Instructions to use gdvstd/korean-style-sketch-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use gdvstd/korean-style-sketch-sd3-lora 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-3-medium-diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("gdvstd/korean-style-sketch-sd3-lora") prompt = "sks sketch" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-5000/optimizer.bin from gdvstd/korean-style-sketch-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 9.6 MB
-
https://huggingface.co/gdvstd/korean-style-sketch-sd3-lora/resolve/main/checkpoint-5000/optimizer.bin
- Command line
-
hf download hf://gdvstd/korean-style-sketch-sd3-lora/checkpoint-5000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/gdvstd/korean-style-sketch-sd3-lora/resolve/main/checkpoint-5000/optimizer.bin
9.6 MB
- Xet hash:
- 0b79fc1ca4b01847ee30fa27fbeb271391a62cbb9ecbc1dc5df5bb4434ed4ac2
- Size of remote file:
- 9.6 MB
- SHA256:
- a91bfe1c7d2a87a336defd38c7d82d1305a2bc5b1224ade68733d78f37705e75
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.