543 lines
24 KiB
Python
543 lines
24 KiB
Python
import os
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from typing import Dict, Optional, Tuple, List, Union
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import cv2
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import torch
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import modules.scripts as scripts
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from modules import shared, script_callbacks, masking, images
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from modules.ui_components import InputAccordion
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from modules.api.api import decode_base64_to_image
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import gradio as gr
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from lib_controlnet import global_state, external_code
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from lib_controlnet.utils import align_dim_latent, image_dict_from_any, set_numpy_seed, crop_and_resize_image, \
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prepare_mask, judge_image_type
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from lib_controlnet.controlnet_ui.controlnet_ui_group import ControlNetUiGroup, UiControlNetUnit
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from lib_controlnet.controlnet_ui.photopea import Photopea
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from lib_controlnet.logging import logger
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from modules.processing import StableDiffusionProcessingImg2Img, StableDiffusionProcessingTxt2Img, \
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StableDiffusionProcessing
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from lib_controlnet.infotext import Infotext
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from modules_forge.forge_util import HWC3, numpy_to_pytorch
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import numpy as np
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import functools
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from PIL import Image
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from modules_forge.shared import try_load_supported_control_model
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from modules_forge.supported_controlnet import ControlModelPatcher
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# Gradio 3.32 bug fix
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import tempfile
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gradio_tempfile_path = os.path.join(tempfile.gettempdir(), 'gradio')
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os.makedirs(gradio_tempfile_path, exist_ok=True)
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global_state.update_controlnet_filenames()
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@functools.lru_cache(maxsize=shared.opts.data.get("control_net_model_cache_size", 5))
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def cached_controlnet_loader(filename):
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return try_load_supported_control_model(filename)
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class ControlNetCachedParameters:
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def __init__(self):
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self.preprocessor = None
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self.model = None
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self.control_cond = None
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self.control_cond_for_hr_fix = None
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self.control_mask = None
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self.control_mask_for_hr_fix = None
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class ControlNetForForgeOfficial(scripts.Script):
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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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return scripts.AlwaysVisible
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def ui(self, is_img2img):
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infotext = Infotext()
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ui_groups = []
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controls = []
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max_models = shared.opts.data.get("control_net_unit_count", 3)
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gen_type = "img2img" if is_img2img else "txt2img"
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elem_id_tabname = gen_type + "_controlnet"
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default_unit = UiControlNetUnit(enabled=False, module="None", model="None")
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with gr.Group(elem_id=elem_id_tabname):
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with gr.Accordion(f"ControlNet Integrated", open=False, elem_id="controlnet",
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elem_classes=["controlnet"]):
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photopea = (
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Photopea()
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if not shared.opts.data.get("controlnet_disable_photopea_edit", False)
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else None
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)
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with gr.Row(elem_id=elem_id_tabname + "_accordions", elem_classes="accordions"):
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for i in range(max_models):
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with InputAccordion(
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value=False,
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label=f"ControlNet Unit {i}",
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elem_classes=["cnet-unit-enabled-accordion"], # Class on accordion
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):
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group = ControlNetUiGroup(is_img2img, default_unit, photopea)
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ui_groups.append(group)
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controls.append(group.render(f"ControlNet-{i}", elem_id_tabname))
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for i, ui_group in enumerate(ui_groups):
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infotext.register_unit(i, ui_group)
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if shared.opts.data.get("control_net_sync_field_args", True):
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self.infotext_fields = infotext.infotext_fields
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self.paste_field_names = infotext.paste_field_names
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return tuple(controls)
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def get_enabled_units(self, units):
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enabled_units = [x for x in units if x.enabled]
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return enabled_units
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@staticmethod
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def try_crop_image_with_a1111_mask(
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p: StableDiffusionProcessing,
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unit: external_code.ControlNetUnit,
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input_image: np.ndarray,
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resize_mode: external_code.ResizeMode,
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preprocessor
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) -> np.ndarray:
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a1111_mask_image: Optional[Image.Image] = getattr(p, "image_mask", None)
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is_only_masked_inpaint = (
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issubclass(type(p), StableDiffusionProcessingImg2Img) and
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p.inpaint_full_res and
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a1111_mask_image is not None
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)
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if (
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preprocessor.corp_image_with_a1111_mask_when_in_img2img_inpaint_tab
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and is_only_masked_inpaint
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):
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logger.info("Crop input image based on A1111 mask.")
