mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-14 14:52:31 +03:00
2373 lines
100 KiB
Python
2373 lines
100 KiB
Python
import argparse, os, sys, glob, re
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import cv2
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from perlin import perlinNoise
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from frontend.frontend import draw_gradio_ui
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from frontend.job_manager import JobManager, JobInfo
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from frontend.image_metadata import ImageMetadata
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from frontend.ui_functions import resize_image
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",)
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parser.add_argument("--cli", type=str, help="don't launch web server, take Python function kwargs from this file.", default=None)
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parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",)
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parser.add_argument("--defaults", type=str, help="path to configuration file providing UI defaults, uses same format as cli parameter", default='configs/webui/webui.yaml')
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parser.add_argument("--esrgan-cpu", action='store_true', help="run ESRGAN on cpu", default=False)
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parser.add_argument("--esrgan-gpu", type=int, help="run ESRGAN on specific gpu (overrides --gpu)", default=0)
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parser.add_argument("--extra-models-cpu", action='store_true', help="run extra models (GFGPAN/ESRGAN) on cpu", default=False)
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parser.add_argument("--extra-models-gpu", action='store_true', help="run extra models (GFGPAN/ESRGAN) on cpu", default=False)
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parser.add_argument("--gfpgan-cpu", action='store_true', help="run GFPGAN on cpu", default=False)
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parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) # i disagree with where you're putting it but since all guidefags are doing it this way, there you go
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parser.add_argument("--gfpgan-gpu", type=int, help="run GFPGAN on specific gpu (overrides --gpu) ", default=0)
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parser.add_argument("--gpu", type=int, help="choose which GPU to use if you have multiple", default=0)
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parser.add_argument("--grid-format", type=str, help="png for lossless png files; jpg:quality for lossy jpeg; webp:quality for lossy webp, or webp:-compression for lossless webp", default="jpg:95")
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parser.add_argument("--inbrowser", action='store_true', help="automatically launch the interface in a new tab on the default browser", default=False)
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parser.add_argument("--ldsr-dir", type=str, help="LDSR directory", default=('./src/latent-diffusion' if os.path.exists('./src/latent-diffusion') else './LDSR'))
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parser.add_argument("--n_rows", type=int, default=-1, help="rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default: -1)",)
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parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats", default=False)
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parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware accleration in browser)", default=False)
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parser.add_argument("--no-verify-input", action='store_true', help="do not verify input to check if it's too long", default=False)
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parser.add_argument("--optimized-turbo", action='store_true', help="alternative optimization mode that does not save as much VRAM but runs siginificantly faster")
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parser.add_argument("--optimized", action='store_true', help="load the model onto the device piecemeal instead of all at once to reduce VRAM usage at the cost of performance")
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parser.add_argument("--outdir_img2img", type=str, nargs="?", help="dir to write img2img results to (overrides --outdir)", default=None)
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parser.add_argument("--outdir_imglab", type=str, nargs="?", help="dir to write imglab results to (overrides --outdir)", default=None)
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parser.add_argument("--outdir_txt2img", type=str, nargs="?", help="dir to write txt2img results to (overrides --outdir)", default=None)
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parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)
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parser.add_argument("--filename_format", type=str, nargs="?", help="filenames format", default=None)
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parser.add_argument("--port", type=int, help="choose the port for the gradio webserver to use", default=7860)
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parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
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parser.add_argument("--realesrgan-dir", type=str, help="RealESRGAN directory", default=('./src/realesrgan' if os.path.exists('./src/realesrgan') else './RealESRGAN'))
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parser.add_argument("--realesrgan-model", type=str, help="Upscaling model for RealESRGAN", default=('RealESRGAN_x4plus'))
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parser.add_argument("--save-metadata", action='store_true', help="Store generation parameters in the output png. Drop saved png into Image Lab to read parameters", default=False)
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parser.add_argument("--share-password", type=str, help="Sharing is open by default, use this to set a password. Username: webui", default=None)
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parser.add_argument("--share", action='store_true', help="Should share your server on gradio.app, this allows you to use the UI from your mobile app", default=False)
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parser.add_argument("--skip-grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples", default=False)
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parser.add_argument("--skip-save", action='store_true', help="do not save indiviual samples. For speed measurements.", default=False)
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parser.add_argument('--no-job-manager', action='store_true', help="Don't use the experimental job manager on top of gradio", default=False)
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parser.add_argument("--max-jobs", type=int, help="Maximum number of concurrent 'generate' commands", default=1)
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parser.add_argument("--tiling", action='store_true', help="Generate tiling images", default=False)
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opt = parser.parse_args()
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#Should not be needed anymore
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#os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
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# all selected gpus, can probably be done nicer
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#if opt.extra_models_gpu:
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# gpus = set([opt.gpu, opt.esrgan_gpu, opt.gfpgan_gpu])
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# os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(g) for g in set(gpus))
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#else:
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# os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu)
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import gradio as gr
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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 random
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import threading, asyncio
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import time
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import torch
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import torch.nn as nn
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import yaml
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import glob
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import copy
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from typing import List, Union, Dict, Callable, Any, Optional
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from pathlib import Path
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from collections import namedtuple
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from contextlib import contextmanager, nullcontext
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from einops import rearrange, repeat
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from itertools import islice
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from omegaconf import OmegaConf
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from PIL import Image, ImageFont, ImageDraw, ImageFilter, ImageOps, ImageChops
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from io import BytesIO
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import base64
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import re
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from torch import autocast
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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from ldm.util import instantiate_from_config
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# add global options to models
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def patch_conv(**patch):
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cls = torch.nn.Conv2d
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init = cls.__init__
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def __init__(self, *args, **kwargs):
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return init(self, *args, **kwargs, **patch)
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cls.__init__ = __init__
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if opt.tiling:
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patch_conv(padding_mode='circular')
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print("patched for tiling")
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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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# 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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LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
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invalid_filename_chars = '<>:"/\|?*\n'
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GFPGAN_dir = opt.gfpgan_dir
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RealESRGAN_dir = opt.realesrgan_dir
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LDSR_dir = opt.ldsr_dir
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if opt.optimized_turbo:
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opt.optimized = True
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if opt.no_job_manager:
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job_manager = None
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else:
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job_manager = JobManager(opt.max_jobs)
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opt.max_jobs += 1 # Leave a free job open for button clicks
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# should probably be moved to a settings menu in the UI at some point
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grid_format = [s.lower() for s in opt.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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def chunk(it, size):
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it = iter(it)
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return iter(lambda: tuple(islice(it, size)), ())
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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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def crash(e, s):
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global model
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global device
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print(s, '\n', e)
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try:
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del model
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del device
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except:
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try:
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del device
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except:
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pass
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pass
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print('exiting...calling os._exit(0)')
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t = threading.Timer(0.25, os._exit, args=[0])
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t.start()
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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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handle = pynvml.nvmlDeviceGetHandleByIndex(opt.gpu)
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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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class KDiffusionSampler:
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def __init__(self, m, sampler):
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self.model = m
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self.model_wrap = K.external.CompVisDenoiser(m)
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self.schedule = sampler
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def get_sampler_name(self):
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return self.schedule
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def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T):
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sigmas = self.model_wrap.get_sigmas(S)
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x = x_T * sigmas[0]
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model_wrap_cfg = CFGDenoiser(self.model_wrap)
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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)
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return samples_ddim, None
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def create_random_tensors(shape, seeds):
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xs = []
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for seed in seeds:
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torch.manual_seed(seed)
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# randn results depend on device; gpu and cpu get different results for same seed;
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# the way I see it, it's better to do this on CPU, so that everyone gets same result;
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# but the original script had it like this so i do not dare change it for now because
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# it will break everyone's seeds.
