Instructions to use vadimcusnir/vadim-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vadimcusnir/vadim-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("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("vadimcusnir/vadim-lora") prompt = "VADIM" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee

- Xet hash:
- f5a990f7008236783e42af787ab3397afa1e33c5ba9e26eb33a67974add74fe5
- Size of remote file:
- 1.22 MB
- SHA256:
- 5afdb04cdfde4633db099f8349b11f97db4eb303e484a62f9627e6b644bc8cc9
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