import gc
import math
import os
import platform
if platform.system() == "Darwin":
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
import random
import re
import traceback
import cv2
import gradio as gr
import numpy as np
import torch
from diffusers import (DDIMScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
KDPM2AncestralDiscreteScheduler, KDPM2DiscreteScheduler,
StableDiffusionInpaintPipeline)
from modules import devices, script_callbacks, shared
from modules.processing import create_infotext, process_images
from modules.sd_models import get_closet_checkpoint_match
from modules.sd_samplers import samplers_for_img2img
from PIL import Image, ImageFilter, ImageOps
from PIL.PngImagePlugin import PngInfo
from torch.hub import download_url_to_file
from torchvision import transforms
import inpalib
from ia_check_versions import ia_check_versions
from ia_config import (IAConfig, get_ia_config_index, get_webui_setting, set_ia_config,
setup_ia_config_ini)
from ia_file_manager import IAFileManager, download_model_from_hf, ia_file_manager
from ia_logging import draw_text_image, ia_logging
from ia_threading import (async_post_reload_model_weights, await_backup_reload_ckpt_info,
await_pre_reload_model_weights, clear_cache_decorator,
offload_reload_decorator)
from ia_ui_items import (get_cleaner_model_ids, get_inp_model_ids, get_inp_webui_model_ids,
get_padding_mode_names, get_sam_model_ids, get_sampler_names)
from ia_webui_controlnet import (backup_alwayson_scripts, clear_controlnet_cache,
disable_all_alwayson_scripts, disable_alwayson_scripts_wo_cn,
find_controlnet, get_controlnet_args_to, get_max_args_to,
get_sd_img2img_processing, restore_alwayson_scripts)
from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import Config, HDStrategy, LDMSampler, SDSampler
@clear_cache_decorator
def download_model(sam_model_id):
"""Download SAM model.
Args:
sam_model_id (str): SAM model id
Returns:
str: download status
"""
if "_hq_" in sam_model_id:
url_sam = "https://huggingface.co/Uminosachi/sam-hq/resolve/main/" + sam_model_id
elif "FastSAM" in sam_model_id:
url_sam = "https://huggingface.co/Uminosachi/FastSAM/resolve/main/" + sam_model_id
elif "mobile_sam" in sam_model_id:
url_sam = "https://huggingface.co/Uminosachi/MobileSAM/resolve/main/" + sam_model_id
else:
# url_sam_vit_h_4b8939 = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
url_sam = "https://dl.fbaipublicfiles.com/segment_anything/" + sam_model_id
sam_checkpoint = os.path.join(ia_file_manager.models_dir, sam_model_id)
if not os.path.isfile(sam_checkpoint):
try:
download_url_to_file(url_sam, sam_checkpoint)
except Exception as e:
ia_logging.error(str(e))
return str(e)
return IAFileManager.DOWNLOAD_COMPLETE
else:
return "Model already exists"
sam_dict = dict(sam_masks=None, mask_image=None, cnet=None, orig_image=None, pad_mask=None)
def save_mask_image(mask_image, save_mask_chk=False):
"""Save mask image.
Args:
mask_image (np.ndarray): mask image
save_mask_chk (bool, optional): If True, save mask image. Defaults to False.