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input_image = [input_image[:, :, i] for i in range(input_image.shape[2])]
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input_image = [Image.fromarray(x) for x in input_image]
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mask = prepare_mask(a1111_mask_image, p)
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crop_region = masking.get_crop_region(np.array(mask), p.inpaint_full_res_padding)
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crop_region = masking.expand_crop_region(crop_region, p.width, p.height, mask.width, mask.height)
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input_image = [
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images.resize_image(resize_mode.int_value(), i, mask.width, mask.height)
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for i in input_image
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]
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input_image = [x.crop(crop_region) for x in input_image]
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input_image = [
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images.resize_image(external_code.ResizeMode.OUTER_FIT.int_value(), x, p.width, p.height)
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for x in input_image
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]
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input_image = [np.asarray(x)[:, :, 0] for x in input_image]
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input_image = np.stack(input_image, axis=2)
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return input_image
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def get_input_data(self, p, unit, preprocessor):
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logger.info(f'ControlNet Input Mode: {unit.input_mode}')
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resize_mode = external_code.resize_mode_from_value(unit.resize_mode)
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if unit.input_mode == external_code.InputMode.MERGE:
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image_list = []
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for idx, item in enumerate(unit.batch_input_gallery):
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img_path = item['name']
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logger.info(f'Try to read image: {img_path}')
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img = np.ascontiguousarray(cv2.imread(img_path)[:, :, ::-1]).copy()
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mask = None
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if len(unit.batch_mask_gallery) > 0:
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if len(unit.batch_mask_gallery) >= len(unit.batch_input_gallery):
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mask_path = unit.batch_mask_gallery[idx]['name']
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else:
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mask_path = unit.batch_mask_gallery[0]['name']
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mask = np.ascontiguousarray(cv2.imread(mask_path)[:, :, ::-1]).copy()
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if img is not None:
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image_list.append([img, mask])
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return image_list, resize_mode
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if unit.input_mode == external_code.InputMode.BATCH:
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image_list = []
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image_extensions = ['.jpg', '.jpeg', '.png', '.bmp']
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for idx, filename in enumerate(os.listdir(unit.batch_image_dir)):
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if any(filename.lower().endswith(ext) for ext in image_extensions):
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img_path = os.path.join(unit.batch_image_dir, filename)
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logger.info(f'Try to read image: {img_path}')
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img = np.ascontiguousarray(cv2.imread(img_path)[:, :, ::-1]).copy()
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mask = None
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if len(unit.batch_mask_dir) > 0:
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if len(unit.batch_mask_dir) >= len(unit.batch_image_dir):
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mask_path = unit.batch_mask_dir[idx]
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else:
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mask_path = unit.batch_mask_dir[0]
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mask_path = os.path.join(unit.batch_mask_dir, mask_path)
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mask = np.ascontiguousarray(cv2.imread(mask_path)[:, :, ::-1]).copy()
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if img is not None:
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image_list.append([img, mask])
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return image_list, resize_mode
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a1111_i2i_image = getattr(p, "init_images", [None])[0]
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a1111_i2i_mask = getattr(p, "image_mask", None)
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using_a1111_data = False
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if unit.use_preview_as_input and unit.generated_image is not None:
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image = unit.generated_image
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elif unit.image is None:
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resize_mode = external_code.resize_mode_from_value(p.resize_mode)
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image = HWC3(np.asarray(a1111_i2i_image))
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using_a1111_data = True
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elif (unit.image['image'] < 5).all() and (unit.image['mask'] > 5).any():
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image = unit.image['mask']
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else:
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image = unit.image['image']
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if not isinstance(image, np.ndarray):
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raise ValueError("controlnet is enabled but no input image is given")
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image = HWC3(image)
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if using_a1111_data:
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mask = HWC3(np.asarray(a1111_i2i_mask)) if a1111_i2i_mask is not None else None
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elif unit.mask_image is not None and (unit.mask_image['image'] > 5).any():
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mask = unit.mask_image['image']
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elif unit.mask_image is not None and (unit.mask_image['mask'] > 5).any():
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mask = unit.mask_image['mask']
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elif unit.image is not None and (unit.image['mask'] > 5).any():
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mask = unit.image['mask']
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else:
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mask = None
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image = self.try_crop_image_with_a1111_mask(p, unit, image, resize_mode, preprocessor)
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if mask is not None:
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mask = cv2.resize(HWC3(mask), (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST)
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mask = self.try_crop_image_with_a1111_mask(p, unit, mask, resize_mode, preprocessor)
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return [[image, mask]], resize_mode
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@staticmethod
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def get_target_dimensions(p: StableDiffusionProcessing) -> Tuple[int, int, int, int]:
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"""Returns (h, w, hr_h, hr_w)."""
