1343 lines
68 KiB
Python
1343 lines
68 KiB
Python
import gc
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import math
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import os
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import platform
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if platform.system() == "Darwin":
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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if platform.system() == "Windows":
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os.environ["XFORMERS_FORCE_DISABLE_TRITON"] = "1"
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import random
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import re
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import traceback
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import cv2
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import gradio as gr
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import numpy as np
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import torch
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from diffusers import (DDIMScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
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KDPM2AncestralDiscreteScheduler, KDPM2DiscreteScheduler,
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StableDiffusionInpaintPipeline)
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from modules import devices, script_callbacks, shared
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from modules.processing import create_infotext, process_images
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from modules.sd_models import get_closet_checkpoint_match
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from modules.sd_samplers import samplers_for_img2img
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from PIL import Image, ImageFilter, ImageOps
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from PIL.PngImagePlugin import PngInfo
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from torch.hub import download_url_to_file
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from torchvision import transforms
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import inpalib
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from ia_check_versions import ia_check_versions
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from ia_config import (IAConfig, get_ia_config_index, get_webui_setting, set_ia_config,
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setup_ia_config_ini)
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from ia_file_manager import IAFileManager, download_model_from_hf, ia_file_manager
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from ia_logging import draw_text_image, ia_logging
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from ia_threading import (async_post_reload_model_weights, await_backup_reload_ckpt_info,
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await_pre_reload_model_weights, clear_cache_decorator,
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offload_reload_decorator)
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from ia_ui_items import (get_cleaner_model_ids, get_inp_model_ids, get_inp_webui_model_ids,
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get_padding_mode_names, get_sam_model_ids, get_sampler_names)
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from ia_webui_controlnet import (backup_alwayson_scripts, clear_controlnet_cache,
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disable_all_alwayson_scripts, disable_alwayson_scripts_wo_cn,
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find_controlnet, get_controlnet_args_to, get_max_args_to,
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get_sd_img2img_processing, restore_alwayson_scripts)
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from lama_cleaner.model_manager import ModelManager
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from lama_cleaner.schema import Config, HDStrategy, LDMSampler, SDSampler
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@clear_cache_decorator
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def download_model(sam_model_id):
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"""Download SAM model.
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Args:
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sam_model_id (str): SAM model id
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Returns:
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str: download status
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"""
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if "_hq_" in sam_model_id:
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url_sam = "https://huggingface.co/Uminosachi/sam-hq/resolve/main/" + sam_model_id
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elif "FastSAM" in sam_model_id:
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url_sam = "https://huggingface.co/Uminosachi/FastSAM/resolve/main/" + sam_model_id
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elif "mobile_sam" in sam_model_id:
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url_sam = "https://huggingface.co/Uminosachi/MobileSAM/resolve/main/" + sam_model_id
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elif "sam2_" in sam_model_id:
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url_sam = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/" + sam_model_id
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else:
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url_sam = "https://dl.fbaipublicfiles.com/segment_anything/" + sam_model_id
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sam_checkpoint = os.path.join(ia_file_manager.models_dir, sam_model_id)
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if not os.path.isfile(sam_checkpoint):
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try:
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download_url_to_file(url_sam, sam_checkpoint)
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except Exception as e:
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ia_logging.error(str(e))
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return str(e)
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return IAFileManager.DOWNLOAD_COMPLETE
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else:
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return "Model already exists"
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sam_dict = dict(sam_masks=None, mask_image=None, cnet=None, orig_image=None, pad_mask=None)
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def save_mask_image(mask_image, save_mask_chk=False):
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"""Save mask image.
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Args:
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mask_image (np.ndarray): mask image
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save_mask_chk (bool, optional): If True, save mask image. Defaults to False.
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Returns:
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None
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"""
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if save_mask_chk:
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save_name = "_".join([ia_file_manager.savename_prefix, "created_mask"]) + ".png"
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save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
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Image.fromarray(mask_image).save(save_name)
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@clear_cache_decorator
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def input_image_upload(input_image, sam_image, sel_mask):
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global sam_dict
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sam_dict["orig_image"] = input_image
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sam_dict["pad_mask"] = None
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if (sam_dict["mask_image"] is None or not isinstance(sam_dict["mask_image"], np.ndarray) or
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sam_dict["mask_image"].shape != input_image.shape):
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sam_dict["mask_image"] = np.zeros_like(input_image, dtype=np.uint8)
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ret_sel_image = cv2.addWeighted(input_image, 0.5, sam_dict["mask_image"], 0.5, 0)
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if sam_image is None or not isinstance(sam_image, dict) or "image" not in sam_image:
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sam_dict["sam_masks"] = None
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ret_sam_image = np.zeros_like(input_image, dtype=np.uint8)
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elif sam_image["image"].shape == input_image.shape:
