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Create app.py
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app.py
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from huggingface_hub import from_pretrained_keras
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from keras_cv import models
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import gradio as gr
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import tensorflow as tf
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# load keras model
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resolution = 512
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dreambooth_model = models.StableDiffusion(
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img_width=resolution, img_height=resolution, jit_compile=True,
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)
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loaded_diffusion_model = from_pretrained_keras("keras-dreambooth/dreambooth_diffusion_akitainu")
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dreambooth_model._diffusion_model = loaded_diffusion_model
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# generate images
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def inference(prompt, negative_prompt, num_imgs_to_gen, num_steps, guidance_scale):
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generated_images = dreambooth_model.text_to_image(
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prompt,
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negative_prompt=negative_prompt,
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batch_size=num_imgs_to_gen,
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num_steps=num_steps,
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unconditional_guidance_scale=guidance_scale,
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)
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return generated_images
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# pass function, input type for prompt, the output for multiple images
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gr.Interface(
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inference, [
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gr.Textbox(label="Positive Prompt", value="a photo of hks## toy"),
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gr.Textbox(label="Negative Prompt", value="bad anatomy, soft blurry"),
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gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
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gr.Slider(label="Inference Steps",value=100),
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gr.Number(label='Guidance scale', value=12),
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], [
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gr.Gallery(show_label=False),
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],
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title="Keras Dreambooth - Aikta dog Demo 🐶",
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description = "This model has been fine tuned to learn the concept of Akita dog-a famous and very cute dog of Japane. To use this demo, you should have {akt## dog} in the input",
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examples = [["akt## dog as an anime character in overwatch", "((ugly)), blurry, ((bad anatomy)), duplicate", 4, 100, 12],
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["cute and adorable cartoon fluffy akt## dog with cap, fantasy, dreamlike, city scenario, surrealism, super cute, trending on artstation", "((ugly)), blurry, ((bad anatomy)), duplicate", 4, 100, 12]],
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cache_examples=True
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).queue().launch(debug=True, share=True)
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