stable-diffusion-webui/webui.py

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import argparse
import os
import sys
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from collections import namedtuple
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import torch
import torch.nn as nn
import numpy as np
import gradio as gr
from omegaconf import OmegaConf
from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
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from torch import autocast
import mimetypes
import random
import math
import html
import time
import json
import traceback
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import k_diffusion.sampling
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from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
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
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# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
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"
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parser = argparse.ArgumentParser()
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("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
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")
cmd_opts = parser.parse_args()
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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; }
"""
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SamplerData = namedtuple('SamplerData', ['name', 'constructor'])
samplers = [
*[SamplerData(x[0], lambda funcname=x[1]: KDiffusionSampler(funcname)) for x in [
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('LMS', 'sample_lms'),
('Heun', 'sample_heun'),
('Euler', 'sample_euler'),
('Euler ancestral', 'sample_euler_ancestral'),
('DPM 2', 'sample_dpm_2'),
('DPM 2 Ancestral', 'sample_dpm_2_ancestral'),
] if hasattr(k_diffusion.sampling, x[1])],
SamplerData('DDIM', lambda: VanillaStableDiffusionSampler(DDIMSampler)),
SamplerData('PLMS', lambda: VanillaStableDiffusionSampler(PLMSSampler)),
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]
samplers_for_img2img = [x for x in samplers if x.name != 'DDIM' and 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)
),
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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 loading Real-ESRGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
realesrgan_models = [RealesrganModelInfo('None', '', 0, None)]
have_realesrgan = False
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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
}
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": OptionInfo("", "Output dictectory; if empty, defaults to 'outputs/*'"),
"samples_save": OptionInfo(True, "Save indiviual samples"),
"samples_format": OptionInfo('png', 'File format for indiviual samples'),
"grid_save": OptionInfo(True, "Save image grids"),
"grid_format": OptionInfo('png', 'File format for grids'),
"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
"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"),
"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"),
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"sd_upscale_upscaler_index": OptionInfo("RealESRGAN", "Upscaler to use for SD upscale", gr.Radio, {"choices": list(sd_upscalers.keys())}),
"sd_upscale_overlap": OptionInfo(64, "Overlap for tiles for SD upscale. The smaller it is, the less smooth transition from one tile to another", gr.Slider, {"minimum": 0, "maximum": 256, "step": 16}),
}
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)
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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 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 save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False):
if short_filename or prompt is None or seed is None:
filename = f"{basename}"
else:
filename = f"{basename}-{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
os.makedirs(path, exist_ok=True)
fullfn = os.path.join(path, f"{filename}.{extension}")
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(os.path.join(path, f"{filename}.jpg"), quality=opts.jpeg_quality, pnginfo=pnginfo)
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 load_gfpgan():
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model_name = 'GFPGANv1.3'
model_path = os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models', model_name + '.pth')
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if not os.path.isfile(model_path):
raise Exception("GFPGAN model not found at path "+model_path)
sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir))
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from gfpgan import GFPGANer
return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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)
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w, h = imgs[0].size
grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
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for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
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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.float).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
for y, h, row in grid.tiles:
combined_row = Image.new("RGB", (grid.image_w, h))
for x, w, tile in row:
if x == 0:
combined_row.paste(tile, (0, 0))
continue
combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w)
combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0))
if y == 0:
combined_image.paste(combined_row, (0, 0))
continue
combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h)
combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap))
return combined_image
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):
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lines = ['']
for word in text.split():
line = f'{lines[-1]} {word}'.strip()
if drawing.textlength(line, font=font) <= line_length:
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lines[-1] = line
else:
lines.append(word)
return lines
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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")
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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)
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draw_y += line.size[1] + line_spacing
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fontsize = (width + height) // 25
line_spacing = fontsize // 2
fnt = ImageFont.truetype("arial.ttf", fontsize)
color_active = (0, 0, 0)
color_inactive = (153, 153, 153)
pad_left = width * 3 // 4 if len(hor_texts) > 1 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 in hor_texts + ver_texts:
items = [] + texts
texts.clear()
for line in items:
wrapped = wrap(calc_d, line.text, fnt, 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
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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
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draw_texts(d, x, y, hor_texts[col])
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for row in range(rows):
x = pad_left / 2
y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2
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draw_texts(d, x, y, ver_texts[row])
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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_call(func):
def f(*p1, **p2):
t = time.perf_counter()
res = list(func(*p1, **p2))
elapsed = time.perf_counter() - t
# last item is always HTML
res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
return tuple(res)
return f
GFPGAN = None
if os.path.exists(cmd_opts.gfpgan_dir):
try:
GFPGAN = load_gfpgan()
print("Loaded GFPGAN")
except Exception:
print("Error loading GFPGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
class StableDiffuionModelHijack:
ids_lookup = {}
word_embeddings = {}
word_embeddings_checksums = {}
fixes = None
comments = None
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']
