462 lines
21 KiB
Python
462 lines
21 KiB
Python
import os
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import stat
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from collections import OrderedDict
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import torch
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import modules.scripts as scripts
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from modules import shared, devices, script_callbacks
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import gradio as gr
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import numpy as np
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from einops import rearrange
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from modules import sd_models
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from torchvision.transforms import Resize, InterpolationMode, CenterCrop, Compose
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from scripts.cldm import PlugableControlModel
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from scripts.processor import *
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from modules.ui_components import ToolButton
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CN_MODEL_EXTS = [".pt", ".pth", ".ckpt", ".safetensors"]
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cn_models = {} # "My_Lora(abcd1234)" -> C:/path/to/model.safetensors
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cn_models_names = {} # "my_lora" -> "My_Lora(abcd1234)"
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cn_models_dir = os.path.join(scripts.basedir(), "models")
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os.makedirs(cn_models_dir, exist_ok=True)
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default_conf = os.path.join(cn_models_dir, "cldm_v15.yaml")
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refresh_symbol = '\U0001f504' # 🔄
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def traverse_all_files(curr_path, model_list):
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f_list = [(os.path.join(curr_path, entry.name), entry.stat())
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for entry in os.scandir(curr_path)]
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for f_info in f_list:
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fname, fstat = f_info
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if os.path.splitext(fname)[1] in CN_MODEL_EXTS:
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model_list.append(f_info)
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elif stat.S_ISDIR(fstat.st_mode):
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model_list = traverse_all_files(fname, model_list)
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return model_list
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def get_all_models(sort_by, filter_by, path):
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res = OrderedDict()
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fileinfos = traverse_all_files(path, [])
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filter_by = filter_by.strip(" ")
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if len(filter_by) != 0:
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fileinfos = [x for x in fileinfos if filter_by.lower()
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in os.path.basename(x[0]).lower()]
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if sort_by == "name":
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fileinfos = sorted(fileinfos, key=lambda x: os.path.basename(x[0]))
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elif sort_by == "date":
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fileinfos = sorted(fileinfos, key=lambda x: -x[1].st_mtime)
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elif sort_by == "path name":
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fileinfos = sorted(fileinfos)
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for finfo in fileinfos:
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filename = finfo[0]
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name = os.path.splitext(os.path.basename(filename))[0]
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# Prevent a hypothetical "None.pt" from being listed.
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if name != "None":
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res[name + f" [{sd_models.model_hash(filename)}]"] = filename
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return res
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def find_closest_lora_model_name(search: str):
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if not search:
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return None
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if search in cn_models:
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return search
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search = search.lower()
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if search in cn_models_names:
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return cn_models_names.get(search)
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applicable = [name for name in cn_models_names.keys()
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if search in name.lower()]
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if not applicable:
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return None
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applicable = sorted(applicable, key=lambda name: len(name))
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return cn_models_names[applicable[0]]
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def update_cn_models():
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global cn_models, cn_models_names
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res = OrderedDict()
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ext_dirs = (shared.opts.data.get("control_net_models_path", None), getattr(shared.cmd_opts, 'controlnet_dir', None))
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extra_lora_paths = (extra_lora_path for extra_lora_path in ext_dirs
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if extra_lora_path is not None and os.path.exists(extra_lora_path))
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paths = [cn_models_dir, *extra_lora_paths]
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for path in paths:
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sort_by = shared.opts.data.get(
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"control_net_models_sort_models_by", "name")
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filter_by = shared.opts.data.get("control_net_models_name_filter", "")
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found = get_all_models(sort_by, filter_by, path)
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res = {**found, **res}
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cn_models = OrderedDict(**{"None": None}, **res)
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cn_models_names = {}
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for name_and_hash, filename in cn_models.items():
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if filename == None:
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continue
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name = os.path.splitext(os.path.basename(filename))[0].lower()
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cn_models_names[name] = name_and_hash
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update_cn_models()
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class Script(scripts.Script):
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def __init__(self) -> None:
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super().__init__()
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self.latest_params = (None, None)
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self.latest_network = None
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self.preprocessor = {
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"none": lambda x, *args, **kwargs: x,
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"canny": canny,
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"depth": midas,
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"hed": hed,
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"mlsd": mlsd,
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"normal_map": midas_normal,
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"openpose": openpose,
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"openpose_hand": openpose_hand,
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"scribble": simple_scribble,
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"fake_scribble": fake_scribble,
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"segmentation": uniformer,
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}
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self.unloadable = {
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"hed": unload_hed,
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"fake_scribble": unload_hed,
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"mlsd": unload_mlsd,
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"depth": unload_midas,
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"normal_map": unload_midas,
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"openpose": unload_openpose,
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"openpose_hand": unload_openpose,
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"segmentation": unload_uniformer,
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}
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self.input_image = None
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self.latest_model_hash = ""
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def title(self):
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return "ControlNet for generating"
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def show(self, is_img2img):
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# if is_img2img:
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# return False
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return scripts.AlwaysVisible
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def get_threshold_block(self, proc):
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pass
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def ui(self, is_img2img):
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"""this function should create gradio UI elements. See https://gradio.app/docs/#components
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The return value should be an array of all components that are used in processing.
