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example_multi_image.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 numpy as np
+import imageio
+from PIL import Image
+from trellis.pipelines import TrellisImageTo3DPipeline
+from trellis.utils import render_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
+images = [
+    Image.open("assets/example_multi_image/character_1.png"),
+    Image.open("assets/example_multi_image/character_2.png"),
+    Image.open("assets/example_multi_image/character_3.png"),
+]
+
+# Run the pipeline
+outputs = pipeline.run_multi_image(
+    images,
+    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
+
+video_gs = render_utils.render_video(outputs['gaussian'][0])['color']
+video_mesh = render_utils.render_video(outputs['mesh'][0])['normal']
+video = [np.concatenate([frame_gs, frame_mesh], axis=1) for frame_gs, frame_mesh in zip(video_gs, video_mesh)]
+imageio.mimsave("sample_multi.mp4", video, fps=30)