sd-webui/stable-diffusion-webui
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stable-diffusion-webui/webui.py
hlky f19029aa6b
#158 
Fixed

At most should produce 2 samples
>original & gfpgan
>original & esrgan
>original & gfpgan -> esrgan

Turn off save individual samples to save only gfpgan, esrgan or gfpgan -> esrgan
2022-08-27 23:26:42 +01:00

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import argparse, os, sys, glob, re
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("--outdir_goBig", 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("--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=int(os.environ.get('CUDA_VISIBLE_DEVICES', 0)))
parser.add_argument("--extra-models-cpu", 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("--cli", type=str, help="don't launch web server, take Python function kwargs from this file.", default=None)
parser.add_argument("--scale",type=float,default=10,help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",)
opt = parser.parse_args()
# this should force GFPGAN and RealESRGAN onto the selected gpu as well
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 tqdm import tqdm, trange
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
# 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 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 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
instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
if opt.gfpgan_cpu or opt.extra_models_cpu:
instance.device = torch.device('cpu')
else:
instance.device = torch.device(f'cuda:{opt.gpu}') # another way to set gpu device
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)
instance.model.name = model_name
instance.device = torch.device('cpu')
instance.device = torch.device('cpu')
instance.model.to('cpu')
else:
instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not opt.no_half)
instance.model.name = model_name
instance.device = torch.device(f'cuda:{opt.gpu}') # another way to set gpu device
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("models/ldm/stable-diffusion-v1/model.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")
config.modelUNet.params.small_batch = False
model = instantiate_from_config(config.modelUNet)
_, _ = model.load_state_dict(sd, strict=False)
model.eval()
modelCS = instantiate_from_config(config.modelCondStage)
_, _ = modelCS.load_state_dict(sd, strict=False)
modelCS.eval()
modelFS = instantiate_from_config(config.modelFirstStage)
_, _ = modelFS.load_state_dict(sd, strict=False)
modelFS.eval()
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model if opt.no_half else model.half()
modelCS = modelCS if opt.no_half else modelCS.half()
else:
config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")
device = torch.device("cuda") 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:
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 resize_image(resize_mode, im, width, height):
if resize_mode == 0:
res = im.resize((width, height), resample=LANCZOS)
elif resize_mode == 1:
ratio = width / height
src_ratio = im.width / im.height
src_w = width if ratio > src_ratio else im.width * height // im.height
src_h = height if ratio <= src_ratio else im.height * width // im.width
resized = im.resize((src_w, src_h), resample=LANCZOS)
res = Image.new("RGB", (width, height))
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
else:
ratio = width / height
src_ratio = im.width / im.height
src_w = width if ratio < src_ratio else im.width * height // im.height
src_h = height if ratio >= src_ratio else im.height * width // im.width
resized = im.resize((src_w, src_h), resample=LANCZOS)
res = Image.new("RGB", (width, height))
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
if ratio < src_ratio:
fill_height = height // 2 - src_h // 2
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
elif ratio > src_ratio:
fill_width = width // 2 - src_w // 2
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
return res
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))
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)
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, 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 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 ):
texts = []
parts = []
for item in items:
texts.append( item.text )
parts.append( "\n".join(item.parts) )
return texts, parts
all_prompts, prompt_matrix_parts = classToArrays(getmatrix( prompt ))
n_iter = math.ceil(len(all_prompts) / batch_size)
all_seeds = len(all_prompts) * [seed]
return all_seeds, n_iter, prompt_matrix_parts, all_prompts
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,use_GoBIG, 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):
"""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)
comments = []
prompt_matrix_parts = []
if prompt_matrix:
if prompt.startswith("@"):
all_seeds, n_iter, prompt_matrix_parts, all_prompts = oxlamon_matrix(prompt, seed, 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 = []
stats = []
with torch.no_grad(), precision_scope("cuda"), (model.ema_scope() if not opt.optimized else nullcontext()):
init_data = func_init()
tic = time.time()
for n in range(n_iter):
print(f"Iteration: {n+1}/{n_iter}")
prompts = all_prompts[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??