Returns:
None
"""
if save_mask_chk:
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)
@clear_cache_decorator
def input_image_upload(input_image, sam_image, sel_mask):
global sam_dict
sam_dict["orig_image"] = input_image
sam_dict["pad_mask"] = None
if (sam_dict["mask_image"] is None or not isinstance(sam_dict["mask_image"], np.ndarray) or
sam_dict["mask_image"].shape != input_image.shape):
sam_dict["mask_image"] = np.zeros_like(input_image, dtype=np.uint8)
ret_sel_image = cv2.addWeighted(input_image, 0.5, sam_dict["mask_image"], 0.5, 0)
if sam_image is None or not isinstance(sam_image, dict) or "image" not in sam_image:
sam_dict["sam_masks"] = None
ret_sam_image = np.zeros_like(input_image, dtype=np.uint8)
elif sam_image["image"].shape == input_image.shape:
ret_sam_image = gr.update()
else:
sam_dict["sam_masks"] = None
ret_sam_image = gr.update(value=np.zeros_like(input_image, dtype=np.uint8))
if sel_mask is None or not isinstance(sel_mask, dict) or "image" not in sel_mask:
ret_sel_mask = ret_sel_image
elif sel_mask["image"].shape == ret_sel_image.shape and np.all(sel_mask["image"] == ret_sel_image):
ret_sel_mask = gr.update()
else:
ret_sel_mask = gr.update(value=ret_sel_image)
return ret_sam_image, ret_sel_mask, gr.update(interactive=True)
@clear_cache_decorator
def run_padding(input_image, pad_scale_width, pad_scale_height, pad_lr_barance, pad_tb_barance, padding_mode="edge"):
global sam_dict
if input_image is None or sam_dict["orig_image"] is None:
sam_dict["orig_image"] = None
sam_dict["pad_mask"] = None
return None, "Input image not found"
orig_image = sam_dict["orig_image"]
height, width = orig_image.shape[:2]
pad_width, pad_height = (int(width * pad_scale_width), int(height * pad_scale_height))
ia_logging.info(f"resize by padding: ({height}, {width}) -> ({pad_height}, {pad_width})")
pad_size_w, pad_size_h = (pad_width - width, pad_height - height)
pad_size_l = int(pad_size_w * pad_lr_barance)
pad_size_r = pad_size_w - pad_size_l
pad_size_t = int(pad_size_h * pad_tb_barance)
pad_size_b = pad_size_h - pad_size_t
pad_width = [(pad_size_t, pad_size_b), (pad_size_l, pad_size_r), (0, 0)]
if padding_mode == "constant":
fill_value = get_webui_setting("inpaint_anything_padding_fill", 127)
pad_image = np.pad(orig_image, pad_width=pad_width, mode=padding_mode, constant_values=fill_value)
else:
pad_image = np.pad(orig_image, pad_width=pad_width, mode=padding_mode)
mask_pad_width = [(pad_size_t, pad_size_b), (pad_size_l, pad_size_r)]
pad_mask = np.zeros((height, width), dtype=np.uint8)
pad_mask = np.pad(pad_mask, pad_width=mask_pad_width, mode="constant", constant_values=255)
sam_dict["pad_mask"] = dict(segmentation=pad_mask.astype(bool))
return pad_image, "Padding done"
@offload_reload_decorator
@clear_cache_decorator
def run_sam(input_image, sam_model_id, sam_image, anime_style_chk=False):
global sam_dict
if not inpalib.sam_file_exists(sam_model_id):
ret_sam_image = None if sam_image is None else gr.update()
return ret_sam_image, f"{sam_model_id} not found, please download"
if input_image is None:
ret_sam_image = None if sam_image is None else gr.update()
return ret_sam_image, "Input image not found"
set_ia_config(IAConfig.KEYS.SAM_MODEL_ID, sam_model_id, IAConfig.SECTIONS.USER)
if sam_dict["sam_masks"] is not None:
sam_dict["sam_masks"] = None
gc.collect()
ia_logging.info(f"input_image: {input_image.shape} {input_image.dtype}")
try:
sam_masks = inpalib.generate_sam_masks(input_image, sam_model_id, anime_style_chk)
sam_masks = inpalib.sort_masks_by_area(sam_masks)
sam_masks = inpalib.insert_mask_to_sam_masks(sam_masks, sam_dict["pad_mask"])
seg_image = inpalib.create_seg_color_image(input_image, sam_masks)
sam_dict["sam_masks"] = sam_masks
except Exception as e:
