Instructions to use tmklein/path-to-save-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tmklein/path-to-save-model 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("tmklein/path-to-save-model") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- ab23f1a2251ee87e9c99559d2cf736ee9b114d053202d04b86433e78e46e8099
- Size of remote file:
- 6.59 MB
- SHA256:
- e21f194ccc9ae2c4e5ed3b3eb9cbea4d85bcd42af2c950abe6bd5201b5c952cf
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