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("artokun/nic0le-krea2")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]nic0le — Krea 2 LoRA
Character LoRA for Krea 2 Raw. Trigger word: nic0le.
Trained with ostris/ai-toolkit.
Checkpoints
| File | Steps |
|---|---|
nic0le_000002000.safetensors |
2000 |
nic0le_000002250.safetensors |
2250 |
nic0le_000002500.safetensors |
2500 |
nic0le_000002750.safetensors |
2750 |
nic0le.safetensors |
3000 (final) |
Identity holds from roughly step 2000 onward. Earlier checkpoints are included so you can pick your own point on the likeness-vs-flexibility curve.
Training
| Base | krea/Krea-2-Raw (arch krea2, qfloat8 quantized for training) |
| Network | LoRA, linear 32 / alpha 32 |
| Steps | 3000, batch size 1 |
| Optimizer | adamw8bit, lr 1e-4, constant |
| Scheduler | flowmatch, linear timesteps |
| Dataset | 19 images at 1024px, captioned with nic0le as the leading token |
Usage in ComfyUI
UNETLoader krea2_raw_bf16.safetensors → LoraLoaderModelOnly (this LoRA) → KSampler
CLIPLoader qwen3vl_4b_bf16.safetensors type=krea2
VAELoader qwen_image_vae.safetensors
Sampler settings for raw (not turbo): ~40 steps, cfg 3.0–3.5, euler / simple.
Turbo is the distilled variant and wants ~8 steps at cfg 1 instead.
Use the bf16 text encoder, not fp8
qwen3vl_4b_fp8_scaled.safetensors produced uniform black images on any
descriptive prompt in our testing — no error raised, full step count, black output.
Single-token prompts survived numerically but carried no usable signal, so results
were driven by the seed alone. Switching to qwen3vl_4b_bf16.safetensors (from
Comfy-Org/Krea-2) fixed it completely.
If you are chaining Krea 2 with a second model family in one workflow, free VRAM between model loads. Loading Krea 2 alongside another large model corrupted the second model's weights silently — sampling to NaN with no error in the log.
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