Merge branch 'master' into #1484_fix_empty_styles_pattern

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AUTOMATIC1111 2022-10-03 07:54:57 +03:00 committed by GitHub
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38 changed files with 1588 additions and 409 deletions

1
.gitignore vendored
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@ -25,3 +25,4 @@ __pycache__
/.idea
notification.mp3
/SwinIR
/textual_inversion

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@ -11,12 +11,12 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- One click install and run script (but you still must install python and git)
- Outpainting
- Inpainting
- Prompt
- Stable Diffusion upscale
- Prompt Matrix
- Stable Diffusion Upscale
- Attention, specify parts of text that the model should pay more attention to
- a man in a ((txuedo)) - will pay more attentinoto tuxedo
- a man in a (txuedo:1.21) - alternative syntax
- Loopback, run img2img procvessing multiple times
- a man in a ((tuxedo)) - will pay more attention to tuxedo
- a man in a (tuxedo:1.21) - alternative syntax
- Loopback, run img2img processing multiple times
- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
- Textual Inversion
- have as many embeddings as you want and use any names you like for them
@ -35,15 +35,15 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- 4GB video card support (also reports of 2GB working)
- Correct seeds for batches
- Prompt length validation
- get length of prompt in tokensas you type
- get a warning after geenration if some text was truncated
- get length of prompt in tokens as you type
- get a warning after generation if some text was truncated
- Generation parameters
- parameters you used to generate images are saved with that image
- in PNG chunks for PNG, in EXIF for JPEG
- can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
- can be disabled in settings
- Settings page
- Running arbitrary python code from UI (must run with commandline flag to enable)
- Running arbitrary python code from UI (must run with --allow-code to enable)
- Mouseover hints for most UI elements
- Possible to change defaults/mix/max/step values for UI elements via text config
- Random artist button
@ -113,6 +113,7 @@ The documentation was moved from this README over to the project's [wiki](https:
- LDSR - https://github.com/Hafiidz/latent-diffusion
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator

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@ -30,6 +30,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_inte
onUiUpdate(function(){
check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', 'ti_interrupt', 'ti_preview', 'ti_gallery')
})
function requestMoreProgress(id_part, id_progressbar_span, id_interrupt){

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@ -0,0 +1,8 @@
function start_training_textual_inversion(){
requestProgress('ti')
gradioApp().querySelector('#ti_error').innerHTML=''
return args_to_array(arguments)
}

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@ -199,6 +199,26 @@ let txt2img_textarea, img2img_textarea = undefined;
let wait_time = 800
let token_timeout;
function update_txt2img_tokens(...args) {
update_token_counter("txt2img_token_button")
if (args.length == 2)
return args[0]
return args;
}
function update_img2img_tokens(...args) {
update_token_counter("img2img_token_button")
if (args.length == 2)
return args[0]
return args;
}
function update_token_counter(button_id) {
if (token_timeout)
clearTimeout(token_timeout);
token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
}
function submit_prompt(event, generate_button_id) {
if (event.altKey && event.keyCode === 13) {
event.preventDefault();
@ -207,8 +227,7 @@ function submit_prompt(event, generate_button_id) {
}
}
function update_token_counter(button_id) {
if (token_timeout)
clearTimeout(token_timeout);
token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
function restart_reload(){
document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
setTimeout(function(){location.reload()},2000)
}

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@ -15,6 +15,7 @@ requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
@ -111,6 +112,9 @@ if not skip_torch_cuda_test:
if not is_installed("gfpgan"):
run_pip(f"install {gfpgan_package}", "gfpgan")
if not is_installed("clip"):
run_pip(f"install {clip_package}", "clip")
os.makedirs(dir_repos, exist_ok=True)
git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)

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@ -32,10 +32,9 @@ def enable_tf32():
errors.run(enable_tf32, "Enabling TF32")
device = get_optimal_device()
device_codeformer = cpu if has_mps else device
dtype = torch.float16
def randn(seed, shape):
# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.

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@ -73,8 +73,8 @@ def fix_model_layers(crt_model, pretrained_net):
class UpscalerESRGAN(Upscaler):
def __init__(self, dirname):
self.name = "ESRGAN"
self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
self.model_name = "ESRGAN 4x"
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
self.model_name = "ESRGAN_4x"
self.scalers = []
self.user_path = dirname
self.model_path = os.path.join(models_path, self.name)

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@ -311,7 +311,12 @@ def apply_filename_pattern(x, p, seed, prompt):
x = x.replace("[cfg]", str(p.cfg_scale))
x = x.replace("[width]", str(p.width))
x = x.replace("[height]", str(p.height))
x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "No styles", replace_spaces=False))
#currently disabled if using the save button, will work otherwise
# if enabled it will cause a bug because styles is not included in the save_files data dictionary
if hasattr(p, "styles"):
x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False))
x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)

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@ -103,6 +103,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
inpaint_full_res_padding=inpaint_full_res_padding,
inpainting_mask_invert=inpainting_mask_invert,
)
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
p.extra_generation_params["Mask blur"] = mask_blur

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@ -21,6 +21,7 @@ Category = namedtuple("Category", ["name", "topn", "items"])
re_topn = re.compile(r"\.top(\d+)\.")
class InterrogateModels:
blip_model = None
clip_model = None

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@ -5,7 +5,6 @@ import importlib
from urllib.parse import urlparse
from basicsr.utils.download_util import load_file_from_url
from modules import shared
from modules.upscaler import Upscaler
from modules.paths import script_path, models_path
@ -43,7 +42,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
for place in places:
if os.path.exists(place):
for file in glob.iglob(place + '**/**', recursive=True):
full_path = os.path.join(place, file)
full_path = file
if os.path.isdir(full_path):
continue
if len(ext_filter) != 0:
@ -121,16 +120,30 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
def load_upscalers():
sd = shared.script_path
# We can only do this 'magic' method to dynamically load upscalers if they are referenced,
# so we'll try to import any _model.py files before looking in __subclasses__
modules_dir = os.path.join(sd, "modules")
for file in os.listdir(modules_dir):
if "_model.py" in file:
model_name = file.replace("_model.py", "")
full_model = f"modules.{model_name}_model"
try:
importlib.import_module(full_model)
except:
pass
datas = []
c_o = vars(shared.cmd_opts)
for cls in Upscaler.__subclasses__():
name = cls.__name__
module_name = cls.__module__
module = importlib.import_module(module_name)
class_ = getattr(module, name)
cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
opt_string = None
try:
opt_string = shared.opts.__getattr__(cmd_name)
if cmd_name in c_o:
opt_string = c_o[cmd_name]
except:
pass
scaler = class_(opt_string)

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@ -20,7 +20,6 @@ path_dirs = [
(os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),
(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
(os.path.join(sd_path, '../latent-diffusion'), 'LDSR.py', 'LDSR', []),
(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
]

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@ -56,7 +56,7 @@ class StableDiffusionProcessing:
self.prompt: str = prompt
self.prompt_for_display: str = None
self.negative_prompt: str = (negative_prompt or "")
self.styles: str = styles
self.styles: list = styles or []
self.seed: int = seed
self.subseed: int = subseed
self.subseed_strength: float = subseed_strength
@ -271,7 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Denoising strength": getattr(p, 'denoising_strength', None),
"Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
"Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
}
generation_params.update(p.extra_generation_params)
@ -295,7 +295,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
fix_seed(p)
if p.outpath_samples is not None:
os.makedirs(p.outpath_samples, exist_ok=True)
if p.outpath_grids is not None:
os.makedirs(p.outpath_grids, exist_ok=True)
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
@ -323,7 +326,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
if os.path.exists(cmd_opts.embeddings_dir):
model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
model_hijack.embedding_db.load_textual_inversion_embeddings()
infotexts = []
output_images = []