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xs.append(torch.randn(shape, device=device))
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x = torch.stack(xs)
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return x
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def torch_gc():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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def load_LDSR(checking=False):
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model_name = 'model'
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yaml_name = 'project'
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model_path = os.path.join(LDSR_dir, 'experiments/pretrained_models', model_name + '.ckpt')
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yaml_path = os.path.join(LDSR_dir, 'experiments/pretrained_models', yaml_name + '.yaml')
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if not os.path.isfile(model_path):
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raise Exception("LDSR model not found at path "+model_path)
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if not os.path.isfile(yaml_path):
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raise Exception("LDSR model not found at path "+yaml_path)
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if checking == True:
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return True
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sys.path.append(os.path.abspath(LDSR_dir))
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from LDSR import LDSR
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LDSRObject = LDSR(model_path, yaml_path)
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return LDSRObject
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def load_GFPGAN(checking=False):
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model_name = 'GFPGANv1.3'
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model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
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if not os.path.isfile(model_path):
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raise Exception("GFPGAN model not found at path "+model_path)
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if checking == True:
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return True
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sys.path.append(os.path.abspath(GFPGAN_dir))
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from gfpgan import GFPGANer
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if opt.gfpgan_cpu or opt.extra_models_cpu:
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instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cpu'))
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elif opt.extra_models_gpu:
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instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f'cuda:{opt.gfpgan_gpu}'))
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else:
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instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f'cuda:{opt.gpu}'))
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return instance
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def load_RealESRGAN(model_name: str, checking = False):
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from basicsr.archs.rrdbnet_arch import RRDBNet
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RealESRGAN_models = {
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'RealESRGAN_x4plus': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4),
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'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)
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}
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model_path = os.path.join(RealESRGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
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if not os.path.isfile(model_path):
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raise Exception(model_name+".pth not found at path "+model_path)
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if checking == True:
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return True
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sys.path.append(os.path.abspath(RealESRGAN_dir))
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from realesrgan import RealESRGANer
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if opt.esrgan_cpu or opt.extra_models_cpu:
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instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=False) # cpu does not support half
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instance.device = torch.device('cpu')
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instance.model.to('cpu')
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elif opt.extra_models_gpu:
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instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not opt.no_half, gpu_id=opt.esrgan_gpu)
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else:
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instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not opt.no_half)
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instance.model.name = model_name
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return instance
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GFPGAN = None
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if os.path.exists(GFPGAN_dir):
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try:
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GFPGAN = load_GFPGAN(checking=True)
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print("Found 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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RealESRGAN = None
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def try_loading_RealESRGAN(model_name: str,checking=False):
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global RealESRGAN
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if os.path.exists(RealESRGAN_dir):
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try:
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RealESRGAN = load_RealESRGAN(model_name,checking) # TODO: Should try to load both models before giving up
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if checking == True:
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print("Found RealESRGAN")
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return True
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print("Loaded RealESRGAN with model "+RealESRGAN.model.name)
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except Exception:
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import traceback
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|
print("Error loading RealESRGAN:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
try_loading_RealESRGAN('RealESRGAN_x4plus',checking=True)
|
|
|
|
LDSR = None
|
|
def try_loading_LDSR(model_name: str,checking=False):
|
|
global LDSR
|
|
if os.path.exists(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)
|
|
|
|
def load_SD_model():
|
|
if opt.optimized:
|
|
sd = load_sd_from_config(opt.ckpt)
|
|
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)
|
|
|
|
config = OmegaConf.load("optimizedSD/v1-inference.yaml")
|
|
device = torch.device(f"cuda:{opt.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 = opt.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 opt.no_half:
|
|
model = model.half()
|
|
modelCS = modelCS.half()
|
|
modelFS = modelFS.half()
|
|
return model,modelCS,modelFS,device, config
|
|
else:
|
|
config = OmegaConf.load(opt.config)
|
|
model = load_model_from_config(config, opt.ckpt)
|
|
|
|
device = torch.device(f"cuda:{opt.gpu}") if torch.cuda.is_available() else torch.device("cpu")
|
|
model = (model if opt.no_half else model.half()).to(device)
|
|
return model, device,config
|
|
|
|
if opt.optimized:
|
|
model,modelCS,modelFS,device, config = load_SD_model()
|
|
else:
|
|
model, device,config = load_SD_model()
|
|
|
|
|
|
def load_embeddings(fp):
|
|
if fp is not None and hasattr(model, "embedding_manager"):
|
|
model.embedding_manager.load(fp.name)
|
|
|
|
|
|
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 image_grid(imgs, batch_size, force_n_rows=None, captions=None):
|
|
if force_n_rows is not None:
|
|
rows = force_n_rows
|
|
elif opt.n_rows > 0:
|
|
rows = opt.n_rows
|
|
elif opt.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)
|
|
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 = (model if not opt.optimized else modelCS).cond_stage_model.tokenizer
|
|
max_length = (model if not opt.optimized else modelCS).cond_stage_model.max_length
|
|
|
|
info = (model if not opt.optimized else 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, write_info_files, write_sample_info_to_log_file, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
|
|
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, skip_metadata=False):
|
|
''' saves the image according to selected parameters. Expects to find generation parameters on image, set by ImageMetadata.set_on_image() '''
|
|
metadata = ImageMetadata.get_from_image(image)
|
|
if not skip_metadata and metadata is None:
|
|
print("No metadata passed in to save. Set metadata on the image before calling save_sample using the ImageMetadata.set_on_image() function.")
|
|
skip_metadata = True
|
|
filename_i = os.path.join(sample_path_i, filename)
|
|
if not jpg_sample:
|
|
if opt.save_metadata and not skip_metadata:
|
|
image.save(f"{filename_i}.png", pnginfo=metadata.as_png_info())
|
|
else:
|
|
image.save(f"{filename_i}.png")
|
|
else:
|
|
image.save(f"{filename_i}.jpg", 'jpeg', quality=100, optimize=True)
|
|
if write_info_files or write_sample_info_to_log_file:
|
|
# toggles differ for txt2img vs. img2img:
|
|
offset = 0 if init_img is None else 2
|
|
toggles = []
|
|
if prompt_matrix:
|
|
toggles.append(0)
|
|
if metadata.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 not skip_save:
|
|
toggles.append(2 + offset)
|
|
if not skip_grid:
|
|
toggles.append(3 + offset)
|
|
if sort_samples:
|
|
toggles.append(4 + offset)
|
|
if write_info_files:
|
|
toggles.append(5 + offset)
|
|
if write_sample_info_to_log_file:
|
|
toggles.append(6+offset)
|
|
if metadata.GFPGAN:
|
|
toggles.append(7 + offset)
|
|
|
|
info_dict = dict(
|
|
target="txt2img" if init_img is None else "img2img",
|
|
prompt=metadata.prompt, ddim_steps=metadata.steps, toggles=toggles, sampler_name=sampler_name,
|
|
ddim_eta=ddim_eta, n_iter=n_iter, batch_size=batch_size, cfg_scale=metadata.cfg_scale,
|
|
seed=metadata.seed, width=metadata.width, height=metadata.height
|
|
)
|
|
if init_img is not None:
|
|
# Not yet any use for these, but they bloat up the files:
|
|
#info_dict["init_img"] = init_img
|
|
#info_dict["init_mask"] = init_mask
|
|
info_dict["denoising_strength"] = denoising_strength
|
|
info_dict["resize_mode"] = resize_mode
|
|
if write_info_files:
|
|
with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
|
|
yaml.dump(info_dict, f, allow_unicode=True, width=10000)
|
|
|
|
if write_sample_info_to_log_file:
|
|
ignore_list = ["prompt", "target", "toggles", "ddim_eta", "batch_size"]
|
|
rename_dict = {"ddim_steps": "steps", "n_iter": "number", "sampler_name": "sampler"} #changes the name of parameters to match with dynamic parameters
|
|
sample_log_path = os.path.join(sample_path_i, "log.yaml")
|
|
log_dump = info_dict.get("prompt") # making sure the first item that is listed in the txt is the prompt text
|
|
for key, value in info_dict.items():
|
|
if key in ignore_list:
|
|
continue
|
|
found_key = rename_dict.get(key)
|
|
|
|
if key == "cfg_scale": #adds zeros to to cfg_scale necessary for dynamic params
|
|
value = str(value).zfill(2)
|
|
|
|
if found_key:
|
|
key = found_key
|
|
log_dump += f" {key} {value}"
|
|
|
|
log_dump = log_dump + " \n" #space at the end for dynamic params to accept the last param
|
|
with open(sample_log_path, "a", encoding="utf8") as log_file:
|
|
log_file.write(log_dump)
|
|
|
|
|
|
|
|
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 perform_color_correction(img_rgb, correction_target_lab, do_color_correction):
|
|
try:
|
|
from skimage import exposure
|
|
except:
|
|
print("Install scikit-image to perform color correction")
|
|
return img_rgb
|
|
|
|
if not do_color_correction: return img_rgb
|
|
if correction_target_lab is None: return img_rgb
|
|
|
|
return (
|
|
Image.fromarray(cv2.cvtColor(exposure.match_histograms(
|
|
cv2.cvtColor(
|
|
np.asarray(img_rgb),
|
|
cv2.COLOR_RGB2LAB
|
|
),
|
|
correction_target_lab,
|
|
channel_axis=2
|
|
), cv2.COLOR_LAB2RGB).astype("uint8")
|
|
)
|
|
)
|
|
|
|
def process_images(
|
|
outpath, func_init, func_sample, prompt, seed, sampler_name, skip_grid, skip_save, batch_size,
|
|
n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, use_RealESRGAN, realesrgan_model_name,
|
|
fp, ddim_eta=0.0, do_not_save_grid=False, normalize_prompt_weights=True, init_img=None, init_mask=None,
|
|
keep_mask=False, mask_blur_strength=3, mask_restore=False, denoising_strength=0.75, resize_mode=None, uses_loopback=False,
|
|
uses_random_seed_loopback=False, sort_samples=True, write_info_files=True, write_sample_info_to_log_file=False, jpg_sample=False,
|
|
variant_amount=0.0, variant_seed=None,imgProcessorTask=False, job_info: JobInfo = None, do_color_correction=False, correction_target=None):
|
|
"""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"""
|
|
prompt = prompt or ''
|
|
torch_gc()
