Create app.py

app.py

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+from huggingface_hub import from_pretrained_keras
+import keras_cv
+import gradio as gr
+from tensorflow import keras
+
+keras.mixed_precision.set_global_policy("mixed_float16")
+# load keras model
+resolution = 512
+dreambooth_model = keras_cv.models.StableDiffusion(
+        img_width=resolution, img_height=resolution, jit_compile=True, 
+    )
+loaded_diffusion_model = from_pretrained_keras("keras-dreambooth/dreambooth-galaxy-mergers")
+dreambooth_model._diffusion_model = loaded_diffusion_model
+
+
+def generate_images(prompt: str, negative_prompt:str, num_imgs_to_gen: int, num_steps: int, ugs: int):
+    generated_img = dreambooth_model.text_to_image(
+        prompt, 
+        negative_prompt=negative_prompt,
+        batch_size=num_imgs_to_gen,
+        num_steps=num_steps,
+        unconditional_guidance_scale=ugs,
+    )
+   
+    return generated_img
+    
+with gr.Blocks() as demo:
+    gr.HTML("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\"> Dreambooth Galaxy Mergers </h2>")    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(lines=1, value="image of sks galaxies merging in space", label="Base Prompt")
+            negative_prompt = gr.Textbox(lines=1, value="deformed", label="Negative Prompt")
+            samples = gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of Image")
+            num_steps = gr.Slider(label="Inference Steps",value=40)
+            ugs = gr.Slider(value=15, minimum=5, maximum=25, step=1, label="Unconditional Guidance Scale")
+            run = gr.Button(value="Run")
+        with gr.Column():
+            gallery = gr.Gallery(label="Outputs").style(grid=(1,2))
+
+    run.click(generate_images, inputs=[prompt,negative_prompt, samples, num_steps, ugs], outputs=gallery)
+    
+    gr.Examples([["image of monument in sks style, 8k, high quality, old paper","colored, deformed, blurry, grain, artifacts, low quality", 1, 30, 18],
+                 ],
+                [prompt,negative_prompt, samples,num_steps, ugs], gallery, generate_images)
+
+demo.launch(debug=True)