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)
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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 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(self.wrapped.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=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):
self.outpath: str = outpath
self.prompt: str = prompt
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
def init(self):
pass
def sample(self, x, conditioning, unconditional_conditioning):
raise NotImplementedError()
class VanillaStableDiffusionSampler:
def __init__(self, constructor):
self.sampler = constructor(sd_model)
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
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, 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(self, p: StableDiffusionProcessing, x, conditioning, unconditional_conditioning):
sigmas = self.model_wrap.get_sigmas(p.steps)
x = x * sigmas[0]
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
Processed = namedtuple('Processed', ['images','seed', 'info'])
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"""
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prompt = p.prompt
model = sd_model
assert p.prompt is not None
torch_gc()
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seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
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sample_path = os.path.join(p.outpath, "samples")
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base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(p.outpath)) - 1
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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 and GFPGAN is not None 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)
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output_images = []
with torch.no_grad(), autocast("cuda"), model.ema_scope():
p.init()
for n in range(p.n_iter):
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) * [""])
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)
samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc)
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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)
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if p.prompt_matrix or opts.samples_save or opts.grid_save:
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 and GFPGAN is not None:
torch_gc()
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
x_sample = restored_img
image = Image.fromarray(x_sample)
if not p.do_not_save_samples:
save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opts.samples_format, info=infotext())
output_images.append(image)
base_count += 1
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if (p.prompt_matrix or opts.grid_save) and not p.do_not_save_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)
output_images.insert(0, grid)
else:
grid = image_grid(output_images, p.batch_size)
save_image(grid, p.outpath, f"grid-{grid_count:04}", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
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grid_count += 1
torch_gc()
return Processed(output_images, seed, infotext())
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class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
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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, 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):
outpath = opts.outdir or "outputs/txt2img-samples"
p = StableDiffusionProcessingTxt2Img(
outpath=outpath,
prompt=prompt,
seed=seed,
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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(*display_result_data)
else:
processed = process_images(p)
return processed.images, processed.seed, plaintext_to_html(processed.info)
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
prompt, ddim_steps, sampler_name, use_gfpgan, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, request_seed, height, width, code, images, seed, comment = 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(["prompt", "seed", "width", "height", "cfgs", "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, cfg_scale, ddim_steps, filenames[0]])
print("Logged:", filenames[0])
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txt2img_interface = gr.Interface(
wrap_gradio_call(txt2img),
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inputs=[
gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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gr.Radio(label='Sampling method', choices=[x.name for x in samplers], value=samplers[0].name, type="index"),
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
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gr.Number(label='Seed', value=-1),
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
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],
outputs=[
gr.Gallery(label="Images"),
gr.Number(label='Seed'),
gr.HTML(),
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],
title="Stable Diffusion Text-to-Image",
flagging_callback=Flagging()
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)
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sampler = None
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def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, **kwargs):
super().__init__(**kwargs)
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self.init_images = init_images
self.resize_mode: int = resize_mode
self.denoising_strength: float = denoising_strength
self.init_latent = None
def init(self):
self.sampler = samplers_for_img2img[self.sampler_index].constructor()
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imgs = []
for img in self.init_images:
image = img.convert("RGB")
image = resize_image(self.resize_mode, image, self.width, self.height)
image = np.array(image).astype(np.float32) / 255.0
image = np.moveaxis(image, 2, 0)
imgs.append(image)
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if len(imgs) == 1:
batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
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))
def sample(self, x, conditioning, unconditional_conditioning):
t_enc = int(self.denoising_strength * self.steps)
sigmas = self.sampler.model_wrap.get_sigmas(self.steps)
noise = x * sigmas[self.steps - t_enc - 1]
xi = self.init_latent + noise
sigma_sched = sigmas[self.steps - t_enc - 1:]
samples_ddim = self.sampler.func(self.sampler.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': self.cfg_scale}, disable=False)
return samples_ddim
def img2img(prompt: str, init_img, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, sd_upscale: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int):
outpath = opts.outdir or "outputs/img2img-samples"
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
p = StableDiffusionProcessingImg2Img(
outpath=outpath,
prompt=prompt,
seed=seed,
sampler_index=sampler_index,
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,
init_images=[init_img],
resize_mode=resize_mode,
denoising_strength=denoising_strength,
extra_generation_params={"Denoising Strength": denoising_strength}
)
if 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
processed = process_images(p)
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
p.init_img = 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_count = len(os.listdir(outpath)) - 1
grid = image_grid(history, batch_size, rows=1)
save_image(grid, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
processed = Processed(history, initial_seed, initial_info)
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elif sd_upscale:
initial_seed = None
initial_info = None
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upscaler = sd_upscalers[opts.sd_upscale_upscaler_index]
img = upscaler(init_img)
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torch_gc()
grid = split_grid(img, tile_w=width, tile_h=height, overlap=opts.sd_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.")
