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stable-diffusion-webui/webui_streamlit.py

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from gc import callbacks
import warnings
import streamlit as st
from streamlit import StopException, StreamlitAPIException
import base64
import argparse, os, sys, glob, re, random, datetime
from PIL import Image, ImageFont, ImageDraw, ImageFilter, ImageOps
from PIL.PngImagePlugin import PngInfo
import requests
from scipy import integrate
import torch
from torchdiffeq import odeint
from tqdm.auto import trange, tqdm
import k_diffusion as K
import math
import mimetypes
import numpy as np
import pynvml
import threading, asyncio
import time
import torch
from torch import autocast
from torchvision import transforms
import torch.nn as nn
import yaml
from typing import List, Union
from pathlib import Path
from tqdm import tqdm
from contextlib import contextmanager, nullcontext
from einops import rearrange, repeat
from itertools import islice
from omegaconf import OmegaConf
from io import BytesIO
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.util import instantiate_from_config
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
extract_into_tensor
# we use python-slugify to make the filenames safe for windows and linux, its better than doing it manually
# install it with 'pip install python-slugify'
from slugify import slugify
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
# remove some annoying deprecation warnings that show every now and then.
warnings.filterwarnings("ignore", category=DeprecationWarning)
defaults = OmegaConf.load("configs/webui/webui_streamlit.yaml")
# 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
# should and will be moved to a settings menu in the UI at some point
grid_format = [s.lower() for s in defaults.general.grid_format.split(':')]
grid_lossless = False
grid_quality = 100
if grid_format[0] == 'png':
grid_ext = 'png'
grid_format = 'png'
elif grid_format[0] in ['jpg', 'jpeg']:
grid_quality = int(grid_format[1]) if len(grid_format) > 1 else 100
grid_ext = 'jpg'
grid_format = 'jpeg'
elif grid_format[0] == 'webp':
grid_quality = int(grid_format[1]) if len(grid_format) > 1 else 100
grid_ext = 'webp'
grid_format = 'webp'
if grid_quality < 0: # e.g. webp:-100 for lossless mode
grid_lossless = True
grid_quality = abs(grid_quality)
# this should force GFPGAN and RealESRGAN onto the selected gpu as well
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = str(defaults.general.gpu)
def load_models(continue_prev_run = False, use_GFPGAN=False, use_RealESRGAN=False, RealESRGAN_model="RealESRGAN_x4plus"):
"""Load the different models. We also reuse the models that are already in memory to speed things up instead of loading them again. """
# Generate random run ID
# Used to link runs linked w/ continue_prev_run which is not yet implemented
# Use URL and filesystem safe version just in case.
st.session_state["run_id"] = base64.urlsafe_b64encode(
os.urandom(6)
).decode("ascii")
# check what models we want to use and if the they are already loaded.
if use_GFPGAN:
if "GFPGAN" in st.session_state:
print("GFPGAN already loaded")
else:
# Load GFPGAN
if os.path.exists(defaults.general.GFPGAN_dir):
try:
st.session_state["GFPGAN"] = load_GFPGAN()
print("Loaded GFPGAN")
except Exception:
import traceback
print("Error loading GFPGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
else:
if "GFPGAN" in st.session_state:
del st.session_state["GFPGAN"]
if use_RealESRGAN:
if "RealESRGAN" in st.session_state and st.session_state["RealESRGAN"].model.name == RealESRGAN_model:
print("RealESRGAN already loaded")
else:
#Load RealESRGAN
try:
# We first remove the variable in case it has something there,
# some errors can load the model incorrectly and leave things in memory.
del st.session_state["RealESRGAN"]
except KeyError:
pass
if os.path.exists(defaults.general.RealESRGAN_dir):
# st.session_state is used for keeping the models in memory across multiple pages or runs.
st.session_state["RealESRGAN"] = load_RealESRGAN(RealESRGAN_model)
print("Loaded RealESRGAN with model "+ st.session_state["RealESRGAN"].model.name)
else:
if "RealESRGAN" in st.session_state:
del st.session_state["RealESRGAN"]
if "model" in st.session_state:
print("Model already loaded")
else:
config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
model = load_model_from_config(config, defaults.general.ckpt)
device = torch.device(f"cuda:{defaults.general.gpu}") if torch.cuda.is_available() else torch.device("cpu")
st.session_state["model"] = (model if defaults.general.no_half else model.half()).to(device)
print("Model loaded.")
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
def load_sd_from_config(ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
return sd
#
def generation_callback(img, i=0):
if i % int(defaults.general.update_preview_frequency) == 0 and defaults.general.update_preview:
#print (img)
#print (type(img))
