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Laxhar/
sensenova-u1-lora-trainer

Name: sensenova-u1-lora-trainer
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sensenova/SenseNova-U1-8B-MoT

finetune

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train_u1 — simple LoRA trainer for SenseNova-U1-8B-MoT

A single-GPU LoRA / partial-finetune trainer for SenseNova-U1-8B-MoT. Drives the entire run from one YAML file. Save format follows the upstream LoRA convention (<key>.lora_down.weight / lora_up.weight / .alpha) so checkpoints drop straight into the official inference scripts.

./train.sh configs/default.yaml                                  # train
./sample.sh configs/default.yaml output.safetensors --prompt …  # sample

Fits on a 32 GB GPU (RTX 5090 / A100-40 / RTX 6000 Ada). Peak VRAM ~20 GB on the train dataset at 2048².

What you get out of the box

Config-first: every run is one YAML file (configs/default.yaml).
Per-module rank + enable: each LoRA target (q_proj_mot_gen, mlp_mot_gen.down_proj, fm_modules.fm_head.0, …) takes its own rank / alpha / on-off independently.
Experimental MoE target grammar: A3B-style generation experts can be addressed explicitly (gen_moe_mlp, gen_moe_router, mlp_mot_gen.experts.*.gate_proj) without changing the stable 8B main path.
Default = small-data style baseline: configs/default.yaml uses x0 + uniform t + no condition dropout, short captions, LoRA on attn+mlp, and full fine-tuning of the timestep/noise embedders, gen vision bridge, and fm_head.
Official-alignment recipe is optional: configs/official_alignment.yaml keeps the public report knobs together for research ablations, but it is not the safest first run for small style datasets.
Upstream-format save: load straight into examples/t2i/inference.py via --lora_path, or stack with the official 8-step LoRA.
bf16 training, not 4/8-bit. Earlier 4-bit nf4 LoRA training produced grid artefacts and limb collapse on the gen tower; switching the base to bf16 (with offload + static prefix-KV cache) eliminated both.

See the ablation write-up with training curves and sample grids: docs/small_data_style_ablation.html or docs/small_data_style_ablation.pdf.

Hardware

Resource	Required
GPU	32 GB CUDA-12-class card (sm_90 / sm_100 / sm_120). 24 GB cards can sample but cannot train at default bucket.
CPU RAM	≥ 64 GB (bf16 base lives on CPU; one prefetched batch is staged in pinned memory)
Disk	≥ 80 GB (HF snapshot ~17 GB + checkpoints)

bitsandbytes>=0.45 and torch>=2.9 must be linked against your CUDA runtime. On RTX 5090 (sm_120) you'll likely need the cu128 torch wheel.

A3B / MoE Status

The trainer now has experimental target grammar for future SenseNova-U1-A3B-MoT generation-side MoE LoRA work:

yaml

lora:
  spec: "attn=r8a8;gen_moe_mlp=r8a8;gen_moe_router=r8a8"

This is a compatibility layer, not the main training path and not an end-to-end A3B training claim. The stable release target remains SenseNova-U1-8B-MoT; A3B training depends on public MoE runtime support that can instantiate the mlp_mot_gen.experts.* modules.

Before training, estimate MoE LoRA size from metadata only:

bash

python -m train_u1.scripts.inspect_lora_targets \
    --model path/to/A3B/config.json \
    --spec "attn=r8a8;gen_moe_mlp=r8a8;fm_head=r8a8"

Install

bash

git clone <this-repo-url> sensenovenove
cd sensenovenove

python -m venv .venv --system-site-packages
source .venv/bin/activate
pip install -r requirements.txt

# Drop in the pinned upstream modeling .py files (sha256-guarded):
git clone https://github.com/OpenSenseNova/SenseNova-U1 u1_src
git -C u1_src checkout df86ca90bfcd95fbdd1e2b3a590822721dba8cd1

# One-time HF snapshot download (~17 GB):
HF_HOME=$PWD/hf_cache python -m train_u1.scripts.download_model
HF_HOME=$PWD/hf_cache python -m train_u1.scripts.install_modeling_into_snapshot \
    --src u1_src/src/sensenova_u1/models/neo_unify

Train

Lay out your data. Two formats are supported.

