGAN"]
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except KeyError:
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pass
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if os.path.exists(st.session_state["defaults"].general.RealESRGAN_dir):
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# st.session_state is used for keeping the models in memory across multiple pages or runs.
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st.session_state["RealESRGAN"] = load_RealESRGAN(RealESRGAN_model)
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print("Loaded RealESRGAN with model "+ st.session_state["RealESRGAN"].model.name)
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else:
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if "RealESRGAN" in st.session_state:
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del st.session_state["RealESRGAN"]
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if "model" in st.session_state:
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if "model" in st.session_state and st.session_state["custom_model"] == custom_model:
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# TODO: check if the optimized mode was changed?
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print("Model already loaded")
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return
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else:
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try:
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del st.session_state.model
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del st.session_state.modelCS
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del st.session_state.modelFS
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except KeyError:
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pass
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# At this point the model is either
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# is not loaded yet or have been evicted:
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# load new model into memory
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st.session_state.custom_model = custom_model
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config, device, model, modelCS, modelFS = load_sd_model(custom_model)
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st.session_state.device = device
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st.session_state.model = model
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st.session_state.modelCS = modelCS
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st.session_state.modelFS = modelFS
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print("Model loaded.")
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def load_model_from_config(config, ckpt, verbose=False):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = pl_sd["state_dict"]
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model = instantiate_from_config(config.model)
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m, u = model.load_state_dict(sd, strict=False)
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if len(m) > 0 and verbose:
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print("missing keys:")
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print(m)
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if len(u) > 0 and verbose:
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print("unexpected keys:")
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print(u)
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model.cuda()
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model.eval()
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return model
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def load_sd_from_config(ckpt, verbose=False):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = pl_sd["state_dict"]
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return sd
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class MemUsageMonitor(threading.Thread):
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stop_flag = False
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max_usage = 0
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total = -1
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def __init__(self, name):
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threading.Thread.__init__(self)
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self.name = name
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def run(self):
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try:
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pynvml.nvmlInit()
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except:
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print(f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n")
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return
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print(f"[{self.name}] Recording max memory usage...\n")
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# Missing context
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#handle = pynvml.nvmlDeviceGetHandleByIndex(st.session_state['defaults'].general.gpu)
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handle = pynvml.nvmlDeviceGetHandleByIndex(0)
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self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
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while not self.stop_flag:
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m = pynvml.nvmlDeviceGetMemoryInfo(handle)
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self.max_usage = max(self.max_usage, m.used)
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# print(self.max_usage)
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time.sleep(0.1)
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print(f"[{self.name}] Stopped recording.\n")
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pynvml.nvmlShutdown()
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def read(self):
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return self.max_usage, self.total
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def stop(self):
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self.stop_flag = True
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def read_and_stop(self):
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self.stop_flag = True
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return self.max_usage, self.total
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class CFGMaskedDenoiser(nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale, mask, x0, xi):
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x_in = x
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x_in = torch.cat([x_in] * 2)
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sigma_in = torch.cat([sigma] * 2)
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cond_in = torch.cat([uncond, cond])
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uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
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denoised = uncond + (cond - uncond) * cond_scale
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if mask is not None:
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assert x0 is not None
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img_orig = x0
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mask_inv = 1. - mask
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denoised = (img_orig * mask_inv) + (mask * denoised)
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return denoised
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class CFGDenoiser(nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale):
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x_in = torch.cat([x] * 2)
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sigma_in = torch.cat([sigma] * 2)
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cond_in = torch.cat([uncond, cond])
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uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
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return uncond + (cond - uncond) * cond_scale
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def append_zero(x):
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return torch.cat([x, x.new_zeros([1])])
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def append_dims(x, target_dims):
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"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
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dims_to_append = target_dims - x.ndim
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if dims_to_append < 0:
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raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
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return x[(...,) + (None,) * dims_to_append]
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def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
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"""Constructs the noise schedule of Karras et al. (2022)."""
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ramp = torch.linspace(0, 1, n)
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min_inv_rho = sigma_min ** (1 / rho)
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max_inv_rho = sigma_max ** (1 / rho)
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sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
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return append_zero(sigmas).to(device)
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#
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# helper fft routines that keep ortho normalization and auto-shift before and after fft
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def _fft2(data):
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if data.ndim > 2: # has channels
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out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
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for c in range(data.shape[2]):
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c_data = data[:,:,c]
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out_fft[:,:,c] = np.fft.fft2(np.fft.fftshift(c_data),norm="ortho")
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out_fft[:,:,c] = np.fft.ifftshift(out_fft[:,:,c])
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else: # one channel
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out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
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out_fft[:,:] = np.fft.fft2(np.fft.fftshift(data),norm="ortho")
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out_fft[:,:] = np.fft.ifftshift(out_fft[:,:])
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return out_fft
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def _ifft2(data):
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if data.ndim > 2: # has channels
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out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
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for c in range(data.shape[2]):
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c_data = data[:,:,c]
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out_ifft[:,:,c] = np.fft.ifft2(np.fft.fftshift(c_data),norm="ortho")
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out_ifft[:,:,c] = np.fft.ifftshift(out_ifft[:,:,c])
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else: # one channel
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out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
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out_ifft[:,:] = np.fft.ifft2(np.fft.fftshift(data),norm="ortho")
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out_ifft[:,:] = np.fft.ifftshift(out_ifft[:,:])
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return out_ifft
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def _get_gaussian_window(width, height, std=3.14, mode=0):
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window_scale_x = float(width / min(width, height))
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window_scale_y = float(height / min(width, height))
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window = np.zeros((width, height))
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x = (np.arange(width) / width * 2. - 1.) * window_scale_x
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for y in range(height):
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fy = (y / height * 2. - 1.) * window_scale_y
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if mode == 0:
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window[:, y] = np.exp(-(x**2+fy**2) * std)
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else:
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window[:, y] = (1/((x**2+1.) * (fy**2+1.))) ** (std/3.14) # hey wait a minute that's not gaussian
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return window
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def _get_masked_window_rgb(np_mask_grey, hardness=1.):
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np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
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if hardness != 1.:
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hardened = np_mask_grey[:] ** hardness
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else:
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hardened = np_mask_grey[:]
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for c in range(3):
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np_mask_rgb[:,:,c] = hardened[:]
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return np_mask_rgb
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def get_matched_noise(_np_src_image, np_mask_rgb, noise_q, color_variation):
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"""
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Explanation:
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Getting good results in/out-painting with stable diffusion can be challenging.
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Although there are simpler effective solutions for in-painting, out-painting can be especially challenging because there is no color data
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in the masked area to help prompt the generator. Ideally, even for in-painting we'd like work effectively without that data as well.
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Provided here is my take on a potential solution to this problem.
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By taking a fourier transform of the masked src img we get a function that tells us the presence and orientation of each feature scale in the unmasked src.
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Shaping the init/seed noise for in/outpainting to the same distribution of feature scales, orientations, and positions increases output coherence
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by helping keep features aligned. This technique is applicable to any continuous generation task such as audio or video, each of which can
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be conceptualized as a series of out-painting steps where the last half of the input "frame" is erased. For multi-channel data such as color
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or stereo sound the "color tone" or histogram of the seed noise can be matched to improve quality (using scikit-image currently)
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This method is quite robust and has the added benefit of being fast independently of the size of the out-painted area.
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The effects of this method include things like helping the generator integrate the pre-existing view distance and camera angle.
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Carefully managing color and brightness with histogram matching is also essential to achieving good coherence.
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noise_q controls the exponent in the fall-off of the distribution can be any positive number, lower values means higher detail (range > 0, default 1.)
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color_variation controls how much freedom is allowed for the colors/palette of the out-painted area (range 0..1, default 0.01)
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This code is provided as is under the Unlicense (https://unlicense.org/)
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Although you have no obligation to do so, if you found this code helpful please find it in your heart to credit me [parlance-zz].
