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-200/optimizer.bin from Lonuhbow/eats2: direct link, hf CLI and curl.
- Browser
- Download file 195 MB
-
https://huggingface.co/Lonuhbow/eats2/resolve/main/checkpoint-200/optimizer.bin
- Command line
-
hf download hf://Lonuhbow/eats2/checkpoint-200/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Lonuhbow/eats2/resolve/main/checkpoint-200/optimizer.bin
195 MB
- Xet hash:
- 2cc007f4b55c571e5be9ac4e6d31f433a12e4f9342ead0bcf13622c9f025fc95
- Size of remote file:
- 195 MB
- SHA256:
- 6b56753311b0fbd61ed79844d9a709aac46c88200752a5f19f3ecc634a4872db
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