Instructions to use ankurcw/TrainedLoRAs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ankurcw/TrainedLoRAs with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ankurcw/TrainedLoRAs") prompt = "Kurukshetra" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-200/optimizer.bin from ankurcw/TrainedLoRAs: direct link, hf CLI and curl.
- Browser
- Download file 15 MB
-
https://huggingface.co/ankurcw/TrainedLoRAs/resolve/main/checkpoint-200/optimizer.bin
- Command line
-
hf download hf://ankurcw/TrainedLoRAs/checkpoint-200/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ankurcw/TrainedLoRAs/resolve/main/checkpoint-200/optimizer.bin
15 MB
- Xet hash:
- 239d094c8c21662e82488e65e9fa6e57b573e387da57358ada3f21d314418cff
- Size of remote file:
- 15 MB
- SHA256:
- 981d7df296bd82a413b3d5da0520e2210ab679dc936561aa22fcf2b68ea1ac08
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