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ret_sam_image = gr.update()
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else:
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sam_dict["sam_masks"] = None
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ret_sam_image = gr.update(value=np.zeros_like(input_image, dtype=np.uint8))
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if sel_mask is None or not isinstance(sel_mask, dict) or "image" not in sel_mask:
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ret_sel_mask = ret_sel_image
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elif sel_mask["image"].shape == ret_sel_image.shape and np.all(sel_mask["image"] == ret_sel_image):
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ret_sel_mask = gr.update()
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else:
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ret_sel_mask = gr.update(value=ret_sel_image)
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return ret_sam_image, ret_sel_mask, gr.update(interactive=True)
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@clear_cache_decorator
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def run_padding(input_image, pad_scale_width, pad_scale_height, pad_lr_barance, pad_tb_barance, padding_mode="edge"):
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global sam_dict
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if input_image is None or sam_dict["orig_image"] is None:
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sam_dict["orig_image"] = None
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sam_dict["pad_mask"] = None
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return None, "Input image not found"
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orig_image = sam_dict["orig_image"]
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height, width = orig_image.shape[:2]
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pad_width, pad_height = (int(width * pad_scale_width), int(height * pad_scale_height))
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ia_logging.info(f"resize by padding: ({height}, {width}) -> ({pad_height}, {pad_width})")
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pad_size_w, pad_size_h = (pad_width - width, pad_height - height)
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pad_size_l = int(pad_size_w * pad_lr_barance)
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pad_size_r = pad_size_w - pad_size_l
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pad_size_t = int(pad_size_h * pad_tb_barance)
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pad_size_b = pad_size_h - pad_size_t
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pad_width = [(pad_size_t, pad_size_b), (pad_size_l, pad_size_r), (0, 0)]
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if padding_mode == "constant":
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fill_value = get_webui_setting("inpaint_anything_padding_fill", 127)
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pad_image = np.pad(orig_image, pad_width=pad_width, mode=padding_mode, constant_values=fill_value)
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else:
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pad_image = np.pad(orig_image, pad_width=pad_width, mode=padding_mode)
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mask_pad_width = [(pad_size_t, pad_size_b), (pad_size_l, pad_size_r)]
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pad_mask = np.zeros((height, width), dtype=np.uint8)
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pad_mask = np.pad(pad_mask, pad_width=mask_pad_width, mode="constant", constant_values=255)
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sam_dict["pad_mask"] = dict(segmentation=pad_mask.astype(bool))
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return pad_image, "Padding done"
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@offload_reload_decorator
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@clear_cache_decorator
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def run_sam(input_image, sam_model_id, sam_image, anime_style_chk=False):
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global sam_dict
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if not inpalib.sam_file_exists(sam_model_id):
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ret_sam_image = None if sam_image is None else gr.update()
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return ret_sam_image, f"{sam_model_id} not found, please download"
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if input_image is None:
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ret_sam_image = None if sam_image is None else gr.update()
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return ret_sam_image, "Input image not found"
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set_ia_config(IAConfig.KEYS.SAM_MODEL_ID, sam_model_id, IAConfig.SECTIONS.USER)
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if sam_dict["sam_masks"] is not None:
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sam_dict["sam_masks"] = None
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gc.collect()
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ia_logging.info(f"input_image: {input_image.shape} {input_image.dtype}")
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try:
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sam_masks = inpalib.generate_sam_masks(input_image, sam_model_id, anime_style_chk)
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sam_masks = inpalib.sort_masks_by_area(sam_masks)
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sam_masks = inpalib.insert_mask_to_sam_masks(sam_masks, sam_dict["pad_mask"])
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seg_image = inpalib.create_seg_color_image(input_image, sam_masks)
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sam_dict["sam_masks"] = sam_masks
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except Exception as e:
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print(traceback.format_exc())
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ia_logging.error(str(e))
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ret_sam_image = None if sam_image is None else gr.update()
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return ret_sam_image, "Segment Anything failed"
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if sam_image is None or not isinstance(sam_image, dict) or "image" not in sam_image:
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return seg_image, "Segment Anything complete"
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else:
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if sam_image["image"].shape == seg_image.shape and np.all(sam_image["image"] == seg_image):
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return gr.update(), "Segment Anything complete"
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else:
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return gr.update(value=seg_image), "Segment Anything complete"
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@clear_cache_decorator
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def select_mask(input_image, sam_image, invert_chk, ignore_black_chk, sel_mask):
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global sam_dict
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if sam_dict["sam_masks"] is None or sam_image is None:
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ret_sel_mask = None if sel_mask is None else gr.update()
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return ret_sel_mask
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sam_masks = sam_dict["sam_masks"]
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# image = sam_image["image"]
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mask = sam_image["mask"][:, :, 0:1]
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try:
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seg_image = inpalib.create_mask_image(mask, sam_masks, ignore_black_chk)
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if invert_chk:
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seg_image = inpalib.invert_mask(seg_image)
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sam_dict["mask_image"] = seg_image
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except Exception as e:
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print(traceback.format_exc())
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ia_logging.error(str(e))
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ret_sel_mask = None if sel_mask is None else gr.update()
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return ret_sel_mask
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if input_image is not None and input_image.shape == seg_image.shape:
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ret_image = cv2.addWeighted(input_image, 0.5, seg_image, 0.5, 0)
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else:
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ret_image = seg_image
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if sel_mask is None or not isinstance(sel_mask, dict) or "image" not in sel_mask:
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return ret_image
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else:
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if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
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return gr.update()
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else:
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return gr.update(value=ret_image)
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@clear_cache_decorator
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def expand_mask(input_image, sel_mask, expand_iteration=1):
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global sam_dict
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if sam_dict["mask_image"] is None or sel_mask is None:
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return None
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new_sel_mask = sam_dict["mask_image"]
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expand_iteration = int(np.clip(expand_iteration, 1, 100))
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new_sel_mask = cv2.dilate(new_sel_mask, np.ones((3, 3), dtype=np.uint8), iterations=expand_iteration)
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sam_dict["mask_image"] = new_sel_mask
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if input_image is not None and input_image.shape == new_sel_mask.shape:
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ret_image = cv2.addWeighted(input_image, 0.5, new_sel_mask, 0.5, 0)
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else:
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ret_image = new_sel_mask
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if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
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return gr.update()
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else:
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return gr.update(value=ret_image)
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@clear_cache_decorator
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def apply_mask(input_image, sel_mask):
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global sam_dict
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if sam_dict["mask_image"] is None or sel_mask is None:
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return None
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sel_mask_image = sam_dict["mask_image"]
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sel_mask_mask = np.logical_not(sel_mask["mask"][:, :, 0:3].astype(bool)).astype(np.uint8)
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new_sel_mask = sel_mask_image * sel_mask_mask
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sam_dict["mask_image"] = new_sel_mask
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if input_image is not None and input_image.shape == new_sel_mask.shape:
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ret_image = cv2.addWeighted(input_image, 0.5, new_sel_mask, 0.5, 0)
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else:
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ret_image = new_sel_mask
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if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
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return gr.update()
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else:
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return gr.update(value=ret_image)
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@clear_cache_decorator
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def add_mask(input_image, sel_mask):
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global sam_dict
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if sam_dict["mask_image"] is None or sel_mask is None:
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return None
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sel_mask_image = sam_dict["mask_image"]
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sel_mask_mask = sel_mask["mask"][:, :, 0:3].astype(bool).astype(np.uint8)
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new_sel_mask = sel_mask_image + (sel_mask_mask * np.invert(sel_mask_image, dtype=np.uint8))
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sam_dict["mask_image"] = new_sel_mask
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if input_image is not None and input_image.shape == new_sel_mask.shape:
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ret_image = cv2.addWeighted(input_image, 0.5, new_sel_mask, 0.5, 0)
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else:
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ret_image = new_sel_mask
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if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
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return gr.update()
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else:
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return gr.update(value=ret_image)
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def auto_resize_to_pil(input_image, mask_image):
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init_image = Image.fromarray(input_image).convert("RGB")
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mask_image = Image.fromarray(mask_image).convert("RGB")
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assert init_image.size == mask_image.size, "The sizes of the image and mask do not match"
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width, height = init_image.size
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new_height = (height // 8) * 8
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new_width = (width // 8) * 8
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if new_width < width or new_height < height:
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if (new_width / width) < (new_height / height):
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scale = new_height / height
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else:
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scale = new_width / width
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resize_height = int(height*scale+0.5)
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resize_width = int(width*scale+0.5)
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if height != resize_height or width != resize_width:
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ia_logging.info(f"resize: ({height}, {width}) -> ({resize_height}, {resize_width})")
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init_image = transforms.functional.resize(init_image, (resize_height, resize_width), transforms.InterpolationMode.LANCZOS)
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mask_image = transforms.functional.resize(mask_image, (resize_height, resize_width), transforms.InterpolationMode.LANCZOS)
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if resize_height != new_height or resize_width != new_width:
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ia_logging.info(f"center_crop: ({resize_height}, {resize_width}) -> ({new_height}, {new_width})")
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init_image = transforms.functional.center_crop(init_image, (new_height, new_width))
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mask_image = transforms.functional.center_crop(mask_image, (new_height, new_width))
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return init_image, mask_image
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@offload_reload_decorator
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@clear_cache_decorator
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def run_inpaint(input_image, sel_mask, prompt, n_prompt, ddim_steps, cfg_scale, seed, inp_model_id, save_mask_chk, composite_chk,
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sampler_name="DDIM", iteration_count=1):
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global sam_dict
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if input_image is None or sam_dict["mask_image"] is None or sel_mask is None:
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ia_logging.error("The image or mask does not exist")
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return
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mask_image = sam_dict["mask_image"]
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if input_image.shape != mask_image.shape:
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ia_logging.error("The sizes of the image and mask do not match")
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return
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set_ia_config(IAConfig.KEYS.INP_MODEL_ID, inp_model_id, IAConfig.SECTIONS.USER)
