sygil-webui/webui.py
bryanlyon 9caf41f5a1
Fix GFPGAN color errors.
GFPGAN requires images in BGR color space.  Using the wrong color space leads to color-shift of the face after it's put through GFPGAN.  To fix, convert the color space before sending to GFPGAN and again when it's returned.
2022-08-24 20:18:29 -07:00

975 lines
40 KiB
Python

import argparse, os, sys, glob
import gradio as gr
import k_diffusion as K
import math
import mimetypes
import numpy as np
import pynvml
import random
import threading
import time
import torch
import torch.nn as nn
from contextlib import contextmanager, nullcontext
from einops import rearrange, repeat
from itertools import islice
from omegaconf import OmegaConf
from PIL import Image, ImageFont, ImageDraw, ImageFilter, ImageOps
from torch import autocast
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.util import instantiate_from_config
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except:
pass
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
mimetypes.add_type('application/javascript', '.js')
# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
invalid_filename_chars = '<>:"/\|?*\n'
parser = argparse.ArgumentParser()
parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)
parser.add_argument("--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",)
parser.add_argument("--skip_save", action='store_true', help="do not save indiviual samples. For speed measurements.",)
parser.add_argument("--n_rows", type=int, default=-1, help="rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default: -1)",)
parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",)
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
parser.add_argument("--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-verify-input", action='store_true', help="do not verify input to check if it's too long")
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)")
opt = parser.parse_args()
GFPGAN_dir = opt.gfpgan_dir
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; }
"""
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
def crash(e, s):
global model
global device
print(s, '\n', e)
del model
del device
print('exiting...calling os._exit(0)')
t = threading.Timer(0.25, os._exit, args=[0])
t.start()
class MemUsageMonitor(threading.Thread):
stop_flag = False
max_usage = 0
total = 0
def __init__(self, name):
threading.Thread.__init__(self)
self.name = name
def run(self):
print(f"[{self.name}] Recording max memory usage...\n")
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
while not self.stop_flag:
m = pynvml.nvmlDeviceGetMemoryInfo(handle)
self.max_usage = max(self.max_usage, m.used)
# print(self.max_usage)
time.sleep(0.1)
print(f"[{self.name}] Stopped recording.\n")
pynvml.nvmlShutdown()
def read(self):
return self.max_usage, self.total
def stop(self):
self.stop_flag = True
def read_and_stop(self):
self.stop_flag = True
return self.max_usage, self.total
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, x, sigma, uncond, cond, cond_scale):
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigma] * 2)
cond_in = torch.cat([uncond, cond])
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
return uncond + (cond - uncond) * cond_scale
class KDiffusionSampler:
def __init__(self, m, sampler):
self.model = m
self.model_wrap = K.external.CompVisDenoiser(m)
self.schedule = sampler
def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T):
sigmas = self.model_wrap.get_sigmas(S)
x = x_T * sigmas[0]
model_wrap_cfg = CFGDenoiser(self.model_wrap)
samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False)
return samples_ddim, None
class MemUsageMonitor(threading.Thread):
stop_flag = False
max_usage = 0
total = 0
def __init__(self, name):
threading.Thread.__init__(self)
self.name = name
def run(self):
print(f"[{self.name}] Recording max memory usage...\n")
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
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
def create_random_tensors(shape, seeds):
xs = []
for seed in seeds:
torch.manual_seed(seed)
