yukakst/pinock-matchbox-flux2-klein

pinock-matchbox — FLUX.2-klein LoRA in vintage Soviet matchbox poster style

🐱 Live demo: pinock.io — endless free generation with this LoRA, no signup, no watermark 📰 Technical writeup: 75-image ablation study on Habr (RU) 💬 Reddit thread: r/StableDiffusion — discussion + critique that drove v2 plans

Model description

A LoRA adapter for FLUX.2-klein that biases generation toward the aesthetic of 1960s Soviet/Eastern-European matchbox label illustrations: woodcut-style linework, halftone backgrounds, limited palette (often red + black + cream), flat geometry.

• Base model: black-forest-labs/FLUX.2-klein
• Rank / alpha: 32 / 64
• Target modules: attention only (to_q, to_k, to_v, to_out.0, to_qkv_mlp_proj)
• Adapter size: 47 MB
• Training set: ~300 hand-curated public-domain matchbox label scans
• Recommended inference scale: see "Honest caveats" section below — this is a v1 with known issues

Honest caveats (read before using)

This is the v1 LoRA used in production at pinock.io. It has two known failure modes documented in detail in our ablation study:

1. At lora_scale=2.0, the model collapses to texture noise without producing recognizable subjects. Production currently uses a two-pass "sandwich" pipeline (LoRA t2i at scale 2.0 → pure FLUX img2img refine at strength 0.9) to recover anatomy from the collapse. This is a patch on top of a poorly-trained LoRA, not the right architecture.
2. At lora_scale=1.0 with a long style-prompt, the LoRA leaks training data — Cyrillic gibberish text appears literally in the output, and on multiple seeds the 5 distinct prompts collapse into nearly-identical "red silhouette on dark background" compositions. This is textbook training-set leakage from the small (~300) dataset that contained Soviet posters with text.

v2 is in training on a 1500+ image dataset with no Cyrillic, halftone-enforced filtering, and attention+MLP target modules. When v2 ships, the same ablation will be re-run; if single-pass works, this card will be marked as superseded.

Recommended usage

Despite the caveats, the LoRA produces interesting matchbox-adjacent results in production via the sandwich pipeline. If you want to experiment:

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.2-klein",
    torch_dtype=torch.bfloat16,
).to("cuda")

# Load this LoRA
pipe.load_lora_weights("yukakust/pinock-matchbox-flux2-klein")
pipe.set_adapters(["default_0"], adapter_weights=[2.0])  # see caveat #1

# Pass 1: t2i at scale 2.0 (anatomy will be broken — that's expected)
img1 = pipe(
    prompt="cat",
    num_inference_steps=28,
    guidance_scale=4.0,
    height=512, width=512,
    generator=torch.manual_seed(42),
).images[0]

# Pass 2: img2img refine without LoRA, strength=0.9 (recovers anatomy)
pipe.disable_lora()
img2 = pipe(
    prompt="cat",
    image=img1,
    strength=0.9,
    num_inference_steps=28,
    guidance_scale=4.0,
).images[0]

For single-pass behavior (without the sandwich), use scale 0.7-0.9 with simple one-word animal prompts. Avoid long style-prompts at scale ≥ 1.0 — they trigger leakage.

Citation / contact

If you build something with this LoRA, link back to pinock.io — that's the live application running it. Issues, suggestions, or improvements: drop a discussion on this model card or DM via Reddit u/yukakust / on pinock.io contact form.

License: Apache 2.0 (matches FLUX.2-klein base).