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Questions or comments can be sent to parlance@fifth-harmonic.com (https://github.com/parlance-zz/)
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This code is part of a new branch of a discord bot I am working on integrating with diffusers (https://github.com/parlance-zz/g-diffuser-bot)
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"""
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global DEBUG_MODE
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global TMP_ROOT_PATH
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width = _np_src_image.shape[0]
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height = _np_src_image.shape[1]
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num_channels = _np_src_image.shape[2]
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np_src_image = _np_src_image[:] * (1. - np_mask_rgb)
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np_mask_grey = (np.sum(np_mask_rgb, axis=2)/3.)
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np_src_grey = (np.sum(np_src_image, axis=2)/3.)
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all_mask = np.ones((width, height), dtype=bool)
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img_mask = np_mask_grey > 1e-6
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ref_mask = np_mask_grey < 1e-3
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windowed_image = _np_src_image * (1.-_get_masked_window_rgb(np_mask_grey))
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windowed_image /= np.max(windowed_image)
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windowed_image += np.average(_np_src_image) * np_mask_rgb# / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
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#windowed_image += np.average(_np_src_image) * (np_mask_rgb * (1.- np_mask_rgb)) / (1.-np.average(np_mask_rgb)) # compensate for darkening across the mask transition area
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#_save_debug_img(windowed_image, "windowed_src_img")
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src_fft = _fft2(windowed_image) # get feature statistics from masked src img
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src_dist = np.absolute(src_fft)
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src_phase = src_fft / src_dist
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#_save_debug_img(src_dist, "windowed_src_dist")
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noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
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noise_rgb = np.random.random_sample((width, height, num_channels))
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noise_grey = (np.sum(noise_rgb, axis=2)/3.)
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noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter
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for c in range(num_channels):
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noise_rgb[:,:,c] += (1. - color_variation) * noise_grey
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noise_fft = _fft2(noise_rgb)
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for c in range(num_channels):
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noise_fft[:,:,c] *= noise_window
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noise_rgb = np.real(_ifft2(noise_fft))
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shaped_noise_fft = _fft2(noise_rgb)
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shaped_noise_fft[:,:,:] = np.absolute(shaped_noise_fft[:,:,:])**2 * (src_dist ** noise_q) * src_phase # perform the actual shaping
|
|
|
|
brightness_variation = 0.#color_variation # todo: temporarily tieing brightness variation to color variation for now
|
|
contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
|
|
|
|
# scikit-image is used for histogram matching, very convenient!
|
|
shaped_noise = np.real(_ifft2(shaped_noise_fft))
|
|
shaped_noise -= np.min(shaped_noise)
|
|
shaped_noise /= np.max(shaped_noise)
|
|
shaped_noise[img_mask,:] = skimage.exposure.match_histograms(shaped_noise[img_mask,:]**1., contrast_adjusted_np_src[ref_mask,:], channel_axis=1)
|
|
shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
|
|
#_save_debug_img(shaped_noise, "shaped_noise")
|
|
|
|
matched_noise = np.zeros((width, height, num_channels))
|
|
matched_noise = shaped_noise[:]
|
|
#matched_noise[all_mask,:] = skimage.exposure.match_histograms(shaped_noise[all_mask,:], _np_src_image[ref_mask,:], channel_axis=1)
|
|
#matched_noise = _np_src_image[:] * (1. - np_mask_rgb) + matched_noise * np_mask_rgb
|
|
|
|
#_save_debug_img(matched_noise, "matched_noise")
|
|
|
|
"""
|
|
todo:
|
|
color_variation doesnt have to be a single number, the overall color tone of the out-painted area could be param controlled
|
|
"""
|
|
|
|
return np.clip(matched_noise, 0., 1.)
|
|
|
|
|
|
#
|
|
def find_noise_for_image(model, device, init_image, prompt, steps=200, cond_scale=2.0, verbose=False, normalize=False, generation_callback=None):
|
|
image = np.array(init_image).astype(np.float32) / 255.0
|
|
image = image[None].transpose(0, 3, 1, 2)
|
|
image = torch.from_numpy(image)
|
|
image = 2. * image - 1.
|
|
image = image.to(device)
|
|
x = model.get_first_stage_encoding(model.encode_first_stage(image))
|
|
|
|
uncond = model.get_learned_conditioning([''])
|
|
cond = model.get_learned_conditioning([prompt])
|
|
|
|
s_in = x.new_ones([x.shape[0]])
|
|
dnw = K.external.CompVisDenoiser(model)
|
|
sigmas = dnw.get_sigmas(steps).flip(0)
|
|
|
|
if verbose:
|
|
print(sigmas)
|
|
|
|
for i in trange(1, len(sigmas)):
|
|
x_in = torch.cat([x] * 2)
|
|
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
|
|
cond_in = torch.cat([uncond, cond])
|
|
|
|
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
|
|
|
|
if i == 1:
|
|
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
|
|
else:
|
|
t = dnw.sigma_to_t(sigma_in)
|
|
|
|
eps = model.apply_model(x_in * c_in, t, cond=cond_in)
|
|
denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
|
|
|
|
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cond_scale
|
|
|
|
if i == 1:
|
|
d = (x - denoised) / (2 * sigmas[i])
|
|
else:
|
|
d = (x - denoised) / sigmas[i - 1]
|
|
|
|
if generation_callback is not None:
|
|
generation_callback(x, i)
|
|
|
|
dt = sigmas[i] - sigmas[i - 1]
|
|
x = x + d * dt
|
|
|
|
return x / sigmas[-1]
|
|
|
|
|
|
def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
|
|
"""Constructs an exponential noise schedule."""
|
|
sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()
|
|
return append_zero(sigmas)
|
|
|
|
|
|
def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
|
|
"""Constructs a continuous VP noise schedule."""
|
|
t = torch.linspace(1, eps_s, n, device=device)
|
|
sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
|
|
return append_zero(sigmas)
|
|
|
|
|
|
def to_d(x, sigma, denoised):
|
|
"""Converts a denoiser output to a Karras ODE derivative."""
|
|
return (x - denoised) / append_dims(sigma, x.ndim)
|
|
def linear_multistep_coeff(order, t, i, j):
|
|
if order - 1 > i:
|
|
raise ValueError(f'Order {order} too high for step {i}')
|
|
def fn(tau):
|
|
prod = 1.
|
|
for k in range(order):
|
|
if j == k:
|
|
continue
|
|
prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
|
|
return prod
|
|
return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
|
|
|
|
class KDiffusionSampler:
|
|
def __init__(self, m, sampler):
|
|
self.model = m
|
|
self.model_wrap = K.external.CompVisDenoiser(m)
|
|
self.schedule = sampler
|
|
def get_sampler_name(self):
|
|
return self.schedule
|
|
def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T, img_callback=None, log_every_t=None):
|
|
sigmas = self.model_wrap.get_sigmas(S)
|
|
x = x_T * sigmas[0]
|
|
model_wrap_cfg = CFGDenoiser(self.model_wrap)
|
|
samples_ddim = None
|
|
samples_ddim = K.sampling.__dict__[f'sample_{self.schedule}'](model_wrap_cfg, x, sigmas,
|
|
extra_args={'cond': conditioning, 'uncond': unconditional_conditioning,
|
|
'cond_scale': unconditional_guidance_scale}, disable=False, callback=generation_callback)
|
|
#
|
|
return samples_ddim, None
|
|
|
|
|
|
@torch.no_grad()
|
|
def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4):
|
|
extra_args = {} if extra_args is None else extra_args
|
|
s_in = x.new_ones([x.shape[0]])
|
|
v = torch.randint_like(x, 2) * 2 - 1
|
|
fevals = 0
|
|
def ode_fn(sigma, x):
|
|
nonlocal fevals
|
|
with torch.enable_grad():
|
|
x = x[0].detach().requires_grad_()
|
|
denoised = model(x, sigma * s_in, **extra_args)
|
|
d = to_d(x, sigma, denoised)
|
|
fevals += 1
|
|
grad = torch.autograd.grad((d * v).sum(), x)[0]
|
|
d_ll = (v * grad).flatten(1).sum(1)
|
|
return d.detach(), d_ll
|
|
x_min = x, x.new_zeros([x.shape[0]])
|
|
t = x.new_tensor([sigma_min, sigma_max])
|
|
sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5')
|
|
latent, delta_ll = sol[0][-1], sol[1][-1]
|
|
ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1)
|
|
return ll_prior + delta_ll, {'fevals': fevals}
|
|
|
|
|
|
def create_random_tensors(shape, seeds):
|
|
xs = []
|
|
for seed in seeds:
|
|
torch.manual_seed(seed)
