Diffusers
Safetensors
English
motionbricks
modular-diffusers
motion-generation
motion-inbetweening
soma
Instructions to use OzzyGT/motionbricks_soma_diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OzzyGT/motionbricks_soma_diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OzzyGT/motionbricks_soma_diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download NOTICE from OzzyGT/motionbricks_soma_diffusers: direct link, hf CLI and curl.
- Browser
- Download file 1.01 kB
-
https://huggingface.co/OzzyGT/motionbricks_soma_diffusers/resolve/main/NOTICE
- Command line
-
hf download hf://OzzyGT/motionbricks_soma_diffusers/NOTICE
-
curl -L -o NOTICE https://huggingface.co/OzzyGT/motionbricks_soma_diffusers/resolve/main/NOTICE
1.01 kB
| Licensed by NVIDIA Corporation under the NVIDIA Open Model Agreement. | |
| Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| The agreement is included as LICENSE and published at | |
| https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/ | |
| This repository repacks the SOMA checkpoints of nvidia/MotionBricks (soma/soma-v6-nova-4n8g-ea43dd8-fp32-v1) for | |
| Diffusers. The float32 tensors of the root model, the pose model and the decoder of the pose VQ-VAE are unchanged, | |
| renamed for the Diffusers model code next to them; the fused attention input projections were split into query, key | |
| and value. The VQ-VAE encoder and the training-only codebook statistics were left out. The normalization statistics and | |
| the neutral joint positions of the skeleton come from the same package and are stored as buffers, with the standard | |
| deviations as sqrt(std^2 + 1e-5), as in NVIDIA's exported ONNX model. | |
| The model code is new, Apache-2.0, and written by The HuggingFace Team. | |