# randn results depend on device; gpu and cpu get different results for same seed;
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
# but the original script had it like this so i do not dare change it for now because
# it will break everyone's seeds.
xs.append(torch.randn(shape, device=device))
x = torch.stack(xs)
return x
def torch_gc():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
def load_GFPGAN():
model_name = 'GFPGANv1.3'
model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
if not os.path.isfile(model_path):
raise Exception("GFPGAN model not found at path "+model_path)
sys.path.append(os.path.abspath(GFPGAN_dir))
from gfpgan import GFPGANer
return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
GFPGAN = None
if os.path.exists(GFPGAN_dir):
try:
GFPGAN = load_GFPGAN()
print("Loaded GFPGAN")
except Exception:
import traceback
print("Error loading GFPGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = (model if opt.no_half else model.half()).to(device)
def load_embeddings(fp):
if fp is not None and hasattr(model, "embedding_manager"):
model.embedding_manager.load(fp.name)
def image_grid(imgs, batch_size, round_down=False, force_n_rows=None):
if force_n_rows is not None:
rows = force_n_rows
elif opt.n_rows > 0:
rows = opt.n_rows
elif opt.n_rows == 0:
rows = batch_size
else:
rows = math.sqrt(len(imgs))
rows = 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 seed_to_int(s):
if s == 'random':
return random.randint(0,2**32)
n = abs(int(s) if s.isdigit() else hash(s))
while n > 2**32:
n = n >> 32
return n
def draw_prompt_matrix(im, width, height, all_prompts):
def wrap(text, d, font, line_length):
lines = ['']
for word in text.split():
line = f'{lines[-1]} {word}'.strip()
if d.textlength(line, font=font) <= line_length:
lines[-1] = line
else:
lines.append(word)
return '\n'.join(lines)
def draw_texts(pos, x, y, texts, sizes):
for i, (text, size) in enumerate(zip(texts, sizes)):
active = pos & (1 << i) != 0
if not active:
text = '\u0336'.join(text) + '\u0336'
d.multiline_text((x, y + size[1] / 2), text, font=fnt, fill=color_active if active else color_inactive, anchor="mm", align="center")
y += size[1] + line_spacing
fontsize = (width + height) // 25
line_spacing = fontsize // 2
fnt = 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 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,
, fp, do_not_save_grid=False, normalize_prompt_weights=True, init_img=None, init_mask=None, keep_mask=True):
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
assert prompt is not None
torch_gc()
# start time after garbage collection (or before?)
start_time = time.time()
mem_mon = MemUsageMonitor('MemMon')
mem_mon.start()
if hasattr(model, "embedding_manager"):
load_embeddings(fp)
os.makedirs(outpath, exist_ok=True)
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
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):
current = prompt_matrix_parts[0]
for n, text in enumerate(prompt_matrix_parts[1:]):
if combination_num & (2 ** n) > 0:
current += ("" if text.strip().startswith(",") else ", ") + text
all_prompts.append(current)
n_iter = math.ceil(len(all_prompts) / batch_size)
all_seeds = len(all_prompts) * [seed]
print(f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches.")
else:
if not opt.no_verify_input:
try:
check_prompt_length(prompt, comments)
except:
import traceback
print("Error verifying input:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
all_prompts = batch_size * n_iter * [prompt]
all_seeds = [seed + x for x in range(len(all_prompts))]
precision_scope = autocast if opt.precision == "autocast" else nullcontext
output_images = []
stats = []
with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
init_data = func_init()
tic = time.time()
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 = None
if cfg_scale != 1.0:
uc = model.get_learned_conditioning(len(prompts) * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
