WebUI/webui.py

1019 lines
38 KiB
Python

import argparse, os, sys, glob
from collections import namedtuple
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
from itertools import islice
from einops import rearrange, repeat
from torch import autocast
import mimetypes
import random
import math
import html
import time
import json
import traceback
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
import ldm.modules.encoders.modules
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except:
pass
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
mimetypes.add_type('application/javascript', '.js')
# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
invalid_filename_chars = '<>:"/\|?*\n'
config_filename = "config.json"
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')) # i disagree with where you're putting it but since all guidefags are doing it this way, there you go
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)")
cmd_opts = parser.parse_args()
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 model: KDiffusionSampler(model, x[1])) for x in [
('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 model: DDIMSampler(model)),
SamplerData('PLMS', lambda model: PLMSSampler(model)),
]
class Options:
data = None
data_labels = {
"outdir": ("", "Output dictectory; if empty, defaults to 'outputs/*'"),
"samples_save": (True, "Save indiviual samples"),
"samples_format": ('png', 'File format for indiviual samples'),
"grid_save": (True, "Save image grids"),
"grid_format": ('png', 'File format for grids'),
"grid_extended_filename": (False, "Add extended info (seed, prompt) to filename when saving grid"),
"n_rows": (-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", -1, 16),
"jpeg_quality": (80, "Quality for saved jpeg images", 1, 100),
"verify_input": (True, "Check input, and produce warning if it's too long"),
"enable_pnginfo": (True, "Save text information about generation parameters as chunks to png files"),
"prompt_matrix_add_to_start": (True, "In prompt matrix, add the variable combination of text to the start of the prompt, rather than the end"),
}
def __init__(self):
self.data = {k: v[0] 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][0]
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 chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, x, sigma, uncond, cond, cond_scale):
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigma] * 2)
cond_in = torch.cat([uncond, cond])
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
return uncond + (cond - uncond) * cond_scale
class KDiffusionSampler:
def __init__(self, m, funcname):
self.model = m
self.model_wrap = k_diffusion.external.CompVisDenoiser(m)
self.funcname = funcname
def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T):
sigmas = self.model_wrap.get_sigmas(S)
x = x_T * sigmas[0]
model_wrap_cfg = CFGDenoiser(self.model_wrap)
fun = getattr(k_diffusion.sampling, self.funcname)
samples_ddim = fun(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False)
return samples_ddim, None
def create_random_tensors(shape, seeds):
xs = []
for seed in seeds:
torch.manual_seed(seed)
# randn results depend on device; gpu and cpu get different results for same seed;
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
# but the original script had it like this so i do not dare change it for now because
# it will break everyone's seeds.
xs.append(torch.randn(shape, device=device))
x = torch.stack(xs)
return x
def torch_gc():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
def sanitize_filename_part(text):
return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
def save_image(image, path, basename, seed, prompt, extension, info=None, short_filename=False):
prompt = sanitize_filename_part(prompt)
if short_filename:
filename = f"{basename}.{extension}"
else:
filename = f"{basename}-{seed}-{prompt[:128]}.{extension}"
if extension == 'png' and opts.enable_pnginfo:
pnginfo = PngImagePlugin.PngInfo()
pnginfo.add_text("parameters", info)
else:
pnginfo = None
image.save(os.path.join(path, filename), quality=opts.jpeg_quality, pnginfo=pnginfo)
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():
model_name = 'GFPGANv1.3'
model_path = os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models', model_name + '.pth')
if not os.path.isfile(model_path):
raise Exception("GFPGAN model not found at path "+model_path)
sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir))
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, round_down=False, force_n_rows=None):
if force_n_rows is not None:
rows = force_n_rows
elif opts.n_rows > 0:
rows = opts.n_rows
elif opts.n_rows == 0:
rows = batch_size
else:
rows = math.sqrt(len(imgs))
rows = int(rows) if round_down else 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
def draw_prompt_matrix(im, width, height, all_prompts):
def wrap(text, d, font, line_length):
lines = ['']
for word in text.split():
line = f'{lines[-1]} {word}'.strip()
if d.textlength(line, font=font) <= line_length:
lines[-1] = line
else:
