Text-to-Image
Diffusers
stable-diffusion
stable-diffusion-diffusers
simpletuner
lora
template:sd-lora
Instructions to use Disra/lora-training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Disra/lora-training with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Disra/lora-training") prompt = "unconditional (blank prompt)" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 051a3c6b257d88a7d5be38c33e96db265101c539fc9108e86167aeca7fc244f6
- Size of remote file:
- 7.58 MB
- SHA256:
- 2872d95b3da0bf2cc82134a5df8a97533e44f65854923e4df67887bc366f14e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.