app.py

from stable_diffusion_tf.stable_diffusion import Text2Image
from PIL import Image
import gradio as gr

generator = Text2Image(
    img_height=512,
    img_width=768,
    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(1, 25, value = 10, label = 'Guidance Scale: How close to follow Prompt'),
    gr.Slider(20, 150, value = 50, step = 1, label = 'Number of Iterations, more take longer and may or may not improve image quality'),
    gr.Slider(.01, 100, value=1, label='Temperature: Changes probability of Diffusion to Image Array, more info in community comments')], 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. Simply type in what you wish to see, adjust the sliders (optional) and click submit. For more information on Keras see https://keras.io/about/ For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic', article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>")
iface.launch()