Instructions to use takuma104/lora-test-text-encoder-lora-target with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use takuma104/lora-test-text-encoder-lora-target 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("takuma104/lora-test-text-encoder-lora-target") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-200/optimizer.bin from takuma104/lora-test-text-encoder-lora-target: direct link, hf CLI and curl.
- Browser
- Download file 9.03 MB
-
https://huggingface.co/takuma104/lora-test-text-encoder-lora-target/resolve/main/checkpoint-200/optimizer.bin
- Command line
-
hf download hf://takuma104/lora-test-text-encoder-lora-target/checkpoint-200/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/takuma104/lora-test-text-encoder-lora-target/resolve/main/checkpoint-200/optimizer.bin
9.03 MB
- Xet hash:
- eb32d6173f297812ab04bc6aabbdca29e1d23fbfea458e97de4c7c73f25ecda1
- Size of remote file:
- 9.03 MB
- SHA256:
- 9608c377b04e8028aba773c0ca83fdfd55b7252101d471f1920ad93ad97f902c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.