How to train a MOSS Image LoRA

Which trainers support MOSS Image (OpenMOSS, ~5B, Apache 2.0), how much VRAM you need, and settings that work. Every row links to its source.

MOSS Image character recipe that works

Source	AI Toolkit Discord, Oct 20 2026
Trainer	ai-toolkit
Base model	MOSS Image Base
Quantization	transformer and text encoder in 8-bit ConvRot
Network	LoKr, factor 8
Optimizer / LR	AdamW 8-bit, 1e-4
Weight decay	5e-5
Schedule	shift timesteps
EMA	on, 0.99
Batch	2, gradient accumulation 2
Differential guidance	On, scale 3
Resolution	512
Dataset	100 images reached the same likeness as 25 images, with much more flexibility

Trainer support

Trainer	Status
ai-toolkit	supported (SFT + Base, Oct 16; edit training Oct 17)
OneTrainer	unknown
musubi-tuner	unknown
kohya sd-scripts	supported (v0.12.2, Oct 20; LoRA incl. ConvRot INT8, faster H2D-only block swap)
SimpleTuner	supported (v4.10.0, Oct 17; Base/SFT incl. edit LoRAs)

VRAM

kohya sd-scripts guide (1024px, batch 1, LoRA dim 16, AdamW, gradient checkpointing, text encoder outputs cached): ~13 GB training with bf16 DiT, ~9 GB with ConvRot INT8 DiT + text encoder, ~5-7 GB adding block swap (needs ~15 GB system RAM). v0.12.2's H2D-only block swap on an RTX 3090 (bf16, 1024px, batch 1, --blocks_to_swap 36): ~7 GB at 9.4 s/step vs 11.7 s/step with classic block swap. Caching text encoder outputs needs ~8.3 GB even in INT8, so 8 GB GPUs are not supported yet. Training the text encoder LoRA too: ~17 GB INT8, ~32 GB bf16. SimpleTuner ships 24, 32, 48, 80 and 144 GB recipes at 512px. ai-toolkit: ~15 GB at batch 2, 1024px, AdamW