# The following lines will convert the tensor we got on img to an actual image we can render on the UI.
# It can probably be done in a better way for someone who knows what they're doing. I don't.
if torch.is_tensor(img):
x_samples_ddim = (st.session_state["model"] if not defaults.general.optimized else modelFS).decode_first_stage(img)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
pil_image = transforms.ToPILImage()(x_samples_ddim.squeeze_(0))
else:
# When using the k Diffusion samplers they return a dict instead of a tensor that look like this:
# {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}
x_samples_ddim = (st.session_state["model"] if not defaults.general.optimized else modelFS).decode_first_stage(img["denoised"])
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
pil_image = transforms.ToPILImage()(x_samples_ddim.squeeze_(0))
st.session_state["preview_image"].image(pil_image, width=512)
class MemUsageMonitor(threading.Thread):
stop_flag = False
max_usage = 0
total = -1
def __init__(self, name):
threading.Thread.__init__(self)
self.name = name
def run(self):
try:
pynvml.nvmlInit()
except:
print(f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n")
return
print(f"[{self.name}] Recording max memory usage...\n")
handle = pynvml.nvmlDeviceGetHandleByIndex(defaults.general.gpu)
self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
while not self.stop_flag:
m = pynvml.nvmlDeviceGetMemoryInfo(handle)
self.max_usage = max(self.max_usage, m.used)
# print(self.max_usage)
time.sleep(0.1)
print(f"[{self.name}] Stopped recording.\n")
pynvml.nvmlShutdown()
def read(self):
return self.max_usage, self.total
def stop(self):
self.stop_flag = True
def read_and_stop(self):
self.stop_flag = True
return self.max_usage, self.total
class CFGMaskedDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, x, sigma, uncond, cond, cond_scale, mask, x0, xi):
x_in = x
x_in = torch.cat([x_in] * 2)
sigma_in = torch.cat([sigma] * 2)
cond_in = torch.cat([uncond, cond])
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
denoised = uncond + (cond - uncond) * cond_scale
if mask is not None:
assert x0 is not None
img_orig = x0
mask_inv = 1. - mask
denoised = (img_orig * mask_inv) + (mask * denoised)
return denoised
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, x, sigma, uncond, cond, cond_scale):
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigma] * 2)
cond_in = torch.cat([uncond, cond])
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
return uncond + (cond - uncond) * cond_scale
def append_zero(x):
return torch.cat([x, x.new_zeros([1])])
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
return x[(...,) + (None,) * dims_to_append]
def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
"""Constructs the noise schedule of Karras et al. (2022)."""
ramp = torch.linspace(0, 1, n)
min_inv_rho = sigma_min ** (1 / rho)
max_inv_rho = sigma_max ** (1 / rho)
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return append_zero(sigmas).to(device)
def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
"""Constructs an exponential noise schedule."""
sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()
return append_zero(sigmas)
def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
"""Constructs a continuous VP noise schedule."""
t = torch.linspace(1, eps_s, n, device=device)
sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
return append_zero(sigmas)
def to_d(x, sigma, denoised):
"""Converts a denoiser output to a Karras ODE derivative."""
return (x - denoised) / append_dims(sigma, x.ndim)
def linear_multistep_coeff(order, t, i, j):
if order - 1 > i:
raise ValueError(f'Order {order} too high for step {i}')
def fn(tau):
prod = 1.
for k in range(order):
if j == k:
continue
prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
return prod
return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
def get_ancestral_step(sigma_from, sigma_to):
"""Calculates the noise level (sigma_down) to step down to and the amount
of noise to add (sigma_up) when doing an ancestral sampling step."""
sigma_up = (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5
sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
return sigma_down, sigma_up
class KDiffusionSampler:
def __init__(self, m, sampler):
self.model = m
self.model_wrap = K.external.CompVisDenoiser(m)
self.schedule = sampler
def get_sampler_name(self):
return self.schedule
def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T, img_callback=None, log_every_t=None):
sigmas = self.model_wrap.get_sigmas(S)
x = x_T * sigmas[0]
model_wrap_cfg = CFGDenoiser(self.model_wrap)
samples_ddim = None
samples_ddim = K.sampling.__dict__[f'sample_{self.schedule}'](model_wrap_cfg, x, sigmas,
extra_args={'cond': conditioning, 'uncond': unconditional_conditioning,
'cond_scale': unconditional_guidance_scale}, disable=False, callback=generation_callback)
#
return samples_ddim, None
@torch.no_grad()
def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4):
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
v = torch.randint_like(x, 2) * 2 - 1
fevals = 0
def ode_fn(sigma, x):
nonlocal fevals
with torch.enable_grad():
x = x[0].detach().requires_grad_()
denoised = model(x, sigma * s_in, **extra_args)
d = to_d(x, sigma, denoised)
fevals += 1
grad = torch.autograd.grad((d * v).sum(), x)[0]
d_ll = (v * grad).flatten(1).sum(1)
return d.detach(), d_ll
x_min = x, x.new_zeros([x.shape[0]])
t = x.new_tensor([sigma_min, sigma_max])
sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5')
latent, delta_ll = sol[0][-1], sol[1][-1]
ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1)
return ll_prior + delta_ll, {'fevals': fevals}
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=defaults.general.gpu))
