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from stable_diffusion_tf.stable_diffusion import Text2Image
from PIL import Image
import gradio as gr
generator = Text2Image(
img_height=512,
img_width=512,
jit_compile=False)
def txt2img(prompt, guide, steps, Temp):
img = generator.generate(prompt,
num_steps=steps,
unconditional_guidance_scale=guide,
temperature=Temp,
batch_size=1)
image=Image.fromarray(img[0])
return image
iface = gr.Interface(fn=txt2img, inputs=[
gr.Textbox(label = 'Input Text Prompt'),
gr.Slider(2, 20, value = 9, label = 'Guidence Scale'),
gr.Slider(10, 100, value = 50, step = 1, label = 'Number of Iterations'),
gr.Slider(.01, 100, value=1)], outputs = 'image',title='Stable Diffusion with Keras and TensorFlow CPU or GPU', description='Now Using Keras and TensorFlow with Stable Diffusion. This allows very complex image generation with less code footprint, and less text.', footer='About Keras: Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research. https://keras.io/about/')
iface.launch() |