mlx-community/MOSS-Image-Base-bf16

MLX (bf16) conversion of OpenMOSS-Team/MOSS-Image-Base (Apache-2.0) for Apple Silicon. MOSS-Image-Base is the pretrained checkpoint that MOSS-Image-SFT is fine-tuned from: same architecture and prompts, with less curated composition. It is the starting point for LoRA training. For generation, prefer MOSS-Image-SFT-bf16.

Model

MOSS-Image pairs a 5B single-stream DiT with the Qwen3.5-9B text encoder and the FLUX.2 VAE with BFL's small decoder. Its DiT design:

• 42 blocks with one shared adaLN modulation
• SiTU-GLU FFN
• 4-axis RoPE

What it does well

• Long text, in English and Chinese. Line breaks in the prompt are kept.
• JSON layout prompts that place text and objects by bounding box: summary.caption plus items, with bbox_format: "yxyx" and bbox_coord_space: "abs_centralized" (pixels, centred on the canvas, same size as the request).

The snapshot is a standard diffusers tree (transformer/ text_encoder/ tokenizer/ vae/ scheduler/):

• The transformer and text encoder are bf16, the VAE fp32. The files are byte-identical to upstream revision 1ceede80.
• The PyTorch custom pipeline (pipeline_moss_image.py) stays upstream.

Sampling

Setting	Value
Sampler	Euler flow matching
Steps	50
Shift	5 (snapped to the training grid)
CFG	3, with block 22 skipped in the negative branch
Negative prompt	empty

Parity (Swift/MLX port vs the PyTorch reference, fp32, TF32 off)

Component	Result
Qwen3.5-9B encoder, final hidden state	relL2 4.8e-6
DiT, one forward (12 timestep / prompt / skip-layer cases)	relL2 1.1e-6 – 5.3e-6
Scheduler sigmas (4 – 50 steps)	bit-exact
FLUX.2 small decoder	relL2 1.2e-6
bf16 end-to-end (8 steps) vs fp32	inside PyTorch's own bf16 drift

Memory and speed (M5 Max, 1024², 50 steps)

DiT tier	Resident	Peak	Denoise
bf16	10.3 GB	26.2 GB	113 s (2.27 s/step)
int8 (quantized at load)	5.6 GB	21.5 GB	about the same

• Every step runs two transformer forwards (CFG).
• The peak is set by the bf16 text encoder (16.8 GB). The port loads it per request and releases it before denoising.
• Sizes from 256² to 1456² by area work; width and height must be multiples of 16.

Use (Swift / MLXEngine)

import MLXMossImage
import MLXToolKit

let package = MossImageBasePackage(configuration: MossImageConfiguration(quant: .bf16))  // weights materialize on load
try await package.load()
let r = try await package.run(T2IRequest(prompt: "A hand-painted sign that reads OPEN LATE", width: 1024, height: 1024, seed: 42)) as! T2IResponse

Code: https://github.com/xocialize/moss-image-swift (Swift/MLX port, MIT).

Training data (from the upstream technical report)

• Sources: about 3.2 billion image-text records from 16 public datasets. About 98% come from ReLAION, OmniCorpus and DataComp.
• Filtering: AI-generated images were filtered out.
• Captions: written by Gemma 4 and a Qwen model.
• Rights: the report makes no rights statement for the data.

Review the technical report before you use the model.

License

The weights are Apache-2.0, as released by OpenMOSS. Components:

• Text encoder: Qwen3.5-9B, Apache-2.0.
• VAE: FLUX.2 small decoder (Black Forest Labs), Apache-2.0.