535 lines
24 KiB
Python
535 lines
24 KiB
Python
import os
|
|
import stat
|
|
from collections import OrderedDict
|
|
|
|
import torch
|
|
|
|
import modules.scripts as scripts
|
|
from modules import shared, devices, script_callbacks
|
|
import gradio as gr
|
|
|
|
import numpy as np
|
|
from einops import rearrange
|
|
from modules import sd_models
|
|
from torchvision.transforms import Resize, InterpolationMode, CenterCrop, Compose
|
|
from scripts.cldm import PlugableControlModel
|
|
from scripts.processor import *
|
|
from modules.ui_components import ToolButton
|
|
from modules.processing import StableDiffusionProcessingImg2Img
|
|
|
|
gradio_compat = True
|
|
try:
|
|
from distutils.version import LooseVersion
|
|
from importlib_metadata import version
|
|
if LooseVersion(version("gradio")) < LooseVersion("3.10"):
|
|
gradio_compat = False
|
|
except ImportError:
|
|
pass
|
|
|
|
# svgsupports
|
|
import io
|
|
import base64
|
|
from svglib.svglib import svg2rlg
|
|
from reportlab.graphics import renderPM
|
|
|
|
CN_MODEL_EXTS = [".pt", ".pth", ".ckpt", ".safetensors"]
|
|
cn_models = {} # "My_Lora(abcd1234)" -> C:/path/to/model.safetensors
|
|
cn_models_names = {} # "my_lora" -> "My_Lora(abcd1234)"
|
|
cn_models_dir = os.path.join(scripts.basedir(), "models")
|
|
os.makedirs(cn_models_dir, exist_ok=True)
|
|
default_conf = os.path.join(cn_models_dir, "cldm_v15.yaml")
|
|
refresh_symbol = '\U0001f504' # 🔄
|
|
switch_values_symbol = '\U000021C5' # ⇅
|
|
active_img2img_tab = 'img2img_img2img_tab'
|
|
|
|
def traverse_all_files(curr_path, model_list):
|
|
f_list = [(os.path.join(curr_path, entry.name), entry.stat())
|
|
for entry in os.scandir(curr_path)]
|
|
for f_info in f_list:
|
|
fname, fstat = f_info
|
|
if os.path.splitext(fname)[1] in CN_MODEL_EXTS:
|
|
model_list.append(f_info)
|
|
elif stat.S_ISDIR(fstat.st_mode):
|
|
model_list = traverse_all_files(fname, model_list)
|
|
return model_list
|
|
|
|
|
|
def get_all_models(sort_by, filter_by, path):
|
|
res = OrderedDict()
|
|
fileinfos = traverse_all_files(path, [])
|
|
filter_by = filter_by.strip(" ")
|
|
if len(filter_by) != 0:
|
|
fileinfos = [x for x in fileinfos if filter_by.lower()
|
|
in os.path.basename(x[0]).lower()]
|
|
if sort_by == "name":
|
|
fileinfos = sorted(fileinfos, key=lambda x: os.path.basename(x[0]))
|
|
elif sort_by == "date":
|
|
fileinfos = sorted(fileinfos, key=lambda x: -x[1].st_mtime)
|
|
elif sort_by == "path name":
|
|
fileinfos = sorted(fileinfos)
|
|
|
|
for finfo in fileinfos:
|
|
filename = finfo[0]
|
|
name = os.path.splitext(os.path.basename(filename))[0]
