1118 lines
52 KiB
Python
1118 lines
52 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 annotator import annotator_path
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from scripts import global_state, hook, external_code, processor, xyz_grid_support
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importlib.reload(annotator_path)
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importlib.reload(processor)
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importlib.reload(global_state)
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importlib.reload(hook)
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importlib.reload(external_code)
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importlib.reload(xyz_grid_support)
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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 modules.processing import StableDiffusionProcessingImg2Img, StableDiffusionProcessingTxt2Img
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from modules.images import save_image
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from modules.ui_components import FormRow
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import cv2
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from pathlib import Path
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from PIL import Image, ImageFilter, ImageOps
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from scripts.lvminthin import lvmin_thin, nake_nms
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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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trigger_symbol = '\U0001F4A5' # 💥
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webcam_enabled = False
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webcam_mirrored = False
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txt2img_submit_button = None
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img2img_submit_button = None
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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_module_basename(self, module):
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return global_state.reverse_preprocessor_aliases.get(module, module)
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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', brush_radius=20, 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="Preprocessor Preview", 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>Set the preprocessor to [invert] If your image has white background and black lines.</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 FormRow(elem_classes="checkboxes-row", variant="compact"):
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enabled = gr.Checkbox(label='Enable', value=default_unit.enabled)
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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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pixel_perfect = gr.Checkbox(label='Pixel Perfect', value=default_unit.pixel_perfect)
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preprocessor_preview = gr.Checkbox(label='Allow Preview', value=False)
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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(global_state.ui_preprocessor_keys, label=f"Preprocessor", value=default_unit.module)
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trigger_preprocessor = ToolButton(value=trigger_symbol, visible=False)
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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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with gr.Row():
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weight = gr.Slider(label=f"Control Weight", value=default_unit.weight, minimum=0.0, maximum=2.0, step=.05)
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guidance_start = gr.Slider(label="Starting Control Step", value=default_unit.guidance_start, minimum=0.0, maximum=1.0, interactive=True)
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guidance_end = gr.Slider(label="Ending Control Step", 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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def build_sliders(module, pp):
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module = self.get_module_basename(module)
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if module == "canny":
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return [
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gr.update(label="Preprocessor resolution", value=512, minimum=64, maximum=2048, step=1, visible=not pp, interactive=not pp),
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gr.update(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1, visible=True, interactive=True),
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gr.update(label="Canny high threshold", minimum=1, maximum=255, value=200, step=1, visible=True, 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="Preprocessor Resolution", minimum=64, maximum=2048, value=512, step=1, visible=not pp, interactive=not pp),
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gr.update(label="Hough value threshold (MLSD)", minimum=0.01, maximum=2.0, value=0.1, step=0.01, visible=True, 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, visible=True, interactive=True),
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gr.update(visible=True)
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]
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elif module in ["hed", "scribble_hed", "hed_safe"]:
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return [
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gr.update(label="Preprocessor Resolution", minimum=64, maximum=2048, value=512, step=1, visible=not pp, interactive=not pp),
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gr.update(visible=False, interactive=False),
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gr.update(visible=False, interactive=False),
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gr.update(visible=True)
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]
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elif module in ["openpose", "openpose_full", "segmentation"]:
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return [
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gr.update(label="Preprocessor Resolution", minimum=64, maximum=2048, value=512, step=1, visible=not pp, interactive=not pp),
