Instructions to use daeunni/CL_rank4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use daeunni/CL_rank4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("daeunni/CL_rank4") prompt = "a photo of sks teddybear" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 7bc9e463e6632349dde11ebf544a69ea03216aa8bf8fd539990f5defa7189bf9
- Size of remote file:
- 6.6 MB
- SHA256:
- 06fd5d58322231c88e9b3e31b04575ce0b3c1503d1da9bb87d8bb4417e41f850
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