Folder of paired files (recommended for ≤ ~10k images):
```
dataset/my_style/
├── 001.jpg     ├── 001.txt
├── 002.jpg     ├── 002.txt
└── …           └── …
```
Each .txt is a single-paragraph natural-language caption. Put the style or artist anchor in a stable way and keep style.trigger aligned with how you will sample later. The default config prepends that trigger to every caption.

Optional: append a <think>...</think> reasoning label inside the same .txt after a ---think--- delimiter line:
```
An illustration by Hayateluc depicting a wisteria-trellis path under
morning glow, painterly composition, no people.
---think---
1. **Instruction Understanding:** ...
6. **Explicit Prompt:** ...
```
Think labels are ignored by default because low-quality or highly templated think text can dominate the prefix and hurt style binding. To use them, set data.use_think_labels: true and evaluate with the same think distribution at sample time. Do this only when your think labels are curated and repeatable.

Parquet/arrow shards (recommended for ≥ ~10k images, e.g. 1M scaling):

bash
1# Pack a folder dataset → single parquet shard 2python -m train_u1.scripts.dataset_tools pack-arrow dataset/my_style \ 3 --out artifacts/my_style.parquet 4# Inspect first 3 rows 5python -m train_u1.scripts.dataset_tools inspect-arrow artifacts/my_style.parquet

Schema: sample_id, caption, think (nullable), image (binary). Set data.data_dir in the YAML to point at the parquet path; the training script auto-detects .parquet and uses ArrowT2IDataset instead of PairedFolderT2IDataset.
Edit configs/default.yaml. The only fields you must touch:

yaml
1run_name: my_run 2data: 3 data_dir: dataset/my_style 4style: 5 trigger: "my style"
Launch:

./train.sh configs/default.yaml

Long-running tip — for 2 h+ runs use setsid + disown so an SSH/IDE disconnect can't SIGHUP-kill the process:

bash
1setsid nohup ./train.sh configs/default.yaml </dev/null >run.log 2>&1 & 2disown

Output:

artifacts/my_run/
├── checkpoints/
│   ├── step_000600.safetensors
│   └── …
├── trainable_state.safetensors      # final
└── train_log.jsonl

Each .safetensors is in upstream format (<key>.lora_down.weight / .lora_up.weight / .alpha).

Sample

bash

./sample.sh configs/default.yaml \
    artifacts/my_run/trainable_state.safetensors \
    --prompt "anime girl in dark kimono on a veranda…" \
    --image-h 1024 --image-w 1024 \
    --num-steps 50 --cfg-scale 4.0 --timestep-shift 3.0 \
    --out preview.png

Optional --think-mode --think-max-tokens 1024 adds a chain-of-thought window before image generation for prompt-fidelity boosts (+~95 s/sample).

Configuration

Everything below the data path is opinionated but tunable. The full schema lives in train_u1/config.py.

yaml

run_name: my_run

data:
  data_dir: dataset/my_style
  cap_max_pixels: 4194304          # 2048² hard cap per image
  snap_bucket: true                 # snap to upstream bucket grid
  use_think_labels: false           # keep prefixes short by default
  # n_samples: 56                   # cap dataset size; default = use everything

style:
  trigger: "my style"              # prepended to every caption
  prompt_template: official        # 'official' (recommended) | 'plain'

lora:
  preset: attn_mlp_no_head         # attn+mlp LoRA; fm_head is full-FT below
  # spec: "attn=r64a64;mlp=r64a64;mlp_mot_gen.down_proj=off"
  dropout: 0.0

unfreeze:                          # full-FT (non-LoRA) regex patterns
  - '^fm_modules\.timestep_embedder\.'
  - '^fm_modules\.noise_scale_embedder\.'
  - '^fm_modules\.vision_model_mot_gen\.'
  - '^fm_modules\.fm_head\.'