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gr.update(visible=False, interactive=False),
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gr.update(visible=False, 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="Preprocessor Resolution", minimum=64, maximum=2048, value=512, step=1, visible=not pp, interactive=not pp),
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gr.update(visible=False, interactive=False),
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gr.update(visible=False, 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="Preprocessor Resolution", minimum=64, maximum=2048, value=512, step=1, visible=not pp, interactive=not pp),
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gr.update(label="Remove Near %", value=0, minimum=0, maximum=100, step=0.1, visible=True, interactive=True),
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gr.update(label="Remove Background %", value=0, minimum=0, maximum=100, step=0.1, visible=True, 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="Preprocessor Resolution", minimum=64, maximum=2048, value=512, step=1, visible=not pp, interactive=not pp),
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gr.update(label="Normal background threshold", minimum=0.0, maximum=1.0, value=0.4, step=0.01, visible=True, interactive=True),
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gr.update(visible=False, interactive=False),
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gr.update(visible=True)
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]
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elif module == "threshold":
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return [
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gr.update(label="Preprocessor resolution", value=512, minimum=64, maximum=2048, step=1, visible=not pp, interactive=not pp),
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gr.update(label="Binarization Threshold", minimum=0, maximum=255, value=127, step=1, visible=True, interactive=True),
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gr.update(visible=False, interactive=False),
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gr.update(visible=True)
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]
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elif module == "scribble_xdog":
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return [
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gr.update(label="Preprocessor resolution", value=512, minimum=64, maximum=2048, step=1, visible=not pp, interactive=not pp),
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gr.update(label="XDoG Threshold", minimum=1, maximum=64, value=32, step=1, visible=True, interactive=True),
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gr.update(visible=False, interactive=False),
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gr.update(visible=True)
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]
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elif module == "tile_gaussian":
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return [
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gr.update(label="Preprocessor resolution", value=512, minimum=64, maximum=2048, step=1, visible=not pp, interactive=not pp),
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gr.update(label="Noise", value=16.0, minimum=0.1, maximum=48.0, step=0.01, visible=True, interactive=True),
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gr.update(visible=False, 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="Preprocessor Resolution", value=512, minimum=64, maximum=2048, step=8, visible=not pp, interactive=not pp),
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gr.update(visible=False, interactive=False),
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gr.update(visible=False, interactive=False),
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gr.update(visible=True)
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]
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elif module == "mediapipe_face":
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return [
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gr.update(label="Preprocessor Resolution", value=512, minimum=64, maximum=2048, step=8, visible=not pp, interactive=not pp),
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gr.update(label="Max Faces", value=1, minimum=1, maximum=10, step=1, visible=True, interactive=True),
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gr.update(label="Min Face Confidence", value=0.5, minimum=0.01, maximum=1.0, step=0.01, visible=True, interactive=True),
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gr.update(visible=True)
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]
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elif module == "none" or "inpaint" in module:
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return [
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gr.update(visible=False, interactive=False),
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gr.update(visible=False, interactive=False),
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gr.update(visible=False, 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="Preprocessor resolution", value=512, minimum=64, maximum=2048, step=1, visible=not pp, interactive=not pp),
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gr.update(visible=False, interactive=False),
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gr.update(visible=False, 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="Preprocessor resolution", value=default_unit.processor_res, minimum=64, maximum=2048, visible=False, interactive=False)
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threshold_a = gr.Slider(label="Threshold A", value=default_unit.threshold_a, minimum=64, maximum=1024, visible=False, interactive=False)
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threshold_b = gr.Slider(label="Threshold B", value=default_unit.threshold_b, minimum=64, maximum=1024, visible=False, interactive=False)
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if gradio_compat:
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module.change(build_sliders, inputs=[module, pixel_perfect], outputs=[processor_res, threshold_a, threshold_b, advanced])
