892 lines
42 KiB
Python
892 lines
42 KiB
Python
import gc
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import os
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from collections import OrderedDict
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from typing import Union, Dict, Any, Optional
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import importlib
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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, processing, masking, images
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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 scripts.cldm import PlugableControlModel
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from scripts.processor import *
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from scripts.adapter import PlugableAdapter
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from scripts.utils import load_state_dict
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from scripts.hook import ControlParams, UnetHook
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from scripts import external_code, global_state
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importlib.reload(global_state)
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importlib.reload(external_code)
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from modules.processing import StableDiffusionProcessingImg2Img
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from modules.images import save_image
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from PIL import Image
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from torchvision.transforms import Resize, InterpolationMode, CenterCrop, Compose
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gradio_compat = True
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try:
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from distutils.version import LooseVersion
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from importlib_metadata import version
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if LooseVersion(version("gradio")) < LooseVersion("3.10"):
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gradio_compat = False
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except ImportError:
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pass
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# svgsupports
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svgsupport = False
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try:
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import io
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import base64
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from svglib.svglib import svg2rlg
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from reportlab.graphics import renderPM
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svgsupport = True
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except ImportError:
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pass
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refresh_symbol = '\U0001f504' # 🔄
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switch_values_symbol = '\U000021C5' # ⇅
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camera_symbol = '\U0001F4F7' # 📷
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reverse_symbol = '\U000021C4' # ⇄
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tossup_symbol = '\u2934'
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webcam_enabled = False
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webcam_mirrored = False
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class ToolButton(gr.Button, gr.components.FormComponent):
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"""Small button with single emoji as text, fits inside gradio forms"""
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def __init__(self, **kwargs):
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super().__init__(variant="tool", **kwargs)
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def get_block_name(self):
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return "button"
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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 global_state.cn_models:
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return search
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search = search.lower()
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if search in global_state.cn_models_names:
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return global_state.cn_models_names.get(search)
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applicable = [name for name in global_state.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 global_state.cn_models_names[applicable[0]]
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def swap_img2img_pipeline(p: processing.StableDiffusionProcessingImg2Img):
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p.__class__ = processing.StableDiffusionProcessingTxt2Img
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dummy = processing.StableDiffusionProcessingTxt2Img()
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for k,v in dummy.__dict__.items():
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if hasattr(p, k):
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continue
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setattr(p, k, v)
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global_state.update_cn_models()
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def image_dict_from_unit(unit) -> Optional[Dict[str, np.ndarray]]:
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image = unit.image
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if image is None:
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return None
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if isinstance(image, (tuple, list)):
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image = {'image': image[0], 'mask': image[1]}
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elif not isinstance(image, dict):
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image = {'image': image, 'mask': None}
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# copy to enable modifying the dict and prevent response serialization error
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result = {'image': image['image'], 'mask': image['mask']}
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if isinstance(result['image'], str):
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result['image'] = external_code.to_base64_nparray(result['image'])
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if isinstance(result['mask'], str):
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result['mask'] = external_code.to_base64_nparray(result['mask'])
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elif result['mask'] is None:
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result['mask'] = np.zeros_like(result['image'], dtype=np.uint8)
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return result
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class Script(scripts.Script):
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model_cache = OrderedDict()
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def __init__(self) -> None:
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super().__init__()
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self.latest_network = None
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self.preprocessor = global_state.cn_preprocessor_modules
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self.unloadable = global_state.cn_preprocessor_unloadable
