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webui.py
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webui.py
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import argparse, os, sys, glob, re
from frontend.frontend import draw_gradio_ui
from frontend.ui_functions import resize_image
parser = argparse.ArgumentParser()
parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)
parser.add_argument("--outdir_txt2img", type=str, nargs="?", help="dir to write txt2img results to (overrides --outdir)", default=None)
parser.add_argument("--outdir_img2img", type=str, nargs="?", help="dir to write img2img results to (overrides --outdir)", default=None)
parser.add_argument("--save-metadata", action='store_true', help="Whether to embed the generation parameters in the sample images", default=False)
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)
parser.add_argument("--skip-save", action='store_true', help="do not save indiviual samples. For speed measurements.", default=False)
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")
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)",)
parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",)
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
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")
parser.add_argument("--optimized-turbo", action='store_true', help="alternative optimization mode that does not save as much VRAM but runs siginificantly faster")
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
parser.add_argument("--realesrgan-dir", type=str, help="RealESRGAN directory", default=('./src/realesrgan' if os.path.exists('./src/realesrgan') else './RealESRGAN'))
parser.add_argument("--realesrgan-model", type=str, help="Upscaling model for RealESRGAN", default=('RealESRGAN_x4plus'))
parser.add_argument("--no-verify-input", action='store_true', help="do not verify input to check if it's too long", default=False)
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats", default=False)
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)
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)
parser.add_argument("--share-password", type=str, help="Sharing is open by default, use this to set a password. Username: webui", default=None)
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')
parser.add_argument("--gpu", type=int, help="choose which GPU to use if you have multiple", default=0)
parser.add_argument("--extra-models-cpu", action='store_true', help="run extra models (GFGPAN/ESRGAN) on cpu", default=False)
parser.add_argument("--extra-models-gpu", action='store_true', help="run extra models (GFGPAN/ESRGAN) on cpu", default=False)
parser.add_argument("--esrgan-cpu", action='store_true', help="run ESRGAN on cpu", default=False)
parser.add_argument("--gfpgan-cpu", action='store_true', help="run GFPGAN on cpu", default=False)
parser.add_argument("--esrgan-gpu", type=int, help="run ESRGAN on specific gpu (overrides --gpu)", default=0)
parser.add_argument("--gfpgan-gpu", type=int, help="run GFPGAN on specific gpu (overrides --gpu) ", default=0)
parser.add_argument("--cli", type=str, help="don't launch web server, take Python function kwargs from this file.", default=None)
opt = parser.parse_args()
#Should not be needed anymore
#os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
# all selected gpus, can probably be done nicer
#if opt.extra_models_gpu:
# gpus = set([opt.gpu, opt.esrgan_gpu, opt.gfpgan_gpu])
# os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(g) for g in set(gpus))
#else:
# os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu)
import gradio as gr
import k_diffusion as K
import math
import mimetypes
import numpy as np
import pynvml
import random
import threading, asyncio
import time
import torch
import torch.nn as nn
import yaml
import glob
from typing import List, Union
from pathlib import Path