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h = align_dim_latent(p.height)
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w = align_dim_latent(p.width)
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high_res_fix = (
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isinstance(p, StableDiffusionProcessingTxt2Img)
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and getattr(p, 'enable_hr', False)
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)
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if high_res_fix:
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if p.hr_resize_x == 0 and p.hr_resize_y == 0:
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hr_y = int(p.height * p.hr_scale)
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hr_x = int(p.width * p.hr_scale)
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else:
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hr_y, hr_x = p.hr_resize_y, p.hr_resize_x
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hr_y = align_dim_latent(hr_y)
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hr_x = align_dim_latent(hr_x)
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else:
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hr_y = h
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hr_x = w
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return h, w, hr_y, hr_x
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@torch.no_grad()
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def process_unit_after_click_generate(self,
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p: StableDiffusionProcessing,
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unit: external_code.ControlNetUnit,
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params: ControlNetCachedParameters,
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*args, **kwargs):
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h, w, hr_y, hr_x = self.get_target_dimensions(p)
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has_high_res_fix = (
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isinstance(p, StableDiffusionProcessingTxt2Img)
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and getattr(p, 'enable_hr', False)
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)
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if unit.use_preview_as_input:
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unit.module = 'None'
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preprocessor = global_state.get_preprocessor(unit.module)
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input_list, resize_mode = self.get_input_data(p, unit, preprocessor)
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preprocessor_outputs = []
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control_masks = []
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preprocessor_output_is_image = False
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preprocessor_output = None
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def optional_tqdm(iterable, use_tqdm):
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from tqdm import tqdm
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return tqdm(iterable) if use_tqdm else iterable
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for input_image, input_mask in optional_tqdm(input_list, len(input_list) > 1):
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# p.extra_result_images.append(input_image)
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if unit.pixel_perfect:
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unit.processor_res = external_code.pixel_perfect_resolution(
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input_image,
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target_H=h,
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target_W=w,
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resize_mode=resize_mode,
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)
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seed = set_numpy_seed(p)
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logger.debug(f"Use numpy seed {seed}.")
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logger.info(f"Using preprocessor: {unit.module}")
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logger.info(f'preprocessor resolution = {unit.processor_res}')
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preprocessor_output = preprocessor(
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input_image=input_image,
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input_mask=input_mask,
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resolution=unit.processor_res,
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slider_1=unit.threshold_a,
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slider_2=unit.threshold_b,
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)
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preprocessor_outputs.append(preprocessor_output)
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preprocessor_output_is_image = judge_image_type(preprocessor_output)
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if input_mask is not None:
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control_masks.append(input_mask)
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if len(input_list) > 1 and not preprocessor_output_is_image:
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logger.info('Batch wise input only support controlnet, control-lora, and t2i adapters!')
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break
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alignment_indices = [i % len(preprocessor_outputs) for i in range(p.batch_size)]
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if preprocessor_output_is_image:
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params.control_cond = []
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params.control_cond_for_hr_fix = []
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for preprocessor_output in preprocessor_outputs:
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control_cond = crop_and_resize_image(preprocessor_output, resize_mode, h, w)
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p.extra_result_images.append(external_code.visualize_inpaint_mask(control_cond))
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params.control_cond.append(numpy_to_pytorch(control_cond).movedim(-1, 1))
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params.control_cond = torch.cat(params.control_cond, dim=0)[alignment_indices].contiguous()
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if has_high_res_fix:
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for preprocessor_output in preprocessor_outputs:
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control_cond_for_hr_fix = crop_and_resize_image(preprocessor_output, resize_mode, hr_y, hr_x)
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p.extra_result_images.append(external_code.visualize_inpaint_mask(control_cond_for_hr_fix))
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params.control_cond_for_hr_fix.append(numpy_to_pytorch(control_cond_for_hr_fix).movedim(-1, 1))
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params.control_cond_for_hr_fix = torch.cat(params.control_cond_for_hr_fix, dim=0)[alignment_indices].contiguous()
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else:
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params.control_cond_for_hr_fix = params.control_cond
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else:
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params.control_cond = preprocessor_output
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params.control_cond_for_hr_fix = preprocessor_output
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p.extra_result_images.append(input_image)
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if len(control_masks) > 0:
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params.control_mask = []
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params.control_mask_for_hr_fix = []
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for input_mask in control_masks:
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fill_border = preprocessor.fill_mask_with_one_when_resize_and_fill
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control_mask = crop_and_resize_image(input_mask, resize_mode, h, w, fill_border)
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p.extra_result_images.append(control_mask)
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control_mask = numpy_to_pytorch(control_mask).movedim(-1, 1)[:, :1]
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params.control_mask.append(control_mask)
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if has_high_res_fix:
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control_mask_for_hr_fix = crop_and_resize_image(input_mask, resize_mode, hr_y, hr_x, fill_border)
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p.extra_result_images.append(control_mask_for_hr_fix)
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control_mask_for_hr_fix = numpy_to_pytorch(control_mask_for_hr_fix).movedim(-1, 1)[:, :1]
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params.control_mask_for_hr_fix.append(control_mask_for_hr_fix)
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params.control_mask = torch.cat(params.control_mask, dim=0)[alignment_indices].contiguous()
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if has_high_res_fix:
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params.control_mask_for_hr_fix = torch.cat(params.control_mask_for_hr_fix, dim=0)[alignment_indices].contiguous()
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else:
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params.control_mask_for_hr_fix = params.control_mask
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if preprocessor.do_not_need_model:
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model_filename = 'Not Needed'
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params.model = ControlModelPatcher()
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else:
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assert unit.model != 'None', 'You have not selected any control model!'