subprompts,weights = split_weighted_subprompts(prompts[0])
# get total weight for normalizing, this gets weird if large negative values used
totalPromptWeight = sum(weights)
# sub-prompt weighting used if more than 1
if len(subprompts) > 1:
c = torch.zeros_like(uc) # i dont know if this is correct.. but it works
for i in range(0,len(subprompts)): # normalize each prompt and add it
weight = weights[i]
if normalize_prompt_weights:
weight = weight / totalPromptWeight
#print(f"{subprompts[i]} {weight*100.0}%")
# note if alpha negative, it functions same as torch.sub
c = torch.add(c, (model if not opt.optimized else modelCS).get_learned_conditioning(subprompts[i]), alpha=weight)
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)
# we manually generate all input noises because each one should have a specific seed
x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=seeds)
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)
original_sample = x_sample
original_filename = filename
if use_GFPGAN and GFPGAN is not None and not use_RealESRGAN:
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]
image = Image.fromarray(gfpgan_sample)
gfpgan_filename = original_filename + '-gfpgan'
save_sample(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)
x_sample = original_sample
if use_RealESRGAN and RealESRGAN is not None and not use_GFPGAN:
torch_gc()
if RealESRGAN.model.name != realesrgan_model_name:
try_loading_RealESRGAN(realesrgan_model_name)
output, img_mode = RealESRGAN.enhance(x_sample[:,:,::-1])
esrgan_filename = original_filename + '-esrgan4x'
esrgan_sample = output[:,:,::-1]
image = Image.fromarray(esrgan_sample)
save_sample(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)
x_sample = original_sample
if use_RealESRGAN and RealESRGAN is not None and use_GFPGAN and GFPGAN is not None:
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])
esrgan_filename = original_filename + '-gfpgan-esrgan4x'
esrgan_sample = output[:,:,::-1]
image = Image.fromarray(esrgan_sample)
save_sample(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)
x_sample = original_sample
if use_GoBIG and RealESRGAN is not None:
if use_GFPGAN:
x_sample = gfpgan_sample
original_sample = gfpgan_sample
else:
original_sample = x_sample
original_filename = filename
def addalpha(im, mask):
imr, img, imb, ima = im.split()
mmr, mmg, mmb, mma = mask.split()
im = Image.merge('RGBA', [imr, img, imb, mma]) # we want the RGB from the original, but the transparency from the mask
return(im)
def grid_merge(source, slices):
source.convert("RGBA")
for slice, posx, posy in slices: # go in reverse to get proper stacking
source.alpha_composite(slice, (posx, posy))
return source
def grid_slice(source, overlap, og_size, maximize=False):
def grid_coords(target, original, overlap):
#generate a list of coordinate tuples for our sections, in order of how they'll be rendered
#target should be the size for the gobig result, original is the size of each chunk being rendered
center = []
target_x, target_y = target
center_x = int(target_x / 2)
center_y = int(target_y / 2)
original_x, original_y = original
x = center_x - int(original_x / 2)
y = center_y - int(original_y / 2)
center.append((x,y)) #center chunk
uy = y #up
uy_list = []
dy = y #down
dy_list = []
lx = x #left
lx_list = []
rx = x #right
rx_list = []
while uy > 0: #center row vertical up
uy = uy - original_y + overlap
uy_list.append((lx, uy))
while (dy + original_y) <= target_y: #center row vertical down
dy = dy + original_y - overlap
dy_list.append((rx, dy))
while lx > 0:
lx = lx - original_x + overlap
lx_list.append((lx, y))
uy = y
while uy > 0:
uy = uy - original_y + overlap
uy_list.append((lx, uy))
dy = y
while (dy + original_y) <= target_y:
dy = dy + original_y - overlap
dy_list.append((lx, dy))
while (rx + original_x) <= target_x:
rx = rx + original_x - overlap
rx_list.append((rx, y))
uy = y
while uy > 0:
uy = uy - original_y + overlap
uy_list.append((rx, uy))
dy = y
while (dy + original_y) <= target_y:
dy = dy + original_y - overlap
dy_list.append((rx, dy))
# calculate a new size that will fill the canvas, which will be optionally used in grid_slice and go_big
last_coordx, last_coordy = dy_list[-1:][0]
render_edgey = last_coordy + original_y # outer bottom edge of the render canvas
render_edgex = last_coordx + original_x # outer side edge of the render canvas
scalarx = render_edgex / target_x
scalary = render_edgey / target_y
if scalarx <= scalary:
new_edgex = int(target_x * scalarx)
new_edgey = int(target_y * scalarx)
else:
new_edgex = int(target_x * scalary)
new_edgey = int(target_y * scalary)
# now put all the chunks into one master list of coordinates (essentially reverse of how we calculated them so that the central slices will be on top)
result = []
for coords in dy_list[::-1]:
result.append(coords)
for coords in uy_list[::-1]:
result.append(coords)
for coords in rx_list[::-1]:
result.append(coords)
for coords in lx_list[::-1]:
result.append(coords)
result.append(center[0])
return result, (new_edgex, new_edgey)
def get_resampling_mode():
try:
from PIL import __version__, Image
major_ver = int(__version__.split('.')[0])
if major_ver >= 9:
return Image.Resampling.LANCZOS
else:
return LANCZOS
except Exception as ex:
return 1 # 'Lanczos' irrespective of version
width, height = og_size # size of the slices to be rendered
coordinates, new_size = grid_coords(source.size, og_size, overlap)
if maximize == True:
source = source.resize(new_size, get_resampling_mode()) # minor concern that we're resizing twice
coordinates, new_size = grid_coords(source.size, og_size, overlap) # re-do the coordinates with the new canvas size
# loc_width and loc_height are the center point of the goal size, and we'll start there and work our way out
slices = []
for coordinate in coordinates:
x, y = coordinate
slices.append(((source.crop((x, y, x+width, y+height))), x, y))
global slices_todo
slices_todo = len(slices) - 1
return slices, new_size
def convert_pil_img(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1.
