openvinotoolkit/stable-diffusion-webui
1
1
Fork 0
mirror of https://github.com/openvinotoolkit/stable-diffusion-webui.git synced 2024-12-14 06:28:12 +03:00
Code Issues Packages Projects Releases Wiki Activity

removed aesthetic gradients as built-in

Browse Source 
added support for extensions
This commit is contained in:
AUTOMATIC 2022-10-22 12:23:45 +03:00
parent 26d1073745
commit 2b91251637
14 changed files with 249 additions and 410 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.gitignore vendored
View File

@ -27,4 +27,4 @@ __pycache__
notification.mp3
/SwinIR
/textual_inversion
.vscode
.vscode

0
extensions/put extension here.txt Normal file
View File

241
modules/aesthetic_clip.py
View File

@ -1,241 +0,0 @@
import copy
import itertools
import os
from pathlib import Path
import html
import gc
import gradio as gr
import torch
from PIL import Image
from torch import optim
from modules import shared
from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer
from tqdm.auto import tqdm, trange
from modules.shared import opts, device
def get_all_images_in_folder(folder):
return [os.path.join(folder, f) for f in os.listdir(folder) if
os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)]
def check_is_valid_image_file(filename):
return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp"))
def batched(dataset, total, n=1):
for ndx in range(0, total, n):
yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
def iter_to_batched(iterable, n=1):
it = iter(iterable)
while True:
chunk = tuple(itertools.islice(it, n))
if not chunk:
return
yield chunk
def create_ui():
import modules.ui
with gr.Group():
with gr.Accordion("Open for Clip Aesthetic!", open=False):
with gr.Row():
aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight",
value=0.9)
aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
with gr.Row():
aesthetic_lr = gr.Textbox(label='Aesthetic learning rate',
placeholder="Aesthetic learning rate", value="0.0001")
aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()),
label="Aesthetic imgs embedding",
value="None")
modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings")
with gr.Row():
aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs',
placeholder="This text is used to rotate the feature space of the imgs embs",
value="")
aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01,
value=0.1)
aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative
aesthetic_clip_model = None
def aesthetic_clip():
global aesthetic_clip_model
if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path:
aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path)
aesthetic_clip_model.cpu()
return aesthetic_clip_model
def generate_imgs_embd(name, folder, batch_size):
model = aesthetic_clip().to(device)
processor = CLIPProcessor.from_pretrained(model.name_or_path)
with torch.no_grad():
embs = []
for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size),
desc=f"Generating embeddings for {name}"):
if shared.state.interrupted:
break
inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device)
outputs = model.get_image_features(**inputs).cpu()
embs.append(torch.clone(outputs))
inputs.to("cpu")
del inputs, outputs
embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
# The generated embedding will be located here
path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
torch.save(embs, path)
model.cpu()
del processor
del embs
gc.collect()
torch.cuda.empty_cache()
res = f"""
Done generating embedding for {name}!
Aesthetic embedding saved to {html.escape(path)}
"""
shared.update_aesthetic_embeddings()
return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
value="None"), \
gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()),
label="Imgs embedding",
value="None"), res, ""
def slerp(low, high, val):
low_norm = low / torch.norm(low, dim=1, keepdim=True)
high_norm = high / torch.norm(high, dim=1, keepdim=True)
omega = torch.acos((low_norm * high_norm).sum(1))
so = torch.sin(omega)
res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
return res
class AestheticCLIP:
def __init__(self):
self.skip = False
self.aesthetic_steps = 0
self.aesthetic_weight = 0
self.aesthetic_lr = 0
self.slerp = False
self.aesthetic_text_negative = ""
self.aesthetic_slerp_angle = 0
self.aesthetic_imgs_text = ""
self.image_embs_name = None
self.image_embs = None
self.load_image_embs(None)
def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
aesthetic_slerp=True, aesthetic_imgs_text="",
aesthetic_slerp_angle=0.15,
aesthetic_text_negative=False):
self.aesthetic_imgs_text = aesthetic_imgs_text
self.aesthetic_slerp_angle = aesthetic_slerp_angle
self.aesthetic_text_negative = aesthetic_text_negative
self.slerp = aesthetic_slerp
self.aesthetic_lr = aesthetic_lr
self.aesthetic_weight = aesthetic_weight
self.aesthetic_steps = aesthetic_steps
self.load_image_embs(image_embs_name)
if self.image_embs_name is not None:
p.extra_generation_params.update({
"Aesthetic LR": aesthetic_lr,
"Aesthetic weight": aesthetic_weight,
"Aesthetic steps": aesthetic_steps,
"Aesthetic embedding": self.image_embs_name,
"Aesthetic slerp": aesthetic_slerp,
"Aesthetic text": aesthetic_imgs_text,
"Aesthetic text negative": aesthetic_text_negative,
"Aesthetic slerp angle": aesthetic_slerp_angle,
})
def set_skip(self, skip):
self.skip = skip
def load_image_embs(self, image_embs_name):
if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
image_embs_name = None
self.image_embs_name = None
if image_embs_name is not None and self.image_embs_name != image_embs_name:
self.image_embs_name = image_embs_name
self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
self.image_embs.requires_grad_(False)
def __call__(self, z, remade_batch_tokens):
if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None:
tokenizer = shared.sd_model.cond_stage_model.tokenizer
if not opts.use_old_emphasis_implementation:
remade_batch_tokens = [
[tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in
remade_batch_tokens]
tokens = torch.asarray(remade_batch_tokens).to(device)
model = copy.deepcopy(aesthetic_clip()).to(device)
model.requires_grad_(True)
if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
text_embs_2 = model.get_text_features(
**tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
if self.aesthetic_text_negative:
text_embs_2 = self.image_embs - text_embs_2
text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
else:
img_embs = self.image_embs
with torch.enable_grad():
# We optimize the model to maximize the similarity
optimizer = optim.Adam(
model.text_model.parameters(), lr=self.aesthetic_lr
)
for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
text_embs = model.get_text_features(input_ids=tokens)
text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
sim = text_embs @ img_embs.T
loss = -sim
optimizer.zero_grad()
loss.mean().backward()
optimizer.step()
zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
if opts.CLIP_stop_at_last_layers > 1:
zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
zn = model.text_model.final_layer_norm(zn)
else:
zn = zn.last_hidden_state
model.cpu()
del model
gc.collect()
torch.cuda.empty_cache()
zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1)
if self.slerp:
z = slerp(z, zn, self.aesthetic_weight)
else:
z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
return z

