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