import gradio as gr from PIL import Image import numpy as np import os,sys import torch import cv2 import io import multiprocessing import random import time from loguru import logger from utils import * from share_btn import * from lama_cleaner.model_manager import ModelManager from lama_cleaner.schema import Config try: torch._C._jit_override_can_fuse_on_cpu(False) torch._C._jit_override_can_fuse_on_gpu(False) torch._C._jit_set_texpr_fuser_enabled(False) torch._C._jit_set_nvfuser_enabled(False) except: pass from lama_cleaner.helper import ( load_img, numpy_to_bytes, resize_max_size, ) NUM_THREADS = str(multiprocessing.cpu_count()) # fix libomp problem on windows https://github.com/Sanster/lama-cleaner/issues/56 os.environ["KMP_DUPLICATE_LIB_OK"] = "True" os.environ["OMP_NUM_THREADS"] = NUM_THREADS os.environ["OPENBLAS_NUM_THREADS"] = NUM_THREADS os.environ["MKL_NUM_THREADS"] = NUM_THREADS os.environ["VECLIB_MAXIMUM_THREADS"] = NUM_THREADS os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS if os.environ.get("CACHE_DIR"): os.environ["TORCH_HOME"] = os.environ["CACHE_DIR"] HF_TOKEN_SD = os.environ.get('HF_TOKEN_SD') device = "cuda" if torch.cuda.is_available() else "cpu" print(f'device = {device}') def read_content(file_path: str) -> str: """read the content of target file """ with open(file_path, 'r', encoding='utf-8') as f: content = f.read() return content def get_image_enhancer(scale = 2, device='cuda:0'): from basicsr.archs.rrdbnet_arch import RRDBNet from realesrgan import RealESRGANer from realesrgan.archs.srvgg_arch import SRVGGNetCompact from gfpgan import GFPGANer realesrgan_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4 ) netscale = scale model_realesrgan = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth' upsampler = RealESRGANer( scale=netscale, model_path=model_realesrgan, model=realesrgan_model, tile=0, tile_pad=10, pre_pad=0, half=False if device=='cpu' else True, device=device ) model_GFPGAN = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth' img_enhancer = GFPGANer( model_path=model_GFPGAN, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler, device=device ) return img_enhancer image_enhancer = None if sys.platform == 'linux' and 0==1: image_enhancer = get_image_enhancer(scale = 1, device=device) model = None def model_process(image, mask, img_enhancer): global model,image_enhancer ori_image = image if mask.shape[0] == image.shape[1] and mask.shape[1] == image.shape[0] and mask.shape[0] != mask.shape[1]: # rotate image ori_image = np.transpose(image[::-1, ...][:, ::-1], axes=(1, 0, 2))[::-1, ...] image = ori_image original_shape = ori_image.shape interpolation = cv2.INTER_CUBIC size_limit = 1080 if size_limit == "Original": size_limit = max(image.shape) else: size_limit = int(size_limit) config = Config( ldm_steps=25, ldm_sampler='plms', zits_wireframe=True, hd_strategy='Original', hd_strategy_crop_margin=196, hd_strategy_crop_trigger_size=1280, hd_strategy_resize_limit=2048, prompt='', use_croper=False, croper_x=0, croper_y=0, croper_height=512, croper_width=512, sd_mask_blur=5, sd_strength=0.75, sd_steps=50, sd_guidance_scale=7.5, sd_sampler='ddim', sd_seed=42, cv2_flag='INPAINT_NS', cv2_radius=5, ) if config.sd_seed == -1: config.sd_seed = random.randint(1, 999999999) logger.info(f"Origin image shape_0_: {original_shape} / {size_limit}") image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation) logger.info(f"Resized image shape_1_: {image.shape}") logger.info(f"mask image shape_0_: {mask.shape} / {type(mask)}") mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation) logger.info(f"mask image shape_1_: {mask.shape} / {type(mask)}") if model is None: return None res_np_img = model(image, mask, config) torch.cuda.empty_cache() image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, 'png'))) if image_enhancer is not None and img_enhancer: start = time.time() input_img_rgb = np.array(image) input_img_bgr = input_img_rgb[...,[2,1,0]] _, _, enhance_img = image_enhancer.enhance(input_img_bgr, has_aligned=False, only_center_face=False, paste_back=True) input_img_rgb = enhance_img[...,[2,1,0]] img_enhance = Image.fromarray(np.uint8(input_img_rgb)) image = img_enhance