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@ -162,6 +162,40 @@ class ScriptRunner:
return processed
def reload_sources(self):
for si, script in list(enumerate(self.scripts)):
with open(script.filename, "r", encoding="utf8") as file:
args_from = script.args_from
args_to = script.args_to
filename = script.filename
text = file.read()
from types import ModuleType
compiled = compile(text, filename, 'exec')
module = ModuleType(script.filename)
exec(compiled, module.__dict__)
for key, script_class in module.__dict__.items():
if type(script_class) == type and issubclass(script_class, Script):
self.scripts[si] = script_class()
self.scripts[si].filename = filename
self.scripts[si].args_from = args_from
self.scripts[si].args_to = args_to
scripts_txt2img = ScriptRunner()
scripts_img2img = ScriptRunner()
def reload_script_body_only():
scripts_txt2img.reload_sources()
scripts_img2img.reload_sources()
def reload_scripts(basedir):
global scripts_txt2img, scripts_img2img
scripts_data.clear()
load_scripts(basedir)
scripts_txt2img = ScriptRunner()
scripts_img2img = ScriptRunner()

90
modules/scunet_model.py Normal file
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@ -0,0 +1,90 @@
import os.path
import sys
import traceback
import PIL.Image
import numpy as np
import torch
from basicsr.utils.download_util import load_file_from_url
import modules.upscaler
from modules import shared, modelloader
from modules.paths import models_path
from modules.scunet_model_arch import SCUNet as net
class UpscalerScuNET(modules.upscaler.Upscaler):
def __init__(self, dirname):
self.name = "ScuNET"
self.model_path = os.path.join(models_path, self.name)
self.model_name = "ScuNET GAN"
self.model_name2 = "ScuNET PSNR"
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
self.user_path = dirname
super().__init__()
model_paths = self.find_models(ext_filter=[".pth"])
scalers = []
add_model2 = True
for file in model_paths:
if "http" in file:
name = self.model_name
else:
name = modelloader.friendly_name(file)
if name == self.model_name2 or file == self.model_url2:
add_model2 = False
try:
scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
scalers.append(scaler_data)
except Exception:
print(f"Error loading ScuNET model: {file}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
if add_model2:
scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
scalers.append(scaler_data2)
self.scalers = scalers
def do_upscale(self, img: PIL.Image, selected_file):
torch.cuda.empty_cache()
model = self.load_model(selected_file)
if model is None:
return img
device = shared.device
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(shared.device)
img = img.to(device)
with torch.no_grad():
output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
output = 255. * np.moveaxis(output, 0, 2)
output = output.astype(np.uint8)
output = output[:, :, ::-1]
torch.cuda.empty_cache()
return PIL.Image.fromarray(output, 'RGB')
def load_model(self, path: str):
device = shared.device
if "http" in path:
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
progress=True)
else:
filename = path
if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
return None
model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
model.load_state_dict(torch.load(filename), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
return model

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@ -0,0 +1,265 @@
# -*- coding: utf-8 -*-
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from einops.layers.torch import Rearrange
from timm.models.layers import trunc_normal_, DropPath
class WMSA(nn.Module):
""" Self-attention module in Swin Transformer
"""
def __init__(self, input_dim, output_dim, head_dim, window_size, type):
super(WMSA, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.head_dim = head_dim
self.scale = self.head_dim ** -0.5
self.n_heads = input_dim // head_dim
self.window_size = window_size
self.type = type
self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
self.relative_position_params = nn.Parameter(
torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
self.linear = nn.Linear(self.input_dim, self.output_dim)
trunc_normal_(self.relative_position_params, std=.02)
self.relative_position_params = torch.nn.Parameter(
self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
2).transpose(
0, 1))
def generate_mask(self, h, w, p, shift):
""" generating the mask of SW-MSA
Args:
shift: shift parameters in CyclicShift.
Returns:
attn_mask: should be (1 1 w p p),
"""
# supporting sqaure.
attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
if self.type == 'W':
return attn_mask
s = p - shift
attn_mask[-1, :, :s, :, s:, :] = True
attn_mask[-1, :, s:, :, :s, :] = True
attn_mask[:, -1, :, :s, :, s:] = True
attn_mask[:, -1, :, s:, :, :s] = True
attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
return attn_mask
def forward(self, x):
""" Forward pass of Window Multi-head Self-attention module.
Args:
x: input tensor with shape of [b h w c];
attn_mask: attention mask, fill -inf where the value is True;
Returns:
output: tensor shape [b h w c]
"""
if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
h_windows = x.size(1)
w_windows = x.size(2)
# sqaure validation
# assert h_windows == w_windows
x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
qkv = self.embedding_layer(x)
q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
# Adding learnable relative embedding
sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
# Using Attn Mask to distinguish different subwindows.
if self.type != 'W':
attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
sim = sim.masked_fill_(attn_mask, float("-inf"))
probs = nn.functional.softmax(sim, dim=-1)
output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
output = rearrange(output, 'h b w p c -> b w p (h c)')
output = self.linear(output)
output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
dims=(1, 2))
return output
def relative_embedding(self):
cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
# negative is allowed
return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
class Block(nn.Module):
def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
""" SwinTransformer Block
"""
super(Block, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
assert type in ['W', 'SW']
self.type = type
if input_resolution <= window_size:
self.type = 'W'
self.ln1 = nn.LayerNorm(input_dim)
self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.ln2 = nn.LayerNorm(input_dim)
self.mlp = nn.Sequential(
nn.Linear(input_dim, 4 * input_dim),
nn.GELU(),
nn.Linear(4 * input_dim, output_dim),
)
def forward(self, x):
x = x + self.drop_path(self.msa(self.ln1(x)))
x = x + self.drop_path(self.mlp(self.ln2(x)))
return x
class ConvTransBlock(nn.Module):
def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
""" SwinTransformer and Conv Block
"""
super(ConvTransBlock, self).__init__()
self.conv_dim = conv_dim
self.trans_dim = trans_dim
self.head_dim = head_dim
self.window_size = window_size
self.drop_path = drop_path
self.type = type
self.input_resolution = input_resolution
assert self.type in ['W', 'SW']
if self.input_resolution <= self.window_size:
self.type = 'W'
self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
self.type, self.input_resolution)
self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
self.conv_block = nn.Sequential(
nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
nn.ReLU(True),
nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
)
def forward(self, x):
conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
conv_x = self.conv_block(conv_x) + conv_x
trans_x = Rearrange('b c h w -> b h w c')(trans_x)
trans_x = self.trans_block(trans_x)
trans_x = Rearrange('b h w c -> b c h w')(trans_x)
res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
x = x + res
return x
class SCUNet(nn.Module):
# def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
super(SCUNet, self).__init__()
if config is None:
config = [2, 2, 2, 2, 2, 2, 2]
self.config = config
self.dim = dim
self.head_dim = 32
self.window_size = 8
# drop path rate for each layer
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
begin = 0
self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution)
for i in range(config[0])] + \
[nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
begin += config[0]
self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 2)
for i in range(config[1])] + \
[nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
begin += config[1]
self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 4)
for i in range(config[2])] + \
[nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
begin += config[2]
self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 8)
for i in range(config[3])]
begin += config[3]
self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
[ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 4)
for i in range(config[4])]
begin += config[4]
self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
[ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 2)
for i in range(config[5])]
begin += config[5]
self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
[ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution)
for i in range(config[6])]
self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
self.m_head = nn.Sequential(*self.m_head)
self.m_down1 = nn.Sequential(*self.m_down1)
self.m_down2 = nn.Sequential(*self.m_down2)
self.m_down3 = nn.Sequential(*self.m_down3)
self.m_body = nn.Sequential(*self.m_body)
self.m_up3 = nn.Sequential(*self.m_up3)
self.m_up2 = nn.Sequential(*self.m_up2)
self.m_up1 = nn.Sequential(*self.m_up1)
self.m_tail = nn.Sequential(*self.m_tail)
# self.apply(self._init_weights)
def forward(self, x0):
h, w = x0.size()[-2:]
paddingBottom = int(np.ceil(h / 64) * 64 - h)
paddingRight = int(np.ceil(w / 64) * 64 - w)
x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
x1 = self.m_head(x0)
x2 = self.m_down1(x1)
x3 = self.m_down2(x2)
x4 = self.m_down3(x3)
x = self.m_body(x4)
x = self.m_up3(x + x4)
x = self.m_up2(x + x3)
x = self.m_up1(x + x2)
x = self.m_tail(x + x1)
x = x[..., :h, :w]
return x
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)

