moss_pht_lora

A photo-oriented LoRA (rank 128) for OpenMOSS-Team/MOSS-Image-Base. It adapts only the transformer; the text encoder (Qwen3.5-9B), VAE (FLUX.2 small decoder), tokenizer, scheduler and inference pipeline are the base release's, unchanged. Load those from their official repositories, or swap in any compatible ones (for example a quantized text encoder).

Both files hold the same weights.

Diffusers

pip install "diffusers>=0.38" "transformers>=5.3" "accelerate>=1.10" "safetensors>=0.6" "peft>=0.21" "Pillow>=10"

import torch
from diffusers import AutoencoderKLFlux2
from diffusers.utils.dynamic_modules_utils import get_class_from_dynamic_module
from peft import PeftModel
from transformers import AutoModel, AutoTokenizer

BASE = "OpenMOSS-Team/MOSS-Image-Base"


def moss_class(name):
    return get_class_from_dynamic_module(
        BASE, "pipeline_moss_image.py", class_name=name, trust_remote_code=True
    )


transformer = moss_class("MOSSImageTransformer").from_pretrained(
    BASE, subfolder="transformer", torch_dtype=torch.bfloat16
)
transformer = PeftModel.from_pretrained(
    transformer, "data-archetype/moss_pht_lora", subfolder="transformer"
).get_base_model()

pipe = moss_class("MOSSImagePipeline")(
    transformer=transformer,
    scheduler=moss_class("MOSSImageScheduler").from_pretrained(BASE, subfolder="scheduler"),
    text_encoder=AutoModel.from_pretrained(BASE, subfolder="text_encoder", dtype=torch.bfloat16),
    tokenizer=AutoTokenizer.from_pretrained(BASE, subfolder="tokenizer"),
    vae=AutoencoderKLFlux2.from_pretrained(BASE, subfolder="vae", torch_dtype=torch.float32),
)
pipe.to("cuda")  # or pipe.enable_model_cpu_offload() on a smaller GPU

image = pipe(
    "A candid street photograph of a cyclist crossing a rain-soaked intersection at dusk",
    height=1024,
    width=1024,
    num_inference_steps=50,
    guidance_scale=3.0,
    uncond_skip_layers=[22],
    generator=torch.Generator("cpu").manual_seed(42),
).images[0]

The adapter is applied unmerged, at strength 1. Sampling settings, prompt formats (natural language, tags and JSON layouts) and requirements are the base model's; see its model card and user guide.

ComfyUI

Use ComfyUI's native MOSS-Image support with the Comfy-Org/MOSS-Image base model, text encoder and VAE, and add the LoRA after Load Diffusion Model.

Recommended: a DMD LoRA

Like the base checkpoint, generations with the standard multi-step sampler can be a bit unstable. Using a DMD LoRA distilled from MOSS-Image-Base is recommended: it sharpens the output distribution and stabilises generations.

Training

License

The weights are released under the base model's license, Apache 2.0 (LICENSE, copied from OpenMOSS-Team/MOSS-Image-Base).

We thank the OpenMOSS team for MOSS-Image.