Instructions to use Lonuhbow/makeout2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lonuhbow/makeout2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Lonuhbow/makeout2") prompt = "Makeout2" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download training_metrics.jsonl from Lonuhbow/makeout2: direct link, hf CLI and curl.
- Browser
- Download file 698 kB
-
https://huggingface.co/Lonuhbow/makeout2/resolve/main/training_metrics.jsonl
- Command line
-
hf download hf://Lonuhbow/makeout2/training_metrics.jsonl
-
curl -L -o training_metrics.jsonl https://huggingface.co/Lonuhbow/makeout2/resolve/main/training_metrics.jsonl
698 kB
File too large to display, you can check the raw version instead.