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save_mask_image(mask_image, save_mask_chk)
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ia_logging.info(f"Loading model {inp_model_id}")
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config_offline_inpainting = get_webui_setting("inpaint_anything_offline_inpainting", False)
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if config_offline_inpainting:
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ia_logging.info("Run Inpainting on offline network: {}".format(str(config_offline_inpainting)))
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local_files_only = False
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local_file_status = download_model_from_hf(inp_model_id, local_files_only=True)
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if local_file_status != IAFileManager.DOWNLOAD_COMPLETE:
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if config_offline_inpainting:
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ia_logging.warning(local_file_status)
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return
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else:
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local_files_only = True
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ia_logging.info("local_files_only: {}".format(str(local_files_only)))
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if platform.system() == "Darwin" or devices.device == devices.cpu or ia_check_versions.torch_on_amd_rocm:
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torch_dtype = torch.float32
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else:
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torch_dtype = torch.float16
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try:
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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inp_model_id, torch_dtype=torch_dtype, local_files_only=local_files_only, use_safetensors=True)
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except Exception as e:
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ia_logging.error(str(e))
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if not config_offline_inpainting:
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try:
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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inp_model_id, torch_dtype=torch_dtype, use_safetensors=True)
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except Exception as e:
|
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ia_logging.error(str(e))
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try:
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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inp_model_id, torch_dtype=torch_dtype, force_download=True, use_safetensors=True)
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except Exception as e:
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ia_logging.error(str(e))
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return
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else:
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return
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pipe.safety_checker = None
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|
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ia_logging.info(f"Using sampler {sampler_name}")
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if sampler_name == "DDIM":
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pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
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elif sampler_name == "Euler":
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pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
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elif sampler_name == "Euler a":
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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elif sampler_name == "DPM2 Karras":
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pipe.scheduler = KDPM2DiscreteScheduler.from_config(pipe.scheduler.config)
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elif sampler_name == "DPM2 a Karras":
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pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
|
|
else:
|
|
ia_logging.info("Sampler fallback to DDIM")
|
|
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
|
|
|
|
if platform.system() == "Darwin":
|
|
pipe = pipe.to("mps" if ia_check_versions.torch_mps_is_available else "cpu")
|
|
pipe.enable_attention_slicing()
|
|
torch_generator = torch.Generator(devices.cpu)
|
|
else:
|
|
if ia_check_versions.diffusers_enable_cpu_offload and devices.device != devices.cpu:
|
|
ia_logging.info("Enable model cpu offload")
|
|
pipe.enable_model_cpu_offload()
|
|
else:
|
|
pipe = pipe.to(devices.device)
|
|
if shared.xformers_available:
|
|
ia_logging.info("Enable xformers memory efficient attention")
|
|
pipe.enable_xformers_memory_efficient_attention()
|
|
else:
|
|
ia_logging.info("Enable attention slicing")
|
|
pipe.enable_attention_slicing()
|
|
if "privateuseone" in str(getattr(devices.device, "type", "")):
|
|
torch_generator = torch.Generator(devices.cpu)
|
|
else:
|
|
torch_generator = torch.Generator(devices.device)
|
|
|
|
init_image, mask_image = auto_resize_to_pil(input_image, mask_image)
|
|
width, height = init_image.size
|
|
|
|
output_list = []
|
|
iteration_count = iteration_count if iteration_count is not None else 1
|
|
for count in range(int(iteration_count)):
|
|
gc.collect()
|
|
if seed < 0 or count > 0:
|
|
seed = random.randint(0, 2147483647)
|
|
|
|
generator = torch_generator.manual_seed(seed)
|
|
|
|
pipe_args_dict = {
|
|
"prompt": prompt,
|
|
"image": init_image,
|
|
"width": width,
|
|
"height": height,
|
|
"mask_image": mask_image,
|
|
"num_inference_steps": ddim_steps,
|
|
"guidance_scale": cfg_scale,
|
|
"negative_prompt": n_prompt,
|
|
"generator": generator,
|
|
}
|
|
|
|
output_image = pipe(**pipe_args_dict).images[0]
|
|
|
|
if composite_chk:
|
|
dilate_mask_image = Image.fromarray(cv2.dilate(np.array(mask_image), np.ones((3, 3), dtype=np.uint8), iterations=4))
|
|
output_image = Image.composite(output_image, init_image, dilate_mask_image.convert("L").filter(ImageFilter.GaussianBlur(3)))
|
|
|
|
generation_params = {
|
|
"Steps": ddim_steps,
|
|
"Sampler": sampler_name,
|
|
"CFG scale": cfg_scale,
|
|
"Seed": seed,
|
|
"Size": f"{width}x{height}",
|
|
"Model": inp_model_id,
|
|
}
|
|
|
|
generation_params_text = ", ".join([k if k == v else f"{k}: {v}" for k, v in generation_params.items() if v is not None])
|
|
prompt_text = prompt if prompt else ""
|
|
negative_prompt_text = "\nNegative prompt: " + n_prompt if n_prompt else ""
|
|
infotext = f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
|
|
|
|
metadata = PngInfo()
|
|
metadata.add_text("parameters", infotext)
|
|
|
|
save_name = "_".join([ia_file_manager.savename_prefix, os.path.basename(inp_model_id), str(seed)]) + ".png"
|
|
save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
|
|
output_image.save(save_name, pnginfo=metadata)
|
|
|
|
output_list.append(output_image)
|
|
|
|
yield output_list, max([1, iteration_count - (count + 1)])
|
|
|
|
|
|
@offload_reload_decorator
|
|
@clear_cache_decorator
|
|
def run_cleaner(input_image, sel_mask, cleaner_model_id, cleaner_save_mask_chk):
|
|
global sam_dict
|
|
if input_image is None or sam_dict["mask_image"] is None or sel_mask is None:
|
|
ia_logging.error("The image or mask does not exist")
|
|
return None
|
|
|
|
mask_image = sam_dict["mask_image"]
|
|
if input_image.shape != mask_image.shape:
|
|
ia_logging.error("The sizes of the image and mask do not match")
|
|
return None
|
|
|
|
save_mask_image(mask_image, cleaner_save_mask_chk)
|
|
|
|
ia_logging.info(f"Loading model {cleaner_model_id}")
|
|
if platform.system() == "Darwin":
|
|
model = ModelManager(name=cleaner_model_id, device=devices.cpu)
|
|
else:
|
|
model = ModelManager(name=cleaner_model_id, device=devices.device)
|
|
|
|
init_image, mask_image = auto_resize_to_pil(input_image, mask_image)
|
|
width, height = init_image.size
|
|
|
|
init_image = np.array(init_image)
|
|
mask_image = np.array(mask_image.convert("L"))
|
|
|
|
config = Config(
|
|
ldm_steps=20,
|
|
ldm_sampler=LDMSampler.ddim,
|
|
hd_strategy=HDStrategy.ORIGINAL,
|
|
hd_strategy_crop_margin=32,
|
|
hd_strategy_crop_trigger_size=512,
|
|
hd_strategy_resize_limit=512,
|
|
prompt="",
|
|
sd_steps=20,
|
|
sd_sampler=SDSampler.ddim
|
|
)
|
|
|
|
output_image = model(image=init_image, mask=mask_image, config=config)
|
|
output_image = cv2.cvtColor(output_image.astype(np.uint8), cv2.COLOR_BGR2RGB)
|
|
output_image = Image.fromarray(output_image)
|
|
|
|
save_name = "_".join([ia_file_manager.savename_prefix, os.path.basename(cleaner_model_id)]) + ".png"
|
|
save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
|
|
output_image.save(save_name)
|
|
|
|
del model
|
|
return [output_image]
|
|
|
|
|
|
@clear_cache_decorator
|
|
def run_get_alpha_image(input_image, sel_mask):
|
|
global sam_dict
|
|