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pixel_perfect.change(build_sliders, inputs=[module, pixel_perfect], 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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def run_annotator(image, module, pres, pthr_a, pthr_b):
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if image is None:
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return gr.update(value=None, visible=True)
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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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if 'inpaint' in module:
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color = HWC3(image['image'])
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alpha = image['mask'][:, :, 0:1]
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img = np.concatenate([color, alpha], axis=2)
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module = self.get_module_basename(module)
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preprocessor = self.preprocessor[module]
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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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if result.ndim == 3 and result.shape[2] == 4:
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inpaint_mask = result[:, :, 3]
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result = result[:, :, 0:3]
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result[inpaint_mask > 127] = 0
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return gr.update(value=result, visible=True, interactive=False)
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return gr.update(value=None, visible=True)
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def shift_preview(is_on):
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if is_on:
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return gr.update(visible=True), gr.update(value=None, visible=True)
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else:
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return gr.update(visible=False), gr.update(visible=False)
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preprocessor_preview.change(fn=shift_preview, inputs=[preprocessor_preview], outputs=[trigger_preprocessor, generated_image])
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trigger_preprocessor.click(fn=run_annotator, inputs=[input_image, module, processor_res, threshold_a, threshold_b], 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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|
|
resize_mode = gr.Radio(choices=[e.value for e in external_code.ResizeMode], value=default_unit.resize_mode.value, label="Resize Mode")
|
|
|
|
with gr.Accordion(label='Directly Draw Scribbles', open=False):
|
|
with gr.Row():
|
|
canvas_width = gr.Slider(label="New Scribble Drawing Width", minimum=256, maximum=1024, value=512, step=64)
|
|
canvas_height = gr.Slider(label="New Scribble Drawing Height", minimum=256, maximum=1024, value=512, step=64)
|
|
with gr.Row():
|
|
create_button = gr.Button(value="Open New Scribble Drawing Canvas")
|
|
create_button.click(fn=create_canvas, inputs=[canvas_height, canvas_width], outputs=[input_image])
|
|
|
|
ctrls += (input_image, resize_mode)
|
|
ctrls += (lowvram,)
|
|
ctrls += (processor_res, threshold_a, threshold_b, guidance_start, guidance_end, guess_mode, pixel_perfect)
|
|
self.register_modules(tabname, ctrls)
|
|
|
|
input_image.orgpreprocess=input_image.preprocess
|
|
input_image.preprocess=svgPreprocess
|
|
|
|
def controlnet_unit_from_args(*args):
|
|
unit = external_code.ControlNetUnit(*args)
|
|
setattr(unit, 'is_ui', True)
|
|
return unit
|
|
|
|
unit = gr.State(default_unit)
|
|
for comp in ctrls:
|
|
event_subscribers = []
|
|
if hasattr(comp, 'edit'):
|
|
event_subscribers.append(comp.edit)
|
|
elif hasattr(comp, 'click'):
|
|
event_subscribers.append(comp.click)
|
|
else:
|
|
event_subscribers.append(comp.change)
|
|
|
|
if hasattr(comp, 'clear'):
|
|
event_subscribers.append(comp.clear)
|
|
|
|
for event_subscriber in event_subscribers:
|
|
event_subscriber(fn=controlnet_unit_from_args, inputs=list(ctrls), outputs=unit)
|
|
|
|
if is_img2img:
|
|
img2img_submit_button.click(fn=controlnet_unit_from_args, inputs=list(ctrls), outputs=unit, queue=False)
|
|
else:
|
|
txt2img_submit_button.click(fn=controlnet_unit_from_args, inputs=list(ctrls), outputs=unit, queue=False)
|
|
|
|
return unit
|
|
|
|
|
|
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.
|
|
"""
|
|
self.infotext_fields = []
|
|
self.paste_field_names = []
|
|
controls = ()
|
|
max_models = shared.opts.data.get("control_net_max_models_num", 1)
|
|
elem_id_tabname = ("img2img" if is_img2img else "txt2img") + "_controlnet"
|
|
with gr.Group(elem_id=elem_id_tabname):
|
|
with gr.Accordion("ControlNet", open = False, elem_id="controlnet"):
|
|
if max_models > 1:
|
|
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, pixel_perfect = params[-4:]
|
|
|
|
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)
|
|
|
|
model_path = os.path.abspath(model_path)
|
|
model_stem = Path(model_path).stem
|
|
model_dir_name = os.path.dirname(model_path)
|
|
|
|
possible_config_filenames = [
|
|
os.path.join(model_dir_name, model_stem + ".yaml"),
|
|
os.path.join(global_state.script_dir, 'models', model_stem + ".yaml"),
|
|
os.path.join(model_dir_name, model_stem.replace('_fp16', '') + ".yaml"),
|
|
os.path.join(global_state.script_dir, 'models', model_stem.replace('_fp16', '') + ".yaml"),
|
|
os.path.join(model_dir_name, model_stem.replace('_diff', '') + ".yaml"),
|
|
os.path.join(global_state.script_dir, 'models', model_stem.replace('_diff', '') + ".yaml"),
|
|
os.path.join(model_dir_name, model_stem.replace('-fp16', '') + ".yaml"),
|
|
os.path.join(global_state.script_dir, 'models', model_stem.replace('-fp16', '') + ".yaml"),
|
|
os.path.join(model_dir_name, model_stem.replace('-diff', '') + ".yaml"),
|
|
os.path.join(global_state.script_dir, 'models', model_stem.replace('-diff', '') + ".yaml")
|
|
]
|
|
|
|
override_config = possible_config_filenames[0]
|
|
|
|
for possible_config_filename in possible_config_filenames:
|
|
if os.path.exists(possible_config_filename):
|
|
override_config = possible_config_filename
|
|
break
|
|
|
|
if 'v11' in model_stem.lower() or 'shuffle' in model_stem.lower():
|
|
assert os.path.exists(override_config), f'Error: The model config {override_config} is missing. ControlNet 1.1 must have configs.'