2
modules/images_history.py
View File

@ -310,7 +310,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
forward = gr.Button('Prev batch')
backward = gr.Button('Next batch')
with gr.Column(scale=3):
load_info = gr.HTML(visible=not custom_dir)
load_info = gr.HTML(visible=not custom_dir)
with gr.Row(visible=False) as warning:
warning_box = gr.Textbox("Message", interactive=False)

5
modules/img2img.py
View File

@ -56,7 +56,7 @@ def process_batch(p, input_dir, output_dir, args):
processed_image.save(os.path.join(output_dir, filename))
def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args):
def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
is_inpaint = mode == 1
is_batch = mode == 2
@ -109,7 +109,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
inpainting_mask_invert=inpainting_mask_invert,
)
shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
p.scripts = modules.scripts.scripts_txt2img
p.script_args = args
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)

35
modules/processing.py
View File

@ -104,6 +104,12 @@ class StableDiffusionProcessing():
self.seed_resize_from_h = 0
self.seed_resize_from_w = 0
self.scripts = None
self.script_args = None
self.all_prompts = None
self.all_seeds = None
self.all_subseeds = None
def init(self, all_prompts, all_seeds, all_subseeds):
pass
@ -350,32 +356,35 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
shared.prompt_styles.apply_styles(p)
if type(p.prompt) == list:
all_prompts = p.prompt
p.all_prompts = p.prompt
else:
all_prompts = p.batch_size * p.n_iter * [p.prompt]
p.all_prompts = p.batch_size * p.n_iter * [p.prompt]
if type(seed) == list:
all_seeds = seed
p.all_seeds = seed
else:
all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
if type(subseed) == list:
all_subseeds = subseed
p.all_subseeds = subseed
else:
all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
def infotext(iteration=0, position_in_batch=0):
return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
if p.scripts is not None:
p.scripts.run_alwayson_scripts(p)
infotexts = []
output_images = []
with torch.no_grad(), p.sd_model.ema_scope():
with devices.autocast():
p.init(all_prompts, all_seeds, all_subseeds)
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
if state.job_count == -1:
state.job_count = p.n_iter
@ -387,9 +396,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if state.interrupted:
break
prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
if (len(prompts) == 0):
break
@ -490,10 +499,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
index_of_first_image = 1
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
devices.torch_gc()
return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