x = torch.stack(xs)
return x
def torch_gc():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
def load_GFPGAN():
model_name = 'GFPGANv1.3'
model_path = os.path.join(defaults.general.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(defaults.general.GFPGAN_dir))
from gfpgan import GFPGANer
if defaults.general.gfpgan_cpu or defaults.general.extra_models_cpu:
instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cpu'))
elif defaults.general.extra_models_gpu:
instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f'cuda:{defaults.general.gfpgan_gpu}'))
else:
instance = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device(f'cuda:{defaults.general.gpu}'))
return instance
def load_RealESRGAN(model_name: str):
from basicsr.archs.rrdbnet_arch import RRDBNet
RealESRGAN_models = {
'RealESRGAN_x4plus': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4),
'RealESRGAN_x4plus_anime_6B': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
}
model_path = os.path.join(defaults.general.RealESRGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
if not os.path.isfile(model_path):
raise Exception(model_name+".pth not found at path "+model_path)
sys.path.append(os.path.abspath(defaults.general.RealESRGAN_dir))
from realesrgan import RealESRGANer
if defaults.general.esrgan_cpu or defaults.general.extra_models_cpu:
instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=False) # cpu does not support half
instance.device = torch.device('cpu')
instance.model.to('cpu')
elif defaults.general.extra_models_gpu:
instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not defaults.general.no_half, device=torch.device(f'cuda:{defaults.general.esrgan_gpu}'))
else:
instance = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name], pre_pad=0, half=not defaults.general.no_half, device=torch.device(f'cuda:{defaults.general.gpu}'))
instance.model.name = model_name
return instance
prompt_parser = re.compile("""
(?P<prompt> # capture group for 'prompt'
[^:]+ # match one or more non ':' characters
) # end 'prompt'
(?: # non-capture group
:+ # match one or more ':' characters
(?P<weight> # capture group for 'weight'
-?\d+(?:\.\d+)? # match positive or negative decimal number
)? # end weight capture group, make optional
\s* # strip spaces after weight
| # OR
$ # else, if no ':' then match end of line
) # end non-capture group
""", re.VERBOSE)
# grabs all text up to the first occurrence of ':' as sub-prompt
# takes the value following ':' as weight
# if ':' has no value defined, defaults to 1.0
# repeats until no text remaining
def split_weighted_subprompts(input_string, normalize=True):
parsed_prompts = [(match.group("prompt"), float(match.group("weight") or 1)) for match in re.finditer(prompt_parser, input_string)]
if not normalize:
return parsed_prompts
# this probably still doesn't handle negative weights very well
weight_sum = sum(map(lambda x: x[1], parsed_prompts))
return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
def slerp(device, t, v0:torch.Tensor, v1:torch.Tensor, DOT_THRESHOLD=0.9995):
v0 = v0.detach().cpu().numpy()
v1 = v1.detach().cpu().numpy()
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
if np.abs(dot) > DOT_THRESHOLD:
v2 = (1 - t) * v0 + t * v1
else:
theta_0 = np.arccos(dot)
sin_theta_0 = np.sin(theta_0)
theta_t = theta_0 * t
sin_theta_t = np.sin(theta_t)
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
s1 = sin_theta_t / sin_theta_0
v2 = s0 * v0 + s1 * v1
v2 = torch.from_numpy(v2).to(device)
return v2
def run_GFPGAN(image, strength):
image = image.convert("RGB")
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
res = Image.fromarray(restored_img)
if strength < 1.0:
res = Image.blend(image, res, strength)
return res
def run_RealESRGAN(image, model_name: str):
if RealESRGAN.model.name != model_name:
try_loading_RealESRGAN(model_name)
image = image.convert("RGB")
output, img_mode = RealESRGAN.enhance(np.array(image, dtype=np.uint8))
res = Image.fromarray(output)
return res
def get_font(fontsize):
fonts = ["arial.ttf", "DejaVuSans.ttf"]
for font_name in fonts:
try:
return ImageFont.truetype(font_name, fontsize)
except OSError:
pass
# ImageFont.load_default() is practically unusable as it only supports
# latin1, so raise an exception instead if no usable font was found
raise Exception(f"No usable font found (tried {', '.join(fonts)})")
def load_embeddings(fp):
if fp is not None and hasattr(st.session_state["model"], "embedding_manager"):
st.session_state["model"].embedding_manager.load(fp['name'])
def image_grid(imgs, batch_size, force_n_rows=None, captions=None):
if force_n_rows is not None:
rows = force_n_rows
elif defaults.general.n_rows > 0:
rows = defaults.general.n_rows
elif defaults.general.n_rows == 0:
rows = batch_size
else:
rows = math.sqrt(len(imgs))
rows = round(rows)
cols = math.ceil(len(imgs) / rows)
w, h = imgs[0].size
grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
fnt = get_font(30)
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
if captions and i<len(captions):
d = ImageDraw.Draw( grid )
size = d.textbbox( (0,0), captions[i], font=fnt, stroke_width=2, align="center" )
d.multiline_text((i % cols * w + w/2, i // cols * h + h - size[3]), captions[i], font=fnt, fill=(255,255,255), stroke_width=2, stroke_fill=(0,0,0), anchor="mm", align="center")
return grid
def seed_to_int(s):
if type(s) is int:
return s
if s is None or s == '':
return random.randint(0, 2**32 - 1)
n = abs(int(s) if s.isdigit() else random.Random(s).randint(0, 2**32 - 1))
while n >= 2**32:
n = n >> 32
return n
def check_prompt_length(prompt, comments):
"""this function tests if prompt is too long, and if so, adds a message to comments"""
tokenizer = (st.session_state["model"] if not defaults.general.optimized else modelCS).cond_stage_model.tokenizer