lines.append(word)
return '\n'.join(lines)
def draw_texts(pos, x, y, texts, sizes):
for i, (text, size) in enumerate(zip(texts, sizes)):
active = pos & (1 << i) != 0
if not active:
text = '\u0336'.join(text) + '\u0336'
d.multiline_text((x, y + size[1] / 2), text, font=fnt, fill=color_active if active else color_inactive, anchor="mm", align="center")
y += size[1] + line_spacing
fontsize = (width + height) // 25
line_spacing = fontsize // 2
fnt = ImageFont.truetype("arial.ttf", fontsize)
color_active = (0, 0, 0)
color_inactive = (153, 153, 153)
pad_top = height // 4
pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0
cols = im.width // width
rows = im.height // height
prompts = all_prompts[1:]
result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
result.paste(im, (pad_left, pad_top))
d = ImageDraw.Draw(result)
boundary = math.ceil(len(prompts) / 2)
prompts_horiz = [wrap(x, d, fnt, width) for x in prompts[:boundary]]
prompts_vert = [wrap(x, d, fnt, pad_left) for x in prompts[boundary:]]
sizes_hor = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_horiz]]
sizes_ver = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_vert]]
hor_text_height = sum([x[1] + line_spacing for x in sizes_hor]) - line_spacing
ver_text_height = sum([x[1] + line_spacing for x in sizes_ver]) - line_spacing
for col in range(cols):
x = pad_left + width * col + width / 2
y = pad_top / 2 - hor_text_height / 2
draw_texts(col, x, y, prompts_horiz, sizes_hor)
for row in range(rows):
x = pad_left / 2
y = pad_top + height * row + height / 2 - ver_text_height / 2
draw_texts(row, x, y, prompts_vert, sizes_ver)
return result
def resize_image(resize_mode, im, width, height):
if resize_mode == 0:
res = im.resize((width, height), resample=LANCZOS)
elif resize_mode == 1:
ratio = width / height
src_ratio = im.width / im.height
src_w = width if ratio > src_ratio else im.width * height // im.height
src_h = height if ratio <= src_ratio else im.height * width // im.width
resized = im.resize((src_w, src_h), resample=LANCZOS)
res = Image.new("RGB", (width, height))
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
else:
ratio = width / height
src_ratio = im.width / im.height
src_w = width if ratio < src_ratio else im.width * height // im.height
src_h = height if ratio >= src_ratio else im.height * width // im.width
resized = im.resize((src_w, src_h), resample=LANCZOS)
res = Image.new("RGB", (width, height))
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
if ratio < src_ratio:
fill_height = height // 2 - src_h // 2
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
elif ratio > src_ratio:
fill_width = width // 2 - src_w // 2
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
return res
def check_prompt_length(prompt, comments):
"""this function tests if prompt is too long, and if so, adds a message to comments"""
tokenizer = model.cond_stage_model.tokenizer
max_length = model.cond_stage_model.max_length
info = model.cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length, return_overflowing_tokens=True, padding="max_length", return_tensors="pt")
ovf = info['overflowing_tokens'][0]
overflowing_count = ovf.shape[0]
if overflowing_count == 0:
return
vocab = {v: k for k, v in tokenizer.get_vocab().items()}
overflowing_words = [vocab.get(int(x), "") for x in ovf]
overflowing_text = tokenizer.convert_tokens_to_string(''.join(overflowing_words))
comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
def 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 TextInversionEmbeddings:
ids_lookup = {}
word_embeddings = {}
word_embeddings_checksums = {}
fixes = []
used_custom_terms = []
dir_mtime = None
def load(self, dir, model):
mt = os.path.getmtime(dir)
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(dir):
try:
process_file(os.path.join(dir, fn), fn)
except:
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, embeddings):
super().__init__()
self.wrapped = wrapped
self.embeddings = embeddings
self.tokenizer = wrapped.tokenizer
self.max_length = wrapped.max_length
def forward(self, text):
self.embeddings.fixes = []
self.embeddings.used_custom_terms = []
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
cache = {}
batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
for tokens in batch_tokens:
tuple_tokens = tuple(tokens)
if tuple_tokens in cache:
remade_tokens, fixes = cache[tuple_tokens]
else:
fixes = []
remade_tokens = []
i = 0
while i < len(tokens):
token = tokens[i]
possible_matches = self.embeddings.ids_lookup.get(token, None)
if possible_matches is None:
remade_tokens.append(token)
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)
i += len(ids) - 1
found = True
self.embeddings.used_custom_terms.append((word, self.embeddings.word_embeddings_checksums[word]))
break
if not found:
remade_tokens.append(token)
i += 1
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)
remade_batch_tokens.append(remade_tokens)
self.embeddings.fixes.append(fixes)
tokens = torch.asarray(remade_batch_tokens).to(self.wrapped.device)
outputs = self.wrapped.transformer(input_ids=tokens)
