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 Settings
- Draw Things
- DiffusionBee

- Xet hash:
- c590ab372faaee94b2743e2e5570d56611342e9531d0a1cce7674eac54834905
- Size of remote file:
- 6.71 MB
- SHA256:
- 247726fbd49fdc4e5ec717a598286fd968c94a821dd5595189b4a7159b0e3ab8
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