if input_image is None or sam_dict["mask_image"] is None or sel_mask is None:
|
|
ia_logging.error("The image or mask does not exist")
|
|
return None, ""
|
|
|
|
mask_image = sam_dict["mask_image"]
|
|
if input_image.shape != mask_image.shape:
|
|
ia_logging.error("The sizes of the image and mask do not match")
|
|
return None, ""
|
|
|
|
alpha_image = Image.fromarray(input_image).convert("RGBA")
|
|
mask_image = Image.fromarray(mask_image).convert("L")
|
|
|
|
alpha_image.putalpha(mask_image)
|
|
|
|
save_name = "_".join([ia_file_manager.savename_prefix, "rgba_image"]) + ".png"
|
|
save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
|
|
alpha_image.save(save_name)
|
|
|
|
return alpha_image, f"saved: {save_name}"
|
|
|
|
|
|
@clear_cache_decorator
|
|
def run_get_mask(sel_mask):
|
|
global sam_dict
|
|
if sam_dict["mask_image"] is None or sel_mask is None:
|
|
return None
|
|
|
|
mask_image = sam_dict["mask_image"]
|
|
|
|
save_name = "_".join([ia_file_manager.savename_prefix, "created_mask"]) + ".png"
|
|
save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
|
|
Image.fromarray(mask_image).save(save_name)
|
|
|
|
return mask_image
|
|
|
|
|
|
@clear_cache_decorator
|
|
def run_cn_inpaint(input_image, sel_mask,
|
|
cn_prompt, cn_n_prompt, cn_sampler_id, cn_ddim_steps, cn_cfg_scale, cn_strength, cn_seed,
|
|
cn_module_id, cn_model_id, cn_save_mask_chk,
|
|
cn_low_vram_chk, cn_weight, cn_mode, cn_iteration_count=1,
|
|
cn_ref_module_id=None, cn_ref_image=None, cn_ref_weight=1.0, cn_ref_mode="Balanced", cn_ref_resize_mode="resize",
|
|
cn_ipa_or_ref=None, cn_ipa_model_id=None):
|
|
global sam_dict
|
|
if input_image is None or sam_dict["mask_image"] is None or sel_mask is None:
|
|
ia_logging.error("The image or mask does not exist")
|
|
return
|
|
|
|
mask_image = sam_dict["mask_image"]
|
|
if input_image.shape != mask_image.shape:
|
|
ia_logging.error("The sizes of the image and mask do not match")
|
|
return
|
|
|
|
await_pre_reload_model_weights()
|
|
|
|
if (shared.sd_model.parameterization == "v" and "sd15" in cn_model_id):
|
|
ia_logging.error("The SDv2 model is not compatible with the ControlNet model")
|
|
ret_image = draw_text_image(input_image, "The SD v2 model is not compatible with the ControlNet model")
|
|
yield [ret_image], 1
|
|
return
|
|
|
|
if (getattr(shared.sd_model, "is_sdxl", False) and "sd15" in cn_model_id):
|
|
ia_logging.error("The SDXL model is not compatible with the ControlNet model")
|
|
ret_image = draw_text_image(input_image, "The SD XL model is not compatible with the ControlNet model")
|
|
yield [ret_image], 1
|
|
return
|
|
|
|
cnet = sam_dict.get("cnet", None)
|
|
if cnet is None:
|
|
ia_logging.warning("The ControlNet extension is not loaded")
|
|
return
|
|
|
|
save_mask_image(mask_image, cn_save_mask_chk)
|
|
|
|
init_image, mask_image = auto_resize_to_pil(input_image, mask_image)
|
|
width, height = init_image.size
|
|
|
|
input_mask = None if "inpaint_only" in cn_module_id else mask_image
|
|
p = get_sd_img2img_processing(init_image, input_mask,
|
|
cn_prompt, cn_n_prompt, cn_sampler_id, cn_ddim_steps, cn_cfg_scale, cn_strength, cn_seed)
|
|
|
|
backup_alwayson_scripts(p.scripts)
|
|
disable_alwayson_scripts_wo_cn(cnet, p.scripts)
|
|
|
|
cn_units = [cnet.to_processing_unit(dict(
|
|
enabled=True,
|
|
module=cn_module_id,
|
|
model=cn_model_id,
|
|
weight=cn_weight,
|
|
image={"image": np.array(init_image), "mask": np.array(mask_image)},
|
|
resize_mode=cnet.ResizeMode.RESIZE,
|
|
low_vram=cn_low_vram_chk,
|
|
processor_res=min(width, height),
|
|
guidance_start=0.0,
|
|
guidance_end=1.0,
|
|
pixel_perfect=True,
|
|
control_mode=cn_mode,
|
|
threshold_a=0.5,
|
|
threshold_b=0.5,
|
|
))]
|
|
|
|
if cn_ref_module_id is not None and cn_ref_image is not None:
|
|
if cn_ref_resize_mode == "tile":
|
|
ref_height, ref_width = cn_ref_image.shape[:2]
|
|
num_h = math.ceil(height / ref_height) if height > ref_height else 1
|
|
num_h = num_h + 1 if (num_h % 2) == 0 else num_h
|
|
num_w = math.ceil(width / ref_width) if width > ref_width else 1
|
|
num_w = num_w + 1 if (num_w % 2) == 0 else num_w
|
|
cn_ref_image = np.tile(cn_ref_image, (num_h, num_w, 1))
|
|
cn_ref_image = transforms.functional.center_crop(Image.fromarray(cn_ref_image), (height, width))
|
|
ia_logging.info(f"Reference image is tiled ({num_h}, {num_w}) times and cropped to ({height}, {width})")
|
|
else:
|
|
cn_ref_image = ImageOps.fit(Image.fromarray(cn_ref_image), (width, height), method=Image.Resampling.LANCZOS)
|
|
ia_logging.info(f"Reference image is resized and cropped to ({height}, {width})")
|
|
assert cn_ref_image.size == init_image.size, "The sizes of the reference image and input image do not match"
|
|
|
|
cn_ref_model_id = "None"
|
|
if cn_ipa_or_ref is not None and cn_ipa_model_id is not None:
|
|
cn_ipa_module_ids = [cn for cn in cnet.get_modules() if "ip-adapter" in cn and "sd15" in cn]
|
|
if len(cn_ipa_module_ids) > 0 and cn_ipa_or_ref == "IP-Adapter":
|
|
cn_ref_module_id = cn_ipa_module_ids[0]
|
|
cn_ref_model_id = cn_ipa_model_id
|
|
|
|
cn_units.append(cnet.to_processing_unit(dict(
|
|
enabled=True,
|
|
module=cn_ref_module_id,
|
|
model=cn_ref_model_id,
|
|
weight=cn_ref_weight,
|
|
image={"image": np.array(cn_ref_image), "mask": None},
|
|
resize_mode=cnet.ResizeMode.RESIZE,
|
|
low_vram=cn_low_vram_chk,
|
|
processor_res=min(width, height),
|
|
guidance_start=0.0,
|
|
guidance_end=1.0,
|
|
pixel_perfect=True,
|
|
control_mode=cn_ref_mode,
|
|
threshold_a=0.5,
|
|
threshold_b=0.5,
|
|
)))
|
|
|
|
p.script_args = np.zeros(get_controlnet_args_to(cnet, p.scripts)).tolist()
|
|
cnet.update_cn_script_in_processing(p, cn_units)
|
|
|
|
no_hash_cn_model_id = re.sub(r"\s\[[0-9a-f]{8,10}\]", "", cn_model_id).strip()
|
|
|
|
output_list = []
|
|
cn_iteration_count = cn_iteration_count if cn_iteration_count is not None else 1
|
|
for count in range(int(cn_iteration_count)):
|
|
gc.collect()
|
|
if cn_seed < 0 or count > 0:
|
|
cn_seed = random.randint(0, 2147483647)
|
|
|
|
p.init_images = [init_image]
|
|
p.seed = cn_seed
|
|
|
|
try:
|
|
processed = process_images(p)
|
|
except devices.NansException:
|
|
ia_logging.error("A tensor with all NaNs was produced in VAE")
|
|
ret_image = draw_text_image(
|
|
input_image, "A tensor with all NaNs was produced in VAE")
|
|
clear_controlnet_cache(cnet, p.scripts)
|
|
restore_alwayson_scripts(p.scripts)
|
|
yield [ret_image], 1
|
|
return
|
|
|
|
if processed is not None and len(processed.images) > 0:
|
|
output_image = processed.images[0]
|
|
|
|
infotext = create_infotext(p, all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds)
|
|
|
|
metadata = PngInfo()
|
|
metadata.add_text("parameters", infotext)
|
|
|
|
save_name = "_".join([ia_file_manager.savename_prefix, os.path.basename(no_hash_cn_model_id), str(cn_seed)]) + ".png"
|
|
save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
|
|
output_image.save(save_name, pnginfo=metadata)
|
|
|
|
output_list.append(output_image)
|
|
|
|
yield output_list, max([1, cn_iteration_count - (count + 1)])
|
|
|
|
clear_controlnet_cache(cnet, p.scripts)
|
|
restore_alwayson_scripts(p.scripts)
|
|
|
|
|
|
@clear_cache_decorator
|
|
def run_webui_inpaint(input_image, sel_mask,
|
|
webui_prompt, webui_n_prompt, webui_sampler_id, webui_ddim_steps, webui_cfg_scale, webui_strength, webui_seed,
|
|
webui_model_id, webui_save_mask_chk,
|
|
webui_mask_blur, webui_fill_mode, webui_iteration_count=1,
|
|
webui_enable_refiner_chk=False, webui_refiner_checkpoint="", webui_refiner_switch_at=0.8):
|
|
global sam_dict
|
|
if input_image is None or sam_dict["mask_image"] is None or sel_mask is None:
|
|
ia_logging.error("The image or mask does not exist")
|
|
return
|
|
|
|
mask_image = sam_dict["mask_image"]
|
|
if input_image.shape != mask_image.shape:
|
|
ia_logging.error("The sizes of the image and mask do not match")
|
|
return
|
|
|
|
info = get_closet_checkpoint_match(webui_model_id)
|
|
if info is None:
|
|
ia_logging.error(f"No model found: {webui_model_id}")
|
|
return
|
|
|
|
await_backup_reload_ckpt_info(info=info)
|
|
|
|
if not getattr(shared.sd_model, "is_sdxl", False) and "sdxl_vae" in getattr(shared.opts, "sd_vae", ""):
|
|
ia_logging.error("The SDXL VAE is not compatible with the inpainting model")
|
|
ret_image = draw_text_image(
|
|
input_image, "The SDXL VAE is not compatible with the inpainting model")
|
|
yield [ret_image], 1
|
|
return
|
|
|
|
set_ia_config(IAConfig.KEYS.INP_WEBUI_MODEL_ID, webui_model_id, IAConfig.SECTIONS.USER)
|
|
|
|
save_mask_image(mask_image, webui_save_mask_chk)
|
|
|
|
init_image, mask_image = auto_resize_to_pil(input_image, mask_image)
|
|
width, height = init_image.size
|
|
|
|
p = get_sd_img2img_processing(init_image, mask_image,
|
|
webui_prompt, webui_n_prompt, webui_sampler_id, webui_ddim_steps, webui_cfg_scale, webui_strength, webui_seed,
|
|
webui_mask_blur, webui_fill_mode)
|
|
|
|
backup_alwayson_scripts(p.scripts)
|
|
disable_all_alwayson_scripts(p.scripts)
|
|
|
|
p.script_args = np.zeros(get_max_args_to(p.scripts)).tolist()
|
|
|
|
if ia_check_versions.webui_refiner_is_available and webui_enable_refiner_chk:
|
|
p.refiner_checkpoint = webui_refiner_checkpoint
|
|