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self.input_image = None
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self.latest_model_hash = ""
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self.txt2img_w_slider = gr.Slider()
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self.txt2img_h_slider = gr.Slider()
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self.img2img_w_slider = gr.Slider()
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self.img2img_h_slider = gr.Slider()
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def title(self):
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return "ControlNet"
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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 after_component(self, component, **kwargs):
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if component.elem_id == "txt2img_width":
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self.txt2img_w_slider = component
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return self.txt2img_w_slider
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if component.elem_id == "txt2img_height":
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self.txt2img_h_slider = component
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return self.txt2img_h_slider
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if component.elem_id == "img2img_width":
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self.img2img_w_slider = component
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return self.img2img_w_slider
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if component.elem_id == "img2img_height":
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self.img2img_h_slider = component
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return self.img2img_h_slider
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def get_threshold_block(self, proc):
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pass
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def get_default_ui_unit(self):
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return external_code.ControlNetUnit(
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enabled=False,
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module="none",
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model="None",
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guess_mode=False,
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)
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def uigroup(self, tabname, is_img2img, elem_id_tabname):
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ctrls = ()
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infotext_fields = []
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default_unit = self.get_default_ui_unit()
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with gr.Row():
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input_image = gr.Image(source='upload', mirror_webcam=False, type='numpy', tool='sketch', elem_id=f'{elem_id_tabname}_{tabname}_input_image')
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generated_image = gr.Image(label="Annotator result", visible=False, elem_id=f'{elem_id_tabname}_{tabname}_generated_image')
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with gr.Row():
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gr.HTML(value='<p>Invert colors 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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webcam_enable = ToolButton(value=camera_symbol)
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webcam_mirror = ToolButton(value=reverse_symbol)
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send_dimen_button = ToolButton(value=tossup_symbol)
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with gr.Row():
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enabled = gr.Checkbox(label='Enable', value=default_unit.enabled)
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scribble_mode = gr.Checkbox(label='Invert Input Color', value=default_unit.invert_image)
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rgbbgr_mode = gr.Checkbox(label='RGB to BGR', value=default_unit.rgbbgr_mode)
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lowvram = gr.Checkbox(label='Low VRAM', value=default_unit.low_vram)
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guess_mode = gr.Checkbox(label='Guess Mode', value=default_unit.guess_mode)
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ctrls += (enabled,)
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# infotext_fields.append((enabled, "ControlNet Enabled"))
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def send_dimensions(image):
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def closesteight(num):
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rem = num % 8
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if rem <= 4:
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return round(num - rem)
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else:
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return round(num + (8 - rem))
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if(image):
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interm = np.asarray(image.get('image'))
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return closesteight(interm.shape[1]), closesteight(interm.shape[0])
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else:
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return gr.Slider.update(), gr.Slider.update()
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def webcam_toggle():
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global webcam_enabled
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webcam_enabled = not webcam_enabled
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return {"value": None, "source": "webcam" if webcam_enabled else "upload", "__type__": "update"}
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def webcam_mirror_toggle():
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global webcam_mirrored
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webcam_mirrored = not webcam_mirrored
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return {"mirror_webcam": webcam_mirrored, "__type__": "update"}
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webcam_enable.click(fn=webcam_toggle, inputs=None, outputs=input_image)
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webcam_mirror.click(fn=webcam_mirror_toggle, inputs=None, outputs=input_image)
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def refresh_all_models(*inputs):
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global_state.update_cn_models()
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dd = inputs[0]
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selected = dd if dd in global_state.cn_models else "None"
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return gr.Dropdown.update(value=selected, choices=list(global_state.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=default_unit.module)
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model = gr.Dropdown(list(global_state.cn_models.keys()), label=f"Model", value=default_unit.model)
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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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with gr.Row():
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weight = gr.Slider(label=f"Weight", value=default_unit.weight, minimum=0.0, maximum=2.0, step=.05)