|
|
|
|
# randn results depend on device; gpu and cpu get different results for same seed;
|
|
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
|
|
# but the original script had it like this so i do not dare change it for now because
|
|
# it will break everyone's seeds.
|
|
xs.append(torch.randn(shape, device=st.session_state['defaults'].general.gpu))
|
|
x = torch.stack(xs)
|
|
return x
|
|
|
|
def torch_gc():
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
|
|
def load_GFPGAN():
|
|
model_name = 'GFPGANv1.3'
|
|
model_path = os.path.join(st.session_state['defaults'].general.GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
|
|
if not os.path.isfile(model_path):
|
|
raise Exception("GFPGAN model not found at path "+model_path)
|
|
|
|
sys.path.append(os.path.abspath(st.session_state['defaults'].general.GFPGAN_dir))
|
|
from gfpgan import GFPGANer
|
|
|
|
if st.session_state['defaults'].general.gfpgan_cpu or st.session_state['defaults'].general.extra_models_cpu:
|
|
instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cpu'))
|
|
elif st.session_state['defaults'].general.extra_models_gpu:
|
|
instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f"cuda:{st.session_state['defaults'].general.gfpgan_gpu}"))
|
|
else:
|
|
instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"))
|
|
return instance
|
|
|
|
def load_RealESRGAN(model_name: str):
|
|
from basicsr.archs.rrdbnet_arch import RRDBNet
|
|
RealESRGAN_models = {
|
|
'RealESRGAN_x4plus': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4),
|
|
'RealESRGAN_x4plus_anime_6B': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
|
|
}
|
|
|
|
model_path = os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
|
|
if not os.path.exists(os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, "experiments","pretrained_models", f"{model_name}.pth")):
|
|
raise Exception(model_name+".pth not found at path "+model_path)
|
|
|
|
sys.path.append(os.path.abspath(st.session_state['defaults'].general.RealESRGAN_dir))
|
|
from realesrgan import RealESRGANer
|
|
|
|
if st.session_state['defaults'].general.esrgan_cpu or st.session_state['defaults'].general.extra_models_cpu:
|
|
instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=False) # cpu does not support half
|
|
instance.device = torch.device('cpu')
|
|
instance.model.to('cpu')
|
|
elif st.session_state['defaults'].general.extra_models_gpu:
|
|
instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not st.session_state['defaults'].general.no_half, device=torch.device(f"cuda:{st.session_state['defaults'].general.esrgan_gpu}"))
|
|
else:
|
|
instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not st.session_state['defaults'].general.no_half, device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"))
|
|
instance.model.name = model_name
|
|
|
|
return instance
|
|
|
|
#
|
|
def load_LDSR(checking=False):
|
|
model_name = 'model'
|
|
yaml_name = 'project'
|
|
model_path = os.path.join(st.session_state['defaults'].general.LDSR_dir, 'experiments/pretrained_models', model_name + '.ckpt')
|
|
yaml_path = os.path.join(st.session_state['defaults'].general.LDSR_dir, 'experiments/pretrained_models', yaml_name + '.yaml')
|
|
if not os.path.isfile(model_path):
|
|
raise Exception("LDSR model not found at path "+model_path)
|
|
if not os.path.isfile(yaml_path):
|
|
raise Exception("LDSR model not found at path "+yaml_path)
|
|
if checking == True:
|
|
return True
|
|
|
|
sys.path.append(os.path.abspath(st.session_state['defaults'].general.LDSR_dir))
|
|
from LDSR import LDSR
|
|
LDSRObject = LDSR(model_path, yaml_path)
|
|
return LDSRObject
|
|
|
|
#
|
|
LDSR = None
|
|
def try_loading_LDSR(model_name: str,checking=False):
|
|
global LDSR
|
|
if os.path.exists(st.session_state['defaults'].general.LDSR_dir):
|
|
try:
|
|
LDSR = load_LDSR(checking=True) # TODO: Should try to load both models before giving up
|
|
if checking == True:
|
|
print("Found LDSR")
|
|
return True
|
|
print("Latent Diffusion Super Sampling (LDSR) model loaded")
|
|
except Exception:
|
|
import traceback
|
|
print("Error loading LDSR:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
else:
|
|
print("LDSR not found at path, please make sure you have cloned the LDSR repo to ./src/latent-diffusion/")
|
|
|
|
#try_loading_LDSR('model',checking=True)
|
|
|
|
|
|
# Loads Stable Diffusion model by name
|
|
def load_sd_model(model_name: str) -> [any, any, any, any, any]:
|
|
ckpt_path = st.session_state.defaults.general.default_model_path
|
|
if model_name != st.session_state.defaults.general.default_model:
|
|
ckpt_path = os.path.join("models", "custom", f"{model_name}.ckpt")
|
|
|
|
if st.session_state.defaults.general.optimized:
|
|
config = OmegaConf.load(st.session_state.defaults.general.optimized_config)
|
|
|
|
sd = load_sd_from_config(ckpt_path)
|
|
li, lo = [], []
|
|
for key, v_ in sd.items():
|
|
sp = key.split('.')