|
|
# Prevent a hypothetical "None.pt" from being listed.
|
|
if name != "None":
|
|
res[name + f" [{sd_models.model_hash(filename)}]"] = filename
|
|
|
|
return res
|
|
|
|
|
|
def find_closest_lora_model_name(search: str):
|
|
if not search:
|
|
return None
|
|
if search in cn_models:
|
|
return search
|
|
search = search.lower()
|
|
if search in cn_models_names:
|
|
return cn_models_names.get(search)
|
|
applicable = [name for name in cn_models_names.keys()
|
|
if search in name.lower()]
|
|
if not applicable:
|
|
return None
|
|
applicable = sorted(applicable, key=lambda name: len(name))
|
|
return cn_models_names[applicable[0]]
|
|
|
|
|
|
def update_cn_models():
|
|
global cn_models, cn_models_names
|
|
res = OrderedDict()
|
|
ext_dirs = (shared.opts.data.get("control_net_models_path", None), getattr(shared.cmd_opts, 'controlnet_dir', None))
|
|
extra_lora_paths = (extra_lora_path for extra_lora_path in ext_dirs
|
|
if extra_lora_path is not None and os.path.exists(extra_lora_path))
|
|
paths = [cn_models_dir, *extra_lora_paths]
|
|
|
|
for path in paths:
|
|
sort_by = shared.opts.data.get(
|
|
"control_net_models_sort_models_by", "name")
|
|
filter_by = shared.opts.data.get("control_net_models_name_filter", "")
|
|
found = get_all_models(sort_by, filter_by, path)
|
|
res = {**found, **res}
|
|
|
|
cn_models = OrderedDict(**{"None": None}, **res)
|
|
cn_models_names = {}
|
|
for name_and_hash, filename in cn_models.items():
|
|
if filename == None:
|
|
continue
|
|
name = os.path.splitext(os.path.basename(filename))[0].lower()
|
|
cn_models_names[name] = name_and_hash
|
|
|
|
|
|
update_cn_models()
|
|
|
|
|
|
class Script(scripts.Script):
|
|
def __init__(self) -> None:
|
|
super().__init__()
|
|
self.latest_params = (None, None)
|
|
self.latest_network = None
|
|
self.preprocessor = {
|
|
"none": lambda x, *args, **kwargs: x,
|
|
"canny": canny,
|
|
"depth": midas,
|
|
"hed": hed,
|
|
"mlsd": mlsd,
|
|
"normal_map": midas_normal,
|
|
"openpose": openpose,
|
|
"openpose_hand": openpose_hand,
|
|
"scribble": simple_scribble,
|
|
"fake_scribble": fake_scribble,
|
|
"segmentation": uniformer,
|
|
}
|
|
self.unloadable = {
|
|
"hed": unload_hed,
|
|
"fake_scribble": unload_hed,
|
|
"mlsd": unload_mlsd,
|
|
"depth": unload_midas,
|
|
"normal_map": unload_midas,
|
|
"openpose": unload_openpose,
|
|
"openpose_hand": unload_openpose,
|
|
"segmentation": unload_uniformer,
|
|
}
|
|
self.input_image = None
|
|
self.latest_model_hash = ""
|
|
|
|
def title(self):
|
|
return "ControlNet for generating"
|
|
|
|
def show(self, is_img2img):
|
|
# if is_img2img:
|
|
# return False
|
|
return scripts.AlwaysVisible
|
|
|
|
def get_threshold_block(self, proc):
|
|
pass
|
|
|
|
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 components will be passed to run() and process() functions.
|
|
"""
|
|
ctrls = ()
|
|
model_dropdowns = []
|
|
self.infotext_fields = []
|
|
with gr.Group():
|
|
with gr.Accordion('ControlNet', open=False):
|
|
input_image = gr.Image(source='upload', type='numpy', tool='sketch')
|
|
gr.HTML(value='<p>Enable scribble mode if your image has white background.<br >Change your brush width to make it thinner if you want to draw something.<br ></p>')
|
|
|
|
with gr.Row():
|
|
enabled = gr.Checkbox(label='Enable', value=False)
|
|
scribble_mode = gr.Checkbox(label='Scribble Mode (Invert colors)', value=False)
|
|
rgbbgr_mode = gr.Checkbox(label='RGB to BGR', value=False)
|
|
lowvram = gr.Checkbox(label='Low VRAM', value=False)
|
|
|
|
ctrls += (enabled,)
|
|
self.infotext_fields.append((enabled, "ControlNet Enabled"))
|
|
|
|
