Qwen-Image-Flash-8bit (MLX)

An int8 MLX snapshot of nvidia/Qwen-Image-Flash — NVIDIA's DMD2 four-step distillation of Qwen/Qwen-Image. 28 GB total, versus 57.7 GB for the bf16 snapshot.

This is the tier that makes a 20B-parameter image model reachable on ordinary Apple Silicon: a 30.0 GB peak at 1024²/4 steps instead of bf16's 57.4 GB, and it is 4× faster.

Contents

file	precision	size
transformer/model-int8.safetensors	int8 attention + feed-forward + modulation, group 64	21.75 GB
text_encoder/model-int8.safetensors	int8 Qwen2.5-VL-7B language model only	7.51 GB
vae/	unquantized	0.25 GB

The img_in / txt_in / time_text_embed / norm_out / proj_out projections are left at full precision, and the VAE is never quantized — decode is where precision loss shows up as visible colour and banding artifacts.

The text encoder carries no vision tower: text-to-image conditions on text alone, so the ViT is dead weight in this pipeline and is omitted entirely.

Measured quality

Against PyTorch fp32 goldens (diffusers 0.37.1), on identical injected inputs:

	int8	bf16	fp32 oracle
DiT step-0 cosine	0.9973	0.99836	1.0
VL-7B prompt-embed cosine	0.99992	0.9999926	1.0
1024²/4-step render	19.8 s	83.3 s	—
load	2.3 s	~60 s	—
peak memory	30.0 GB	57.4 GB	—

Renders at this tier are visually indistinguishable from bf16 at the same seed.

Why there is no 4-bit tier

int4 was built and measured, not skipped: DiT step-0 cosine 0.9623 at group 64 and 0.9659 at group 32, with the VL encoder at 0.9845. The 1024² render came out visibly soft and washed out, with fine fur and snow detail gone. Finer scale groups did not rescue it — this DiT is intrinsically lossy at 4 bits — so no 4-bit snapshot is published rather than shipping one that looks like that.

Inference notes that are easy to get wrong

The distillation internalized CFG 4.0, and the packaged scheduler is static shift-3 (use_dynamic_shifting: false):

• num_inference_steps = 4, true_cfg_scale = 1.0 — applying CFG again double-counts guidance the student already absorbed, and doubles the transformer evaluations per step for nothing.
• The four-step trajectory is sigmas [1.0, 0.9, 0.75, 0.5, 0.0].
• Tested at 1024 × 1024. Use width/height divisible by 16.

Use from Swift (MLXEngine)

import MLXQwenImageFlash
import MLXToolKit

let package = QwenImageFlashPackage(configuration: .init(quant: .int8))
try await package.load()
let response = try await package.run(T2IRequest(
    prompt: "A red fox in a snowy pine forest at golden hour, photorealistic",
    width: 1024, height: 1024, seed: 42)) as! T2IResponse

Port: xocialize/qwen-image-edit-swift (MIT). The package also selects this tier automatically when the engine's memory governor reports a budget that cannot seat bf16 — and it resolves that before downloading, so a constrained machine fetches this 28 GB snapshot rather than 41 GB of bf16 it could never load.

These are pre-quantized weights. Consumers never materialize bf16 at any point, which is the difference between a tier that runs on a 32–48 GB machine and one that merely claims to: quantizing at load would require holding the 41 GB bf16 transformer first.

License

Governing terms: NVIDIA Open Model License Agreement. Additional information: Apache License 2.0 (LICENSE).

Licensed by NVIDIA Corporation under the NVIDIA Open Model License

Commercial use, derivative models, and redistribution are permitted; if you redistribute these weights you must pass on the Agreement and this notice (§3.1). See NOTICE.