|
|
|
|
if os.path.exists(override_config):
|
|
network_config = override_config
|
|
|
|
print(f"Loading config: {network_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.resize_mode = selector(p, "control_net_resize_mode", unit.resize_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)
|
|
unit.pixel_perfect = selector(p, "control_net_pixel_perfect", unit.pixel_perfect, idx)
|
|
|
|
return unit
|
|
|
|
def detectmap_proc(self, detected_map, module, resize_mode, h, w):
|
|
|
|
if 'inpaint' in module:
|
|
detected_map = detected_map.astype(np.float32)
|
|
else:
|
|
detected_map = HWC3(detected_map)
|
|
|
|
def get_pytorch_control(x):
|
|
# A very safe method to make sure that Apple/Mac works
|
|
y = x
|
|
|
|
# below is very boring but do not change these. If you change these Apple or Mac may fail.
|
|
y = y.copy()
|
|
y = y.ascontiguousarray()
|
|
y = y.copy()
|
|
y = torch.from_numpy(y)
|
|
y = y.float() / 255.0
|
|
y = rearrange(y, 'h w c -> c h w')
|
|
y = y.clone()
|
|
y = y.to(devices.get_device_for("controlnet"))
|
|
y = y.clone()
|
|
return y
|
|
|
|
def high_quality_resize(x, size):
|
|
# Written by lvmin
|
|
# Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
|
|
|
|
inpaint_mask = None
|
|
if x.ndim == 3 and x.shape[2] == 4:
|
|
inpaint_mask = x[:, :, 3]
|
|
x = x[:, :, 0:3]
|
|
|
|
new_size_is_smaller = (size[0] * size[1]) < (x.shape[0] * x.shape[1])
|
|
new_size_is_bigger = (size[0] * size[1]) > (x.shape[0] * x.shape[1])
|
|
unique_color_count = np.unique(x.reshape(-1, x.shape[2]), axis=0).shape[0]
|
|
is_one_pixel_edge = False
|
|
is_binary = False
|
|
if unique_color_count == 2:
|
|
is_binary = np.min(x) < 16 and np.max(x) > 240
|
|
if is_binary:
|
|
xc = x
|
|
xc = cv2.erode(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
|
|
xc = cv2.dilate(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
|
|
one_pixel_edge_count = np.where(xc < x)[0].shape[0]
|
|
all_edge_count = np.where(x > 127)[0].shape[0]
|
|
is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count
|
|
|
|
if 2 < unique_color_count < 200:
|
|
interpolation = cv2.INTER_NEAREST
|
|
elif new_size_is_smaller:
|
|
interpolation = cv2.INTER_AREA
|
|
else:
|
|
interpolation = cv2.INTER_CUBIC # Must be CUBIC because we now use nms. NEVER CHANGE THIS
|
|
|
|
y = cv2.resize(x, size, interpolation=interpolation)
|
|
if inpaint_mask is not None:
|
|
inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation)
|
|
|
|
if is_binary:
|
|
y = np.mean(y.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8)
|
|
if is_one_pixel_edge:
|
|
y = nake_nms(y)
|
|
_, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
|
y = lvmin_thin(y, prunings=new_size_is_bigger)
|
|
else:
|
|
_, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
|
y = np.stack([y] * 3, axis=2)
|
|
|
|
if inpaint_mask is not None:
|
|
y[inpaint_mask > 127] = - 255
|
|
|
|
return y
|
|
|
|
if resize_mode == external_code.ResizeMode.RESIZE:
|
|
detected_map = high_quality_resize(detected_map, (w, h))
|
|
return get_pytorch_control(detected_map), detected_map
|
|
|
|
old_h, old_w, _ = detected_map.shape
|
|
old_w = float(old_w)
|
|
old_h = float(old_h)
|
|
k0 = float(h) / old_h
|
|
k1 = float(w) / old_w
|
|
|
|
safeint = lambda x: int(np.round(x))
|
|
|
|
if resize_mode == external_code.ResizeMode.OUTER_FIT:
