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WebUI/webui.py

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import argparse
import os
import sys
script_path = os.path.dirname(os.path.realpath(__file__))
# use current directory as SD dir if it has related files, otherwise parent dir of script as stated in guide
sd_path = os.path.abspath('.') if os.path.exists('./ldm/models/diffusion/ddpm.py') else os.path.dirname(script_path)
# add parent directory to path; this is where Stable diffusion repo should be
path_dirs = [
(sd_path, 'ldm', 'Stable Diffusion'),
(os.path.join(sd_path,'../taming-transformers'), 'taming', 'Taming Transformers')
]
for d, must_exist, what in path_dirs:
must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist))
if not os.path.exists(must_exist_path):
print(f"Warning: {what} not found at path {must_exist_path}", file=sys.stderr)
else:
sys.path.append(os.path.join(script_path, d))
import torch
import torch.nn as nn
import numpy as np
import gradio as gr
import gradio.utils
from omegaconf import OmegaConf
from PIL import Image, ImageFont, ImageDraw, PngImagePlugin, ImageFilter, ImageOps
from torch import autocast
import mimetypes
import random
import math
import html
import time
import json
import traceback
from collections import namedtuple
from contextlib import nullcontext
import signal
import tqdm
import re
import threading
import time
import base64
import io
import k_diffusion.sampling
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
# 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'
config_filename = "config.json"
sd_model_file = os.path.join(script_path, 'model.ckpt')
if not os.path.exists(sd_model_file):
sd_model_file = "models/ldm/stable-diffusion-v1/model.ckpt"
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default=os.path.join(sd_path, sd_model_file), help="path to checkpoint of model",)
parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default='GFPGANv1.3.pth')
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
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)")
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
parser.add_argument("--embeddings-dir", type=str, default='embeddings', help="embeddings dirtectory for textual inversion (default: embeddings)")
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrficing a little speed for low VRM usage")
parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrficing a lot of speed for very low VRM usage")
parser.add_argument("--always-batch-cond-uncond", action='store_true', help="a workaround test; may help with speed in you use --lowvram")
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
cmd_opts = parser.parse_args()
cpu = torch.device("cpu")
gpu = torch.device("cuda")
device = gpu if torch.cuda.is_available() else cpu
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
queue_lock = threading.Lock()
def gr_show(visible=True):
return {"visible": visible, "__type__": "update"}
class State:
interrupted = False
job = ""
def interrupt(self):
self.interrupted = True
state = State()
if not cmd_opts.share:
# fix gradio phoning home
gradio.utils.version_check = lambda: None
gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
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; }
"""
SamplerData = namedtuple('SamplerData', ['name', 'constructor'])
samplers = [
*[SamplerData(x[0], lambda funcname=x[1]: KDiffusionSampler(funcname)) for x in [
('Euler a', 'sample_euler_ancestral'),
('Euler', 'sample_euler'),
('LMS', 'sample_lms'),
('Heun', 'sample_heun'),
('DPM2', 'sample_dpm_2'),
('DPM2 a', 'sample_dpm_2_ancestral'),
] if hasattr(k_diffusion.sampling, x[1])],
SamplerData('DDIM', lambda: VanillaStableDiffusionSampler(DDIMSampler)),
SamplerData('PLMS', lambda: VanillaStableDiffusionSampler(PLMSSampler)),
]
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
RealesrganModelInfo = namedtuple("RealesrganModelInfo", ["name", "location", "model", "netscale"])
try:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
realesrgan_models = [
RealesrganModelInfo(
name="Real-ESRGAN 4x plus",
location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
netscale=4, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
),
RealesrganModelInfo(
name="Real-ESRGAN 4x plus anime 6B",
location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
netscale=4, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
),
RealesrganModelInfo(
name="Real-ESRGAN 2x plus",
location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
netscale=2, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
),
]
have_realesrgan = True
except Exception:
print("Error importing Real-ESRGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
realesrgan_models = [RealesrganModelInfo('None', '', 0, None)]
have_realesrgan = False
sd_upscalers = {
"RealESRGAN": lambda img: upscale_with_realesrgan(img, 2, 0),
"Lanczos": lambda img: img.resize((img.width*2, img.height*2), resample=LANCZOS),
"None": lambda img: img
}
def gfpgan_model_path():
places = [script_path, '.', os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models')]
files = [cmd_opts.gfpgan_model] + [os.path.join(dirname, cmd_opts.gfpgan_model) for dirname in places]
found = [x for x in files if os.path.exists(x)]
if len(found) == 0:
raise Exception("GFPGAN model not found in paths: " + ", ".join(files))
return found[0]
def gfpgan():
return GFPGANer(model_path=gfpgan_model_path(), upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
def gfpgan_fix_faces(gfpgan_model, np_image):
np_image_bgr = np_image[:, :, ::-1]
cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan_model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
np_image = gfpgan_output_bgr[:, :, ::-1]
return np_image
have_gfpgan = False
try:
model_path = gfpgan_model_path()
if os.path.exists(cmd_opts.gfpgan_dir):
sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir))
from gfpgan import GFPGANer
have_gfpgan = True
except Exception:
print("Error setting up GFPGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
class Options:
class OptionInfo:
def __init__(self, default=None, label="", component=None, component_args=None):
self.default = default
self.label = label
self.component = component
self.component_args = component_args
data = None
data_labels = {