from contextlib import contextmanager, nullcontext
from einops import rearrange, repeat
from itertools import islice
from omegaconf import OmegaConf
from PIL import Image, ImageFont, ImageDraw, ImageFilter, ImageOps
from PIL.PngImagePlugin import PngInfo
from io import BytesIO
import base64
import re
from torch import autocast
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.util import instantiate_from_config
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except:
pass
# 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
mimetypes.init()
mimetypes.add_type('application/javascript', '.js')
# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
invalid_filename_chars = '<>:"/\|?*\n'
GFPGAN_dir = opt.gfpgan_dir
RealESRGAN_dir = opt.realesrgan_dir
if opt.optimized_turbo:
opt.optimized = True
# should probably be moved to a settings menu in the UI at some point
grid_format = [s.lower() for s in opt.grid_format.split(':')]
grid_lossless = False
grid_quality = 100
if grid_format[0] == 'png':
grid_ext = 'png'
grid_format = 'png'
elif grid_format[0] in ['jpg', 'jpeg']:
grid_quality = int(grid_format[1]) if len(grid_format) > 1 else 100
grid_ext = 'jpg'
grid_format = 'jpeg'
elif grid_format[0] == 'webp':
grid_quality = int(grid_format[1]) if len(grid_format) > 1 else 100
grid_ext = 'webp'
grid_format = 'webp'
if grid_quality < 0: # e.g. webp:-100 for lossless mode
grid_lossless = True
grid_quality = abs(grid_quality)
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
def load_sd_from_config(ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
return sd
def crash(e, s):
global model
global device
print(s, '\n', e)
del model
del device
print('exiting...calling os._exit(0)')
t = threading.Timer(0.25, os._exit, args=[0])
t.start()
class MemUsageMonitor(threading.Thread):
stop_flag = False
max_usage = 0
total = -1
def __init__(self, name):
threading.Thread.__init__(self)
self.name = name
def run(self):
try:
pynvml.nvmlInit()
except:
print(f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n")
return
print(f"[{self.name}] Recording max memory usage...\n")
handle = pynvml.nvmlDeviceGetHandleByIndex(opt.gpu)
self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
while not self.stop_flag:
m = pynvml.nvmlDeviceGetMemoryInfo(handle)
self.max_usage = max(self.max_usage, m.used)
# print(self.max_usage)
time.sleep(0.1)
print(f"[{self.name}] Stopped recording.\n")
pynvml.nvmlShutdown()
def read(self):
return self.max_usage, self.total
def stop(self):
self.stop_flag = True
def read_and_stop(self):
self.stop_flag = True
return self.max_usage, self.total
class CFGMaskedDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, x, sigma, uncond, cond, cond_scale, mask, x0, xi):
x_in = x
x_in = torch.cat([x_in] * 2)
sigma_in = torch.cat([sigma] * 2)
cond_in = torch.cat([uncond, cond])
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
denoised = uncond + (cond - uncond) * cond_scale
if mask is not None:
assert x0 is not None
img_orig = x0
mask_inv = 1. - mask
denoised = (img_orig * mask_inv) + (mask * denoised)
return denoised
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, x, sigma, uncond, cond, cond_scale):
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigma] * 2)
cond_in = torch.cat([uncond, cond])
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
return uncond + (cond - uncond) * cond_scale
class KDiffusionSampler:
def __init__(self, m, sampler):
self.model = m
self.model_wrap = K.external.CompVisDenoiser(m)
self.schedule = sampler
def get_sampler_name(self):
return self.schedule
def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T):
sigmas = self.model_wrap.get_sigmas(S)
x = x_T * sigmas[0]
model_wrap_cfg = CFGDenoiser(self.model_wrap)
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)
return samples_ddim, None
def create_random_tensors(shape, seeds):
xs = []
for seed in seeds:
torch.manual_seed(seed)
