Instructions to use suvadityamuk/TRELLIS-text-large-diffusers-3d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use suvadityamuk/TRELLIS-text-large-diffusers-3d with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("suvadityamuk/TRELLIS-text-large-diffusers-3d", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Trellis
How to use suvadityamuk/TRELLIS-text-large-diffusers-3d with Trellis:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
suvadityamuk HF Staff
Convert microsoft/TRELLIS-text-large with diffusers-3d 0.1.0.dev0
8567a6b verified Download trellis_conversion.json from suvadityamuk/TRELLIS-text-large-diffusers-3d: direct link, hf CLI and curl.
- Browser
- Download file 4.27 kB
-
https://huggingface.co/suvadityamuk/TRELLIS-text-large-diffusers-3d/resolve/main/trellis_conversion.json
- Command line
-
hf download hf://suvadityamuk/TRELLIS-text-large-diffusers-3d/trellis_conversion.json
-
curl -L -o trellis_conversion.json https://huggingface.co/suvadityamuk/TRELLIS-text-large-diffusers-3d/resolve/main/trellis_conversion.json
4.27 kB
| { | |
| "components": { | |
| "slat_decoder_gs": { | |
| "class": "TrellisSLatGaussianDecoder", | |
| "config": { | |
| "attn_mode": "swin", | |
| "latent_channels": 8, | |
| "mlp_ratio": 4, | |
| "model_channels": 768, | |
| "num_blocks": 12, | |
| "num_heads": 12, | |
| "representation_config": { | |
| "2d_filter_kernel_size": 0.1, | |
| "3d_filter_kernel_size": 0.0009, | |
| "lr": { | |
| "_features_dc": 1.0, | |
| "_opacity": 1.0, | |
| "_rotation": 0.1, | |
| "_scaling": 1.0, | |
| "_xyz": 1.0 | |
| }, | |
| "num_gaussians": 32, | |
| "opacity_bias": 0.1, | |
| "perturb_offset": true, | |
| "scaling_activation": "softplus", | |
| "scaling_bias": 0.004, | |
| "voxel_size": 1.5 | |
| }, | |
| "resolution": 64, | |
| "use_fp16": true, | |
| "window_size": 8 | |
| }, | |
| "source": "JeffreyXiang/TRELLIS-image-large/ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16", | |
| "target": "gaussian_decoder" | |
| }, | |
| "slat_decoder_mesh": { | |
| "class": "TrellisSLatMeshDecoder", | |
| "config": { | |
| "attn_mode": "swin", | |
| "latent_channels": 8, | |
| "mlp_ratio": 4, | |
| "model_channels": 768, | |
| "num_blocks": 12, | |
| "num_heads": 12, | |
| "representation_config": { | |
| "use_color": true | |
| }, | |
| "resolution": 64, | |
| "use_fp16": true, | |
| "window_size": 8 | |
| }, | |
| "source": "JeffreyXiang/TRELLIS-image-large/ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16", | |
| "target": "mesh_decoder" | |
| }, | |
| "slat_decoder_rf": { | |
| "class": "TrellisSLatRadianceFieldDecoder", | |
| "config": { | |
| "attn_mode": "swin", | |
| "latent_channels": 8, | |
| "mlp_ratio": 4, | |
| "model_channels": 768, | |
| "num_blocks": 12, | |
| "num_heads": 12, | |
| "representation_config": { | |
| "dim": 8, | |
| "rank": 16 | |
| }, | |
| "resolution": 64, | |
| "use_fp16": true, | |
| "window_size": 8 | |
| }, | |
| "source": "JeffreyXiang/TRELLIS-image-large/ckpts/slat_dec_rf_swin8_B_64l8r16_fp16", | |
| "target": "radiance_field_decoder" | |
| }, | |
| "slat_flow_model": { | |
| "class": "TrellisSLatFlowModel", | |
| "config": { | |
| "cond_channels": 768, | |
| "in_channels": 8, | |
| "io_block_channels": [ | |
| 128 | |
| ], | |
| "mlp_ratio": 4, | |
| "model_channels": 1024, | |
| "num_blocks": 24, | |
| "num_heads": 16, | |
| "num_io_res_blocks": 2, | |
| "out_channels": 8, | |
| "patch_size": 2, | |
| "pe_mode": "ape", | |
| "qk_rms_norm": true, | |
| "resolution": 64, | |
| "use_fp16": true | |
| }, | |
| "source": "ckpts/slat_flow_txt_dit_L_64l8p2_fp16", | |
| "target": "slat_flow_model" | |
| }, | |
| "sparse_structure_decoder": { | |
| "class": "TrellisSparseStructureDecoder", | |
| "config": { | |
| "channels": [ | |
| 512, | |
| 128, | |
| 32 | |
| ], | |
| "latent_channels": 8, | |
| "num_res_blocks": 2, | |
| "num_res_blocks_middle": 2, | |
| "out_channels": 1, | |
| "use_fp16": true | |
| }, | |
| "source": "JeffreyXiang/TRELLIS-image-large/ckpts/ss_dec_conv3d_16l8_fp16", | |
| "target": "sparse_structure_decoder" | |
| }, | |
| "sparse_structure_flow_model": { | |
| "class": "TrellisSparseStructureFlowModel", | |
| "config": { | |
| "cond_channels": 768, | |
| "in_channels": 8, | |
| "mlp_ratio": 4, | |
| "model_channels": 1024, | |
| "num_blocks": 24, | |
| "num_heads": 16, | |
| "out_channels": 8, | |
| "patch_size": 1, | |
| "pe_mode": "ape", | |
| "qk_rms_norm": true, | |
| "resolution": 16, | |
| "use_fp16": true | |
| }, | |
| "source": "ckpts/ss_flow_txt_dit_L_16l8_fp16", | |
| "target": "sparse_structure_flow_model" | |
| } | |
| }, | |
| "production_gpu_quality_verified": false, | |
| "reference_revision": "442aa1e1afb9014e80681d3bf604e8d728a86ee7", | |
| "samplers": { | |
| "slat": { | |
| "cfg_interval": [ | |
| 0.5, | |
| 0.95 | |
| ], | |
| "cfg_strength": 7.5, | |
| "rescale_t": 3.0, | |
| "steps": 25 | |
| }, | |
| "sparse_structure": { | |
| "cfg_interval": [ | |
| 0.5, | |
| 0.95 | |
| ], | |
| "cfg_strength": 7.5, | |
| "rescale_t": 3.0, | |
| "steps": 25 | |
| } | |
| }, | |
| "skipped_components": {}, | |
| "source_pipeline": "/work/publish/microsoft__TRELLIS-text-large/pipeline.json" | |
| } | |