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| import gradio as gr | |
| import numpy as np | |
| import time | |
| import os | |
| from PIL import Image | |
| import requests | |
| from io import BytesIO | |
| def create_gif(images): | |
| pil_images = [] | |
| for image in images: | |
| if isinstance(image, str): | |
| response = requests.get(image) | |
| image = Image.open(BytesIO(response.content)) | |
| else: | |
| image = Image.fromarray((image * 255).astype(np.uint8)) | |
| pil_images.append(image) | |
| fp_out = os.path.join(os.path.dirname(__file__), "image.gif") | |
| img = pil_images.pop(0) | |
| img.save(fp=fp_out, format='GIF', append_images=pil_images, | |
| save_all=True, duration=400, loop=0) | |
| return fp_out | |
| def fake_diffusion(steps): | |
| rng = np.random.default_rng() | |
| images = [] | |
| for _ in range(steps): | |
| time.sleep(1) | |
| image = rng.random((600, 600, 3)) | |
| images.append(image) | |
| yield image, gr.Image(visible=False) | |
| time.sleep(1) | |
| image = "https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg" | |
| images.append(image) | |
| gif_path = create_gif(images) | |
| yield image, gr.Image(value=gif_path, visible=True) | |
| demo = gr.Interface(fake_diffusion, | |
| inputs=gr.Slider(1, 10, 3, step=1), | |
| outputs=["image", gr.Image(label="All Images", visible=False)]) | |
| demo.queue() | |
| if __name__ == "__main__": | |
| demo.launch() | |