|
|
if (sp[0]) == 'model':
|
|
if 'input_blocks' in sp:
|
|
li.append(key)
|
|
elif 'middle_block' in sp:
|
|
li.append(key)
|
|
elif 'time_embed' in sp:
|
|
li.append(key)
|
|
else:
|
|
lo.append(key)
|
|
for key in li:
|
|
sd['model1.' + key[6:]] = sd.pop(key)
|
|
for key in lo:
|
|
sd['model2.' + key[6:]] = sd.pop(key)
|
|
|
|
device = torch.device(f"cuda:{st.session_state.defaults.general.gpu}") \
|
|
if torch.cuda.is_available() else torch.device("cpu")
|
|
|
|
model = instantiate_from_config(config.modelUNet)
|
|
_, _ = model.load_state_dict(sd, strict=False)
|
|
model.cuda()
|
|
model.eval()
|
|
model.turbo = st.session_state.defaults.general.optimized_turbo
|
|
|
|
modelCS = instantiate_from_config(config.modelCondStage)
|
|
_, _ = modelCS.load_state_dict(sd, strict=False)
|
|
modelCS.cond_stage_model.device = device
|
|
modelCS.eval()
|
|
|
|
modelFS = instantiate_from_config(config.modelFirstStage)
|
|
_, _ = modelFS.load_state_dict(sd, strict=False)
|
|
modelFS.eval()
|
|
|
|
del sd
|
|
|
|
if not st.session_state.defaults.general.no_half:
|
|
model = model.half()
|
|
modelCS = modelCS.half()
|
|
modelFS = modelFS.half()
|
|
|
|
return config, device, model, modelCS, modelFS
|
|
else:
|
|
config = OmegaConf.load(st.session_state.defaults.general.default_model_config)
|
|
model = load_model_from_config(config, ckpt_path)
|
|
|
|
device = torch.device(f"cuda:{st.session_state.defaults.general.gpu}") \
|
|
if torch.cuda.is_available() else torch.device("cpu")
|
|
model = (model if st.session_state.defaults.general.no_half
|
|
else model.half()).to(device)
|
|
|
|
return config, device, model, None, None
|
|
|
|
|
|
# @codedealer: No usages
|
|
def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='RealESRGAN_x4plus'):
|
|
#get global variables
|
|
global_vars = globals()
|
|
#check if m is in globals
|
|
if unload:
|
|
for m in models:
|
|
if m in global_vars:
|
|
#if it is, delete it
|
|
del global_vars[m]
|
|
if st.session_state['defaults'].general.optimized:
|
|
if m == 'model':
|
|
del global_vars[m+'FS']
|
|
del global_vars[m+'CS']
|
|
if m == 'model':
|
|
m = 'Stable Diffusion'
|
|
print('Unloaded ' + m)
|
|
if load:
|
|
for m in models:
|
|
if m not in global_vars or m in global_vars and type(global_vars[m]) == bool:
|
|
#if it isn't, load it
|
|
if m == 'GFPGAN':
|
|
global_vars[m] = load_GFPGAN()
|
|
elif m == 'model':
|
|
sdLoader = load_sd_from_config()
|
|
global_vars[m] = sdLoader[0]
|
|
if st.session_state['defaults'].general.optimized:
|
|
global_vars[m+'CS'] = sdLoader[1]
|
|
global_vars[m+'FS'] = sdLoader[2]
|
|
elif m == 'RealESRGAN':
|
|
global_vars[m] = load_RealESRGAN(imgproc_realesrgan_model_name)
|
|
elif m == 'LDSR':
|
|
global_vars[m] = load_LDSR()
|
|
if m =='model':
|
|
m='Stable Diffusion'
|
|
print('Loaded ' + m)
|
|
torch_gc()
|
|
|
|
|
|
#
|
|
@retry(tries=5)
|
|
def generation_callback(img, i=0):
|
|
|
|
try:
|
|
if i == 0:
|
|
if img['i']: i = img['i']
|
|
except TypeError:
|
|
pass
|
|
|
|
if i % int(st.session_state['defaults'].general.update_preview_frequency) == 0 and st.session_state['defaults'].general.update_preview:
|
|
#print (img)
|
|
#print (type(img))
|
|
# The following lines will convert the tensor we got on img to an actual image we can render on the UI.
|
|
# It can probably be done in a better way for someone who knows what they're doing. I don't.
|
|
#print (img,isinstance(img, torch.Tensor))
|
|
if isinstance(img, torch.Tensor):
|
|
x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).decode_first_stage(img)
|
|
else:
|
|
# When using the k Diffusion samplers they return a dict instead of a tensor that look like this:
|
|
# {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}
|
|
x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).decode_first_stage(img["denoised"])
|
|
|
|
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
|
|
|
pil_image = transforms.ToPILImage()(x_samples_ddim.squeeze_(0))
|
|
|
|
# update image on the UI so we can see the progress
|
|
st.session_state["preview_image"].image(pil_image)
|
|
|
|
# Show a progress bar so we can keep track of the progress even when the image progress is not been shown,
|
|
# Dont worry, it doesnt affect the performance.
|
|
if st.session_state["generation_mode"] == "txt2img":
|
|
percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps))
|
|
st.session_state["progress_bar_text"].text(
|
|
f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} {percent if percent < 100 else 100}%")
|
|
else:
|
|
if st.session_state["generation_mode"] == "img2img":
|
|
round_sampling_steps = round(st.session_state.sampling_steps * st.session_state["denoising_strength"])
|
|
percent = int(100 * float(i+1 if i+1 < round_sampling_steps else round_sampling_steps)/float(round_sampling_steps))
|
|
st.session_state["progress_bar_text"].text(
|
|
f"""Running step: {i+1 if i+1 < round_sampling_steps else round_sampling_steps}/{round_sampling_steps} {percent if percent < 100 else 100}%""")
|
|
else:
|
|
if st.session_state["generation_mode"] == "txt2vid":
|
|
percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps))
|
|
st.session_state["progress_bar_text"].text(
|
|
f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps}"
|
|
f"{percent if percent < 100 else 100}%")
|
|
|
|
st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
|
|
|
|
|
|
prompt_parser = re.compile("""
|
|
(?P<prompt> # capture group for 'prompt'
|
|
[^:]+ # match one or more non ':' characters
|
|
) # end 'prompt'
|
|
(?: # non-capture group
|
|
:+ # match one or more ':' characters
|
|
(?P<weight> # capture group for 'weight'
|
|
-?\\d+(?:\\.\\d+)? # match positive or negative decimal number
|
|
)? # end weight capture group, make optional
|
|
\\s* # strip spaces after weight
|
|
| # OR
|
|
$ # else, if no ':' then match end of line
|
|
) # end non-capture group
|
|
""", re.VERBOSE)
|
|
|
|
# grabs all text up to the first occurrence of ':' as sub-prompt
|
|
# takes the value following ':' as weight
|
|
# if ':' has no value defined, defaults to 1.0
|
|
# repeats until no text remaining
|
|
def split_weighted_subprompts(input_string, normalize=True):
|
|
parsed_prompts = [(match.group("prompt"), float(match.group("weight") or 1)) for match in re.finditer(prompt_parser, input_string)]
|
|
if not normalize:
|
|
return parsed_prompts
|
|
# this probably still doesn't handle negative weights very well
|
|
weight_sum = sum(map(lambda x: x[1], parsed_prompts))
|
|
return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
|
|
|
|
def slerp(device, t, v0:torch.Tensor, v1:torch.Tensor, DOT_THRESHOLD=0.9995):
|
|
v0 = v0.detach().cpu().numpy()
|
|
v1 = v1.detach().cpu().numpy()
|
|
|
|
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
|
|
if np.abs(dot) > DOT_THRESHOLD:
|
|
v2 = (1 - t) * v0 + t * v1
|
|
else:
|
|
theta_0 = np.arccos(dot)
|
|
sin_theta_0 = np.sin(theta_0)
|
|
theta_t = theta_0 * t
|
|
sin_theta_t = np.sin(theta_t)
|
|
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
|
|
s1 = sin_theta_t / sin_theta_0
|
|
v2 = s0 * v0 + s1 * v1
|
|
|
|
v2 = torch.from_numpy(v2).to(device)
|
|
|
|
return v2
|
|
|
|
#
|
|
def optimize_update_preview_frequency(current_chunk_speed, previous_chunk_speed_list, update_preview_frequency, update_preview_frequency_list):
|
|
"""Find the optimal update_preview_frequency value maximizing
|
|
performance while minimizing the time between updates."""