# split the prompt if it has : for weighting
# TODO for speed it might help to have this occur when all_prompts filled??
subprompts,weights = split_weighted_subprompts(prompts[0])
# get total weight for normalizing, this gets weird if large negative values used
totalPromptWeight = sum(weights)
# sub-prompt weighting used if more than 1
if len(subprompts) > 1:
c = torch.zeros_like(uc) # i dont know if this is correct.. but it works
for i in range(0,len(subprompts)): # normalize each prompt and add it
weight = weights[i]
if normalize_prompt_weights:
weight = weight / totalPromptWeight
#print(f"{subprompts[i]} {weight*100.0}%")
# note if alpha negative, it functions same as torch.sub
c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
else: # just behave like usual
c = model.get_learned_conditioning(prompts)
shape = [opt_C, height // opt_f, width // opt_f]
# we manually generate all input noises because each one should have a specific seed
x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=seeds)
samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc, sampler_name=sampler_name)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
for i, x_sample in enumerate(x_samples_ddim):
x_sample = 255. * 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[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
x_sample = restored_img[:,:,::-1]
image = Image.fromarray(x_sample)
if init_mask:
init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
init_mask = init_mask.filter(ImageFilter.GaussianBlur(3))
init_mask = init_mask.convert('L')
init_img = init_img.convert('RGB')
image = image.convert('RGB')
image = Image.composite(init_img, image, init_mask)
filename = f"{base_count:05}-{seeds[i]}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"
if not skip_save:
image.save(os.path.join(sample_path, filename))
output_images.append(image)
base_count += 1
if (prompt_matrix or not skip_grid) 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)
grid_file = f"grid-{grid_count:05}-{seed}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.jpg"
grid.save(os.path.join(outpath, grid_file), 'jpeg', quality=100, optimize=True)
grid_count += 1
toc = time.time()
mem_max_used, mem_total = mem_mon.read_and_stop()
time_diff = time.time()-start_time
info = f"""
{prompt}
Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}{', 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, use_GFPGAN: bool, prompt_matrix: bool, skip_grid: bool, skip_save: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, normalize_prompt_weights: bool, fp):
outpath = opt.outdir or "outputs/txt2img-samples"
err = False
seed = seed_to_int(seed)
if sampler_name == 'PLMS':
sampler = PLMSSampler(model)
elif sampler_name == 'DDIM':
sampler = DDIMSampler(model)
elif sampler_name == 'k_dpm_2_a':
sampler = KDiffusionSampler(model,'dpm_2_ancestral')
elif sampler_name == 'k_dpm_2':
sampler = KDiffusionSampler(model,'dpm_2')
elif sampler_name == 'k_euler_a':
sampler = KDiffusionSampler(model,'euler_ancestral')
elif sampler_name == 'k_euler':
sampler = KDiffusionSampler(model,'euler')
elif sampler_name == 'k_heun':
sampler = KDiffusionSampler(model,'heun')
elif sampler_name == 'k_lms':
sampler = KDiffusionSampler(model,'lms')
else:
raise Exception("Unknown sampler: " + sampler_name)
def init():
pass
def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x)
return samples_ddim
try:
output_images, seed, info, stats = process_images(
outpath=outpath,
func_init=init,
func_sample=sample,
prompt=prompt,
seed=seed,
sampler_name=sampler_name,
skip_save=skip_save,
skip_grid=skip_grid,
batch_size=batch_size,
n_iter=n_iter,
steps=ddim_steps,
cfg_scale=cfg_scale,
width=width,
height=height,
prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN,
fp=fp,
normalize_prompt_weights=normalize_prompt_weights
)
del sampler
return output_images, seed, info, stats
except RuntimeError as e:
err = e
err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
stats = err_msg
return [], seed, 'err', stats
finally:
if err:
crash(err, '!!Runtime error (txt2img)!!')
class Flagging(gr.FlaggingCallback):
def setup(self, components, flagging_dir: str):
pass
def flag(self, flag_data, flag_option=None, flag_index=None, username=None):
import csv
os.makedirs("log/images", exist_ok=True)
# those must match the "txt2img" function !! + images, seed, comment, stats !! NOTE: changes to UI output must be reflected here too
prompt, ddim_steps, sampler_name, use_GFPGAN, skip_grid, skip_save, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, input_seed, height, width, normalize_prompt_weights, fp, images, seed, comment, stats = flag_data
filenames = []
with open("log/log.csv", "a", encoding="utf8", newline='') as file:
import time
import base64
at_start = file.tell() == 0
writer = csv.writer(file)
if at_start:
writer.writerow(["prompt", "seed", "width", "height", "sampler", "use_GFPGAN", "prompt_matrix", "n_iter", "n_samples", "cfg_scale", "steps", "filename"])
filename_base = str(int(time.time() * 1000))
for i, filedata in enumerate(images):
filename = "log/images/"+filename_base + ("" if len(images) == 1 else "-"+str(i+1)) + ".png"
