Instructions to use ggomma/test_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ggomma/test_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("ggomma/test", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ggomma/test_lora") prompt = "A photo of 76301216-c872-4193-a1c8-5e138d14e453 person" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-900/optimizer.bin from ggomma/test_lora: direct link, hf CLI and curl.
- Browser
- Download file 9.03 MB
-
https://huggingface.co/ggomma/test_lora/resolve/main/checkpoint-900/optimizer.bin
- Command line
-
hf download hf://ggomma/test_lora/checkpoint-900/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ggomma/test_lora/resolve/main/checkpoint-900/optimizer.bin
9.03 MB
- Xet hash:
- 5ce3707538069b0aa33e9d2e2da4e4e55e7a84cae0401b1a5ef1468997a84bc7
- Size of remote file:
- 9.03 MB
- SHA256:
- 35fb4af43ddda1d3d5021f894b232938d27cb145d6e60859ff8418f7c11254c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.