import gradio as gr class CodeformerUpscalerGenerator: def generate_image( self, image_path: str, background_enhance: bool, face_upsample: bool, upscale: int, codeformer_fidelity: int, ): from codeformer.app import inference_app pipe = inference_app( image=image_path, background_enhance=background_enhance, face_upsample=face_upsample, upscale=upscale, codeformer_fidelity=codeformer_fidelity, ) return [pipe] def app(): with gr.Blocks(): with gr.Row(): with gr.Column(): codeformer_upscale_image_file = gr.Image( type="filepath", label="Image" ).style(height=260) with gr.Row(): with gr.Column(): codeformer_face_upsample = gr.Checkbox( label="Face Upsample", value=True, ) codeformer_upscale = gr.Slider( label="Upscale", minimum=1, maximum=4, step=1, value=2, ) with gr.Row(): with gr.Column(): codeformer_background_enhance = gr.Checkbox( label="Background Enhance", value=True, ) codeformer_upscale_fidelity = gr.Slider( label="Codeformer Fidelity", minimum=0.1, maximum=1.0, step=0.1, value=0.5, ) codeformer_upscale_predict_button = gr.Button( value="Generator" ) with gr.Column(): output_image = gr.Gallery( label="Generated images", show_label=False, elem_id="gallery", ).style(grid=(1, 2)) codeformer_upscale_predict_button.click( fn=CodeformerUpscalerGenerator().generate_image, inputs=[ codeformer_upscale_image_file, codeformer_background_enhance, codeformer_face_upsample, codeformer_upscale, codeformer_upscale_fidelity, ], outputs=[output_image], )