Instructions to use willhx/train_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use willhx/train_lora 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("willhx/train_lora") prompt = "a photo of sofa" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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("willhx/train_lora")
prompt = "a photo of sofa"
image = pipe(prompt).images[0]LoRA DreamBooth - willhx/train_lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sofa using DreamBooth. You can find some example images in the following.
- Downloads last month
- 7
Model tree for willhx/train_lora
Base model
runwayml/stable-diffusion-v1-5


