MOSS-Image

MOSS-Image is OpenMOSS’s 5B single-stream diffusion-transformer text-to-image family. It encodes prompts with a Qwen3.5 9B text encoder and decodes with the 32-channel FLUX.2 VAE. Two checkpoints are supported. They share the architecture, the text encoder and the VAE, and differ only in their transformer weights:

MOSS-Image Base is the pretrained model, without any post-training. OpenMOSS publishes it as the starting point for finetunes.
MOSS-Image SFT is a supervised finetune of Base. In OpenMOSS’s comparison it groups text more clearly and lays out scenes more cleanly.

Both run at 50 steps with CFG 3 and support negative prompts. MOSS-Image works in the Generate tab, on the Canvas (image-to-image, inpainting, outpainting and regional guidance) and in the workflow editor, and it can decode with PiD.

License

MOSS-Image Base and SFT, including the bundled Qwen3.5 9B text encoder and VAE, are released under Apache 2.0, which allows commercial use. Neither download is gated.

This is a summary, not legal advice. Read the full license on the model’s HuggingFace page before using a model commercially.

Hardware

See the System Requirements table. For scale, measured on an RTX 4090 in bf16:

Component	VRAM in bf16	With FP8 Storage
Transformer	10.2 GB	5.3 GB
Qwen3.5 9B text encoder	16.8 GB	16.8 GB

The transformer and the encoder do not fit on a 24 GB card together. InvokeAI runs the encoder first and then swaps it out for the transformer, so plan for about 32 GB of system RAM for the model cache to hold both. FP8 Storage applies to the transformer only; the text encoder is never stored in FP8. On smaller cards, Low-VRAM mode lets InvokeAI stream models that don’t fit entirely in VRAM.

A 1024×1024 image takes about 70 s at the default 50 steps and CFG 3, because each step runs the transformer twice, once for the prompt and once for the negative prompt. Images match OpenMOSS’s reference pipeline for the same seed and settings (51–53 dB PSNR).

Installing

The easiest path is the MOSS-Image bundle in the Model Manager. It installs MOSS-Image Base as a Diffusers pipeline. MOSS-Image SFT is left out of the bundle and can be installed individually from the starter models.

Starter	Source	Also installs
MOSS-Image Base	`OpenMOSS-Team/MOSS-Image-Base` (~27 GB)	nothing, everything is bundled
MOSS-Image SFT	`OpenMOSS-Team/MOSS-Image-SFT` (~27 GB)	nothing, everything is bundled

MOSS-Image needs three components:

Component	Diffusers install	Single-file install
Transformer	bundled	the `.safetensors` file, such as the ComfyUI export `moss-image-dit.safetensors`
Text encoder (Qwen3.5 9B)	bundled	installed separately
VAE (FLUX.2)	bundled	installed separately

When a single-file MOSS-Image model is selected, the Components section next to the model asks for:

Qwen3.5 Encoder: a single-file Qwen3.5 9B encoder, such as the ComfyUI export moss-image-qwen35.safetensors. A file that carries the vision tower as well loads; the vision tower stays on disk. The shards of a sharded export, such as the files in the pipeline’s text_encoder/ folder, do not work on their own: the first shard is refused at install with a message saying so, and the others install as unknown models. The shards in transformer/ install as unknown models too. Install the whole Diffusers folder or a merged single file instead. Anima-3.8B uses the Qwen3.5 4B, which installs under the same encoder type. The two are not interchangeable, so the picker lists only 9B encoders.
VAE: a FLUX.2 VAE. MOSS-Image ships the FLUX.2 VAE with a smaller decoder (the ComfyUI export moss-image-vae.safetensors); it installs under the FLUX.2 base. Any other FLUX.2 VAE works too, for example the FLUX.2 VAE starter model.

Quantized MOSS-Image transformers (fp8, int8, nvfp4, MXFP8 and GGUF) are not supported yet and are refused at install with a message saying so. Install the bf16 file, and to save VRAM turn on FP8 Storage in the model’s Default Settings.

Generation settings

Selecting a MOSS-Image model applies these defaults:

Model	Steps	CFG Scale	Scheduler	Size
MOSS-Image Base	50	3.0	Euler	1024×1024
MOSS-Image SFT	50	3.0	Euler	1024×1024

Steps: OpenMOSS gets its best results at 50 steps or more.
CFG Scale: OpenMOSS recommends 3.0 to 6.0. 3.0 is usually enough for a long prompt; raise it for a short one. 1.0 turns CFG off, and values below 1.0 are not accepted.
Negative prompt: only used when CFG Scale is above 1.0. OpenMOSS’s defaults leave it empty and suggest nsfw, worst quality, bad anatomy, watermark, jpeg artifact when you want one.
Scheduler: MOSS-Image uses its own Euler schedule, so the Scheduler field is not shown.
Resolution: width and height must be multiples of 16. OpenMOSS recommends 1, 1.5 or 2 megapixels, in any aspect ratio; the model extrapolates from 0.25 megapixels (512×512) up to 4 megapixels.

