Krea 2 Style LoRA โ€” 2.5D Solid Brush (Knife Strokes)

A style LoRA trained on Krea 2, capturing a flat-shaded 2.5D concept-art brushwork style (front/back character turnaround sheets, knife-stroke rendering) from a 25-image curated dataset.

Details

  • Base model trained on: krea/Krea-2-Raw (LoRA training requires the undistilled Raw checkpoint)
  • Inference model: apply this LoRA to krea/Krea-2-Turbo for fast 8-step generation
  • Trigger token: kbrsh2d
  • Rank: 32
  • Steps: 1000 (checkpoints saved every 200 steps: 200/400/600/800/1000)
  • Trainer: DiffSynth-Studio

Usage

import torch
from diffsynth.pipelines.krea2 import Krea2Pipeline, ModelConfig

pipe = Krea2Pipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="krea/Krea-2-Turbo", origin_file_pattern="turbo.safetensors"),
        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
)
pipe.load_lora(pipe.dit, "checkpoints/step-1000.safetensors")

prompt = "kbrsh2d, a knight in ornate plate armor holding a sword and shield, standing on a plain background"
image = pipe(prompt, seed=42, height=1024, width=1024, num_inference_steps=8, cfg_scale=1, mu=1.15)
image.save("image.png")

Checkpoints

File Steps
checkpoints/step-200.safetensors 200
checkpoints/step-400.safetensors 400
checkpoints/step-600.safetensors 600
checkpoints/step-800.safetensors 800
checkpoints/step-1000.safetensors 1000

Dataset

Trained on 25 curated concept-art images (front/back character turnaround sheets in a flat 2.5D painterly brush style), captioned content-only (no style words) with the layout described explicitly to avoid it bleeding into the learned style.

Sample outputs

See samples/ for evaluation renders across checkpoints (Turbo + LoRA, fixed seed/prompt per checkpoint).

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