torch_gc()
if RealESRGAN.model.name != realesrgan_model_name:
try_loading_RealESRGAN(realesrgan_model_name)
output,img_mode = RealESRGAN.enhance(x_sample[:,:,::-1])
x_sample2 = output[:,:,::-1]
res = Image.fromarray(x_sample2)
X2_Output = res.resize((int(res.width/2), int(res.height/2)), LANCZOS)
filename = filename + '-esrgan2x'
save_sample(X2_Output, 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 = original_filename
output_images.append(X2_Output)
sampler = DDIMSampler(model)
data = [batch_size * [prompt]]
gobig_overlap = 64
for _ in trange(1, desc="Passes"):
source_image = X2_Output
og_size = (int(source_image.size[0] / 2), int(source_image.size[1] / 2))
slices, _ = grid_slice(source_image, gobig_overlap, og_size, False)
betterslices = []
for _, chunk_w_coords in tqdm(enumerate(slices), "Slices"):
chunk, coord_x, coord_y = chunk_w_coords
init_image = convert_pil_img(chunk).to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=150, ddim_eta=0, verbose=False)
assert 0. <= 0.3 <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(0.3 * 150)
with torch.no_grad():
with precision_scope("cuda"):
with model.ema_scope():
for prompts in tqdm(data, desc="data"):
uc = None
if opt.scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [prompt])
if isinstance(prompts, tuple):
prompts2 = list(prompts)
else:
prompts2 = prompts
c = model.get_learned_conditioning(prompts2)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))
# decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,)
x_samples = model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
for x_sample2 in x_samples:
x_sample2 = 255. * rearrange(x_sample2.cpu().numpy(), 'c h w -> h w c')
resultslice = Image.fromarray(x_sample2.astype(np.uint8)).convert('RGBA')
betterslices.append((resultslice.copy(), coord_x, coord_y))
alpha = Image.new('L', og_size, color=0xFF)
alpha_gradient = ImageDraw.Draw(alpha)
a = 0
ia = 0
overlap = gobig_overlap
shape = (og_size, (0,0))
while ia < overlap:
alpha_gradient.rectangle(shape, fill = a)
a += 4
ia += 1
shape = ((og_size[0] - ia, og_size[1]- ia), (ia,ia))
mask = Image.new('RGBA', og_size, color=0)
mask.putalpha(alpha)
finished_slices = []
for betterslice, x, y in betterslices:
finished_slice = addalpha(betterslice, mask)
finished_slices.append((finished_slice, x, y))
# # Once we have all our images, use grid_merge back onto the source, then save
goBig_output = grid_merge(source_image.convert("RGBA"), finished_slices).convert("RGB")
filename = filename + '-gobig'
save_sample(goBig_output, 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 use_GFPGAN and GFPGAN is not None:
torch_gc()
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(goBig_output, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
x_sample3 = restored_img[:,:,::1]
goBig_output = Image.fromarray(x_sample3)
filename = filename + '-gfpgan'
save_sample(goBig_output, 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 = original_filename
x_sample = original_sample
output_images.append(goBig_output)
torch_gc()
image = Image.fromarray(x_sample)
if 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, 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(image)
if (prompt_matrix or not skip_grid) and not do_not_save_grid:
if prompt_matrix:
grid = image_grid(output_images, batch_size, force_n_rows=1 << ((len(prompt_matrix_parts)-1)//2), captions=prompt_matrix_parts if prompt.startswith("@") else None)
else:
grid = image_grid(output_images, batch_size)
if prompt_matrix:
if not prompt.startswith("@"):
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)
output_images.insert(0, grid)
else:
grid = image_grid(output_images, batch_size)
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)
if opt.optimized:
mem = torch.cuda.memory_allocated()/1e6
modelFS.to("cpu")
while(torch.cuda.memory_allocated()/1e6 >= mem):
time.sleep(1)
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}{', 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):
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_GoBIG = 7 in toggles
use_GFPGAN = 8 in toggles
use_RealESRGAN = 9 in toggles # possible index shift
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,
use_GoBIG=use_GoBIG,
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,
jpg_sample=jpg_sample,
)
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, 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 img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask_blur_strength: int, ddim_steps: int, sampler_name: str,
toggles: List[int], realesrgan_model_name: str, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float,
seed: int, height: int, width: int, resize_mode: int, fp):
outpath = opt.outdir_img2img or opt.outdir or "outputs/img2img-samples"
err = False
seed = seed_to_int(seed)
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
jpg_sample = 8 in toggles
use_GoBIG = 9 in toggles
use_GFPGAN = 10 in toggles
use_RealESRGAN = 11 in toggles
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["image"]
init_img = init_img.convert("RGB")
init_img = resize_image(resize_mode, init_img, width, height)
init_mask = init_info["mask"]
init_mask = init_mask.convert("RGB")
init_mask = resize_image(resize_mode, init_mask, width, height)
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 = 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, _ = 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=0.0, x_T=x)
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
try:
if loopback:
output_images, info = None, None
history = []
initial_seed = None
for i in range(n_iter):
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
use_GoBIG=use_GoBIG,
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,
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,
jpg_sample=jpg_sample,
)
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:
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,
use_GoBIG=use_GoBIG,
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,
resize_mode=resize_mode,
uses_loopback=loopback,
sort_samples=sort_samples,
write_info_files=write_info_files,
jpg_sample=jpg_sample,
)
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 (img2img)!!')