max_length = (st.session_state["model"] if not defaults.general.optimized else modelCS).cond_stage_model.max_length
info = (st.session_state["model"] if not defaults.general.optimized else modelCS).cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length,
return_overflowing_tokens=True, padding="max_length", return_tensors="pt")
ovf = info['overflowing_tokens'][0]
overflowing_count = ovf.shape[0]
if overflowing_count == 0:
return
vocab = {v: k for k, v in tokenizer.get_vocab().items()}
overflowing_words = [vocab.get(int(x), "") for x in ovf]
overflowing_text = tokenizer.convert_tokens_to_string(''.join(overflowing_words))
comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
def save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode):
filename_i = os.path.join(sample_path_i, filename)
if not jpg_sample:
if defaults.general.save_metadata:
metadata = PngInfo()
metadata.add_text("SD:prompt", prompts[i])
metadata.add_text("SD:seed", str(seeds[i]))
metadata.add_text("SD:width", str(width))
metadata.add_text("SD:height", str(height))
metadata.add_text("SD:steps", str(steps))
metadata.add_text("SD:cfg_scale", str(cfg_scale))
metadata.add_text("SD:normalize_prompt_weights", str(normalize_prompt_weights))
if init_img is not None:
metadata.add_text("SD:denoising_strength", str(denoising_strength))
metadata.add_text("SD:GFPGAN", str(use_GFPGAN and st.session_state["GFPGAN"] is not None))
image.save(f"{filename_i}.png", pnginfo=metadata)
else:
image.save(f"{filename_i}.png")
else:
image.save(f"{filename_i}.jpg", 'jpeg', quality=100, optimize=True)
if write_info_files:
# toggles differ for txt2img vs. img2img:
offset = 0 if init_img is None else 2
toggles = []
if prompt_matrix:
toggles.append(0)
if normalize_prompt_weights:
toggles.append(1)
if init_img is not None:
if uses_loopback:
toggles.append(2)
if uses_random_seed_loopback:
toggles.append(3)
if not skip_save:
toggles.append(2 + offset)
if not skip_grid:
toggles.append(3 + offset)
if sort_samples:
toggles.append(4 + offset)
if write_info_files:
toggles.append(5 + offset)
if use_GFPGAN:
toggles.append(6 + offset)
info_dict = dict(
target="txt2img" if init_img is None else "img2img",
prompt=prompts[i], ddim_steps=steps, toggles=toggles, sampler_name=sampler_name,
ddim_eta=ddim_eta, n_iter=n_iter, batch_size=batch_size, cfg_scale=cfg_scale,
seed=seeds[i], width=width, height=height
)
if init_img is not None:
# Not yet any use for these, but they bloat up the files:
#info_dict["init_img"] = init_img
#info_dict["init_mask"] = init_mask
info_dict["denoising_strength"] = denoising_strength
info_dict["resize_mode"] = resize_mode
with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
yaml.dump(info_dict, f, allow_unicode=True, width=10000)
# render the image on the frontend
st.session_state["preview_image"].image(image, width=512)
def get_next_sequence_number(path, prefix=''):
"""
Determines and returns the next sequence number to use when saving an
image in the specified directory.
If a prefix is given, only consider files whose names start with that
prefix, and strip the prefix from filenames before extracting their
sequence number.
The sequence starts at 0.
"""
result = -1
for p in Path(path).iterdir():
if p.name.endswith(('.png', '.jpg')) and p.name.startswith(prefix):
tmp = p.name[len(prefix):]
try:
result = max(int(tmp.split('-')[0]), result)
except ValueError:
pass
return result + 1
def oxlamon_matrix(prompt, seed, n_iter, batch_size):
pattern = re.compile(r'(,\s){2,}')
class PromptItem:
def __init__(self, text, parts, item):
self.text = text
self.parts = parts
if item:
self.parts.append( item )
def clean(txt):
return re.sub(pattern, ', ', txt)
def getrowcount( txt ):
for data in re.finditer( ".*?\\((.*?)\\).*", txt ):
if data:
return len(data.group(1).split("|"))
break
return None
def repliter( txt ):
for data in re.finditer( ".*?\\((.*?)\\).*", txt ):
if data:
r = data.span(1)
for item in data.group(1).split("|"):
yield (clean(txt[:r[0]-1] + item.strip() + txt[r[1]+1:]), item.strip())
break
def iterlist( items ):
outitems = []
for item in items:
for newitem, newpart in repliter(item.text):
outitems.append( PromptItem(newitem, item.parts.copy(), newpart) )
return outitems
def getmatrix( prompt ):
dataitems = [ PromptItem( prompt[1:].strip(), [], None ) ]
while True:
newdataitems = iterlist( dataitems )
if len( newdataitems ) == 0:
return dataitems
dataitems = newdataitems
def classToArrays( items, seed, n_iter ):
texts = []
parts = []
seeds = []
for item in items:
itemseed = seed
for i in range(n_iter):
texts.append( item.text )
parts.append( f"Seed: {itemseed}\n" + "\n".join(item.parts) )
seeds.append( itemseed )
itemseed += 1
return seeds, texts, parts
all_seeds, all_prompts, prompt_matrix_parts = classToArrays(getmatrix( prompt ), seed, n_iter)
n_iter = math.ceil(len(all_prompts) / batch_size)
needrows = getrowcount(prompt)
if needrows:
xrows = math.sqrt(len(all_prompts))
xrows = round(xrows)
# if columns is to much
cols = math.ceil(len(all_prompts) / xrows)
if cols > needrows*4:
needrows *= 2
return all_seeds, n_iter, prompt_matrix_parts, all_prompts, needrows
def process_images(
outpath, func_init, func_sample, prompt, seed, sampler_name, skip_grid, skip_save, batch_size,
n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, use_RealESRGAN, realesrgan_model_name,
fp=None, ddim_eta=0.0, do_not_save_grid=False, normalize_prompt_weights=True, init_img=None, init_mask=None,
keep_mask=False, mask_blur_strength=3, denoising_strength=0.75, resize_mode=None, uses_loopback=False,
uses_random_seed_loopback=False, sort_samples=True, write_info_files=True, jpg_sample=False,
variant_amount=0.0, variant_seed=None):
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
assert prompt is not None
torch_gc()