|
|
# start time after garbage collection (or before?)
|
|
start_time = time.time()
|
|
|
|
mem_mon = MemUsageMonitor('MemMon')
|
|
mem_mon.start()
|
|
|
|
if hasattr(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 = True
|
|
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 opt.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))]
|
|
original_seeds = all_seeds.copy()
|
|
|
|
precision_scope = autocast if opt.precision == "autocast" else nullcontext
|
|
if job_info:
|
|
output_images = job_info.images
|
|
else:
|
|
output_images = []
|
|
grid_captions = []
|
|
stats = []
|
|
with torch.no_grad(), precision_scope("cuda"), (model.ema_scope() if not opt.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):
|
|
if job_info and job_info.should_stop.is_set():
|
|
print("Early exit requested")
|
|
break
|
|
|
|
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]
|
|
current_seeds = original_seeds[n * batch_size:(n + 1) * batch_size]
|
|
|
|
if job_info:
|
|
job_info.job_status = f"Processing Iteration {n+1}/{n_iter}. Batch size {batch_size}"
|
|
for idx,(p,s) in enumerate(zip(prompts,seeds)):
|
|
job_info.job_status += f"\nItem {idx}: Seed {s}\nPrompt: {p}"
|
|
|
|
if opt.optimized:
|
|
modelCS.to(device)
|
|
uc = (model if not opt.optimized else 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, (model if not opt.optimized else modelCS).get_learned_conditioning(weighted_subprompts[i][0]), alpha=weighted_subprompts[i][1])
|
|
else: # just behave like usual
|
|
c = (model if not opt.optimized else modelCS).get_learned_conditioning(prompts)
|
|
|
|
shape = [opt_C, height // opt_f, width // opt_f]
|
|
|
|
if opt.optimized:
|
|
mem = torch.cuda.memory_allocated()/1e6
|
|
modelCS.to("cpu")
|
|
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
|
time.sleep(1)
|
|
|
|
cur_variant_amount = variant_amount
|
|
if variant_amount == 0.0:
|
|
# we manually generate all input noises because each one should have a specific seed
|
|
x = create_random_tensors(shape, seeds=seeds)
|
|
else: # 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)
|
|
target_x = create_random_tensors(shape, seeds=[specified_variant_seed])
|
|
# with a variant seed we would end up with the same variant as the basic seed
|
|
# does not change. But we can increase the steps to get an interesting result
|
|
# that shows more and more deviation of the original image and let us adjust
|
|
# how far we will go (using 10 iterations with variation amount set to 0.02 will
|
|
# generate an icreasingly variated image which is very interesting for movies)
|
|
cur_variant_amount += n*variant_amount
|
|
else:
|
|
target_x = create_random_tensors(shape, seeds=seeds)
|
|
# finally, slerp base_x noise to target_x noise for creating a variant
|
|
x = slerp(device, max(0.0, min(1.0, cur_variant_amount)), base_x, target_x)
|
|
|
|
samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc, sampler_name=sampler_name)
|
|
|
|
if opt.optimized:
|
|
modelFS.to(device)
|
|
|
|
|
|
|
|
x_samples_ddim = (model if not opt.optimized else 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 = prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})
|
|
if variant_seed != None and variant_seed != '':
|
|
if variant_amount == 0.0:
|
|
seed_used = f"{current_seeds[i]}-{variant_seed}"
|
|
else:
|
|
seed_used = f"{seed}-{variant_seed}"
|
|
else:
|
|
seed_used = f"{current_seeds[i]}"
|
|
if sort_samples:
|
|
sanitized_prompt = sanitized_prompt[:128] #200 is too long
|
|
sample_path_i = os.path.join(sample_path, sanitized_prompt)
|
|
os.makedirs(sample_path_i, exist_ok=True)
|
|
base_count = get_next_sequence_number(sample_path_i)
|
|
filename = opt.filename_format or "[STEPS]_[SAMPLER]_[SEED]_[VARIANT_AMOUNT]"
|
|
else:
|
|
sample_path_i = sample_path
|
|
base_count = get_next_sequence_number(sample_path_i)
|
|
filename = opt.filename_format or "[STEPS]_[SAMPLER]_[SEED]_[VARIANT_AMOUNT]_[PROMPT]"
|
|
|
|
#Add new filenames tags here
|
|
filename = f"{base_count:05}-" + filename
|
|
filename = filename.replace("[STEPS]", str(steps))
|
|
filename = filename.replace("[CFG]", str(cfg_scale))
|
|
filename = filename.replace("[PROMPT]", sanitized_prompt[:128])
|
|
filename = filename.replace("[PROMPT_SPACES]", prompts[i].translate({ord(x): '' for x in invalid_filename_chars})[:128])
|
|
filename = filename.replace("[WIDTH]", str(width))
|
|
filename = filename.replace("[HEIGHT]", str(height))
|
|
filename = filename.replace("[SAMPLER]", sampler_name)
|
|
filename = filename.replace("[SEED]", seed_used)
|
|
filename = filename.replace("[VARIANT_AMOUNT]", f"{cur_variant_amount:.2f}")
|
|
|
|
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
|
x_sample = x_sample.astype(np.uint8)
|
|
metadata = ImageMetadata(prompt=prompts[i], seed=seeds[i], height=height, width=width, steps=steps,
|
|
cfg_scale=cfg_scale, normalize_prompt_weights=normalize_prompt_weights, denoising_strength=denoising_strength,
|
|
GFPGAN=use_GFPGAN )
|
|
image = Image.fromarray(x_sample)
|
|
image = perform_color_correction(image, correction_target, do_color_correction)
|
|
ImageMetadata.set_on_image(image, metadata)
|
|
|
|
original_sample = x_sample
|
|
original_filename = filename
|
|
if use_GFPGAN and GFPGAN is not None and not use_RealESRGAN:
|
|
skip_save = True # #287 >_>
|
|
torch_gc()
|
|
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(original_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
|
gfpgan_sample = restored_img[:,:,::-1]
|
|
gfpgan_image = Image.fromarray(gfpgan_sample)
|
|
gfpgan_image = perform_color_correction(gfpgan_image, correction_target, do_color_correction)
|
|
gfpgan_metadata = copy.copy(metadata)
|
|
gfpgan_metadata.GFPGAN = True
|
|
ImageMetadata.set_on_image( gfpgan_image, gfpgan_metadata )
|
|
gfpgan_filename = original_filename + '-gfpgan'
|
|
save_sample(gfpgan_image, sample_path_i, gfpgan_filename, jpg_sample, write_info_files, write_sample_info_to_log_file, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
|
|
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, skip_metadata=False)
|
|
output_images.append(gfpgan_image) #287
|
|
#if simple_templating:
|
|
# grid_captions.append( captions[i] + "\ngfpgan" )
|
|
|
|
if use_RealESRGAN and RealESRGAN is not None and not use_GFPGAN:
|
|
skip_save = True # #287 >_>
|
|
torch_gc()
|
|
output, img_mode = RealESRGAN.enhance(original_sample[:,:,::-1])
|
|
esrgan_filename = original_filename + '-esrgan4x'
|
|
esrgan_sample = output[:,:,::-1]
|
|
esrgan_image = Image.fromarray(esrgan_sample)
|
|
esrgan_image = perform_color_correction(esrgan_image, correction_target, do_color_correction)
|
|
ImageMetadata.set_on_image( esrgan_image, metadata )
|
|
save_sample(esrgan_image, sample_path_i, esrgan_filename, jpg_sample, write_info_files, write_sample_info_to_log_file, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
|
|
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, skip_metadata=False)
|
|
output_images.append(esrgan_image) #287
|
|
#if simple_templating:
|
|
# grid_captions.append( captions[i] + "\nesrgan" )
|
|
|
|
if use_RealESRGAN and RealESRGAN is not None and use_GFPGAN and GFPGAN is not None:
|
|
skip_save = True # #287 >_>
|
|
torch_gc()
|
|
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
|
gfpgan_sample = restored_img[:,:,::-1]
|
|
output, img_mode = 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)
|
|
gfpgan_esrgan_image = perform_color_correction(gfpgan_esrgan_image, correction_target, do_color_correction)
|
|
ImageMetadata.set_on_image(gfpgan_esrgan_image, metadata)
|
|
save_sample(gfpgan_esrgan_image, sample_path_i, gfpgan_esrgan_filename, jpg_sample, write_info_files, write_sample_info_to_log_file, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
|
skip_save, skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, skip_metadata=False)
|
|
output_images.append(gfpgan_esrgan_image) #287
|
|
#if simple_templating:
|
|
# grid_captions.append( captions[i] + "\ngfpgan_esrgan" )
|
|
|
|
# this flag is used for imgProcessorTasks like GoBig, will return the image without saving it
|
|
if imgProcessorTask == True:
|
|
output_images.append(image)
|
|
|
|
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 RealESRGAN is not None:
|
|
if RealESRGAN.model.name != realesrgan_model_name:
|
|
try_loading_RealESRGAN(realesrgan_model_name)
|
|
output, img_mode = RealESRGAN.enhance(np.array(init_img, dtype=np.uint8))
|
|
init_img = Image.fromarray(output)
|
|
init_img = init_img.convert('RGB')
|
|
|
|
output, img_mode = 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 not skip_save:
|
|
save_sample(image, sample_path_i, filename, jpg_sample, write_info_files, write_sample_info_to_log_file, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
|
|
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, False)
|
|
if add_original_image or not simple_templating:
|
|
output_images.append(image)
|
|
if simple_templating:
|
|
grid_captions.append( captions[i] )
|
|
|
|
if opt.optimized:
|
|
mem = torch.cuda.memory_allocated()/1e6
|
|
modelFS.to("cpu")
|
|
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
|
time.sleep(1)
|
|
|
|
if (prompt_matrix or not skip_grid) and not do_not_save_grid:
|
|
grid = None
|
|
if prompt_matrix:
|
|
if simple_templating:
|
|
grid = image_grid(output_images, batch_size, force_n_rows=frows, captions=grid_captions)
|
|
else:
|
|
grid = image_grid(output_images, batch_size, 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)
|
|
elif batch_size > 1 or n_iter > 1:
|
|
grid = image_grid(output_images, batch_size)
|
|
if grid is not None:
|
|
grid_count = get_next_sequence_number(outpath, 'grid-')
|
|
grid_file = f"grid-{grid_count:05}-{seed}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.{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
|
|
args_and_names = {
|
|
"seed": seed,
|
|
"width": width,
|
|
"height": height,
|
|
"steps": steps,
|
|
"cfg_scale": cfg_scale,
|
|
"sampler": sampler_name,
|
|
}
|
|
|
|
full_string = f"{prompt}\n"+ " ".join([f"{k}:" for k,v in args_and_names.items()])