View File

@ -6,244 +6,41 @@ import torch
import numpy as np
from torch import einsum
from modules import prompt_parser
import modules.textual_inversion.textual_inversion
from modules import prompt_parser, devices, sd_hijack_optimizations, shared
from modules.shared import opts, device, cmd_opts
from ldm.util import default
from einops import rearrange
import ldm.modules.attention
import ldm.modules.diffusionmodules.model
# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
def split_cross_attention_forward_v1(self, x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
del context, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
for i in range(0, q.shape[0], 2):
end = i + 2
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
s1 *= self.scale
s2 = s1.softmax(dim=-1)
del s1
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
del s2
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
return self.to_out(r2)
attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
# taken from https://github.com/Doggettx/stable-diffusion
def split_cross_attention_forward(self, x, context=None, mask=None):
h = self.heads
def apply_optimizations():
if cmd_opts.opt_split_attention_v1:
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
q_in = self.to_q(x)
context = default(context, x)
k_in = self.to_k(context) * self.scale
v_in = self.to_v(context)
del context, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
def undo_optimizations():
ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
modifier = 3 if q.element_size() == 2 else 2.5
mem_required = tensor_size * modifier
steps = 1
if mem_required > mem_free_total:
steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
if steps > 64:
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
s2 = s1.softmax(dim=-1, dtype=q.dtype)
del s1
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
del s2
del q, k, v
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
return self.to_out(r2)
def nonlinearity_hijack(x):
# swish
t = torch.sigmoid(x)
x *= t
del t
return x
def cross_attention_attnblock_forward(self, x):
h_ = x
h_ = self.norm(h_)
q1 = self.q(h_)
k1 = self.k(h_)
v = self.v(h_)
# compute attention
b, c, h, w = q1.shape
q2 = q1.reshape(b, c, h*w)
del q1
q = q2.permute(0, 2, 1) # b,hw,c
del q2
k = k1.reshape(b, c, h*w) # b,c,hw
del k1
h_ = torch.zeros_like(k, device=q.device)
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
mem_required = tensor_size * 2.5
steps = 1
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w2 = w1 * (int(c)**(-0.5))
del w1
w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
del w2
# attend to values
v1 = v.reshape(b, c, h*w)
w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
del w3
h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
del v1, w4
h2 = h_.reshape(b, c, h, w)
del h_
h3 = self.proj_out(h2)
del h2
h3 += x
return h3
class StableDiffusionModelHijack:
ids_lookup = {}
word_embeddings = {}
word_embeddings_checksums = {}
fixes = None
comments = []
dir_mtime = None
layers = None
circular_enabled = False
clip = None
def load_textual_inversion_embeddings(self, dirname, model):
mt = os.path.getmtime(dirname)
if self.dir_mtime is not None and mt <= self.dir_mtime:
return
self.dir_mtime = mt
self.ids_lookup.clear()
self.word_embeddings.clear()
tokenizer = model.cond_stage_model.tokenizer
def const_hash(a):
r = 0
for v in a:
r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
return r
def process_file(path, filename):
name = os.path.splitext(filename)[0]
data = torch.load(path, map_location="cpu")
# textual inversion embeddings
if 'string_to_param' in data:
param_dict = data['string_to_param']
if hasattr(param_dict, '_parameters'):
param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1]
# diffuser concepts
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
emb = next(iter(data.values()))
if len(emb.shape) == 1:
emb = emb.unsqueeze(0)
self.word_embeddings[name] = emb.detach().to(device)
self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1)*100)&0xffff:04x}'
ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
first_id = ids[0]
if first_id not in self.ids_lookup:
self.ids_lookup[first_id] = []
self.ids_lookup[first_id].append((ids, name))
for fn in os.listdir(dirname):
try:
fullfn = os.path.join(dirname, fn)
if os.stat(fullfn).st_size == 0:
continue
process_file(fullfn, fn)
except Exception:
print(f"Error loading emedding {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
continue
print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
def hijack(self, m):
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
@ -253,12 +50,7 @@ class StableDiffusionModelHijack:
self.clip = m.cond_stage_model
if cmd_opts.opt_split_attention_v1:
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack
ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
apply_optimizations()
def flatten(el):
flattened = [flatten(children) for children in el.children()]
@ -296,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
def __init__(self, wrapped, hijack):
super().__init__()
self.wrapped = wrapped
self.hijack = hijack
self.hijack: StableDiffusionModelHijack = hijack
self.tokenizer = wrapped.tokenizer
self.max_length = wrapped.max_length
self.token_mults = {}
@ -317,7 +109,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
if mult != 1.0:
self.token_mults[ident] = mult
def tokenize_line(self, line, used_custom_terms, hijack_comments):
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
@ -339,28 +130,19 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
while i < len(tokens):
token = tokens[i]
possible_matches = self.hijack.ids_lookup.get(token, None)
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
if possible_matches is None:
remade_tokens.append(token)
multipliers.append(weight)
else:
found = False
for ids, word in possible_matches:
if tokens[i:i + len(ids)] == ids:
emb_len = int(self.hijack.word_embeddings[word].shape[0])
fixes.append((len(remade_tokens), word))
remade_tokens += [0] * emb_len
multipliers += [weight] * emb_len
i += len(ids) - 1
found = True
used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
break
if not found:
if embedding is None:
remade_tokens.append(token)
multipliers.append(weight)
i += 1
else:
emb_len = int(embedding.vec.shape[0])
fixes.append((len(remade_tokens), embedding))
remade_tokens += [0] * emb_len
multipliers += [weight] * emb_len
used_custom_terms.append((embedding.name, embedding.checksum()))
i += embedding_length_in_tokens
if len(remade_tokens) > maxlen - 2:
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
@ -431,32 +213,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
while i < len(tokens):
token = tokens[i]
possible_matches = self.hijack.ids_lookup.get(token, None)
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
if mult_change is not None:
mult *= mult_change
elif possible_matches is None:
i += 1
elif embedding is None:
remade_tokens.append(token)
multipliers.append(mult)
i += 1
else:
found = False
for ids, word in possible_matches:
if tokens[i:i+len(ids)] == ids:
emb_len = int(self.hijack.word_embeddings[word].shape[0])
fixes.append((len(remade_tokens), word))
emb_len = int(embedding.vec.shape[0])
fixes.append((len(remade_tokens), embedding))
remade_tokens += [0] * emb_len
multipliers += [mult] * emb_len
i += len(ids) - 1
found = True
used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
break
if not found:
remade_tokens.append(token)
multipliers.append(mult)
i += 1
used_custom_terms.append((embedding.name, embedding.checksum()))
i += embedding_length_in_tokens
if len(remade_tokens) > maxlen - 2:
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
@ -464,6 +237,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
overflowing_words = [vocab.get(int(x), "") for x in ovf]
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
token_count = len(remade_tokens)
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
@ -484,7 +258,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
else:
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
self.hijack.fixes = hijack_fixes
self.hijack.comments = hijack_comments
@ -517,15 +290,20 @@ class EmbeddingsWithFixes(torch.nn.Module):
inputs_embeds = self.wrapped(input_ids)
if batch_fixes is not None:
for fixes, tensor in zip(batch_fixes, inputs_embeds):
for offset, word in fixes:
emb = self.embeddings.word_embeddings[word]
emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
tensor[offset+1:offset+1+emb_len] = self.embeddings.word_embeddings[word][0:emb_len]
if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
return inputs_embeds
vecs = []
for fixes, tensor in zip(batch_fixes, inputs_embeds):
for offset, embedding in fixes:
emb = embedding.vec
emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
vecs.append(tensor)
return torch.stack(vecs)
def add_circular_option_to_conv_2d():
conv2d_constructor = torch.nn.Conv2d.__init__