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guidance_start = gr.Slider(label="Guidance Start (T)", value=default_unit.guidance_start, minimum=0.0, maximum=1.0, interactive=True)
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guidance_end = gr.Slider(label="Guidance End (T)", value=default_unit.guidance_end, minimum=0.0, maximum=1.0, interactive=True)
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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=2048, 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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gr.update(visible=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=64, maximum=2048, 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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gr.update(visible=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=64, maximum=2048, 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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gr.update(visible=True)
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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=64, maximum=2048, 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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gr.update(visible=True)
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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=64, maximum=2048, 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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gr.update(visible=True)
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]
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elif module in ["depth_leres", "depth_leres_boost"]:
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return [
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gr.update(label="LeReS Resolution", minimum=64, maximum=2048, value=512, step=1, interactive=True),
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gr.update(label="Remove Near %", value=0, minimum=0, maximum=100, step=0.1, interactive=True),
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gr.update(label="Remove Background %", value=0, minimum=0, maximum=100, step=0.1, interactive=True),
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gr.update(visible=True)
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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=64, maximum=2048, 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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gr.update(visible=True)
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]
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elif module == "binary":
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return [
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gr.update(label="Annotator resolution", value=512, minimum=64, maximum=2048, step=1, interactive=True),
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gr.update(label="Binary threshold", minimum=0, maximum=255, value=0, step=1, interactive=True),
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gr.update(label="Threshold B", value=64, minimum=64, maximum=1024, interactive=False),
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gr.update(visible=True)
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]
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elif module == "color":
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return [
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gr.update(label="Annotator Resolution", value=512, minimum=64, maximum=2048, step=8, 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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gr.update(visible=True)
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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=2048, 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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gr.update(visible=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=2048, 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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gr.update(visible=True)
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]
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# advanced options
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advanced = gr.Column(visible=False)
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with advanced:
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processor_res = gr.Slider(label="Annotator resolution", value=default_unit.processor_res, minimum=64, maximum=2048, interactive=False)
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threshold_a = gr.Slider(label="Threshold A", value=default_unit.threshold_a, minimum=64, maximum=1024, interactive=False)
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threshold_b = gr.Slider(label="Threshold B", value=default_unit.threshold_b, minimum=64, maximum=1024, interactive=False)
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if gradio_compat:
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module.change(build_sliders, inputs=[module], outputs=[processor_res, threshold_a, threshold_b, advanced])
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# infotext_fields.extend((module, model, weight))
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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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def svgPreprocess(inputs):
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if (inputs):
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if (inputs['image'].startswith("data:image/svg+xml;base64,") and svgsupport):
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svg_data = base64.b64decode(inputs['image'].replace('data:image/svg+xml;base64,',''))
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drawing = svg2rlg(io.BytesIO(svg_data))
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png_data = renderPM.drawToString(drawing, fmt='PNG')
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encoded_string = base64.b64encode(png_data)
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base64_str = str(encoded_string, "utf-8")
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base64_str = "data:image/png;base64,"+ base64_str
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inputs['image'] = base64_str
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return input_image.orgpreprocess(inputs)
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return None
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resize_mode = gr.Radio(choices=[e.value for e in external_code.ResizeMode], value=default_unit.resize_mode.value, label="Resize Mode")
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with gr.Row():
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with gr.Column():
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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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if gradio_compat:
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canvas_swap_res = ToolButton(value=switch_values_symbol)
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canvas_swap_res.click(lambda w, h: (h, w), inputs=[canvas_width, canvas_height], outputs=[canvas_width, canvas_height])
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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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def run_annotator(image, module, pres, pthr_a, pthr_b):
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img = HWC3(image['image'])
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if not ((image['mask'][:, :, 0]==0).all() or (image['mask'][:, :, 0]==255).all()):
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img = HWC3(image['mask'][:, :, 0])