# start time after garbage collection (or before?)
start_time = time.time()
# We will use this date here later for the folder name, need to start_time if not need
run_start_dt = datetime.datetime.now()
mem_mon = MemUsageMonitor('MemMon')
mem_mon.start()
if hasattr(st.session_state["model"], "embedding_manager"):
load_embeddings(fp)
os.makedirs(outpath, exist_ok=True)
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
if not ("|" in prompt) and prompt.startswith("@"):
prompt = prompt[1:]
comments = []
prompt_matrix_parts = []
simple_templating = False
add_original_image = True
if prompt_matrix:
if prompt.startswith("@"):
simple_templating = True
add_original_image = not (use_RealESRGAN or use_GFPGAN)
all_seeds, n_iter, prompt_matrix_parts, all_prompts, frows = oxlamon_matrix(prompt, seed, n_iter, batch_size)
else:
all_prompts = []
prompt_matrix_parts = prompt.split("|")
combination_count = 2 ** (len(prompt_matrix_parts) - 1)
for combination_num in range(combination_count):
current = prompt_matrix_parts[0]
for n, text in enumerate(prompt_matrix_parts[1:]):
if combination_num & (2 ** n) > 0:
current += ("" if text.strip().startswith(",") else ", ") + text
all_prompts.append(current)
n_iter = math.ceil(len(all_prompts) / batch_size)
all_seeds = len(all_prompts) * [seed]
print(f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches.")
else:
if not defaults.general.no_verify_input:
try:
check_prompt_length(prompt, comments)
except:
import traceback
print("Error verifying input:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
all_prompts = batch_size * n_iter * [prompt]
all_seeds = [seed + x for x in range(len(all_prompts))]
precision_scope = autocast if defaults.general.precision == "autocast" else nullcontext
output_images = []
grid_captions = []
stats = []
with torch.no_grad(), precision_scope("cuda"), (st.session_state["model"].ema_scope() if not defaults.general.optimized else nullcontext()):
init_data = func_init()
tic = time.time()
# if variant_amount > 0.0 create noise from base seed
base_x = None
if variant_amount > 0.0:
target_seed_randomizer = seed_to_int('') # random seed
torch.manual_seed(seed) # this has to be the single starting seed (not per-iteration)
base_x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=[seed])
# we don't want all_seeds to be sequential from starting seed with variants,
# since that makes the same variants each time,
# so we add target_seed_randomizer as a random offset
for si in range(len(all_seeds)):
all_seeds[si] += target_seed_randomizer
for n in range(n_iter):
print(f"Iteration: {n+1}/{n_iter}")
prompts = all_prompts[n * batch_size:(n + 1) * batch_size]
captions = prompt_matrix_parts[n * batch_size:(n + 1) * batch_size]
seeds = all_seeds[n * batch_size:(n + 1) * batch_size]
print(prompt)
if defaults.general.optimized:
modelCS.to(defaults.general.gpu)
uc = (st.session_state["model"] if not defaults.general.optimized else modelCS).get_learned_conditioning(len(prompts) * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
# split the prompt if it has : for weighting
# TODO for speed it might help to have this occur when all_prompts filled??
weighted_subprompts = split_weighted_subprompts(prompts[0], normalize_prompt_weights)
# sub-prompt weighting used if more than 1
if len(weighted_subprompts) > 1:
c = torch.zeros_like(uc) # i dont know if this is correct.. but it works
for i in range(0, len(weighted_subprompts)):
# note if alpha negative, it functions same as torch.sub
c = torch.add(c, (st.session_state["model"] if not defaults.general.optimized else modelCS).get_learned_conditioning(weighted_subprompts[i][0]), alpha=weighted_subprompts[i][1])
else: # just behave like usual
c = (st.session_state["model"] if not defaults.general.optimized else modelCS).get_learned_conditioning(prompts)
shape = [opt_C, height // opt_f, width // opt_f]
if defaults.general.optimized:
mem = torch.cuda.memory_allocated()/1e6
modelCS.to("cpu")
while(torch.cuda.memory_allocated()/1e6 >= mem):
time.sleep(1)
if variant_amount == 0.0:
# we manually generate all input noises because each one should have a specific seed
x = create_random_tensors(shape, seeds=seeds)
else: # we are making variants
# using variant_seed as sneaky toggle,
# when not None or '' use the variant_seed
# otherwise use seeds
if variant_seed != None and variant_seed != '':
specified_variant_seed = seed_to_int(variant_seed)
torch.manual_seed(specified_variant_seed)
seeds = [specified_variant_seed]
target_x = create_random_tensors(shape, seeds=seeds)
# finally, slerp base_x noise to target_x noise for creating a variant
x = slerp(defaults.general.gpu, max(0.0, min(1.0, variant_amount)), base_x, target_x)
samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc, sampler_name=sampler_name)
if defaults.general.optimized:
modelFS.to(defaults.general.gpu)
x_samples_ddim = (st.session_state["model"] if not defaults.general.optimized else modelFS).decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
for i, x_sample in enumerate(x_samples_ddim):
# use the date and a path safe filename, this should help avoid troubles with long paths or invalid characters in the filenames/folders.