View File

@ -0,0 +1,164 @@
import math
import torch
from torch import einsum
from ldm.util import default
from einops import rearrange
# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
def split_cross_attention_forward_v1(self, x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
del context, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
for i in range(0, q.shape[0], 2):
end = i + 2
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
s1 *= self.scale
s2 = s1.softmax(dim=-1)
del s1
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
del s2
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
return self.to_out(r2)
# taken from https://github.com/Doggettx/stable-diffusion
def split_cross_attention_forward(self, x, context=None, mask=None):
h = self.heads
q_in = self.to_q(x)
context = default(context, x)
k_in = self.to_k(context) * self.scale
v_in = self.to_v(context)
del context, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
modifier = 3 if q.element_size() == 2 else 2.5
mem_required = tensor_size * modifier
steps = 1
if mem_required > mem_free_total:
steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
if steps > 64:
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
s2 = s1.softmax(dim=-1, dtype=q.dtype)
del s1
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
del s2
del q, k, v
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
return self.to_out(r2)
def nonlinearity_hijack(x):
# swish
t = torch.sigmoid(x)
x *= t
del t
return x
def cross_attention_attnblock_forward(self, x):
h_ = x
h_ = self.norm(h_)
q1 = self.q(h_)
k1 = self.k(h_)
v = self.v(h_)
# compute attention
b, c, h, w = q1.shape
q2 = q1.reshape(b, c, h*w)
del q1
q = q2.permute(0, 2, 1) # b,hw,c
del q2
k = k1.reshape(b, c, h*w) # b,c,hw
del k1
h_ = torch.zeros_like(k, device=q.device)
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
mem_required = tensor_size * 2.5
steps = 1
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w2 = w1 * (int(c)**(-0.5))
del w1
w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
del w2
# attend to values
v1 = v.reshape(b, c, h*w)
w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
del w3
h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
del v1, w4
h2 = h_.reshape(b, c, h, w)
del h_
h3 = self.proj_out(h2)
del h2
h3 += x
return h3

View File

@ -8,14 +8,11 @@ from omegaconf import OmegaConf
from ldm.util import instantiate_from_config
from modules import shared, modelloader
from modules import shared, modelloader, devices
from modules.paths import models_path
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(models_path, model_dir))
model_name = "sd-v1-4.ckpt"
model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"
user_dir = None
CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
checkpoints_list = {}
@ -30,12 +27,10 @@ except Exception:
pass
def setup_model(dirname):
global user_dir
user_dir = dirname
def setup_model():
if not os.path.exists(model_path):
os.makedirs(model_path)
checkpoints_list.clear()
list_models()
@ -45,13 +40,13 @@ def checkpoint_tiles():
def list_models():
checkpoints_list.clear()
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=user_dir, ext_filter=[".ckpt"], download_name=model_name)
model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
def modeltitle(path, shorthash):
abspath = os.path.abspath(path)
if user_dir is not None and abspath.startswith(user_dir):
name = abspath.replace(user_dir, '')
if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
elif abspath.startswith(model_path):
name = abspath.replace(model_path, '')
else:
@ -69,7 +64,7 @@ def list_models():
h = model_hash(cmd_ckpt)
title, short_model_name = modeltitle(cmd_ckpt, h)
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
shared.opts.sd_model_checkpoint = title
shared.opts.data['sd_model_checkpoint'] = title
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
for filename in model_list:
@ -106,7 +101,10 @@ def select_checkpoint():
if len(checkpoints_list) == 0:
print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
if shared.cmd_opts.ckpt is not None:
print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
print(f" - directory {model_path}", file=sys.stderr)
if shared.cmd_opts.ckpt_dir is not None:
print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
exit(1)
@ -134,6 +132,8 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
if not shared.cmd_opts.no_half:
model.half()
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
model.sd_model_hash = sd_model_hash
model.sd_model_checkpint = checkpoint_file

View File

@ -77,7 +77,9 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs):
state.sampling_steps = len(sequence)
state.sampling_step = 0
for x in tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs):
seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
for x in seq:
if state.interrupted:
break
@ -207,7 +209,9 @@ def extended_trange(sampler, count, *args, **kwargs):
state.sampling_steps = count
state.sampling_step = 0
for x in tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs):
seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
for x in seq:
if state.interrupted:
break

View File

@ -40,6 +40,7 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi
parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET'))
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
@ -57,6 +58,9 @@ parser.add_argument("--opt-channelslast", action='store_true', help="change memo
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
cmd_opts = parser.parse_args()
device = get_optimal_device()
@ -78,6 +82,7 @@ class State:
current_latent = None
current_image = None
current_image_sampling_step = 0
textinfo = None
def interrupt(self):
self.interrupted = True
@ -88,7 +93,7 @@ class State:
self.current_image_sampling_step = 0
def get_job_timestamp(self):
return datetime.datetime.now().strftime("%Y%m%d%H%M%S")
return datetime.datetime.now().strftime("%Y%m%d%H%M%S") # shouldn't this return job_timestamp?
state = State()
@ -165,9 +170,10 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
"save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
"grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"),
"grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
"directories_filename_pattern": OptionInfo("", "Directory name pattern"),
"directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
}))
options_templates.update(options_section(('upscaling', "Upscaling"), {
@ -318,14 +324,14 @@ class TotalTQDM:
)
def update(self):
if not opts.multiple_tqdm:
if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
return
if self._tqdm is None:
self.reset()
self._tqdm.update()
def updateTotal(self, new_total):
if not opts.multiple_tqdm:
if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
return
if self._tqdm is None:
self.reset()

