Update README.md

README.md

@@ -1,11 +1,37 @@
----
-language:
-- en
-- bg
-metrics:
-- accuracy
-- character
-pipeline_tag: image-to-3d
-tags:
-- music
----
+
+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')