print(traceback.format_exc())
ia_logging.error(str(e))
ret_sam_image = None if sam_image is None else gr.update()
return ret_sam_image, "Segment Anything failed"
if sam_image is None:
return seg_image, "Segment Anything complete"
else:
if sam_image["image"].shape == seg_image.shape and np.all(sam_image["image"] == seg_image):
return gr.update(), "Segment Anything complete"
else:
return gr.update(value=seg_image), "Segment Anything complete"
@clear_cache_decorator
def select_mask(input_image, sam_image, invert_chk, ignore_black_chk, sel_mask):
global sam_dict
if sam_dict["sam_masks"] is None or sam_image is None:
ret_sel_mask = None if sel_mask is None else gr.update()
return ret_sel_mask
sam_masks = sam_dict["sam_masks"]
# image = sam_image["image"]
mask = sam_image["mask"][:, :, 0:1]
try:
seg_image = inpalib.create_mask_image(mask, sam_masks, ignore_black_chk)
if invert_chk:
seg_image = inpalib.invert_mask(seg_image)
sam_dict["mask_image"] = seg_image
except Exception as e:
print(traceback.format_exc())
ia_logging.error(str(e))
ret_sel_mask = None if sel_mask is None else gr.update()
return ret_sel_mask
if input_image is not None and input_image.shape == seg_image.shape:
ret_image = cv2.addWeighted(input_image, 0.5, seg_image, 0.5, 0)
else:
ret_image = seg_image
if sel_mask is None:
return ret_image
else:
if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
return gr.update()
else:
return gr.update(value=ret_image)
@clear_cache_decorator
def expand_mask(input_image, sel_mask, expand_iteration=1):
global sam_dict
if sam_dict["mask_image"] is None or sel_mask is None:
return None
new_sel_mask = sam_dict["mask_image"]
expand_iteration = int(np.clip(expand_iteration, 1, 100))
new_sel_mask = cv2.dilate(new_sel_mask, np.ones((3, 3), dtype=np.uint8), iterations=expand_iteration)
sam_dict["mask_image"] = new_sel_mask
if input_image is not None and input_image.shape == new_sel_mask.shape:
ret_image = cv2.addWeighted(input_image, 0.5, new_sel_mask, 0.5, 0)
else:
ret_image = new_sel_mask
if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
return gr.update()
else:
return gr.update(value=ret_image)
@clear_cache_decorator
def apply_mask(input_image, sel_mask):
global sam_dict
if sam_dict["mask_image"] is None or sel_mask is None:
return None
sel_mask_image = sam_dict["mask_image"]
sel_mask_mask = np.logical_not(sel_mask["mask"][:, :, 0:3].astype(bool)).astype(np.uint8)
new_sel_mask = sel_mask_image * sel_mask_mask
sam_dict["mask_image"] = new_sel_mask
if input_image is not None and input_image.shape == new_sel_mask.shape:
ret_image = cv2.addWeighted(input_image, 0.5, new_sel_mask, 0.5, 0)
else:
ret_image = new_sel_mask
if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
return gr.update()
else:
return gr.update(value=ret_image)
@clear_cache_decorator
def add_mask(input_image, sel_mask):
global sam_dict
if sam_dict["mask_image"] is None or sel_mask is None:
return None
sel_mask_image = sam_dict["mask_image"]
sel_mask_mask = sel_mask["mask"][:, :, 0:3].astype(bool).astype(np.uint8)
new_sel_mask = sel_mask_image + (sel_mask_mask * np.invert(sel_mask_image, dtype=np.uint8))
sam_dict["mask_image"] = new_sel_mask
if input_image is not None and input_image.shape == new_sel_mask.shape:
ret_image = cv2.addWeighted(input_image, 0.5, new_sel_mask, 0.5, 0)
else:
ret_image = new_sel_mask
if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
return gr.update()
else:
return gr.update(value=ret_image)
def auto_resize_to_pil(input_image, mask_image):
init_image = Image.fromarray(input_image).convert("RGB")
mask_image = Image.fromarray(mask_image).convert("RGB")
assert init_image.size == mask_image.size, "The sizes of the image and mask do not match"
