Text-to-Image
Diffusers
TensorBoard
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
Instructions to use pedrohsmoura/lora-brasilia-rank8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pedrohsmoura/lora-brasilia-rank8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("pedrohsmoura/lora-brasilia-rank8") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 7a4a59b3a1ef13a863743103224fa06067c0f690a479f5c752b8b5b52d0e2ab8
- Size of remote file:
- 434 kB
- SHA256:
- a45ca0b4c0529094c228da86e1fd29450ba8af042c0705bf5d1f6e032f3a9e28
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.