if filedata.startswith("data:image/png;base64,"):
filedata = filedata[len("data:image/png;base64,"):]
with open(filename, "wb") as imgfile:
imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
filenames.append(filename)
writer.writerow([prompt, seed, width, height, sampler_name, use_GFPGAN, prompt_matrix, n_iter, n_samples, cfg_scale, ddim_steps, filenames[0]])
print("Logged:", filenames[0])
txt2img_interface = gr.Interface(
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 (k_lms is default k-diffusion sampler)', choices=["DDIM", "PLMS", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms'], value="k_lms"),
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='Skip grid', value=False),
gr.Checkbox(label='Skip save individual images', 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=250, 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.Textbox(label="Seed ('random' to randomize)", lines=1, value="random"),
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.Checkbox(label="Normalize Prompt Weights (ensure sum of weights add up to 1.0)", value=True),
gr.File(label = "Embeddings file for textual inversion", visible=hasattr(model, "embedding_manager")),
],
outputs=[
gr.Gallery(label="Images"),
gr.Number(label='Seed'),
gr.Textbox(label="Copy-paste generation parameters"),
gr.HTML(label='Stats'),
],
title="Stable Diffusion Text-to-Image Unified",
description="Generate images from text with Stable Diffusion",
flagging_callback=Flagging(),
theme="default",
)
def img2img(prompt: str, init_info, mask_mode, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix, loopback: bool, skip_grid: bool, skip_save: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, normalize_prompt_weights: bool, fp):
outpath = opt.outdir or "outputs/img2img-samples"
err = False
seed = seed_to_int(seed)
if sampler_name == 'DDIM':
sampler = DDIMSampler(model)
elif sampler_name == 'k_dpm_2_a':
sampler = KDiffusionSampler(model,'dpm_2_ancestral')
elif sampler_name == 'k_dpm_2':
sampler = KDiffusionSampler(model,'dpm_2')
elif sampler_name == 'k_euler_a':
sampler = KDiffusionSampler(model,'euler_ancestral')
elif sampler_name == 'k_euler':
sampler = KDiffusionSampler(model,'euler')
elif sampler_name == 'k_heun':
sampler = KDiffusionSampler(model,'heun')
elif sampler_name == 'k_lms':
sampler = KDiffusionSampler(model,'lms')
else:
raise Exception("Unknown sampler: " + sampler_name)
init_img = init_info["image"]
init_img = init_img.convert("RGB")
init_img = resize_image(resize_mode, init_img, width, height)
init_mask = init_info["mask"]
init_mask = init_mask.convert("RGB")
init_mask = resize_image(resize_mode, init_mask, width, height)
keep_mask = mask_mode == "Keep masked area"
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(denoising_strength * ddim_steps)
def init():
image = init_img.convert("RGB")
image = resize_image(resize_mode, image, width, height)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
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, sampler_name):
if sampler_name == 'k-diffusion':
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.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False)
else:
x0, = init_data
sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=0.0, verbose=False)
z_enc = sampler.stochastic_encode(x0, torch.tensor([t_enc]*batch_size).to(device))
# decode it
samples_ddim = sampler.decode(z_enc, conditioning, t_enc,
unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=unconditional_conditioning,)
return samples_ddim
try:
if loopback:
output_images, info = None, None
history = []
initial_seed = None
for i in range(n_iter):
output_images, seed, info, stats = process_images(
outpath=outpath,
func_init=init,
func_sample=sample,
prompt=prompt,
seed=seed,
sampler_name=sampler_name,
skip_save=skip_save,
skip_grid=skip_grid,
batch_size=1,
n_iter=1,
steps=ddim_steps,
cfg_scale=cfg_scale,
width=width,
height=height,
prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN,
fp=fp,
do_not_save_grid=True
)
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)
if not skip_grid:
grid_count = len(os.listdir(outpath)) - 1
grid = image_grid(history, batch_size, force_n_rows=1)
grid_file = f"grid-{grid_count:05}-{seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.jpg"
grid.save(os.path.join(outpath, grid_file), 'jpeg', quality=100, optimize=True)
output_images = history
seed = initial_seed
else:
output_images, seed, info, stats = process_images(
outpath=outpath,
func_init=init,
func_sample=sample,
prompt=prompt,
seed=seed,
sampler_name=sampler_name,
skip_save=skip_save,
skip_grid=skip_grid,
batch_size=batch_size,
n_iter=n_iter,
steps=ddim_steps,
cfg_scale=cfg_scale,
width=width,
height=height,
prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN,
fp=fp,
normalize_prompt_weights=normalize_prompt_weights,
init_img=init_img,
init_mask=init_mask,
keep_mask=keep_mask
)
del sampler
return output_images, seed, info, stats
except RuntimeError as e:
err = e
err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
stats = err_msg
return [], seed, 'err', stats
finally:
if err:
crash(err, '!!Runtime error (img2img)!!')
