Upload 3 files

app.py

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+from utils_app import keras_stable_diffusion_app
+import gradio as gr
+
+app = gr.Blocks()
+with app:
+    gr.HTML(
+        """
+        <h1 style='text-align: center'>
+        Keras Diffusion WebUI
+        </h1>
+        """
+    )
+    gr.Markdown(
+        """
+        <h4 style='text-align: center'>
+        Follow me for more! 
+        <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>
+        </h4>
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            keras_diffusion_app = keras_stable_diffusion_app()
+
+app.launch(debug=True)  

requirements.txt

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+keras_cv
+tensorflow

utils_app.py

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+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_model_list = [
+    "keras-dreambooth/keras_diffusion_lowpoly_world",
+
+]
+
+stable_prompt_list = [
+        "a photo of lowpoly_world",
+    ]
+
+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,
+    ):
+        
+    with tf.device('/GPU:0'):      
+        keras.mixed_precision.set_global_policy("mixed_float16")
+    
+        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
+        )
+
+    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
+        )