example_text.py

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 trellis.pipelines import TrellisTextTo3DPipeline
from trellis.utils import render_utils, postprocessing_utils

# Load a pipeline from a model folder or a Hugging Face model hub.
pipeline = TrellisTextTo3DPipeline.from_pretrained("./pretrained/TRELLIS-text-xlarge")
# pipeline.cuda()
pipeline.low_vram = True 

# Run the pipeline
outputs = pipeline.run(
    "A chair looking like a avocado.",
    seed=1,
    # Optional parameters
    # sparse_structure_sampler_params={
    #     "steps": 12,
    #     "cfg_strength": 7.5,
    # },
    # slat_sampler_params={
    #     "steps": 12,
    #     "cfg_strength": 7.5,
    # },
)
# 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("tmp/sample_gs.mp4", video, fps=30)
video = render_utils.render_video(outputs["radiance_field"][0])["color"]
imageio.mimsave("tmp/sample_rf.mp4", video, fps=30)
video = render_utils.render_video(outputs["mesh"][0])["normal"]
imageio.mimsave("tmp/sample_mesh.mp4", video, fps=30)

# GLB files can be extracted from the outputs
glb = postprocessing_utils.to_trimesh(
    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("tmp/sample.glb")

# Save Gaussians as PLY files
outputs["gaussian"][0].save_ply("tmp/sample.ply")