|
|
from statistics import mean
|
|
|
|
previous_chunk_avg_speed = mean(previous_chunk_speed_list)
|
|
|
|
previous_chunk_speed_list.append(current_chunk_speed)
|
|
current_chunk_avg_speed = mean(previous_chunk_speed_list)
|
|
|
|
if current_chunk_avg_speed >= previous_chunk_avg_speed:
|
|
#print(f"{current_chunk_speed} >= {previous_chunk_speed}")
|
|
update_preview_frequency_list.append(update_preview_frequency + 1)
|
|
else:
|
|
#print(f"{current_chunk_speed} <= {previous_chunk_speed}")
|
|
update_preview_frequency_list.append(update_preview_frequency - 1)
|
|
|
|
update_preview_frequency = round(mean(update_preview_frequency_list))
|
|
|
|
return current_chunk_speed, previous_chunk_speed_list, update_preview_frequency, update_preview_frequency_list
|
|
|
|
|
|
def get_font(fontsize):
|
|
fonts = ["arial.ttf", "DejaVuSans.ttf"]
|
|
for font_name in fonts:
|
|
try:
|
|
return ImageFont.truetype(font_name, fontsize)
|
|
except OSError:
|
|
pass
|
|
|
|
# ImageFont.load_default() is practically unusable as it only supports
|
|
# latin1, so raise an exception instead if no usable font was found
|
|
raise Exception(f"No usable font found (tried {', '.join(fonts)})")
|
|
|
|
def load_embeddings(fp):
|
|
if fp is not None and hasattr(st.session_state["model"], "embedding_manager"):
|
|
st.session_state["model"].embedding_manager.load(fp['name'])
|
|
|
|
def image_grid(imgs, batch_size, force_n_rows=None, captions=None):
|
|
#print (len(imgs))
|
|
if force_n_rows is not None:
|
|
rows = force_n_rows
|
|
elif st.session_state['defaults'].general.n_rows > 0:
|
|
rows = st.session_state['defaults'].general.n_rows
|
|
elif st.session_state['defaults'].general.n_rows == 0:
|
|
rows = batch_size
|
|
else:
|
|
rows = math.sqrt(len(imgs))
|
|
rows = round(rows)
|
|
|
|
cols = math.ceil(len(imgs) / rows)
|
|
|
|
w, h = imgs[0].size
|
|
grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
|
|
|
|
fnt = get_font(30)
|
|
|
|
for i, img in enumerate(imgs):
|
|
grid.paste(img, box=(i % cols * w, i // cols * h))
|
|
if captions and i<len(captions):
|
|
d = ImageDraw.Draw( grid )
|
|
size = d.textbbox( (0,0), captions[i], font=fnt, stroke_width=2, align="center" )
|
|
d.multiline_text((i % cols * w + w/2, i // cols * h + h - size[3]), captions[i], font=fnt, fill=(255,255,255), stroke_width=2, stroke_fill=(0,0,0), anchor="mm", align="center")
|
|
|
|
return grid
|
|
|
|
def seed_to_int(s):
|
|
if type(s) is int:
|
|
return s
|
|
if s is None or s == '':
|
|
return random.randint(0, 2**32 - 1)
|
|
|
|
if type(s) is list:
|
|
seed_list = []
|
|
for seed in s:
|
|
if seed is None or seed == '':
|
|
seed_list.append(random.randint(0, 2**32 - 1))
|
|
else:
|
|
seed_list = s
|
|
|
|
return seed_list
|
|
|
|
n = abs(int(s) if s.isdigit() else random.Random(s).randint(0, 2**32 - 1))
|
|
while n >= 2**32:
|
|
n = n >> 32
|
|
return n
|
|
|
|
#
|
|
def draw_prompt_matrix(im, width, height, all_prompts):
|
|
def wrap(text, d, font, line_length):
|
|
lines = ['']
|
|
for word in text.split():
|
|
line = f'{lines[-1]} {word}'.strip()
|
|
if d.textlength(line, font=font) <= line_length:
|
|
lines[-1] = line
|
|
else:
|
|
lines.append(word)
|
|
return '\n'.join(lines)
|
|
|
|
def draw_texts(pos, x, y, texts, sizes):
|
|
for i, (text, size) in enumerate(zip(texts, sizes)):
|
|
active = pos & (1 << i) != 0
|
|
|
|
if not active:
|
|
text = '\u0336'.join(text) + '\u0336'
|
|
|
|
d.multiline_text((x, y + size[1] / 2), text, font=fnt, fill=color_active if active else color_inactive, anchor="mm", align="center")
|
|
|
|
y += size[1] + line_spacing
|
|
|
|
fontsize = (width + height) // 25
|
|
line_spacing = fontsize // 2
|
|
fnt = get_font(fontsize)
|
|
color_active = (0, 0, 0)
|
|
color_inactive = (153, 153, 153)
|
|
|
|
pad_top = height // 4
|
|
pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0
|
|
|
|
cols = im.width // width
|
|
rows = im.height // height
|
|
|
|
prompts = all_prompts[1:]
|
|
|
|
result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
|
|
result.paste(im, (pad_left, pad_top))
|
|
|
|
d = ImageDraw.Draw(result)
|
|
|
|
boundary = math.ceil(len(prompts) / 2)
|
|
prompts_horiz = [wrap(x, d, fnt, width) for x in prompts[:boundary]]
|
|
prompts_vert = [wrap(x, d, fnt, pad_left) for x in prompts[boundary:]]
|
|
|
|
sizes_hor = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_horiz]]
|
|
sizes_ver = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_vert]]
|
|
hor_text_height = sum([x[1] + line_spacing for x in sizes_hor]) - line_spacing
|
|
ver_text_height = sum([x[1] + line_spacing for x in sizes_ver]) - line_spacing
|
|
|
|
for col in range(cols):
|
|
x = pad_left + width * col + width / 2
|
|
y = pad_top / 2 - hor_text_height / 2
|
|
|
|
draw_texts(col, x, y, prompts_horiz, sizes_hor)
|
|
|
|
for row in range(rows):
|
|
x = pad_left / 2
|
|
y = pad_top + height * row + height / 2 - ver_text_height / 2
|
|
|
|
draw_texts(row, x, y, prompts_vert, sizes_ver)
|
|
|
|
return result
|
|
|
|
def check_prompt_length(prompt, comments):
|
|
"""this function tests if prompt is too long, and if so, adds a message to comments"""
|
|
|
|
tokenizer = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer
|
|
max_length = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.max_length
|
|
|
|
info = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length,
|
|
return_overflowing_tokens=True, padding="max_length", return_tensors="pt")
|
|
ovf = info['overflowing_tokens'][0]
|
|
overflowing_count = ovf.shape[0]
|
|
if overflowing_count == 0:
|
|
return
|
|
|
|
vocab = {v: k for k, v in tokenizer.get_vocab().items()}
|
|
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
|
overflowing_text = tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
|
|
|
comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
|
|
|
def save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
|
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
|
save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, save_individual_images):
|
|
|
|
filename_i = os.path.join(sample_path_i, filename)
|
|
|
|
if st.session_state['defaults'].general.save_metadata or write_info_files:
|
|
# toggles differ for txt2img vs. img2img:
|
|
offset = 0 if init_img is None else 2
|
|
toggles = []
|
|
if prompt_matrix:
|
|
toggles.append(0)
|
|
if normalize_prompt_weights:
|
|
toggles.append(1)
|
|
if init_img is not None:
|
|
if uses_loopback:
|
|
toggles.append(2)
|
|
if uses_random_seed_loopback:
|
|
toggles.append(3)
|
|
if save_individual_images:
|
|
toggles.append(2 + offset)
|
|
if save_grid:
|
|
toggles.append(3 + offset)
|
|
if sort_samples:
|
|
toggles.append(4 + offset)
|
|
if write_info_files:
|
|
toggles.append(5 + offset)
|
|
if use_GFPGAN:
|
|
toggles.append(6 + offset)
|
|
metadata = \
|
|
dict(
|
|
target="txt2img" if init_img is None else "img2img",
|
|
prompt=prompts[i], ddim_steps=steps, toggles=toggles, sampler_name=sampler_name,
|
|
ddim_eta=ddim_eta, n_iter=n_iter, batch_size=batch_size, cfg_scale=cfg_scale,
|
|
seed=seeds[i], width=width, height=height, normalize_prompt_weights=normalize_prompt_weights)
|
|
# Not yet any use for these, but they bloat up the files:
|
|
# info_dict["init_img"] = init_img
|
|
# info_dict["init_mask"] = init_mask
|
|
if init_img is not None:
|
|
metadata["denoising_strength"] = str(denoising_strength)
|
|
metadata["resize_mode"] = resize_mode
|
|
|
|
if write_info_files:
|
|
with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
|
|
yaml.dump(metadata, f, allow_unicode=True, width=10000)
|
|
|
|
if st.session_state['defaults'].general.save_metadata:
|
|
# metadata = {
|
|
# "SD:prompt": prompts[i],
|
|
# "SD:seed": str(seeds[i]),
|
|
# "SD:width": str(width),
|
|
# "SD:height": str(height),
|
|
# "SD:steps": str(steps),
|
|
# "SD:cfg_scale": str(cfg_scale),
|
|
# "SD:normalize_prompt_weights": str(normalize_prompt_weights),
|
|
# }
|
|
metadata = {"SD:" + k:v for (k,v) in metadata.items()}
|
|
|
|
if save_ext == "png":
|
|
mdata = PngInfo()
|
|
for key in metadata:
|
|
mdata.add_text(key, str(metadata[key]))
|
|
image.save(f"{filename_i}.png", pnginfo=mdata)
|
|
else:
|
|
if jpg_sample:
|
|
image.save(f"{filename_i}.jpg", quality=save_quality,
|
|
optimize=True)
|
|
elif save_ext == "webp":
|
|
image.save(f"{filename_i}.{save_ext}", f"webp", quality=save_quality,
|
|
lossless=save_lossless)
|
|
else:
|
|
# not sure what file format this is
|
|
image.save(f"{filename_i}.{save_ext}", f"{save_ext}")
|
|
try:
|
|
exif_dict = piexif.load(f"{filename_i}.{save_ext}")
|
|
except:
|
|
exif_dict = { "Exif": dict() }
|
|
exif_dict["Exif"][piexif.ExifIFD.UserComment] = piexif.helper.UserComment.dump(
|
|
json.dumps(metadata), encoding="unicode")
|
|
piexif.insert(piexif.dump(exif_dict), f"{filename_i}.{save_ext}")
|
|
|
|
# render the image on the frontend
|
|
st.session_state["preview_image"].image(image)
|
|
|
|
def get_next_sequence_number(path, prefix=''):
|
|
"""
|
|
Determines and returns the next sequence number to use when saving an
|
|
image in the specified directory.
