import torch import requests from PIL import Image import numpy as np from torchvision.utils import make_grid, save_image from diffusers import DiffusionPipeline # only tested on diffusers[torch]==0.19.3, may have conflicts with newer versions of diffusers

def load_wonder3d_pipeline():

pipeline = DiffusionPipeline.from_pretrained(
'flamehaze1115/wonder3d-v1.0', # or use local checkpoint './ckpts'
custom_pipeline='flamehaze1115/wonder3d-pipeline',
torch_dtype=torch.float16
)

# enable xformers
pipeline.unet.enable_xformers_memory_efficient_attention()

if torch.cuda.is_available():
    pipeline.to('cuda:0')
return pipeline

pipeline = load_wonder3d_pipeline()

Download an example image.

cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/lysol.png", stream=True).raw)

The object should be located in the center and resized to 80% of image height.

cond = Image.fromarray(np.array(cond)[:, :, :3])

Run the pipeline!

images = pipeline(cond, num_inference_steps=20, output_type='pt', guidance_scale=1.0).images

result = make_grid(images, nrow=6, ncol=2, padding=0, value_range=(0, 1))

save_image(result, 'result.png')