View File

@ -5,6 +5,7 @@ import numpy as np
import torch
from PIL import Image
from basicsr.utils.download_util import load_file_from_url
from tqdm import tqdm
from modules import modelloader
from modules.paths import models_path
@ -122,6 +123,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
W = torch.zeros_like(E, dtype=torch.half, device=device)
with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
for h_idx in h_idx_list:
for w_idx in w_idx_list:
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
@ -134,6 +136,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
W[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch_mask)
pbar.update(1)
output = E.div_(W)
return output

View File

@ -0,0 +1,78 @@
import os
import numpy as np
import PIL
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
import random
import tqdm
from modules import devices
class PersonalizedBase(Dataset):
def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
self.placeholder_token = placeholder_token
self.size = size
self.width = width
self.height = height
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
self.dataset = []
with open(template_file, "r") as file:
lines = [x.strip() for x in file.readlines()]
self.lines = lines
assert data_root, 'dataset directory not specified'
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
print("Preparing dataset...")
for path in tqdm.tqdm(self.image_paths):
image = Image.open(path)
image = image.convert('RGB')
image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
filename = os.path.basename(path)
filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
filename_tokens = [token for token in filename_tokens if token.isalpha()]
npimage = np.array(image).astype(np.uint8)
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
torchdata = torch.moveaxis(torchdata, 2, 0)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
init_latent = init_latent.to(devices.cpu)
self.dataset.append((init_latent, filename_tokens))
self.length = len(self.dataset) * repeats
self.initial_indexes = np.arange(self.length) % len(self.dataset)
self.indexes = None
self.shuffle()
def shuffle(self):
self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
def __len__(self):
return self.length
def __getitem__(self, i):
if i % len(self.dataset) == 0:
self.shuffle()
index = self.indexes[i % len(self.indexes)]
x, filename_tokens = self.dataset[index]
text = random.choice(self.lines)
text = text.replace("[name]", self.placeholder_token)
text = text.replace("[filewords]", ' '.join(filename_tokens))
return x, text

View File

@ -0,0 +1,75 @@
import os
from PIL import Image, ImageOps
import tqdm
from modules import shared, images
def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
size = 512
src = os.path.abspath(process_src)
dst = os.path.abspath(process_dst)
assert src != dst, 'same directory specified as source and desitnation'
os.makedirs(dst, exist_ok=True)
files = os.listdir(src)
shared.state.textinfo = "Preprocessing..."
shared.state.job_count = len(files)
if process_caption:
shared.interrogator.load()
def save_pic_with_caption(image, index):
if process_caption:
caption = "-" + shared.interrogator.generate_caption(image)
else:
caption = ""
image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
subindex[0] += 1
def save_pic(image, index):
save_pic_with_caption(image, index)
if process_flip:
save_pic_with_caption(ImageOps.mirror(image), index)
for index, imagefile in enumerate(tqdm.tqdm(files)):
subindex = [0]
filename = os.path.join(src, imagefile)
img = Image.open(filename).convert("RGB")
if shared.state.interrupted:
break
ratio = img.height / img.width
is_tall = ratio > 1.35
is_wide = ratio < 1 / 1.35
if process_split and is_tall:
img = img.resize((size, size * img.height // img.width))
top = img.crop((0, 0, size, size))
save_pic(top, index)
bot = img.crop((0, img.height - size, size, img.height))
save_pic(bot, index)
elif process_split and is_wide:
img = img.resize((size * img.width // img.height, size))
left = img.crop((0, 0, size, size))
save_pic(left, index)
right = img.crop((img.width - size, 0, img.width, size))
save_pic(right, index)
else:
img = images.resize_image(1, img, size, size)
save_pic(img, index)
shared.state.nextjob()
if process_caption:
shared.interrogator.send_blip_to_ram()

View File

@ -0,0 +1,271 @@
import os
import sys
import traceback
import torch
import tqdm
import html
import datetime
from modules import shared, devices, sd_hijack, processing, sd_models
import modules.textual_inversion.dataset
class Embedding:
def __init__(self, vec, name, step=None):
self.vec = vec
self.name = name
self.step = step
self.cached_checksum = None
self.sd_checkpoint = None
self.sd_checkpoint_name = None
def save(self, filename):
embedding_data = {
"string_to_token": {"*": 265},
"string_to_param": {"*": self.vec},
"name": self.name,
"step": self.step,
"sd_checkpoint": self.sd_checkpoint,
"sd_checkpoint_name": self.sd_checkpoint_name,
}
torch.save(embedding_data, filename)
def checksum(self):
if self.cached_checksum is not None:
return self.cached_checksum
def const_hash(a):
r = 0
for v in a:
r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
return r
self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
return self.cached_checksum
class EmbeddingDatabase:
def __init__(self, embeddings_dir):
self.ids_lookup = {}
self.word_embeddings = {}
self.dir_mtime = None
self.embeddings_dir = embeddings_dir
def register_embedding(self, embedding, model):
self.word_embeddings[embedding.name] = embedding
ids = model.cond_stage_model.tokenizer([embedding.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] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)
return embedding
def load_textual_inversion_embeddings(self):
mt = os.path.getmtime(self.embeddings_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()
def process_file(path, filename):
name = os.path.splitext(filename)[0]
data = torch.load(path, map_location="cpu")
# textual inversion embeddings
if 'string_to_param' in data:
param_dict = data['string_to_param']
if hasattr(param_dict, '_parameters'):
param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1]
# diffuser concepts
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
emb = next(iter(data.values()))
if len(emb.shape) == 1:
emb = emb.unsqueeze(0)
else:
raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
vec = emb.detach().to(devices.device, dtype=torch.float32)
embedding = Embedding(vec, name)
embedding.step = data.get('step', None)
embedding.sd_checkpoint = data.get('hash', None)
embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
self.register_embedding(embedding, shared.sd_model)
for fn in os.listdir(self.embeddings_dir):
try:
fullfn = os.path.join(self.embeddings_dir, fn)
if os.stat(fullfn).st_size == 0:
continue
process_file(fullfn, fn)
except Exception:
print(f"Error loading emedding {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
continue
print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
def find_embedding_at_position(self, tokens, offset):
token = tokens[offset]
possible_matches = self.ids_lookup.get(token, None)
if possible_matches is None:
return None, None
for ids, embedding in possible_matches:
if tokens[offset:offset + len(ids)] == ids:
return embedding, len(ids)
return None, None
def create_embedding(name, num_vectors_per_token, init_text='*'):
cond_model = shared.sd_model.cond_stage_model
embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
for i in range(num_vectors_per_token):
vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
assert not os.path.exists(fn), f"file {fn} already exists"
embedding = Embedding(vec, name)
embedding.step = 0
embedding.save(fn)
return fn
def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
assert embedding_name, 'embedding not selected'
shared.state.textinfo = "Initializing textual inversion training..."
shared.state.job_count = steps
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name)
if save_embedding_every > 0:
embedding_dir = os.path.join(log_directory, "embeddings")
os.makedirs(embedding_dir, exist_ok=True)
else:
embedding_dir = None
if create_image_every > 0:
images_dir = os.path.join(log_directory, "images")
os.makedirs(images_dir, exist_ok=True)
else:
images_dir = None
cond_model = shared.sd_model.cond_stage_model
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
with torch.autocast("cuda"):
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
hijack = sd_hijack.model_hijack
embedding = hijack.embedding_db.word_embeddings[embedding_name]
embedding.vec.requires_grad = True
optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
losses = torch.zeros((32,))
last_saved_file = "<none>"
last_saved_image = "<none>"
ititial_step = embedding.step or 0
if ititial_step > steps:
return embedding, filename
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, (x, text) in pbar:
embedding.step = i + ititial_step
if embedding.step > steps:
break
if shared.state.interrupted:
break
with torch.autocast("cuda"):
c = cond_model([text])
x = x.to(devices.device)
loss = shared.sd_model(x.unsqueeze(0), c)[0]
del x
losses[embedding.step % losses.shape[0]] = loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
pbar.set_description(f"loss: {losses.mean():.7f}")
if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
embedding.save(last_saved_file)
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
prompt=text,
steps=20,
do_not_save_grid=True,
do_not_save_samples=True,
)
processed = processing.process_images(p)
image = processed.images[0]
shared.state.current_image = image
image.save(last_saved_image)
last_saved_image += f", prompt: {text}"
shared.state.job_no = embedding.step
shared.state.textinfo = f"""
<p>
Loss: {losses.mean():.7f}<br/>
Step: {embedding.step}<br/>
Last prompt: {html.escape(text)}<br/>
Last saved embedding: {html.escape(last_saved_file)}<br/>
Last saved image: {html.escape(last_saved_image)}<br/>
</p>
"""
checkpoint = sd_models.select_checkpoint()
embedding.sd_checkpoint = checkpoint.hash
embedding.sd_checkpoint_name = checkpoint.model_name
embedding.cached_checksum = None
embedding.save(filename)
return embedding, filename

