Added the app for the demo space

README.md

@@ -1,8 +1,8 @@
 ---
-title: Ignatius
-emoji: 🌍
+title: Ignatius Farray - "All right!!!"
+emoji: 🤡
 colorFrom: yellow
-colorTo: pink
+colorTo: purple
 sdk: gradio
 sdk_version: 3.21.0
 app_file: app.py

app.py

@@ -0,0 +1,58 @@
+from huggingface_hub import from_pretrained_keras
+from 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/ignatius")
+dreambooth_model._diffusion_model = loaded_diffusion_model
+
+# generate images
+def generate_images(prompt, negative_prompt, num_imgs_to_gen, num_steps, guidance_scale):
+    """
+    This function is used to generate images using our fine-tuned keras dreambooth stable diffusion model.
+    Args:
+        prompt (str): The text input given by the user based on which images will be generated.
+        negative_prompt (srt): The text to eliminate from the generation some concepts.
+        num_imgs_to_gen (int): The number of images to be generated using given prompt.
+        num_steps (int): The number of denoising steps
+        guidance_scale (double): Increasing guidance makes generation follow more closely to the prompt.
+    Returns:
+        generated_img (List): List of images that were generated using the model
+    """
+    generated_images = sd_dreambooth_model.text_to_image(
+        prompt,
+        negative_prompt=negative_prompt,
+        batch_size=num_imgs_to_gen,
+        num_steps=num_steps,
+        unconditional_guidance_scale=guidance_scale
+    )
+    return generated_images 
+
+with gr.Blocks() as demo:
+    gr.HTML("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\">Ignatius Farray - The cavern of the muffled scream</h2>")    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(lines=1, value="ignatius in a standup comedy spectacle", label="Base Prompt")
+            negative_prompt = gr.Textbox(lines=1, value="deformed", value="bad anatomy, blurry, ugly", label="Negative Prompt")
+            samples = gr.Slider(minimum=1, maximum=10, default=1, step=1, label="Number of Image")
+            num_steps = gr.Slider(label="Inference Steps",value=50)
+            guidance_scale = gr.Number(label="Guidance scale", value=7.5)
+            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, guidance_scale], outputs=gallery)
+    
+    gr.Examples([["ignatius on the moon","bad anatomy, blurry, ugly", 2, 150, 15],
+                 ["A photo of ignatius person inside a box","bad anatomy, blurry, ugly", 2, 150, 15],
+                 ["A closeup portrait of ignatius, highly detailed, high qulity","bad anatomy, blurry, ugly", 2, 150, 15]],
+                [prompt, negative_prompt, samples, num_steps, guidance_scale], gallery, generate_images)
+    gr.Markdown('\n Demo created by: <a href=\"https://huggingface.co/matallanas/\">Eduardo Matallanas</a>')
+
+demo.launch(debug=True)

requirements.txt

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+keras-cv
+tensorflow
+huggingface-hub