def refresh_all_models(*inputs):
|
|
update_cn_models()
|
|
|
|
dd = inputs[0]
|
|
selected = dd if dd in cn_models else "None"
|
|
return gr.Dropdown.update(value=selected, choices=list(cn_models.keys()))
|
|
|
|
|
|
with gr.Row():
|
|
module = gr.Dropdown(list(self.preprocessor.keys()), label=f"Preprocessor", value="none")
|
|
model = gr.Dropdown(list(cn_models.keys()), label=f"Model", value="None")
|
|
refresh_models = ToolButton(value=refresh_symbol)
|
|
refresh_models.click(refresh_all_models, model, model)
|
|
# ctrls += (refresh_models, )
|
|
weight = gr.Slider(label=f"Weight", value=1.0, minimum=0.0, maximum=2.0, step=.05)
|
|
|
|
ctrls += (module, model, weight,)
|
|
# model_dropdowns.append(model)
|
|
|
|
def build_sliders(module):
|
|
if module == "canny":
|
|
return [
|
|
gr.update(label="Annotator resolution", value=512, minimum=64, maximum=2048, step=1, interactive=True),
|
|
gr.update(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1, interactive=True),
|
|
gr.update(label="Canny high threshold", minimum=1, maximum=255, value=200, step=1, interactive=True),
|
|
]
|
|
elif module == "mlsd": #Hough
|
|
return [
|
|
gr.update(label="Hough Resolution", minimum=64, maximum=2048, value=512, step=1, interactive=True),
|
|
gr.update(label="Hough value threshold (MLSD)", minimum=0.01, maximum=2.0, value=0.1, step=0.01, interactive=True),
|
|
gr.update(label="Hough distance threshold (MLSD)", minimum=0.01, maximum=20.0, value=0.1, step=0.01, interactive=True)
|
|
]
|
|
elif module in ["hed", "fake_scribble"]:
|
|
return [
|
|
gr.update(label="HED Resolution", minimum=64, maximum=2048, value=512, step=1, interactive=True),
|
|
gr.update(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False),
|
|
gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
|
|
]
|
|
elif module in ["openpose", "openpose_hand", "segmentation"]:
|
|
return [
|
|
gr.update(label="Annotator Resolution", minimum=64, maximum=2048, value=512, step=1, interactive=True),
|
|
gr.update(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False),
|
|
gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
|
|
]
|
|
elif module == "depth":
|
|
return [
|
|
gr.update(label="Midas Resolution", minimum=64, maximum=2048, value=384, step=1, interactive=True),
|
|
gr.update(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False),
|
|
gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
|
|
]
|
|
elif module == "normal_map":
|
|
return [
|
|
gr.update(label="Normal Resolution", minimum=64, maximum=2048, value=512, step=1, interactive=True),
|
|
gr.update(label="Normal background threshold", minimum=0.0, maximum=1.0, value=0.4, step=0.01, interactive=True),
|
|
gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
|
|
]
|
|
elif module == "none":
|
|
return [
|
|
gr.update(label="Normal Resolution", value=64, minimum=64, maximum=2048, interactive=False),
|
|
gr.update(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False),
|
|
gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
|
|
]
|
|
else:
|
|
return [
|
|
gr.update(label="Annotator resolution", value=512, minimum=64, maximum=2048, step=1, interactive=True),
|
|
gr.update(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False),
|
|
gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
|
|
]
|
|
|
|
# advanced options
|
|
with gr.Column(visible=gradio_compat):
|
|
processor_res = gr.Slider(label="Annotator resolution", value=64, minimum=64, maximum=2048, interactive=False)
|
|
threshold_a = gr.Slider(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False)
|
|