"outdir_samples": OptionInfo("", "Output dictectory for images; if empty, defaults to two directories below"),
"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output dictectory for txt2img images'),
"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output dictectory for img2img images'),
"outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output dictectory for images from extras tab'),
"outdir_grids": OptionInfo("", "Output dictectory for grids; if empty, defaults to two directories below"),
"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output dictectory for txt2img grids'),
"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output dictectory for img2img grids'),
"save_to_dirs": OptionInfo(False, "When writing images/grids, create a directory with name derived from the prompt"),
"save_to_dirs_prompt_len": OptionInfo(10, "When using above, how many words from prompt to put into directory name", gr.Slider, {"minimum": 1, "maximum": 32, "step": 1}),
"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button"),
"samples_save": OptionInfo(True, "Save indiviual samples"),
"samples_format": OptionInfo('png', 'File format for indiviual samples'),
"grid_save": OptionInfo(True, "Save image grids"),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"grid_format": OptionInfo('png', 'File format for grids'),
"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
"export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
"font": OptionInfo("arial.ttf", "Font for image grids that have text"),
"prompt_matrix_add_to_start": OptionInfo(True, "In prompt matrix, add the variable combination of text to the start of the prompt, rather than the end"),
"enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text text and [text] to make it pay less attention"),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
}
def __init__(self):
self.data = {k: v.default for k, v in self.data_labels.items()}
def __setattr__(self, key, value):
if self.data is not None:
if key in self.data:
self.data[key] = value
return super(Options, self).__setattr__(key, value)
def __getattr__(self, item):
if self.data is not None:
if item in self.data:
return self.data[item]
if item in self.data_labels:
return self.data_labels[item].default
return super(Options, self).__getattribute__(item)
def save(self, filename):
with open(filename, "w", encoding="utf8") as file:
json.dump(self.data, file)
def load(self, filename):
with open(filename, "r", encoding="utf8") as file:
self.data = json.load(file)
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.eval()
return model
module_in_gpu = None
def setup_for_low_vram(sd_model):
parents = {}
def send_me_to_gpu(module, _):
"""send this module to GPU; send whatever tracked module was previous in GPU to CPU;
we add this as forward_pre_hook to a lot of modules and this way all but one of them will
be in CPU
"""
global module_in_gpu
module = parents.get(module, module)
if module_in_gpu == module:
return
if module_in_gpu is not None:
module_in_gpu.to(cpu)
module.to(gpu)
module_in_gpu = module
# see below for register_forward_pre_hook;
# first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
# useless here, and we just replace those methods
def first_stage_model_encode_wrap(self, encoder, x):
send_me_to_gpu(self, None)
return encoder(x)
def first_stage_model_decode_wrap(self, decoder, z):
send_me_to_gpu(self, None)
return decoder(z)
# remove three big modules, cond, first_stage, and unet from the model and then
# send the model to GPU. Then put modules back. the modules will be in CPU.
stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None
sd_model.to(device)
sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored
# register hooks for those the first two models
sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z)
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
if cmd_opts.medvram:
sd_model.model.register_forward_pre_hook(send_me_to_gpu)
else:
diff_model = sd_model.model.diffusion_model
# the third remaining model is still too big for 4GB, so we also do the same for its submodules
# so that only one of them is in GPU at a time
stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
sd_model.model.to(device)
diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
# install hooks for bits of third model
diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
for block in diff_model.input_blocks:
block.register_forward_pre_hook(send_me_to_gpu)
diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
for block in diff_model.output_blocks:
block.register_forward_pre_hook(send_me_to_gpu)
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():
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False):
if short_filename or prompt is None or seed is None:
file_decoration = ""
elif opts.save_to_dirs:
file_decoration = f"-{seed}"
else:
file_decoration = f"-{seed}-{sanitize_filename_part(prompt)[:128]}"
if extension == 'png' and opts.enable_pnginfo and info is not None:
pnginfo = PngImagePlugin.PngInfo()
pnginfo.add_text("parameters", info)
else:
pnginfo = None
if opts.save_to_dirs and not no_prompt:
words = re.findall(r'\w+', prompt or "")
if len(words) == 0:
words = ["empty"]
dirname = " ".join(words[0:opts.save_to_dirs_prompt_len])
path = os.path.join(path, dirname)
os.makedirs(path, exist_ok=True)
filecount = len(os.listdir(path))
fullfn = "a.png"
fullfn_without_extension = "a"
for i in range(100):
fn = f"{filecount:05}" if basename == '' else f"{basename}-{filecount:04}"
fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
if not os.path.exists(fullfn):
break
image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
target_side_length = 4000
oversize = image.width > target_side_length or image.height > target_side_length
if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
ratio = image.width / image.height
if oversize and ratio > 1:
image = image.resize((target_side_length, image.height * target_side_length // image.width), LANCZOS)
elif oversize:
image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
image.save(f"{fullfn_without_extension}.jpg", quality=opts.jpeg_quality, pnginfo=pnginfo)
if opts.save_txt and info is not None:
with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
file.write(info + "\n")
def sanitize_filename_part(text):