# randn results depend on device; gpu and cpu get different results for same seed;
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
# but the original script had it like this so i do not dare change it for now because
# it will break everyone's seeds.
xs.append(torch.randn(shape, device=device))
x = torch.stack(xs)
return x
def torch_gc():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
def load_GFPGAN():
model_name = 'GFPGANv1.3'
model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
if not os.path.isfile(model_path):
raise Exception("GFPGAN model not found at path "+model_path)
sys.path.append(os.path.abspath(GFPGAN_dir))
from gfpgan import GFPGANer
if opt.gfpgan_cpu or opt.extra_models_cpu:
instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cpu'))
elif opt.extra_models_gpu:
instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f'cuda:{opt.gfpgan_gpu}'))
else:
instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f'cuda:{opt.gpu}'))
return instance
def load_RealESRGAN(model_name: str):
from basicsr.archs.rrdbnet_arch import RRDBNet
RealESRGAN_models = {
'RealESRGAN_x4plus': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4),
'RealESRGAN_x4plus_anime_6B': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
}
model_path = os.path.join(RealESRGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
if not os.path.isfile(model_path):
raise Exception(model_name+".pth not found at path "+model_path)
sys.path.append(os.path.abspath(RealESRGAN_dir))
from realesrgan import RealESRGANer
if opt.esrgan_cpu or opt.extra_models_cpu:
instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=False) # cpu does not support half
instance.device = torch.device('cpu')
instance.model.to('cpu')
elif opt.extra_models_gpu:
instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not opt.no_half, device=torch.device(f'cuda:{opt.esrgan_gpu}'))
else:
instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not opt.no_half, device=torch.device(f'cuda:{opt.gpu}'))
instance.model.name = model_name
return instance
GFPGAN = None
if os.path.exists(GFPGAN_dir):
try:
GFPGAN = load_GFPGAN()
print("Loaded GFPGAN")
except Exception:
import traceback
print("Error loading GFPGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
RealESRGAN = None
def try_loading_RealESRGAN(model_name: str):
global RealESRGAN
if os.path.exists(RealESRGAN_dir):
try:
RealESRGAN = load_RealESRGAN(model_name) # TODO: Should try to load both models before giving up
print("Loaded RealESRGAN with model "+RealESRGAN.model.name)
except Exception:
import traceback
print("Error loading RealESRGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
try_loading_RealESRGAN('RealESRGAN_x4plus')
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()
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)
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, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode):
filename_i = os.path.join(sample_path_i, filename)
if not jpg_sample:
if opt.save_metadata:
metadata = PngInfo()
metadata.add_text("SD:prompt", prompts[i])
metadata.add_text("SD:seed", str(seeds[i]))
metadata.add_text("SD:width", str(width))
metadata.add_text("SD:height", str(height))
metadata.add_text("SD:steps", str(steps))
metadata.add_text("SD:cfg_scale", str(cfg_scale))
metadata.add_text("SD:normalize_prompt_weights", str(normalize_prompt_weights))
if init_img is not None:
metadata.add_text("SD:denoising_strength", str(denoising_strength))
metadata.add_text("SD:GFPGAN", str(use_GFPGAN and GFPGAN is not None))
image.save(f"{filename_i}.png", pnginfo=metadata)
else:
image.save(f"{filename_i}.png")
else:
image.save(f"{filename_i}.jpg", 'jpeg', quality=100, optimize=True)
if write_info_files:
# toggles differ for txt2img vs. img2img:
offset = 0 if init_img is None else 2
toggles = []
if prompt_matrix:
toggles.append(0)
if normalize_prompt_weights:
toggles.append(1)
if init_img is not None:
if uses_loopback:
toggles.append(2)
if uses_random_seed_loopback:
toggles.append(3)
if 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 use_GFPGAN:
toggles.append(6 + offset)
info_dict = dict(
target="txt2img" if init_img is None else "img2img",
prompt=prompts[i], ddim_steps=steps, toggles=toggles, sampler_name=sampler_name,
ddim_eta=ddim_eta, n_iter=n_iter, batch_size=batch_size, cfg_scale=cfg_scale,
seed=seeds[i], width=width, height=height
)
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
with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
yaml.dump(info_dict, f, allow_unicode=True, width=10000)
def get_next_sequence_number(path, prefix=''):
"""
Determines and returns the next sequence number to use when saving an
image in the specified directory.
If a prefix is given, only consider files whose names start with that
prefix, and strip the prefix from filenames before extracting their
sequence number.
The sequence starts at 0.
