Instructions to use Lonuhbow/eats2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lonuhbow/eats2 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/eats2") prompt = "Eats2" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2000/optimizer.bin from Lonuhbow/eats2: direct link, hf CLI and curl.
- Browser
- Download file 195 MB
-
https://huggingface.co/Lonuhbow/eats2/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://Lonuhbow/eats2/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Lonuhbow/eats2/resolve/main/checkpoint-2000/optimizer.bin
195 MB
- Xet hash:
- 6c217c9acd11c6a745f4c23adfea4dfcad9a40c14e7c499e90ad012275d31c83
- Size of remote file:
- 195 MB
- SHA256:
- b6725c41d38f33279e36065ded8a32c3e8d6b5541cef934aec06451d4033026d
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