PedroMarinhoDev/Moss-Image-Edit-ComfyUI

Moss-Image-Edit LoRA for ComfyUI

chinoll/Moss-Image-Edit, the image-editing LoRA for MOSS-Image, with its keys renamed for ComfyUI's native MOSS-Image model. Edit an image (or combine up to four) with a text instruction.

Watch the overview (62 s) · Gallery with every prompt

14 common edit types (after the GEdit-Bench and ImgEdit categories) on MOSS-Image SFT at 1024 px, CFG 5, layer 22 skipped on the negative pass; columns: source, seed 42, seed 7. Sources made with FLUX.2 Klein 9B.

Use

Install the node pack ComfyUI-Moss-Image-Edit-Native (stock ComfyUI loaders, LoraLoaderModelOnly, samplers and memory management; no diffusers or transformers) and open a workflow from Templates → ComfyUI-Moss-Image-Edit-Native. The pack also runs akhaliq/MOSS-Image-Edit-HQ v3 and v5, in their own ai-toolkit layout (v5 with the 512 px vision it was trained with); the node pack's README compares all three. ComfyUI master can also run this LoRA itself (Text Encode MOSS-Image Edit, edit_format Moss-Image-Edit); that isn't in a release yet, and the node pack works on the 0.42 releases and on master.

folder	file
loras	moss_image_edit_lora_bf16.safetensors (this repo)
diffusion_models	moss_image_sft_bf16 or moss_image_sft_int8_convrot from Comfy-Org/MOSS-Image
text_encoders	qwen3.5_9b_w4a8_gptq from Comfy-Org/MOSS-Image, or a bf16 Qwen3.5-9B
vae	flux2-vae_small from Comfy-Org/MOSS-Image, or flux2-vae (same encoder)

hf download PedroMarinhoDev/Moss-Image-Edit-ComfyUI moss_image_edit_lora_bf16.safetensors --local-dir ComfyUI/models/loras

Settings

Tested on the RTX 3090: one change at a time on 7 edit types, then 14 edit types on SFT and Base with two seeds.

• LoRA strength 1.0. Below it the scene is redrawn instead of edited, removals don't happen and two-image edits return image 2:
• References and output the same size. The LoRA was trained with source and target at one size (256 px); with smaller references than the output the scene zooms out or the edit fails. The nodes' default (reference_max_edge and output_max_edge 0) takes care of it: each image becomes a ~1 MP bucket of its own aspect ratio, and the output follows image 1:
• CFG 5, 30 steps, euler / simple, layer 22 skipped on the negative pass (the official MOSS sampler default), empty negative. At CFG 4 removals also take nearby objects. (The HQ LoRA works best at CFG 3.)
• MOSS-Image SFT, not Base. The LoRA was trained on SFT; on Base text, subject replacement and two-image edits fail much more often:
• Weak spots on SFT too: pose changes and replacing one subject with another (it often adds the new one beside the old).
• kv_cache auto (the default) computes the reference images once per run instead of at every step.

What changed

Only the key names; the tensors are the original bf16 values.

	original adapter_model.safetensors	this file
keys	layers.N.proj.X.lora_A / .lora_B	diffusion_model.layers.N.proj.X.lora_A.weight / .lora_B.weight / .alpha
scale	alpha / rank from adapter_config.json	alpha = 64 per module (rank 64, scale 1.0)
targets	q, k, v, o, ffn_up_gate, ffn_down of all 42 blocks (252 linears)	same; ComfyUI maps q/k/v onto the fused qkv of the int8 files
sha256	17a48b8c605dcffdedb92a2b887d18cf316505be90802717669fb6556098dcdf (revision e19b6ab, unchanged in the republished 3be9c40)	f4bf2905dd40ae490ffda37d6be717f3361a4a723225cf0bf1f3b941eb03315d

Made with tools/convert_lora.py from the node pack, which checks the checksum, targets, shapes and rank/alpha. The original adapter config is kept in the file's metadata (adapter_config).

Credits and license

The LoRA was trained and released by chinoll / Chenkin-x (Moss-Image-Edit, released under GPLv3) on MOSS-Image by OpenMOSS (Apache-2.0). The original model repo names no separate license for the weights; this copy follows the project's GPL-3.0. All credit for the training goes to the original authors.