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| 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("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\">Radiance Shoe Demo</h2>") | |
| 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) |