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| """ | |
| Adapted from https://huggingface.co/spaces/stabilityai/stable-diffusion | |
| """ | |
| from tensorflow import keras | |
| 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 | |
| from huggingface_hub import from_pretrained_keras | |
| # MODEL_CKPT = "chansung/textual-inversion-pipeline@v1673026791" | |
| # MODEL = from_pretrained_keras(MODEL_CKPT) | |
| # model = keras_cv.models.StableDiffusion( | |
| # img_width=img_width, img_height=img_height, jit_compile=True | |
| # ) | |
| # model._text_encoder = MODEL | |
| # model._text_encoder.compile(jit_compile=True) | |
| # # Warm-up the model. | |
| # _ = 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 = 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] | |
| demoInterface = 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"), | |
| # examples=[["cute Sundar Pichai creature", 8], ["Hello kitty", 8]], | |
| allow_flagging=False, | |
| ) | |
| def welcome(name): | |
| return f"Welcome to Gradio, {name}!" | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # Hello World! | |
| Start typing below to see the output. | |
| """) | |
| gr.Markdown( | |
| """ | |
| GCP project ID | |
| """) | |
| gcp_project_id = gr.Textbox() | |
| gr.Markdown( | |
| """ | |
| GCP region | |
| """) | |
| gcp_region = gr.Textbox() | |
| gr.Markdown( | |
| """ | |
| Minimum number of nodes? | |
| """) | |
| min_nodes = gr.Slider(label="minimum number of nodes"), | |
| gr.Markdown( | |
| """ | |
| Maximum number of nodes? | |
| """) | |
| max_nodes = gr.Slider(label="maximum number of nodes"), | |
| btn = gr.Button(value="Ready to Deploy!") | |
| # btn.click(mirror, inputs=[im], outputs=[im_2]) | |
| gr.TabbedInterface( | |
| [demoInterface, demo], ["Try-out", "Deployment"] | |
| ).launch(enable_queue=True) |