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| import random | |
| import gradio as gr | |
| from sample import (arg_parse, | |
| sampling, | |
| load_fontdiffuer_pipeline) | |
| def run_fontdiffuer(source_image, | |
| character, | |
| reference_image, | |
| sampling_step, | |
| guidance_scale, | |
| batch_size): | |
| args.character_input = False if source_image is not None else True | |
| args.content_character = character | |
| args.sampling_step = sampling_step | |
| args.guidance_scale = guidance_scale | |
| args.batch_size = batch_size | |
| args.seed = random.randint(0, 10000) | |
| out_image = sampling( | |
| args=args, | |
| pipe=pipe, | |
| content_image=source_image, | |
| style_image=reference_image) | |
| return out_image | |
| if __name__ == '__main__': | |
| args = arg_parse() | |
| args.demo = True | |
| args.ckpt_dir = 'ckpt' | |
| args.ttf_path = 'ttf/KaiXinSongA.ttf' | |
| # load fontdiffuer pipeline | |
| pipe = load_fontdiffuer_pipeline(args=args) | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.HTML(""" | |
| <div style="text-align: center; max-width: 1200px; margin: 20px auto;"> | |
| <h1 style="font-weight: 900; font-size: 3rem; margin: 0rem"> | |
| FontDiffuser | |
| </h1> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
| <a href="https://yeungchenwa.github.io/"">Zhenhua Yang</a>, | |
| <a href="https://scholar.google.com/citations?user=6zNgcjAAAAAJ&hl=zh-CN&oi=ao"">Dezhi Peng</a>, | |
| Yuxin Kong, Yuyi Zhang, | |
| <a href="https://scholar.google.com/citations?user=IpmnLFcAAAAJ&hl=zh-CN&oi=ao"">Cong Yao</a>, | |
| <a href="http://www.dlvc-lab.net/lianwen/Index.html"">Lianwen Jin</a>† | |
| </h2> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
| <strong>South China University of Technology</strong>, Alibaba DAMO Academy | |
| </h2> | |
| <h3 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
| [<a href="https://github.com/yeungchenwa/FontDiffuser" style="color:blue;">arXiv</a>] | |
| [<a href="https://github.com/yeungchenwa/FontDiffuser" style="color:green;">Github</a>] | |
| </h3> | |
| <h2 style="text-align: left; font-weight: 600; font-size: 1rem; margin-top: 0.5rem; margin-bottom: 0.5rem"> | |
| 1.We propose FontDiffuser, which is capable to generate unseen characters and styles, and it can be extended to the cross-lingual generation, such as Chinese to Korean. | |
| </h2> | |
| <h2 style="text-align: left; font-weight: 600; font-size: 1rem; margin-top: 0.5rem; margin-bottom: 0.5rem"> | |
| 2. FontDiffuser excels in generating complex character and handling large style variation. And it achieves state-of-the-art performance. | |
| </h2> | |
| </div> | |
| """) | |
| gr.Image('figures/result_vis.png') | |
| gr.Image('figures/demo_tips.png') | |
| with gr.Column(scale=1): | |
| with gr.Row(): | |
| source_image = gr.Image(width=320, label='[Option 1] Source Image', image_mode='RGB', type='pil') | |
| reference_image = gr.Image(width=320, label='Reference Image', image_mode='RGB', type='pil') | |
| with gr.Row(): | |
| character = gr.Textbox(value='隆', label='[Option 2] Source Character') | |
| with gr.Row(): | |
| fontdiffuer_output_image = gr.Image(height=200, label="FontDiffuser Output Image", image_mode='RGB', type='pil') | |
| sampling_step = gr.Slider(20, 50, value=20, step=10, | |
| label="Sampling Step", info="The sampling step by FontDiffuser.") | |
| guidance_scale = gr.Slider(1, 12, value=7.5, step=0.5, | |
| label="Scale of Classifier-free Guidance", | |
| info="The scale used for classifier-free guidance sampling") | |
| batch_size = gr.Slider(1, 4, value=1, step=1, | |
| label="Batch Size", info="The number of images to be sampled.") | |
| FontDiffuser = gr.Button('Run FontDiffuser') | |
| gr.Markdown("## <font color=#008000, size=6>Examples that You Can Choose Below⬇️</font>") | |
| with gr.Row(): | |
| gr.Markdown("## Examples") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("## Example 1️⃣: Source Image and Reference Image") | |
| gr.Markdown("### In this mode, we provide both the source image and \ | |
| the reference image for you to try our demo!") | |
| gr.Examples( | |
| examples=[['figures/source_imgs/source_灨.jpg', 'figures/ref_imgs/ref_籍.jpg'], | |
| ['figures/source_imgs/source_鑻.jpg', 'figures/ref_imgs/ref_鹰.jpg'], | |
| ['figures/source_imgs/source_鑫.jpg', 'figures/ref_imgs/ref_壤.jpg'], | |
| ['figures/source_imgs/source_釅.jpg', 'figures/ref_imgs/ref_雕.jpg']], | |
| inputs=[source_image, reference_image] | |
| ) | |
| with gr.Column(scale=1): | |
| gr.Markdown("## Example 2️⃣: Character and Reference Image") | |
| gr.Markdown("### In this mode, we provide the content character and the reference image \ | |
| for you to try our demo!") | |
| gr.Examples( | |
| examples=[['龍', 'figures/ref_imgs/ref_鷢.jpg'], | |
| ['轉', 'figures/ref_imgs/ref_鲸.jpg'], | |
| ['懭', 'figures/ref_imgs/ref_籍_1.jpg'], | |
| ['識', 'figures/ref_imgs/ref_鞣.jpg']], | |
| inputs=[character, reference_image] | |
| ) | |
| with gr.Column(scale=1): | |
| gr.Markdown("## Example 3️⃣: Reference Image") | |
| gr.Markdown("### In this mode, we provide only the reference image, \ | |
| you can upload your own source image or you choose the character above \ | |
| to try our demo!") | |
| gr.Examples( | |
| examples=['figures/ref_imgs/ref_闡.jpg', | |
| 'figures/ref_imgs/ref_雕.jpg', | |
| 'figures/ref_imgs/ref_豄.jpg', | |
| 'figures/ref_imgs/ref_馨.jpg', | |
| 'figures/ref_imgs/ref_鲸.jpg', | |
| 'figures/ref_imgs/ref_檀.jpg', | |
| 'figures/ref_imgs/ref_鞣.jpg', | |
| 'figures/ref_imgs/ref_穗.jpg', | |
| 'figures/ref_imgs/ref_欟.jpg', | |
| 'figures/ref_imgs/ref_籍_1.jpg', | |
| 'figures/ref_imgs/ref_鷢.jpg', | |
| 'figures/ref_imgs/ref_媚.jpg', | |
| 'figures/ref_imgs/ref_籍.jpg', | |
| 'figures/ref_imgs/ref_壤.jpg', | |
| 'figures/ref_imgs/ref_蜓.jpg', | |
| 'figures/ref_imgs/ref_鹰.jpg'], | |
| examples_per_page=20, | |
| inputs=reference_image | |
| ) | |
| FontDiffuser.click( | |
| fn=run_fontdiffuer, | |
| inputs=[source_image, | |
| character, | |
| reference_image, | |
| sampling_step, | |
| guidance_scale, | |
| batch_size], | |
| outputs=fontdiffuer_output_image) | |
| demo.launch(debug=True) |