threshold_b = gr.Slider(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False)
|
|
|
|
if gradio_compat:
|
|
module.change(build_sliders, inputs=[module], outputs=[processor_res, threshold_a, threshold_b])
|
|
|
|
self.infotext_fields.extend([
|
|
(module, f"ControlNet Preprocessor"),
|
|
(model, f"ControlNet Model"),
|
|
(weight, f"ControlNet Weight"),
|
|
])
|
|
|
|
def create_canvas(h, w):
|
|
return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
|
|
|
|
def svgPreprocess(inputs):
|
|
if (inputs):
|
|
if (inputs['image'].startswith("data:image/svg+xml;base64,")):
|
|
svg_data = base64.b64decode(inputs['image'].replace('data:image/svg+xml;base64,',''))
|
|
drawing = svg2rlg(io.BytesIO(svg_data))
|
|
png_data = renderPM.drawToString(drawing, fmt='PNG')
|
|
encoded_string = base64.b64encode(png_data)
|
|
base64_str = str(encoded_string, "utf-8")
|
|
base64_str = "data:image/png;base64,"+ base64_str
|
|
inputs['image'] = base64_str
|
|
return input_image.orgpreprocess(inputs)
|
|
return None
|
|
|
|
resize_mode = gr.Radio(choices=["Envelope (Outer Fit)", "Scale to Fit (Inner Fit)", "Just Resize"], value="Scale to Fit (Inner Fit)", label="Resize Mode")
|
|
with gr.Row():
|
|
with gr.Column():
|
|
canvas_width = gr.Slider(label="Canvas Width", minimum=256, maximum=1024, value=512, step=64)
|
|
canvas_height = gr.Slider(label="Canvas Height", minimum=256, maximum=1024, value=512, step=64)
|
|
|
|
if gradio_compat:
|
|
canvas_swap_res = ToolButton(value=switch_values_symbol)
|
|
|
|
create_button = gr.Button(value="Create blank canvas")
|
|
create_button.click(fn=create_canvas, inputs=[canvas_height, canvas_width], outputs=[input_image])
|
|
|
|
if gradio_compat:
|
|
canvas_swap_res.click(lambda w, h: (h, w), inputs=[canvas_width, canvas_height], outputs=[canvas_width, canvas_height])
|
|
|
|
ctrls += (input_image, scribble_mode, resize_mode, rgbbgr_mode)
|
|
ctrls += (lowvram,)
|
|
ctrls += (processor_res, threshold_a, threshold_b)
|
|
|
|
input_image.orgpreprocess=input_image.preprocess
|
|
input_image.preprocess=svgPreprocess
|
|
|
|
return ctrls
|
|
|
|
def set_infotext_fields(self, p, params, weight):
|
|
module, model = params
|
|
if model == "None" or model == "none":
|
|
return
|
|
p.extra_generation_params.update({
|
|
"ControlNet Enabled": True,
|
|
f"ControlNet Module": module,
|
|
f"ControlNet Model": model,
|
|
f"ControlNet Weight": weight,
|
|
})
|
|
|
|
def process(self, p, *args):
|
|
"""
|
|
This function is called before processing begins for AlwaysVisible scripts.
|
|
You can modify the processing object (p) here, inject hooks, etc.
|
|
args contains all values returned by components from ui()
|
|
"""
|
|
unet = p.sd_model.model.diffusion_model
|
|
|
|
def restore_networks():
|
|
if self.latest_network is not None:
|
|
print("restoring last networks")
|
|
self.input_image = None
|
|
self.latest_network.restore(unet)
|
|
self.latest_network = None
|
|
|
|
last_module = self.latest_params[0]
|
|
if last_module is not None:
|
|
self.unloadable.get(last_module, lambda:None)()
|
|
|
|
enabled, module, model, weight, image, scribble_mode, \
|
|
resize_mode, rgbbgr_mode, lowvram, pres, pthr_a, pthr_b = args
|
|
|
|
# Other scripts can control this extension now
|
|
if shared.opts.data.get("control_net_allow_script_control", False):
|
|
enabled = getattr(p, 'control_net_enabled', enabled)
|
|
module = getattr(p, 'control_net_module', module)
|
|
model = getattr(p, 'control_net_model', model)
|
|
weight = getattr(p, 'control_net_weight', weight)
|
|
image = getattr(p, 'control_net_image', image)
|
|
scribble_mode = getattr(p, 'control_net_scribble_mode', scribble_mode)