z = outputs.last_hidden_state
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 = []
inputs_embeds = self.wrapped(input_ids)
for fixes, tensor in zip(batch_fixes, inputs_embeds):
for offset, word in fixes:
tensor[offset] = self.embeddings.word_embeddings[word]
return inputs_embeds
def get_learned_conditioning_with_embeddings(model, prompts):
if os.path.exists(cmd_opts.embeddings_dir):
text_inversion_embeddings.load(cmd_opts.embeddings_dir, model)
return model.get_learned_conditioning(prompts)
def process_images(outpath, func_init, func_sample, prompt, seed, sampler_index, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False, extra_generation_params=None):
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
assert prompt is not None
torch_gc()
if seed == -1:
seed = random.randrange(4294967294)
seed = int(seed)
os.makedirs(outpath, exist_ok=True)
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
comments = []
prompt_matrix_parts = []
if 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))
n_iter = math.ceil(len(all_prompts) / batch_size)
all_seeds = len(all_prompts) * [seed]
print(f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches.")
else:
if opts.verify_input:
try:
check_prompt_length(prompt, comments)
except:
import traceback
print("Error verifying input:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
all_prompts = batch_size * n_iter * [prompt]
all_seeds = [seed + x for x in range(len(all_prompts))]
generation_params = {
"Steps": steps,
"Sampler": samplers[sampler_index].name,
"CFG scale": cfg_scale,
"Seed": seed,
"GFPGAN": ("GFPGAN" if use_GFPGAN and GFPGAN is not None else None)
}
if extra_generation_params is not None:
generation_params.update(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):
text_inversion_embeddings.load(cmd_opts.embeddings_dir, model)
output_images = []
with torch.no_grad(), autocast("cuda"), model.ema_scope():
init_data = func_init()
for n in range(n_iter):
prompts = all_prompts[n * batch_size:(n + 1) * batch_size]
seeds = all_seeds[n * batch_size:(n + 1) * batch_size]
uc = model.get_learned_conditioning(len(prompts) * [""])
c = model.get_learned_conditioning(prompts)
if len(text_inversion_embeddings.used_custom_terms) > 0:
comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in text_inversion_embeddings.used_custom_terms]))
# we manually generate all input noises because each one should have a specific seed
x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=seeds)
samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc)
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)
if prompt_matrix or opts.samples_save or opts.grid_save:
for i, x_sample in enumerate(x_samples_ddim):
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
x_sample = x_sample.astype(np.uint8)
if use_GFPGAN and GFPGAN is not None:
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)
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
if (prompt_matrix or opts.grid_save) and not do_not_save_grid:
grid = image_grid(output_images, batch_size, round_down=prompt_matrix)
if prompt_matrix:
try:
grid = draw_prompt_matrix(grid, width, 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)
save_image(grid, outpath, f"grid-{grid_count:04}", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
grid_count += 1
torch_gc()
return output_images, seed, infotext()
def txt2img(prompt: str, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int):
outpath = opts.outdir or "outputs/txt2img-samples"
sampler = samplers[sampler_index].constructor(model)
def init():
pass
def sample(init_data, x, conditioning, unconditional_conditioning):
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x)
return samples_ddim
output_images, seed, info = process_images(
outpath=outpath,
func_init=init,
func_sample=sample,
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
)
del sampler
return output_images, seed, plaintext_to_html(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, 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])
txt2img_interface = gr.Interface(
wrap_gradio_call(txt2img),
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),
gr.Radio(label='Sampling method', choices=[x.name for x in samplers], value=samplers[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.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=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),
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),
],
outputs=[
gr.Gallery(label="Images"),
gr.Number(label='Seed'),
gr.HTML(),
],
title="Stable Diffusion Text-to-Image",
flagging_callback=Flagging()
)
def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, loopback: 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"
sampler = KDiffusionSampler(model, 'sample_lms')
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
def init():
image = init_img.convert("RGB")
image = resize_image(resize_mode, image, width, height)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
init_image = 2. * image - 1.
