MOSS-Image (5B, Apache 2.0) on a 12 GB RTX 3060: INT8 quant + lodestones' 8-step DMD turbo in ComfyUI - 35 hard prompts, one seed, no cherry-picking

Key points

• Model: u/XCEED123's INT8 ConvRot quant (HF), ~15 GB total, with the official ComfyUI node (comfyui/moss-image from the GitHub repo).1
• Turbo: lodestones' 8-step DMD (still training).1
• It isn't a normal LoRA: the card says the delta is smaller than bf16 rounding, so it has to run unmerged in fp32 along with fp32 time-embedding/modulation biases and its own data-space sampler (no CFG, no scheduler).1
• I wrote a small ComfyUI node that does this on top of the INT8 base model. ~24 s at 1 MP, ~36 s at 1.6 MP, about 6× faster.1

Sources (1)

• [1]MOSS-Image (5B, Apache 2.0) on a 12 GB RTX 3060: INT8 quant + lodestones' 8-step DMD turbo in ComfyUI - 35 hard prompts, one seed, no cherry-picking
  r/StableDiffusion (top, day) · Oct 16, 04:09 PM

  Tested the new MOSS-Image (5B single-stream DiT, Qwen3.5-9B text encoder, FLUX.2 VAE, Apache 2.0) on a plain RTX 3060 12 GB / 16 GB RAM with 35 deliberately hard prompts: dense on-image text in English/Chinese/Japanese, accents, exact counts, attribute binding, spatial relations, negation, hands, clocks, very long prompts, and MOSS's Layout JSON.

  - Model: u/XCEED123's INT8 ConvRot quant (HF), ~15 GB total, with the official ComfyUI node (comfyui/moss-image from the GitHub repo).

Extractive summary: sentences quoted from the sources.