"""
result = -1
for p in Path(path).iterdir():
if p.name.endswith(('.png', '.jpg')) and p.name.startswith(prefix):
tmp = p.name[len(prefix):]
try:
result = max(int(tmp.split('-')[0]), result)
except ValueError:
pass
return result + 1
def oxlamon_matrix(prompt, seed, n_iter, batch_size):
pattern = re.compile(r'(,\s){2,}')
class PromptItem:
def __init__(self, text, parts, item):
self.text = text
self.parts = parts
if item:
self.parts.append( item )
def clean(txt):
return re.sub(pattern, ', ', txt)
def getrowcount( txt ):
for data in re.finditer( ".*?\\((.*?)\\).*", txt ):
if data:
return len(data.group(1).split("|"))
break
return None
def repliter( txt ):
for data in re.finditer( ".*?\\((.*?)\\).*", txt ):
if data:
r = data.span(1)
for item in data.group(1).split("|"):
yield (clean(txt[:r[0]-1] + item.strip() + txt[r[1]+1:]), item.strip())
break
def iterlist( items ):
outitems = []
for item in items:
for newitem, newpart in repliter(item.text):
outitems.append( PromptItem(newitem, item.parts.copy(), newpart) )
return outitems
def getmatrix( prompt ):
dataitems = [ PromptItem( prompt[1:].strip(), [], None ) ]
while True:
newdataitems = iterlist( dataitems )
if len( newdataitems ) == 0:
return dataitems
dataitems = newdataitems
def classToArrays( items, seed, n_iter ):
texts = []
parts = []
seeds = []
for item in items:
itemseed = seed
for i in range(n_iter):
texts.append( item.text )
parts.append( f"Seed: {itemseed}\n" + "\n".join(item.parts) )
seeds.append( itemseed )
itemseed += 1
return seeds, texts, parts
all_seeds, all_prompts, prompt_matrix_parts = classToArrays(getmatrix( prompt ), seed, n_iter)
n_iter = math.ceil(len(all_prompts) / batch_size)
needrows = getrowcount(prompt)
if needrows:
xrows = math.sqrt(len(all_prompts))
xrows = round(xrows)
# if columns is to much
cols = math.ceil(len(all_prompts) / xrows)
if cols > needrows*4:
needrows *= 2
return all_seeds, n_iter, prompt_matrix_parts, all_prompts, needrows
def process_images(
outpath, func_init, func_sample, prompt, seed, sampler_name, 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, denoising_strength=0.75, resize_mode=None, uses_loopback=False,
uses_random_seed_loopback=False, sort_samples=True, write_info_files=True, jpg_sample=False,
variant_amount=0.0, variant_seed=None):
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
assert prompt is not None
torch_gc()
# start time after garbage collection (or before?)
start_time = time.time()
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))]
precision_scope = autocast if opt.precision == "autocast" else nullcontext
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):
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]
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)
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)
seeds = [specified_variant_seed]
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, 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 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 = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}"
else:
sample_path_i = sample_path
base_count = get_next_sequence_number(sample_path_i)
sanitized_prompt = sanitized_prompt
filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}_{sanitized_prompt}"[:128] #same as before
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
x_sample = x_sample.astype(np.uint8)
image = Image.fromarray(x_sample)
original_sample = x_sample
original_filename = filename
if use_GFPGAN and 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_filename = original_filename + '-gfpgan'
save_sample(gfpgan_image, sample_path_i, gfpgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
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()
if RealESRGAN.model.name != realesrgan_model_name:
try_loading_RealESRGAN(realesrgan_model_name)
output, img_mode = RealESRGAN.enhance(original_sample[:,:,::-1])
esrgan_filename = original_filename + '-esrgan4x'
esrgan_sample = output[:,:,::-1]
esrgan_image = Image.fromarray(esrgan_sample)
save_sample(esrgan_image, sample_path_i, esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
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]
if RealESRGAN.model.name != realesrgan_model_name:
try_loading_RealESRGAN(realesrgan_model_name)
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)
save_sample(gfpgan_esrgan_image, sample_path_i, gfpgan_esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
output_images.append(gfpgan_esrgan_image) #287
if simple_templating:
grid_captions.append( captions[i] + "\ngfpgan_esrgan" )
if not skip_save:
save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
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:
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)
else:
grid = image_grid(output_images, batch_size)
if grid and (batch_size > 1 or n_iter > 1):
output_images.insert(0, grid)
grid_count = get_next_sequence_number(outpath, 'grid-')
grid_file = f"grid-{grid_count:05}-{seed}_{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
info = f"""
{prompt}
Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', Denoising strength: '+str(denoising_strength) if init_img is not None else ''}{', GFPGAN' if use_GFPGAN and 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 += "\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):
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
jpg_sample = 6 in toggles
use_GFPGAN = 7 in toggles
use_RealESRGAN = 8 in toggles
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,