return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
def plaintext_to_html(text):
text = "".join([f"<p>{html.escape(x)}</p>\n" for x in text.split('\n')])
return text
def image_grid(imgs, batch_size=1, rows=None):
if rows is None:
if opts.n_rows > 0:
rows = opts.n_rows
elif opts.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')
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])
def split_grid(image, tile_w=512, tile_h=512, overlap=64):
w = image.width
h = image.height
now = tile_w - overlap # non-overlap width
noh = tile_h - overlap
cols = math.ceil((w - overlap) / now)
rows = math.ceil((h - overlap) / noh)
grid = Grid([], tile_w, tile_h, w, h, overlap)
for row in range(rows):
row_images = []
y = row * noh
if y + tile_h >= h:
y = h - tile_h
for col in range(cols):
x = col * now
if x+tile_w >= w:
x = w - tile_w
tile = image.crop((x, y, x + tile_w, y + tile_h))
row_images.append([x, tile_w, tile])
grid.tiles.append([y, tile_h, row_images])
return grid
def combine_grid(grid):
def make_mask_image(r):
r = r * 255 / grid.overlap
r = r.astype(np.uint8)
return Image.fromarray(r, 'L')
mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
for y, h, row in grid.tiles:
combined_row = Image.new("RGB", (grid.image_w, h))
for x, w, tile in row:
if x == 0:
combined_row.paste(tile, (0, 0))
continue
combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w)
combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0))
if y == 0:
combined_image.paste(combined_row, (0, 0))
continue
combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h)
combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap))
return combined_image
class GridAnnotation:
def __init__(self, text='', is_active=True):
self.text = text
self.is_active = is_active
self.size = None
def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
def wrap(drawing, text, font, line_length):
lines = ['']
for word in text.split():
line = f'{lines[-1]} {word}'.strip()
if drawing.textlength(line, font=font) <= line_length:
lines[-1] = line
else:
lines.append(word)
return lines
def draw_texts(drawing, draw_x, draw_y, lines):
for i, line in enumerate(lines):
drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
if not line.is_active:
drawing.line((draw_x - line.size[0]//2, draw_y + line.size[1]//2, draw_x + line.size[0]//2, draw_y + line.size[1]//2), fill=color_inactive, width=4)
draw_y += line.size[1] + line_spacing
fontsize = (width + height) // 25
line_spacing = fontsize // 2
fnt = ImageFont.truetype(opts.font, fontsize)
color_active = (0, 0, 0)
color_inactive = (153, 153, 153)
pad_left = width * 3 // 4 if len(ver_texts) > 0 else 0
cols = im.width // width
rows = im.height // height
assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
calc_img = Image.new("RGB", (1, 1), "white")
calc_d = ImageDraw.Draw(calc_img)
for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)):
items = [] + texts
texts.clear()
for line in items:
wrapped = wrap(calc_d, line.text, fnt, allowed_width)
texts += [GridAnnotation(x, line.is_active) for x in wrapped]
for line in texts:
bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt)
line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])
hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]
pad_top = max(hor_text_heights) + line_spacing * 2
result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
result.paste(im, (pad_left, pad_top))
d = ImageDraw.Draw(result)
for col in range(cols):
x = pad_left + width * col + width / 2
y = pad_top / 2 - hor_text_heights[col] / 2
draw_texts(d, x, y, hor_texts[col])
for row in range(rows):
x = pad_left / 2
y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2
draw_texts(d, x, y, ver_texts[row])
return result
def draw_prompt_matrix(im, width, height, all_prompts):
prompts = all_prompts[1:]
boundary = math.ceil(len(prompts) / 2)
prompts_horiz = prompts[:boundary]
prompts_vert = prompts[boundary:]
hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]
return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
def draw_xy_grid(xs, ys, x_label, y_label, cell):
res = []
ver_texts = [[GridAnnotation(y_label(y))] for y in ys]
hor_texts = [[GridAnnotation(x_label(x))] for x in xs]
for y in ys:
for x in xs:
res.append(cell(x, y))
grid = image_grid(res, rows=len(ys))
grid = draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
return grid
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 wrap_gradio_gpu_call(func):
def f(*args, **kwargs):
with queue_lock:
res = func(*args, **kwargs)
return res
return wrap_gradio_call(f)
def wrap_gradio_call(func):
def f(*args, **kwargs):
t = time.perf_counter()
try:
res = list(func(*args, **kwargs))
except Exception as e:
print("Error completing request", file=sys.stderr)
print("Arguments:", args, kwargs, file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
elapsed = time.perf_counter() - t
# last item is always HTML
res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
state.interrupted = False
return tuple(res)
return f
class StableDiffusionModelHijack:
ids_lookup = {}
word_embeddings = {}
word_embeddings_checksums = {}
fixes = None
comments = []
dir_mtime = None
def load_textual_inversion_embeddings(self, dirname, model):
mt = os.path.getmtime(dirname)
if self.dir_mtime is not None and mt <= self.dir_mtime:
return
self.dir_mtime = mt
self.ids_lookup.clear()
self.word_embeddings.clear()
tokenizer = model.cond_stage_model.tokenizer
def const_hash(a):
r = 0
for v in a:
r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
return r
def process_file(path, filename):
name = os.path.splitext(filename)[0]
data = torch.load(path)
param_dict = data['string_to_param']
if hasattr(param_dict, '_parameters'):
param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1].reshape(768)
self.word_embeddings[name] = emb
self.word_embeddings_checksums[name] = f'{const_hash(emb)&0xffff:04x}'
ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
first_id = ids[0]
if first_id not in self.ids_lookup:
self.ids_lookup[first_id] = []
self.ids_lookup[first_id].append((ids, name))
for fn in os.listdir(dirname):
try:
process_file(os.path.join(dirname, fn), fn)
except Exception:
print(f"Error loading emedding {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
continue
print(f"Loaded a total of {len(self.word_embeddings)} text inversion embeddings.")