|
|
resize_mode = getattr(p, 'control_net_resize_mode', resize_mode)
|
|
rgbbgr_mode = getattr(p, 'control_net_rgbbgr_mode', rgbbgr_mode)
|
|
lowvram = getattr(p, 'control_net_lowvram', lowvram)
|
|
pres = getattr(p, 'control_net_pres', pres)
|
|
pthr_a = getattr(p, 'control_net_pthr_a', pthr_a)
|
|
pthr_b = getattr(p, 'control_net_pthr_b', pthr_b)
|
|
|
|
input_image = getattr(p, 'control_net_input_image', None)
|
|
else:
|
|
input_image = None
|
|
|
|
if not enabled:
|
|
restore_networks()
|
|
return
|
|
|
|
models_changed = self.latest_params[1] != model \
|
|
or self.latest_model_hash != p.sd_model.sd_model_hash or self.latest_network == None \
|
|
or (self.latest_network is not None and self.latest_network.lowvram != lowvram)
|
|
|
|
self.latest_params = (module, model)
|
|
self.latest_model_hash = p.sd_model.sd_model_hash
|
|
if models_changed:
|
|
restore_networks()
|
|
model_path = cn_models.get(model, None)
|
|
|
|
if model_path is None:
|
|
raise RuntimeError(f"model not found: {model}")
|
|
|
|
# trim '"' at start/end
|
|
if model_path.startswith("\"") and model_path.endswith("\""):
|
|
model_path = model_path[1:-1]
|
|
|
|
if not os.path.exists(model_path):
|
|
raise ValueError(f"file not found: {model_path}")
|
|
|
|
print(f"Loading preprocessor: {module}, model: {model}")
|
|
network = PlugableControlModel(
|
|
model_path=model_path,
|
|
config_path=shared.opts.data.get("control_net_model_config", default_conf),
|
|
weight=weight,
|
|
lowvram=lowvram,
|
|
base_model=unet,
|
|
)
|
|
network.to(p.sd_model.device, dtype=p.sd_model.dtype)
|
|
network.hook(unet, p.sd_model)
|
|
|
|
print(f"ControlNet model {model} loaded.")
|
|
self.latest_network = network
|
|
|
|
if input_image is not None:
|
|
input_image = HWC3(np.asarray(input_image))
|
|
elif image is not None:
|
|
input_image = HWC3(image['image'])
|
|
if not ((image['mask'][:, :, 0]==0).all() or (image['mask'][:, :, 0]==255).all()):
|
|
print("using mask as input")
|
|
input_image = HWC3(image['mask'][:, :, 0])
|
|
scribble_mode = True
|
|
else:
|
|
# use img2img init_image as default
|
|
input_image = getattr(p, "init_images", [None])[0]
|
|
if input_image is None:
|
|
raise ValueError('controlnet is enabled but no input image is given')
|
|
input_image = HWC3(np.asarray(input_image))
|
|
|
|
if scribble_mode:
|
|
detected_map = np.zeros_like(input_image, dtype=np.uint8)
|
|
detected_map[np.min(input_image, axis=2) < 127] = 255
|
|
input_image = detected_map
|
|
|
|
preprocessor = self.preprocessor[self.latest_params[0]]
|
|
h, w, bsz = p.height, p.width, p.batch_size
|
|
if pres > 64:
|
|
detected_map = preprocessor(input_image, res=pres, thr_a=pthr_a, thr_b=pthr_b)
|
|
else:
|
|
detected_map = preprocessor(input_image)
|
|
detected_map = HWC3(detected_map)
|
|
|
|
if module == "normal_map" or rgbbgr_mode:
|
|
control = torch.from_numpy(detected_map[:, :, ::-1].copy()).float().to(devices.get_device_for("controlnet")) / 255.0
|
|
else:
|
|
control = torch.from_numpy(detected_map.copy()).float().to(devices.get_device_for("controlnet")) / 255.0
|
|
|
|
control = rearrange(control, 'h w c -> c h w')
|
|
detected_map = rearrange(torch.from_numpy(detected_map), 'h w c -> c h w')
|
|
if resize_mode == "Scale to Fit (Inner Fit)":
|
|
transform = Compose([
|
|
Resize(h if h<w else w, interpolation=InterpolationMode.BICUBIC),
|
|
CenterCrop(size=(h, w))
|
|
])
|
|
control = transform(control)
|
|
detected_map = transform(detected_map)
|
|
elif resize_mode == "Envelope (Outer Fit)":
|
|
transform = Compose([
|
|
Resize(h if h>w else w, interpolation=InterpolationMode.BICUBIC),
