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-1900/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-1900/optimizer.bin
- Command line
-
hf download hf://ggomma/test_lora/checkpoint-1900/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ggomma/test_lora/resolve/main/checkpoint-1900/optimizer.bin
9.03 MB
- Xet hash:
- 2b8faa78d0e1f542f2568f5da73e71590250dc09e807f1040be01706af096869
- Size of remote file:
- 9.03 MB
- SHA256:
- 8b76dda22cefe9447bd724bf028b3357b9cb14c1d98ee8ce3fd3c1713a6d506a
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