def hijack(self, m):
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
def __init__(self, wrapped, hijack):
super().__init__()
self.wrapped = wrapped
self.hijack = hijack
self.tokenizer = wrapped.tokenizer
self.max_length = wrapped.max_length
self.token_mults = {}
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
for text, ident in tokens_with_parens:
mult = 1.0
for c in text:
if c == '[':
mult /= 1.1
if c == ']':
mult *= 1.1
if c == '(':
mult *= 1.1
if c == ')':
mult /= 1.1
if mult != 1.0:
self.token_mults[ident] = mult
def forward(self, text):
self.hijack.fixes = []
self.hijack.comments = []
remade_batch_tokens = []
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
maxlen = self.wrapped.max_length - 2
used_custom_terms = []
cache = {}
batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
batch_multipliers = []
for tokens in batch_tokens:
tuple_tokens = tuple(tokens)
if tuple_tokens in cache:
remade_tokens, fixes, multipliers = cache[tuple_tokens]
else:
fixes = []
remade_tokens = []
multipliers = []
mult = 1.0
i = 0
while i < len(tokens):
token = tokens[i]
possible_matches = self.hijack.ids_lookup.get(token, None)
mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
if mult_change is not None:
mult *= mult_change
elif possible_matches is None:
remade_tokens.append(token)
multipliers.append(mult)
else:
found = False
for ids, word in possible_matches:
if tokens[i:i+len(ids)] == ids:
fixes.append((len(remade_tokens), word))
remade_tokens.append(777)
multipliers.append(mult)
i += len(ids) - 1
found = True
used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
break
if not found:
remade_tokens.append(token)
multipliers.append(mult)
i += 1
if len(remade_tokens) > maxlen - 2:
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
ovf = remade_tokens[maxlen - 2:]
overflowing_words = [vocab.get(int(x), "") for x in ovf]
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
self.hijack.comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
remade_batch_tokens.append(remade_tokens)
self.hijack.fixes.append(fixes)
batch_multipliers.append(multipliers)
if len(used_custom_terms) > 0:
self.hijack.comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
tokens = torch.asarray(remade_batch_tokens).to(device)
outputs = self.wrapped.transformer(input_ids=tokens)
z = outputs.last_hidden_state
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
batch_multipliers = torch.asarray(np.array(batch_multipliers)).to(device)
original_mean = z.mean()
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
new_mean = z.mean()
z *= original_mean / new_mean
return z
class EmbeddingsWithFixes(nn.Module):
def __init__(self, wrapped, embeddings):
super().__init__()
self.wrapped = wrapped
self.embeddings = embeddings
def forward(self, input_ids):
batch_fixes = self.embeddings.fixes
self.embeddings.fixes = None
inputs_embeds = self.wrapped(input_ids)
if batch_fixes is not None:
for fixes, tensor in zip(batch_fixes, inputs_embeds):
for offset, word in fixes:
tensor[offset] = self.embeddings.word_embeddings[word]
return inputs_embeds
class StableDiffusionProcessing:
def __init__(self, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
self.outpath_samples: str = outpath_samples
self.outpath_grids: str = outpath_grids
self.prompt: str = prompt
self.negative_prompt: str = (negative_prompt or "")
self.seed: int = seed
self.sampler_index: int = sampler_index
self.batch_size: int = batch_size
self.n_iter: int = n_iter
self.steps: int = steps
self.cfg_scale: float = cfg_scale
self.width: int = width
self.height: int = height
self.prompt_matrix: bool = prompt_matrix
self.use_GFPGAN: bool = use_GFPGAN
self.do_not_save_samples: bool = do_not_save_samples
self.do_not_save_grid: bool = do_not_save_grid
self.extra_generation_params: dict = extra_generation_params
self.overlay_images = overlay_images
self.paste_to = None
def init(self):
pass
def sample(self, x, conditioning, unconditional_conditioning):
raise NotImplementedError()
def p_sample_ddim_hook(sampler_wrapper, x_dec, cond, ts, *args, **kwargs):
if sampler_wrapper.mask is not None:
img_orig = sampler_wrapper.sampler.model.q_sample(sampler_wrapper.init_latent, ts)
x_dec = img_orig * sampler_wrapper.mask + sampler_wrapper.nmask * x_dec
return sampler_wrapper.orig_p_sample_ddim(x_dec, cond, ts, *args, **kwargs)
class VanillaStableDiffusionSampler:
def __init__(self, constructor):
self.sampler = constructor(sd_model)
self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else None
self.mask = None
self.nmask = None
self.init_latent = None
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
# existing code fails with cetin step counts, like 9
try:
self.sampler.make_schedule(ddim_num_steps=p.steps, verbose=False)
except Exception:
self.sampler.make_schedule(ddim_num_steps=p.steps+1, verbose=False)
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(device), noise=noise)
self.sampler.p_sample_ddim = lambda x_dec, cond, ts, *args, **kwargs: p_sample_ddim_hook(self, x_dec, cond, ts, *args, **kwargs)
self.mask = p.mask
self.nmask = p.nmask
self.init_latent = p.init_latent
samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
return samples
def sample(self, p: StableDiffusionProcessing, x, conditioning, unconditional_conditioning):
samples_ddim, _ = self.sampler.sample(S=p.steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
return samples_ddim
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
self.mask = None
self.nmask = None
self.init_latent = None
def forward(self, x, sigma, uncond, cond, cond_scale):
if batch_cond_uncond:
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)
denoised = uncond + (cond - uncond) * cond_scale
else:
uncond = self.inner_model(x, sigma, cond=uncond)
cond = self.inner_model(x, sigma, cond=cond)
denoised = uncond + (cond - uncond) * cond_scale
if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
return denoised
def extended_trange(*args, **kwargs):
for x in tqdm.trange(*args, desc=state.job, **kwargs):
if state.interrupted:
break
yield x
class KDiffusionSampler:
def __init__(self, funcname):
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model)
self.funcname = funcname
self.func = getattr(k_diffusion.sampling, self.funcname)
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
sigmas = self.model_wrap.get_sigmas(p.steps)
noise = noise * sigmas[p.steps - t_enc - 1]
xi = x + noise
sigma_sched = sigmas[p.steps - t_enc - 1:]