42
modules/script_callbacks.py Normal file
View File

@ -0,0 +1,42 @@
callbacks_model_loaded = []
callbacks_ui_tabs = []
def clear_callbacks():
callbacks_model_loaded.clear()
callbacks_ui_tabs.clear()
def model_loaded_callback(sd_model):
for callback in callbacks_model_loaded:
callback(sd_model)
def ui_tabs_callback():
res = []
for callback in callbacks_ui_tabs:
res += callback() or []
return res
def on_model_loaded(callback):
"""register a function to be called when the stable diffusion model is created; the model is
passed as an argument"""
callbacks_model_loaded.append(callback)
def on_ui_tabs(callback):
"""register a function to be called when the UI is creating new tabs.
The function must either return a None, which means no new tabs to be added, or a list, where
each element is a tuple:
(gradio_component, title, elem_id)
gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
title is tab text displayed to user in the UI
elem_id is HTML id for the tab
"""
callbacks_ui_tabs.append(callback)

210
modules/scripts.py
View File

@ -1,86 +1,153 @@
import os
import sys
import traceback
from collections import namedtuple
import modules.ui as ui
import gradio as gr
from modules.processing import StableDiffusionProcessing
from modules import shared
from modules import shared, paths, script_callbacks
AlwaysVisible = object()
class Script:
filename = None
args_from = None
args_to = None
alwayson = False
infotext_fields = None
"""if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
"""
# The title of the script. This is what will be displayed in the dropdown menu.
def title(self):
"""this function should return the title of the script. This is what will be displayed in the dropdown menu."""
raise NotImplementedError()
# How the script is displayed in the UI. See https://gradio.app/docs/#components
# for the different UI components you can use and how to create them.
# Most UI components can return a value, such as a boolean for a checkbox.
# The returned values are passed to the run method as parameters.
def ui(self, is_img2img):
"""this function should create gradio UI elements. See https://gradio.app/docs/#components
The return value should be an array of all components that are used in processing.
Values of those returned componenbts will be passed to run() and process() functions.
"""
pass
# Determines when the script should be shown in the dropdown menu via the
# returned value. As an example:
# is_img2img is True if the current tab is img2img, and False if it is txt2img.
# Thus, return is_img2img to only show the script on the img2img tab.
def show(self, is_img2img):
"""
is_img2img is True if this function is called for the img2img interface, and Fasle otherwise
This function should return:
- False if the script should not be shown in UI at all
- True if the script should be shown in UI if it's scelected in the scripts drowpdown
- script.AlwaysVisible if the script should be shown in UI at all times
"""
return True
# This is where the additional processing is implemented. The parameters include
# self, the model object "p" (a StableDiffusionProcessing class, see
# processing.py), and the parameters returned by the ui method.
# Custom functions can be defined here, and additional libraries can be imported
# to be used in processing. The return value should be a Processed object, which is
# what is returned by the process_images method.
def run(self, *args):
def run(self, p, *args):
"""
This function is called if the script has been selected in the script dropdown.
It must do all processing and return the Processed object with results, same as
one returned by processing.process_images.
Usually the processing is done by calling the processing.process_images function.
args contains all values returned by components from ui()
"""
raise NotImplementedError()
# The description method is currently unused.
# To add a description that appears when hovering over the title, amend the "titles"
# dict in script.js to include the script title (returned by title) as a key, and
# your description as the value.
def process(self, p, *args):
"""
This function is called before processing begins for AlwaysVisible scripts.
scripts. You can modify the processing object (p) here, inject hooks, etc.
"""
pass
def describe(self):
"""unused"""
return ""
current_basedir = paths.script_path
def basedir():
"""returns the base directory for the current script. For scripts in the main scripts directory,
this is the main directory (where webui.py resides), and for scripts in extensions directory
(ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic)
"""
return current_basedir
scripts_data = []
ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir"])
def load_scripts(basedir):
if not os.path.exists(basedir):
return
def list_scripts(scriptdirname, extension):
scripts_list = []
for filename in sorted(os.listdir(basedir)):
path = os.path.join(basedir, filename)
basedir = os.path.join(paths.script_path, scriptdirname)
if os.path.exists(basedir):
for filename in sorted(os.listdir(basedir)):
scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
