| import os | |
| import gradio as gr | |
| from PIL import Image | |
| os.system('pip install dlib') | |
| os.system("wget https://www.dropbox.com/s/fgupbov77x4rrru/blendgan.pt") | |
| os.system("wget https://www.dropbox.com/s/v8q0dd3r4u20659/psp_encoder.pt") | |
| os.system("wget https://github.com/kim-ninh/align_face_ffhq/raw/main/shape_predictor_68_face_landmarks.dat -P /home/user/app/ffhq_dataset/") | |
| from ffhq_dataset.gen_aligned_image import FaceAlign | |
| fa = FaceAlign() | |
| import cv2 | |
| def inference(content, style, index): | |
| content.save('content.png') | |
| style.save('style.png') | |
| imgc = cv2.imread('content.png') | |
| img_cropc = fa.get_crop_image(imgc) | |
| cv2.imwrite('contentcrop.png', img_cropc) | |
| os.system("""python style_transfer_folder.py --size 1024 --add_weight_index """+str(int(index))+""" --ckpt ./blendgan.pt --psp_encoder_ckpt ./psp_encoder.pt --style_img_path style.png --input_img_path contentcrop.png""") | |
| return "out.jpg" | |
| title = "BlendGAN" | |
| description = "Gradio Demo for BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation. To use it, simply upload your images, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2110.11728' target='_blank'>BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation</a> | <a href='https://github.com/onion-liu/BlendGAN' target='_blank'>Github Repo</a></p><p style='text-align: center'>samples from repo: <img src='https://user-images.githubusercontent.com/6346064/142623312-3e6f09aa-ce88-465c-b956-a8b4db95b4da.gif' alt='animation'/> <img src='https://user-images.githubusercontent.com/6346064/142621044-086cde48-8604-467b-8c43-8768b6670ec2.gif' alt='animation'/></p>" | |
| examples=[['000000.png','100001.png',6]] | |
| gr.Interface(inference, [gr.inputs.Image(type="pil"),gr.inputs.Image(type="pil"),gr.inputs.Slider(minimum=1, maximum=30, step=1, default=6, label="Weight Index") | |
| ], gr.outputs.Image(type="file"),title=title,description=description,article=article,enable_queue=True,examples=examples,allow_flagging=False).launch() |