|
|
info = {
|
|
'text': full_string,
|
|
'entities': [{'entity':str(v), 'start': full_string.find(f"{k}:"),'end': full_string.find(f"{k}:") + len(f"{k} ")} for k,v in args_and_names.items()]
|
|
}
|
|
# info = f"""
|
|
# {prompt} --seed {seed} --W {width} --H {height} -s {steps} -C {cfg_scale} --sampler {sampler_name} {', Denoising strength: '+str(denoising_strength) if init_img is not None else ''}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}{', '+realesrgan_model_name if use_RealESRGAN and 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['text'] += "\n\n" + comment
|
|
|
|
#mem_mon.stop()
|
|
#del mem_mon
|
|
torch_gc()
|
|
|
|
return output_images, seed, info, stats
|
|
|
|
|
|
def txt2img(prompt: str, ddim_steps: int, sampler_name: str, toggles: List[int], realesrgan_model_name: str,
|
|
ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: Union[int, str, None],
|
|
height: int, width: int, fp, variant_amount: float = None, variant_seed: int = None, job_info: JobInfo = None):
|
|
outpath = opt.outdir_txt2img or opt.outdir or "outputs/txt2img-samples"
|
|
err = False
|
|
seed = seed_to_int(seed)
|
|
prompt_matrix = 0 in toggles
|
|
normalize_prompt_weights = 1 in toggles
|
|
skip_save = 2 not in toggles
|
|
skip_grid = 3 not in toggles
|
|
sort_samples = 4 in toggles
|
|
write_info_files = 5 in toggles
|
|
write_to_one_file = 6 in toggles
|
|
jpg_sample = 7 in toggles
|
|
use_GFPGAN = 8 in toggles
|
|
use_RealESRGAN = 9 in toggles
|
|
|
|
do_color_correction = False
|
|
correction_target = None
|
|
|
|
ModelLoader(['model'],True,False)
|
|
if use_GFPGAN and not use_RealESRGAN:
|
|
ModelLoader(['GFPGAN'],True,False)
|
|
ModelLoader(['RealESRGAN'],False,True)
|
|
if use_RealESRGAN and not use_GFPGAN:
|
|
ModelLoader(['GFPGAN'],False,True)
|
|
ModelLoader(['RealESRGAN'],True,False,realesrgan_model_name)
|
|
if use_RealESRGAN and use_GFPGAN:
|
|
ModelLoader(['GFPGAN','RealESRGAN'],True,False,realesrgan_model_name)
|
|
if sampler_name == 'PLMS':
|
|
sampler = PLMSSampler(model)
|
|
elif sampler_name == 'DDIM':
|
|
sampler = DDIMSampler(model)
|
|
elif sampler_name == 'k_dpm_2_a':
|
|
sampler = KDiffusionSampler(model,'dpm_2_ancestral')
|
|
elif sampler_name == 'k_dpm_2':
|
|
sampler = KDiffusionSampler(model,'dpm_2')
|
|
elif sampler_name == 'k_euler_a':
|
|
sampler = KDiffusionSampler(model,'euler_ancestral')
|
|
elif sampler_name == 'k_euler':
|
|
sampler = KDiffusionSampler(model,'euler')
|
|
elif sampler_name == 'k_heun':
|
|
sampler = KDiffusionSampler(model,'heun')
|
|
elif sampler_name == 'k_lms':
|
|
sampler = KDiffusionSampler(model,'lms')
|
|
else:
|
|
raise Exception("Unknown sampler: " + sampler_name)
|
|
|
|
def init():
|
|
pass
|
|
|
|
def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
|
|
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x)
|
|
return samples_ddim
|
|
|
|
try:
|
|
output_images, seed, info, stats = process_images(
|
|
outpath=outpath,
|
|
func_init=init,
|
|
func_sample=sample,
|
|
prompt=prompt,
|
|
seed=seed,
|
|
sampler_name=sampler_name,
|
|
skip_save=skip_save,
|
|
skip_grid=skip_grid,
|
|
batch_size=batch_size,
|
|
n_iter=n_iter,
|
|
steps=ddim_steps,
|
|
cfg_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
prompt_matrix=prompt_matrix,
|
|
use_GFPGAN=use_GFPGAN,
|
|
use_RealESRGAN=use_RealESRGAN,
|
|
realesrgan_model_name=realesrgan_model_name,
|
|
fp=fp,
|
|
ddim_eta=ddim_eta,
|
|
normalize_prompt_weights=normalize_prompt_weights,
|
|
sort_samples=sort_samples,
|
|
write_info_files=write_info_files,
|
|
write_sample_info_to_log_file=write_to_one_file,
|
|
jpg_sample=jpg_sample,
|
|
variant_amount=variant_amount,
|
|
variant_seed=variant_seed,
|
|
job_info=job_info,
|
|
do_color_correction=do_color_correction,
|
|
correction_target=correction_target
|
|
)
|
|
|
|
del sampler
|
|
|
|
return output_images, seed, info, stats
|
|
except RuntimeError as e:
|
|
err = e
|
|
err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
|
|
stats = err_msg
|
|
return [], seed, 'err', stats
|
|
finally:
|
|
if err:
|
|
crash(err, '!!Runtime error (txt2img)!!')
|
|
|
|
|
|
class Flagging(gr.FlaggingCallback):
|
|
|
|
def setup(self, components, flagging_dir: str):
|
|
pass
|
|
|
|
def flag(self, flag_data, flag_option=None, flag_index=None, username=None):
|
|
import csv
|
|
|
|
os.makedirs("log/images", exist_ok=True)
|
|
|
|
# those must match the "txt2img" function !! + images, seed, comment, stats !! NOTE: changes to UI output must be reflected here too
|
|
prompt, ddim_steps, sampler_name, toggles, ddim_eta, n_iter, batch_size, cfg_scale, seed, height, width, fp, variant_amount, variant_seed, images, seed, comment, stats = flag_data
|
|
|
|
filenames = []
|
|
|
|
with open("log/log.csv", "a", encoding="utf8", newline='') as file:
|
|
import time
|
|
import base64
|
|
|
|
at_start = file.tell() == 0
|
|
writer = csv.writer(file)
|
|
if at_start:
|
|
writer.writerow(["sep=,"])
|
|
writer.writerow(["prompt", "seed", "width", "height", "sampler", "toggles", "n_iter", "n_samples", "cfg_scale", "steps", "filename"])
|
|
|
|
filename_base = str(int(time.time() * 1000))
|
|
for i, filedata in enumerate(images):
|
|
filename = "log/images/"+filename_base + ("" if len(images) == 1 else "-"+str(i+1)) + ".png"
|
|
|
|
if filedata.startswith("data:image/png;base64,"):
|
|
filedata = filedata[len("data:image/png;base64,"):]
|
|
|
|
with open(filename, "wb") as imgfile:
|
|
imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
|
|
|
|
filenames.append(filename)
|
|
|
|
writer.writerow([prompt, seed, width, height, sampler_name, toggles, n_iter, batch_size, cfg_scale, ddim_steps, filenames[0]])
|
|
|
|
print("Logged:", filenames[0])
|
|
|
|
|
|
def blurArr(a,r=8):
|
|
im1=Image.fromarray((a*255).astype(np.int8),"L")
|
|
im2 = im1.filter(ImageFilter.GaussianBlur(radius = r))
|
|
out= np.array(im2)/255
|
|
return out
|
|
|
|
|
|
|
|
def img2img(prompt: str, image_editor_mode: str, mask_mode: str, mask_blur_strength: int, mask_restore: bool, ddim_steps: int, sampler_name: str,
|
|
toggles: List[int], realesrgan_model_name: str, n_iter: int, cfg_scale: float, denoising_strength: float,
|
|
seed: int, height: int, width: int, resize_mode: int, init_info: any = None, init_info_mask: any = None, fp = None, job_info: JobInfo = None):
|
|
# print([prompt, image_editor_mode, init_info, init_info_mask, mask_mode,
|
|
# mask_blur_strength, ddim_steps, sampler_name, toggles,
|
|
# realesrgan_model_name, n_iter, cfg_scale,
|
|
# denoising_strength, seed, height, width, resize_mode,
|
|
# fp])
|
|
outpath = opt.outdir_img2img or opt.outdir or "outputs/img2img-samples"
|
|
err = False
|
|
seed = seed_to_int(seed)
|
|
|
|
batch_size = 1
|
|
|
|
prompt_matrix = 0 in toggles
|
|
normalize_prompt_weights = 1 in toggles
|
|
loopback = 2 in toggles
|
|
random_seed_loopback = 3 in toggles
|
|
skip_save = 4 not in toggles
|
|
skip_grid = 5 not in toggles
|
|
sort_samples = 6 in toggles
|
|
write_info_files = 7 in toggles
|
|
write_sample_info_to_log_file = 8 in toggles
|
|
jpg_sample = 9 in toggles
|
|
do_color_correction = 10 in toggles
|
|
use_GFPGAN = 11 in toggles
|
|
use_RealESRGAN = 12 in toggles
|
|
ModelLoader(['model'],True,False)
|
|
if use_GFPGAN and not use_RealESRGAN:
|
|
ModelLoader(['GFPGAN'],True,False)
|
|
ModelLoader(['RealESRGAN'],False,True)
|
|
if use_RealESRGAN and not use_GFPGAN:
|
|
ModelLoader(['GFPGAN'],False,True)
|
|
ModelLoader(['RealESRGAN'],True,False,realesrgan_model_name)
|
|
if use_RealESRGAN and use_GFPGAN:
|
|
ModelLoader(['GFPGAN','RealESRGAN'],True,False,realesrgan_model_name)
|
|
if sampler_name == 'DDIM':
|
|
sampler = DDIMSampler(model)
|
|
elif sampler_name == 'k_dpm_2_a':
|
|
sampler = KDiffusionSampler(model,'dpm_2_ancestral')
|
|
elif sampler_name == 'k_dpm_2':
|
|
sampler = KDiffusionSampler(model,'dpm_2')
|
|
elif sampler_name == 'k_euler_a':
|
|
sampler = KDiffusionSampler(model,'euler_ancestral')
|
|
elif sampler_name == 'k_euler':
|
|
sampler = KDiffusionSampler(model,'euler')
|
|
elif sampler_name == 'k_heun':
|
|
sampler = KDiffusionSampler(model,'heun')
|
|
elif sampler_name == 'k_lms':
|
|
sampler = KDiffusionSampler(model,'lms')
|
|
else:
|
|
raise Exception("Unknown sampler: " + sampler_name)
|
|
|
|
if image_editor_mode == 'Mask':
|
|
init_img = init_info_mask["image"]
|
|
init_img_transparency = ImageOps.invert(init_img.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
|
|
init_img = init_img.convert("RGB")
|
|
init_img = resize_image(resize_mode, init_img, width, height)
|
|
init_img = init_img.convert("RGB")
|
|
init_mask = init_info_mask["mask"]
|
|
init_mask = ImageChops.lighter(init_img_transparency, init_mask.convert('L')).convert('RGBA')
|
|
init_mask = init_mask.convert("RGB")
|
|
init_mask = resize_image(resize_mode, init_mask, width, height)
|
|
init_mask = init_mask.convert("RGB")
|
|
keep_mask = mask_mode == 0
|
|
init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
|
|
else:
|
|
init_img = init_info
|
|
init_mask = None
|
|
keep_mask = False
|
|
|
|
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
|
t_enc = int(denoising_strength * ddim_steps)
|
|
|
|
def init():
|
|
image = init_img.convert("RGB")
|
|
image = resize_image(resize_mode, image, width, height)
|
|
#image = image.convert("RGB")
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = image[None].transpose(0, 3, 1, 2)
|
|
image = torch.from_numpy(image)
|
|
|
|
mask_channel = None
|
|
if image_editor_mode == "Mask":
|
|
alpha = init_mask.convert("RGBA")
|
|
alpha = resize_image(resize_mode, alpha, width // 8, height // 8)
|
|
mask_channel = alpha.split()[1]
|
|
|
|
mask = None
|
|
if mask_channel is not None:
|
|
mask = np.array(mask_channel).astype(np.float32) / 255.0
|
|
mask = (1 - mask)
|
|
mask = np.tile(mask, (4, 1, 1))
|
|
mask = mask[None].transpose(0, 1, 2, 3)
|
|
mask = torch.from_numpy(mask).to(device)
|
|
if opt.optimized:
|
|
modelFS.to(device)
|
|
|
|
#let's try and find where init_image is 0's
|
|
#shape is probably (3,width,height)?
