Instructions to use ssdxc/lora_ckpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ssdxc/lora_ckpt with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ssdxc/lora_ckpt") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2000/optimizer.bin from ssdxc/lora_ckpt: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/ssdxc/lora_ckpt/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://ssdxc/lora_ckpt/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ssdxc/lora_ckpt/resolve/main/checkpoint-2000/optimizer.bin
6.59 MB
- Xet hash:
- 250afbc6fe1db70b0f33a21cf589ae91508b827e5ebceb2f28ef69b6b6f0f773
- Size of remote file:
- 6.59 MB
- SHA256:
- d80ff6fc8f0485747988e96a660af00e2c6114d08bf2df522c3d7fbc2bbcc877
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.