Generating with MOSS Serenity
Everything needed to run the model in ComfyUI, the settings the samples were made with, and how to prompt it. Back to the model card.
Files
| ComfyUI folder | File |
|---|---|
models/diffusion_models |
serenity_base_v1_24000_bf16.safetensors or serenity_base_v1_24000_int8_convrot.safetensors (this repository; the INT8 one is half the size and faster) |
models/text_encoders |
qwen3.5_9b_int8_convrot.safetensors (what the model trained with), or qwen3.5_9b_bf16.safetensors |
models/vae |
flux2_vae.safetensors (the FLUX.2 VAE) |
models/loras |
optional: lodestones' MOSS-Image-Base-8-steps-dmd for the turbo workflows; the extracted LoRAs |
Use the Base DMD LoRA, not the SFT one: Serenity was finetuned from MOSS-Image Base.
The workflows
All four are in workflow/; drag one into ComfyUI and pick your own paths in the
loaders (the model and LoRA paths are the author's folders).
Turbo: base-turbo-no-lora.json
Load Diffusion Model → Power Lora Loader (empty, for your own LoRAs) → Load LoRA (DMD, 1.0) → SkipLayerGuidanceDiTSimple → KSampler.
| Node | Setting |
|---|---|
| Load CLIP | qwen3.5_9b_int8_convrot, type moss_image |
| Load LoRA (model only) | MOSS-Image-Base-8-steps-dmd, strength 1.00 |
| SkipLayerGuidanceDiTSimple | single_layers 22, double_layers empty, start 0.0, end 0.5 |
| Negative | bad ugly blurry |
| EmptyFlux2LatentImage | 1584 × 1776 |
| KSampler | 8 steps, CFG 4.0, euler, simple, denoise 1.00 |
About 16 s per image on an RTX 5090. This is the setup the portrait samples were made with: not necessarily the best possible, but very good, and fast.
Full: base-no-turbo-no-lora.json
The same without the DMD LoRA, at 50 steps:
| Node | Setting |
|---|---|
| SkipLayerGuidanceDiTSimple | layer 22, start 0.0, end 0.5 |
| Negative | bad ugly blurry |
| EmptyFlux2LatentImage | 1920 × 1088 (Full HD, on the 16 px latent grid) |
| KSampler | 50 steps, CFG 4.0, euler, simple |
On an RTX 5090 at 1920 × 1088: ~62 s at 50 steps, ~25 s at 20 (1.24 s per step either way). Same seeds at 20 and 50 steps give the same composition; 50 adds fine structure (an insect's head, lettering on signs, small figures). For landscapes 20 is close.
Turbo with a person LoRA: base-turbo-lora.json
The turbo workflow with a separate person LoRA switched on in the Power Lora Loader (strength
1.0), for people Serenity was not trained on. MOSS-Image person LoRAs trained on Base load on top
of Serenity. Prompt them the way they were trained: this example's LoRA uses the class word as its
keyword, so the prompt starts photo of a woman, ... with no name trigger.
Base + the extracted LoRA: lora-extract-turbo-lora.json
The original MOSS-Image Base (moss_image_base_bf16.safetensors) instead of Serenity, with
Serenity added back as a LoRA: Load Diffusion Model → Power Lora Loader (serenity_v1_lora_r256,
strength 1.0) → Load LoRA (DMD, 1.0) → SkipLayerGuidanceDiTSimple → KSampler at 12 steps, CFG
4.0, euler / simple, 1584 × 1776, negative bad ugly blurry. For when you keep the base model
around and switch LoRAs. The LoRA keeps only part of the finetune (see
The extracted LoRAs); the full model is the reference.
Notes
- Shift: ComfyUI already runs MOSS-Image at shift 5. To change it, add ModelSamplingAuraFlow before the KSampler. Not ModelSamplingSD3: its timestep scale is 1000× off for MOSS-Image.
- Size: keep both sides multiples of 16. Portrait (1584 × 1776) suits people; 1920 × 1088 suits scenes.
- "widescreen" / "anamorphic" in a prompt bake black letterbox bars into the image. Leave the words out if you don't want bars.
The extracted LoRAs
To keep one base model and switch LoRAs (the
lora-extract-turbo-lora.json workflow does this at 12 steps): Load Diffusion Model moss_image_base_bf16 → Power Lora Loader or Load LoRA
(serenity_v1_lora_r256, strength 1.0) → (DMD LoRA for turbo) → SkipLayerGuidanceDiTSimple →
KSampler, with the settings above. Higher ranks keep more of the finetune; compare with the full
model on the same seed.
Prompting
The shape
The captions were photo of <trigger>, <one sentence>, so prompts work best the same way:
photo of <trigger>, a <woman|man>, <look>, <expression>, <framing>, <clothing>, <setting>, <light>
The trigger is one lower-case word, the person's name without spaces (the list is in the
model card). The class word after it (a woman, a man) matches how
the model was trained.
Examples
photo of jenniferlawrence, a woman with long dark brown hair in a single side braid, a determined
serious expression, looking slightly off-camera to her left, medium close-up from the chest up,
holding a wooden recurve bow with an arrow drawn, a quiver strapped over her shoulder, wearing a dark
green hooded hunting jacket over a gray shirt, standing in a dense misty pine forest, dappled
morning sunlight through the trees, cool green and amber tones, cinematic film still, shallow depth
of field
photo of nathanfillion, a man with short dark hair, a wry lopsided smile, standing in the cargo bay
of a worn frontier spaceship, wearing a long brown leather duster coat over suspenders and a red
shirt, a holstered pistol at his hip, warm amber light, medium shot, TV series still
photo of taylorswift, a woman with long blonde hair and bangs, bold red lipstick, a soft joyful
expression with her eyes looking toward the crowd, close-up of her face and shoulders, holding a
microphone near her chin, wearing a glittering silver sequined outfit, bokeh of thousands of
colorful wristband lights behind her, warm golden spotlight
What works
- Large faces. Chest-up or closer. In a full-body stadium shot the face was a few dozen pixels and came out wrong; the same scene as a close-up was clean.
- Describe the role, not the title. The model knows the person, not the film. Costume, hair and setting make the scene: the side braid, bow and pine forest above read as a well-known role without naming it.
- Name the hair when it matters. A prompt that does not mention someone's signature hair can lose it (red hair especially).
- Two people: a man and a woman work; two of the same gender are hit-or-miss. Name both with their
class word, place them (
on the left ...; on the right ...) and describe each separately. A man and a woman keep their own faces; two women (or two men) always keep their costumes, but in some seeds their faces drift toward each other. Three people follow the same rule: one woman with two men holds, three women converge on one face. See Two people in one shot and Three people in one shot. - No trigger, no person:
photo of a woman, ...gives an ordinary person; prior preservation kept the class words close to the base model. - Seeds: likeness varies a little by seed; try a few.