Skip layers

Skip layers (negative branch) in the Render section lists the transformer blocks the negative-prompt pass leaves out, as comma-separated numbers from 0 to 41. The default is 22, OpenMOSS’s recommendation. OpenMOSS reports that blocks 22, 24 and 26 all help image quality. Empty skips no block, which makes images look very realistic but can make some details collapse. The setting has no effect at CFG 1.0, where no negative pass runs, so the field is disabled there.

The setting is saved in the image’s metadata. Recall All and Remix Image restore it.

Prompting

MOSS-Image reads natural language, tag sequences and JSON layouts, and you can mix them in one prompt.

Language: English works best, because most of the training data is English. Chinese and other languages work as well.
Tags: OpenMOSS recommends the order quality, score, meta, year, character, series, artist, general, for example best quality, highres, 1girl, blue hair.
Text in the image: put the text in quotation marks. The model breaks lines by itself; to place the breaks yourself, write \n or start a new line in the prompt.
Length: a prompt can be up to 4096 tokens long. A longer one is cut off at the end, as in OpenMOSS’s pipeline, and the server log says so.

JSON layouts

MOSS-Image can also place described elements in regions of the image. Paste a JSON layout into the prompt field in the format OpenMOSS defines; OpenMOSS calls this experimental. For a 1024×1024 image:

{
  "summary": {
    "meta": ["poster"],
    "canvas": "single",
    "caption": "A vintage travel poster with a warm sunset sky over a quiet harbor."
  },
  "items": [
    {
      "label": "text",
      "caption": "Bold cream-colored serif title across the top.",
      "text": "HARBOR NIGHTS",
      "bbox": [-448, -400, -320, 400]
    },
    {
      "label": "object",
      "caption": "A small red lighthouse on a rocky pier in the lower half.",
      "bbox": [-64, -192, 448, 192]
    }
  ],
  "bbox_coord_space": "abs_centralized",
  "bbox_format": "yxyx"
}

summary.caption describes the whole scene and its style.
Each entry in items has a label (object, character or text), a caption describing it, its bbox, and for text the exact text to render.
bbox is [y_min, x_min, y_max, x_max] in pixels, measured from the center of the image, with x growing to the right and y downward. A 1024×1024 image runs from -512 to 512 on both axes. To convert coordinates measured from the top-left corner, subtract half the width from x and half the height from y.

The coordinates are absolute pixels, so a layout made for one size needs new coordinates when you change the width or height. See OpenMOSS’s user guide for the format.

Layout with canvas regions

On the Canvas, InvokeAI builds the JSON layout for you from Regional Guidance layers. The main prompt becomes the layout’s summary.caption, and each enabled regional guidance layer becomes one element: the region’s Positive prompt describes it, and its box is the rectangle around everything painted on the layer inside the generation frame, scaled to the generation size. The shape does not matter, and separate painted areas on one layer share one box.

With a MOSS-Image model selected, the layer’s Layer section in Properties has a Region type:

Character is for people and creatures, Object, the default, for everything else.
Text renders text. Enter the exact characters in Visible text, and describe how they look in the positive prompt. The field takes several lines; each line break reaches the model as \n, and the text breaks there.

A region without a positive prompt, or a Text region without visible text, is left out of the layout, and the layer’s settings say why. When a region can be placed, the main prompt cannot be blank: Invoke is disabled with MOSS-Image needs a prompt to place regional guidance in its layout. Enter a prompt or disable the regional guidance layers. Regional negative prompts, auto-negative and regional reference images are not used with MOSS-Image; the layer hides its negative prompt and auto-negative controls.

Without regions the prompt is sent unchanged. If the prompt is a layout draft, it is compiled and the regions follow its items. If it already is a JSON layout in the format above, the regions are added to its items; a layout with another bbox_coord_space or bbox_format makes the generation fail with a message. Any other prompt, including JSON that is not a layout, becomes the caption. The layout is built when the generation runs, so for a plain-text prompt dynamic prompts and batches vary the caption inside it. A prompt that already is a JSON layout or draft is sent as written, without expanding dynamic prompts or wildcards and without a prompt template’s positive text.