|
|
|
|
if image_editor_mode == "Uncrop":
|
|
_image=image.numpy()[0]
|
|
_mask=np.ones((_image.shape[1],_image.shape[2]))
|
|
|
|
#compute bounding box
|
|
cmax=np.max(_image,axis=0)
|
|
rowmax=np.max(cmax,axis=0)
|
|
colmax=np.max(cmax,axis=1)
|
|
rowwhere=np.where(rowmax>0)[0]
|
|
colwhere=np.where(colmax>0)[0]
|
|
rowstart=rowwhere[0]
|
|
rowend=rowwhere[-1]+1
|
|
colstart=colwhere[0]
|
|
colend=colwhere[-1]+1
|
|
print('bounding box: ',rowstart,rowend,colstart,colend)
|
|
|
|
#this is where noise will get added
|
|
PAD_IMG=16
|
|
boundingbox=np.zeros(shape=(height,width))
|
|
boundingbox[colstart+PAD_IMG:colend-PAD_IMG,rowstart+PAD_IMG:rowend-PAD_IMG]=1
|
|
boundingbox=blurArr(boundingbox,4)
|
|
|
|
#this is the mask for outpainting
|
|
PAD_MASK=24
|
|
boundingbox2=np.zeros(shape=(height,width))
|
|
boundingbox2[colstart+PAD_MASK:colend-PAD_MASK,rowstart+PAD_MASK:rowend-PAD_MASK]=1
|
|
boundingbox2=blurArr(boundingbox2,4)
|
|
|
|
#noise=np.random.randn(*_image.shape)
|
|
noise=np.array([perlinNoise(height,width,height/64,width/64) for i in range(3)])
|
|
_mask*=1-boundingbox2
|
|
|
|
#convert 0,1 to -1,1
|
|
_image = 2. * _image - 1.
|
|
|
|
#add noise
|
|
boundingbox=np.tile(boundingbox,(3,1,1))
|
|
_image=_image*boundingbox+noise*(1-boundingbox)
|
|
|
|
#resize mask
|
|
_mask = np.array(resize_image(resize_mode, Image.fromarray(_mask*255), width // 8, height // 8))/255
|
|
|
|
#convert back to torch tensor
|
|
init_image=torch.from_numpy(np.expand_dims(_image,axis=0).astype(np.float32)).to(device)
|
|
mask=torch.from_numpy(_mask.astype(np.float32)).to(device)
|
|
|
|
else:
|
|
init_image = 2. * image - 1.
|
|
|
|
init_image = init_image.to(device)
|
|
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
|
|
init_latent = (model if not opt.optimized else modelFS).get_first_stage_encoding((model if not opt.optimized else modelFS).encode_first_stage(init_image)) # move to latent space
|
|
|
|
if opt.optimized:
|
|
mem = torch.cuda.memory_allocated()/1e6
|
|
modelFS.to("cpu")
|
|
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
|
time.sleep(1)
|
|
|
|
return init_latent, mask,
|
|
|
|
def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
|
|
t_enc_steps = t_enc
|
|
obliterate = False
|
|
if ddim_steps == t_enc_steps:
|
|
t_enc_steps = t_enc_steps - 1
|
|
obliterate = True
|
|
|
|
if sampler_name != 'DDIM':
|
|
x0, z_mask = init_data
|
|
|
|
sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
|
|
noise = x * sigmas[ddim_steps - t_enc_steps - 1]
|
|
|
|
xi = x0 + noise
|
|
|
|
# Obliterate masked image
|
|
if z_mask is not None and obliterate:
|
|
random = torch.randn(z_mask.shape, device=xi.device)
|
|
xi = (z_mask * noise) + ((1-z_mask) * xi)
|
|
|
|
sigma_sched = sigmas[ddim_steps - t_enc_steps - 1:]
|
|
model_wrap_cfg = CFGMaskedDenoiser(sampler.model_wrap)
|
|
samples_ddim = K.sampling.__dict__[f'sample_{sampler.get_sampler_name()}'](model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale, 'mask': z_mask, 'x0': x0, 'xi': xi}, disable=False)
|
|
else:
|
|
|
|
x0, z_mask = init_data
|
|
|
|
sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=0.0, verbose=False)
|
|
z_enc = sampler.stochastic_encode(x0, torch.tensor([t_enc_steps]*batch_size).to(device))
|
|
|
|
# Obliterate masked image
|
|
if z_mask is not None and obliterate:
|
|
random = torch.randn(z_mask.shape, device=z_enc.device)
|
|
z_enc = (z_mask * random) + ((1-z_mask) * z_enc)
|
|
|
|
# decode it
|
|
samples_ddim = sampler.decode(z_enc, conditioning, t_enc_steps,
|
|
unconditional_guidance_scale=cfg_scale,
|
|
unconditional_conditioning=unconditional_conditioning,
|
|
z_mask=z_mask, x0=x0)
|
|
return samples_ddim
|
|
|
|
|
|
correction_target = None
|
|
if loopback:
|
|
output_images, info = None, None
|
|
history = []
|
|
initial_seed = None
|
|
|
|
# turn on color correction for loopback to prevent known issue of color drift
|
|
do_color_correction = True
|
|
|
|
for i in range(n_iter):
|
|
if do_color_correction and i == 0:
|
|
correction_target = cv2.cvtColor(np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB)
|
|
|
|
output_images, seed, info, stats = process_images(
|
|
outpath=outpath,
|
|
func_init=init,
|
|
func_sample=sample,
|
|
prompt=prompt,
|
|
seed=seed,
|
|
sampler_name=sampler_name,
|
|
skip_save=skip_save,
|
|
skip_grid=skip_grid,
|
|
batch_size=1,
|
|
n_iter=1,
|
|
steps=ddim_steps,
|
|
cfg_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
prompt_matrix=prompt_matrix,
|
|
use_GFPGAN=use_GFPGAN,
|
|
use_RealESRGAN=False, # Forcefully disable upscaling when using loopback
|
|
realesrgan_model_name=realesrgan_model_name,
|
|
fp=fp,
|
|
do_not_save_grid=True,
|
|
normalize_prompt_weights=normalize_prompt_weights,
|
|
init_img=init_img,
|
|
init_mask=init_mask,
|
|
keep_mask=keep_mask,
|
|
mask_blur_strength=mask_blur_strength,
|
|
mask_restore=mask_restore,
|
|
denoising_strength=denoising_strength,
|
|
resize_mode=resize_mode,
|
|
uses_loopback=loopback,
|
|
uses_random_seed_loopback=random_seed_loopback,
|
|
sort_samples=sort_samples,
|
|
write_info_files=write_info_files,
|
|
write_sample_info_to_log_file=write_sample_info_to_log_file,
|
|
jpg_sample=jpg_sample,
|
|
job_info=job_info,
|
|
do_color_correction=do_color_correction,
|
|
correction_target=correction_target
|
|
)
|
|
|
|
if initial_seed is None:
|
|
initial_seed = seed
|
|
|
|
init_img = output_images[0]
|
|
|
|
if not random_seed_loopback:
|
|
seed = seed + 1
|
|
else:
|
|
seed = seed_to_int(None)
|
|
denoising_strength = max(denoising_strength * 0.95, 0.1)
|
|
history.append(init_img)
|
|
|
|
if not skip_grid:
|
|
grid_count = get_next_sequence_number(outpath, 'grid-')
|
|
grid = image_grid(history, batch_size, force_n_rows=1)
|
|
grid_file = f"grid-{grid_count:05}-{seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.{grid_ext}"
|
|
grid.save(os.path.join(outpath, grid_file), grid_format, quality=grid_quality, lossless=grid_lossless, optimize=True)
|
|
|
|
|
|
output_images = history
|
|
seed = initial_seed
|
|
|
|
else:
|
|
if do_color_correction:
|
|
correction_target = cv2.cvtColor(np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB)
|
|
|
|
output_images, seed, info, stats = process_images(
|
|
outpath=outpath,
|
|
func_init=init,
|
|
func_sample=sample,
|
|
prompt=prompt,
|
|
seed=seed,
|
|
sampler_name=sampler_name,
|
|
skip_save=skip_save,
|
|
skip_grid=skip_grid,
|
|
batch_size=batch_size,
|
|
n_iter=n_iter,
|
|
steps=ddim_steps,
|
|
cfg_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
prompt_matrix=prompt_matrix,
|
|
use_GFPGAN=use_GFPGAN,
|
|
use_RealESRGAN=use_RealESRGAN,
|
|
realesrgan_model_name=realesrgan_model_name,
|
|
fp=fp,
|
|
normalize_prompt_weights=normalize_prompt_weights,
|
|
init_img=init_img,
|
|
init_mask=init_mask,
|
|
keep_mask=keep_mask,
|
|
mask_blur_strength=mask_blur_strength,
|
|
denoising_strength=denoising_strength,
|
|
mask_restore=mask_restore,
|
|
resize_mode=resize_mode,
|
|
uses_loopback=loopback,
|
|
sort_samples=sort_samples,
|
|
write_info_files=write_info_files,
|
|
write_sample_info_to_log_file=write_sample_info_to_log_file,
|
|
jpg_sample=jpg_sample,
|
|
job_info=job_info,
|
|
do_color_correction=do_color_correction,
|
|
correction_target=correction_target
|
|
)
|
|
|
|
del sampler
|
|
|
|
return output_images, seed, info, stats
|
|
|
|
|
|
prompt_parser = re.compile("""
|
|
(?P<prompt> # capture group for 'prompt'
|
|
(?:\\\:|[^:])+ # match one or more non ':' characters or escaped colons '\:'
|
|
) # end 'prompt'
|
|
(?: # non-capture group
|
|
:+ # match one or more ':' characters
|
|
(?P<weight> # capture group for 'weight'
|
|
-?\d+(?:\.\d+)? # match positive or negative integer or 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").replace("\\:", ":"), float(match.group("weight") or 1)) for match in re.finditer(prompt_parser, input_string)]
|
|
if not normalize:
|
|
return parsed_prompts
|
|
weight_sum = sum(map(lambda x: x[1], parsed_prompts))
|
|
if weight_sum == 0:
|
|
print("Warning: Subprompt weights add up to zero. Discarding and using even weights instead.")