|
|
k = min(k0, k1)
|
|
borders = np.concatenate([detected_map[0, :, 0:3], detected_map[-1, :, 0:3], detected_map[:, 0, 0:3], detected_map[:, -1, 0:3]], axis=0)
|
|
high_quality_border_color = np.median(borders, axis=0).astype(detected_map.dtype)
|
|
high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
|
|
detected_map = high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
|
|
new_h, new_w, _ = detected_map.shape
|
|
pad_h = max(0, (h - new_h) // 2)
|
|
pad_w = max(0, (w - new_w) // 2)
|
|
high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = detected_map
|
|
detected_map = high_quality_background
|
|
return get_pytorch_control(detected_map), detected_map
|
|
else:
|
|
k = max(k0, k1)
|
|
detected_map = high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
|
|
new_h, new_w, _ = detected_map.shape
|
|
pad_h = max(0, (new_h - h) // 2)
|
|
pad_w = max(0, (new_w - w) // 2)
|
|
detected_map = detected_map[pad_h:pad_h+h, pad_w:pad_w+w]
|
|
return get_pytorch_control(detected_map), 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(key, lambda:None)()
|
|
|
|
self.latest_model_hash = p.sd_model.sd_model_hash
|
|
for idx, unit in enumerate(enabled_units):
|
|
unit.module = self.get_module_basename(unit.module)
|
|
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)
|
|
|
|
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:
|
|
if isinstance(p_input_image, dict) and "mask" in p_input_image and "image" in p_input_image:
|
|
color = HWC3(np.asarray(p_input_image['image']))
|
|
alpha = np.asarray(p_input_image['mask'])[..., None]
|
|
input_image = np.concatenate([color, alpha], axis=2)
|
|
else:
|
|
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'])
|
|
|
|
have_mask = 'mask' in image and not ((image['mask'][:, :, 0] == 0).all() or (image['mask'][:, :, 0] == 255).all())
|
|
|
|
if 'inpaint' in unit.module:
|
|
print("using inpaint as input")
|
|
color = HWC3(image['image'])
|
|
if have_mask:
|
|
alpha = image['mask'][:, :, 0:1]
|
|
else:
|
|
alpha = np.zeros_like(color)[:, :, 0:1]
|
|
input_image = np.concatenate([color, alpha], axis=2)
|
|
else:
|
|
if have_mask:
|
|
print("using mask as input")
|
|
input_image = HWC3(image['mask'][:, :, 0])
|
|
unit.module = 'none' # Always use black bg and white line
|
|
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 = [input_image[:, :, i] for i in range(input_image.shape[2])]
|
|
input_image = [Image.fromarray(x) for x in input_image]
|
|
|
|
mask = p.image_mask.convert('L')
|
|
if p.inpainting_mask_invert:
|
|
mask = ImageOps.invert(mask)
|
|
if p.mask_blur > 0:
|
|
mask = mask.filter(ImageFilter.GaussianBlur(p.mask_blur))
|
|
|
|
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 = mask.width / float(input_image[0].width), mask.height / float(input_image[0].height)
|
|
crop_region = int(x1 / scale_x), int(y1 / scale_y), int(x2 / scale_x), int(y2 / scale_y)
|
|
|
|
input_image = [x.crop(crop_region) for x in input_image]
|
|
input_image = [images.resize_image(2, x, p.width, p.height) for x in input_image]
|
|
input_image = [np.asarray(x)[:, :, 0] for x in input_image]
|
|
input_image = np.stack(input_image, axis=2)
|
|
|
|
tmp_seed = int(p.all_seeds[0] if p.seed == -1 else max(int(p.seed),0))
|
|
tmp_subseed = int(p.all_seeds[0] if p.subseed == -1 else max(int(p.subseed),0))
|