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Values of those returned components will be passed to run() and process() functions.
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"""
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ctrls = ()
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model_dropdowns = []
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self.infotext_fields = []
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with gr.Group():
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with gr.Accordion('ControlNet', open=False):
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input_image = gr.Image(source='upload', type='numpy', tool='sketch')
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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>')
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with gr.Row():
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enabled = gr.Checkbox(label='Enable', value=False)
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scribble_mode = gr.Checkbox(label='Scribble Mode (Invert colors)', value=False)
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rgbbgr_mode = gr.Checkbox(label='RGB to BGR', value=False)
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lowvram = gr.Checkbox(label='Low VRAM', value=False)
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ctrls += (enabled,)
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self.infotext_fields.append((enabled, "ControlNet Enabled"))
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def refresh_all_models(*inputs):
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update_cn_models()
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dd = inputs[0]
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selected = dd if dd in cn_models else "None"
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return gr.Dropdown.update(value=selected, choices=list(cn_models.keys()))
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with gr.Row():
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module = gr.Dropdown(list(self.preprocessor.keys()), label=f"Preprocessor", value="none")
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model = gr.Dropdown(list(cn_models.keys()), label=f"Model", value="None")
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refresh_models = ToolButton(value=refresh_symbol)
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refresh_models.click(refresh_all_models, model, model)
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# ctrls += (refresh_models, )
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weight = gr.Slider(label=f"Weight", value=1.0, minimum=0.0, maximum=2.0, step=.05)
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ctrls += (module, model, weight,)
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# model_dropdowns.append(model)
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def build_sliders(module):
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if module == "canny":
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return [
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gr.update(label="Annotator resolution", value=512, minimum=64, maximum=1024, step=1, interactive=True),
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gr.update(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1, interactive=True),
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gr.update(label="Canny high threshold", minimum=1, maximum=255, value=200, step=1, interactive=True),
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]
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elif module == "mlsd": #Hough
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return [
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gr.update(label="Hough Resolution", minimum=128, maximum=1024, value=512, step=1, interactive=True),
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gr.update(label="Hough value threshold (MLSD)", minimum=0.01, maximum=2.0, value=0.1, step=0.01, interactive=True),
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gr.update(label="Hough distance threshold (MLSD)", minimum=0.01, maximum=20.0, value=0.1, step=0.01, interactive=True)
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]
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elif module in ["hed", "fake_scribble"]:
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return [
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gr.update(label="HED Resolution", minimum=128, maximum=1024, value=512, step=1, interactive=True),
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gr.update(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False),
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gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
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]
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elif module in ["openpose", "openpose_hand", "segmentation"]:
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return [
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gr.update(label="Annotator Resolution", minimum=128, maximum=1024, value=512, step=1, interactive=True),
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gr.update(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False),
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gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
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]
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elif module == "depth":
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return [
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gr.update(label="Midas Resolution", minimum=128, maximum=1024, value=384, step=1, interactive=True),
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gr.update(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False),
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gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
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]
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elif module == "normal_map":
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return [
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gr.update(label="Normal Resolution", minimum=128, maximum=1024, value=512, step=1, interactive=True),
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gr.update(label="Normal background threshold", minimum=0.0, maximum=1.0, value=0.4, step=0.01, interactive=True),
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gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
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]
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elif module == "none":
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return [
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gr.update(label="Normal Resolution", value=64, minimum=64, maximum=1024, interactive=False),
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gr.update(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False),
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gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
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]
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else:
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return [
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gr.update(label="Annotator resolution", value=512, minimum=64, maximum=1024, step=1, interactive=True),
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gr.update(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False),
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gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
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]
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# advanced options
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with gr.Column():
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processor_res = gr.Slider(label="Annotator resolution", value=64, minimum=64, maximum=1024, interactive=False)
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threshold_a = gr.Slider(label="Threshold A", value=64, minimum=64, maximum=1024, interactive=False)
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threshold_b = gr.Slider(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False)