|
|
equal_weight = 1 / (len(parsed_prompts) or 1)
|
|
return [(x[0], equal_weight) for x in 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 imgproc(image,image_batch,imgproc_prompt,imgproc_toggles, imgproc_upscale_toggles,imgproc_realesrgan_model_name,imgproc_sampling,
|
|
imgproc_steps, imgproc_height, imgproc_width, imgproc_cfg, imgproc_denoising, imgproc_seed,imgproc_gfpgan_strength,imgproc_ldsr_steps,imgproc_ldsr_pre_downSample,imgproc_ldsr_post_downSample):
|
|
|
|
outpath = opt.outdir_imglab or opt.outdir or "outputs/imglab-samples"
|
|
output = []
|
|
images = []
|
|
def processGFPGAN(image,strength):
|
|
image = image.convert("RGB")
|
|
metadata = ImageMetadata.get_from_image(image)
|
|
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
|
|
result = Image.fromarray(restored_img)
|
|
if metadata:
|
|
metadata.GFPGAN = True
|
|
ImageMetadata.set_on_image(image, metadata)
|
|
|
|
if strength < 1.0:
|
|
result = Image.blend(image, result, strength)
|
|
|
|
return result
|
|
def processRealESRGAN(image):
|
|
if 'x2' in imgproc_realesrgan_model_name:
|
|
# downscale to 1/2 size
|
|
modelMode = imgproc_realesrgan_model_name.replace('x2','x4')
|
|
else:
|
|
modelMode = imgproc_realesrgan_model_name
|
|
image = image.convert("RGB")
|
|
metadata = ImageMetadata.get_from_image(image)
|
|
RealESRGAN = load_RealESRGAN(modelMode)
|
|
result, res = RealESRGAN.enhance(np.array(image, dtype=np.uint8))
|
|
result = Image.fromarray(result)
|
|
ImageMetadata.set_on_image(result, metadata)
|
|
if 'x2' in imgproc_realesrgan_model_name:
|
|
# downscale to 1/2 size
|
|
result = result.resize((result.width//2, result.height//2), LANCZOS)
|
|
|
|
return result
|
|
def processGoBig(image):
|
|
metadata = ImageMetadata.get_from_image(image)
|
|
result = processRealESRGAN(image,)
|
|
if 'x4' in imgproc_realesrgan_model_name:
|
|
#downscale to 1/2 size
|
|
result = result.resize((result.width//2, result.height//2), LANCZOS)
|
|
|
|
|
|
|
|
#make sense of parameters
|
|
n_iter = 1
|
|
batch_size = 1
|
|
seed = seed_to_int(imgproc_seed)
|
|
ddim_steps = int(imgproc_steps)
|
|
resize_mode = 0 #need to add resize mode to form, or infer correct resolution from file name
|
|
width = int(imgproc_width)
|
|
height = int(imgproc_height)
|
|
cfg_scale = float(imgproc_cfg)
|
|
denoising_strength = float(imgproc_denoising)
|
|
skip_save = True
|
|
skip_grid = True
|
|
prompt = imgproc_prompt
|
|
t_enc = int(denoising_strength * ddim_steps)
|
|
sampler_name = imgproc_sampling
|
|
|
|
|
|
if sampler_name == 'DDIM':
|
|
sampler = DDIMSampler(model)
|
|
elif sampler_name == 'k_dpm_2_a':
|
|
sampler = KDiffusionSampler(model,'dpm_2_ancestral')
|
|
elif sampler_name == 'k_dpm_2':
|
|
sampler_name = KDiffusionSampler(model,'dpm_2')
|
|
elif sampler_name == 'k_euler_a':
|
|
sampler = KDiffusionSampler(model,'euler_ancestral')
|
|
elif sampler_name == 'k_euler':
|
|
sampler = KDiffusionSampler(model,'euler')
|
|
elif sampler_name == 'k_heun':
|
|
sampler = KDiffusionSampler(model,'heun')
|
|
elif sampler_name == 'k_lms':
|
|
sampler = KDiffusionSampler(model,'lms')
|
|
else:
|
|
raise Exception("Unknown sampler: " + sampler_name)
|
|
pass
|
|
init_img = result
|
|
init_mask = None
|
|
keep_mask = False
|
|
mask_restore = False
|
|
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
|
|
|
def init():
|
|
image = init_img.convert("RGB")
|
|
image = resize_image(resize_mode, image, width, height)
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = image[None].transpose(0, 3, 1, 2)
|
|
image = torch.from_numpy(image)
|
|
|
|
if opt.optimized:
|
|
modelFS.to(device)
|
|
|
|
init_image = 2. * image - 1.
|
|
init_image = init_image.to(device)
|
|
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
|
|
init_latent = (model if not opt.optimized else modelFS).get_first_stage_encoding((model if not opt.optimized else modelFS).encode_first_stage(init_image)) # move to latent space
|
|
|
|
if opt.optimized:
|
|
mem = torch.cuda.memory_allocated()/1e6
|
|
modelFS.to("cpu")
|
|
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
|
time.sleep(1)
|
|
|
|
return init_latent,
|
|
|
|
def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
|
|
if sampler_name != 'DDIM':
|
|
x0, = init_data
|
|
|
|
sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
|
|
noise = x * sigmas[ddim_steps - t_enc - 1]
|
|
|
|
xi = x0 + noise
|
|
sigma_sched = sigmas[ddim_steps - t_enc - 1:]
|
|
model_wrap_cfg = CFGDenoiser(sampler.model_wrap)
|
|
samples_ddim = K.sampling.__dict__[f'sample_{sampler.get_sampler_name()}'](model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False)
|
|
else:
|
|
x0, = init_data
|
|
sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=0.0, verbose=False)
|
|
z_enc = sampler.stochastic_encode(x0, torch.tensor([t_enc]*batch_size).to(device))
|
|
# decode it
|
|
samples_ddim = sampler.decode(z_enc, conditioning, t_enc,
|
|
unconditional_guidance_scale=cfg_scale,
|
|
unconditional_conditioning=unconditional_conditioning,)
|
|
return samples_ddim
|
|
def split_grid(image, tile_w=512, tile_h=512, overlap=64):
|
|
Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])
|
|
w = image.width
|
|
h = image.height
|
|
|
|
now = tile_w - overlap # non-overlap width
|
|
noh = tile_h - overlap
|
|
|
|
cols = math.ceil((w - overlap) / now)
|
|
rows = math.ceil((h - overlap) / noh)
|
|
|
|
grid = Grid([], tile_w, tile_h, w, h, overlap)
|
|
for row in range(rows):
|
|
row_images = []
|
|
|
|
y = row * noh
|
|
|
|
if y + tile_h >= h:
|
|
y = h - tile_h
|
|
|
|
for col in range(cols):
|
|
x = col * now
|
|
|
|
if x+tile_w >= w:
|
|
x = w - tile_w
|
|
|
|
tile = image.crop((x, y, x + tile_w, y + tile_h))
|
|
|
|
row_images.append([x, tile_w, tile])
|
|
|
|
grid.tiles.append([y, tile_h, row_images])
|
|
|
|
return grid
|
|
|
|
|
|
def combine_grid(grid):
|
|
def make_mask_image(r):
|
|
r = r * 255 / grid.overlap
|
|
r = r.astype(np.uint8)
|
|
return Image.fromarray(r, 'L')
|
|
|
|
mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
|
|
mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
|
|
|
|
combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
|
|
for y, h, row in grid.tiles:
|
|
combined_row = Image.new("RGB", (grid.image_w, h))
|
|
for x, w, tile in row:
|
|
if x == 0:
|
|
combined_row.paste(tile, (0, 0))
|
|
continue
|
|
|
|
combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w)
|
|
combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0))
|
|
|
|
if y == 0:
|
|
combined_image.paste(combined_row, (0, 0))
|
|
continue
|
|
|
|
combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h)
|
|
combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap))
|
|
|
|
return combined_image
|
|
|
|
grid = split_grid(result, tile_w=width, tile_h=height, overlap=64)
|
|
work = []
|
|
work_results = []
|
|
|
|
for y, h, row in grid.tiles:
|
|
for tiledata in row:
|
|
work.append(tiledata[2])
|
|
batch_count = math.ceil(len(work) / batch_size)
|
|
print(f"GoBig upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} in a total of {batch_count} batches.")