self.model_wrap_cfg.mask = p.mask
self.model_wrap_cfg.nmask = p.nmask
self.model_wrap_cfg.init_latent = p.init_latent
if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
def sample(self, p: StableDiffusionProcessing, x, conditioning, unconditional_conditioning):
sigmas = self.model_wrap.get_sigmas(p.steps)
x = x * sigmas[0]
if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
def cb(d):
n = d['i']
img = d['denoised']
x_samples_ddim = sd_model.decode_first_stage(img)
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):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
image = Image.fromarray(x_sample)
image.save(f'a/{n}.png')
samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
return samples_ddim
class Processed:
def __init__(self, p: StableDiffusionProcessing, images, seed, info):
self.images = images
self.prompt = p.prompt
self.seed = seed
self.info = info
self.width = p.width
self.height = p.height
self.sampler = samplers[p.sampler_index].name
self.cfg_scale = p.cfg_scale
self.steps = p.steps
def js(self):
obj = {
"prompt": self.prompt,
"seed": int(self.seed),
"width": self.width,
"height": self.height,
"sampler": self.sampler,
"cfg_scale": self.cfg_scale,
"steps": self.steps,
}
return json.dumps(obj)
def process_images(p: StableDiffusionProcessing) -> Processed:
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
prompt = p.prompt
model = sd_model
assert p.prompt is not None
torch_gc()
seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
os.makedirs(p.outpath_samples, exist_ok=True)
os.makedirs(p.outpath_grids, exist_ok=True)
comments = []
prompt_matrix_parts = []
if p.prompt_matrix:
all_prompts = []
prompt_matrix_parts = prompt.split("|")
combination_count = 2 ** (len(prompt_matrix_parts) - 1)
for combination_num in range(combination_count):
selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1 << n)]
if opts.prompt_matrix_add_to_start:
selected_prompts = selected_prompts + [prompt_matrix_parts[0]]
else:
selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
all_prompts.append(", ".join(selected_prompts))
p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
all_seeds = len(all_prompts) * [seed]
print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
else:
all_prompts = p.batch_size * p.n_iter * [prompt]
all_seeds = [seed + x for x in range(len(all_prompts))]
generation_params = {
"Steps": p.steps,
"Sampler": samplers[p.sampler_index].name,
"CFG scale": p.cfg_scale,
"Seed": seed,
"GFPGAN": ("GFPGAN" if p.use_GFPGAN else None)
}
if p.extra_generation_params is not None:
generation_params.update(p.extra_generation_params)
generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
def infotext():
return f"{prompt}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments])
if os.path.exists(cmd_opts.embeddings_dir):
model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, model)
output_images = []
precision_scope = autocast if cmd_opts.precision == "autocast" else nullcontext
ema_scope = (nullcontext if cmd_opts.lowvram else model.ema_scope)
with torch.no_grad(), precision_scope("cuda"), ema_scope():
p.init()
for n in range(p.n_iter):
if state.interrupted:
break
prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
uc = model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
c = model.get_learned_conditioning(prompts)
if len(model_hijack.comments) > 0:
comments += model_hijack.comments
# we manually generate all input noises because each one should have a specific seed
x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds)
if p.n_iter > 1:
state.job = f"Batch {n+1} out of {p.n_iter}"
samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc)
x_samples_ddim = model.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):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
if p.use_GFPGAN:
torch_gc()
gfpgan_model = gfpgan()
x_sample = gfpgan_fix_faces(gfpgan_model, x_sample)
image = Image.fromarray(x_sample)
if p.overlay_images is not None and i < len(p.overlay_images):
overlay = p.overlay_images[i]
if p.paste_to is not None:
x, y, w, h = p.paste_to
base_image = Image.new('RGBA', (overlay.width, overlay.height))
image = resize_image(1, image, w, h)
base_image.paste(image, (x, y))
image = base_image
image = image.convert('RGBA')
image.alpha_composite(overlay)
image = image.convert('RGB')
if opts.samples_save and not p.do_not_save_samples:
save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext())
output_images.append(image)
unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
if not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
return_grid = opts.return_grid
if p.prompt_matrix:
grid = image_grid(output_images, p.batch_size, rows=1 << ((len(prompt_matrix_parts)-1)//2))
try:
grid = draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts)
except Exception:
import traceback
print("Error creating prompt_matrix text:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
return_grid = True
else:
grid = image_grid(output_images, p.batch_size)
if return_grid:
output_images.insert(0, grid)
if opts.grid_save:
save_image(grid, p.outpath_grids, "grid", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
torch_gc()
return Processed(p, output_images, seed, infotext())
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
def init(self):
self.sampler = samplers[self.sampler_index].constructor()
def sample(self, x, conditioning, unconditional_conditioning):
samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
return samples_ddim
def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
p = StableDiffusionProcessingTxt2Img(
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
prompt=prompt,
negative_prompt=negative_prompt,
seed=seed,
sampler_index=sampler_index,
batch_size=batch_size,
n_iter=n_iter,
steps=steps,
cfg_scale=cfg_scale,
width=width,
height=height,
prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN
)
if code != '' and cmd_opts.allow_code:
p.do_not_save_grid = True
p.do_not_save_samples = True
display_result_data = [[], -1, ""]
def display(imgs, s=display_result_data[1], i=display_result_data[2]):
display_result_data[0] = imgs
display_result_data[1] = s
display_result_data[2] = i
from types import ModuleType
compiled = compile(code, '', 'exec')
module = ModuleType("testmodule")
module.__dict__.update(globals())
module.p = p
module.display = display
exec(compiled, module.__dict__)
processed = Processed(p, *display_result_data)
else:
processed = process_images(p)
return processed.images, processed.js(), plaintext_to_html(processed.info)
def image_from_url_text(filedata):
if filedata.startswith("data:image/png;base64,"):
filedata = filedata[len("data:image/png;base64,"):]
filedata = base64.decodebytes(filedata.encode('utf-8'))
image = Image.open(io.BytesIO(filedata))
return image
def send_gradio_gallery_to_image(x):
if len(x) == 0:
return None
return image_from_url_text(x[0])
def save_files(js_data, images):
import csv
os.makedirs(opts.outdir_save, exist_ok=True)
filenames = []
data = json.loads(js_data)
with open("log/log.csv", "a", encoding="utf8", newline='') as file:
at_start = file.tell() == 0
writer = csv.writer(file)
if at_start:
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename"])
filename_base = str(int(time.time() * 1000))
for i, filedata in enumerate(images):
filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + ".png"
filepath = os.path.join(opts.outdir_save, filename)
if filedata.startswith("data:image/png;base64,"):
filedata = filedata[len("data:image/png;base64,"):]
with open(filepath, "wb") as imgfile:
imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
filenames.append(filename)
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0]])
return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
with gr.Row():
prompt = gr.Textbox(label="Prompt", elem_id="txt2img_prompt", show_label=False, placeholder="Prompt", lines=1)
negative_prompt = gr.Textbox(label="Negative prompt", elem_id="txt2img_negative_prompt", show_label=False, placeholder="Negative prompt", lines=1, visible=False)
submit = gr.Button('Generate', elem_id="txt2img_generate", variant='primary')
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):
steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
with gr.Row():
use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=have_gfpgan)
prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
with gr.Row():
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.0)
with gr.Group():
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
seed = gr.Number(label='Seed', value=-1)
code = gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
with gr.Column(variant='panel'):
with gr.Group():
txt2img_gallery = gr.Gallery(label='Output')
with gr.Group():
with gr.Row():
save = gr.Button('Save')
send_to_img2img = gr.Button('Send to img2img')
send_to_inpaint = gr.Button('Send to inpaint')
send_to_extras = gr.Button('Send to extras')
interrupt = gr.Button('Interrupt')
with gr.Group():
html_info = gr.HTML()
generation_info = gr.Textbox(visible=False)
txt2img_args = dict(
fn=wrap_gradio_gpu_call(txt2img),
inputs=[
prompt,
negative_prompt,
steps,
sampler_index,
use_GFPGAN,
prompt_matrix,
batch_count,
batch_size,
cfg_scale,
seed,
height,
width,
code
],
outputs=[
txt2img_gallery,
generation_info,
html_info
]
)
prompt.submit(**txt2img_args)
submit.click(**txt2img_args)
interrupt.click(
fn=lambda: state.interrupt(),
inputs=[],
outputs=[],
)
save.click(
fn=wrap_gradio_call(save_files),
inputs=[
generation_info,
txt2img_gallery,
],
outputs=[
html_info,
html_info,
html_info,
]
)
def get_crop_region(mask, pad=0):
h, w = mask.shape
crop_left = 0
for i in range(w):
if not (mask[:,i] == 0).all():
break
crop_left += 1
crop_right = 0
for i in reversed(range(w)):
if not (mask[:,i] == 0).all():
break
crop_right += 1
crop_top = 0
for i in range(h):
if not (mask[i] == 0).all():
break
crop_top += 1
crop_bottom = 0
for i in reversed(range(h)):
if not (mask[i] == 0).all():
break
crop_bottom += 1
return (
int(max(crop_left-pad, 0)),
int(max(crop_top-pad, 0)),
int(min(w - crop_right + pad, w)),
int(min(h - crop_bottom + pad, h))
)
def fill(image, mask):
image_mod = Image.new('RGBA', (image.width, image.height))
image_masked = Image.new('RGBa', (image.width, image.height))
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L')))
image_masked = image_masked.convert('RGBa')
for radius, repeats in [(64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]:
blurred = image_masked.filter(ImageFilter.GaussianBlur(radius)).convert('RGBA')
for _ in range(repeats):
image_mod.alpha_composite(blurred)
return image_mod.convert("RGB")
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sampler = None
def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, **kwargs):
super().__init__(**kwargs)
self.init_images = init_images
self.resize_mode: int = resize_mode
self.denoising_strength: float = denoising_strength
self.init_latent = None
self.image_mask = mask
self.mask_for_overlay = None
self.mask_blur = mask_blur
self.inpainting_fill = inpainting_fill
self.inpaint_full_res = inpaint_full_res
self.mask = None
self.nmask = None
def init(self):
self.sampler = samplers_for_img2img[self.sampler_index].constructor()
crop_region = None
if self.image_mask is not None:
if self.mask_blur > 0:
self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)).convert('L')
if self.inpaint_full_res:
self.mask_for_overlay = self.image_mask
mask = self.image_mask.convert('L')
crop_region = get_crop_region(np.array(mask), 64)
x1, y1, x2, y2 = crop_region
mask = mask.crop(crop_region)
self.image_mask = resize_image(2, mask, self.width, self.height)
self.paste_to = (x1, y1, x2-x1, y2-y1)
else:
self.image_mask = resize_image(self.resize_mode, self.image_mask, self.width, self.height)
self.mask_for_overlay = self.image_mask
self.overlay_images = []
imgs = []
for img in self.init_images:
image = img.convert("RGB")
if crop_region is None:
image = resize_image(self.resize_mode, image, self.width, self.height)
if self.image_mask is not None:
if self.inpainting_fill != 1:
image = fill(image, self.mask_for_overlay)
image_masked = Image.new('RGBa', (image.width, image.height))
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
self.overlay_images.append(image_masked.convert('RGBA'))
if crop_region is not None:
image = image.crop(crop_region)
image = resize_image(2, image, self.width, self.height)
image = np.array(image).astype(np.float32) / 255.0
image = np.moveaxis(image, 2, 0)
imgs.append(image)
if len(imgs) == 1:
batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
if self.overlay_images is not None:
self.overlay_images = self.overlay_images * self.batch_size
elif len(imgs) <= self.batch_size:
self.batch_size = len(imgs)
batch_images = np.array(imgs)
else:
raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
image = torch.from_numpy(batch_images)
image = 2. * image - 1.