p.refiner_switch_at = webui_refiner_switch_at
|
|
|
|
no_hash_webui_model_id = re.sub(r"\s\[[0-9a-f]{8,10}\]", "", webui_model_id).strip()
|
|
no_hash_webui_model_id = os.path.splitext(no_hash_webui_model_id)[0]
|
|
|
|
output_list = []
|
|
webui_iteration_count = webui_iteration_count if webui_iteration_count is not None else 1
|
|
for count in range(int(webui_iteration_count)):
|
|
gc.collect()
|
|
if webui_seed < 0 or count > 0:
|
|
webui_seed = random.randint(0, 2147483647)
|
|
|
|
p.init_images = [init_image]
|
|
p.seed = webui_seed
|
|
|
|
try:
|
|
processed = process_images(p)
|
|
except devices.NansException:
|
|
ia_logging.error("A tensor with all NaNs was produced in VAE")
|
|
ret_image = draw_text_image(
|
|
input_image, "A tensor with all NaNs was produced in VAE")
|
|
restore_alwayson_scripts(p.scripts)
|
|
yield [ret_image], 1
|
|
return
|
|
|
|
if processed is not None and len(processed.images) > 0:
|
|
output_image = processed.images[0]
|
|
|
|
infotext = create_infotext(p, all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds)
|
|
|
|
metadata = PngInfo()
|
|
metadata.add_text("parameters", infotext)
|
|
|
|
save_name = "_".join([ia_file_manager.savename_prefix, os.path.basename(no_hash_webui_model_id), str(webui_seed)]) + ".png"
|
|
save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
|
|
output_image.save(save_name, pnginfo=metadata)
|
|
|
|
output_list.append(output_image)
|
|
|
|
yield output_list, max([1, webui_iteration_count - (count + 1)])
|
|
|
|
restore_alwayson_scripts(p.scripts)
|
|
|
|
|
|
def on_ui_tabs():
|
|
global sam_dict
|
|
|
|
setup_ia_config_ini()
|
|
sampler_names = get_sampler_names()
|
|
sam_model_ids = get_sam_model_ids()
|
|
sam_model_index = get_ia_config_index(IAConfig.KEYS.SAM_MODEL_ID, IAConfig.SECTIONS.USER)
|
|
inp_model_ids = get_inp_model_ids()
|
|
inp_model_index = get_ia_config_index(IAConfig.KEYS.INP_MODEL_ID, IAConfig.SECTIONS.USER)
|
|
cleaner_model_ids = get_cleaner_model_ids()
|
|
padding_mode_names = get_padding_mode_names()
|
|
sam_dict["cnet"] = find_controlnet()
|
|
|
|
cn_enabled = False
|
|
if sam_dict["cnet"] is not None:
|
|
cn_module_ids = [cn for cn in sam_dict["cnet"].get_modules() if "inpaint" in cn]
|
|
cn_module_index = cn_module_ids.index("inpaint_only") if "inpaint_only" in cn_module_ids else 0
|
|
|
|
cn_model_ids = [cn for cn in sam_dict["cnet"].get_models() if "inpaint" in cn]
|
|
cn_modes = [mode.value for mode in sam_dict["cnet"].ControlMode]
|
|
|
|
if len(cn_module_ids) > 0 and len(cn_model_ids) > 0:
|
|
cn_enabled = True
|
|
|
|
if samplers_for_img2img is not None and len(samplers_for_img2img) > 0:
|
|
cn_sampler_ids = [sampler.name for sampler in samplers_for_img2img]
|
|
else:
|
|
cn_sampler_ids = ["DDIM"]
|
|
cn_sampler_index = cn_sampler_ids.index("DDIM") if "DDIM" in cn_sampler_ids else 0
|
|
|
|
cn_ref_only = False
|
|
try:
|
|
if cn_enabled and sam_dict["cnet"].get_max_models_num() > 1:
|
|
cn_ref_module_ids = [cn for cn in sam_dict["cnet"].get_modules() if "reference" in cn]
|
|
if len(cn_ref_module_ids) > 0:
|
|
cn_ref_only = True
|
|
except AttributeError:
|
|
pass
|
|
|
|
cn_ip_adapter = False
|
|
if cn_ref_only:
|
|
cn_ipa_module_ids = [cn for cn in sam_dict["cnet"].get_modules() if "ip-adapter" in cn and "sd15" in cn]
|
|
cn_ipa_model_ids = [cn for cn in sam_dict["cnet"].get_models() if "ip-adapter" in cn and "sd15" in cn]
|
|
|
|
if len(cn_ipa_module_ids) > 0 and len(cn_ipa_model_ids) > 0:
|
|
cn_ip_adapter = True
|
|
|
|
webui_inpaint_enabled = False
|
|
webui_model_ids = get_inp_webui_model_ids()
|
|
if len(webui_model_ids) > 0:
|
|
webui_inpaint_enabled = True
|
|
webui_model_index = get_ia_config_index(IAConfig.KEYS.INP_WEBUI_MODEL_ID, IAConfig.SECTIONS.USER)
|
|
|
|
if samplers_for_img2img is not None and len(samplers_for_img2img) > 0:
|
|
webui_sampler_ids = [sampler.name for sampler in samplers_for_img2img]
|
|
else:
|
|
webui_sampler_ids = ["DDIM"]
|
|
webui_sampler_index = webui_sampler_ids.index("DDIM") if "DDIM" in webui_sampler_ids else 0
|
|
|
|
out_gallery_kwargs = dict(columns=2, height=520, object_fit="contain", preview=True)
|
|
|
|
with gr.Blocks(analytics_enabled=False) as inpaint_anything_interface:
|
|
with gr.Row():
|
|
with gr.Column():
|
|
with gr.Row():
|
|
with gr.Column():
|
|
sam_model_id = gr.Dropdown(label="Segment Anything Model ID", elem_id="sam_model_id", choices=sam_model_ids,
|
|
value=sam_model_ids[sam_model_index], show_label=True)
|
|
with gr.Column():
|
|
with gr.Row():
|
|
load_model_btn = gr.Button("Download model", elem_id="load_model_btn")
|
|
with gr.Row():
|
|
status_text = gr.Textbox(label="", elem_id="status_text", max_lines=1, show_label=False, interactive=False)
|
|
with gr.Row():
|
|
input_image = gr.Image(label="Input image", elem_id="ia_input_image", source="upload", type="numpy", interactive=True)
|
|
|
|
with gr.Row():
|
|
with gr.Accordion("Padding options", elem_id="padding_options", open=False):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
pad_scale_width = gr.Slider(label="Scale Width", elem_id="pad_scale_width", minimum=1.0, maximum=1.5, value=1.0, step=0.01)
|
|
with gr.Column():
|
|
pad_lr_barance = gr.Slider(label="Left/Right Balance", elem_id="pad_lr_barance", minimum=0.0, maximum=1.0, value=0.5, step=0.01)
|
|
with gr.Row():
|
|
with gr.Column():
|
|
pad_scale_height = gr.Slider(label="Scale Height", elem_id="pad_scale_height", minimum=1.0, maximum=1.5, value=1.0, step=0.01)
|
|
with gr.Column():
|
|
pad_tb_barance = gr.Slider(label="Top/Bottom Balance", elem_id="pad_tb_barance", minimum=0.0, maximum=1.0, value=0.5, step=0.01)
|
|
with gr.Row():
|
|
with gr.Column():
|
|
padding_mode = gr.Dropdown(label="Padding Mode", elem_id="padding_mode", choices=padding_mode_names, value="edge")
|
|
with gr.Column():
|
|
padding_btn = gr.Button("Run Padding", elem_id="padding_btn")
|
|
|
|
with gr.Row():
|
|
with gr.Column():
|
|
anime_style_chk = gr.Checkbox(label="Anime Style (Up Detection, Down mask Quality)", elem_id="anime_style_chk",
|
|
show_label=True, interactive=True)
|
|
with gr.Column():
|
|
sam_btn = gr.Button("Run Segment Anything", elem_id="sam_btn", variant="primary", interactive=False)
|
|
|
|
with gr.Tab("Inpainting", elem_id="inpainting_tab"):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
prompt = gr.Textbox(label="Inpainting Prompt", elem_id="ia_sd_prompt")
|
|
n_prompt = gr.Textbox(label="Negative Prompt", elem_id="ia_sd_n_prompt")
|
|
with gr.Column(scale=0, min_width=128):
|
|
gr.Markdown("Get prompt from:")
|
|
get_txt2img_prompt_btn = gr.Button("txt2img", elem_id="get_txt2img_prompt_btn")
|
|
get_img2img_prompt_btn = gr.Button("img2img", elem_id="get_img2img_prompt_btn")
|
|
with gr.Accordion("Advanced options", elem_id="inp_advanced_options", open=False):
|
|
composite_chk = gr.Checkbox(label="Mask area Only", elem_id="composite_chk", value=True, show_label=True, interactive=True)
|
|
with gr.Row():
|
|
with gr.Column():
|
|
sampler_name = gr.Dropdown(label="Sampler", elem_id="sampler_name", choices=sampler_names,
|
|
value=sampler_names[0], show_label=True)
|
|
with gr.Column():
|
|
ddim_steps = gr.Slider(label="Sampling Steps", elem_id="ddim_steps", minimum=1, maximum=100, value=20, step=1)
|
|
cfg_scale = gr.Slider(label="Guidance Scale", elem_id="cfg_scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
|
|
seed = gr.Slider(
|
|
label="Seed",
|
|
elem_id="sd_seed",
|
|
minimum=-1,
|
|
maximum=2147483647,
|
|
step=1,
|
|
value=-1,
|
|
)
|
|
with gr.Row():
|
|
with gr.Column():
|
|
inp_model_id = gr.Dropdown(label="Inpainting Model ID", elem_id="inp_model_id",
|
|
choices=inp_model_ids, value=inp_model_ids[inp_model_index], show_label=True)
|
|
with gr.Column():
|
|
with gr.Row():
|
|
inpaint_btn = gr.Button("Run Inpainting", elem_id="inpaint_btn", variant="primary")
|
|
with gr.Row():
|
|
save_mask_chk = gr.Checkbox(label="Save mask", elem_id="save_mask_chk",
|
|
value=False, show_label=False, interactive=False, visible=False)
|
|
iteration_count = gr.Slider(label="Iterations", elem_id="iteration_count", minimum=1, maximum=10, value=1, step=1)
|
|
|
|
with gr.Row():
|
|
if ia_check_versions.gradio_version_is_old:
|
|
out_image = gr.Gallery(label="Inpainted image", elem_id="ia_out_image", show_label=False
|
|
).style(**out_gallery_kwargs)
|
|
else:
|
|
out_image = gr.Gallery(label="Inpainted image", elem_id="ia_out_image", show_label=False,
|
|
**out_gallery_kwargs)
|
|
|
|
with gr.Tab("Cleaner", elem_id="cleaner_tab"):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
cleaner_model_id = gr.Dropdown(label="Cleaner Model ID", elem_id="cleaner_model_id",
|
|
choices=cleaner_model_ids, value=cleaner_model_ids[0], show_label=True)
|
|
with gr.Column():
|
|
with gr.Row():
|
|
cleaner_btn = gr.Button("Run Cleaner", elem_id="cleaner_btn", variant="primary")
|
|
with gr.Row():
|
|
cleaner_save_mask_chk = gr.Checkbox(label="Save mask", elem_id="cleaner_save_mask_chk",
|
|
value=False, show_label=False, interactive=False, visible=False)
|
|
|
|
with gr.Row():
|
|
if ia_check_versions.gradio_version_is_old:
|
|
cleaner_out_image = gr.Gallery(label="Cleaned image", elem_id="ia_cleaner_out_image", show_label=False
|
|
).style(**out_gallery_kwargs)