|
|
|
|
If a prefix is given, only consider files whose names start with that
|
|
prefix, and strip the prefix from filenames before extracting their
|
|
sequence number.
|
|
|
|
The sequence starts at 0.
|
|
"""
|
|
result = -1
|
|
for p in Path(path).iterdir():
|
|
if p.name.endswith(('.png', '.jpg')) and p.name.startswith(prefix):
|
|
tmp = p.name[len(prefix):]
|
|
try:
|
|
result = max(int(tmp.split('-')[0]), result)
|
|
except ValueError:
|
|
pass
|
|
return result + 1
|
|
|
|
|
|
def oxlamon_matrix(prompt, seed, n_iter, batch_size):
|
|
pattern = re.compile(r'(,\s){2,}')
|
|
|
|
class PromptItem:
|
|
def __init__(self, text, parts, item):
|
|
self.text = text
|
|
self.parts = parts
|
|
if item:
|
|
self.parts.append( item )
|
|
|
|
def clean(txt):
|
|
return re.sub(pattern, ', ', txt)
|
|
|
|
def getrowcount( txt ):
|
|
for data in re.finditer( ".*?\\((.*?)\\).*", txt ):
|
|
if data:
|
|
return len(data.group(1).split("|"))
|
|
break
|
|
return None
|
|
|
|
def repliter( txt ):
|
|
for data in re.finditer( ".*?\\((.*?)\\).*", txt ):
|
|
if data:
|
|
r = data.span(1)
|
|
for item in data.group(1).split("|"):
|
|
yield (clean(txt[:r[0]-1] + item.strip() + txt[r[1]+1:]), item.strip())
|
|
break
|
|
|
|
def iterlist( items ):
|
|
outitems = []
|
|
for item in items:
|
|
for newitem, newpart in repliter(item.text):
|
|
outitems.append( PromptItem(newitem, item.parts.copy(), newpart) )
|
|
|
|
return outitems
|
|
|
|
def getmatrix( prompt ):
|
|
dataitems = [ PromptItem( prompt[1:].strip(), [], None ) ]
|
|
while True:
|
|
newdataitems = iterlist( dataitems )
|
|
if len( newdataitems ) == 0:
|
|
return dataitems
|
|
dataitems = newdataitems
|
|
|
|
def classToArrays( items, seed, n_iter ):
|
|
texts = []
|
|
parts = []
|
|
seeds = []
|
|
|
|
for item in items:
|
|
itemseed = seed
|
|
for i in range(n_iter):
|
|
texts.append( item.text )
|
|
parts.append( f"Seed: {itemseed}\n" + "\n".join(item.parts) )
|
|
seeds.append( itemseed )
|
|
itemseed += 1
|
|
|
|
return seeds, texts, parts
|
|
|
|
all_seeds, all_prompts, prompt_matrix_parts = classToArrays(getmatrix( prompt ), seed, n_iter)
|
|
n_iter = math.ceil(len(all_prompts) / batch_size)
|
|
|
|
needrows = getrowcount(prompt)
|
|
if needrows:
|
|
xrows = math.sqrt(len(all_prompts))
|
|
xrows = round(xrows)
|
|
# if columns is to much
|
|
cols = math.ceil(len(all_prompts) / xrows)
|
|
if cols > needrows*4:
|
|
needrows *= 2
|
|
|
|
return all_seeds, n_iter, prompt_matrix_parts, all_prompts, needrows
|
|
|
|
#
|
|
def process_images(
|
|
outpath, func_init, func_sample, prompt, seed, sampler_name, save_grid, batch_size,
|
|
n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, use_RealESRGAN, realesrgan_model_name,
|
|
fp=None, ddim_eta=0.0, normalize_prompt_weights=True, init_img=None, init_mask=None,
|
|
mask_blur_strength=3, mask_restore=False, denoising_strength=0.75, noise_mode=0, find_noise_steps=1, resize_mode=None, uses_loopback=False,
|
|
uses_random_seed_loopback=False, sort_samples=True, write_info_files=True, jpg_sample=False,
|
|
variant_amount=0.0, variant_seed=None, save_individual_images: bool = True):
|
|
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
|
|
assert prompt is not None
|
|
torch_gc()
|
|
# start time after garbage collection (or before?)
|
|
start_time = time.time()
|
|
|
|
# We will use this date here later for the folder name, need to start_time if not need
|
|
run_start_dt = datetime.datetime.now()
|
|
|
|
mem_mon = MemUsageMonitor('MemMon')
|
|
mem_mon.start()
|
|
|
|
if hasattr(st.session_state["model"], "embedding_manager"):
|
|
load_embeddings(fp)
|
|
|
|
os.makedirs(outpath, exist_ok=True)
|
|
|
|
sample_path = os.path.join(outpath, "samples")
|
|
os.makedirs(sample_path, exist_ok=True)
|
|
|
|
if not ("|" in prompt) and prompt.startswith("@"):
|
|
prompt = prompt[1:]
|
|
|
|
comments = []
|
|
|
|
prompt_matrix_parts = []
|
|
simple_templating = False
|
|
add_original_image = not (use_RealESRGAN or use_GFPGAN)
|
|
|
|
if prompt_matrix:
|
|
if prompt.startswith("@"):
|
|
simple_templating = True
|
|
add_original_image = not (use_RealESRGAN or use_GFPGAN)
|
|
all_seeds, n_iter, prompt_matrix_parts, all_prompts, frows = oxlamon_matrix(prompt, seed, n_iter, batch_size)
|
|
else:
|
|
all_prompts = []
|
|
prompt_matrix_parts = prompt.split("|")
|
|
combination_count = 2 ** (len(prompt_matrix_parts) - 1)
|
|
for combination_num in range(combination_count):
|
|
current = prompt_matrix_parts[0]
|
|
|
|
for n, text in enumerate(prompt_matrix_parts[1:]):
|
|
if combination_num & (2 ** n) > 0:
|
|
current += ("" if text.strip().startswith(",") else ", ") + text
|
|
|
|
all_prompts.append(current)
|
|
|
|
n_iter = math.ceil(len(all_prompts) / batch_size)
|
|
all_seeds = len(all_prompts) * [seed]
|
|
|
|
print(f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches.")