#runoutputdir = os.path.join(sample_path, run_start_dt.strftime("%Y-%m-%d %H-%M-%S") + "_" + st.session_state["run_id"])
#print (runoutputdir)
sanitized_prompt = slugify(prompts[i])
if sort_samples:
sanitized_prompt = sanitized_prompt[:128] #200 is too long
sample_path_i = os.path.join(sample_path, sanitized_prompt)
os.makedirs(sample_path_i, exist_ok=True)
base_count = get_next_sequence_number(sample_path_i)
filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}"
else:
sample_path_i = sample_path
base_count = get_next_sequence_number(sample_path_i)
filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}_{sanitized_prompt}"[:128] #same as before
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
x_sample = x_sample.astype(np.uint8)
image = Image.fromarray(x_sample)
original_sample = x_sample
original_filename = filename
if use_GFPGAN and st.session_state["GFPGAN"] is not None and not use_RealESRGAN:
skip_save = True # #287 >_>
torch_gc()
cropped_faces, restored_faces, restored_img = st.session_state["GFPGAN"].enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
gfpgan_sample = restored_img[:,:,::-1]
gfpgan_image = Image.fromarray(gfpgan_sample)
gfpgan_filename = original_filename + '-gfpgan'
save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
save_sample(gfpgan_image, sample_path_i, gfpgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
output_images.append(gfpgan_image) #287
if simple_templating:
grid_captions.append( captions[i] + "\ngfpgan" )
if use_RealESRGAN and st.session_state["RealESRGAN"] is not None and not use_GFPGAN:
skip_save = True # #287 >_>
torch_gc()
if st.session_state["RealESRGAN"].model.name != realesrgan_model_name:
try_loading_RealESRGAN(realesrgan_model_name)
output, img_mode = st.session_state["RealESRGAN"].enhance(x_sample[:,:,::-1])
esrgan_filename = original_filename + '-esrgan4x'
esrgan_sample = output[:,:,::-1]
esrgan_image = Image.fromarray(esrgan_sample)
save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
save_sample(esrgan_image, sample_path_i, esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
output_images.append(esrgan_image) #287
if simple_templating:
grid_captions.append( captions[i] + "\nesrgan" )
if use_RealESRGAN and st.session_state["RealESRGAN"] is not None and use_GFPGAN and st.session_state["GFPGAN"] is not None:
skip_save = True # #287 >_>
torch_gc()
cropped_faces, restored_faces, restored_img = st.session_state["GFPGAN"].enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
gfpgan_sample = restored_img[:,:,::-1]
if st.session_state["RealESRGAN"].model.name != realesrgan_model_name:
try_loading_RealESRGAN(realesrgan_model_name)
output, img_mode = st.session_state["RealESRGAN"].enhance(gfpgan_sample[:,:,::-1])
gfpgan_esrgan_filename = original_filename + '-gfpgan-esrgan4x'
gfpgan_esrgan_sample = output[:,:,::-1]
gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)
save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
save_sample(gfpgan_esrgan_image, sample_path_i, gfpgan_esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
output_images.append(gfpgan_esrgan_image) #287
if simple_templating:
grid_captions.append( captions[i] + "\ngfpgan_esrgan" )
if not skip_save or (not use_GFPGAN or not use_RealESRGAN):
save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
if add_original_image or not simple_templating:
output_images.append(image)
if simple_templating:
grid_captions.append( captions[i] )
if defaults.general.optimized:
mem = torch.cuda.memory_allocated()/1e6
modelFS.to("cpu")
while(torch.cuda.memory_allocated()/1e6 >= mem):
time.sleep(1)
if (prompt_matrix or skip_grid) and not do_not_save_grid:
if prompt_matrix:
if simple_templating:
grid = image_grid(output_images, batch_size, force_n_rows=frows, captions=grid_captions)
else:
grid = image_grid(output_images, batch_size, force_n_rows=1 << ((len(prompt_matrix_parts)-1)//2))
try:
grid = draw_prompt_matrix(grid, width, height, prompt_matrix_parts)
except:
import traceback
print("Error creating prompt_matrix text:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
else:
grid = image_grid(output_images, batch_size)
if grid and (batch_size > 1 or n_iter > 1):
output_images.insert(0, grid)
grid_count = get_next_sequence_number(outpath, 'grid-')
grid_file = f"grid-{grid_count:05}-{seed}_{slugify(prompts[i].replace(' ', '_')[:128])}.{grid_ext}"
grid.save(os.path.join(outpath, grid_file), grid_format, quality=grid_quality, lossless=grid_lossless, optimize=True)
toc = time.time()