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preprocessor = self.preprocessor[module]
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result = None
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if pres > 64:
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result, is_image = preprocessor(img, res=pres, thr_a=pthr_a, thr_b=pthr_b)
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else:
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result, is_image = preprocessor(img)
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if is_image:
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return gr.update(value=result, visible=True, interactive=False)
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with gr.Row():
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annotator_button = gr.Button(value="Preview annotator result")
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annotator_button_hide = gr.Button(value="Hide annotator result")
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annotator_button.click(fn=run_annotator, inputs=[input_image, module, processor_res, threshold_a, threshold_b], outputs=[generated_image])
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annotator_button_hide.click(fn=lambda: gr.update(visible=False), inputs=None, outputs=[generated_image])
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if is_img2img:
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send_dimen_button.click(fn=send_dimensions, inputs=[input_image], outputs=[self.img2img_w_slider, self.img2img_h_slider])
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else:
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send_dimen_button.click(fn=send_dimensions, inputs=[input_image], outputs=[self.txt2img_w_slider, self.txt2img_h_slider])
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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, guidance_start, guidance_end, guess_mode)
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self.register_modules(tabname, ctrls)
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input_image.orgpreprocess=input_image.preprocess
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input_image.preprocess=svgPreprocess
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def controlnet_unit_from_args(*args):
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unit = external_code.ControlNetUnit(*args)
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setattr(unit, 'is_ui', True)
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return unit
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unit = gr.State(default_unit)
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for comp in ctrls:
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event_subscribers = []
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if hasattr(comp, 'edit'):
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event_subscribers.append(comp.edit)
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elif hasattr(comp, 'click'):
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event_subscribers.append(comp.click)
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else:
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event_subscribers.append(comp.change)
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if hasattr(comp, 'clear'):
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event_subscribers.append(comp.clear)
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for event_subscriber in event_subscribers:
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event_subscriber(fn=controlnet_unit_from_args, inputs=list(ctrls), outputs=unit)
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return unit
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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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self.infotext_fields = []
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self.paste_field_names = []
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controls = ()
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max_models = shared.opts.data.get("control_net_max_models_num", 1)
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elem_id_tabname = ("img2img" if is_img2img else "txt2img") + "_controlnet"
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with gr.Group(elem_id=elem_id_tabname):
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with gr.Accordion("ControlNet", open = False, elem_id="controlnet"):
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if max_models > 1:
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with gr.Tabs(elem_id=f"{elem_id_tabname}_tabs"):
|
|
for i in range(max_models):
|
|
with gr.Tab(f"Control Model - {i}"):
|
|
controls += (self.uigroup(f"ControlNet-{i}", is_img2img, elem_id_tabname),)
|
|
else:
|
|
with gr.Column():
|
|
controls += (self.uigroup(f"ControlNet", is_img2img, elem_id_tabname),)
|
|
|
|
if shared.opts.data.get("control_net_sync_field_args", False):
|
|
for _, field_name in self.infotext_fields:
|
|
self.paste_field_names.append(field_name)
|
|
|
|
return controls
|
|
|
|
def register_modules(self, tabname, params):
|
|
enabled, module, model, weight = params[:4]
|
|
guidance_start, guidance_end, guess_mode = params[-3:]
|
|
|
|
self.infotext_fields.extend([
|
|
(enabled, f"{tabname} Enabled"),
|
|
(module, f"{tabname} Preprocessor"),
|
|
(model, f"{tabname} Model"),
|
|
(weight, f"{tabname} Weight"),
|
|
(guidance_start, f"{tabname} Guidance Start"),
|
|
(guidance_end, f"{tabname} Guidance End"),
|
|
])
|
|
|
|
def clear_control_model_cache(self):
|
|
Script.model_cache.clear()
|
|
gc.collect()
|
|
devices.torch_gc()
|
|
|
|
def load_control_model(self, p, unet, model, lowvram):
|
|
if model in Script.model_cache:
|
|
print(f"Loading model from cache: {model}")
|
|
return Script.model_cache[model]
|
|
|
|
# Remove model from cache to clear space before building another model
|
|
if len(Script.model_cache) > 0 and len(Script.model_cache) >= shared.opts.data.get("control_net_model_cache_size", 2):
|
|
Script.model_cache.popitem(last=False)
|
|
gc.collect()
|
|
devices.torch_gc()
|
|
|
|
model_net = self.build_control_model(p, unet, model, lowvram)
|
|
|
|
if shared.opts.data.get("control_net_model_cache_size", 2) > 0:
|
|
Script.model_cache[model] = model_net
|
|
|
|
return model_net
|
|
|
|
def build_control_model(self, p, unet, model, lowvram):
|
|
|
|
model_path = global_state.cn_models.get(model, None)
|
|
if model_path is None:
|
|
model = find_closest_lora_model_name(model)
|
|
model_path = global_state.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 model: {model}")
|
|
state_dict = load_state_dict(model_path)
|
|
network_module = PlugableControlModel
|
|
network_config = shared.opts.data.get("control_net_model_config", global_state.default_conf)
|
|
if not os.path.isabs(network_config):
|
|
network_config = os.path.join(global_state.script_dir, network_config)
|
|
|
|
if any([k.startswith("body.") or k == 'style_embedding' for k, v in state_dict.items()]):
|
|
# adapter model
|
|
network_module = PlugableAdapter
|
|
network_config = shared.opts.data.get("control_net_model_adapter_config", global_state.default_conf_adapter)
|
|
if not os.path.isabs(network_config):
|
|
network_config = os.path.join(global_state.script_dir, network_config)
|
|
|
|
override_config = os.path.splitext(model_path)[0] + ".yaml"
|
|
if os.path.exists(override_config):
|
|
network_config = override_config
|
|
|
|
network = network_module(
|
|
state_dict=state_dict,
|
|
config_path=network_config,
|
|
lowvram=lowvram,
|
|
base_model=unet,
|
|
)
|
|
network.to(p.sd_model.device, dtype=p.sd_model.dtype)
|
|
print(f"ControlNet model {model} loaded.")
