from cgitb import enable from ctypes.wintypes import HFONT import os import sys import torch import gradio as gr import numpy as np import torchvision.transforms as transforms from torch.autograd import Variable from network.Transformer import Transformer from huggingface_hub import hf_hub_download from PIL import Image import logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Constants MAX_DIMENSION = 1280 MODEL_PATH = "models" COLOUR_MODEL = "RGB" STYLE_SHINKAI = "Makoto Shinkai" STYLE_HOSODA = "Mamoru Hosoda" STYLE_MIYAZAKI = "Hayao Miyazaki" STYLE_KON = "Satoshi Kon" DEFAULT_STYLE = STYLE_SHINKAI STYLE_CHOICE_LIST = [STYLE_SHINKAI, STYLE_HOSODA, STYLE_MIYAZAKI, STYLE_KON] MODEL_REPO_SHINKAI = "akiyamasho/AnimeBackgroundGAN-Shinkai" MODEL_FILE_SHINKAI = "shinkai_makoto.pth" MODEL_REPO_HOSODA = "akiyamasho/AnimeBackgroundGAN-Hosoda" MODEL_FILE_HOSODA = "hosoda_mamoru.pth" MODEL_REPO_MIYAZAKI = "akiyamasho/AnimeBackgroundGAN-Miyazaki" MODEL_FILE_MIYAZAKI = "miyazaki_hayao.pth" MODEL_REPO_KON = "akiyamasho/AnimeBackgroundGAN-Kon" MODEL_FILE_KON = "kon_satoshi.pth" # Model Initalisation shinkai_model_hfhub = hf_hub_download(repo_id=MODEL_REPO_SHINKAI, filename=MODEL_FILE_SHINKAI) hosoda_model_hfhub = hf_hub_download(repo_id=MODEL_REPO_HOSODA, filename=MODEL_FILE_HOSODA) miyazaki_model_hfhub = hf_hub_download(repo_id=MODEL_REPO_MIYAZAKI, filename=MODEL_FILE_MIYAZAKI) kon_model_hfhub = hf_hub_download(repo_id=MODEL_REPO_KON, filename=MODEL_FILE_KON) shinkai_model = Transformer() hosoda_model = Transformer() miyazaki_model = Transformer() kon_model = Transformer() enable_gpu = torch.cuda.is_available() if enable_gpu: # If you have multiple cards, # you can assign to a specific card, eg: "cuda:0"("cuda") or "cuda:1" # Use the first card by default: "cuda" device = torch.device("cuda") else: device = "cpu" shinkai_model.load_state_dict( torch.load(shinkai_model_hfhub, device) ) hosoda_model.load_state_dict( torch.load(hosoda_model_hfhub, device) ) miyazaki_model.load_state_dict( torch.load(miyazaki_model_hfhub, device) ) kon_model.load_state_dict( torch.load(kon_model_hfhub, device) ) if enable_gpu: shinkai_model = shinkai_model.to(device) hosoda_model = hosoda_model.to(device) miyazaki_model = miyazaki_model.to(device) kon_model = kon_model.to(device) shinkai_model.eval() hosoda_model.eval() miyazaki_model.eval() kon_model.eval() # Functions def get_model(style): if style == STYLE_SHINKAI: return shinkai_model elif style == STYLE_HOSODA: return hosoda_model elif style == STYLE_MIYAZAKI: return miyazaki_model elif style == STYLE_KON: return kon_model else: logger.warning( f"Style {style} not found. Defaulting to Makoto Shinkai" ) return shinkai_model def adjust_image_for_model(img): logger.info(f"Image Height: {img.height}, Image Width: {img.width}") if img.height > MAX_DIMENSION or img.width > MAX_DIMENSION: logger.info(f"Dimensions too large. Resizing to {MAX_DIMENSION}px.") img.thumbnail((MAX_DIMENSION, MAX_DIMENSION), Image.ANTIALIAS) return img def inference(img, style): img = adjust_image_for_model(img) # load image input_image = img.convert(COLOUR_MODEL) input_image = np.asarray(input_image) # RGB -> BGR input_image = input_image[:, :, [2, 1, 0]] input_image = transforms.ToTensor()(input_image).unsqueeze(0) # preprocess, (-1, 1) input_image = -1 + 2 * input_image if enable_gpu: logger.info(f"CUDA found. Using GPU.") # Allows to specify a card for calculation input_image = Variable(input_image).to(device) else: logger.info(f"CUDA not found. Using CPU.") input_image = Variable(input_image).float() # forward model = get_model(style) output_image = model(input_image) output_image = output_image[0] # BGR -> RGB output_image = output_image[[2, 1, 0], :, :] output_image = output_image.data.cpu().float() * 0.5 + 0.5 return transforms.ToPILImage()(output_image) # Gradio setup title = "Anime Image Convertor" description = "Anime image convertor in different styles" article = "" examples = [ ["examples/garden_in.jpg", STYLE_SHINKAI], ["examples/library_in.jpg", STYLE_KON], ] gr.Interface( fn=inference, inputs=[ gr.inputs.Image( type="pil", label="Input Photo (less than 1280px on both width and height)", ), gr.inputs.Dropdown( STYLE_CHOICE_LIST, default=DEFAULT_STYLE, label="Style", ), ], outputs=gr.outputs.Image( type="pil", label="Output Image", ), title=title, description=description, article=article, examples=examples, allow_flagging="never", allow_screenshot=False, ).launch(enable_queue=True)