|
|
else:
|
|
cleaner_out_image = gr.Gallery(label="Cleaned image", elem_id="ia_cleaner_out_image", show_label=False,
|
|
**out_gallery_kwargs)
|
|
|
|
if webui_inpaint_enabled:
|
|
with gr.Tab("Inpainting webui", elem_id="webui_inpainting_tab"):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
webui_prompt = gr.Textbox(label="Inpainting Prompt", elem_id="ia_webui_sd_prompt")
|
|
webui_n_prompt = gr.Textbox(label="Negative Prompt", elem_id="ia_webui_sd_n_prompt")
|
|
with gr.Column(scale=0, min_width=128):
|
|
gr.Markdown("Get prompt from:")
|
|
webui_get_txt2img_prompt_btn = gr.Button("txt2img", elem_id="webui_get_txt2img_prompt_btn")
|
|
webui_get_img2img_prompt_btn = gr.Button("img2img", elem_id="webui_get_img2img_prompt_btn")
|
|
with gr.Accordion("Advanced options", elem_id="webui_advanced_options", open=False):
|
|
webui_mask_blur = gr.Slider(label="Mask blur", minimum=0, maximum=64, step=1, value=4, elem_id="webui_mask_blur")
|
|
webui_fill_mode = gr.Radio(label="Masked content", elem_id="webui_fill_mode",
|
|
choices=["fill", "original", "latent noise", "latent nothing"], value="original", type="index")
|
|
with gr.Row():
|
|
with gr.Column():
|
|
webui_sampler_id = gr.Dropdown(label="Sampling method webui", elem_id="webui_sampler_id",
|
|
choices=webui_sampler_ids, value=webui_sampler_ids[webui_sampler_index], show_label=True)
|
|
with gr.Column():
|
|
webui_ddim_steps = gr.Slider(label="Sampling steps webui", elem_id="webui_ddim_steps",
|
|
minimum=1, maximum=150, value=30, step=1)
|
|
webui_cfg_scale = gr.Slider(label="Guidance scale webui", elem_id="webui_cfg_scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
|
|
webui_strength = gr.Slider(label="Denoising strength webui", elem_id="webui_strength",
|
|
minimum=0.0, maximum=1.0, value=0.75, step=0.01)
|
|
webui_seed = gr.Slider(
|
|
label="Seed",
|
|
elem_id="webui_sd_seed",
|
|
minimum=-1,
|
|
maximum=2147483647,
|
|
step=1,
|
|
value=-1,
|
|
)
|
|
if ia_check_versions.webui_refiner_is_available:
|
|
with gr.Accordion("Refiner options", elem_id="webui_refiner_options", open=False):
|
|
with gr.Row():
|
|
webui_enable_refiner_chk = gr.Checkbox(label="Enable Refiner", elem_id="webui_enable_refiner_chk",
|
|
value=False, show_label=True, interactive=True)
|
|
with gr.Row():
|
|
webui_refiner_checkpoint = gr.Dropdown(label="Refiner Model ID", elem_id="webui_refiner_checkpoint",
|
|
choices=shared.list_checkpoint_tiles(), value="")
|
|
webui_refiner_switch_at = gr.Slider(value=0.8, label="Switch at", minimum=0.01, maximum=1.0, step=0.01,
|
|
elem_id="webui_refiner_switch_at")
|
|
|
|
with gr.Row():
|
|
with gr.Column():
|
|
webui_model_id = gr.Dropdown(label="Inpainting Model ID webui", elem_id="webui_model_id",
|
|
choices=webui_model_ids, value=webui_model_ids[webui_model_index], show_label=True)
|
|
with gr.Column():
|
|
with gr.Row():
|
|
webui_inpaint_btn = gr.Button("Run Inpainting", elem_id="webui_inpaint_btn", variant="primary")
|
|
with gr.Row():
|
|
webui_save_mask_chk = gr.Checkbox(label="Save mask", elem_id="webui_save_mask_chk",
|
|
value=False, show_label=False, interactive=False, visible=False)
|
|
webui_iteration_count = gr.Slider(label="Iterations", elem_id="webui_iteration_count",
|
|
minimum=1, maximum=10, value=1, step=1)
|
|
|
|
with gr.Row():
|
|
if ia_check_versions.gradio_version_is_old:
|
|
webui_out_image = gr.Gallery(label="Inpainted image", elem_id="ia_webui_out_image", show_label=False
|
|
).style(**out_gallery_kwargs)
|
|
else:
|
|
webui_out_image = gr.Gallery(label="Inpainted image", elem_id="ia_webui_out_image", show_label=False,
|
|
**out_gallery_kwargs)
|
|
|
|
with gr.Tab("ControlNet Inpaint", elem_id="cn_inpaint_tab"):
|
|
if cn_enabled:
|
|
with gr.Row():
|
|
with gr.Column():
|
|
cn_prompt = gr.Textbox(label="Inpainting Prompt", elem_id="ia_cn_sd_prompt")
|
|
cn_n_prompt = gr.Textbox(label="Negative Prompt", elem_id="ia_cn_sd_n_prompt")
|
|
with gr.Column(scale=0, min_width=128):
|
|
gr.Markdown("Get prompt from:")
|
|
cn_get_txt2img_prompt_btn = gr.Button("txt2img", elem_id="cn_get_txt2img_prompt_btn")
|
|
cn_get_img2img_prompt_btn = gr.Button("img2img", elem_id="cn_get_img2img_prompt_btn")
|
|
with gr.Accordion("Advanced options", elem_id="cn_advanced_options", open=False):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
cn_sampler_id = gr.Dropdown(label="Sampling method", elem_id="cn_sampler_id",
|
|
choices=cn_sampler_ids, value=cn_sampler_ids[cn_sampler_index], show_label=True)
|
|
with gr.Column():
|
|
cn_ddim_steps = gr.Slider(label="Sampling steps", elem_id="cn_ddim_steps", minimum=1, maximum=150, value=30, step=1)
|
|
cn_cfg_scale = gr.Slider(label="Guidance scale", elem_id="cn_cfg_scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
|
|
cn_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Denoising strength", value=0.75, elem_id="cn_strength")
|
|
cn_seed = gr.Slider(
|
|
label="Seed",
|
|
elem_id="cn_sd_seed",
|
|
minimum=-1,
|
|
maximum=2147483647,
|
|
step=1,
|
|
value=-1,
|
|
)
|
|
with gr.Accordion("ControlNet options", elem_id="cn_cn_options", open=False):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
cn_low_vram_chk = gr.Checkbox(label="Low VRAM", elem_id="cn_low_vram_chk", value=True, show_label=True, interactive=True)
|
|
cn_weight = gr.Slider(label="Control Weight", elem_id="cn_weight", minimum=0.0, maximum=2.0, value=1.0, step=0.05)
|
|
with gr.Column():
|
|
cn_mode = gr.Dropdown(label="Control Mode", elem_id="cn_mode", choices=cn_modes, value=cn_modes[-1], show_label=True)
|
|
|
|
if cn_ref_only:
|
|
with gr.Row():
|
|
with gr.Column():
|
|
cn_md_text = "Reference Control (enabled with image below)"
|
|
if not cn_ip_adapter:
|
|
cn_md_text = cn_md_text + ("<br><span style='color: gray;'>"
|
|
"[IP-Adapter](https://huggingface.co/lllyasviel/sd_control_collection/tree/main) "
|
|
"is not available. Reference-Only is used.</span>")
|
|
gr.Markdown(cn_md_text)
|
|
if cn_ip_adapter:
|
|
cn_ipa_or_ref = gr.Radio(label="IP-Adapter or Reference-Only", elem_id="cn_ipa_or_ref",
|
|
choices=["IP-Adapter", "Reference-Only"], value="IP-Adapter", show_label=False)
|
|
cn_ref_image = gr.Image(label="Reference Image", elem_id="cn_ref_image", source="upload", type="numpy",
|
|
interactive=True)
|
|
with gr.Column():
|
|
cn_ref_resize_mode = gr.Radio(label="Reference Image Resize Mode", elem_id="cn_ref_resize_mode",
|
|
choices=["resize", "tile"], value="resize", show_label=True)
|
|
if cn_ip_adapter:
|
|
cn_ipa_model_id = gr.Dropdown(label="IP-Adapter Model ID", elem_id="cn_ipa_model_id",
|
|
choices=cn_ipa_model_ids, value=cn_ipa_model_ids[0], show_label=True)
|
|
cn_ref_module_id = gr.Dropdown(label="Reference Type for Reference-Only", elem_id="cn_ref_module_id",
|
|
choices=cn_ref_module_ids, value=cn_ref_module_ids[-1], show_label=True)
|
|
cn_ref_weight = gr.Slider(label="Reference Control Weight", elem_id="cn_ref_weight",
|
|
minimum=0.0, maximum=2.0, value=1.0, step=0.05)
|
|
cn_ref_mode = gr.Dropdown(label="Reference Control Mode", elem_id="cn_ref_mode",
|
|
choices=cn_modes, value=cn_modes[0], show_label=True)
|
|
else:
|
|
with gr.Row():
|
|
gr.Markdown("The Multi ControlNet setting is currently set to 1.<br>"
|
|
"If you wish to use the Reference-Only Control, "
|
|
"please adjust the Multi ControlNet setting to 2 or more and restart the Web UI.")
|
|
|
|
with gr.Row():
|
|
with gr.Column():
|
|
cn_module_id = gr.Dropdown(label="ControlNet Preprocessor", elem_id="cn_module_id",
|
|
choices=cn_module_ids, value=cn_module_ids[cn_module_index], show_label=True)
|
|
cn_model_id = gr.Dropdown(label="ControlNet Model ID", elem_id="cn_model_id",
|
|
choices=cn_model_ids, value=cn_model_ids[0], show_label=True)
|
|
with gr.Column():
|
|
with gr.Row():
|
|
cn_inpaint_btn = gr.Button("Run ControlNet Inpaint", elem_id="cn_inpaint_btn", variant="primary")
|
|
with gr.Row():
|
|
cn_save_mask_chk = gr.Checkbox(label="Save mask", elem_id="cn_save_mask_chk",
|
|
value=False, show_label=False, interactive=False, visible=False)
|
|
cn_iteration_count = gr.Slider(label="Iterations", elem_id="cn_iteration_count",
|
|
minimum=1, maximum=10, value=1, step=1)
|
|
|
|
with gr.Row():
|
|
if ia_check_versions.gradio_version_is_old:
|
|
cn_out_image = gr.Gallery(label="Inpainted image", elem_id="ia_cn_out_image", show_label=False
|
|
).style(**out_gallery_kwargs)
|
|
else:
|
|
cn_out_image = gr.Gallery(label="Inpainted image", elem_id="ia_cn_out_image", show_label=False,
|
|
**out_gallery_kwargs)
|
|
|
|
else:
|
|
if sam_dict["cnet"] is None:
|
|
gr.Markdown("ControlNet extension is not available.<br>"
|
|
"Requires the [sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet) extension.")
|
|
elif len(cn_module_ids) > 0:
|
|
cn_models_directory = os.path.join("extensions", "sd-webui-controlnet", "models")
|
|
gr.Markdown("ControlNet inpaint model is not available.<br>"
|
|
"Requires the [ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1/tree/main) inpaint model "
|
|
f"in the {cn_models_directory} directory.")
|
|
else:
|
|
gr.Markdown("ControlNet inpaint preprocessor is not available.<br>"
|
|
"The local version of [sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet) extension may be old.")