View File

@ -0,0 +1,40 @@
import html
import gradio as gr
import modules.textual_inversion.textual_inversion
import modules.textual_inversion.preprocess
from modules import sd_hijack, shared
def create_embedding(name, initialization_text, nvpt):
filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
def preprocess(*args):
modules.textual_inversion.preprocess.preprocess(*args)
return "Preprocessing finished.", ""
def train_embedding(*args):
try:
sd_hijack.undo_optimizations()
embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
res = f"""
Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.
Embedding saved to {html.escape(filename)}
"""
return res, ""
except Exception:
raise
finally:
sd_hijack.apply_optimizations()

View File

@ -34,7 +34,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
denoising_strength=denoising_strength if enable_hr else None,
)
if cmd_opts.enable_console_prompts:
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
processed = modules.scripts.scripts_txt2img.run(p, *args)
if processed is None:

View File

@ -11,6 +11,7 @@ import time
import traceback
import platform
import subprocess as sp
from functools import reduce
import numpy as np
import torch
@ -21,6 +22,7 @@ import gradio as gr
import gradio.utils
import gradio.routes
from modules import sd_hijack
from modules.paths import script_path
from modules.shared import opts, cmd_opts
import modules.shared as shared
@ -32,6 +34,9 @@ import modules.gfpgan_model
import modules.codeformer_model
import modules.styles
import modules.generation_parameters_copypaste
from modules.prompt_parser import get_learned_conditioning_prompt_schedules
from modules.images import apply_filename_pattern, get_next_sequence_number
import modules.textual_inversion.ui
# 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()
@ -95,13 +100,30 @@ def send_gradio_gallery_to_image(x):
def save_files(js_data, images, index):
import csv
os.makedirs(opts.outdir_save, exist_ok=True)
filenames = []
#quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it
class MyObject:
def __init__(self, d=None):
if d is not None:
for key, value in d.items():
setattr(self, key, value)
data = json.loads(js_data)
p = MyObject(data)
path = opts.outdir_save
save_to_dirs = opts.use_save_to_dirs_for_ui
if save_to_dirs:
dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt)
path = os.path.join(opts.outdir_save, dirname)
os.makedirs(path, exist_ok=True)
if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
images = [images[index]]
infotexts = [data["infotexts"][index]]
else:
@ -113,11 +135,20 @@ def save_files(js_data, images, index):
if at_start:
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
filename_base = str(int(time.time() * 1000))
file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
if file_decoration != "":
file_decoration = "-" + file_decoration.lower()
file_decoration = apply_filename_pattern(file_decoration, p, p.seed, p.prompt)
truncated = (file_decoration[:240] + '..') if len(file_decoration) > 240 else file_decoration
filename_base = truncated
extension = opts.samples_format.lower()
basecount = get_next_sequence_number(path, "")
for i, filedata in enumerate(images):
filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + f".{extension}"
filepath = os.path.join(opts.outdir_save, filename)
file_number = f"{basecount+i:05}"
filename = file_number + filename_base + f".{extension}"
filepath = os.path.join(path, filename)
if filedata.startswith("data:image/png;base64,"):
filedata = filedata[len("data:image/png;base64,"):]
@ -142,8 +173,8 @@ def save_files(js_data, images, index):
return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
def wrap_gradio_call(func):
def f(*args, **kwargs):
def wrap_gradio_call(func, extra_outputs=None):
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
if run_memmon:
shared.mem_mon.monitor()
@ -159,7 +190,10 @@ def wrap_gradio_call(func):
shared.state.job = ""
shared.state.job_count = 0
res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
if extra_outputs_array is None:
extra_outputs_array = [None, '']
res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
elapsed = time.perf_counter() - t
@ -179,6 +213,7 @@ def wrap_gradio_call(func):
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
shared.state.interrupted = False
shared.state.job_count = 0
return tuple(res)
@ -187,7 +222,7 @@ def wrap_gradio_call(func):
def check_progress_call(id_part):
if shared.state.job_count == 0:
return "", gr_show(False), gr_show(False)
return "", gr_show(False), gr_show(False), gr_show(False)
progress = 0
@ -219,13 +254,19 @@ def check_progress_call(id_part):
else:
preview_visibility = gr_show(True)
return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
if shared.state.textinfo is not None:
textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
else:
textinfo_result = gr_show(False)
return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
def check_progress_call_initial(id_part):
shared.state.job_count = -1
shared.state.current_latent = None
shared.state.current_image = None
shared.state.textinfo = None
return check_progress_call(id_part)
@ -345,8 +386,11 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
outputs=[seed, dummy_component]
)
def update_token_counter(text):
tokens, token_count, max_length = model_hijack.tokenize(text)
def update_token_counter(text, steps):
prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
prompts = [prompt_text for step,prompt_text in flat_prompts]
tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
style_class = ' class="red"' if (token_count > max_length) else ""
return f"<span {style_class}>{token_count}/{max_length}</span>"
@ -364,8 +408,7 @@ def create_toprow(is_img2img):
roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
paste = gr.Button(value=paste_symbol, elem_id="paste")
token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
hidden_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
hidden_button.click(fn=update_token_counter, inputs=[prompt], outputs=[token_counter])
token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
with gr.Column(scale=10, elem_id="style_pos_col"):
prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
@ -396,16 +439,19 @@ def create_toprow(is_img2img):
prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
save_style = gr.Button('Create style', elem_id="style_create")
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
def setup_progressbar(progressbar, preview, id_part):
def setup_progressbar(progressbar, preview, id_part, textinfo=None):
if textinfo is None:
textinfo = gr.HTML(visible=False)
check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
check_progress.click(
fn=lambda: check_progress_call(id_part),
show_progress=False,
inputs=[],
outputs=[progressbar, preview, preview],
outputs=[progressbar, preview, preview, textinfo],
)
check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
@ -413,13 +459,16 @@ def setup_progressbar(progressbar, preview, id_part):
fn=lambda: check_progress_call_initial(id_part),
show_progress=False,
inputs=[],
outputs=[progressbar, preview, preview],
outputs=[progressbar, preview, preview, textinfo],
)
def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
def create_ui(wrap_gradio_gpu_call):
import modules.img2img
import modules.txt2img
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
dummy_component = gr.Label(visible=False)
with gr.Row(elem_id='txt2img_progress_row'):
@ -483,7 +532,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
