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
- Draw Things
- DiffusionBee
- Xet hash:
- d6010b0054a387b0ec4dbf3f7bf0e65d7d234db799072863f0b425cbfe44691e
- Size of remote file:
- 9.03 MB
- SHA256:
- b9502d35f6f6ff93983f9c83cf18264785f385237dcab3d19053447931b88625
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