|
|
|
|
with gr.Tab("Mask only", elem_id="mask_only_tab"):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
get_alpha_image_btn = gr.Button("Get mask as alpha of image", elem_id="get_alpha_image_btn")
|
|
with gr.Column():
|
|
get_mask_btn = gr.Button("Get mask", elem_id="get_mask_btn")
|
|
|
|
with gr.Row():
|
|
with gr.Column():
|
|
alpha_out_image = gr.Image(label="Alpha channel image", elem_id="alpha_out_image", type="pil", image_mode="RGBA", interactive=False)
|
|
with gr.Column():
|
|
mask_out_image = gr.Image(label="Mask image", elem_id="mask_out_image", type="numpy", interactive=False)
|
|
|
|
with gr.Row():
|
|
with gr.Column():
|
|
get_alpha_status_text = gr.Textbox(label="", elem_id="get_alpha_status_text", max_lines=1, show_label=False, interactive=False)
|
|
with gr.Column():
|
|
mask_send_to_inpaint_btn = gr.Button("Send to img2img inpaint", elem_id="mask_send_to_inpaint_btn")
|
|
|
|
with gr.Column():
|
|
with gr.Row():
|
|
gr.Markdown("Mouse over image: Press `S` key for Fullscreen mode, `R` key to Reset zoom")
|
|
with gr.Row():
|
|
if ia_check_versions.gradio_version_is_old:
|
|
sam_image = gr.Image(label="Segment Anything image", elem_id="ia_sam_image", type="numpy", tool="sketch", brush_radius=8,
|
|
show_label=False, interactive=True).style(height=480)
|
|
else:
|
|
sam_image = gr.Image(label="Segment Anything image", elem_id="ia_sam_image", type="numpy", tool="sketch", brush_radius=8,
|
|
show_label=False, interactive=True, height=480)
|
|
|
|
with gr.Row():
|
|
with gr.Column():
|
|
select_btn = gr.Button("Create Mask", elem_id="select_btn", variant="primary")
|
|
with gr.Column():
|
|
with gr.Row():
|
|
invert_chk = gr.Checkbox(label="Invert mask", elem_id="invert_chk", show_label=True, interactive=True)
|
|
ignore_black_chk = gr.Checkbox(label="Ignore black area", elem_id="ignore_black_chk", value=True, show_label=True, interactive=True)
|
|
|
|
with gr.Row():
|
|
if ia_check_versions.gradio_version_is_old:
|
|
sel_mask = gr.Image(label="Selected mask image", elem_id="ia_sel_mask", type="numpy", tool="sketch", brush_radius=12,
|
|
show_label=False, interactive=True).style(height=480)
|
|
else:
|
|
sel_mask = gr.Image(label="Selected mask image", elem_id="ia_sel_mask", type="numpy", tool="sketch", brush_radius=12,
|
|
show_label=False, interactive=True, height=480)
|
|
|
|
with gr.Row():
|
|
with gr.Column():
|
|
expand_mask_btn = gr.Button("Expand mask region", elem_id="expand_mask_btn")
|
|
expand_mask_iteration_count = gr.Slider(label="Expand Mask Iterations",
|
|
elem_id="expand_mask_iteration_count", minimum=1, maximum=100, value=1, step=1)
|
|
with gr.Column():
|
|
apply_mask_btn = gr.Button("Trim mask by sketch", elem_id="apply_mask_btn")
|
|
add_mask_btn = gr.Button("Add mask by sketch", elem_id="add_mask_btn")
|
|
|
|
load_model_btn.click(download_model, inputs=[sam_model_id], outputs=[status_text])
|
|
input_image.upload(input_image_upload, inputs=[input_image, sam_image, sel_mask], outputs=[sam_image, sel_mask, sam_btn]).then(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_initSamSelMask")
|
|
padding_btn.click(run_padding, inputs=[input_image, pad_scale_width, pad_scale_height, pad_lr_barance, pad_tb_barance, padding_mode],
|
|
outputs=[input_image, status_text])
|
|
sam_btn.click(run_sam, inputs=[input_image, sam_model_id, sam_image, anime_style_chk], outputs=[sam_image, status_text]).then(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_clearSamMask")
|
|
select_btn.click(select_mask, inputs=[input_image, sam_image, invert_chk, ignore_black_chk, sel_mask], outputs=[sel_mask]).then(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_clearSelMask")
|
|
expand_mask_btn.click(expand_mask, inputs=[input_image, sel_mask, expand_mask_iteration_count], outputs=[sel_mask]).then(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_clearSelMask")
|
|
apply_mask_btn.click(apply_mask, inputs=[input_image, sel_mask], outputs=[sel_mask]).then(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_clearSelMask")
|
|
add_mask_btn.click(add_mask, inputs=[input_image, sel_mask], outputs=[sel_mask]).then(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_clearSelMask")
|
|
get_txt2img_prompt_btn.click(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_getTxt2imgPrompt")
|
|
get_img2img_prompt_btn.click(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_getImg2imgPrompt")
|
|
|
|
inpaint_btn.click(
|
|
run_inpaint,
|
|
inputs=[input_image, sel_mask, prompt, n_prompt, ddim_steps, cfg_scale, seed, inp_model_id, save_mask_chk, composite_chk,
|
|
sampler_name, iteration_count],
|
|
outputs=[out_image, iteration_count])
|
|
cleaner_btn.click(
|
|
run_cleaner,
|
|
inputs=[input_image, sel_mask, cleaner_model_id, cleaner_save_mask_chk],
|
|
outputs=[cleaner_out_image])
|
|
get_alpha_image_btn.click(
|
|
run_get_alpha_image,
|
|
inputs=[input_image, sel_mask],
|
|
outputs=[alpha_out_image, get_alpha_status_text])
|
|
get_mask_btn.click(
|
|
run_get_mask,
|
|
inputs=[sel_mask],
|
|
outputs=[mask_out_image])
|
|
mask_send_to_inpaint_btn.click(
|
|
fn=None,
|
|
_js="inpaintAnything_sendToInpaint",
|
|
inputs=None,
|
|
outputs=None)
|
|
if cn_enabled:
|
|
cn_get_txt2img_prompt_btn.click(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_cnGetTxt2imgPrompt")
|
|
cn_get_img2img_prompt_btn.click(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_cnGetImg2imgPrompt")
|
|
if cn_enabled:
|
|
cn_inputs = [input_image, sel_mask,
|
|
cn_prompt, cn_n_prompt, cn_sampler_id, cn_ddim_steps, cn_cfg_scale, cn_strength, cn_seed,
|
|
cn_module_id, cn_model_id, cn_save_mask_chk,
|
|
cn_low_vram_chk, cn_weight, cn_mode, cn_iteration_count]
|
|
if cn_ref_only:
|
|
cn_inputs.extend([cn_ref_module_id, cn_ref_image, cn_ref_weight, cn_ref_mode, cn_ref_resize_mode])
|
|
if cn_ip_adapter:
|
|
cn_inputs.extend([cn_ipa_or_ref, cn_ipa_model_id])
|
|
cn_inpaint_btn.click(
|
|
run_cn_inpaint,
|
|
inputs=cn_inputs,
|
|
outputs=[cn_out_image, cn_iteration_count]).then(
|
|
fn=async_post_reload_model_weights, inputs=None, outputs=None)
|
|
if webui_inpaint_enabled:
|
|
webui_get_txt2img_prompt_btn.click(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_webuiGetTxt2imgPrompt")
|
|
webui_get_img2img_prompt_btn.click(
|
|
fn=None, inputs=None, outputs=None, _js="inpaintAnything_webuiGetImg2imgPrompt")
|
|
wi_inputs = [input_image, sel_mask,
|
|
webui_prompt, webui_n_prompt, webui_sampler_id, webui_ddim_steps, webui_cfg_scale, webui_strength, webui_seed,
|
|
webui_model_id, webui_save_mask_chk,
|
|
webui_mask_blur, webui_fill_mode, webui_iteration_count]
|
|
if ia_check_versions.webui_refiner_is_available:
|
|
wi_inputs.extend([webui_enable_refiner_chk, webui_refiner_checkpoint, webui_refiner_switch_at])
|
|
webui_inpaint_btn.click(
|
|
run_webui_inpaint,
|
|
inputs=wi_inputs,
|
|
outputs=[webui_out_image, webui_iteration_count]).then(
|
|
fn=async_post_reload_model_weights, inputs=None, outputs=None)
|
|
|
|
return [(inpaint_anything_interface, "Inpaint Anything", "inpaint_anything")]
|
|
|
|
|
|
def on_ui_settings():
|
|
section = ("inpaint_anything", "Inpaint Anything")
|
|
shared.opts.add_option("inpaint_anything_save_folder",
|
|
shared.OptionInfo(
|
|
default="inpaint-anything",
|
|
label="Folder name where output images will be saved",
|
|
component=gr.Radio,
|
|
component_args={"choices": ["inpaint-anything", "img2img-images (img2img output setting of web UI)"]},
|
|
section=section))
|
|
shared.opts.add_option("inpaint_anything_sam_oncpu",
|
|
shared.OptionInfo(
|
|
default=False,
|
|
label="Run Segment Anything on CPU",
|
|
component=gr.Checkbox,
|
|
component_args={"interactive": True},
|
|
section=section))
|
|
shared.opts.add_option("inpaint_anything_offline_inpainting",
|
|
shared.OptionInfo(
|
|
default=False,
|
|
label="Run Inpainting on offline network (Models not auto-downloaded)",
|
|
component=gr.Checkbox,
|
|
component_args={"interactive": True},
|
|
section=section))
|
|
shared.opts.add_option("inpaint_anything_padding_fill",
|
|
shared.OptionInfo(
|
|
default=127,
|
|
label="Fill value used when Padding is set to constant",
|
|
component=gr.Slider,
|
|
component_args={"minimum": 0, "maximum": 255, "step": 1},
|
|
section=section))
|
|
shared.opts.add_option("inpain_anything_sam_models_dir",
|
|
shared.OptionInfo(
|
|
default="",
|
|
label="Segment Anything Models Directory; If empty, defaults to [Inpaint Anything extension folder]/models",
|
|
component=gr.Textbox,
|
|
component_args={"interactive": True},
|
|
section=section))
|
|
|
|
|
|
script_callbacks.on_ui_settings(on_ui_settings)
|
|
script_callbacks.on_ui_tabs(on_ui_tabs)
|