|
|
else:
|
|
|
|
if not st.session_state['defaults'].general.no_verify_input:
|
|
try:
|
|
check_prompt_length(prompt, comments)
|
|
except:
|
|
import traceback
|
|
print("Error verifying input:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
|
|
all_prompts = batch_size * n_iter * [prompt]
|
|
all_seeds = [seed + x for x in range(len(all_prompts))]
|
|
|
|
precision_scope = autocast if st.session_state['defaults'].general.precision == "autocast" else nullcontext
|
|
output_images = []
|
|
grid_captions = []
|
|
stats = []
|
|
with torch.no_grad(), precision_scope("cuda"), (st.session_state["model"].ema_scope() if not st.session_state['defaults'].general.optimized else nullcontext()):
|
|
init_data = func_init()
|
|
tic = time.time()
|
|
|
|
|
|
# if variant_amount > 0.0 create noise from base seed
|
|
base_x = None
|
|
if variant_amount > 0.0:
|
|
target_seed_randomizer = seed_to_int('') # random seed
|
|
torch.manual_seed(seed) # this has to be the single starting seed (not per-iteration)
|
|
base_x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=[seed])
|
|
# we don't want all_seeds to be sequential from starting seed with variants,
|
|
# since that makes the same variants each time,
|
|
# so we add target_seed_randomizer as a random offset
|
|
for si in range(len(all_seeds)):
|
|
all_seeds[si] += target_seed_randomizer
|
|
|
|
for n in range(n_iter):
|
|
print(f"Iteration: {n+1}/{n_iter}")
|
|
prompts = all_prompts[n * batch_size:(n + 1) * batch_size]
|
|
captions = prompt_matrix_parts[n * batch_size:(n + 1) * batch_size]
|
|
seeds = all_seeds[n * batch_size:(n + 1) * batch_size]
|
|
|
|
print(prompt)
|
|
|
|
if st.session_state['defaults'].general.optimized:
|
|
st.session_state.modelCS.to(st.session_state['defaults'].general.gpu)
|
|
|
|
uc = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).get_learned_conditioning(len(prompts) * [""])
|
|
|
|
if isinstance(prompts, tuple):
|
|
prompts = list(prompts)
|
|
|
|
# split the prompt if it has : for weighting
|
|
# TODO for speed it might help to have this occur when all_prompts filled??
|
|
weighted_subprompts = split_weighted_subprompts(prompts[0], normalize_prompt_weights)
|
|
|
|
# sub-prompt weighting used if more than 1
|
|
if len(weighted_subprompts) > 1:
|
|
c = torch.zeros_like(uc) # i dont know if this is correct.. but it works
|
|
for i in range(0, len(weighted_subprompts)):
|
|
# note if alpha negative, it functions same as torch.sub
|
|
c = torch.add(c, (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).get_learned_conditioning(weighted_subprompts[i][0]), alpha=weighted_subprompts[i][1])
|
|
else: # just behave like usual
|
|
c = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).get_learned_conditioning(prompts)
|
|
|
|
|
|
shape = [opt_C, height // opt_f, width // opt_f]
|
|
|
|
if st.session_state['defaults'].general.optimized:
|
|
mem = torch.cuda.memory_allocated()/1e6
|
|
st.session_state.modelCS.to("cpu")
|
|
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
|
time.sleep(1)
|
|
|
|
if noise_mode == 1 or noise_mode == 3:
|
|
# TODO params for find_noise_to_image
|
|
x = torch.cat(batch_size * [find_noise_for_image.find_noise_for_image(
|
|
st.session_state["model"], st.session_state["device"],
|
|
init_img.convert('RGB'), '', find_noise_steps, 0.0, normalize=True,
|
|
generation_callback=generation_callback,
|
|
)], dim=0)
|
|
else:
|
|
# we manually generate all input noises because each one should have a specific seed
|
|
x = create_random_tensors(shape, seeds=seeds)
|
|
|
|
if variant_amount > 0.0: # we are making variants
|
|
# using variant_seed as sneaky toggle,
|
|
# when not None or '' use the variant_seed
|
|
# otherwise use seeds
|
|
if variant_seed != None and variant_seed != '':
|
|
specified_variant_seed = seed_to_int(variant_seed)
|
|
torch.manual_seed(specified_variant_seed)
|
|
seeds = [specified_variant_seed]
|
|
# finally, slerp base_x noise to target_x noise for creating a variant
|
|
x = slerp(st.session_state['defaults'].general.gpu, max(0.0, min(1.0, variant_amount)), base_x, x)
|
|
|
|
samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc, sampler_name=sampler_name)
|
|
|
|
if st.session_state['defaults'].general.optimized:
|
|
st.session_state.modelFS.to(st.session_state['defaults'].general.gpu)
|
|
|
|
x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).decode_first_stage(samples_ddim)
|
|
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
|
|
|
for i, x_sample in enumerate(x_samples_ddim):
|
|
sanitized_prompt = slugify(prompts[i])
|
|
|
|
if sort_samples:
|
|
full_path = os.path.join(os.getcwd(), sample_path, sanitized_prompt)
|
|
|
|
|
|
sanitized_prompt = sanitized_prompt[:220-len(full_path)]
|
|
sample_path_i = os.path.join(sample_path, sanitized_prompt)
|
|
|
|
#print(f"output folder length: {len(os.path.join(os.getcwd(), sample_path_i))}")
|
|
#print(os.path.join(os.getcwd(), sample_path_i))
|
|
|
|
os.makedirs(sample_path_i, exist_ok=True)
|
|
base_count = get_next_sequence_number(sample_path_i)
|
|
filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}"
|
|
else:
|
|
full_path = os.path.join(os.getcwd(), sample_path)
|
|
sample_path_i = sample_path
|
|
base_count = get_next_sequence_number(sample_path_i)
|
|
filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}_{sanitized_prompt}"[:220-len(full_path)] #same as before
|
|
|
|
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
|
x_sample = x_sample.astype(np.uint8)
|
|
image = Image.fromarray(x_sample)
|
|
original_sample = x_sample
|
|
original_filename = filename
|
|
|
|
if use_GFPGAN and st.session_state["GFPGAN"] is not None and not use_RealESRGAN:
|
|
#skip_save = True # #287 >_>
|
|
torch_gc()
|
|
cropped_faces, restored_faces, restored_img = st.session_state["GFPGAN"].enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
|
gfpgan_sample = restored_img[:,:,::-1]
|
|
gfpgan_image = Image.fromarray(gfpgan_sample)
|
|
gfpgan_filename = original_filename + '-gfpgan'
|
|
|
|
save_sample(gfpgan_image, sample_path_i, gfpgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
|
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback,
|
|
uses_random_seed_loopback, save_grid, sort_samples, sampler_name, ddim_eta,
|
|
n_iter, batch_size, i, denoising_strength, resize_mode, save_individual_images=False)
|
|
|
|
output_images.append(gfpgan_image) #287
|
|
if simple_templating:
|
|
grid_captions.append( captions[i] + "\ngfpgan" )
|
|
|
|
if use_RealESRGAN and st.session_state["RealESRGAN"] is not None and not use_GFPGAN:
|
|
#skip_save = True # #287 >_>
|
|
torch_gc()
|
|
|
|
if st.session_state["RealESRGAN"].model.name != realesrgan_model_name:
|
|
#try_loading_RealESRGAN(realesrgan_model_name)
|
|
load_models(use_GFPGAN=use_GFPGAN, use_RealESRGAN=use_RealESRGAN, RealESRGAN_model=realesrgan_model_name)
|
|
|
|
output, img_mode = st.session_state["RealESRGAN"].enhance(x_sample[:,:,::-1])
|