|
|
for i in range(batch_count):
|
|
init_img = work[i*batch_size:(i+1)*batch_size][0]
|
|
output_images, seed, info, stats = process_images(
|
|
outpath=outpath,
|
|
func_init=init,
|
|
func_sample=sample,
|
|
prompt=prompt,
|
|
seed=seed,
|
|
sampler_name=sampler_name,
|
|
skip_save=skip_save,
|
|
skip_grid=skip_grid,
|
|
batch_size=batch_size,
|
|
n_iter=n_iter,
|
|
steps=ddim_steps,
|
|
cfg_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
prompt_matrix=None,
|
|
use_GFPGAN=None,
|
|
use_RealESRGAN=None,
|
|
realesrgan_model_name=None,
|
|
fp=None,
|
|
normalize_prompt_weights=False,
|
|
init_img=init_img,
|
|
init_mask=None,
|
|
keep_mask=False,
|
|
mask_blur_strength=None,
|
|
denoising_strength=denoising_strength,
|
|
mask_restore=mask_restore,
|
|
resize_mode=resize_mode,
|
|
uses_loopback=False,
|
|
sort_samples=True,
|
|
write_info_files=True,
|
|
write_sample_info_to_log_file=False,
|
|
jpg_sample=False,
|
|
imgProcessorTask=True
|
|
)
|
|
#if initial_seed is None:
|
|
# initial_seed = seed
|
|
#seed = seed + 1
|
|
|
|
work_results.append(output_images[0])
|
|
image_index = 0
|
|
for y, h, row in grid.tiles:
|
|
for tiledata in row:
|
|
tiledata[2] = work_results[image_index]
|
|
image_index += 1
|
|
|
|
combined_image = combine_grid(grid)
|
|
grid_count = len(os.listdir(outpath)) - 1
|
|
del sampler
|
|
|
|
torch.cuda.empty_cache()
|
|
ImageMetadata.set_on_image(combined_image, metadata)
|
|
return combined_image
|
|
def processLDSR(image):
|
|
metadata = ImageMetadata.get_from_image(image)
|
|
result = LDSR.superResolution(image,int(imgproc_ldsr_steps),str(imgproc_ldsr_pre_downSample),str(imgproc_ldsr_post_downSample))
|
|
ImageMetadata.set_on_image(result, metadata)
|
|
return result
|
|
|
|
|
|
if image_batch != None:
|
|
if image != None:
|
|
print("Batch detected and single image detected, please only use one of the two. Aborting.")
|
|
return None
|
|
#convert file to pillow image
|
|
for img in image_batch:
|
|
image = Image.fromarray(np.array(Image.open(img)))
|
|
images.append(image)
|
|
|
|
elif image != None:
|
|
if image_batch != None:
|
|
print("Batch detected and single image detected, please only use one of the two. Aborting.")
|
|
return None
|
|
else:
|
|
images.append(image)
|
|
|
|
if len(images) > 0:
|
|
print("Processing images...")
|
|
#pre load models not in loop
|
|
if 0 in imgproc_toggles:
|
|
ModelLoader(['RealESGAN','LDSR'],False,True) # Unload unused models
|
|
ModelLoader(['GFPGAN'],True,False) # Load used models
|
|
if 1 in imgproc_toggles:
|
|
if imgproc_upscale_toggles == 0:
|
|
ModelLoader(['GFPGAN','LDSR'],False,True) # Unload unused models
|
|
ModelLoader(['RealESGAN'],True,False,imgproc_realesrgan_model_name) # Load used models
|
|
elif imgproc_upscale_toggles == 1:
|
|
ModelLoader(['GFPGAN','LDSR'],False,True) # Unload unused models
|
|
ModelLoader(['RealESGAN','model'],True,False) # Load used models
|
|
elif imgproc_upscale_toggles == 2:
|
|
|
|
ModelLoader(['model','GFPGAN','RealESGAN'],False,True) # Unload unused models
|
|
ModelLoader(['LDSR'],True,False) # Load used models
|
|
elif imgproc_upscale_toggles == 3:
|
|
ModelLoader(['GFPGAN','LDSR'],False,True) # Unload unused models
|
|
ModelLoader(['RealESGAN','model'],True,False,imgproc_realesrgan_model_name) # Load used models
|
|
for image in images:
|
|
metadata = ImageMetadata.get_from_image(image)
|
|
if 0 in imgproc_toggles:
|
|
#recheck if GFPGAN is loaded since it's the only model that can be loaded in the loop as well
|
|
ModelLoader(['GFPGAN'],True,False) # Load used models
|
|
image = processGFPGAN(image,imgproc_gfpgan_strength)
|
|
if metadata:
|
|
metadata.GFPGAN = True
|
|
ImageMetadata.set_on_image(image, metadata)
|
|
outpathDir = os.path.join(outpath,'GFPGAN')
|
|
os.makedirs(outpathDir, exist_ok=True)
|
|
batchNumber = get_next_sequence_number(outpathDir)
|
|
outFilename = str(batchNumber)+'-'+'result'
|
|
|
|
if 1 not in imgproc_toggles:
|
|
output.append(image)
|
|
save_sample(image, outpathDir, outFilename, False, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, False)
|
|
if 1 in imgproc_toggles:
|
|
if imgproc_upscale_toggles == 0:
|
|
image = processRealESRGAN(image)
|
|
ImageMetadata.set_on_image(image, metadata)
|
|
outpathDir = os.path.join(outpath,'RealESRGAN')
|
|
os.makedirs(outpathDir, exist_ok=True)
|
|
batchNumber = get_next_sequence_number(outpathDir)
|
|
outFilename = str(batchNumber)+'-'+'result'
|
|
output.append(image)
|
|
save_sample(image, outpathDir, outFilename, False, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, False)
|
|
|
|
elif imgproc_upscale_toggles == 1:
|
|
image = processGoBig(image)
|
|
ImageMetadata.set_on_image(image, metadata)
|
|
outpathDir = os.path.join(outpath,'GoBig')
|
|
os.makedirs(outpathDir, exist_ok=True)
|
|
batchNumber = get_next_sequence_number(outpathDir)
|
|
outFilename = str(batchNumber)+'-'+'result'
|
|
output.append(image)
|
|
save_sample(image, outpathDir, outFilename, False, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, False)
|
|
|
|
elif imgproc_upscale_toggles == 2:
|
|
image = processLDSR(image)
|
|
ImageMetadata.set_on_image(image, metadata)
|
|
outpathDir = os.path.join(outpath,'LDSR')
|
|
os.makedirs(outpathDir, exist_ok=True)
|
|
batchNumber = get_next_sequence_number(outpathDir)
|
|
outFilename = str(batchNumber)+'-'+'result'
|
|
output.append(image)
|
|
save_sample(image, outpathDir, outFilename, False, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, False)
|
|
|
|
elif imgproc_upscale_toggles == 3:
|
|
image = processGoBig(image)
|
|
ModelLoader(['model','GFPGAN','RealESGAN'],False,True) # Unload unused models
|
|
ModelLoader(['LDSR'],True,False) # Load used models
|
|
image = processLDSR(image)
|
|
ImageMetadata.set_on_image(image, metadata)
|
|
outpathDir = os.path.join(outpath,'GoLatent')
|
|
os.makedirs(outpathDir, exist_ok=True)
|
|
batchNumber = get_next_sequence_number(outpathDir)
|
|
outFilename = str(batchNumber)+'-'+'result'
|
|
output.append(image)
|
|
|
|
save_sample(image, outpathDir, outFilename, None, None, None, None, None, None, False, None, None, None, None, None, None, None, None, None, False)
|
|
|
|
#LDSR is always unloaded to avoid memory issues
|
|
#ModelLoader(['LDSR'],False,True)
|
|
#print("Reloading default models...")
|
|
#ModelLoader(['model','RealESGAN','GFPGAN'],True,False) # load back models
|
|
print("Done.")