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for i in range(batch_count):
p.init_images = work[i*p.batch_size:(i+1)*p.batch_size]
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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
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for y, h, row in grid.tiles:
for tiledata in row:
tiledata[2] = work_results[image_index]
image_index += 1
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combined_image = combine_grid(grid)
grid_count = len(os.listdir(outpath)) - 1
save_image(combined_image, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=initial_info, short_filename=not opts.grid_extended_filename)
processed = Processed([combined_image], initial_seed, initial_info)
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else:
processed = process_images(p)
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return processed.images, processed.seed, plaintext_to_html(processed.info)
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sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
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img2img_interface = gr.Interface(
wrap_gradio_call(img2img),
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inputs=[
gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index"),
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False),
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gr.Checkbox(label='Stable Diffusion upscale', value=False),
gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
gr.Number(label='Seed', value=-1),
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
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],
outputs=[
gr.Gallery(),
gr.Number(label='Seed'),
gr.HTML(),
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],
allow_flagging="never",
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)
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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):
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torch_gc()
image = image.convert("RGB")
outpath = opts.outdir or "outputs/extras-samples"
if GFPGAN is not None and GFPGAN_strength > 0:
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 GFPGAN_strength < 1.0:
res = Image.blend(image, res, GFPGAN_strength)
image = res
if have_realesrgan and RealESRGAN_upscaling != 1.0:
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image = upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index)
base_count = len(os.listdir(outpath))
save_image(image, outpath, f"{base_count:05}", None, '', opts.samples_format, short_filename=True)
return image, 0, ''
extras_interface = gr.Interface(
wrap_gradio_call(run_extras),
inputs=[
gr.Image(label="Source", source="upload", interactive=True, type="pil"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN strength", value=1, interactive=GFPGAN is not None),
gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Real-ESRGAN upscaling", value=2, interactive=have_realesrgan),
gr.Radio(label='Real-ESRGAN model', choices=[x.name for x in realesrgan_models], value=realesrgan_models[0].name, type="index", interactive=have_realesrgan),
],
outputs=[
gr.Image(label="Result"),
gr.Number(label='Seed', visible=False),
gr.HTML(),
],
allow_flagging="never",
)
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(),
],
title=None,
description=None,
allow_flagging="never",
)
interfaces = [
(txt2img_interface, "txt2img"),
(img2img_interface, "img2img"),
(extras_interface, "Extras"),
(settings_interface, "Settings"),
]
sd_config = OmegaConf.load(cmd_opts.config)
sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
sd_model = (sd_model if cmd_opts.no_half else sd_model.half()).to(device)
model_hijack = StableDiffuionModelHijack()
model_hijack.hijack(sd_model)
demo = gr.TabbedInterface(
interface_list=[x[0] for x in interfaces],
tab_names=[x[1] for x in interfaces],
css=("" if cmd_opts.no_progressbar_hiding else css_hide_progressbar) + """
.output-html p {margin: 0 0.5em;}
.performance { font-size: 0.85em; color: #444; }
"""
)
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demo.launch()