if os.path.splitext(path)[1].lower() != '.py':
continue
extdir = os.path.join(paths.script_path, "extensions")
if os.path.exists(extdir):
for dirname in sorted(os.listdir(extdir)):
dirpath = os.path.join(extdir, dirname)
if not os.path.isdir(dirpath):
continue
if not os.path.isfile(path):
continue
for filename in sorted(os.listdir(os.path.join(dirpath, scriptdirname))):
scripts_list.append(ScriptFile(dirpath, filename, os.path.join(dirpath, scriptdirname, filename)))
scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
return scripts_list
def load_scripts():
global current_basedir
scripts_data.clear()
script_callbacks.clear_callbacks()
scripts_list = list_scripts("scripts", ".py")
syspath = sys.path
for scriptfile in sorted(scripts_list):
try:
with open(path, "r", encoding="utf8") as file:
if scriptfile.basedir != paths.script_path:
sys.path = [scriptfile.basedir] + sys.path
current_basedir = scriptfile.basedir
with open(scriptfile.path, "r", encoding="utf8") as file:
text = file.read()
from types import ModuleType
compiled = compile(text, path, 'exec')
module = ModuleType(filename)
compiled = compile(text, scriptfile.path, 'exec')
module = ModuleType(scriptfile.filename)
exec(compiled, module.__dict__)
for key, script_class in module.__dict__.items():
if type(script_class) == type and issubclass(script_class, Script):
scripts_data.append((script_class, path))
scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir))
except Exception:
print(f"Error loading script: {filename}", file=sys.stderr)
print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
finally:
sys.path = syspath
current_basedir = paths.script_path
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
try:
@ -96,56 +163,80 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
class ScriptRunner:
def __init__(self):
self.scripts = []
self.selectable_scripts = []
self.alwayson_scripts = []
self.titles = []
self.infotext_fields = []
def setup_ui(self, is_img2img):
for script_class, path in scripts_data:
for script_class, path, basedir in scripts_data:
script = script_class()
script.filename = path
if not script.show(is_img2img):
continue
visibility = script.show(is_img2img)
self.scripts.append(script)
if visibility == AlwaysVisible:
self.scripts.append(script)
self.alwayson_scripts.append(script)
script.alwayson = True
self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
elif visibility:
self.scripts.append(script)
self.selectable_scripts.append(script)
dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
dropdown.save_to_config = True
inputs = [dropdown]
self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
for script in self.scripts:
inputs = [None]
inputs_alwayson = [True]
def create_script_ui(script, inputs, inputs_alwayson):
script.args_from = len(inputs)
script.args_to = len(inputs)
controls = wrap_call(script.ui, script.filename, "ui", is_img2img)
if controls is None:
continue
return
for control in controls:
control.custom_script_source = os.path.basename(script.filename)
control.visible = False
if not script.alwayson:
control.visible = False
if script.infotext_fields is not None:
self.infotext_fields += script.infotext_fields
inputs += controls
inputs_alwayson += [script.alwayson for _ in controls]
script.args_to = len(inputs)
for script in self.alwayson_scripts:
with gr.Group():
create_script_ui(script, inputs, inputs_alwayson)
dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
dropdown.save_to_config = True
inputs[0] = dropdown
for script in self.selectable_scripts:
create_script_ui(script, inputs, inputs_alwayson)
def select_script(script_index):
if 0 < script_index <= len(self.scripts):
script = self.scripts[script_index-1]
if 0 < script_index <= len(self.selectable_scripts):
script = self.selectable_scripts[script_index-1]
args_from = script.args_from
args_to = script.args_to
else:
args_from = 0
args_to = 0
return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]
def init_field(title):
if title == 'None':
return
script_index = self.titles.index(title)
script = self.scripts[script_index]
script = self.selectable_scripts[script_index]
for i in range(script.args_from, script.args_to):
inputs[i].visible = True
@ -164,7 +255,7 @@ class ScriptRunner:
if script_index == 0:
return None
script = self.scripts[script_index-1]
script = self.selectable_scripts[script_index-1]
if script is None:
return None
@ -176,6 +267,15 @@ class ScriptRunner:
return processed
def run_alwayson_scripts(self, p):
for script in self.alwayson_scripts:
try:
script_args = p.script_args[script.args_from:script.args_to]
script.process(p, *script_args)
except Exception:
print(f"Error running alwayson script: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
def reload_sources(self):
for si, script in list(enumerate(self.scripts)):
with open(script.filename, "r", encoding="utf8") as file:
@ -197,19 +297,21 @@ class ScriptRunner:
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):
def reload_scripts():
global scripts_txt2img, scripts_img2img
scripts_data.clear()
load_scripts(basedir)
load_scripts()
scripts_txt2img = ScriptRunner()
scripts_img2img = ScriptRunner()