|
esrgan_filename = original_filename + '-esrgan4x'
|
|
esrgan_sample = output[:,:,::-1]
|
|
esrgan_image = Image.fromarray(esrgan_sample)
|
|
|
|
#save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
|
#normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
|
|
#save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
|
|
|
|
save_sample(esrgan_image, sample_path_i, esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
|
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
|
save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, save_individual_images=False)
|
|
|
|
output_images.append(esrgan_image) #287
|
|
if simple_templating:
|
|
grid_captions.append( captions[i] + "\nesrgan" )
|
|
|
|
if use_RealESRGAN and st.session_state["RealESRGAN"] is not None and use_GFPGAN and st.session_state["GFPGAN"] is not None:
|
|
#skip_save = True # #287 >_>
|
|
torch_gc()
|
|
cropped_faces, restored_faces, restored_img = st.session_state["GFPGAN"].enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
|
gfpgan_sample = restored_img[:,:,::-1]
|
|
|
|
if st.session_state["RealESRGAN"].model.name != realesrgan_model_name:
|
|
#try_loading_RealESRGAN(realesrgan_model_name)
|
|
load_models(use_GFPGAN=use_GFPGAN, use_RealESRGAN=use_RealESRGAN, RealESRGAN_model=realesrgan_model_name)
|
|
|
|
output, img_mode = st.session_state["RealESRGAN"].enhance(gfpgan_sample[:,:,::-1])
|
|
gfpgan_esrgan_filename = original_filename + '-gfpgan-esrgan4x'
|
|
gfpgan_esrgan_sample = output[:,:,::-1]
|
|
gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)
|
|
|
|
save_sample(gfpgan_esrgan_image, sample_path_i, gfpgan_esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
|
normalize_prompt_weights, False, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
|
save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, save_individual_images=False)
|
|
|
|
output_images.append(gfpgan_esrgan_image) #287
|
|
|
|
if simple_templating:
|
|
grid_captions.append( captions[i] + "\ngfpgan_esrgan" )
|
|
|
|
if mask_restore and init_mask:
|
|
#init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
|
|
init_mask = init_mask.filter(ImageFilter.GaussianBlur(mask_blur_strength))
|
|
init_mask = init_mask.convert('L')
|
|
init_img = init_img.convert('RGB')
|
|
image = image.convert('RGB')
|
|
|
|
if use_RealESRGAN and st.session_state["RealESRGAN"] is not None:
|
|
if st.session_state["RealESRGAN"].model.name != realesrgan_model_name:
|
|
#try_loading_RealESRGAN(realesrgan_model_name)
|
|
load_models(use_GFPGAN=use_GFPGAN, use_RealESRGAN=use_RealESRGAN, RealESRGAN_model=realesrgan_model_name)
|
|
|
|
output, img_mode = st.session_state["RealESRGAN"].enhance(np.array(init_img, dtype=np.uint8))
|
|
init_img = Image.fromarray(output)
|
|
init_img = init_img.convert('RGB')
|
|
|
|
output, img_mode = st.session_state["RealESRGAN"].enhance(np.array(init_mask, dtype=np.uint8))
|
|
init_mask = Image.fromarray(output)
|
|
init_mask = init_mask.convert('L')
|
|
|
|
image = Image.composite(init_img, image, init_mask)
|
|
|
|
if save_individual_images:
|
|
save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
|
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
|
save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, save_individual_images)
|
|
|
|
if not use_GFPGAN or not use_RealESRGAN:
|
|
output_images.append(image)
|
|
|
|
#if add_original_image or not simple_templating:
|
|
#output_images.append(image)
|
|
#if simple_templating:
|
|
#grid_captions.append( captions[i] )
|
|
|
|
if st.session_state['defaults'].general.optimized:
|
|
mem = torch.cuda.memory_allocated()/1e6
|
|
st.session_state.modelFS.to("cpu")
|
|
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
|
time.sleep(1)
|
|
|
|
if prompt_matrix or save_grid:
|
|
if prompt_matrix:
|
|
if simple_templating:
|
|
grid = image_grid(output_images, n_iter, force_n_rows=frows, captions=grid_captions)
|
|
else:
|
|
grid = image_grid(output_images, n_iter, force_n_rows=1 << ((len(prompt_matrix_parts)-1)//2))
|
|
try:
|
|
grid = draw_prompt_matrix(grid, width, height, prompt_matrix_parts)
|
|
except:
|
|
import traceback
|
|
print("Error creating prompt_matrix text:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
else:
|
|
grid = image_grid(output_images, batch_size)
|
|
|
|
if grid and (batch_size > 1 or n_iter > 1):
|
|
output_images.insert(0, grid)
|
|
|
|
grid_count = get_next_sequence_number(outpath, 'grid-')
|
|
grid_file = f"grid-{grid_count:05}-{seed}_{slugify(prompts[i].replace(' ', '_')[:220-len(full_path)])}.{grid_ext}"
|
|
grid.save(os.path.join(outpath, grid_file), grid_format, quality=grid_quality, lossless=grid_lossless, optimize=True)
|
|
|
|
toc = time.time()
|
|
|
|
mem_max_used, mem_total = mem_mon.read_and_stop()
|
|
time_diff = time.time()-start_time
|
|
|
|
info = f"""
|
|
{prompt}
|
|
Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', Denoising strength: '+str(denoising_strength) if init_img is not None else ''}{', GFPGAN' if use_GFPGAN and st.session_state["GFPGAN"] is not None else ''}{', '+realesrgan_model_name if use_RealESRGAN and st.session_state["RealESRGAN"] is not None else ''}{', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()
|
|
stats = f'''
|
|
Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
|
|
Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%'''
|
|
|
|
for comment in comments:
|
|
info += "\n\n" + comment
|
|
|
|
#mem_mon.stop()
|
|
#del mem_mon
|
|
torch_gc()
|
|
|
|
return output_images, seed, info, stats
|
|
|
|
|
|
def resize_image(resize_mode, im, width, height):
|
|
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
|
|
if resize_mode == 0:
|
|
res = im.resize((width, height), resample=LANCZOS)
|
|
elif resize_mode == 1:
|
|
ratio = width / height
|
|
src_ratio = im.width / im.height
|
|
|
|
src_w = width if ratio > src_ratio else im.width * height // im.height
|
|
src_h = height if ratio <= src_ratio else im.height * width // im.width
|
|
|
|
resized = im.resize((src_w, src_h), resample=LANCZOS)
|
|
res = Image.new("RGBA", (width, height))
|
|
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
|
|
else:
|
|
ratio = width / height
|
|
src_ratio = im.width / im.height
|
|
|
|
src_w = width if ratio < src_ratio else im.width * height // im.height
|
|
src_h = height if ratio >= src_ratio else im.height * width // im.width
|
|
|
|
resized = im.resize((src_w, src_h), resample=LANCZOS)
|
|
res = Image.new("RGBA", (width, height))
|
|
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
|
|
|
|
if ratio < src_ratio:
|
|
fill_height = height // 2 - src_h // 2
|
|
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
|
|
res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
|
|
elif ratio > src_ratio:
|
|
fill_width = width // 2 - src_w // 2
|
|
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
|
|
res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
|
|
|
|
return res
|
|
|
|
#
|