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Zaytron40k/
t01nstyle-qie2511-t2i-surgical-lora

Name: t01nstyle-qie2511-t2i-surgical-lora
Availability: InStock

Qwen/Qwen-Image-Edit-2511

adapter

apache-2.0

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t01nstyle LoRA for Qwen-Image-Edit-2511

Style LoRA trained in T2I mode (--model_version original) on Edit-2511 weights with surgical Tier B+ targeting (image-stream AdaLN modulation + image-stream MLP only). Designed to preserve native multi-reference editing and InstantX ControlNet compatibility.

Status: training complete (25 epochs, 3450 steps).

Trigger: t01nstyle

Recommended checkpoint

Start with t01nstyle_qie2511_t2i_surgical-000018.safetensors — global minimum loss + center of the sweet spot.

A/B test plan (LoRA scale 1.0):

epoch 18 — primary candidate (loss 0.07351, the minimum)
epoch 16 — if 18 looks too strong / distorts the base
epoch 22 — if 18 looks too weak in style

The final unnumbered checkpoint t01nstyle_qie2511_t2i_surgical_final.safetensors corresponds to epoch 25.

Per-epoch loss

epoch	loss	epoch	loss
1	0.07772	14	0.07651
2	0.07733	15	0.07539
3	0.07672	16	0.07353
4	0.07606	17	0.07477
5	0.07701	18	0.07351 (min)
6	0.07731	19	0.07443
7	0.07579	20	0.07462
8	0.07678	21	0.07602
9	0.07644	22	0.07664
10	0.07666	23	0.07661
11	0.07833	24	0.07404
12	0.07759	25	0.07389
13	0.07379

Loss curve is very flat (range 0.07351–0.07833, ~6%) — typical for surgical LoRA with low parameter count. Visual quality at lora_scale 1.0 is the deciding factor.

Inference

Pipeline: Qwen-Image-Edit-2511 (multi-reference)
Compatible with InstantX ControlNet for Qwen-Image (use controlnet_conditioning_scale 0.6-0.8)
LoRA scale: 1.0 (surgical LoRA — full strength expected)
Sampler: FlowMatch / Euler, 25–40 steps, true CFG 4.0
Resolution: 1024² or native 1328²

Training

Tuner: musubi-tuner (kohya-ss), --model_version original on Edit-2511 weights
Dataset: 256 art images in t01nstyle, T2I captions (no control images)
Network: rank 16 / alpha 16, surgical Tier B+ targeting via --network_args
Target modules: img_mod.1, img_mlp.net.0.proj, img_mlp.net.2 (image stream only, attention untouched)
180 LoRA modules total (60 transformer blocks × 3)
Learning rate: 1e-4 (adamw8bit, constant_with_warmup, 200 warmup steps)
Timestep sampling: shift, discrete_flow_shift 2.2, num_timestep_buckets 4
25 epochs, 3450 steps, ~2h 00m wall time
Hardware: H100 SXM 80GB (bf16, sdpa attention)

Why this targeting: All attention projections (to_q/k/v, to_out.0, add_q/k/v_proj, to_add_out) are bit-identical to base Edit-2511. Multi-reference image embeddings flow through joint attention unchanged. ControlNet residuals land on unchanged hidden states. The LoRA modulates only per-block style injection (AdaLN) and image-stream FFN output.

Files

t01nstyle_qie2511_t2i_surgical-000001.safetensors … -000024.safetensors — checkpoints per epoch (1–24)
t01nstyle_qie2511_t2i_surgical_final.safetensors — epoch 25 final
train.sh — exact training command
dataset.toml — dataset configuration
training.log — full training log
tensorboard/ — tensorboard event files for loss curves

Zaytron40k/t01nstyle-qie2511-t2i-surgical-lora