Instructions to use cgioo/Filex-lora-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cgioo/Filex-lora-train 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("cgioo/Filex-lora-train") prompt = "TOK" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 3f2b17ccf9bc7d84a3d2f9fe682608549d6b5b6d21dab4c1a607176000c36734
- Size of remote file:
- 172 MB
- SHA256:
- 1cfcd2884307cca2c78d221c8638eafd2dcb2415cc7eb75d8a68205a176d9ce6
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