from huggingface_hub import from_pretrained_keras import keras_cv import gradio as gr from tensorflow import keras keras.mixed_precision.set_global_policy("mixed_float16") resolution = 512 dreambooth_model = keras_cv.models.StableDiffusion( img_width=resolution, img_height=resolution, jit_compile=True, ) loaded_diffusion_model = from_pretrained_keras("ashishtanwer/shoe") dreambooth_model._diffusion_model = loaded_diffusion_model def generate_images(prompt: str, negative_prompt:str, num_imgs_to_gen: int, num_steps: int): generated_img = dreambooth_model.text_to_image( prompt, negative_prompt=negative_prompt, batch_size=num_imgs_to_gen, num_steps=num_steps, ) return generated_img with gr.Blocks() as demo: gr.HTML("

Radiance Shoe Demo

") with gr.Row(): with gr.Column(): prompt = gr.Textbox(lines=1, value="sshh shoe", label="Base Prompt") negative_prompt = gr.Textbox(lines=1, value="deformed", label="Negative Prompt") samples = gr.Slider(minimum=1, maximum=10, default=1, step=1, label="Number of Image") num_steps = gr.Slider(label="Inference Steps",value=50) run = gr.Button(value="Run") with gr.Column(): gallery = gr.Gallery(label="Outputs").style(grid=(1,2)) run.click(generate_images, inputs=[prompt,negative_prompt, samples, num_steps], outputs=gallery) gr.Examples([["photo of sshh shoe","deformed", 1, 50]], [prompt,negative_prompt, samples,num_steps], gallery, generate_images) demo.launch(debug=True)