Instructions to use bewiz/squogglelora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bewiz/squogglelora 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("bewiz/squogglelora") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 185efb383e026dc9547b930bced324807c0f5fefec8bad9e4e63941ba6e157a9
- Size of remote file:
- 215 MB
- SHA256:
- 38ac900cb806d74101b3e6bdf542f63217fcbb829917fe6235d19891efe47a4c
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