iasam_app.py

import argparse 
# import math 
import gc 
import os 
import platform 
 
if platform.system() == "Darwin": 
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" 
 
if platform.system() == "Windows": 
 os.environ["XFORMERS_FORCE_DISABLE_TRITON"] = "1" 
 
import random 
import traceback 
from importlib.util import find_spec 
 
import cv2 
import gradio as gr 
import numpy as np 
import torch 
from diffusers import (DDIMScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, 
 KDPM2AncestralDiscreteScheduler, KDPM2DiscreteScheduler, 
 StableDiffusionInpaintPipeline) 
from PIL import Image, ImageFilter 
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, set_ia_config, setup_ia_config_ini 
from ia_devices import devices 
from ia_file_manager import IAFileManager, download_model_from_hf, ia_file_manager 
from ia_logging import ia_logging 
from ia_threading import clear_cache_decorator 
from ia_ui_gradio import reload_javascript 
from ia_ui_items import (get_cleaner_model_ids, get_inp_model_ids, get_padding_mode_names, 
 get_sam_model_ids, get_sampler_names) 
from lama_cleaner.model_manager import ModelManager 
from lama_cleaner.schema import Config, HDStrategy, LDMSampler, SDSampler 
 
print("platform:", platform.system()) 
 
reload_javascript() 
 
if find_spec("xformers") is not None: 
 xformers_available = True 
else: 
 xformers_available = False 
 
parser = argparse.ArgumentParser(description="Inpaint Anything") 
parser.add_argument("--save-seg", action="store_true", help="Save the segmentation image generated by SAM.") 
parser.add_argument("--offline", action="store_true", help="Execute inpainting using an offline network.") 
parser.add_argument("--sam-cpu", action="store_true", help="Perform the Segment Anything operation on CPU.") 
args = parser.parse_args() 
IAConfig.global_args.update(args.__dict__) 
 
 
@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 
 elif "sam2_" in sam_model_id: 
 url_sam = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/" + sam_model_id 
 else: 
 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 = 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" 
 
 
@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 IAConfig.global_args.get("save_seg", False): 
 save_name = "_".join([ia_file_manager.savename_prefix, os.path.splitext(sam_model_id)[0]]) + ".png" 
 save_name = os.path.join(ia_file_manager.outputs_dir, save_name) 
 Image.fromarray(seg_image).save(save_name) 
 
 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 
 
 
@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 = IAConfig.global_args.get("offline", 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, use_safetensors=True) 
 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, use_safetensors=True) 
 except Exception as e: 
 ia_logging.error(str(e)) 
 try: 
 pipe = StableDiffusionInpaintPipeline.from_pretrained( 
 inp_model_id, torch_dtype=torch_dtype, force_download=True, use_safetensors=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 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)]) 
 
 
@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 
 
 
def on_ui_tabs(): 
 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() 
 
 out_gallery_kwargs = dict(columns=2, height=520, object_fit="contain", preview=True) 
 
 block = gr.Blocks(analytics_enabled=False).queue() 
 block.title = "Inpaint Anything" 
 with block as inpaint_anything_interface: 
 with gr.Row(): 
 gr.Markdown("## Inpainting with Segment Anything") 
 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"): 
 prompt = gr.Textbox(label="Inpainting Prompt", elem_id="sd_prompt") 
 n_prompt = gr.Textbox(label="Negative Prompt", elem_id="sd_n_prompt") 
 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) 
 
 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(): 
 gr.Markdown("") 
 
 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") 
 
 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]) 
 
 return [(inpaint_anything_interface, "Inpaint Anything", "inpaint_anything")] 
 
 
block, _, _ = on_ui_tabs()[0] 
block.launch(share=True)