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-500/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-500/optimizer.bin
- Command line
-
hf download hf://hf-internal-testing/lora-trained/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/hf-internal-testing/lora-trained/resolve/main/checkpoint-500/optimizer.bin
9.03 MB
- Xet hash:
- 54b88115a20ee37759f6e7897cf590a52e76e781f035e63c23ce71bb649d23f2
- Size of remote file:
- 9.03 MB
- SHA256:
- 53300bd98f4b571df67a3bfb8cfcf1cf5c4d24785f544f829cce30b4ad4655c3
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