|
|
CenterCrop(size=(h, w))
|
|
])
|
|
control = transform(control)
|
|
detected_map = transform(detected_map)
|
|
else:
|
|
control = Resize((h,w), interpolation=InterpolationMode.BICUBIC)(control)
|
|
detected_map = Resize((h,w), interpolation=InterpolationMode.BICUBIC)(detected_map)
|
|
|
|
# for log use
|
|
self.detected_map = rearrange(detected_map, 'c h w -> h w c').numpy().astype(np.uint8)
|
|
|
|
# control = torch.stack([control for _ in range(bsz)], dim=0)
|
|
self.latest_network.notify(control, weight)
|
|
self.set_infotext_fields(p, self.latest_params, weight)
|
|
|
|
def postprocess(self, p, processed, *args):
|
|
is_img2img = issubclass(type(p), StableDiffusionProcessingImg2Img)
|
|
no_detectmap_opt = shared.opts.data.get("control_net_no_detectmap", False)
|
|
if self.latest_network is None or no_detectmap_opt or (is_img2img and active_img2img_tab == 'img2img_batch_tab'):
|
|
return
|
|
if hasattr(self, "detected_map") and self.detected_map is not None:
|
|
result = self.detected_map
|
|
if self.latest_params[0] in ["canny", "mlsd", "scribble", "fake_scribble"]:
|
|
result = 255-result
|
|
processed.images.extend([result])
|
|
|
|
def update_script_args(p, value, arg_idx):
|
|
for s in scripts.scripts_txt2img.alwayson_scripts:
|
|
if isinstance(s, Script):
|
|
args = list(p.script_args)
|
|
# print(f"Changed arg {arg_idx} from {args[s.args_from + arg_idx - 1]} to {value}")
|
|
args[s.args_from + arg_idx] = value
|
|
p.script_args = tuple(args)
|
|
break
|
|
|
|
|
|
# def confirm_models(p, xs):
|
|
# for x in xs:
|
|
# if x in ["", "None"]:
|
|
# continue
|
|
# if not find_closest_lora_model_name(x):
|
|
# raise RuntimeError(f"Unknown ControlNet model: {x}")
|
|
|
|
def on_ui_settings():
|
|
section = ('control_net', "ControlNet")
|
|
shared.opts.add_option("control_net_model_config", shared.OptionInfo(
|
|
default_conf, "Config file for Control Net models", section=section))
|
|
shared.opts.add_option("control_net_models_path", shared.OptionInfo(
|
|
"", "Extra path to scan for ControlNet models (e.g. training output directory)", section=section))
|
|
|
|
shared.opts.add_option("control_net_control_transfer", shared.OptionInfo(
|
|
False, "Apply transfer control when loading models", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("control_net_no_detectmap", shared.OptionInfo(
|
|
False, "Do not append detectmap to output", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("control_net_only_midctrl_hires", shared.OptionInfo(
|
|
True, "Use mid-layer control on highres pass (second pass)", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("control_net_allow_script_control", shared.OptionInfo(
|
|
False, "Allow other script to control this extension", gr.Checkbox, {"interactive": True}, section=section))
|
|
|
|
# control_net_skip_hires
|
|
|
|
|
|
script_callbacks.on_ui_settings(on_ui_settings)
|
|
|
|
|
|
def set_active_img2img_tab(tab):
|
|
global active_img2img_tab
|
|
active_img2img_tab = tab.elem_id
|
|
|
|
|
|
def create_on_after_component():
|
|
img2img_tabs = set()
|
|
|
|
def inner(component, **_kwargs):
|
|
if type(component) is gr.State:
|
|
return
|
|
|
|
tab = component.parent
|
|
is_tab = type(tab) is gr.Tab and tab.elem_id is not None
|
|
is_img2img_tab = is_tab and tab.parent is not None and tab.parent.elem_id == 'mode_img2img'
|
|
if is_img2img_tab and tab.elem_id not in img2img_tabs:
|
|
tab.select(fn=set_active_img2img_tab, inputs=gr.State(tab), outputs=[])
|
|
img2img_tabs.add(tab.elem_id)
|
|
|
|
return inner
|
|
|
|
|
|
script_callbacks.on_after_component(create_on_after_component())
|