# 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
# TODO this could probably be done with less code
def split_weighted_subprompts(text):
print(text)
remaining = len(text)
prompts = []
weights = []
while remaining > 0:
if ":" in text:
idx = text.index(":") # first occurrence from start
# grab up to index as sub-prompt
prompt = text[:idx]
remaining -= idx
# remove from main text
text = text[idx+1:]
# find value for weight, assume it is followed by a space or comma
idx = len(text) # default is read to end of text
if " " in text:
idx = min(idx,text.index(" ")) # want the closer idx
if "," in text:
idx = min(idx,text.index(",")) # want the closer idx
if idx != 0:
try:
weight = float(text[:idx])
except: # couldn't treat as float
print(f"Warning: '{text[:idx]}' is not a value, are you missing a space or comma after a value?")
weight = 1.0
else: # no value found
weight = 1.0
# remove from main text
remaining -= idx
text = text[idx+1:]
# append the sub-prompt and its weight
prompts.append(prompt)
weights.append(weight)
else: # no : found
if len(text) > 0: # there is still text though
# take remainder as weight 1
prompts.append(text)
weights.append(1.0)
remaining = 0
return prompts, weights
def run_GFPGAN(image, strength):
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)
if strength < 1.0:
res = Image.blend(image, res, strength)
return res
def run_RealESRGAN(image, model_name: str):
if RealESRGAN.model.name != model_name:
try_loading_RealESRGAN(model_name)
image = image.convert("RGB")
output, img_mode = RealESRGAN.enhance(np.array(image, dtype=np.uint8))
res = Image.fromarray(output)
return res
def run_goBIG(image, model_name: str):
outpath = opt.outdir_goBig or opt.outdir or "outputs/gobig-samples"
os.makedirs(outpath, exist_ok=True)
def addalpha(im, mask):
imr, img, imb, ima = im.split()
mmr, mmg, mmb, mma = mask.split()
im = Image.merge('RGBA', [imr, img, imb, mma]) # we want the RGB from the original, but the transparency from the mask
return(im)
def grid_merge(source, slices):
source.convert("RGBA")
for slice, posx, posy in slices: # go in reverse to get proper stacking
source.alpha_composite(slice, (posx, posy))
return source
def grid_slice(source, overlap, og_size, maximize=False):
def grid_coords(target, original, overlap):
#generate a list of coordinate tuples for our sections, in order of how they'll be rendered
#target should be the size for the gobig result, original is the size of each chunk being rendered
center = []
target_x, target_y = target
center_x = int(target_x / 2)
center_y = int(target_y / 2)
original_x, original_y = original
x = center_x - int(original_x / 2)
y = center_y - int(original_y / 2)
center.append((x,y)) #center chunk
uy = y #up
uy_list = []
dy = y #down
dy_list = []
lx = x #left
lx_list = []
rx = x #right
rx_list = []
while uy > 0: #center row vertical up
uy = uy - original_y + overlap
uy_list.append((lx, uy))
while (dy + original_y) <= target_y: #center row vertical down
dy = dy + original_y - overlap
dy_list.append((rx, dy))
while lx > 0:
lx = lx - original_x + overlap
lx_list.append((lx, y))
uy = y
while uy > 0:
uy = uy - original_y + overlap
uy_list.append((lx, uy))
dy = y
while (dy + original_y) <= target_y:
dy = dy + original_y - overlap
dy_list.append((lx, dy))
while (rx + original_x) <= target_x:
rx = rx + original_x - overlap
rx_list.append((rx, y))
uy = y
while uy > 0:
uy = uy - original_y + overlap
uy_list.append((rx, uy))
dy = y
while (dy + original_y) <= target_y:
dy = dy + original_y - overlap
dy_list.append((rx, dy))
# calculate a new size that will fill the canvas, which will be optionally used in grid_slice and go_big
last_coordx, last_coordy = dy_list[-1:][0]
render_edgey = last_coordy + original_y # outer bottom edge of the render canvas
render_edgex = last_coordx + original_x # outer side edge of the render canvas
scalarx = render_edgex / target_x
scalary = render_edgey / target_y
if scalarx <= scalary:
new_edgex = int(target_x * scalarx)
new_edgey = int(target_y * scalarx)
else:
new_edgex = int(target_x * scalary)
new_edgey = int(target_y * scalary)
# now put all the chunks into one master list of coordinates (essentially reverse of how we calculated them so that the central slices will be on top)
result = []
for coords in dy_list[::-1]:
result.append(coords)
for coords in uy_list[::-1]:
result.append(coords)
for coords in rx_list[::-1]:
result.append(coords)
for coords in lx_list[::-1]:
result.append(coords)
result.append(center[0])
return result, (new_edgex, new_edgey)
def get_resampling_mode():
try:
from PIL import __version__, Image
major_ver = int(__version__.split('.')[0])
if major_ver >= 9:
return Image.Resampling.LANCZOS
else:
return LANCZOS
except Exception as ex:
return 1 # 'Lanczos' irrespective of version
width, height = og_size # size of the slices to be rendered
coordinates, new_size = grid_coords(source.size, og_size, overlap)
if maximize == True:
source = source.resize(new_size, get_resampling_mode()) # minor concern that we're resizing twice
coordinates, new_size = grid_coords(source.size, og_size, overlap) # re-do the coordinates with the new canvas size
# loc_width and loc_height are the center point of the goal size, and we'll start there and work our way out
slices = []
for coordinate in coordinates:
x, y = coordinate
slices.append(((source.crop((x, y, x+width, y+height))), x, y))
global slices_todo
slices_todo = len(slices) - 1
return slices, new_size
def convert_pil_img(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1.