txt2img_args = dict(
fn=txt2img,
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
_js="submit",
inputs=[
txt2img_prompt,
@ -539,6 +588,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
roll.click(
fn=roll_artist,
_js="update_txt2img_tokens",
inputs=[
txt2img_prompt,
],
@ -567,9 +617,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
]
modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
with gr.Blocks(analytics_enabled=False) as img2img_interface:
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste = create_toprow(is_img2img=True)
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
with gr.Row(elem_id='img2img_progress_row'):
with gr.Column(scale=1):
@ -675,7 +726,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
)
img2img_args = dict(
fn=img2img,
fn=wrap_gradio_gpu_call(modules.img2img.img2img),
_js="submit_img2img",
inputs=[
dummy_component,
@ -743,6 +794,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
roll.click(
fn=roll_artist,
_js="update_img2img_tokens",
inputs=[
img2img_prompt,
],
@ -753,6 +805,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
button.click(
@ -764,9 +817,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
)
for button, (prompt, negative_prompt), (style1, style2) in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns):
for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
button.click(
fn=apply_styles,
_js=js_func,
inputs=[prompt, negative_prompt, style1, style2],
outputs=[prompt, negative_prompt, style1, style2],
)
@ -789,6 +843,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
(denoising_strength, "Denoising strength"),
]
modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt)
token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
with gr.Blocks(analytics_enabled=False) as extras_interface:
with gr.Row().style(equal_height=False):
@ -828,7 +883,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
submit.click(
fn=run_extras,
fn=wrap_gradio_gpu_call(modules.extras.run_extras),
_js="get_extras_tab_index",
inputs=[
dummy_component,
@ -878,7 +933,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
pnginfo_send_to_img2img = gr.Button('Send to img2img')
image.change(
fn=wrap_gradio_call(run_pnginfo),
fn=wrap_gradio_call(modules.extras.run_pnginfo),
inputs=[image],
outputs=[html, generation_info, html2],
)
@ -900,6 +955,130 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
with gr.Column(variant='panel'):
submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
with gr.Blocks() as textual_inversion_interface:
with gr.Row().style(equal_height=False):
with gr.Column():
with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
new_embedding_name = gr.Textbox(label="Name")
initialization_text = gr.Textbox(label="Initialization text", value="*")
nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
with gr.Column():
create_embedding = gr.Button(value="Create", variant='primary')
with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
process_src = gr.Textbox(label='Source directory')
process_dst = gr.Textbox(label='Destination directory')
with gr.Row():
process_flip = gr.Checkbox(label='Flip')
process_split = gr.Checkbox(label='Split into two')
process_caption = gr.Checkbox(label='Add caption')
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
with gr.Column():
run_preprocess = gr.Button(value="Preprocess", variant='primary')
with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
steps = gr.Number(label='Max steps', value=100000, precision=0)
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
with gr.Row():
with gr.Column(scale=2):
gr.HTML(value="")
with gr.Column():
with gr.Row():
interrupt_training = gr.Button(value="Interrupt")
train_embedding = gr.Button(value="Train", variant='primary')
with gr.Column():
progressbar = gr.HTML(elem_id="ti_progressbar")
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
ti_preview = gr.Image(elem_id='ti_preview', visible=False)
ti_progress = gr.HTML(elem_id="ti_progress", value="")
ti_outcome = gr.HTML(elem_id="ti_error", value="")
setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
create_embedding.click(
fn=modules.textual_inversion.ui.create_embedding,
inputs=[
new_embedding_name,
initialization_text,
nvpt,
],
outputs=[
train_embedding_name,
ti_output,
ti_outcome,
]
)
run_preprocess.click(
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
process_src,
process_dst,
process_flip,
process_split,
process_caption,
],
outputs=[
ti_output,
ti_outcome,
],
)
train_embedding.click(
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
train_embedding_name,
learn_rate,
dataset_directory,
log_directory,
steps,
create_image_every,
save_embedding_every,
template_file,
],
outputs=[
ti_output,
ti_outcome,
]
)
interrupt_training.click(
fn=lambda: shared.state.interrupt(),
inputs=[],
outputs=[],
)
def create_setting_component(key):
def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default
@ -1002,6 +1181,31 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
_js='function(){}'
)
with gr.Row():
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
def reload_scripts():
modules.scripts.reload_script_body_only()
reload_script_bodies.click(
fn=reload_scripts,
inputs=[],
outputs=[],
_js='function(){}'
)
def request_restart():
settings_interface.gradio_ref.do_restart = True
restart_gradio.click(
fn=request_restart,
inputs=[],
outputs=[],
_js='function(){restart_reload()}'
)
if column is not None:
column.__exit__()
@ -1011,6 +1215,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
(extras_interface, "Extras", "extras"),
(pnginfo_interface, "PNG Info", "pnginfo"),
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
(textual_inversion_interface, "Textual inversion", "ti"),
(settings_interface, "Settings", "settings"),
]
@ -1027,6 +1232,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
settings_interface.gradio_ref = demo
with gr.Tabs() as tabs:
for interface, label, ifid in interfaces:
with gr.TabItem(label, id=ifid):
@ -1044,11 +1251,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
def modelmerger(*args):
try:
results = run_modelmerger(*args)
results = modules.extras.run_modelmerger(*args)
except Exception as e:
print("Error loading/saving model file:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
modules.sd_models.list_models() #To remove the potentially missing models from the list
modules.sd_models.list_models() # to remove the potentially missing models from the list
return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
return results
@ -1206,12 +1413,12 @@ for filename in sorted(os.listdir(jsdir)):
javascript += f"\n<script>{jsfile.read()}</script>"
def template_response(*args, **kwargs):
if 'gradio_routes_templates_response' not in globals():
def template_response(*args, **kwargs):
res = gradio_routes_templates_response(*args, **kwargs)
res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
res.init_headers()
return res
gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
gradio.routes.templates.TemplateResponse = template_response
gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
gradio.routes.templates.TemplateResponse = template_response

View File

@ -13,14 +13,12 @@ Pillow
pytorch_lightning
realesrgan
scikit-image>=0.19
git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379
timm==0.4.12
transformers==4.19.2
torch
einops
jsonmerge
clean-fid
git+https://github.com/openai/CLIP@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
resize-right
torchdiffeq
kornia

