from huggingface_hub import from_pretrained_keras from keras_cv import models from tensorflow import keras import tensorflow as tf import gradio as gr keras.mixed_precision.set_global_policy("mixed_float16") keras_model_list = [ "kadirnar/dreambooth_diffusion_model_v5", "kadirnar/dreambooth_diffusion_model_v3" ] stable_prompt_list = [ "a photo of sks traditional furniture", ] stable_negative_prompt_list = [ "bad, ugly", "deformed" ] def keras_stable_diffusion( model_path:str, prompt:str, negative_prompt:str, guidance_scale:int, num_inference_step:int, height:int, width:int, ): sd_dreambooth_model = models.StableDiffusion( img_width=height, img_height=width ) db_diffusion_model = from_pretrained_keras(model_path) sd_dreambooth_model._diffusion_model = db_diffusion_model generated_images = sd_dreambooth_model.text_to_image( prompt=prompt, negative_prompt=negative_prompt, num_steps=num_inference_step, unconditional_guidance_scale=guidance_scale ) tf.keras.backend.clear_session() return generated_images def keras_stable_diffusion_app(): with gr.Blocks(): with gr.Row(): with gr.Column(): keras_text2image_model_path = gr.Dropdown( choices=keras_model_list, value=keras_model_list[0], label='Text-Image Model Id' ) keras_text2image_prompt = gr.Textbox( lines=1, value=stable_prompt_list[0], label='Prompt' ) keras_text2image_negative_prompt = gr.Textbox( lines=1, value=stable_negative_prompt_list[0], label='Negative Prompt' ) with gr.Accordion("Advanced Options", open=False): keras_text2image_guidance_scale = gr.Slider( minimum=0.1, maximum=15, step=0.1, value=7.5, label='Guidance Scale' ) keras_text2image_num_inference_step = gr.Slider( minimum=1, maximum=100, step=1, value=50, label='Num Inference Step' ) keras_text2image_height = gr.Slider( minimum=128, maximum=1280, step=32, value=512, label='Image Height' ) keras_text2image_width = gr.Slider( minimum=128, maximum=1280, step=32, value=512, label='Image Height' ) keras_text2image_predict = gr.Button(value='Generator') with gr.Column(): output_image = gr.Gallery(label='Output') keras_text2image_predict.click( fn=keras_stable_diffusion, inputs=[ keras_text2image_model_path, keras_text2image_prompt, keras_text2image_negative_prompt, keras_text2image_guidance_scale, keras_text2image_num_inference_step, keras_text2image_height, keras_text2image_width ], outputs=output_image )