if RealESRGAN.model.name != model_name:
try_loading_RealESRGAN(model_name)
image = image.convert("RGB")
output, img_mode = RealESRGAN.enhance(np.array(image, dtype=np.uint8))
#resize output to half size
#convert output to single segment array
res = Image.fromarray(output)
res = res.resize((int(res.width/2), int(res.height/2)), LANCZOS)
sampler = DDIMSampler(model)
gobig_overlap = 64
batch_size = 1
data = [batch_size * [""]]
precision_scope = autocast if opt.precision == "autocast" else nullcontext
base_filename = 'sampleTest'
res.save(os.path.join(outpath, f"{base_filename}.png"))
image.save(os.path.join(outpath, f"{base_filename}ORG.png"))
with torch.no_grad():
with precision_scope("cuda"):
with model.ema_scope():
for _ in trange(1, desc="Passes"):
#realesrgan2x(opt.realesrgan, os.path.join(sample_path, f"{base_filename}.png"), os.path.join(sample_path, f"{base_filename}u.png"))
base_filename = f"{base_filename}u"
source_image = res
og_size = (int(source_image.size[0] / 2), int(source_image.size[1] / 2))
slices, _ = grid_slice(source_image, gobig_overlap, og_size, False)
betterslices = []
for _, chunk_w_coords in tqdm(enumerate(slices), "Slices"):
chunk, coord_x, coord_y = chunk_w_coords
init_image = convert_pil_img(chunk).to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=150, ddim_eta=0, verbose=False)
assert 0. <= 0.3 <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(0.3 * 150)
with torch.no_grad():
with precision_scope("cuda"):
with model.ema_scope():
for prompts in tqdm(data, desc="data"):
uc = None
if opt.scale != 1.0:
uc = model.get_learned_conditioning(batch_size * ['4k'])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))
# decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,)
x_samples = model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
for x_sample in x_samples:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
resultslice = Image.fromarray(x_sample.astype(np.uint8)).convert('RGBA')
betterslices.append((resultslice.copy(), coord_x, coord_y))
alpha = Image.new('L', og_size, color=0xFF)
alpha_gradient = ImageDraw.Draw(alpha)
a = 0
i = 0
overlap = gobig_overlap
shape = (og_size, (0,0))
while i < overlap:
alpha_gradient.rectangle(shape, fill = a)
a += 4
i += 1
shape = ((og_size[0] - i, og_size[1]- i), (i,i))
mask = Image.new('RGBA', og_size, color=0)
mask.putalpha(alpha)
finished_slices = []
for betterslice, x, y in betterslices:
finished_slice = addalpha(betterslice, mask)
finished_slices.append((finished_slice, x, y))
# # Once we have all our images, use grid_merge back onto the source, then save
final_output = grid_merge(source_image.convert("RGBA"), finished_slices).convert("RGB")
final_output.save(os.path.join(outpath, f"{base_filename}d.png"))
base_filename = f"{base_filename}d"
torch_gc()
#put_watermark(final_output, wm_encoder)
final_output.save(os.path.join(outpath, f"{base_filename}.png"))
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',
'jpg samples',
]
if RealESRGAN is not None:
txt2img_toggles.append('Upscale images using goBig')
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,
'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']]
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else 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',
'jpg samples',
]
if RealESRGAN is not None:
img2img_toggles.append('Upscale images goBig')
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,
'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'
def change_image_editor_mode(choice, cropped_image, resize_mode, width, height):
if choice == "Mask":
return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)]
return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
def update_image_mask(cropped_image, resize_mode, width, height):
resized_cropped_image = resize_image(resize_mode, cropped_image, width, height) if cropped_image else None
return gr.update(value=resized_cropped_image)
def copy_img_to_input(selected=1, imgs = []):
try:
idx = int(0 if selected - 1 < 0 else selected - 1)
image_data = re.sub('^data:image/.+;base64,', '', imgs[idx])
processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
