File size: 812 Bytes
14affff a7638ff 14affff a7638ff 14affff a7638ff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 |
import gradio as gr
from diffusers import MarigoldDepthPipeline, DDIMScheduler
import torch
from PIL import Image
CHECKPOINT = "developy/ApDepth"
device = "cpu"
dtype = torch.float32
pipe = MarigoldDepthPipeline.from_pretrained(CHECKPOINT)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
pipe = pipe.to(device=device, dtype=dtype)
def predict(image: Image.Image):
out = pipe(image)
depth_vis = pipe.image_processor.visualize_depth(out.prediction)[0]
return depth_vis
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Input Image"),
outputs=gr.Image(type="pil", label="Depth Map"),
title="ApDepth Demo",
description="Monocular Depth Estimation based on Marigold"
)
if __name__ == "__main__":
demo.launch()
|