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(
img2img,
inputs=[
gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil", tool="sketch"),
gr.Radio(choices=["Keep masked area", "Regenerate only masked area"], label="Mask Mode", value="Keep masked area"),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
gr.Radio(label='Sampling method (k_lms is default k-diffusion sampler)', choices=["DDIM", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms'], value="k_lms"),
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.Checkbox(label='Skip grid', value=False),
gr.Checkbox(label='Skip save individual images', value=False),
gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
gr.Slider(minimum=1, maximum=250, 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.Textbox(label="Seed ('random' to randomize)", lines=1, value="random"),
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"),
gr.Checkbox(label="Normalize Prompt Weights (ensure sum of weights add up to 1.0)", value=True),
gr.File(label = "Embeddings file for textual inversion", visible=hasattr(model, "embedding_manager")),
],
outputs=[
gr.Gallery(),
gr.Number(label='Seed'),
gr.Textbox(label="Copy-paste generation parameters"),
gr.HTML(label='Stats'),
],
title="Stable Diffusion Image-to-Image Unified",
description="Generate images from images with Stable Diffusion",
allow_flagging="never",
theme="default",
)
interfaces = [
(txt2img_interface, "txt2img"),
(img2img_interface, "img2img")
]
# grabs all text up to the first occurrence of ':' as sub-prompt
# takes the value following ':' as weight
# if ':' has no value defined, defaults to 1.0
# repeats until no text remaining
# TODO this could probably be done with less code
def split_weighted_subprompts(text):
print(text)
remaining = len(text)
prompts = []
weights = []
while remaining > 0:
if ":" in text:
idx = text.index(":") # first occurrence from start
# grab up to index as sub-prompt
prompt = text[:idx]
remaining -= idx
# remove from main text
text = text[idx+1:]
# find value for weight, assume it is followed by a space or comma
idx = len(text) # default is read to end of text
if " " in text:
idx = min(idx,text.index(" ")) # want the closer idx
if "," in text:
idx = min(idx,text.index(",")) # want the closer idx
if idx != 0:
try:
weight = float(text[:idx])
except: # couldn't treat as float
print(f"Warning: '{text[:idx]}' is not a value, are you missing a space or comma after a value?")
weight = 1.0
else: # no value found
weight = 1.0
# remove from main text
remaining -= idx
text = text[idx+1:]
# append the sub-prompt and its weight
prompts.append(prompt)
weights.append(weight)
else: # no : found
if len(text) > 0: # there is still text though
# take remainder as weight 1
prompts.append(text)
weights.append(1.0)
remaining = 0
return prompts, weights
def run_GFPGAN(image, strength):
image = image.convert("RGB")
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
res = Image.fromarray(restored_img)
if strength < 1.0:
res = Image.blend(image, res, strength)
return res
if GFPGAN is not None:
interfaces.append((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"),
],
title="GFPGAN",
description="Fix faces on images",
allow_flagging="never",
theme="default",
), "GFPGAN"))
demo = gr.TabbedInterface(
interface_list=[x[0] for x in interfaces],
tab_names=[x[1] for x in interfaces],
css=("" if opt.no_progressbar_hiding else css_hide_progressbar),
theme="default",
)
demo.queue(concurrency_count=1)
demo.launch(show_error=True, server_name='0.0.0.0')