image = image.to(device)
self.init_latent = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image))
if self.image_mask is not None:
latmask = self.image_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
latmask = np.moveaxis(np.array(latmask, dtype=np.float64), 2, 0) / 255
latmask = latmask[0]
latmask = np.tile(latmask[None], (4, 1, 1))
self.mask = torch.asarray(1.0 - latmask).to(device).type(sd_model.dtype)
self.nmask = torch.asarray(latmask).to(device).type(sd_model.dtype)
if self.inpainting_fill == 2:
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], [self.seed + x + 1 for x in range(self.init_latent.shape[0])]) * self.nmask
elif self.inpainting_fill == 3:
self.init_latent = self.init_latent * self.mask
def sample(self, x, conditioning, unconditional_conditioning):
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
if self.mask is not None:
samples = samples * self.nmask + self.init_latent * self.mask
return samples
def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool):
is_classic = mode == 0
is_inpaint = mode == 1
is_loopback = mode == 2
is_upscale = mode == 3
if is_inpaint:
image = init_img_with_mask['image']
mask = init_img_with_mask['mask']
else:
image = init_img
mask = None
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
p = StableDiffusionProcessingImg2Img(
outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
prompt=prompt,
seed=seed,
sampler_index=sampler_index,
batch_size=batch_size,
n_iter=n_iter,
steps=steps,
cfg_scale=cfg_scale,
width=width,
height=height,
prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN,
init_images=[image],
mask=mask,
mask_blur=mask_blur,
inpainting_fill=inpainting_fill,
resize_mode=resize_mode,
denoising_strength=denoising_strength,
inpaint_full_res=inpaint_full_res,
extra_generation_params={"Denoising Strength": denoising_strength}
)
if is_loopback:
output_images, info = None, None
history = []
initial_seed = None
initial_info = None
for i in range(n_iter):
p.n_iter = 1
p.batch_size = 1
p.do_not_save_grid = True
state.job = f"Batch {i + 1} out of {n_iter}"
processed = process_images(p)
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
p.init_images = [processed.images[0]]
p.seed = processed.seed + 1
p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
history.append(processed.images[0])
grid = image_grid(history, batch_size, rows=1)
save_image(grid, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
processed = Processed(p, history, initial_seed, initial_info)
elif is_upscale:
initial_seed = None
initial_info = None
upscaler = sd_upscalers.get(upscaler_name, next(iter(sd_upscalers.values())))
img = upscaler(init_img)
torch_gc()
grid = split_grid(img, tile_w=width, tile_h=height, overlap=upscale_overlap)
p.n_iter = 1
p.do_not_save_grid = True
p.do_not_save_samples = True
work = []
work_results = []
for y, h, row in grid.tiles:
for tiledata in row:
work.append(tiledata[2])
batch_count = math.ceil(len(work) / p.batch_size)
print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} in a total of {batch_count} batches.")
for i in range(batch_count):
p.init_images = work[i*p.batch_size:(i+1)*p.batch_size]
state.job = f"Batch {i + 1} out of {batch_count}"
processed = process_images(p)
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
p.seed = processed.seed + 1
work_results += processed.images
image_index = 0
for y, h, row in grid.tiles:
for tiledata in row:
tiledata[2] = work_results[image_index] if image_index<len(work_results) else Image.new("RGB", (p.width, p.height))
image_index += 1
combined_image = combine_grid(grid)
save_image(combined_image, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=initial_info, short_filename=not opts.grid_extended_filename)
processed = Processed(p, [combined_image], initial_seed, initial_info)
else:
processed = process_images(p)
return processed.images, processed.js(), plaintext_to_html(processed.info)
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
with gr.Blocks(analytics_enabled=False) as img2img_interface:
with gr.Row():
prompt = gr.Textbox(label="Prompt", elem_id="img2img_prompt", show_label=False, placeholder="Prompt", lines=1)
submit = gr.Button('Generate', elem_id="img2img_generate", variant='primary')
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):
with gr.Group():
switch_mode = gr.Radio(label='Mode', elem_id="img2img_mode", choices=['Redraw whole image', 'Inpaint a part of image', 'Loopback', 'SD upscale'], value='Redraw whole image', type="index", show_label=False)
init_img = gr.Image(label="Image for img2img", source="upload", interactive=True, type="pil")
init_img_with_mask = gr.Image(label="Image for inpainting with mask", elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", visible=False)
resize_mode = gr.Radio(label="Resize mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
inpainting_fill = gr.Radio(label='Msked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
with gr.Row():
use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=have_gfpgan)
prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=True, visible=False)
with gr.Row():
sd_upscale_upscaler_name = gr.Radio(label='Upscaler', choices=list(sd_upscalers.keys()), value=list(sd_upscalers.keys())[0], visible=False)
sd_upscale_overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False)
with gr.Row():
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
with gr.Group():
cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.0)
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75)
with gr.Group():
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
seed = gr.Number(label='Seed', value=-1)
with gr.Column(variant='panel'):
with gr.Group():
img2img_gallery = gr.Gallery(label='Output')
with gr.Group():
with gr.Row():
interrupt = gr.Button('Interrupt')
save = gr.Button('Save')
img2img_send_to_extras = gr.Button('Send to extras')
with gr.Group():
html_info = gr.HTML()
generation_info = gr.Textbox(visible=False)
def apply_mode(mode):
is_classic = mode == 0
is_inpaint = mode == 1
is_loopback = mode == 2
is_upscale = mode == 3
return {
init_img: gr_show(not is_inpaint),
init_img_with_mask: gr_show(is_inpaint),
mask_blur: gr_show(is_inpaint),
inpainting_fill: gr_show(is_inpaint),
prompt_matrix: gr_show(is_classic),
batch_count: gr_show(not is_upscale),
batch_size: gr_show(not is_loopback),
sd_upscale_upscaler_name: gr_show(is_upscale),
sd_upscale_overlap:gr_show(is_upscale),
inpaint_full_res: gr_show(is_inpaint),
}
switch_mode.change(
apply_mode,
inputs=[switch_mode],
outputs=[
init_img,
init_img_with_mask,
mask_blur,
