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README.md
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---
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---
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library_name: trellis
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pipeline_tag: text-to-3d
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license: mit
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language:
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- en
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---
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# TRELLIS Text Large
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<!-- Provide a quick summary of what the model is/does. -->
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The text conditioned version of TRELLIS with model size L, a large 3D genetive model. It was introduced in the paper [Structured 3D Latents for Scalable and Versatile 3D Generation](https://huggingface.co/papers/2412.01506).
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Project page: https://trellis3d.github.io/
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Code: https://github.com/Microsoft/TRELLIS
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ckpts/slat_flow_txt_dit_L_64l8p2_fp16.json
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{
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"name": "SLatFlowModel",
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"args": {
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"resolution": 64,
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"in_channels": 8,
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"out_channels": 8,
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"model_channels": 1024,
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"cond_channels": 768,
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"num_blocks": 24,
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"num_heads": 16,
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"mlp_ratio": 4,
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"patch_size": 2,
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"num_io_res_blocks": 2,
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"io_block_channels": [128],
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"pe_mode": "ape",
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"qk_rms_norm": true,
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"use_fp16": true
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}
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}
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ckpts/slat_flow_txt_dit_L_64l8p2_fp16.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3e6609ba6ce2d9e6442a04c06043b0f927bdc5bc21d34795b72811d668a401e3
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size 1178589272
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ckpts/ss_flow_txt_dit_L_16l8_fp16.json
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{
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"name": "SparseStructureFlowModel",
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"args": {
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"resolution": 16,
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"in_channels": 8,
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"out_channels": 8,
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"model_channels": 1024,
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"cond_channels": 768,
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"num_blocks": 24,
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"num_heads": 16,
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"mlp_ratio": 4,
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"patch_size": 1,
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"pe_mode": "ape",
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"qk_rms_norm": true,
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"use_fp16": true
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}
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}
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ckpts/ss_flow_txt_dit_L_16l8_fp16.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fbe712778a6a62fb7df44467972939b3c33e503fd971076caa4cd673e1aa37a3
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size 1105604976
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pipeline.json
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{
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"name": "TrellisTextTo3DPipeline",
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"args": {
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"models": {
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"sparse_structure_decoder": "JeffreyXiang/TRELLIS-image-large/ckpts/ss_dec_conv3d_16l8_fp16",
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"sparse_structure_flow_model": "ckpts/ss_flow_txt_dit_L_16l8_fp16",
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"slat_decoder_gs": "JeffreyXiang/TRELLIS-image-large/ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16",
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"slat_decoder_rf": "JeffreyXiang/TRELLIS-image-large/ckpts/slat_dec_rf_swin8_B_64l8r16_fp16",
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"slat_decoder_mesh": "JeffreyXiang/TRELLIS-image-large/ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16",
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"slat_flow_model": "ckpts/slat_flow_txt_dit_L_64l8p2_fp16"
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},
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"sparse_structure_sampler": {
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"name": "FlowEulerGuidanceIntervalSampler",
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"args": {
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"sigma_min": 1e-5
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},
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"params": {
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"steps": 25,
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"cfg_strength": 7.5,
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"cfg_interval": [0.5, 0.95],
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"rescale_t": 3.0
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}
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},
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"slat_sampler": {
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"name": "FlowEulerGuidanceIntervalSampler",
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"args": {
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"sigma_min": 1e-5
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},
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"params": {
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"steps": 25,
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"cfg_strength": 7.5,
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"cfg_interval": [0.5, 0.95],
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"rescale_t": 3.0
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}
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},
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"slat_normalization": {
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"mean": [
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-2.1687545776367188,
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-0.004347046371549368,
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-0.13352349400520325,
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-0.08418072760105133,
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-0.5271206498146057,
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0.7238689064979553,
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-1.1414450407028198,
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1.2039363384246826
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],
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"std": [
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2.377650737762451,
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2.386378288269043,
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2.124418020248413,
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2.1748552322387695,
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2.663944721221924,
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2.371192216873169,
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2.6217446327209473,
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2.684523105621338
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]
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},
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"text_cond_model": "openai/clip-vit-large-patch14"
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}
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}
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