Instructions to use masafy/masafee-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use masafy/masafee-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("gsdf/Counterfeit-V3.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("masafy/masafee-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("gsdf/Counterfeit-V3.0", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("masafy/masafee-lora")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Masafee LoRA
Stable Diffusion LoRA for the original character Masafee โ a red panda from a personal LINE sticker set. Trained entirely on a single consumer GPU (NVIDIA GeForce RTX 3060, 12GB) with kohya-ss/sd-scripts.
Model details
| Base model | Counterfeit-V3.0 |
| Method | LoRA (rank 32) |
| Learning rate | 1e-4 |
| Epochs | 30 (sweet spot before overfitting) |
| Training images | 13 hand-picked LINE stickers |
| Trigger word | masafee |
| Recommended weight | 0.8 |
| Hardware | RTX 3060 12GB (โ9 min / 16 epochs) |
Usage
from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained(
"gsdf/Counterfeit-V3.0",
torch_dtype=torch.float16,
).to("cuda")
pipe.load_lora_weights(
"masafy/masafee-lora",
weight_name="cf60-000030.safetensors",
)
prompt = "masafee, red panda character, sitting on a park bench, sunny day, masterpiece"
image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
image.save("out.png")
Apply at network_mul=0.8 for the most faithful character likeness without rigidity.
Paper & full reproduction
The full experiment writeup (Japanese / English, two-column PDF), all training scripts, the 6-condition sweep, the epoch ablation, and side-by-side comparison grids are in the companion GitHub repo:
๐ https://github.com/masafykun/masafee-lora
Key findings from the paper
- Base model choice dominates โ switching the base model (SD1.5 โ Counterfeit-V3.0) had a far larger effect on output quality than tuning LR or LoRA rank.
- Epoch ~30 is the sweet spot โ extending to 40โ60 epochs produced visible overfitting without improving character fidelity.
- Output quality is bounded by training image count โ with only 13 stickers, certain poses and lighting conditions remain out of distribution.
Citation
If you use this model in academic work, please cite it via the Zenodo archive:
@software{suzuki_masafee_lora_2026,
author = {Suzuki, Masato},
title = {{Masafee LoRA: Single-GPU LoRA Fine-tuning of an
Original Character on Stable Diffusion}},
year = {2026},
version = {v1.0.0},
doi = {10.5281/zenodo.20397946},
url = {https://doi.org/10.5281/zenodo.20397946},
orcid = {0009-0000-7977-2756}
}
Persistent archive (Zenodo DOI): 10.5281/zenodo.20397946
License
- LoRA weights (this file): released for personal and research use only. The weights encode a specific copyrighted character.
- Code, scripts, and paper text (in the GitHub repo): MIT.
- Character "Masafee" itself, the original sticker images, and any images generated using this LoRA: ยฉ 2026 masafykun / Masato Suzuki. Redistribution, commercial use, and use to imply official endorsement are not permitted.
By downloading this model you agree to use it only for personal experimentation or academic research.
Made with ๐พ by masafykun ยท masafy.org ยท ORCID
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Model tree for masafy/masafee-lora
Base model
gsdf/Counterfeit-V3.0