|
|
return network
|
|
|
|
@staticmethod
|
|
def get_remote_call(p, attribute, default=None, idx=0, strict=False, force=False):
|
|
if not force and not shared.opts.data.get("control_net_allow_script_control", False):
|
|
return default
|
|
|
|
def get_element(obj, strict=False):
|
|
if not isinstance(obj, list):
|
|
return obj if not strict or idx == 0 else None
|
|
elif idx < len(obj):
|
|
return obj[idx]
|
|
else:
|
|
return None
|
|
|
|
attribute_value = get_element(getattr(p, attribute, None), strict)
|
|
default_value = get_element(default)
|
|
return attribute_value if attribute_value is not None else default_value
|
|
|
|
def parse_remote_call(self, p, unit: external_code.ControlNetUnit, idx):
|
|
selector = self.get_remote_call
|
|
|
|
unit.enabled = selector(p, "control_net_enabled", unit.enabled, idx, strict=True)
|
|
unit.module = selector(p, "control_net_module", unit.module, idx)
|
|
unit.model = selector(p, "control_net_model", unit.model, idx)
|
|
unit.weight = selector(p, "control_net_weight", unit.weight, idx)
|
|
unit.image = selector(p, "control_net_image", unit.image, idx)
|
|
unit.scribble_mode = selector(p, "control_net_scribble_mode", unit.invert_image, idx)
|
|
unit.resize_mode = selector(p, "control_net_resize_mode", unit.resize_mode, idx)
|
|
unit.rgbbgr_mode = selector(p, "control_net_rgbbgr_mode", unit.rgbbgr_mode, idx)
|
|
unit.low_vram = selector(p, "control_net_lowvram", unit.low_vram, idx)
|
|
unit.processor_res = selector(p, "control_net_pres", unit.processor_res, idx)
|
|
unit.threshold_a = selector(p, "control_net_pthr_a", unit.threshold_a, idx)
|
|
unit.threshold_b = selector(p, "control_net_pthr_b", unit.threshold_b, idx)
|
|
unit.guidance_start = selector(p, "control_net_guidance_start", unit.guidance_start, idx)
|
|
unit.guidance_end = selector(p, "control_net_guidance_end", unit.guidance_end, idx)
|
|
unit.guidance_end = selector(p, "control_net_guidance_strength", unit.guidance_end, idx)
|
|
unit.guess_mode = selector(p, "control_net_guess_mode", unit.guess_mode, idx)
|
|
|
|
return unit
|
|
|
|
def detectmap_proc(self, detected_map, module, rgbbgr_mode, resize_mode, h, w):
|
|
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 == external_code.ResizeMode.INNER_FIT:
|
|
h0 = detected_map.shape[1]
|
|
w0 = detected_map.shape[2]
|
|
w1 = w0
|
|
h1 = int(w0/w*h)
|
|
if (h/w > h0/w0):
|
|
h1 = h0
|
|
w1 = int(h0/h*w)
|
|
transform = Compose([
|
|
CenterCrop(size=(h1, w1)),
|
|
Resize(size=(h, w), interpolation=InterpolationMode.BICUBIC)
|
|
])
|
|
control = transform(control)
|
|
detected_map = transform(detected_map)
|
|
elif resize_mode == external_code.ResizeMode.OUTER_FIT:
|
|
h0 = detected_map.shape[1]
|
|
w0 = detected_map.shape[2]
|
|
h1 = h0
|
|
w1 = int(h0/h*w)
|
|
if (h/w > h0/w0):
|
|
w1 = w0
|
|
h1 = int(w0/w*h)
|
|
transform = Compose([
|
|
CenterCrop(size=(h1, w1)),
|
|
Resize(size=(h, w),interpolation=InterpolationMode.BICUBIC)
|
|
])
|
|
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
|
|
detected_map = rearrange(detected_map, 'c h w -> h w c').numpy().astype(np.uint8)
|
|
return control, detected_map
|
|
|
|
def is_ui(self, args):
|
|
return args and isinstance(args[0], external_code.ControlNetUnit) and getattr(args[0], 'is_ui', False)
|
|
|
|
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
|
|
if self.latest_network is not None:
|
|
# always restore (~0.05s)
|
|
self.latest_network.restore(unet)
|
|
|
|
params_group = external_code.get_all_units_from(args)
|
|
enabled_units = []
|
|
if len(params_group) == 0:
|
|
# fill a null group
|
|
remote_unit = self.parse_remote_call(p, self.get_default_ui_unit(), 0)
|
|
if remote_unit.enabled:
|
|
params_group.append(remote_unit)
