Instructions to use anm-ol/wan-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use anm-ol/wan-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("Wan-AI/Wan2.1-T2V-1.3B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("anm-ol/wan-lora") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("anm-ol/wan-lora")
prompt = "-"
image = pipe(prompt).images[0]wan-relighting-lora

- Prompt
- -
Model description
"Relighting." as trigger
Trigger words
You should use Relighting. to trigger the image generation.
You should use orbit shot to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for anm-ol/wan-lora
Base model
Wan-AI/Wan2.1-T2V-1.3B