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-3000/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-3000/optimizer.bin
- Command line
-
hf download hf://ssdxc/lora_ckpt/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ssdxc/lora_ckpt/resolve/main/checkpoint-3000/optimizer.bin
6.59 MB
- Xet hash:
- c4357f1959288decbb79fd623e8473643e16c677add52be647f713bdd510001e
- Size of remote file:
- 6.59 MB
- SHA256:
- 6359f48ecc1f15f6aa103c6752b160284e05f167089346a10751b2728b1cef79
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