Instructions to use OzzyGT/Kimodo_SOMA_Modular with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OzzyGT/Kimodo_SOMA_Modular 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/Kimodo_SOMA_Modular", 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/Kimodo_SOMA_Modular: direct link, hf CLI and curl.
- Browser
- Download file 416 Bytes
-
https://huggingface.co/OzzyGT/Kimodo_SOMA_Modular/resolve/main/NOTICE
- Command line
-
hf download hf://OzzyGT/Kimodo_SOMA_Modular/NOTICE
-
curl -L -o NOTICE https://huggingface.co/OzzyGT/Kimodo_SOMA_Modular/resolve/main/NOTICE
416 Bytes
| The two-stage transformer, motion representation and scheduler are adapted from: | |
| Kimodo, Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES, Apache-2.0. | |
| https://github.com/nv-tlabs/kimodo | |
| Reference revision: 58e781898b3d7e328a676a75d3e338c45dce3ad9 | |
| Text tokenization and pooling follow LLM2Vec, Copyright (c) 2024 McGill NLP, MIT. | |
| The package contains no learned weights. Converted checkpoint licensing is separate. | |