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module.change(build_sliders, inputs=[module], outputs=[processor_res, threshold_a, threshold_b])
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self.infotext_fields.extend([
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(module, f"ControlNet Preprocessor"),
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(model, f"ControlNet Model"),
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(weight, f"ControlNet Weight"),
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])
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def create_canvas(h, w):
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return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
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resize_mode = gr.Radio(choices=["Envelope (Outer Fit)", "Scale to Fit (Inner Fit)", "Just Resize"], value="Scale to Fit (Inner Fit)", label="Resize Mode")
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with gr.Row():
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canvas_width = gr.Slider(label="Canvas Width", minimum=256, maximum=1024, value=512, step=64)
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canvas_height = gr.Slider(label="Canvas Height", minimum=256, maximum=1024, value=512, step=64)
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with gr.Row():
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create_button = gr.Button(value="Create blank canvas")
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create_button.click(fn=create_canvas, inputs=[canvas_height, canvas_width], outputs=[input_image])
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ctrls += (input_image, scribble_mode, resize_mode, rgbbgr_mode)
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ctrls += (lowvram,)
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ctrls += (processor_res, threshold_a, threshold_b)
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return ctrls
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def set_infotext_fields(self, p, params, weight):
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module, model = params
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if model == "None" or model == "none":
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return
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p.extra_generation_params.update({
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"ControlNet Enabled": True,
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f"ControlNet Module": module,
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f"ControlNet Model": model,
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f"ControlNet Weight": weight,
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})
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def process(self, p, *args):
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"""
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This function is called before processing begins for AlwaysVisible scripts.
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You can modify the processing object (p) here, inject hooks, etc.
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args contains all values returned by components from ui()
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"""
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unet = p.sd_model.model.diffusion_model
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def restore_networks():
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if self.latest_network is not None:
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print("restoring last networks")
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self.input_image = None
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self.latest_network.restore(unet)
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self.latest_network = None
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last_module = self.latest_params[0]
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if last_module is not None:
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self.unloadable.get(last_module, lambda:None)()
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enabled, module, model, weight, image, scribble_mode, \
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resize_mode, rgbbgr_mode, lowvram, pres, pthr_a, pthr_b = args
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# Other scripts can control this extension now
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if shared.opts.data.get("control_net_allow_script_control", False):
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enabled = getattr(p, 'control_net_enabled', enabled)
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module = getattr(p, 'control_net_module', module)
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model = getattr(p, 'control_net_model', model)
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weight = getattr(p, 'control_net_weight', weight)
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image = getattr(p, 'control_net_image', image)
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scribble_mode = getattr(p, 'control_net_scribble_mode', scribble_mode)
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resize_mode = getattr(p, 'control_net_resize_mode', resize_mode)
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rgbbgr_mode = getattr(p, 'control_net_rgbbgr_mode', rgbbgr_mode)
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lowvram = getattr(p, 'control_net_lowvram', lowvram)
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input_image = getattr(p, 'control_net_input_image', None)
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else:
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input_image = None
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if not enabled:
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restore_networks()
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return
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models_changed = self.latest_params[1] != model \
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or self.latest_model_hash != p.sd_model.sd_model_hash or self.latest_network == None \
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or (self.latest_network is not None and self.latest_network.lowvram != lowvram)
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self.latest_params = (module, model)
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self.latest_model_hash = p.sd_model.sd_model_hash
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if models_changed:
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restore_networks()
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model_path = cn_models.get(model, None)
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if model_path is None:
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raise RuntimeError(f"model not found: {model}")
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# trim '"' at start/end
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if model_path.startswith("\"") and model_path.endswith("\""):
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model_path = model_path[1:-1]
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if not os.path.exists(model_path):
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raise ValueError(f"file not found: {model_path}")
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print(f"Loading preprocessor: {module}, model: {model}")
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network = PlugableControlModel(
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model_path=model_path,
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config_path=shared.opts.data.get("control_net_model_config", default_conf),
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weight=weight,
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lowvram=lowvram,
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base_model=unet,
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)
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network.to(p.sd_model.device, dtype=p.sd_model.dtype)
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network.hook(unet, p.sd_model)
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print(f"ControlNet model {model} loaded.")
