Instructions to use hf-internal-testing/lora-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/lora-trained 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("hf-internal-testing/lora-trained") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-300/optimizer.bin from hf-internal-testing/lora-trained: direct link, hf CLI and curl.
- Browser
- Download file 9.03 MB
-
https://huggingface.co/hf-internal-testing/lora-trained/resolve/main/checkpoint-300/optimizer.bin
- Command line
-
hf download hf://hf-internal-testing/lora-trained/checkpoint-300/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/hf-internal-testing/lora-trained/resolve/main/checkpoint-300/optimizer.bin
9.03 MB
- Xet hash:
- 171e12f3a2c16d3e89c0618d5f49950fbb5b70a6fc3e5811b57546b29db902c6
- Size of remote file:
- 9.03 MB
- SHA256:
- 0cd8a9d977e3f5c733aaaefb36eaf9cd46bb521f90a0944c6455c1a2e5ac030a
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