license: apache-2.0
base_model:
- black-forest-labs/FLUX.2-klein-base-4B
tags:
- lora
- image-to-image
- image-editing
- flux2
- trading-cards
- ai-toolkit
library_name: diffusers
pipeline_tag: image-to-image
Card De-frame LoRA (FLUX.2-Klein 4B)
An instruction-editing LoRA that strips the frame, text, and UI elements from trading-card images (Magic: The Gathering, Pokémon, Yu-Gi-Oh!, Digimon) and extends the artwork to a seamless full-bleed illustration.
| File | Base model | Recommended sampling | Time/image* |
|---|---|---|---|
carddeframe_klein4b_v6.safetensors |
FLUX.2-klein-base-4B | 20–25 steps, CFG 4.0, empty negative | ~115 s |
*measured at 848×1184 on an RTX 4060 Ti 16 GB with layer offloading.
Usage
The input card is passed as the control/reference image (kontext-style edit conditioning) and the prompt is the edit instruction. The instruction is game-specific — use the exact phrasing the LoRA was trained with (shown with each example below).
FLUX.2-klein-base is not guidance-distilled: sample with standard CFG ≈ 4.0 and an
empty negative prompt. Quality saturates at 20 steps; 12 steps is usable (95%).
Python example (MTG)
import torch
from diffusers import Flux2KleinPipeline
from diffusers.utils import load_image
pipe = Flux2KleinPipeline.from_pretrained(
"black-forest-labs/FLUX.2-klein-base-4B", torch_dtype=torch.bfloat16
)
pipe.load_lora_weights("carddeframe_klein4b_v6.safetensors")
pipe.enable_model_cpu_offload() # fits in 16 GB VRAM
card = load_image("mtg_card.png")
prompt = (
"Remove the title, mana cost, type line, rules text box, power and toughness, "
"and set symbol. Paint over those areas with a natural continuation of the "
"existing artwork."
)
image = pipe(
image=[card],
prompt=prompt,
guidance_scale=4.0, # true CFG with an empty negative prompt
num_inference_steps=20,
height=1184,
width=848,
generator=torch.Generator("cpu").manual_seed(42),
).images[0]
image.save("full_bleed.png")
For the other games, swap the prompt for the matching instruction below.
Before / after examples
All examples are held-out validation cards (not in the training set), generated at 20 steps, CFG 4.0, empty negative, 848×1184.
Magic: The Gathering
Prompt:
Remove the title, mana cost, type line, rules text box, power and toughness, and set symbol. Paint over those areas with a natural continuation of the existing artwork.
Pokémon
Prompt:
Remove the card name, HP, energy type icons, attack names and damage, weakness, resistance, retreat cost, set number, and illustrator credit. Paint over those areas with a natural continuation of the existing artwork.
Yu-Gi-Oh!
Prompt:
Remove the card name, attribute icon, level stars, card type line, ATK and DEF values, effect text box, and outer card border frame. Paint over those areas with a natural continuation of the existing artwork.
Digimon
Prompt:
Remove the card name, level indicator, attribute icon, type bar, DP value, play cost, digivolve costs, effect text box, and card border. Paint over those areas with a natural continuation of the existing artwork.
Training
- Dataset: 547 curated (input card → de-framed full-bleed) pairs across the four games, teacher-generated with Qwen-Image-Edit-2511 and manually curated. Captions are the edit instructions above. The full training set is published at pearsonkyle/carddeframe-dataset.
- Network: LoRA rank 64, alpha 32, on the transformer only.
- Recipe: trained with ostris/ai-toolkit at lr 1e-4, bf16, flow-matching with weighted timestep sampling. The 600-step checkpoint was selected by held-out evaluation — later checkpoints did not improve.
Known limitations
- Cards with non-standard layouts (full-art EX / promo Pokémon cards, some Yu-Gi-Oh! frames) may keep their inner border or art box instead of extending to full bleed (~3 of 24 held-out cards).
- Output has a mild painterly softening versus the original art crop.
- Card art is owned by the respective game publishers; this LoRA is intended for research and personal use on imagery you have rights to process.