Instructions to use nvidia/RFD4-Proteina-prep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nvidia/RFD4-Proteina-prep with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nvidia/RFD4-Proteina-prep", 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 modular_config.json from nvidia/RFD4-Proteina-prep: direct link, hf CLI and curl.
- Browser
- Download file 237 Bytes
-
https://huggingface.co/nvidia/RFD4-Proteina-prep/resolve/main/modular_config.json
- Command line
-
hf download hf://nvidia/RFD4-Proteina-prep/modular_config.json
-
curl -L -o modular_config.json https://huggingface.co/nvidia/RFD4-Proteina-prep/resolve/main/modular_config.json
237 Bytes
| { | |
| "_class_name": "RFD4CIFBlocks", | |
| "_diffusers_version": "0.41.0.dev0", | |
| "auto_map": { | |
| "ModularPipelineBlocks": "block.RFD4CIFBlocks" | |
| }, | |
| "requirements": { | |
| "atomworks": "==3.0.3", | |
| "rfd4_proteina_prep": "==0.1.0" | |
| } | |
| } | |