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example.py

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+import os
+# os.environ['ATTN_BACKEND'] = 'xformers'   # Can be 'flash-attn' or 'xformers', default is 'flash-attn'
+os.environ['SPCONV_ALGO'] = 'native'        # Can be 'native' or 'auto', default is 'auto'.
+                                            # 'auto' is faster but will do benchmarking at the beginning.
+                                            # Recommended to set to 'native' if run only once.
+
+import imageio
+from PIL import Image
+from trellis.pipelines import TrellisImageTo3DPipeline
+from trellis.utils import render_utils, postprocessing_utils
+
+# Load a pipeline from a model folder or a Hugging Face model hub.
+pipeline = TrellisImageTo3DPipeline.from_pretrained("microsoft/TRELLIS-image-large")
+pipeline.cuda()
+
+# Load an image
+image = Image.open("assets/example_image/T.png")
+
+# Run the pipeline
+outputs = pipeline.run(
+    image,
+    seed=1,
+    # Optional parameters
+    # sparse_structure_sampler_params={
+    #     "steps": 12,
+    #     "cfg_strength": 7.5,
+    # },
+    # slat_sampler_params={
+    #     "steps": 12,
+    #     "cfg_strength": 3,
+    # },
+)
+# outputs is a dictionary containing generated 3D assets in different formats:
+# - outputs['gaussian']: a list of 3D Gaussians
+# - outputs['radiance_field']: a list of radiance fields
+# - outputs['mesh']: a list of meshes
+
+# Render the outputs
+video = render_utils.render_video(outputs['gaussian'][0])['color']
+imageio.mimsave("sample_gs.mp4", video, fps=30)
+video = render_utils.render_video(outputs['radiance_field'][0])['color']
+imageio.mimsave("sample_rf.mp4", video, fps=30)
+video = render_utils.render_video(outputs['mesh'][0])['normal']
+imageio.mimsave("sample_mesh.mp4", video, fps=30)
+
+# GLB files can be extracted from the outputs
+glb = postprocessing_utils.to_glb(
+    outputs['gaussian'][0],
+    outputs['mesh'][0],
+    # Optional parameters
+    simplify=0.95,          # Ratio of triangles to remove in the simplification process
+    texture_size=1024,      # Size of the texture used for the GLB
+)
+glb.export("sample.glb")
+
+# Save Gaussians as PLY files
+outputs['gaussian'][0].save_ply("sample.ply")