|
np.random.seed((tmp_seed + tmp_subseed) & 0xFFFFFFFF)
|
|
|
|
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:
|
|
preprocessor_resolution = unit.processor_res
|
|
if unit.pixel_perfect:
|
|
raw_H, raw_W, _ = input_image.shape
|
|
target_H, target_W = p.height, p.width
|
|
|
|
k0 = float(target_H) / float(raw_H)
|
|
k1 = float(target_W) / float(raw_W)
|
|
|
|
if resize_mode == external_code.ResizeMode.OUTER_FIT:
|
|
estimation = min(k0, k1) * float(min(raw_H, raw_W))
|
|
else:
|
|
estimation = max(k0, k1) * float(min(raw_H, raw_W))
|
|
|
|
preprocessor_resolution = int(np.round(float(estimation) / 64.0)) * 64
|
|
|
|
print(f'Pixel Perfect Mode Enabled.')
|
|
print(f'resize_mode = {str(resize_mode)}')
|
|
print(f'raw_H = {raw_H}')
|
|
print(f'raw_W = {raw_W}')
|
|
print(f'target_H = {target_H}')
|
|
print(f'target_W = {target_W}')
|
|
print(f'estimation = {estimation}')
|
|
|
|
print(f'preprocessor resolution = {preprocessor_resolution}')
|
|
detected_map, is_image = preprocessor(input_image, res=preprocessor_resolution, 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 isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
|
|
if p.hr_resize_x == 0 and p.hr_resize_y == 0:
|
|
hr_y = int(p.height * p.hr_scale)
|
|
hr_x = int(p.width * p.hr_scale)
|
|
else:
|
|
hr_y, hr_x = p.hr_resize_y, p.hr_resize_x
|
|
|
|
if is_image:
|
|
hr_control, hr_detected_map = self.detectmap_proc(detected_map, unit.module, resize_mode, hr_y, hr_x)
|
|
detected_maps.append((hr_detected_map, unit.module))
|
|
else:
|
|
hr_control = detected_map
|
|
else:
|
|
hr_control = None
|
|
|
|
if is_image:
|
|
control, detected_map = self.detectmap_proc(detected_map, unit.module, 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.detach().cpu().numpy().tobytes())
|
|
detected_maps.append((fake_detected_map, unit.module))
|
|
|
|
is_vanilla_samplers = p.sampler_name in ["DDIM", "PLMS", "UniPC"]
|
|
|
|
|
|
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",
|
|
global_average_pooling=model_net.config.model.params.get("global_average_pooling", False),
|
|
hr_hint_cond=hr_control,
|
|
batch_size=p.batch_size,
|
|
instance_counter=0,
|
|
is_vanilla_samplers=is_vanilla_samplers,
|
|
cfg_scale=p.cfg_scale
|
|
)
|
|
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, is_vanilla_samplers)
|
|
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
|
|
processed.images.extend([Image.fromarray(detect_map.clip(0, 255).astype(np.uint8))])
|
|
|
|
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_modules_path", shared.OptionInfo(
|
|
"", "Path to directory containing annotator model directories (requires restart, overrides corresponding command line flag)", 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_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_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
|
|
|
|
|
|
def on_after_component(component, **_kwargs):
|
|
global txt2img_submit_button
|
|
if getattr(component, 'elem_id', None) == 'txt2img_generate':
|
|
txt2img_submit_button = component
|
|
return
|
|
|
|
global img2img_submit_button
|
|
if getattr(component, 'elem_id', None) == 'img2img_generate':
|
|
img2img_submit_button = component
|
|
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)
|
|
script_callbacks.on_after_component(on_after_component)
|