Instructions to use innople/nb-m-windbreaker-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use innople/nb-m-windbreaker-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-2512", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("innople/nb-m-windbreaker-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
NB Windbreaker LoRA
Qwen/Qwen-Image-2512 ๊ธฐ๋ฐ ์๋๋ธ๋ ์ด์ปค ํจ์ ๋์์ธ LoRA.
์ฌ์ฉ๋ฒ
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained(
"Qwen/Qwen-Image-2512",
torch_dtype=torch.bfloat16,
).to("cuda")
pipe.load_lora_weights(
"innople/nb-m-windbreaker-lora",
weight_name="nb_m_windbreaker_v1_000001200.safetensors",
)
image = pipe(
prompt="A windbreaker jacket, flatlay on white background",
width=1104, height=1472,
num_inference_steps=30,
true_cfg_scale=4.0,
).images[0]
image.save("output.png")
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Model tree for innople/nb-m-windbreaker-lora
Base model
Qwen/Qwen-Image-2512