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model_filename = global_state.get_controlnet_filename(unit.model)
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params.model = cached_controlnet_loader(model_filename)
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assert params.model is not None, logger.error(f"Recognizing Control Model failed: {model_filename}")
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params.preprocessor = preprocessor
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params.preprocessor.process_after_running_preprocessors(process=p, params=params, **kwargs)
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params.model.process_after_running_preprocessors(process=p, params=params, **kwargs)
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logger.info(f"Current ControlNet {type(params.model).__name__}: {model_filename}")
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return
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@torch.no_grad()
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def process_unit_before_every_sampling(self,
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p: StableDiffusionProcessing,
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unit: external_code.ControlNetUnit,
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params: ControlNetCachedParameters,
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*args, **kwargs):
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is_hr_pass = getattr(p, 'is_hr_pass', False)
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if is_hr_pass:
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cond = params.control_cond_for_hr_fix
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mask = params.control_mask_for_hr_fix
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else:
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cond = params.control_cond
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mask = params.control_mask
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kwargs.update(dict(unit=unit, params=params))
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params.model.strength = float(unit.weight)
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params.model.start_percent = float(unit.guidance_start)
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params.model.end_percent = float(unit.guidance_end)
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params.model.positive_advanced_weighting = None
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params.model.negative_advanced_weighting = None
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params.model.advanced_frame_weighting = None
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params.model.advanced_sigma_weighting = None
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soft_weighting = {
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'input': [0.09941396206337118, 0.12050177219802567, 0.14606275417942507, 0.17704576264172736,
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0.214600924414215,
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0.26012233262329093, 0.3152997971191405, 0.3821815722656249, 0.4632503906249999, 0.561515625,
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0.6806249999999999, 0.825],
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'middle': [1.0],
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'output': [0.09941396206337118, 0.12050177219802567, 0.14606275417942507, 0.17704576264172736,
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0.214600924414215,
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0.26012233262329093, 0.3152997971191405, 0.3821815722656249, 0.4632503906249999, 0.561515625,
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0.6806249999999999, 0.825]
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}
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zero_weighting = {
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'input': [0.0] * 12,
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'middle': [0.0],
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'output': [0.0] * 12
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}
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if unit.control_mode == external_code.ControlMode.CONTROL.value:
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params.model.positive_advanced_weighting = soft_weighting.copy()
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params.model.negative_advanced_weighting = zero_weighting.copy()
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# high-ref fix pass always use softer injections
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if is_hr_pass or unit.control_mode == external_code.ControlMode.PROMPT.value:
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params.model.positive_advanced_weighting = soft_weighting.copy()
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params.model.negative_advanced_weighting = soft_weighting.copy()
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cond, mask = params.preprocessor.process_before_every_sampling(p, cond, mask, *args, **kwargs)
|
|
|
|
params.model.advanced_mask_weighting = mask
|
|
|
|
params.model.process_before_every_sampling(p, cond, mask, *args, **kwargs)
|
|
|
|
logger.info(f"ControlNet Method {params.preprocessor.name} patched.")
|
|
return
|
|
|
|
@staticmethod
|
|
def bound_check_params(unit: external_code.ControlNetUnit) -> None:
|
|
"""
|
|
Checks and corrects negative parameters in ControlNetUnit 'unit'.
