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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/andrew27/kerascv_clothing_finetuned/resolve/main/ckpt_epoch_96.h5", | |
| file_hash="4b4348297aa9853ff9dc4da7f52dcb240210564400f164e5155e5f4dc1866626" | |
| ) | |
| 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, | |
| num_steps = 100, | |
| ) | |
| 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=10, minimum=8, maximum=50, step=1), | |
| ], | |
| outputs=[gr.outputs.Image(type="pil"),gr.outputs.Image(type="pil"),gr.outputs.Image(type="pil"),gr.outputs.Image(type="pil")], | |
| title="Generate custom pokemons", | |
| description=description, | |
| article=article, | |
| examples=[["cute Sundar Pichai creature", 40], ["Hello kitty", 40]], | |
| allow_flagging=False, | |
| ).launch() |