mem_max_used, mem_total = mem_mon.read_and_stop()
time_diff = time.time()-start_time
info = f"""
{prompt}
Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', Denoising strength: '+str(denoising_strength) if init_img is not None else ''}{', GFPGAN' if use_GFPGAN and st.session_state["GFPGAN"] is not None else ''}{', '+realesrgan_model_name if use_RealESRGAN and st.session_state["RealESRGAN"] is not None else ''}{', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()
stats = f'''
Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%'''
for comment in comments:
info += "\n\n" + comment
#mem_mon.stop()
#del mem_mon
torch_gc()
return output_images, seed, info, stats
def txt2img(prompt: str, ddim_steps: int, sampler_name: str, realesrgan_model_name: str,
n_iter: int, batch_size: int, cfg_scale: float, seed: Union[int, str, None],
height: int, width: int,separate_prompts:bool = False, normalize_prompt_weights:bool = True,
save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
save_as_jpg: bool = True, use_GFPGAN: bool = True, use_RealESRGAN: bool = True,
RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B", fp = None, variant_amount: float = None,
variant_seed: int = None, ddim_eta:float = 0.0, write_info_files:bool = True):
outpath = defaults.general.outdir_txt2img or defaults.general.outdir or "outputs/txt2img-samples"
err = False
seed = seed_to_int(seed)
#prompt_matrix = 0 in toggles
#normalize_prompt_weights = 1 in toggles
#skip_save = 2 not in toggles
#skip_grid = 3 not in toggles
#sort_samples = 4 in toggles
#write_info_files = 5 in toggles
#jpg_sample = 6 in toggles
#use_GFPGAN = 7 in toggles
#use_RealESRGAN = 8 in toggles
if sampler_name == 'PLMS':
sampler = PLMSSampler(st.session_state["model"])
elif sampler_name == 'DDIM':
sampler = DDIMSampler(st.session_state["model"])
elif sampler_name == 'k_dpm_2_a':
sampler = KDiffusionSampler(st.session_state["model"],'dpm_2_ancestral')
elif sampler_name == 'k_dpm_2':
sampler = KDiffusionSampler(st.session_state["model"],'dpm_2')
elif sampler_name == 'k_euler_a':
sampler = KDiffusionSampler(st.session_state["model"],'euler_ancestral')
elif sampler_name == 'k_euler':
sampler = KDiffusionSampler(st.session_state["model"],'euler')
elif sampler_name == 'k_heun':
sampler = KDiffusionSampler(st.session_state["model"],'heun')
elif sampler_name == 'k_lms':
sampler = KDiffusionSampler(st.session_state["model"],'lms')
else:
raise Exception("Unknown sampler: " + sampler_name)
def init():
pass
def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x, img_callback=generation_callback,
log_every_t=int(defaults.general.update_preview_frequency))
return samples_ddim
try:
output_images, seed, info, stats = process_images(
outpath=outpath,
func_init=init,
func_sample=sample,
prompt=prompt,
seed=seed,
sampler_name=sampler_name,
skip_save=save_individual_images,
skip_grid=save_grid,
batch_size=batch_size,
n_iter=n_iter,
steps=ddim_steps,
cfg_scale=cfg_scale,
width=width,
height=height,
prompt_matrix=separate_prompts,
use_GFPGAN=use_GFPGAN,
use_RealESRGAN=use_RealESRGAN,
realesrgan_model_name=realesrgan_model_name,
fp=fp,
ddim_eta=ddim_eta,
normalize_prompt_weights=normalize_prompt_weights,
sort_samples=group_by_prompt,
write_info_files=write_info_files,
jpg_sample=save_as_jpg,
variant_amount=variant_amount,
variant_seed=variant_seed,
)
del sampler
return output_images, seed, info, stats
except RuntimeError as e:
err = e
err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
stats = err_msg
return [], seed, 'err', stats
def layout():
st.set_page_config(page_title="Stable Diffusion Playground", layout="wide", initial_sidebar_state="collapsed")
css = """
<style>
.css-18e3th9 {
padding-top: 2rem;
padding-bottom: 10rem;
padding-left: 5rem;
padding-right: 5rem;
}
.css-1d391kg {
padding-top: 3.5rem;
padding-right: 1rem;
padding-bottom: 3.5rem;
padding-left: 1rem;
}
button[data-baseweb="tab"] {
font-size: 25px;
}
</style>
"""
st.markdown(css, unsafe_allow_html=True)
with st.sidebar:
# we should use an expander and group things together when more options are added so the sidebar is not too messy.
#with st.expander("Global Settings:"):
st.write("Global Settings:")
defaults.general.update_preview = st.checkbox("Update Image Preview", value=defaults.general.update_preview,
help="If enabled the image preview will be updated during the generation instead of at the end. You can use the Update Preview \
Frequency option bellow to customize how frequent it's updated. By default this is enabled and the frequency is set to 1 step.")
