I've been working with MOSS-Image, the open text-to-image model from the OpenMOSS team (Apache-2.0 weights), and wanted to write down the settings and prompt habits that matter most, since most "settings guides" for image models are vague.
Quick model facts that matter for prompting
- It's a ~5B-parameter diffusion transformer with a Qwen3.5 text encoder and the FLUX.2 VAE. The LLM-class encoder is the reason long, structured prompts are followed better than with CLIP-style stacks.
- There are two checkpoints: Base (for LoRA training / fine-tuning) and SFT (instruction-tuned). For direct prompting, use SFT. Base gives noticeably looser prompt adherence.
Settings worth understanding
- Steps. The recommended range is 50+. Small text and fine textures are the first things to degrade when you cut steps to save time.
- Guidance. 3.0-6.0 is the useful range. The low end is more natural and varied; the high end follows the prompt more literally but can get contrasty. Start in the middle and move in small increments.
- Seed. Fix it while you compare anything else. Changing seed and guidance at the same time makes it impossible to tell what helped.
- Sampler. Euler is the default and a sensible baseline.
- Negative prompt. Short and specific ("blurry text, extra letters, watermark") tends to beat long boilerplate lists.
Prompt structure
- Put any text that should appear in the image in quotes, exactly as written, and say where it goes ("title at the top", "small date at the bottom").
- Keep each text block short. Two or three short strings render far more reliably than one sentence.
- For layouts, describing the composition as a small JSON object (canvas, background, elements with positions, style) tends to be followed more reliably than one long sentence. Again, that's mostly the encoder.
Running it without a local GPU
Local setup needs a lot of VRAM for 2K output. Disclosure: I'm on the team behind Moss Image, an independent hosted front-end for this model (not affiliated with OpenMOSS). New accounts get free credits, so you can test the settings above in a browser: Visit Site
Curious whether anyone has compared it with other open models specifically on text rendering, which is where open models seem to differ most.