init_image = init_image.to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
return init_latent,
def sample(init_data, x, conditioning, unconditional_conditioning):
t_enc = int(denoising_strength * ddim_steps)
x0, = init_data
sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
noise = x * sigmas[ddim_steps - t_enc - 1]
xi = x0 + noise
sigma_sched = sigmas[ddim_steps - t_enc - 1:]
model_wrap_cfg = CFGDenoiser(sampler.model_wrap)
samples_ddim = k_diffusion.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False)
return samples_ddim
if loopback:
output_images, info = None, None
history = []
initial_seed = None
for i in range(n_iter):
output_images, seed, info = process_images(
outpath=outpath,
func_init=init,
func_sample=sample,
prompt=prompt,
seed=seed,
sampler_index=0,
batch_size=1,
n_iter=1,
steps=ddim_steps,
cfg_scale=cfg_scale,
width=width,
height=height,
prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN,
do_not_save_grid=True,
extra_generation_params = {"Denoising Strength": denoising_strength},
)
if initial_seed is None:
initial_seed = seed
init_img = output_images[0]
seed = seed + 1
denoising_strength = max(denoising_strength * 0.95, 0.1)
history.append(init_img)
grid_count = len(os.listdir(outpath)) - 1
grid = image_grid(history, batch_size, force_n_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)
output_images = history
seed = initial_seed
else:
output_images, seed, info = process_images(
outpath=outpath,
func_init=init,
func_sample=sample,
prompt=prompt,
seed=seed,
sampler_index=0,
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,
extra_generation_params = {"Denoising Strength": denoising_strength},
)
del sampler
return output_images, seed, plaintext_to_html(info)
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
img2img_interface = gr.Interface(
wrap_gradio_call(img2img),
inputs=[
gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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),
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),
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")
],
outputs=[
gr.Gallery(),
gr.Number(label='Seed'),
gr.HTML(),
],
allow_flagging="never",
)
def run_GFPGAN(image, strength):
image = image.convert("RGB")
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
res = Image.fromarray(restored_img)
if strength < 1.0:
res = Image.blend(image, res, strength)
return res, 0, ''
gfpgan_interface = gr.Interface(
run_GFPGAN,
inputs=[
gr.Image(label="Source", source="upload", interactive=True, type="pil"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength", value=100),
],
outputs=[
gr.Image(label="Result"),
gr.Number(label='Seed', visible=False),
gr.HTML(),
],
description="Fix faces on images",
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][0]
labelinfo = opts.data_labels[key]
t = type(labelinfo[0])
label = labelinfo[1]
if t == str:
item = gr.Textbox(label=label, value=fun, lines=1)
elif t == int:
if len(labelinfo) == 4:
item = gr.Slider(minimum=labelinfo[2], maximum=labelinfo[3], step=1, label=label, value=fun)
else:
item = gr.Number(label=label, value=fun)
elif t == bool:
item = gr.Checkbox(label=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"),
(gfpgan_interface, "GFPGAN"),
(settings_interface, "Settings"),
]
config = OmegaConf.load(cmd_opts.config)
model = load_model_from_config(config, cmd_opts.ckpt)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = (model if cmd_opts.no_half else model.half()).to(device)
text_inversion_embeddings = TextInversionEmbeddings()
if os.path.exists(cmd_opts.embeddings_dir):
text_inversion_embeddings.hijack(model)
if GFPGAN is None:
interfaces = [x for x in interfaces if x[0] != gfpgan_interface]
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; }
"""
)
demo.launch()