Create app.py

app.py

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+"""
+Adapted from https://huggingface.co/spaces/stabilityai/stable-diffusion
+"""
+
+from tensorflow import keras
+
+keras.mixed_precision.set_global_policy("mixed_float16")
+
+import time
+
+import gradio as gr
+import keras_cv
+
+from constants import css, examples, img_height, img_width, num_images_to_gen
+from share_btn import community_icon_html, loading_icon_html, share_js
+
+# Load model.
+weights_path = keras.utils.get_file(
+    origin="https://huggingface.co/sayakpaul/kerascv_sd_pokemon_finetuned/resolve/main/ckpt_epochs_72_res_512_mp_True.h5",
+    file_hash="10b20bd27912d1da904dafe8c576351c2f373546f446591aeff00d816d701a6e"
+)
+pokemon_model = keras_cv.models.StableDiffusion(
+    img_width=img_width, img_height=img_height
+)
+pokemon_model.diffusion_model.load_weights(weights_path)
+
+pokemon_model.diffusion_model.compile(jit_compile=True)
+pokemon_model.decoder.compile(jit_compile=True)
+pokemon_model.text_encoder.compile(jit_compile=True)
+
+# Warm-up the model.
+_ = pokemon_model.text_to_image("Teddy bear", batch_size=num_images_to_gen)
+
+
+def generate_image_fn(prompt: str, unconditional_guidance_scale: int) -> list:
+    start_time = time.time()
+    # `images is an `np.ndarray`. So we convert it to a list of ndarrays.
+    # Each ndarray represents a generated image.
+    # Reference: https://gradio.app/docs/#gallery
+    images = pokemon_model.text_to_image(
+        prompt,
+        batch_size=num_images_to_gen,
+        unconditional_guidance_scale=unconditional_guidance_scale,
+    )
+    end_time = time.time()
+    print(f"Time taken: {end_time - start_time} seconds.")
+    return [image for image in images]
+
+
+description = "This Space demonstrates a fine-tuned Stable Diffusion model. You can use it for generating custom pokemons. To get started, either enter a prompt and pick one from the examples below. For details on the fine-tuning procedure, refer to [this repository](https://github.com/sayakpaul/stable-diffusion-keras-ft/)."
+article = "This Space leverages a T4 GPU to run the predictions. We use mixed-precision to speed up the inference latency. We further use XLA to carve out maximum performance from TensorFlow."
+gr.Interface(
+    generate_image_fn,
+    inputs=[
+        gr.Textbox(
+            label="Enter your prompt",
+            max_lines=1,
+            placeholder="cute Sundar Pichai creature",
+        ),
+        gr.Slider(value=40, minimum=8, maximum=50, step=1),
+    ],
+    outputs=gr.Gallery().style(grid=[2], height="auto"),
+    title="Generate custom pokemons",
+    description=description,
+    article=article,
+    examples=[["cute Sundar Pichai creature", 40], ["Hello kitty", 40]],
+    allow_flagging=False,
+).launch(enable_queue=True)