Instructions to use DDENK23/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DDENK23/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("wikeeyang/Magic-Wan-Image-V2", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("DDENK23/lora") prompt = "fe" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/d6125cf0-ff92-4141-8b25-8bd7ce074e56.jpeg
text: fe
parameters:
negative_prompt: fe
base_model: wikeeyang/Magic-Wan-Image-V2
instance_prompt: null
Lora1

- Prompt
- fe
- Negative Prompt
- fe
Download model
Download them in the Files & versions tab.