Instructions to use n01e1se/qwen-image-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use n01e1se/qwen-image-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("Qwen/Qwen-Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("n01e1se/qwen-image-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Qwen-Image with Integrated LoRA
This is a Qwen-Image model with integrated LoRA weights for custom character generation.
Model Details
- Base Model: Qwen/Qwen-Image
- LoRA: Integrated custom LoRA for character generation
- Format: Diffusers
- Precision: bfloat16
- Framework: PyTorch
Usage
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained(
"n01e1se/qwen-image-lora",
torch_dtype=torch.bfloat16,
)
pipe.enable_sequential_cpu_offload()
image = pipe(
prompt="ma11en1a elegant woman with voluminous red curls",
num_inference_steps=50,
width=1024,
height=1024,
).images[0]
image.save("output.png")
Example Images
Example 1
Example 2
Example 3
Notes
- The LoRA weights have been merged into the base model weights
- Use CPU offloading for systems with limited GPU memory
- Recommended image size: 1024x1024
- Recommended steps: 50
- Downloads last month
- -
Model tree for n01e1se/qwen-image-lora
Base model
Qwen/Qwen-Image