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self.latest_network = network
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if input_image is not None:
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input_image = HWC3(np.asarray(input_image))
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elif image is not None:
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input_image = HWC3(image['image'])
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if not ((image['mask'][:, :, 0]==0).all() or (image['mask'][:, :, 0]==255).all()):
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print("using mask as input")
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input_image = HWC3(image['mask'][:, :, 0])
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scribble_mode = True
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else:
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# use img2img init_image as default
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input_image = getattr(p, "init_images", [None])[0]
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if input_image is None:
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raise ValueError('controlnet is enabled but no input image is given')
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input_image = HWC3(np.asarray(input_image))
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if scribble_mode:
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detected_map = np.zeros_like(input_image, dtype=np.uint8)
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detected_map[np.min(input_image, axis=2) < 127] = 255
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input_image = detected_map
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preprocessor = self.preprocessor[self.latest_params[0]]
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h, w, bsz = p.height, p.width, p.batch_size
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if pres > 64:
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detected_map = preprocessor(input_image, res=pres, thr_a=pthr_a, thr_b=pthr_b)
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else:
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detected_map = preprocessor(input_image)
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detected_map = HWC3(detected_map)
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if module == "normal_map" or rgbbgr_mode:
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control = torch.from_numpy(detected_map[:, :, ::-1].copy()).float().to(devices.get_device_for("controlnet")) / 255.0
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else:
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control = torch.from_numpy(detected_map.copy()).float().to(devices.get_device_for("controlnet")) / 255.0
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control = rearrange(control, 'h w c -> c h w')
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detected_map = rearrange(torch.from_numpy(detected_map), 'h w c -> c h w')
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if resize_mode == "Scale to Fit (Inner Fit)":
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transform = Compose([
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Resize(h if h<w else w, interpolation=InterpolationMode.BICUBIC),
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CenterCrop(size=(h, w))
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])
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control = transform(control)
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detected_map = transform(detected_map)
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elif resize_mode == "Envelope (Outer Fit)":
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transform = Compose([
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Resize(h if h>w else w, interpolation=InterpolationMode.BICUBIC),
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CenterCrop(size=(h, w))
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])
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control = transform(control)
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detected_map = transform(detected_map)
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else:
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control = Resize((h,w), interpolation=InterpolationMode.BICUBIC)(control)
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detected_map = Resize((h,w), interpolation=InterpolationMode.BICUBIC)(detected_map)
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# for log use
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self.detected_map = rearrange(detected_map, 'c h w -> h w c').numpy().astype(np.uint8)
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# control = torch.stack([control for _ in range(bsz)], dim=0)
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self.latest_network.notify(control, weight)
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self.set_infotext_fields(p, self.latest_params, weight)
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def postprocess(self, p, processed, *args):
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if self.latest_network is None or shared.opts.data.get("control_net_no_detectmap", False):
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return
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if hasattr(self, "detected_map") and self.detected_map is not None:
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result = self.detected_map
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if self.latest_params[0] in ["canny", "mlsd", "scribble", "fake_scribble"]:
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result = 255-result
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processed.images.extend([result])
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def update_script_args(p, value, arg_idx):
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for s in scripts.scripts_txt2img.alwayson_scripts:
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if isinstance(s, Script):
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args = list(p.script_args)
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|
# 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)
|