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Johannes
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Parent(s):
82bdc0d
add code
Browse files- .gitignore +1 -0
- app.py +48 -0
- requirements.txt +2 -0
.gitignore
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venv
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app.py
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from huggingface_hub import from_pretrained_keras
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import keras_cv
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import gradio as gr
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from tensorflow import keras
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keras.mixed_precision.set_global_policy("mixed_float16")
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# load keras model
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resolution = 512
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dreambooth_model = keras_cv.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("johko/monkey_island_style")
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dreambooth_model._diffusion_model = loaded_diffusion_model
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def generate_images(prompt: str, negative_prompt: str, num_imgs_to_gen: int, num_steps: int, guidance_scale: float):
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generated_img = 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_img
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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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title="Keras Dreambooth - Monkey Island Style 🐒",
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description="""This SD model has been fine-tuned to learn the art style of the game *Return To Monkey Island* using Dreambooth.
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While not being perfect at imitating the stlye, my experiments have shown that it works best on fictional characters, such as Geralt of Rivia, Frodo Baggins or Harry Potter.
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To use the new style make sure to include `in mnky style` to your prompt.
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""",
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fn=generate_images,
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inputs=[
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gr.Textbox(label="Positive Prompt", value="a photo of paranoid marvin a robot"),
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gr.Textbox(label="Negative Prompt", value="low quality, deformed"),
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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=50),
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gr.Slider(label='Guidance scale', value=7.5, maximum=15, minimum=0, step=0.5),
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],
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outputs=[
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gr.Gallery(show_label=False).style(grid=(1,2)),
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],
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examples=[["geralt of rivia in mnky style, high quality, 4k, trending on artstation", "bad, ugly", 2, 70, 8]],
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).queue().launch(debug=True)
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requirements.txt
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keras_cv
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tensorflow
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