defaults.general.update_preview_frequency = st.text_input("Update Image Preview Frequency", value=defaults.general.update_preview_frequency,
help="Frequency in steps at which the the preview image is updated. By default the frequency is set to 1 step.")
tab1, tab2, tab3, tab4 = st.tabs(["Stable Diffusion Text-to-Image Unified", "Stable Diffusion Image-to-Image Unified", "GFPGAN", "RealESRGAN"])
with tab1:
with st.form("form-inputs"):
input_col1, generate_col1 = st.columns([10,1])
with input_col1:
#prompt = st.text_area("Input Text","")
prompt = st.text_input("Input Text","")
# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
generate_col1.write("")
generate_col1.write("")
generate_button = generate_col1.form_submit_button("Generate")
# creating the page layout using columns
col1, col2, col3 = st.columns([1,2,1], gap="large")
with col1:
height = st.slider("Height:", min_value=64, max_value=2048, value=defaults.txt2img.height, step=64)
width = st.slider("Width:", min_value=64, max_value=2048, value=defaults.txt2img.width, step=64)
cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=1.0, max_value=30.0, value=defaults.txt2img.cfg_scale, step=0.5, help="How strongly the image should follow the prompt.")
seed = st.text_input("Seed:", value=defaults.txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
batch_count = st.slider("Batch count.", min_value=1, max_value=500, value=defaults.txt2img.batch_count, step=1, help="How many iterations or batches of images to generate in total.")
batch_size = st.slider("Batch size", min_value=1, max_value=500, value=defaults.txt2img.batch_size, step=1,
help="How many images are at once in a batch.\
It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
Default: 1")
with col2:
preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
with preview_tab:
st.write("Image")
#Image for testing
image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw)
new_image = image.resize((175, 240))
#preview_image = st.image(image)
# create an empty container for the image and use session_state to hold it globally.
preview_image = st.empty()
st.session_state["preview_image"] = preview_image
with gallery_tab:
st.write('Here should be the image gallery, if I could make a grid in streamlit.')
with col3:
sampling_steps = st.slider("Sampling Steps", value=defaults.txt2img.sampling_steps, min_value=1, max_value=250)
sampler_name = st.selectbox("Sampling method",
["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"],
index=0, help="Sampling method to use. Default: k_lms")
basic_tab, advanced_tab = st.tabs(["Basic", "Advanced"])
with basic_tab:
summit_on_enter = st.radio("Submit on enter?", ("Yes", "No"), horizontal=True,
help="Press the Enter key to summit, when 'No' is selected you can use the Enter key to write multiple lines.")
with advanced_tab:
separate_prompts = st.checkbox("Create Prompt Matrix.", value=False, help="Separate multiple prompts using the `|` character, and get all combinations of them.")
normalize_prompt_weights = st.checkbox("Normalize Prompt Weights.", value=True, help="Ensure the sum of all weights add up to 1.0")
save_individual_images = st.checkbox("Save individual images.", value=True, help="Save each image generated before any filter or enhancement is applied.")
save_grid = st.checkbox("Save grid",value=True, help="Save a grid with all the images generated into a single image.")
group_by_prompt = st.checkbox("Group results by prompt", value=True,
help="Saves all the images with the same prompt into the same folder. When using a prompt matrix each prompt combination will have its own folder.")
write_info_files = st.checkbox("Write Info file", value=True, help="Save a file next to the image with informartion about the generation.")
save_as_jpg = st.checkbox("Save samples as jpg", value=False, help="Saves the images as jpg instead of png.")
use_GFPGAN = st.checkbox("Use GFPGAN", value=True, help="Uses the GFPGAN model to improve faces after the generation. This greatly improve the quality and consistency of faces but uses extra VRAM. Disable if you need the extra VRAM.")
use_RealESRGAN = st.checkbox("Use RealESRGAN", value=True, help="Uses the RealESRGAN model to upscale the images after the generation. This greatly improve the quality and lets you have high resolution images but uses extra VRAM. Disable if you need the extra VRAM.")
RealESRGAN_model = st.selectbox("RealESRGAN model", ["RealESRGAN_x4plus", "RealESRGAN_x4plus_anime_6B"], index=0)
variant_amount = st.slider("Variant Amount:", value=defaults.txt2img.variant_amount, min_value=0.0, max_value=1.0, step=0.01)
variant_seed = st.text_input("Variant Seed:", value=defaults.txt2img.seed, help="The seed to use when generating a variant, if left blank a random seed will be generated.")
if generate_button:
print("Loading models")
# load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
load_models(False, use_GFPGAN, use_RealESRGAN, RealESRGAN_model)
try:
output_images, seed, info, stats = txt2img(prompt, sampling_steps, sampler_name, RealESRGAN_model, batch_count, batch_size,
cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
save_grid, group_by_prompt, save_as_jpg, use_GFPGAN, use_RealESRGAN, RealESRGAN_model, fp=defaults.general.fp,
variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
except (StopException, KeyError):
print(f"Received Streamlit StopException")
# this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
# use the current col2 first tab to show the preview_img and update it as its generated.
#preview_image.image(output_images, width=750)
if __name__ == '__main__':
layout()
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