| 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() |