log_info = f"image_enhancer_: {(time.time() - start) * 1000}ms, {res_np_img.shape} " logger.info(log_info) return image, Image.fromarray(ori_image) def resize_image(pil_image, new_width=400): width, height = pil_image.size new_height = int(height*(new_width/width)) pil_image = pil_image.resize((new_width, new_height)) return pil_image model = ModelManager( name='lama', device=device, ) image_type = 'pil' # filepath' def predict(input, img_enhancer): if input is None: return None, [], gr.update(visible=False) if image_type == 'filepath': # input: {'image': '/tmp/tmp8mn9xw93.png', 'mask': '/tmp/tmpn5ars4te.png'} origin_image_bytes = open(input["image"], 'rb').read() print(f'origin_image_bytes = ', type(origin_image_bytes), len(origin_image_bytes)) image, _ = load_img(origin_image_bytes) mask, _ = load_img(open(input["mask"], 'rb').read(), gray=True) elif image_type == 'pil': # input: {'image': pil, 'mask': pil} image_pil = input['image'] mask_pil = input['mask'] image = np.array(image_pil) mask = np.array(mask_pil.convert("L")) output, ori_image = model_process(image, mask, img_enhancer) return output, [resize_image(ori_image, new_width=400), resize_image(output, new_width=400)], gr.update(visible=True) image_blocks = gr.Blocks(css=css, title='Image Cleaner') with image_blocks as demo: with gr.Group(elem_id="page_1", visible=True) as page_1: with gr.Box(): with gr.Row(elem_id="gallery_row"): with gr.Column(elem_id="gallery_col"): gallery = gr.Gallery(value=['./sample_00.jpg','./sample_00_e.jpg'], show_label=False) gallery.style(grid=[2], height='500px') with gr.Row(): with gr.Column(): begin_button = gr.Button("Let's GO!", elem_id="begin-btn", visible=True) with gr.Row(): with gr.Column(): gr.HTML("""

Solemnly promise: this application will not collect any user information and image resources.

The model comes from [Lama]. Thanks! ❤️
[huggingface.co] provides code hosting. Thanks! ❤️
""" ) with gr.Group(elem_id="page_2", visible=False) as page_2: with gr.Box(elem_id="work-container"): with gr.Row(elem_id="input-container"): with gr.Column(): image_input = gr.Image(source='upload', elem_id="image_upload",tool='sketch', type=f'{image_type}', label="Upload", show_label=False).style(mobile_collapse=False) with gr.Row(elem_id="scroll_x_row"): with gr.Column(id="scroll_x_col"): gr.HTML("""
""" ) with gr.Row(elem_id="op-container").style(mobile_collapse=False, equal_height=True): with gr.Column(elem_id="erase-btn-container"): erase_btn = gr.Button(value = "Erase(⏬)",elem_id="erase-btn").style( margin=True, rounded=(True, True, True, True), full_width=True, ).style(width=100) with gr.Column(elem_id="enhancer-checkbox", visible=True if image_enhancer is not None else False): enhancer_label = 'Enhanced image(processing is very slow, please check only for blurred images)' img_enhancer = gr.Checkbox(label=enhancer_label).style(width=150) with gr.Row(elem_id="output-container"): with gr.Column(elem_id="image-output-container"): image_out = gr.Image(elem_id="image_output",label="Result", show_label=False, visible=False) with gr.Column(): gallery = gr.Gallery( label="Generated images", show_label=False, elem_id="gallery" ).style(grid=[2], height="600px") with gr.Row(elem_id="download-container", visible=False) as download_container: with gr.Column(elem_id="download-btn-container") as download_btn_container: download_button = gr.Button(elem_id="download-btn", value="Save(⏩)") with gr.Column(elem_id="share-container") as share_container: with gr.Group(elem_id="share-btn-container"): community_icon = gr.HTML(community_icon_html, elem_id="community-icon", visible=True) loading_icon = gr.HTML(loading_icon_html, elem_id="loading-icon", visible=True) share_button = gr.Button("Share to community", elem_id="share-btn", visible=True) with gr.Row(elem_id="log_row"): with gr.Column(id="log_col"): gr.HTML("""
""" ) erase_btn.click(fn=predict, inputs=[image_input, img_enhancer], outputs=[image_out, gallery, download_container]) download_button.click(None, [], [], _js=download_img) share_button.click(None, [], [], _js=share_js) begin_button.click(fn=None, inputs=[], outputs=[page_1, page_2], _js=start_cleaner) image_blocks.launch(server_name='0.0.0.0')