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from stable_diffusion_tf.stable_diffusion import StableDiffusion as StableDiffusionPy
import gradio as gr
from tensorflow import keras
keras.mixed_precision.set_global_policy("float32")
# load keras model
resolution=512
sd_dreambooth_model_1=StableDiffusionPy(resolution, resolution, download_weights=False, jit_compile=True)
sd_dreambooth_model_1.load_weights_from_pytorch_ckpt("riffusion-model-v1.ckpt")
sd_dreambooth_model_1.diffusion_model.load_weights("/dreambooth_riffusion_model_currulao_v1")
def generate_images(prompt: str, num_steps: int, unconditional_guidance_scale: int, temperature: int):
generated_img = sd_dreambooth_model_1.generate(
prompt,
num_steps=num_steps,
unconditional_guidance_scale=unconditional_guidance_scale,
temperature=temperature,
batch_size=1,
)
return generated_img[0]
# pass function, input type for prompt, the output for multiple images
gr.Interface(
title="Keras Dreambooth Riffusion-Currulao",
description="""This SD model has been fine-tuned from Riffusion to generate Currulao spectrograms. Currulao is a traditional Afro-Colombian music and dance genre, characterized by its rhythmic beats, call-and-response singing, and lively percussion instruments, that holds significant cultural and social importance in Colombia, particularly in the Pacific coast region, as a celebration of African heritage and community identity.
To generate the concept, use the phrase 'a $currulao song' in your prompt.
""",
fn=generate_images,
inputs=[
gr.Textbox(label="Prompt", value="a $currulao song, lo-fi"),
gr.Slider(label="Inference steps", value=50),
gr.Slider(label="Guidance scale", value=7.5, maximum=15, minimum=0, step=0.5),
gr.Slider(label='Temperature', value=1, maximum=1.5, minimum=0, step=0.1),
],
outputs=[
gr.Gallery(show_label=False).style(grid=(1,2)),
],
examples=[["a $currulao song", "low quality, deformed, dark", 2, 50, 7.5]],
).queue().launch(debug=True)