Added app.py and requirements.txt

app.py

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+from huggingface_hub import from_pretrained_keras
+from keras_cv import models
+import gradio as gr
+
+dreambooth_model = models.StableDiffusion(img_width=512, img_height=512)
+
+diffusion_model = from_pretrained_keras("moizsajid/dreambooth-markhor")
+dreambooth_model._diffusion_model = diffusion_model
+
+# generate images
+def infer(prompt: str, negative_prompt: str, num_imgs_to_gen: int, num_steps: int, guidance_scale: float):
+    generated_images = 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
+
+# pass function, input type for prompt, the output for multiple images
+gr.Interface(
+    infer, [
+        gr.Textbox(label="Positive Prompt", value="a teddy_holmes dog astronaut in space"),
+        gr.Textbox(label="Negative Prompt", value="bad anatomy, blurry"),
+        gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
+        gr.Slider(label="Inference Steps",value=100),
+        gr.Number(label='Guidance scale', value=10),
+    ], [
+        gr.Gallery(show_label=False),
+    ],
+    title="Dreambooth Markhor Demo",
+    description = "This model is fine-tuned on images of Markhor from the internet (iStock). To use the demo, please add {markhor} to the input string.",
+    examples = [["a picture of markhor upside down", "", 4, 100, 10]],
+    ).launch()
+  

requirements.txt

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+tensorflow
+huggingface-hub
+keras-cv
+pycocotools