Norod78/Yarn-art-style
Viewer • Updated • 18 • 140 • 12
How to use linoyts/krea2-yarn-art-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("krea/Krea-2-Raw", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("linoyts/krea2-yarn-art-lora")
prompt = "a cute puppy, yarn art style"
image = pipe(prompt).images[0]



A yarn art style LoRA for Krea 2, trained with the Krea 2 diffusers trainer on the Norod78/Yarn-art-style dataset.
Training config: rank 64 / alpha 64, the full default layer set, learning rate 3e-4 (constant), 1000 steps.
Krea 2 ships as two checkpoints: RAW (the non-distilled base you fine-tune on) and Turbo (an 8-step distilled checkpoint for fast, high-quality inference). Train your LoRA on RAW and run it on Turbo — LoRAs trained on RAW express strongly on Turbo.
You should use yarn art style to trigger the image generation.
>>> import torch
>>> from diffusers import Krea2Pipeline
>>> # Load the LoRA onto Krea 2 Turbo (the distilled inference model)
>>> pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
>>> pipe.load_lora_weights("linoyts/krea2-yarn-art-lora")
>>> # Turbo recipe: 8 steps, no classifier-free guidance
>>> image = pipe("a tiger prowling through a jungle, yarn art style", num_inference_steps=8, guidance_scale=0.0).images[0]
>>> image.save("output.png")
For more details on loading, weighting, merging and fusing LoRAs, see the diffusers LoRA docs.
Base model
krea/Krea-2-Raw