Known-good settings

Learns fast
Tokenizer loading bug fixed same day (Oct 16) in ai-toolkit; update before training
SFT variant: stripe artifacts, no fix yet
sd-scripts: fp8_base is not supported for MOSS Image; use --convrot_int8 (and --convrot_int8_text_encoder) instead
sd-scripts: --timestep_sampling sigma --weighting_scheme logit_normal --logit_mean -1.09 --logit_std 0.95 --discrete_flow_shift 1.0 reproduces pre-training; simpler alternative: --timestep_sampling shift --discrete_flow_shift 3.0
sd-scripts example: LR 1e-4 constant at network_alpha 1 (lower LR if you raise alpha)
SimpleTuner recipes: rank-32 LoRA, LR 1e-4, REPA, 4,000 steps, 512px-area training; validate at 50 steps, CFG 3, 1024px
Edit/reference LoRAs: SimpleTuner model_flavour sft-edit or base-edit with matching target and reference folders; ai-toolkit also trains Base and SFT as edit models
Base LoRA with ai-toolkit defaults (adamw8bit, shift timesteps, rank 32) gives a good style by ~1,500 steps; Base-trained LoRAs also work on SFT
Try a higher LoRA rank than usual (smaller hidden size than Krea 2); character LoRAs are hard to get right so far
Full fine-tunes on small datasets collapse after a few epochs; use LoRA unless you have a very large dataset
ComfyUI "lora key not loaded": ai-toolkit now saves unfused q/k/v LoRA keys (Oct 17); use the Comfy-Org MOSS files to fix it
ai-toolkit low_vram + cache_text_embeddings could OOM at startup because the text encoder stayed on the GPU; fixed in ai-toolkit on Oct 17, so update first
Inference: an SFT LoRA at ~0.6 strength with the Base model at 30 steps, CFG 3 works well; 8-step DMD LoRAs exist for SFT and Base but can weaken LoRA effect
Base DMD2 speed LoRA is final (update 3000, Oct 20): stock Euler, 4-8 steps, CFG 1
Testing your LoRA's likeness: skip Kromoss and turbo/DMD speed LoRAs, use 50 steps, CFG 2 and LoRA strength ~1.5, and generate at 1.5 MP or more (2-3 MP is best)
OpenMOSS user guide: keep the text encoder frozen when training on MOSS Image Base
OpenMOSS user guide, captioning: an LLM prompt that returns JSON with long, medium and short captions; recommended captioners are Gemma 4, Qwen 3.5 27B or Qwen 3.5 35B-A3B with thinking mode on
OpenMOSS user guide, Base inference: 50+ steps, CFG 3 (3-6 for short prompts), shift 5, Euler sampler
Using the whole JSON caption output as the caption works well; ai-toolkit accepts .json caption files
Standard LoRA recipe that trains well on Base: LoRA on attention + MLP, rank 32, alpha 32, AdamW LR 1e-4 (betas 0.9/0.95, weight decay 0.01), grad clip 1.0, cosine schedule with 100 warmup steps and min multiplier 0.1, bf16, text encoder and VAE frozen, ~1 MP (1024²) with aspect ratio kept, no min-SNR
Low-VRAM ai-toolkit LoKr on Base: rank 4 LoKr, 8-bit quantization, low VRAM on, text encoder fully offloaded, transformer 50% offloaded
A Krea 2 config pasted straight into MOSS Image gives poor results; start from MOSS-specific settings
sd-scripts v0.12.2: cache several caption variants per image (for example the long, medium and short captions) and pick one at random each step; before, only the first was kept
sd-scripts v0.12.2 H2D-only block swap: add --block_swap_h2d_only to --blocks_to_swap (needs --gradient_checkpointing; optional --block_swap_ring_size, default 2, and --use_pinned_memory_for_block_swap). MOSS Image LoRA on an RTX 3090 at 1024px, batch 1, 36 blocks swapped: 9.4 s/step instead of 11.7 (~20% faster) at ~7 GB VRAM. LoRA only, not full fine-tuning
sd-scripts: if you train the text encoder LoRA, don't cache text encoder outputs; older LoRA command examples in the docs did both (fixed in v0.12.2)
Kromoss: Apache 2.0 full fine-tune of Base that fixes Base's blurriness; drop-in for SFT files and workflows (50 steps, CFG 3), with an 8-step turbo LoRA (8 steps, CFG 1, euler/simple) and, since Oct 20, an 8-steps DMD2 LoRA (8 steps, 4 also works, CFG 1, strength 1). Still being updated (update 11000 on Oct 20)
Kromoss int8 ConvRot single file (~5.4 GB) runs in ComfyUI on smaller GPUs; its convert_to_comfy.py turns your own MOSS Image diffusers fine-tune into a ComfyUI single file
ai-toolkit "no module named fla" (flash-linear-attention) in Docker: fixed Oct 19, so pull the latest version and update dependencies; prompt encoding at batch size above 1 also got faster the same day
ComfyUI v0.42.1 fixes crashes in the MOSS Image template workflow; ComfyUI master adds MOSS RoPE scaling (ScaleRope node) for higher resolutions and a MOSS color/depth control node
RoPE scaling for high-res MOSS works best at partial strength; an experimental hi-res reference LoRA using depth/color maps reaches ~8K without duplicated subjects
If SFT feels stiff: extract SFT as a LoRA and apply it to Base at ~0.5 strength
SimpleTuner: LoKr training with torch.compile needs LyCORIS 4.1.3, which SimpleTuner requires since Oct 20

General tips

Gated Hugging Face models: click 'Agree and access' on each model page, or you'll get 403 errors
Steps scale with dataset size (steps = epochs x images at batch 1)
A safe start: LoKr with auto rank (~64), batch/grad accumulation 4

Where to rent a GPU

See cheapest GPU to train a LoRA (RunPod, Vast.ai).

Sources

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Data updated 2026-10-20. RunPod and Vast.ai links are referral links. unknown = no data yet. · Raw data (JSON) · llms.txt