inpainting_fill,
prompt_matrix,
batch_count,
batch_size,
sd_upscale_upscaler_name,
sd_upscale_overlap,
inpaint_full_res,
]
)
img2img_args = dict(
fn=wrap_gradio_gpu_call(img2img),
inputs=[
prompt,
init_img,
init_img_with_mask,
steps,
sampler_index,
mask_blur,
inpainting_fill,
use_GFPGAN,
prompt_matrix,
switch_mode,
batch_count,
batch_size,
cfg_scale,
denoising_strength,
seed,
height,
width,
resize_mode,
sd_upscale_upscaler_name,
sd_upscale_overlap,
inpaint_full_res,
],
outputs=[
img2img_gallery,
generation_info,
html_info
]
)
prompt.submit(**img2img_args)
submit.click(**img2img_args)
interrupt.click(
fn=lambda: state.interrupt(),
inputs=[],
outputs=[],
)
save.click(
fn=wrap_gradio_call(save_files),
inputs=[
generation_info,
img2img_gallery,
],
outputs=[
html_info,
html_info,
html_info,
]
)
send_to_img2img.click(
fn=send_gradio_gallery_to_image,
inputs=[txt2img_gallery],
outputs=[init_img],
)
send_to_inpaint.click(
fn=send_gradio_gallery_to_image,
inputs=[txt2img_gallery],
outputs=[init_img_with_mask],
)
def upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index):
info = realesrgan_models[RealESRGAN_model_index]
model = info.model()
upsampler = RealESRGANer(
scale=info.netscale,
model_path=info.location,
model=model,
half=True
)
upsampled = upsampler.enhance(np.array(image), outscale=RealESRGAN_upscaling)[0]
image = Image.fromarray(upsampled)
return image
def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_index):
torch_gc()
image = image.convert("RGB")
outpath = opts.outdir_samples or opts.outdir_extras_samples
if have_gfpgan is not None and GFPGAN_strength > 0:
gfpgan_model = gfpgan()
restored_img = gfpgan_fix_faces(gfpgan_model, np.array(image, dtype=np.uint8))
res = Image.fromarray(restored_img)
if GFPGAN_strength < 1.0:
res = Image.blend(image, res, GFPGAN_strength)
image = res
if have_realesrgan and RealESRGAN_upscaling != 1.0:
image = upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index)
save_image(image, outpath, "", None, '', opts.samples_format, short_filename=True, no_prompt=True)
return image, '', ''
with gr.Blocks(analytics_enabled=False) as extras_interface:
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):
with gr.Group():
image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN strength", value=1, interactive=have_gfpgan)
realesrgan_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Real-ESRGAN upscaling", value=2, interactive=have_realesrgan)
realesrgan_model = gr.Radio(label='Real-ESRGAN model', choices=[x.name for x in realesrgan_models], value=realesrgan_models[0].name, type="index", interactive=have_realesrgan)
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
with gr.Column(variant='panel'):
result_image = gr.Image(label="Result")
html_info_x = gr.HTML()
html_info = gr.HTML()
extras_args = dict(
fn=wrap_gradio_gpu_call(run_extras),
inputs=[
image,
gfpgan_strength,
realesrgan_resize,
realesrgan_model,
],
outputs=[
result_image,
html_info_x,
html_info,
]
)
submit.click(**extras_args)
send_to_extras.click(
fn=send_gradio_gallery_to_image,
inputs=[txt2img_gallery],
outputs=[image],
)
img2img_send_to_extras.click(
fn=send_gradio_gallery_to_image,
inputs=[img2img_gallery],
outputs=[image],
)
def run_pnginfo(image):
info = ''
for key, text in image.info.items():
info += f"""
<div>
<p><b>{plaintext_to_html(str(key))}</b></p>
<p>{plaintext_to_html(str(text))}</p>
</div>
""".strip()+"\n"
if len(info) == 0:
message = "Nothing found in the image."
info = f"<div><p>{message}<p></div>"
return '', '', info
pnginfo_interface = gr.Interface(
wrap_gradio_call(run_pnginfo),
inputs=[
gr.Image(label="Source", source="upload", interactive=True, type="pil"),
],
outputs=[
gr.HTML(),
gr.HTML(),
gr.HTML(),
],
allow_flagging="never",
analytics_enabled=False,
)
opts = Options()
if os.path.exists(config_filename):
opts.load(config_filename)
def run_settings(*args):
up = []
for key, value, comp in zip(opts.data_labels.keys(), args, settings_interface.input_components):
opts.data[key] = value
up.append(comp.update(value=value))
opts.save(config_filename)
return 'Settings saved.', '', ''
def create_setting_component(key):
def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default
info = opts.data_labels[key]
t = type(info.default)
if info.component is not None:
item = info.component(label=info.label, value=fun, **(info.component_args or {}))
elif t == str:
item = gr.Textbox(label=info.label, value=fun, lines=1)
elif t == int:
item = gr.Number(label=info.label, value=fun)
elif t == bool:
item = gr.Checkbox(label=info.label, value=fun)
else:
raise Exception(f'bad options item type: {str(t)} for key {key}')
return item
settings_interface = gr.Interface(
run_settings,
inputs=[create_setting_component(key) for key in opts.data_labels.keys()],
outputs=[
gr.Textbox(label='Result'),
gr.HTML(),
gr.HTML(),
],
title=None,
description=None,
allow_flagging="never",
analytics_enabled=False,
)
interfaces = [
(txt2img_interface, "txt2img"),
(img2img_interface, "img2img"),
(extras_interface, "Extras"),
(pnginfo_interface, "PNG Info"),
(settings_interface, "Settings"),
]
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 Exception:
pass
sd_config = OmegaConf.load(cmd_opts.config)
sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
sd_model = (sd_model if cmd_opts.no_half else sd_model.half())
if cmd_opts.lowvram or cmd_opts.medvram:
setup_for_low_vram(sd_model)
else:
sd_model = sd_model.to(device)
model_hijack = StableDiffusionModelHijack()
model_hijack.hijack(sd_model)
with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
css = file.read()
if not cmd_opts.no_progressbar_hiding:
css += css_hide_progressbar
with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as file:
javascript = file.read()
# make the program just exit at ctrl+c without waiting for anything
def sigint_handler(signal, frame):
print('Interrupted')
os._exit(0)
signal.signal(signal.SIGINT, sigint_handler)
demo = gr.TabbedInterface(
interface_list=[x[0] for x in interfaces],
tab_names=[x[1] for x in interfaces],
analytics_enabled=False,
css=css,
)
def inject_gradio_html(javascript):
import gradio.routes
def template_response(*args, **kwargs):
res = gradio_routes_templates_response(*args, **kwargs)
res.body = res.body.replace(b'</head>', f'<script>{javascript}</script></head>'.encode("utf8"))
res.init_headers()
return res
gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
gradio.routes.templates.TemplateResponse = template_response
inject_gradio_html(javascript)
demo.launch(share=cmd_opts.share)
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