Instructions to use dn6/RFDiffusion-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dn6/RFDiffusion-3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dn6/RFDiffusion-3", 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
| { | |
| "_class_name": "MPNNModel", | |
| "_diffusers_version": "0.37.0.dev0", | |
| "auto_map": { | |
| "AutoModel": "model_mpnn.MPNNModel" | |
| }, | |
| "model_type": "protein_mpnn", | |
| "hidden_dim": 128, | |
| "num_encoder_layers": 3, | |
| "num_decoder_layers": 3, | |
| "num_neighbors": 48, | |
| "dropout_rate": 0.1, | |
| "num_positional_embeddings": 16, | |
| "min_rbf_mean": 2.0, | |
| "max_rbf_mean": 22.0, | |
| "num_rbf": 16 | |
| } | |