|
|
return output
|
|
|
|
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 opt.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_model()
|
|
global_vars[m] = sdLoader[0]
|
|
if opt.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()
|
|
|
|
|
|
def run_GFPGAN(image, strength):
|
|
ModelLoader(['LDSR','RealESRGAN'],False,True)
|
|
ModelLoader(['GFPGAN'],True,False)
|
|
metadata = ImageMetadata.get_from_image(image)
|
|
image = image.convert("RGB")
|
|
|
|
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
|
|
res = Image.fromarray(restored_img)
|
|
metadata.GFPGAN = True
|
|
ImageMetadata.set_on_image(res, metadata)
|
|
|
|
if strength < 1.0:
|
|
res = Image.blend(image, res, strength)
|
|
|
|
return res
|
|
|
|
def run_RealESRGAN(image, model_name: str):
|
|
ModelLoader(['GFPGAN','LDSR'],False,True)
|
|
ModelLoader(['RealESRGAN'],True,False)
|
|
if RealESRGAN.model.name != model_name:
|
|
try_loading_RealESRGAN(model_name)
|
|
|
|
metadata = ImageMetadata.get_from_image(image)
|
|
image = image.convert("RGB")
|
|
|
|
output, img_mode = RealESRGAN.enhance(np.array(image, dtype=np.uint8))
|
|
res = Image.fromarray(output)
|
|
ImageMetadata.set_on_image(res, metadata)
|
|
|
|
return res
|
|
|
|
|
|
if opt.defaults is not None and os.path.isfile(opt.defaults):
|
|
try:
|
|
with open(opt.defaults, "r", encoding="utf8") as f:
|
|
user_defaults = yaml.safe_load(f)
|
|
except (OSError, yaml.YAMLError) as e:
|
|
print(f"Error loading defaults file {opt.defaults}:", e, file=sys.stderr)
|
|
print("Falling back to program defaults.", file=sys.stderr)
|
|
user_defaults = {}
|
|
else:
|
|
user_defaults = {}
|
|
|
|
# make sure these indicies line up at the top of txt2img()
|
|
txt2img_toggles = [
|
|
'Create prompt matrix (separate multiple prompts using |, and get all combinations of them)',
|
|
'Normalize Prompt Weights (ensure sum of weights add up to 1.0)',
|
|
'Save individual images',
|
|
'Save grid',
|
|
'Sort samples by prompt',
|
|
'Write sample info files',
|
|
'write sample info to log file',
|
|
'jpg samples',
|
|
]
|
|
|
|
if GFPGAN is not None:
|
|
txt2img_toggles.append('Fix faces using GFPGAN')
|
|
if RealESRGAN is not None:
|
|
txt2img_toggles.append('Upscale images using RealESRGAN')
|
|
|
|
txt2img_defaults = {
|
|
'prompt': '',
|
|
'ddim_steps': 50,
|
|
'toggles': [1, 2, 3],
|
|
'sampler_name': 'k_lms',
|
|
'ddim_eta': 0.0,
|
|
'n_iter': 1,
|
|
'batch_size': 1,
|
|
'cfg_scale': 7.5,
|
|
'seed': '',
|
|
'height': 512,
|
|
'width': 512,
|
|
'fp': None,
|
|
'variant_amount': 0.0,
|
|
'variant_seed': '',
|
|
'submit_on_enter': 'Yes',
|
|
}
|
|
|
|
if 'txt2img' in user_defaults:
|
|
txt2img_defaults.update(user_defaults['txt2img'])
|
|
|
|
txt2img_toggle_defaults = [txt2img_toggles[i] for i in txt2img_defaults['toggles']]
|
|
|
|
imgproc_defaults = {
|
|
'prompt': '',
|
|
'ddim_steps': 50,
|
|
'sampler_name': 'k_lms',
|
|
'cfg_scale': 7.5,
|
|
'seed': '',
|
|
'height': 512,
|
|
'width': 512,
|
|
'denoising_strength': 0.30
|
|
}
|
|
imgproc_mode_toggles = [
|
|
'Fix Faces',
|
|
'Upscale'
|
|
]
|
|
|
|
#sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
|
|
#sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
|
|
sample_img2img = None
|
|
# make sure these indicies line up at the top of img2img()
|
|
img2img_toggles = [
|
|
'Create prompt matrix (separate multiple prompts using |, and get all combinations of them)',
|
|
'Normalize Prompt Weights (ensure sum of weights add up to 1.0)',
|
|
'Loopback (use images from previous batch when creating next batch)',
|
|
'Random loopback seed',
|
|
'Save individual images',
|
|
'Save grid',
|
|
'Sort samples by prompt',
|
|
'Write sample info files',
|
|
'Write sample info to one file',
|
|
'jpg samples',
|
|
'Color correction (always enabled on loopback mode)'
|
|
]
|
|
# removed for now becuase of Image Lab implementation
|
|
if GFPGAN is not None:
|
|
img2img_toggles.append('Fix faces using GFPGAN')
|
|
if RealESRGAN is not None:
|
|
img2img_toggles.append('Upscale images using RealESRGAN')
|
|
|
|
img2img_mask_modes = [
|
|
"Keep masked area",
|
|
"Regenerate only masked area",
|
|
]
|
|
|
|
img2img_resize_modes = [
|
|
"Just resize",
|
|
"Crop and resize",
|
|
"Resize and fill",
|
|
]
|
|
|
|
img2img_defaults = {
|
|
'prompt': '',
|
|
'ddim_steps': 50,
|
|
'toggles': [1, 4, 5],
|
|
'sampler_name': 'k_lms',
|
|
'ddim_eta': 0.0,
|
|
'n_iter': 1,
|
|
'batch_size': 1,
|
|
'cfg_scale': 5.0,
|
|
'denoising_strength': 0.75,
|
|
'mask_mode': 0,
|
|
'mask_restore': False,
|
|
'resize_mode': 0,
|
|
'seed': '',
|
|
'height': 512,
|
|
'width': 512,
|
|
'fp': None,
|
|
}
|
|
|
|
if 'img2img' in user_defaults:
|
|
img2img_defaults.update(user_defaults['img2img'])
|
|
|
|
img2img_toggle_defaults = [img2img_toggles[i] for i in img2img_defaults['toggles']]
|
|
img2img_image_mode = 'sketch'
|
|
|
|
help_text = """
|
|
## Mask/Crop
|
|
* The masking/cropping is very temperamental.
|
|
* It may take some time for the image to show when switching from Crop to Mask.
|
|
* If the image doesn't appear after switching to Mask, switch back to Crop and then back again to Mask
|
|
* If the mask appears distorted (the brush is weirdly shaped instead of round), switch back to Crop and then back again to Mask.
|
|
|
|
## Advanced Editor
|
|
* For now the button needs to be clicked twice the first time.
|
|
* Once you have edited your image, you _need_ to click the save button for the next step to work.
|
|
* Clear the image from the crop editor (click the x)
|
|
* Click "Get Image from Advanced Editor" to get the image you saved. If it doesn't work, try opening the editor and saving again.
|
|
|
|
If it keeps not working, try switching modes again, switch tabs, clear the image or reload.
|
|
"""
|
|
|
|
def show_help():
|
|
return [gr.update(visible=False), gr.update(visible=True), gr.update(value=help_text)]
|
|
|
|
def hide_help():
|
|
return [gr.update(visible=True), gr.update(visible=False), gr.update(value="")]
|
|
|
|
|
|
demo = draw_gradio_ui(opt,
|
|
user_defaults=user_defaults,
|
|
txt2img=txt2img,
|
|
img2img=img2img,
|
|
imgproc=imgproc,
|
|
txt2img_defaults=txt2img_defaults,
|
|
txt2img_toggles=txt2img_toggles,
|
|
txt2img_toggle_defaults=txt2img_toggle_defaults,
|
|
show_embeddings=hasattr(model, "embedding_manager"),
|
|
img2img_defaults=img2img_defaults,
|
|
img2img_toggles=img2img_toggles,
|
|
img2img_toggle_defaults=img2img_toggle_defaults,
|
|
img2img_mask_modes=img2img_mask_modes,
|
|
img2img_resize_modes=img2img_resize_modes,
|
|
sample_img2img=sample_img2img,
|
|
imgproc_defaults=imgproc_defaults,
|
|
imgproc_mode_toggles=imgproc_mode_toggles,
|
|
RealESRGAN=RealESRGAN,
|
|
GFPGAN=GFPGAN,
|
|
LDSR=LDSR,
|
|
run_GFPGAN=run_GFPGAN,
|
|
run_RealESRGAN=run_RealESRGAN,
|
|
job_manager=job_manager
|
|
)
|
|
|
|
class ServerLauncher(threading.Thread):
|
|
def __init__(self, demo):
|
|
threading.Thread.__init__(self)
|
|
self.name = 'Gradio Server Thread'
|
|
self.demo = demo
|
|
|
|
def run(self):
|
|
loop = asyncio.new_event_loop()
|
|
asyncio.set_event_loop(loop)
|
|
gradio_params = {
|
|
'inbrowser': opt.inbrowser,
|
|
'server_name': '0.0.0.0',
|
|
'server_port': opt.port,
|
|
'share': opt.share,
|
|
'show_error': True
|
|
}
|
|
if not opt.share:
|
|
demo.queue(concurrency_count=opt.max_jobs)
|
|
if opt.share and opt.share_password:
|
|
gradio_params['auth'] = ('webui', opt.share_password)
|
|
|
|
# Check to see if Port 7860 is open
|
|
port_status = 1
|
|
while port_status != 0:
|
|
try:
|
|
self.demo.launch(**gradio_params)
|
|
except (OSError) as e:
|
|
print (f'Error: Port: {opt.port} is not open yet. Please wait, this may take upwards of 60 seconds...')
|
|
time.sleep(10)
|
|
else:
|
|
port_status = 0
|
|
|
|
def stop(self):
|
|
self.demo.close() # this tends to hang
|
|
|
|
def launch_server():
|
|
server_thread = ServerLauncher(demo)
|
|
server_thread.start()
|
|
|
|
try:
|
|
while server_thread.is_alive():
|
|
time.sleep(60)
|
|
except (KeyboardInterrupt, OSError) as e:
|
|
crash(e, 'Shutting down...')
|
|
|
|
def run_headless():
|
|
with open(opt.cli, 'r', encoding='utf8') as f:
|
|
kwargs = yaml.safe_load(f)
|
|
target = kwargs.pop('target')
|
|
if target == 'txt2img':
|
|
target_func = txt2img
|
|
elif target == 'img2img':
|
|
target_func = img2img
|
|
raise NotImplementedError()
|
|
else:
|
|
raise ValueError(f'Unknown target: {target}')
|
|
prompts = kwargs.pop("prompt")
|
|
prompts = prompts if type(prompts) is list else [prompts]
|
|
for i, prompt_i in enumerate(prompts):
|
|
print(f"===== Prompt {i+1}/{len(prompts)}: {prompt_i} =====")
|
|
output_images, seed, info, stats = target_func(prompt=prompt_i, **kwargs)
|
|
print(f'Seed: {seed}')
|
|
print(info)
|
|
print(stats)
|
|
print()
|
|
|
|
if __name__ == '__main__':
|
|
if opt.cli is None:
|
|
launch_server()
|
|
else:
|
|
run_headless()
|