The prompt the model encodes, in the Generate tab and on the Canvas, is saved in the image’s metadata as moss_image_layout_prompt: the compiled layout when there is one, otherwise the prompt as sent.

Prompt enhancer

MOSS-Image follows long, specific prompts best. Expand Prompt in the prompt toolbar can write them for you with the MOSS-Image Prompt Enhancer system prompt, derived from OpenMOSS’s prompt-enhancer skill. See Prompt Tools for the tool itself.

Select a MOSS-Image model and type a short idea, such as a jazz festival poster titled "BLUE NOTES".
Open Expand prompt in the Generate panel’s prompt toolbar; Canvas generations use the same prompt. With MOSS-Image selected it preselects the MOSS-Image Prompt Enhancer system prompt. The model saved for the project stays selected; without one, Gemma 4 E4B (Prompt Enhancer) is preselected when installed. When Gemma is installed but another model is selected, the popover shows Recommended for this model with a Use it button; when it is not installed, the popover points you to that starter model. A model or system prompt you pick in the open popover overrides the suggestion.
Click Expand, check the result in the prompt box and press Invoke.

The language model decides between two kinds of output:

A caption: one detailed paragraph, for photos, scenes and most images without precise placement. It is sent as written.
A layout draft: JSON for posters, covers, infographics and other images where text or elements must sit in particular places. It holds a summary with the scene caption, a list of items with a label, a caption, the text to render for text items and a box, plus the width and height the boxes refer to. Boxes are [x1, y1, x2, y2] pixel edges from the top-left corner ("bbox_coord_space": "abs", "bbox_format": "xyxy").

You can edit a draft in the prompt box like any other text. It is compiled into the model’s JSON layout when the generation runs:

Size: Expand Prompt never changes the image size. The draft’s width and height only set its coordinate frame, and its boxes are scaled to the width and height you generate at, so you can change the size after expanding.
Tolerance: a Markdown code fence around the JSON (with or without a language tag such as json), unknown fields and unknown labels are accepted, boxes that leave the image are clamped to it, and items whose box has no area are dropped. A draft without a scene caption, without any usable item, or with a text item that has no text fails with a message naming the item.
Canvas regions: on the Canvas, enabled regional guidance layers are appended after the draft’s items.

A JSON object with "bbox_coord_space": "abs" is treated as a draft. When it lacks a summary caption, items, or a numeric width and height, or its bbox_format is not xyxy, Invoke is disabled with a message saying so. A compiled layout (abs_centralized, yxyx) is never blocked. Run Expand Prompt again or fix the JSON.

Recommended language model

Install Gemma 4 E4B (Prompt Enhancer) from the starter models (google/gemma-4-E4B-it, ~16 GB). It is optional and not part of the MOSS-Image bundle. In InvokeAI’s tests on an RTX 4090 in bf16, it chose between caption and layout correctly for all 10 test requests and every draft it wrote was valid. A request took 8 to 26 s and peaked at about 15 GiB of VRAM.

Small language models of about 1.5 to 1.7B parameters, such as Qwen2.5 1.5B Instruct or SmolLM-class models, are not recommended for this system prompt: they write layouts for nearly every request, and their drafts are often invalid.

Limitations

LoRA is not supported.
Quantized transformers are refused at install (see Installing).
ControlNet, reference images and seamless tiling are not available.
Regional guidance places boxes in a layout; it has no regional negative prompts or regional reference images.
MOSS-Image is not offered in the Upscale panel.

Workflow nodes

Node	Purpose
Main Model - MOSS-Image	Loads the transformer, and the Qwen3.5 9B encoder and VAE from the pipeline or from the standalone models selected
Layout Prompt - MOSS-Image	Builds the prompt the model encodes from a prompt, the image size and a list of regions, each with a type, a caption, visible text for text regions, and a pixel box `[x1, y1, x2, y2]` from the image’s top-left. Compiles a layout draft at the given size. Every MOSS-Image graph from the Generate tab and the Canvas uses it; its regions input has no editor widget
Prompt - MOSS-Image	Encodes a prompt with the Qwen3.5 9B encoder
Denoise - MOSS-Image	Runs sampling; accepts image-to-image latents and an inpainting mask, and takes the skip layers as Uncond Skip Layers
Image to Latents - FLUX2	VAE encode, for image-to-image and inpainting
Latents to Image - FLUX2	VAE decode

The denoise node also takes the VAE, whose statistics define the latent space the transformer works in. MOSS-Image latents use the FLUX.2 layout, so the FLUX.2 encode, decode and PiD decode nodes work with them.

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