train:
  steps: 6000
  lr: 5.0e-5
  seed: 0
  shuffle: true
  grad_accum: 1
  checkpoint_every: 600
  # Small-data style baseline. See docs/small_data_style_ablation.html before
  # switching to the official-alignment recipe.
  loss_type: x0
  t_dist: uniform
  t_logit_mean: -0.8
  t_logit_std: 0.8
  # huber_delta: 1.0             # only used for *_huber
  cond_dropout_text: 0.0
  cond_dropout_both: 0.0

runtime:
  keep_kvs_on_gpu: true
  gc_skip_last: 6
  device: cuda
  cpu_device: cpu
  # upstream_lora_path: SenseNova-U1-8B-MoT-LoRA-8step-V1.0.safetensors
  # upstream_lora_skip: ['fm_modules.fm_head']

LoRA spec mini-language

lora.spec (or --lora-spec on the CLI) is a ;-separated list of target=BODY entries. Targets are specific modules or group aliases:

Target	Resolves to
`q_proj_mot_gen` `k_proj_mot_gen` `v_proj_mot_gen` `o_proj_mot_gen`	one attn projection × 42 layers
`mlp_mot_gen.gate_proj` `mlp_mot_gen.up_proj` `mlp_mot_gen.down_proj`	one mlp projection × 42 layers
`fm_modules.fm_head.0` `fm_modules.fm_head.2`	one of the two patch-decoder linears
`attn`	all four attn projections
`mlp`	all three mlp projections
`fm_head`	both fm_head linears
`all`	every supported target

BODY is one of:

r64a64 — enable with rank 64, alpha 64
r128 — enable with rank 128, alpha defaults to rank
r=64,a=32 — comma form, more readable
off / disable — turn that target off

Examples:

all=r64a64                                          # = the 'default' preset
attn=r128a128;mlp=r128a128;fm_head=r128a128         # exact upstream 8-step shape
attn=r64;mlp=r64;mlp_mot_gen.down_proj=off          # ablate one MLP projection
q_proj_mot_gen=r=128,a=64;k_proj_mot_gen=r=64,a=64  # asymmetric ranks

Built-in presets

Preset	Coverage	Trainable LoRA params	Use when
`default`	168 attn + 126 mlp + 2 fm_head, all r=64	~75 M	match upstream 8-step LoRA coverage
`attn_only`	168 attn, r=64	~50 M	ablation
`attn_mlp`	attn + mlp (no fm_head), r=64	~75 M	when fm_head is full-FT'd separately
`attn_only_no_head`	alias for `attn_only`; explicit no-fm_head intent	~50 M	conservative small-data style training
`attn_mlp_no_head`	alias for `attn_mlp`; explicit no-fm_head intent	~75 M	conservative small-data style training
`official_r128`	exact upstream shape (r=128 across all 296 wraps)	~298 M	parameter-matching upstream's 8-step LoRA

The shipped configs/default.yaml uses attn_mlp_no_head and full-FTs fm_head separately because that was the most stable small-data baseline in our ablations. The default preset name inside the LoRA parser still means "match upstream 8-step LoRA coverage"; use it only when that exact module coverage is what you want. For report-alignment research, start from configs/official_alignment.yaml.

