| 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') |