update = gr.update(selected='img2img_tab')
return [processed_image, processed_image, update]
except IndexError:
return [None, None]
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="")]
css_hide_progressbar = """
.wrap .m-12 svg { display:none!important; }
.wrap .m-12::before { content:"Loading..." }
.progress-bar { display:none!important; }
.meta-text { display:none!important; }
"""
styling = """
[data-testid="image"] {min-height: 512px !important}
* #body>.col:nth-child(2){width:250%;max-width:89vw}
#generate{width: 100%; }
#prompt_row input{
font-size:20px
}
input[type=number]:disabled { -moz-appearance: textfield;+ }
"""
css = styling if opt.no_progressbar_hiding else styling + css_hide_progressbar
with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion WebUI") as demo:
with gr.Tabs(elem_id='tabss') as tabs:
with gr.TabItem("Stable Diffusion Text-to-Image Unified", id='txt2img_tab'):
with gr.Row(elem_id="prompt_row"):
txt2img_prompt = gr.Textbox(label="Prompt",
elem_id='prompt_input',
placeholder="A corgi wearing a top hat as an oil painting.",
lines=1,
max_lines=1 if txt2img_defaults['submit_on_enter'] == 'Yes' else 25,
value=txt2img_defaults['prompt'],
show_label=False).style()
with gr.Row(elem_id='body').style(equal_height=False):
with gr.Column():
txt2img_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=txt2img_defaults["height"])
txt2img_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=txt2img_defaults["width"])
txt2img_cfg = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=txt2img_defaults['cfg_scale'])
txt2img_seed = gr.Textbox(label="Seed (blank to randomize)", lines=1, max_lines=1, value=txt2img_defaults["seed"])
txt2img_batch_count = gr.Slider(minimum=1, maximum=250, step=1, label='Batch count (how many batches of images to generate)', value=txt2img_defaults['n_iter'])
txt2img_batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=txt2img_defaults['batch_size'])
with gr.Column():
output_txt2img_gallery = gr.Gallery(label="Images", elem_id="gallery_output").style(grid=[4,4])
with gr.Row():
with gr.Group():
output_txt2img_seed = gr.Number(label='Seed', interactive=False)
output_txt2img_copy_seed = gr.Button("Copy").click(inputs=output_txt2img_seed, outputs=[], _js='(x) => navigator.clipboard.writeText(x)', fn=None, show_progress=False)
with gr.Group():
output_txt2img_select_image = gr.Number(label='Image # and click Copy to copy to img2img', value=1, precision=None)
output_txt2img_copy_to_input_btn = gr.Button("Push to img2img").style(full_width=True)
if RealESRGAN is not None:
#needs to be fixed
#output_txt2img_copy_to_gobig_input_btn = gr.Button("Copy selected image to goBig input")
pass
with gr.Group():
output_txt2img_params = gr.Textbox(label="Copy-paste generation parameters", interactive=False)
output_txt2img_copy_params = gr.Button("Copy").click(inputs=output_txt2img_params, outputs=[], _js='(x) => navigator.clipboard.writeText(x)', fn=None, show_progress=False)
output_txt2img_stats = gr.HTML(label='Stats')
with gr.Column():
txt2img_btn = gr.Button("Generate", elem_id="generate", variant="primary").style(full_width=True)
txt2img_steps = gr.Slider(minimum=1, maximum=250, step=1, label="Sampling Steps", value=txt2img_defaults['ddim_steps'])
txt2img_sampling = gr.Dropdown(label='Sampling method (k_lms is default k-diffusion sampler)', choices=["DDIM", "PLMS", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms'], value=txt2img_defaults['sampler_name'])
with gr.Tabs():
with gr.TabItem('Simple'):
txt2img_submit_on_enter = gr.Radio(['Yes', 'No'], label="Submit on enter? (no means multiline)", value=txt2img_defaults['submit_on_enter'], interactive=True)
txt2img_submit_on_enter.change(lambda x: gr.update(max_lines=1 if x == 'Single' else 25) , txt2img_submit_on_enter, txt2img_prompt)
with gr.TabItem('Advanced'):
txt2img_toggles = gr.CheckboxGroup(label='', choices=txt2img_toggles, value=txt2img_toggle_defaults, type="index")
txt2img_realesrgan_model_name = gr.Dropdown(label='RealESRGAN model', choices=['RealESRGAN_x4plus', 'RealESRGAN_x4plus_anime_6B'], value='RealESRGAN_x4plus', visible=RealESRGAN is not None) # TODO: Feels like I shouldnt slot it in here.
