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): images = [] for _ in range(steps): time.sleep(1) image = np.random.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), outputs=["image", gr.Image(label="All Images", visible=False)]) demo.queue() if __name__ == "__main__": demo.launch()