Stack with the official 8-step distill LoRA

Upstream released a step-distillation LoRA that brings inference down to 8 NFE at cfg_scale=1.0. You can train your own style LoRA on top of it by setting runtime.upstream_lora_path in your YAML — at training time we bake-in the official 8-step delta into the bf16 base (skipping fm_head so we don't clobber our own fm_head LoRA), then wrap our LoRA on top.

yaml

runtime:
  upstream_lora_path: hf_cache/.../SenseNova-U1-8B-MoT-LoRA-8step-V1.0.safetensors
  upstream_lora_skip: ['fm_modules.fm_head']   # don't clobber our fm_head LoRA

At sample time, pass the same upstream LoRA and use 8 steps at cfg=1.0:

bash

./sample.sh configs/my_style.yaml \
    artifacts/my_style/trainable_state.safetensors \
    --prompt "…" \
    --upstream-lora-path SenseNova-U1-8B-MoT-LoRA-8step-V1.0.safetensors \
    --upstream-lora-skip fm_modules.fm_head \
    --num-steps 8 --cfg-scale 1.0 --timestep-shift 3.0 \
    --out preview_8step.png

Layout

.
├── train.sh                       # quick-launch wrapper (calls train_bf16_offload)
├── sample.sh                      # quick-launch wrapper (calls sample_t2i_offload)
├── requirements.txt               # pip dependencies
├── pyproject.toml                 # package metadata
├── LICENSE                        # Apache-2.0
├── configs/
│   ├── default.yaml               # recommended small-data style baseline
│   └── official_alignment.yaml    # optional report-alignment research config
├── train_u1/                      # importable package
│   ├── config.py                  # YAML config schema
│   ├── constants.py               # pinned MODEL_SHA / CODE_COMMIT / arch constants
│   ├── data/                      # collator / dataset / cache I/O
│   ├── model/
│   │   ├── lora.py                # LoraAdapter + per-spec apply
│   │   ├── lora_io.py             # save/load + upstream merge
│   │   ├── loader.py              # bf16 base load + tower offload
│   │   ├── wrapper.py             # forward_t2i_step
│   │   ├── losses.py              # fm_loss_x0 / fm_loss_v / fm_loss dispatcher
│   │   ├── patching.py            # patchify/unpatchify
│   │   └── …
│   ├── scripts/
│   │   ├── train_bf16_offload.py  # main training entry
│   │   ├── sample_t2i_offload.py  # bf16 sampler
│   │   ├── sample_t2i_offload_batch.py  # (state × prompt) sweep
│   │   ├── download_model.py      # HF snapshot download
│   │   └── install_modeling_into_snapshot.py
│   └── tests/
├── docs/
│   ├── SETUP.md                   # data layout, design rationale, pinned-upstream details
│   ├── small_data_style_ablation.html
│   ├── small_data_style_ablation.pdf
│   └── assets/                    # figures used by the ablation document
├── artifacts/                     # local-only: checkpoints + sweeps (gitignored)
├── dataset/                       # local-only: image+caption pairs (gitignored)
├── hf_cache/                      # local-only: HF snapshot (gitignored)
└── u1_src/                        # local-only: upstream clone (gitignored)

Acknowledgements & license

Upstream: OpenSenseNova/SenseNova-U1 (Apache-2.0). We pin commit df86ca90 of the modeling code and load it via trust_remote_code after sha256 verification. The training stack here is independent of upstream training code (none was released).
Model weights: sensenova/SenseNova-U1-8B-MoT (post-RL) and sensenova/SenseNova-U1-8B-MoT-SFT. Use according to their model card.
8-step distill LoRA: sensenova/SenseNova-U1-8B-MoT-LoRAs — public release; consumed via the upstream_lora_path mechanism.
This trainer is licensed under Apache-2.0 (see LICENSE).

Thanks to comfy.org for the GPU power support. The open-source community will not forget.

Laxhar/sensenova-u1-lora-trainer

train_u1 — simple LoRA trainer for SenseNova-U1-8B-MoT

What you get out of the box

Hardware

A3B / MoE Status

Install

Train

Sample

Configuration

LoRA spec mini-language

Built-in presets

Stack with the official 8-step distill LoRA

Layout

Acknowledgements & license

Laxhar/
sensenova-u1-lora-trainer