1
modules/sd_hijack.py
View File

@ -332,7 +332,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
multipliers.append([1.0] * 75)
z1 = self.process_tokens(tokens, multipliers)
z1 = shared.aesthetic_clip(z1, remade_batch_tokens)
z = z1 if z is None else torch.cat((z, z1), axis=-2)
remade_batch_tokens = rem_tokens

7
modules/sd_models.py
View File

@ -7,7 +7,7 @@ from omegaconf import OmegaConf
from ldm.util import instantiate_from_config
from modules import shared, modelloader, devices
from modules import shared, modelloader, devices, script_callbacks
from modules.paths import models_path
from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
@ -238,6 +238,9 @@ def load_model(checkpoint_info=None):
sd_hijack.model_hijack.hijack(sd_model)
sd_model.eval()
shared.sd_model = sd_model
script_callbacks.model_loaded_callback(sd_model)
print(f"Model loaded.")
return sd_model
@ -252,7 +255,7 @@ def reload_model_weights(sd_model, info=None):
if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
checkpoints_loaded.clear()
shared.sd_model = load_model(checkpoint_info)
load_model(checkpoint_info)
return shared.sd_model
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

19
modules/shared.py
View File

@ -31,7 +31,6 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(models_path, 'aesthetic_embeddings'), help="aesthetic_embeddings directory(default: aesthetic_embeddings)")
parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
@ -109,21 +108,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
loaded_hypernetwork = None
os.makedirs(cmd_opts.aesthetic_embeddings_dir, exist_ok=True)
aesthetic_embeddings = {}
def update_aesthetic_embeddings():
global aesthetic_embeddings
aesthetic_embeddings = {f.replace(".pt", ""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings)
update_aesthetic_embeddings()
def reload_hypernetworks():
global hypernetworks
@ -415,9 +399,6 @@ sd_model = None
clip_model = None
from modules.aesthetic_clip import AestheticCLIP
aesthetic_clip = AestheticCLIP()
progress_print_out = sys.stdout

5
modules/txt2img.py
View File

@ -7,7 +7,7 @@ import modules.processing as processing
from modules.ui import plaintext_to_html
def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args):
def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
p = StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@ -36,7 +36,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
firstphase_height=firstphase_height if enable_hr else None,
)
shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
p.scripts = modules.scripts.scripts_txt2img
p.script_args = args
if cmd_opts.enable_console_prompts:
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)