|
|
|
|
for idx, unit in enumerate(params_group):
|
|
unit = self.parse_remote_call(p, unit, idx)
|
|
if not unit.enabled:
|
|
continue
|
|
|
|
enabled_units.append(unit)
|
|
if len(params_group) != 1:
|
|
prefix = f"ControlNet-{idx}"
|
|
else:
|
|
prefix = "ControlNet"
|
|
|
|
p.extra_generation_params.update({
|
|
f"{prefix} Enabled": True,
|
|
f"{prefix} Module": unit.module,
|
|
f"{prefix} Model": unit.model,
|
|
f"{prefix} Weight": unit.weight,
|
|
f"{prefix} Guidance Start": unit.guidance_start,
|
|
f"{prefix} Guidance End": unit.guidance_end,
|
|
})
|
|
|
|
if len(params_group) == 0 or len(enabled_units) == 0:
|
|
self.latest_network = None
|
|
return
|
|
|
|
detected_maps = []
|
|
forward_params = []
|
|
hook_lowvram = False
|
|
|
|
# cache stuff
|
|
if self.latest_model_hash != p.sd_model.sd_model_hash:
|
|
self.clear_control_model_cache()
|
|
|
|
# unload unused preproc
|
|
module_list = [unit.module for unit in enabled_units]
|
|
for key in self.unloadable:
|
|
if key not in module_list:
|
|
self.unloadable.get(unit.module, lambda:None)()
|
|
|
|
self.latest_model_hash = p.sd_model.sd_model_hash
|
|
for idx, unit in enumerate(enabled_units):
|
|
p_input_image = self.get_remote_call(p, "control_net_input_image", None, idx)
|
|
image = image_dict_from_unit(unit)
|
|
if image is not None:
|
|
while len(image['mask'].shape) < 3:
|
|
image['mask'] = image['mask'][..., np.newaxis]
|
|
|
|
resize_mode = external_code.resize_mode_from_value(unit.resize_mode)
|
|
invert_image = unit.invert_image
|
|
|
|
if unit.low_vram:
|
|
hook_lowvram = True
|
|
|
|
model_net = self.load_control_model(p, unet, unit.model, unit.low_vram)
|
|
model_net.reset()
|
|
|
|
is_img2img = img2img_tab_tracker.submit_button == 'img2img_generate'
|
|
is_img2img_batch_tab = is_img2img and img2img_tab_tracker.submit_img2img_tab == 'img2img_batch_tab'
|
|
if is_img2img_batch_tab and getattr(p, "image_control", None) is not None:
|
|
input_image = HWC3(np.asarray(p.image_control))
|
|
elif p_input_image is not None:
|
|
input_image = HWC3(np.asarray(p_input_image))
|
|
elif image is not None:
|
|
# Need to check the image for API compatibility
|
|
if isinstance(image['image'], str):
|
|
from modules.api.api import decode_base64_to_image
|
|
input_image = HWC3(np.asarray(decode_base64_to_image(image['image'])))
|
|
else:
|
|
input_image = HWC3(image['image'])
|
|
|
|
# Adding 'mask' check for API compatibility
|
|
if 'mask' in image and not ((image['mask'][:, :, 0]==0).all() or (image['mask'][:, :, 0]==255).all()):
|
|
print("using mask as input")
|
|
input_image = HWC3(image['mask'][:, :, 0])
|
|
invert_image = 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 issubclass(type(p), StableDiffusionProcessingImg2Img) and p.inpaint_full_res == True and p.image_mask is not None:
|
|
input_image = Image.fromarray(input_image)
|
|
mask = p.image_mask.convert('L')
|
|
crop_region = masking.get_crop_region(np.array(mask), p.inpaint_full_res_padding)
|
|
crop_region = masking.expand_crop_region(crop_region, p.width, p.height, mask.width, mask.height)
|
|
|
|
# scale crop region to the size of our image
|
|
x1, y1, x2, y2 = crop_region
|
|
scale_x, scale_y = p.width / float(input_image.width), p.height / float(input_image.height)
|
|
crop_region = int(x1 / scale_x), int(y1 / scale_y), int(x2 / scale_x), int(y2 / scale_y)
|
|
|
|
input_image = input_image.crop(crop_region)
|
|
input_image = images.resize_image(2, input_image, p.width, p.height)
|
|
input_image = HWC3(np.asarray(input_image))
|
|
|
|
if invert_image:
|
|
detected_map = np.zeros_like(input_image, dtype=np.uint8)
|
|
detected_map[np.min(input_image, axis=2) < 127] = 255
|
|
