Instructions to use Stereo0001/Krea-2-NF4-for-LoRA-Training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Stereo0001/Krea-2-NF4-for-LoRA-Training with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Stereo0001/Krea-2-NF4-for-LoRA-Training", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
| { | |
| "format": "Krea2-NF4", | |
| "version": 1, | |
| "model_id": "./Krea-2-Raw", | |
| "dtype": "bfloat16", | |
| "quant_type": "nf4", | |
| "compute_dtype": "bfloat16", | |
| "quantized_layers": 257, | |
| "unquantized_layers": 7, | |
| "skip_quant": [ | |
| "img_in", | |
| "time_embed", | |
| "time_mod_proj", | |
| "txt_in", | |
| "final_layer" | |
| ], | |
| "bitsandbytes_prequantized": true, | |
| "quant_state_serialization": "as_dict(packed=True)", | |
| "loader": "Params4bit.from_prequantized" | |
| } |