83
modules/ui.py
View File

@ -23,10 +23,10 @@ import gradio as gr
import gradio.utils
import gradio.routes
from modules import sd_hijack, sd_models, localization
from modules import sd_hijack, sd_models, localization, script_callbacks
from modules.paths import script_path
from modules.shared import opts, cmd_opts, restricted_opts, aesthetic_embeddings
from modules.shared import opts, cmd_opts, restricted_opts
if cmd_opts.deepdanbooru:
from modules.deepbooru import get_deepbooru_tags
@ -44,7 +44,6 @@ from modules.images import save_image
import modules.textual_inversion.ui
import modules.hypernetworks.ui
import modules.aesthetic_clip as aesthetic_clip
import modules.images_history as img_his
@ -662,8 +661,6 @@ def create_ui(wrap_gradio_gpu_call):
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative = aesthetic_clip.create_ui()
with gr.Group():
custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
@ -718,14 +715,6 @@ def create_ui(wrap_gradio_gpu_call):
denoising_strength,
firstphase_width,
firstphase_height,
aesthetic_lr,
aesthetic_weight,
aesthetic_steps,
aesthetic_imgs,
aesthetic_slerp,
aesthetic_imgs_text,
aesthetic_slerp_angle,
aesthetic_text_negative
] + custom_inputs,
outputs=[
@ -804,14 +793,7 @@ def create_ui(wrap_gradio_gpu_call):
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
(firstphase_width, "First pass size-1"),
(firstphase_height, "First pass size-2"),
(aesthetic_lr, "Aesthetic LR"),
(aesthetic_weight, "Aesthetic weight"),
(aesthetic_steps, "Aesthetic steps"),
(aesthetic_imgs, "Aesthetic embedding"),
(aesthetic_slerp, "Aesthetic slerp"),
(aesthetic_imgs_text, "Aesthetic text"),
(aesthetic_text_negative, "Aesthetic text negative"),
(aesthetic_slerp_angle, "Aesthetic slerp angle"),
*modules.scripts.scripts_txt2img.infotext_fields
]
txt2img_preview_params = [
@ -896,8 +878,6 @@ def create_ui(wrap_gradio_gpu_call):
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
aesthetic_weight_im, aesthetic_steps_im, aesthetic_lr_im, aesthetic_slerp_im, aesthetic_imgs_im, aesthetic_imgs_text_im, aesthetic_slerp_angle_im, aesthetic_text_negative_im = aesthetic_clip.create_ui()
with gr.Group():
custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
@ -988,14 +968,6 @@ def create_ui(wrap_gradio_gpu_call):
inpainting_mask_invert,
img2img_batch_input_dir,
img2img_batch_output_dir,
aesthetic_lr_im,
aesthetic_weight_im,
aesthetic_steps_im,
aesthetic_imgs_im,
aesthetic_slerp_im,
aesthetic_imgs_text_im,
aesthetic_slerp_angle_im,
aesthetic_text_negative_im,
] + custom_inputs,
outputs=[
img2img_gallery,
@ -1087,14 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
(seed_resize_from_w, "Seed resize from-1"),
(seed_resize_from_h, "Seed resize from-2"),
(denoising_strength, "Denoising strength"),
(aesthetic_lr_im, "Aesthetic LR"),
(aesthetic_weight_im, "Aesthetic weight"),
(aesthetic_steps_im, "Aesthetic steps"),
(aesthetic_imgs_im, "Aesthetic embedding"),
(aesthetic_slerp_im, "Aesthetic slerp"),
(aesthetic_imgs_text_im, "Aesthetic text"),
(aesthetic_text_negative_im, "Aesthetic text negative"),
(aesthetic_slerp_angle_im, "Aesthetic slerp angle"),
*modules.scripts.scripts_img2img.infotext_fields
]
token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
@ -1217,9 +1182,9 @@ def create_ui(wrap_gradio_gpu_call):
)
#images history
images_history_switch_dict = {
"fn":modules.generation_parameters_copypaste.connect_paste,
"t2i":txt2img_paste_fields,
"i2i":img2img_paste_fields
"fn": modules.generation_parameters_copypaste.connect_paste,
"t2i": txt2img_paste_fields,
"i2i": img2img_paste_fields
}
images_history = img_his.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
@ -1264,18 +1229,6 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Column():
create_embedding = gr.Button(value="Create embedding", variant='primary')
with gr.Tab(label="Create aesthetic images embedding"):
new_embedding_name_ae = gr.Textbox(label="Name")
process_src_ae = gr.Textbox(label='Source directory')
batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
with gr.Column():
create_embedding_ae = gr.Button(value="Create images embedding", variant='primary')
with gr.Tab(label="Create hypernetwork"):
new_hypernetwork_name = gr.Textbox(label="Name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
@ -1375,21 +1328,6 @@ def create_ui(wrap_gradio_gpu_call):
]
)
create_embedding_ae.click(
fn=aesthetic_clip.generate_imgs_embd,
inputs=[
new_embedding_name_ae,
process_src_ae,
batch_ae
],
outputs=[
aesthetic_imgs,
aesthetic_imgs_im,
ti_output,
ti_outcome,
]
)
create_hypernetwork.click(
fn=modules.hypernetworks.ui.create_hypernetwork,
inputs=[
@ -1580,10 +1518,10 @@ Requested path was: {f}
if not opts.same_type(value, opts.data_labels[key].default):
return gr.update(visible=True), opts.dumpjson()
oldval = opts.data.get(key, None)
if cmd_opts.hide_ui_dir_config and key in restricted_opts:
return gr.update(value=oldval), opts.dumpjson()
oldval = opts.data.get(key, None)
opts.data[key] = value
if oldval != value:
@ -1692,9 +1630,12 @@ Requested path was: {f}
(images_history, "Image Browser", "images_history"),
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
(train_interface, "Train", "ti"),
(settings_interface, "Settings", "settings"),
]
interfaces += script_callbacks.ui_tabs_callback()
interfaces += [(settings_interface, "Settings", "settings")]
with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
css = file.read()

7
webui.py
View File

@ -71,6 +71,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
def initialize():
modelloader.cleanup_models()
modules.sd_models.setup_model()
@ -79,9 +80,9 @@ def initialize():
shared.face_restorers.append(modules.face_restoration.FaceRestoration())
modelloader.load_upscalers()
modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
modules.scripts.load_scripts()
shared.sd_model = modules.sd_models.load_model()
modules.sd_models.load_model()
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
@ -145,7 +146,7 @@ def webui():
sd_samplers.set_samplers()
print('Reloading Custom Scripts')
modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
modules.scripts.reload_scripts()
print('Reloading modules: modules.ui')
importlib.reload(modules.ui)
print('Refreshing Model List')