|
|
Parameters 'processor_res', 'threshold_a', 'threshold_b' are reset to
|
|
their default values if negative.
|
|
|
|
Args:
|
|
unit (external_code.ControlNetUnit): The ControlNetUnit instance to check.
|
|
"""
|
|
preprocessor = global_state.get_preprocessor(unit.module)
|
|
|
|
if unit.processor_res < 0:
|
|
unit.processor_res = int(preprocessor.slider_resolution.gradio_update_kwargs.get('value', 512))
|
|
|
|
if unit.threshold_a < 0:
|
|
unit.threshold_a = int(preprocessor.slider_1.gradio_update_kwargs.get('value', 1.0))
|
|
|
|
if unit.threshold_b < 0:
|
|
unit.threshold_b = int(preprocessor.slider_2.gradio_update_kwargs.get('value', 1.0))
|
|
|
|
return
|
|
|
|
@torch.no_grad()
|
|
def process_unit_after_every_sampling(self,
|
|
p: StableDiffusionProcessing,
|
|
unit: external_code.ControlNetUnit,
|
|
params: ControlNetCachedParameters,
|
|
*args, **kwargs):
|
|
|
|
params.preprocessor.process_after_every_sampling(p, params, *args, **kwargs)
|
|
params.model.process_after_every_sampling(p, params, *args, **kwargs)
|
|
return
|
|
|
|
@torch.no_grad()
|
|
def process(self, p, *args, **kwargs):
|
|
self.current_params = {}
|
|
enabled_units = self.get_enabled_units(args)
|
|
Infotext.write_infotext(enabled_units, p)
|
|
for i, unit in enumerate(enabled_units):
|
|
self.bound_check_params(unit)
|
|
params = ControlNetCachedParameters()
|
|
self.process_unit_after_click_generate(p, unit, params, *args, **kwargs)
|
|
self.current_params[i] = params
|
|
return
|
|
|
|
@torch.no_grad()
|
|
def process_before_every_sampling(self, p, *args, **kwargs):
|
|
for i, unit in enumerate(self.get_enabled_units(args)):
|
|
self.process_unit_before_every_sampling(p, unit, self.current_params[i], *args, **kwargs)
|
|
return
|
|
|
|
@torch.no_grad()
|
|
def postprocess_batch_list(self, p, pp, *args, **kwargs):
|
|
for i, unit in enumerate(self.get_enabled_units(args)):
|
|
self.process_unit_after_every_sampling(p, unit, self.current_params[i], pp, *args, **kwargs)
|
|
return
|
|
|
|
def postprocess(self, p, processed, *args):
|
|
self.current_params = {}
|
|
return
|
|
|
|
|
|
def on_ui_settings():
|
|
section = ('control_net', "ControlNet")
|
|
shared.opts.add_option("control_net_detectedmap_dir", shared.OptionInfo(
|
|
"detected_maps", "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_unit_count", shared.OptionInfo(
|
|
3, "Multi-ControlNet: ControlNet unit number (requires restart)", gr.Slider,
|
|
{"minimum": 1, "maximum": 10, "step": 1}, section=section))
|
|
shared.opts.add_option("control_net_model_cache_size", shared.OptionInfo(
|
|
5, "Model cache size (requires restart)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}, 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_sync_field_args", shared.OptionInfo(
|
|
True, "Paste ControlNet parameters in infotext", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("controlnet_show_batch_images_in_ui", shared.OptionInfo(
|
|
False, "Show batch images in gradio gallery output", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("controlnet_increment_seed_during_batch", shared.OptionInfo(
|
|
False, "Increment seed after each controlnet batch iteration", gr.Checkbox, {"interactive": True},
|
|
section=section))
|
|
shared.opts.add_option("controlnet_disable_openpose_edit", shared.OptionInfo(
|
|
False, "Disable openpose edit", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("controlnet_disable_photopea_edit", shared.OptionInfo(
|
|
False, "Disable photopea edit", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("controlnet_photopea_warning", shared.OptionInfo(
|
|
True, "Photopea popup warning", gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("controlnet_input_thumbnail", shared.OptionInfo(
|
|
True, "Input image thumbnail on unit header", gr.Checkbox, {"interactive": True}, section=section))
|
|
|
|
|
|
script_callbacks.on_ui_settings(on_ui_settings)
|
|
script_callbacks.on_infotext_pasted(Infotext.on_infotext_pasted)
|
|
script_callbacks.on_after_component(ControlNetUiGroup.on_after_component)
|
|
script_callbacks.on_before_reload(ControlNetUiGroup.reset)
|