mlx-community/Ming-Image-0.1-Design-4bit

Pre-quantized MLX tier of inclusionAI/Ming-Image-0.1-Design (MIT) for Apple Silicon, loaded by the Swift/MLX port ming-image-swift. 4-bit tier, for memory-bound Macs. It fits a 36 GB Mac, at a small, measured quality cost. Keep OCR-verify and re-roll in the loop for text.

A Mac never has to hold the bf16 weights to use this tier. Total size: 20.5 GB; the bf16 repo is 49.8 GB. It is part of the Ming-Image (MLX) collection, next to mlx-community/Ming-Image-0.1-Design-bf16.

What is quantized

Component	This tier
`mllm/`: MoE MLLM (attention, dense and shared-expert MLPs, the 256 routed experts)	4-bit
`connector/`: Qwen2-1.5B connector	8-bit
`transformer/`: DiT attention and feed-forward	8-bit
Kept at full precision: the MoE routers, embeddings, norms, the Qwen2.5 ViT, the f32 projection heads (`mlp/`), the VAE, and the DiT's conditioning layers (adaLN, embedders, final layer)	bf16 / f32

Weight-only affine quantization, group size 64. The DiT never goes below 8 bits: weight-only int4 on a DiT is real quality damage that buys no speed.

The layout is the bf16 repo's upstream tree, with MLX .scales / .biases stored beside each quantized weight and a quantization block in each quantized component's config.json. The Swift loader reads it as published. Loading this repo gives parameters bit-identical to quantizing the bf16 snapshot at load time (verified for every parameter).

Quality

Gate (same noise and scorers as bf16)	bf16	8-bit	4-bit
Render vs the fp32-truth render (1024², same noise)	41.9 dB	36.5 dB	29.0 dB
Text boards: exact strings · fully correct boards	117/122 · 12/16	118/122 · 12/16	116/122 · 10/16
Zones reserved for live text: clean · text-free	19/20 · 20/20	19/20 · 20/20	19/20 · 20/20
Native alpha: the recipe's usable subjects	5/6	5/6	4/6
Conditioning drift vs fp32 truth (learnable relL2, short / json)	0.122 / 0.140	0.118 / 0.155	0.290 / 0.291

The misses are single-character typos on otherwise-correct boards ("Kexmote", "6 frt 8 in"). Composition and zones hold.

Native alpha is the weakest point of this tier. On the gate's six subjects, the alpha recipe loses one (a potted plant). In a live run at a different seed, a coffee cup that comes back transparent on the first try at bf16 and 8-bit came back opaque on both phrases. For transparent assets, use the 8-bit tier or bf16, and check alpha on import either way.

Full tables are in GATE-RESULTS §8 in the port's oracle (https://github.com/xocialize/ming-image-swift).

Memory and speed

Measured on an M5 Max as process phys_footprint, with MLX's buffer cache capped at 2 GB (MLXEngine's default).

Post-load resident: 7.6 GB. Peak process footprint: 19.7 GB at 1024², 2048² and 2560×1440 alike. The peak is the conditioning stage, when the 4-bit MLLM loads, conditions and is released.
The 2048²-class VAE decode is bounded (chunked mid-attention plus a tiled up path, exact).
MLXEngine declares 7.9 GB resident plus 14.6 GB activation, which is admitted on a 36 GB Mac.
Speed at 12 steps (M5 Max): 42 s at 1024² and 243 s at 2048². bf16 takes 39 s and 245 s.

Use (Swift / MLXEngine)

import MLXMingImage
import MLXToolKit

let package = MingImageT2IPackage(configuration: MingImageConfiguration(quant: .int4))
try await package.load()
let response = try await package.run(T2IRequest(prompt: "…", width: 1024, height: 1024, seed: 42)) as! T2IResponse