Instructions to use ckeisc/lora-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ckeisc/lora-trained with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ckeisc/lora-trained") prompt = "a photo of ch1u_bubu toddler" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/optimizer.bin from ckeisc/lora-trained: direct link, hf CLI and curl.
- Browser
- Download file 4 MB
-
https://huggingface.co/ckeisc/lora-trained/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://ckeisc/lora-trained/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ckeisc/lora-trained/resolve/main/checkpoint-500/optimizer.bin
4 MB
- Xet hash:
- 7883af861416ae3172459f0aef38ae560a6828405275dfc61cfd519e896d4311
- Size of remote file:
- 4 MB
- SHA256:
- 11f978247f828b69486ec3a0a078ffe4022e26dfb19d285792c8e6806f6b664b
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