input_image = detected_map
|
|
|
|
print(f"Loading preprocessor: {unit.module}")
|
|
preprocessor = self.preprocessor[unit.module]
|
|
h, w, bsz = p.height, p.width, p.batch_size
|
|
if unit.processor_res > 64:
|
|
detected_map, is_image = preprocessor(input_image, res=unit.processor_res, thr_a=unit.threshold_a, thr_b=unit.threshold_b)
|
|
else:
|
|
detected_map, is_image = preprocessor(input_image)
|
|
|
|
if unit.module == "none" and "style" in unit.model:
|
|
detected_map_bytes = detected_map[:,:,0].tobytes()
|
|
detected_map = np.ndarray((round(input_image.shape[0]/4),input_image.shape[1]),dtype="float32",buffer=detected_map_bytes)
|
|
detected_map = torch.Tensor(detected_map).to(devices.get_device_for("controlnet"))
|
|
is_image = False
|
|
|
|
if is_image:
|
|
control, detected_map = self.detectmap_proc(detected_map, unit.module, unit.rgbbgr_mode, resize_mode, h, w)
|
|
detected_maps.append((detected_map, unit.module))
|
|
else:
|
|
control = detected_map
|
|
if unit.module == 'clip_vision':
|
|
fake_detected_map = np.ndarray((detected_map.shape[0]*4, detected_map.shape[1]),dtype="uint8",buffer=detected_map.numpy(force=True).tobytes())
|
|
detected_maps.append((fake_detected_map, unit.module))
|
|
|
|
forward_param = ControlParams(
|
|
control_model=model_net,
|
|
hint_cond=control,
|
|
guess_mode=unit.guess_mode,
|
|
weight=unit.weight,
|
|
guidance_stopped=False,
|
|
start_guidance_percent=unit.guidance_start,
|
|
stop_guidance_percent=unit.guidance_end,
|
|
advanced_weighting=None,
|
|
is_adapter=isinstance(model_net, PlugableAdapter),
|
|
is_extra_cond=getattr(model_net, "target", "") == "scripts.adapter.StyleAdapter"
|
|
)
|
|
forward_params.append(forward_param)
|
|
|
|
del model_net
|
|
|
|
self.latest_network = UnetHook(lowvram=hook_lowvram)
|
|
self.latest_network.hook(unet)
|
|
self.latest_network.notify(forward_params, p.sampler_name in ["DDIM", "PLMS", "UniPC"])
|
|
self.detected_map = detected_maps
|
|
|
|
if len(enabled_units) > 0 and shared.opts.data.get("control_net_skip_img2img_processing") and hasattr(p, "init_images"):
|
|
swap_img2img_pipeline(p)
|
|
|
|
def postprocess(self, p, processed, *args):
|
|
if shared.opts.data.get("control_net_detectmap_autosaving", False) and self.latest_network is not None:
|
|
for detect_map, module in self.detected_map:
|
|
detectmap_dir = os.path.join(shared.opts.data.get("control_net_detectedmap_dir", False), module)
|
|
if not os.path.isabs(detectmap_dir):
|
|
detectmap_dir = os.path.join(p.outpath_samples, detectmap_dir)
|
|
if module != "none":
|
|
os.makedirs(detectmap_dir, exist_ok=True)
|
|
img = Image.fromarray(detect_map)
|
|
save_image(img, detectmap_dir, module)
|
|
|
|
is_img2img = img2img_tab_tracker.submit_button == 'img2img_generate'
|
|
is_img2img_batch_tab = self.is_ui(args) and is_img2img and img2img_tab_tracker.submit_img2img_tab == 'img2img_batch_tab'
|
|
if self.latest_network is None or is_img2img_batch_tab:
|
|
return
|
|
|
|
no_detectmap_opt = shared.opts.data.get("control_net_no_detectmap", False)
|
|
if not no_detectmap_opt and hasattr(self, "detected_map") and self.detected_map is not None:
|
|
for detect_map, module in self.detected_map:
|
|
if detect_map is None:
|
|
continue
|
|
if module in ["canny", "mlsd", "scribble", "fake_scribble", "pidinet", "binary"]:
|
|
detect_map = 255-detect_map
|
|
processed.images.extend([Image.fromarray(detect_map)])
|
|
|
|
self.input_image = None
|
|
self.latest_network.restore(p.sd_model.model.diffusion_model)
|
|
self.latest_network = None
|
|
|
|
gc.collect()
|
|
devices.torch_gc()
|
|
|
|
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 on_ui_settings():
|
|
section = ('control_net', "ControlNet")
|
|
