

license: apache-2.0 base_model: OpenMOSS-Team/MOSS-Image-SFT tags:
🚀 v3 is out —
armV3is live. Trained on ConceptEdit-12M (the dataset behind chinoll's LoRA) with quality + locality-purity filtering. Head-to-head vs chinoll/Moss-Image-Edit on 15 real portraits — full grids below, judge them yourself. Download:weights/armV3/armV3.safetensors(ai-toolkit format, loads natively in ComfyUI) ·armV3-moss-edit-lora.safetensors(ComfyUI plugin format) · Try the demo Space · Blind eval grid below.
An open effort to build an instruction-based image-editing LoRA for OpenMOSS-Team/MOSS-Image-SFT that beats chinoll/Moss-Image-Edit on quality — every experiment, checkpoint, and side-by-side published, including the ones that lost.
5 real Pexels portraits (never in training), matched settings (1024px long edge, 50 steps, CFG 3, seed 42). Columns: source → chinoll → armV3. The exact instruction is printed on every row — judge it yourself:

Batch 2 — 5 different portraits, different instructions (accessory, lighting, removal, background, outfit):

Batch 3 — 5 different portraits, the hardest surgical categories (eye color, freckles, eyeglasses) plus environment and style:

Three batches, 15 real portraits total, never in training. Every row is source → chinoll → armV3 at identical settings (1024px long edge, 50 steps, CFG 3, seed 42), with the exact instruction printed on the row. We don't call winners — the grids are the evidence, judge them yourself. Full per-candidate images: akhaliq/wiki-eval-images.
ComfyUI merged native MOSS-Image Edit support (PR #17296, Oct 22) — it loads ai-toolkit-format edit LoRAs directly, which is exactly our checkpoint format:
weights/armV3/armV3.safetensors into ComfyUI/models/loras/
(no conversion, no rename needed).edit_format = ai-toolkit
(that's our training layout — index_timestep_zero_isolated with KV cache;
chinoll's LoRA uses the Moss-Image-Edit option instead).Use the armV3-moss-edit-lora.safetensors file (root of this repo) —
it's in the moss-edit-lora format that the
ComfyUI-Moss-Image-Edit
nodes load, same as chinoll's adapter.
See the full description on the original page
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