import gradio as gr import PIL from PIL import Image import numpy as np import os import uuid import torch from torch import autocast import cv2 from io import BytesIO import requests import PIL from PIL import Image import numpy as np import os import uuid import torch from torch import autocast import cv2 from matplotlib import pyplot as plt from torchvision import transforms from diffusers import DiffusionPipeline import io import logging import multiprocessing import random import time import imghdr from pathlib import Path from typing import Union from loguru import logger 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"] from share_btn import community_icon_html, loading_icon_html, share_js HF_TOKEN_SD = os.environ.get('HF_TOKEN_SD') device = "cuda" if torch.cuda.is_available() else "cpu" print(f'device = {device}') def get_image_ext(img_bytes): w = imghdr.what("", img_bytes) if w is None: w = "jpeg" return w def read_content(file_path): """read the content of target file """ with open(file_path, 'rb') as f: content = f.read() return content model = None def model_process(image, mask, alpha_channel, ext): global model original_shape = image.shape interpolation = cv2.INTER_CUBIC size_limit = "Original" print(f'size_limit_2_ = {size_limit}') if size_limit == "Original": size_limit = max(image.shape) else: size_limit = int(size_limit) print(f'size_limit_3_ = {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, ) print(f'config/alpha_channel/size_limit = {config} / {alpha_channel} / {size_limit}') if config.sd_seed == -1: config.sd_seed = random.randint(1, 999999999) logger.info(f"Origin image shape: {original_shape}") print(f"Origin image shape: {original_shape} / {image[250][250]}") image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation) logger.info(f"Resized image shape: {image.shape} / {type(image)}") print(f"Resized image shape: {image.shape} / {image[250][250]}") mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation) print(f"mask image shape: {mask.shape} / {type(mask)} / {mask[250][250]} / {alpha_channel}") if model is None: return None start = time.time() res_np_img = model(image, mask, config) logger.info(f"process time: {(time.time() - start) * 1000}ms, {res_np_img.shape}") print(f"process time_1_: {(time.time() - start) * 1000}ms, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype}") torch.cuda.empty_cache() alpha_channel = None if alpha_channel is not None: print(f"liuyz_here_10_: {alpha_channel.shape} / {res_np_img.dtype}") if alpha_channel.shape[:2] != res_np_img.shape[:2]: print(f"liuyz_here_20_: {res_np_img.shape}") alpha_channel = cv2.resize( alpha_channel, dsize=(res_np_img.shape[1], res_np_img.shape[0]) ) print(f"liuyz_here_30_: {res_np_img.dtype}") res_np_img = np.concatenate( (res_np_img, alpha_channel[:, :, np.newaxis]), axis=-1 ) print(f"liuyz_here_40_: {res_np_img.dtype}") print(f"process time_2_: {(time.time() - start) * 1000}ms, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype} /{ext}") image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, ext))) return image # image model = ModelManager( name='lama', device=device, # hf_access_token=HF_TOKEN_SD, # sd_disable_nsfw=False, # sd_cpu_textencoder=True, # sd_run_local=True, # callback=diffuser_callback, ) ''' pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", dtype=torch.float16, revision="fp16", use_auth_token=auth_token).to(device) transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), transforms.Resize((512, 512)), ]) ''' image_type = 'filepath' #'pil' def predict(input): print(f'liuyz_0_', input) ''' image_np = np.array(input["image"]) print(f'image_np = {image_np.shape}') mask_np = np.array(input["mask"]) print(f'mask_np = {mask_np.shape}') ''' ''' image = dict["image"] # .convert("RGB") #.resize((512, 512)) # target_size = (init_image.shape[0], init_image.shape[1]) print(f'liuyz_1_', image.shape) print(f'liuyz_2_', image.convert("RGB").shape) print(f'liuyz_3_', image.convert("RGB").resize((512, 512)).shape) # mask = dict["mask"] # .convert("RGB") #.resize((512, 512)) ''' if image_type == 'filepath': # input: {'image': '/tmp/tmp8mn9xw93.png', 'mask': '/tmp/tmpn5ars4te.png'} origin_image_bytes = read_content(input["image"]) print(f'origin_image_bytes = ', type(origin_image_bytes), len(origin_image_bytes)) image, _ = load_img(origin_image_bytes) mask, _ = load_img(read_content(input["mask"]), gray=True) alpha_channel = (np.ones((image.shape[0],image.shape[1]))*255).astype(np.uint8) ext = get_image_ext(origin_image_bytes) output = model_process(image, mask, alpha_channel, ext) 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")) alpha_channel = (np.ones((image.shape[0],image.shape[1]))*255).astype(np.uint8) ext = 'png' output = model_process(image, mask, alpha_channel, ext) return output #, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) css = ''' .container {max-width: 1150px;margin: auto;padding-top: 1.5rem} #image_upload{min-height:512px} #image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 512px} #mask_radio .gr-form{background:transparent; border: none} #word_mask{margin-top: .75em !important} #word_mask textarea:disabled{opacity: 0.3} .footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5} .footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white} .dark .footer {border-color: #303030} .dark .footer>p {background: #0b0f19} .acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%} #image_upload .touch-none{display: flex} @keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } } #share-btn-container { display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; } #share-btn { all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important; } #share-btn * { all: unset; } #share-btn-container div:nth-child(-n+2){ width: auto !important; min-height: 0px !important; } #share-btn-container .wrap { display: none !important; } ''' ''' sketchpad = Sketchpad() imageupload = ImageUplaod() interface = gr.Interface(fn=predict, inputs="image", outputs="image", sketchpad, imageupload) interface.launch(share=True) ''' ''' # gr.Interface(fn=predict, inputs="image", outputs="image").launch(share=True) image = gr.Image(source='upload', tool='sketch', type="pil", label="Upload")# .style(height=400) image_blocks = gr.Interface( fn=predict, inputs=image, outputs=image, # examples=[["cheetah.jpg"]], ) image_blocks.launch(inline=True) import gradio as gr def greet(dict, name, is_morning, temperature): image = dict['image'] target_size = (image.shape[0], image.shape[1]) print(f'liuyz_1_', target_size) salutation = "Good morning" if is_morning else "Good evening" greeting = f"{salutation} {name}. It is {temperature} degrees today" celsius = (temperature - 32) * 5 / 9 return image, greeting, round(celsius, 2) image = gr.Image(source='upload', tool='sketch', label="上传")# .style(height=400) demo = gr.Interface( fn=greet, inputs=[image, "text", "checkbox", gr.Slider(0, 100)], outputs=['image', "text", "number"], ) demo.launch() ''' image_blocks = gr.Blocks(css=css) with image_blocks as demo: # gr.HTML(read_content("header.html")) with gr.Group(): with gr.Box(): with gr.Row(): with gr.Column(): image = gr.Image(source='upload', elem_id="image_upload", tool='sketch,editor', type=f'{image_type}', label="Upload").style(height=512) with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): # prompt = gr.Textbox(placeholder = 'Your prompt (what you want in place of what is erased)', show_label=False, elem_id="input-text") btn_in = gr.Button("Done!").style( margin=True, rounded=(True, True, True, True), full_width=True, ) with gr.Column(): image_out = gr.Image(label="Output", elem_id="image_output", visible=True).style(height=512) ''' with gr.Group(elem_id="share-btn-container"): community_icon = gr.HTML(community_icon_html, visible=False) loading_icon = gr.HTML(loading_icon_html, visible=False) share_button = gr.Button("Share to community", elem_id="share-btn", visible=False) ''' # btn.click(fn=predict, inputs=[image, prompt], outputs=[image_out, community_icon, loading_icon, share_button]) btn_in.click(fn=predict, inputs=[image], outputs=[image_out]) #, community_icon, loading_icon, share_button]) #share_button.click(None, [], [], _js=share_js) image_blocks.launch()