Instructions to use privcdd/lorawan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use privcdd/lorawan with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.2-I2V-A14B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("privcdd/lorawan") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
gr3ntwi Wan 2.2 Character LoRA

- Prompt
- -
Model description
Character LoRA for gr3ntwi trained on Wan 2.2 I2V 14B (dual transformer) using ostris ai-toolkit.
Trained for 3000 steps at learning rate 0.0001 with sigmoid timestep, EMA enabled, rank 32. Designed for use with WanAnimate workflows.
Includes both high_noise and low_noise variants. For best results, load both in your WanAnimate workflow โ low_noise at strength 0.85 (identity), high_noise at strength 0.5 (motion).
Trigger words
You should use gr3ntwi to trigger the image generation.
Download model
Download them in the Files & versions tab.
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Model tree for privcdd/lorawan
Base model
Wan-AI/Wan2.2-I2V-A14B