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-700/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-700/optimizer.bin
- Command line
-
hf download hf://ggomma/test_lora/checkpoint-700/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ggomma/test_lora/resolve/main/checkpoint-700/optimizer.bin
9.03 MB
- Xet hash:
- ea2fb70c0a536271b770598c5d77433ae21093de2f84e339fe7d05f0608947c7
- Size of remote file:
- 9.03 MB
- SHA256:
- 62c336ed576a210cee2ab243fa8f33a6007814c8d33ce40fb7221a529e56b519
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