txt2img_ddim_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=txt2img_defaults['ddim_eta'], visible=False)
txt2img_embeddings = gr.File(label = "Embeddings file for textual inversion", visible=hasattr(model, "embedding_manager"))
txt2img_btn.click(
txt2img,
[txt2img_prompt, txt2img_steps, txt2img_sampling, txt2img_toggles, txt2img_realesrgan_model_name, txt2img_ddim_eta, txt2img_batch_count, txt2img_batch_size, txt2img_cfg, txt2img_seed, txt2img_height, txt2img_width, txt2img_embeddings],
[output_txt2img_gallery, output_txt2img_seed, output_txt2img_params, output_txt2img_stats]
)
txt2img_prompt.submit(
txt2img,
[txt2img_prompt, txt2img_steps, txt2img_sampling, txt2img_toggles, txt2img_realesrgan_model_name, txt2img_ddim_eta, txt2img_batch_count, txt2img_batch_size, txt2img_cfg, txt2img_seed, txt2img_height, txt2img_width, txt2img_embeddings],
[output_txt2img_gallery, output_txt2img_seed, output_txt2img_params, output_txt2img_stats]
)
with gr.TabItem("Stable Diffusion Image-to-Image Unified", id="img2img_tab"):
with gr.Row(elem_id="prompt_row"):
img2img_prompt = gr.Textbox(label="Prompt",
elem_id='img2img_prompt_input',
placeholder="A fantasy landscape, trending on artstation.",
lines=1,
max_lines=1 if txt2img_defaults['submit_on_enter'] == 'Yes' else 25,
value=img2img_defaults['prompt'],
show_label=False).style()
img2img_btn_mask = gr.Button("Generate",variant="primary", visible=False, elem_id="img2img_mask_btn").style(full_width=True)
img2img_btn_editor = gr.Button("Generate",variant="primary", elem_id="img2img_editot_btn").style(full_width=True)
with gr.Row().style(equal_height=False):
with gr.Column():
img2img_image_editor_mode = gr.Radio(choices=["Mask", "Crop"], label="Image Editor Mode", value="Crop")
img2img_show_help_btn = gr.Button("Show Hints")
img2img_hide_help_btn = gr.Button("Hide Hints", visible=False)
img2img_help = gr.Markdown(visible=False, value="")
with gr.Row():
img2img_painterro_btn = gr.Button("Advanced Editor")
img2img_copy_from_painterro_btn = gr.Button(value="Get Image from Advanced Editor")
img2img_image_editor = gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil", tool="select")
img2img_image_mask = gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil", tool="sketch", visible=False)
img2img_mask = gr.Radio(choices=["Keep masked area", "Regenerate only masked area"], label="Mask Mode", type="index", value=img2img_mask_modes[img2img_defaults['mask_mode']], visible=False)
img2img_mask_blur_strength = gr.Slider(minimum=1, maximum=10, step=1, label="How much blurry should the mask be? (to avoid hard edges)", value=3, visible=False)
img2img_steps = gr.Slider(minimum=1, maximum=250, step=1, label="Sampling Steps", value=img2img_defaults['ddim_steps'])
img2img_sampling = gr.Dropdown(label='Sampling method (k_lms is default k-diffusion sampler)', choices=["DDIM", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms'], value=img2img_defaults['sampler_name'])
img2img_toggles = gr.CheckboxGroup(label='', choices=img2img_toggles, value=img2img_toggle_defaults, type="index")
img2img_realesrgan_model_name = gr.Dropdown(label='RealESRGAN model', choices=['RealESRGAN_x4plus', 'RealESRGAN_x4plus_anime_6B'], value='RealESRGAN_x4plus', visible=RealESRGAN is not None) # TODO: Feels like I shouldnt slot it in here.
img2img_batch_count = gr.Slider(minimum=1, maximum=250, step=1, label='Batch count (how many batches of images to generate)', value=img2img_defaults['n_iter'])
img2img_batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=img2img_defaults['batch_size'])
img2img_cfg = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=img2img_defaults['cfg_scale'])
img2img_denoising = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=img2img_defaults['denoising_strength'])
img2img_seed = gr.Textbox(label="Seed (blank to randomize)", lines=1, max_lines=1, value=img2img_defaults["seed"])
img2img_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=img2img_defaults["height"])
img2img_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=img2img_defaults["width"])
img2img_resize = gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value=img2img_resize_modes[img2img_defaults['resize_mode']])
img2img_embeddings = gr.File(label = "Embeddings file for textual inversion", visible=hasattr(model, "embedding_manager"))
with gr.Column():
output_img2img_gallery = gr.Gallery(label="Images")
output_img2img_select_image = gr.Number(label='Select image number from results for copying', value=1, precision=None)
gr.Markdown("Clear the input image before copying your output to your input. It may take some time to load the image.")