width, height = init_image.size
new_height = (height // 8) * 8
new_width = (width // 8) * 8
if new_width < width or new_height < height:
if (new_width / width) < (new_height / height):
scale = new_height / height
else:
scale = new_width / width
resize_height = int(height*scale+0.5)
resize_width = int(width*scale+0.5)
if height != resize_height or width != resize_width:
ia_logging.info(f"resize: ({height}, {width}) -> ({resize_height}, {resize_width})")
init_image = transforms.functional.resize(init_image, (resize_height, resize_width), transforms.InterpolationMode.LANCZOS)
mask_image = transforms.functional.resize(mask_image, (resize_height, resize_width), transforms.InterpolationMode.LANCZOS)
if resize_height != new_height or resize_width != new_width:
ia_logging.info(f"center_crop: ({resize_height}, {resize_width}) -> ({new_height}, {new_width})")
init_image = transforms.functional.center_crop(init_image, (new_height, new_width))
mask_image = transforms.functional.center_crop(mask_image, (new_height, new_width))
return init_image, mask_image
@offload_reload_decorator
@clear_cache_decorator
def run_inpaint(input_image, sel_mask, prompt, n_prompt, ddim_steps, cfg_scale, seed, inp_model_id, save_mask_chk, composite_chk,
sampler_name="DDIM", iteration_count=1):
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
set_ia_config(IAConfig.KEYS.INP_MODEL_ID, inp_model_id, IAConfig.SECTIONS.USER)
save_mask_image(mask_image, save_mask_chk)
ia_logging.info(f"Loading model {inp_model_id}")
config_offline_inpainting = get_webui_setting("inpaint_anything_offline_inpainting", False)
if config_offline_inpainting:
ia_logging.info("Run Inpainting on offline network: {}".format(str(config_offline_inpainting)))
local_files_only = False
local_file_status = download_model_from_hf(inp_model_id, local_files_only=True)
if local_file_status != IAFileManager.DOWNLOAD_COMPLETE:
if config_offline_inpainting:
ia_logging.warning(local_file_status)
return
else:
local_files_only = True
ia_logging.info("local_files_only: {}".format(str(local_files_only)))
if platform.system() == "Darwin" or devices.device == devices.cpu or ia_check_versions.torch_on_amd_rocm:
torch_dtype = torch.float32
else:
torch_dtype = torch.float16
try:
pipe = StableDiffusionInpaintPipeline.from_pretrained(inp_model_id, torch_dtype=torch_dtype, local_files_only=local_files_only)
except Exception as e:
ia_logging.error(str(e))
if not config_offline_inpainting:
try:
pipe = StableDiffusionInpaintPipeline.from_pretrained(inp_model_id, torch_dtype=torch_dtype)
except Exception as e:
ia_logging.error(str(e))
try:
pipe = StableDiffusionInpaintPipeline.from_pretrained(inp_model_id, torch_dtype=torch_dtype, force_download=True)
except Exception as e:
ia_logging.error(str(e))
return
else:
return
pipe.safety_checker = None
ia_logging.info(f"Using sampler {sampler_name}")
if sampler_name == "DDIM":
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
elif sampler_name == "Euler":
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
elif sampler_name == "Euler a":
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
elif sampler_name == "DPM2 Karras":
pipe.scheduler = KDPM2DiscreteScheduler.from_config(pipe.scheduler.config)
elif sampler_name == "DPM2 a Karras":
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 + ("
"
"[IP-Adapter](https://huggingface.co/lllyasviel/sd_control_collection/tree/main) "
"is not available. Reference-Only is used.")
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.
"
"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.
"
"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.
"
"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.
"
"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)