View File

@ -18,7 +18,6 @@ piexif==1.1.3
einops==0.4.1
jsonmerge==1.8.0
clean-fid==0.1.29
git+https://github.com/openai/CLIP@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
resize-right==0.0.2
torchdiffeq==0.2.3
kornia==0.6.7

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@ -34,7 +34,11 @@ class Script(scripts.Script):
seed = p.seed
init_img = p.init_images[0]
if(upscaler.name != "None"):
img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)
else:
img = init_img
devices.torch_gc()

View File

@ -157,7 +157,7 @@ button{
max-width: 10em;
}
#txt2img_preview, #img2img_preview{
#txt2img_preview, #img2img_preview, #ti_preview{
position: absolute;
width: 320px;
left: 0;
@ -172,18 +172,18 @@ button{
}
@media screen and (min-width: 768px) {
#txt2img_preview, #img2img_preview {
#txt2img_preview, #img2img_preview, #ti_preview {
position: absolute;
}
}
@media screen and (max-width: 767px) {
#txt2img_preview, #img2img_preview {
#txt2img_preview, #img2img_preview, #ti_preview {
position: relative;
}
}
#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0{
#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0, #ti_preview div.left-0.top-0{
display: none;
}
@ -247,7 +247,7 @@ input[type="range"]{
#txt2img_negative_prompt, #img2img_negative_prompt{
}
#txt2img_progressbar, #img2img_progressbar{
#txt2img_progressbar, #img2img_progressbar, #ti_progressbar{
position: absolute;
z-index: 1000;
right: 0;

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@ -0,0 +1,19 @@
a painting, art by [name]
a rendering, art by [name]
a cropped painting, art by [name]
the painting, art by [name]
a clean painting, art by [name]
a dirty painting, art by [name]
a dark painting, art by [name]
a picture, art by [name]
a cool painting, art by [name]
a close-up painting, art by [name]
a bright painting, art by [name]
a cropped painting, art by [name]
a good painting, art by [name]
a close-up painting, art by [name]
a rendition, art by [name]
a nice painting, art by [name]
a small painting, art by [name]
a weird painting, art by [name]
a large painting, art by [name]

View File

@ -0,0 +1,19 @@
a painting of [filewords], art by [name]
a rendering of [filewords], art by [name]
a cropped painting of [filewords], art by [name]
the painting of [filewords], art by [name]
a clean painting of [filewords], art by [name]
a dirty painting of [filewords], art by [name]
a dark painting of [filewords], art by [name]
a picture of [filewords], art by [name]
a cool painting of [filewords], art by [name]
a close-up painting of [filewords], art by [name]
a bright painting of [filewords], art by [name]
a cropped painting of [filewords], art by [name]
a good painting of [filewords], art by [name]
a close-up painting of [filewords], art by [name]
a rendition of [filewords], art by [name]
a nice painting of [filewords], art by [name]
a small painting of [filewords], art by [name]
a weird painting of [filewords], art by [name]
a large painting of [filewords], art by [name]

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@ -0,0 +1,27 @@
a photo of a [name]
a rendering of a [name]
a cropped photo of the [name]
the photo of a [name]
a photo of a clean [name]
a photo of a dirty [name]
a dark photo of the [name]
a photo of my [name]
a photo of the cool [name]
a close-up photo of a [name]
a bright photo of the [name]
a cropped photo of a [name]
a photo of the [name]
a good photo of the [name]
a photo of one [name]
a close-up photo of the [name]
a rendition of the [name]
a photo of the clean [name]
a rendition of a [name]
a photo of a nice [name]
a good photo of a [name]
a photo of the nice [name]
a photo of the small [name]
a photo of the weird [name]
a photo of the large [name]
a photo of a cool [name]
a photo of a small [name]

View File

@ -0,0 +1,27 @@
a photo of a [name], [filewords]
a rendering of a [name], [filewords]
a cropped photo of the [name], [filewords]
the photo of a [name], [filewords]
a photo of a clean [name], [filewords]
a photo of a dirty [name], [filewords]
a dark photo of the [name], [filewords]
a photo of my [name], [filewords]
a photo of the cool [name], [filewords]
a close-up photo of a [name], [filewords]
a bright photo of the [name], [filewords]
a cropped photo of a [name], [filewords]
a photo of the [name], [filewords]
a good photo of the [name], [filewords]
a photo of one [name], [filewords]
a close-up photo of the [name], [filewords]
a rendition of the [name], [filewords]
a photo of the clean [name], [filewords]
a rendition of a [name], [filewords]
a photo of a nice [name], [filewords]
a good photo of a [name], [filewords]
a photo of the nice [name], [filewords]
a photo of the small [name], [filewords]
a photo of the weird [name], [filewords]
a photo of the large [name], [filewords]
a photo of a cool [name], [filewords]
a photo of a small [name], [filewords]

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@ -1,34 +1,34 @@
import os
import threading
import time
import importlib
from modules import devices
from modules.paths import script_path
import signal
import threading
import modules.paths
import modules.codeformer_model as codeformer
import modules.esrgan_model as esrgan
import modules.bsrgan_model as bsrgan
import modules.extras
import modules.face_restoration
import modules.gfpgan_model as gfpgan
import modules.img2img
import modules.ldsr_model as ldsr
import modules.lowvram
import modules.realesrgan_model as realesrgan
import modules.paths
import modules.scripts
import modules.sd_hijack
import modules.sd_models
import modules.shared as shared
import modules.swinir_model as swinir
import modules.txt2img
import modules.ui
from modules import devices
from modules import modelloader
from modules.paths import script_path
from modules.shared import cmd_opts
modelloader.cleanup_models()
modules.sd_models.setup_model(cmd_opts.ckpt_dir)
modules.sd_models.setup_model()
codeformer.setup_model(cmd_opts.codeformer_models_path)
gfpgan.setup_model(cmd_opts.gfpgan_models_path)
shared.face_restorers.append(modules.face_restoration.FaceRestoration())
@ -46,7 +46,7 @@ def wrap_queued_call(func):
return f
def wrap_gradio_gpu_call(func):
def wrap_gradio_gpu_call(func, extra_outputs=None):
def f(*args, **kwargs):
devices.torch_gc()
@ -58,6 +58,7 @@ def wrap_gradio_gpu_call(func):
shared.state.current_image = None
shared.state.current_image_sampling_step = 0
shared.state.interrupted = False
shared.state.textinfo = None
with queue_lock:
res = func(*args, **kwargs)
@ -69,7 +70,7 @@ def wrap_gradio_gpu_call(func):
return res
return modules.ui.wrap_gradio_call(f)
return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
@ -86,13 +87,9 @@ def webui():
signal.signal(signal.SIGINT, sigint_handler)
demo = modules.ui.create_ui(
txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
run_pnginfo=modules.extras.run_pnginfo,
run_modelmerger=modules.extras.run_modelmerger
)
while 1:
demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
demo.launch(
share=cmd_opts.share,
@ -101,8 +98,24 @@ def webui():
debug=cmd_opts.gradio_debug,
auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
inbrowser=cmd_opts.autolaunch,
prevent_thread_lock=True
)
while 1:
time.sleep(0.5)
if getattr(demo, 'do_restart', False):
time.sleep(0.5)
demo.close()
time.sleep(0.5)
break
print('Reloading Custom Scripts')
modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
print('Reloading modules: modules.ui')
importlib.reload(modules.ui)
print('Restarting Gradio')
if __name__ == "__main__":
webui()