output_img2img_copy_to_input_btn = gr.Button("Copy selected image to input")
if RealESRGAN is not None:
output_txt2img_copy_to_gobig_input_btn = gr.Button("Copy selected image to goBig input")
output_img2img_seed = gr.Number(label='Seed')
output_img2img_params = gr.Textbox(label="Copy-paste generation parameters")
output_img2img_stats = gr.HTML(label='Stats')
img2img_image_editor_mode.change(
change_image_editor_mode,
[img2img_image_editor_mode, img2img_image_editor, img2img_resize, img2img_width, img2img_height],
[img2img_image_editor, img2img_image_mask, img2img_btn_editor, img2img_btn_mask, img2img_painterro_btn, img2img_copy_from_painterro_btn, img2img_mask, img2img_mask_blur_strength]
)
img2img_image_editor.edit(
update_image_mask,
[img2img_image_editor, img2img_resize, img2img_width, img2img_height],
img2img_image_mask
)
img2img_show_help_btn.click(
show_help,
None,
[img2img_show_help_btn, img2img_hide_help_btn, img2img_help]
)
img2img_hide_help_btn.click(
hide_help,
None,
[img2img_show_help_btn, img2img_hide_help_btn, img2img_help]
)
output_img2img_copy_to_input_btn.click(
copy_img_to_input,
[output_img2img_select_image, output_img2img_gallery],
[img2img_image_editor, img2img_image_mask]
)
output_txt2img_copy_to_input_btn.click(
copy_img_to_input,
[output_txt2img_select_image, output_txt2img_gallery],
[img2img_image_editor, img2img_image_mask, tabs]
)
img2img_btn_mask.click(
img2img,
[img2img_prompt, img2img_image_editor_mode, img2img_image_mask, img2img_mask, img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles, img2img_realesrgan_model_name, img2img_batch_count, img2img_batch_size, img2img_cfg, img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize, img2img_embeddings],
[output_img2img_gallery, output_img2img_seed, output_img2img_params, output_img2img_stats]
)
img2img_btn_editor.click(
img2img,
[img2img_prompt, img2img_image_editor_mode, img2img_image_editor, img2img_mask, img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles, img2img_realesrgan_model_name, img2img_batch_count, img2img_batch_size, img2img_cfg, img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize, img2img_embeddings],
[output_img2img_gallery, output_img2img_seed, output_img2img_params, output_img2img_stats]
)
img2img_painterro_btn.click(None, [img2img_image_editor], None, _js="""(img) => {
try {
Painterro({
hiddenTools: ['arrow'],
saveHandler: function (image, done) {
localStorage.setItem('painterro-image', image.asDataURL());
done(true);
},
}).show(Array.isArray(img) ? img[0] : img);
} catch(e) {
const script = document.createElement('script');
script.src = 'https://unpkg.com/painterro@1.2.78/build/painterro.min.js';
document.head.appendChild(script);
const style = document.createElement('style');
style.appendChild(document.createTextNode('.ptro-holder-wrapper { z-index: 9999 !important; }'));
document.head.appendChild(style);
}
return [];
}""")
img2img_copy_from_painterro_btn.click(None, None, [img2img_image_editor, img2img_image_mask], _js="""() => {
const image = localStorage.getItem('painterro-image')
return [image, image];
}""")
if GFPGAN is not None:
gfpgan_defaults = {
'strength': 100,
}
if 'gfpgan' in user_defaults:
gfpgan_defaults.update(user_defaults['gfpgan'])
with gr.TabItem("GFPGAN"):
gr.Markdown("Fix faces on images")
with gr.Row():
with gr.Column():
gfpgan_source = gr.Image(label="Source", source="upload", interactive=True, type="pil")
gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength", value=gfpgan_defaults['strength'])
gfpgan_btn = gr.Button("Generate", variant="primary")
with gr.Column():
gfpgan_output = gr.Image(label="Output")
gfpgan_btn.click(
run_GFPGAN,
[gfpgan_source, gfpgan_strength],
[gfpgan_output]
)
if RealESRGAN is not None:
with gr.TabItem("RealESRGAN"):
gr.Markdown("Upscale images")
with gr.Row():
with gr.Column():
realesrgan_source = gr.Image(label="Source", source="upload", interactive=True, type="pil")
realesrgan_model_name = gr.Dropdown(label='RealESRGAN model', choices=['RealESRGAN_x4plus', 'RealESRGAN_x4plus_anime_6B'], value='RealESRGAN_x4plus')
realesrgan_btn = gr.Button("Generate")
with gr.Column():
realesrgan_output = gr.Image(label="Output")
realesrgan_btn.click(
run_RealESRGAN,
[realesrgan_source, realesrgan_model_name],
[realesrgan_output]
)
with gr.TabItem("goBIG"):
gr.Markdown("Upscale and detail images")
with gr.Row():
with gr.Column():
realesrganGoBig_source = gr.Image(source="upload", interactive=True, type="pil", tool="select")
realesrganGoBig_model_name = gr.Dropdown(label='RealESRGAN model', choices=['RealESRGAN_x4plus', 'RealESRGAN_x4plus_anime_6B'], value='RealESRGAN_x4plus')
realesrganGoBig_btn = gr.Button("Generate")
with gr.Column():
realesrganGoBig_output = gr.Image(label="Output")
realesrganGoBig_btn.click(
run_goBIG,
[realesrganGoBig_source, realesrganGoBig_model_name],
[realesrganGoBig_output]
)
output_txt2img_copy_to_gobig_input_btn.click(
copy_img_to_input,
[output_txt2img_select_image, output_txt2img_gallery],
[realesrganGoBig_source,realesrganGoBig_source]
)
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 = {
'show_error': True,
'server_name': '0.0.0.0',
'share': opt.share
}
if not opt.share:
demo.queue(concurrency_count=1)
if opt.share and opt.share_password:
gradio_params['auth'] = ('webui', opt.share_password)
self.demo.launch(**gradio_params)
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()
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