shared.opts.add_option("control_net_model_config", shared.OptionInfo(
|
|
global_state.default_conf, "Config file for Control Net models", section=section))
|
|
shared.opts.add_option("control_net_model_adapter_config", shared.OptionInfo(
|
|
global_state.default_conf_adapter, "Config file for Adapter models", section=section))
|
|
shared.opts.add_option("control_net_detectedmap_dir", shared.OptionInfo(
|
|
global_state.default_detectedmap_dir, "Directory for detected maps auto saving", 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_max_models_num", shared.OptionInfo(
|
|
1, "Multi ControlNet: Max models amount (requires restart)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}, section=section))
|
|
shared.opts.add_option("control_net_model_cache_size", shared.OptionInfo(
|
|
1, "Model cache size (requires restart)", gr.Slider, {"minimum": 1, "maximum": 5, "step": 1}, 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_detectmap_autosaving", shared.OptionInfo(
|
|
False, "Allow detectmap auto saving", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("control_net_only_midctrl_hires", shared.OptionInfo(
|
|
True, "Use mid-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))
|
|
shared.opts.add_option("control_net_skip_img2img_processing", shared.OptionInfo(
|
|
False, "Skip img2img processing when using img2img initial image", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("control_net_monocular_depth_optim", shared.OptionInfo(
|
|
False, "Enable optimized monocular depth estimation", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("control_net_only_mid_control", shared.OptionInfo(
|
|
False, "Only use mid-control when inference", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("control_net_cfg_based_guidance", shared.OptionInfo(
|
|
False, "Enable CFG-Based guidance", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("control_net_sync_field_args", shared.OptionInfo(
|
|
False, "Passing ControlNet parameters with \"Send to img2img\"", gr.Checkbox, {"interactive": True}, section=section))
|
|
# shared.opts.add_option("control_net_advanced_weighting", shared.OptionInfo(
|
|
# False, "Enable advanced weight tuning", gr.Checkbox, {"interactive": False}, section=section))
|
|
|
|
|
|
class Img2ImgTabTracker:
|
|
def __init__(self):
|
|
self.img2img_tabs = set()
|
|
self.active_img2img_tab = 'img2img_img2img_tab'
|
|
self.submit_img2img_tab = None
|
|
self.submit_button = None
|
|
|
|
def save_submit_img2img_tab(self, button_elem_id):
|
|
self.submit_img2img_tab = self.active_img2img_tab
|
|
self.submit_button = button_elem_id
|
|
|
|
def set_active_img2img_tab(self, tab_elem_id):
|
|
self.active_img2img_tab = tab_elem_id
|
|
|
|
def on_after_component_callback(self, component, **_kwargs):
|
|
if type(component) is gr.State:
|
|
return
|
|
|
|
if type(component) is gr.Button and component.elem_id in ('img2img_generate', 'txt2img_generate'):
|
|
component.click(fn=self.save_submit_img2img_tab, inputs=gr.State(component.elem_id), outputs=[])
|
|
return
|
|
|
|
tab = getattr(component, 'parent', None)
|
|
is_tab = type(tab) is gr.Tab and getattr(tab, 'elem_id', None) is not None
|
|
is_img2img_tab = is_tab and getattr(tab, 'parent', None) is not None and getattr(tab.parent, 'elem_id', None) == 'mode_img2img'
|
|
if is_img2img_tab and tab.elem_id not in self.img2img_tabs:
|
|
tab.select(fn=self.set_active_img2img_tab, inputs=gr.State(tab.elem_id), outputs=[])
|
|
self.img2img_tabs.add(tab.elem_id)
|
|
return
|
|
|
|
|
|
img2img_tab_tracker = Img2ImgTabTracker()
|
|
script_callbacks.on_ui_settings(on_ui_settings)
|
|
script_callbacks.on_after_component(img2img_tab_tracker.on_after_component_callback)
|