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README.md

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+---
+library_name: mlx
+license: apache-2.0
+license_link: https://huggingface.co/Tongyi-MAI/Z-Image/blob/main/LICENSE
+pipeline_tag: text-to-image
+base_model: Tongyi-MAI/Z-Image
+language: [en, zh]
+tags: [mlx, safetensors, apple-silicon, text-to-image, diffusion, s3-dit, z-image]
+---
+
+# mlx-community/Z-Image-bf16
+
+MLX (bf16) conversion of **[Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image)** (Apache-2.0) for Apple
+Silicon — a 6.15B single-stream **S3-DiT** text-to-image model (Qwen3-4B thinking-template
+conditioning → single-stream DiT → FLUX.1-dev AE decode). **Base tier**: non-distilled ~28-step with CFG + negative prompts (scheduler shift 6.0) — the quality / LoRA-substrate tier.
+
+Standard diffusers-tree snapshot (`transformer/ text_encoder/ vae/ tokenizer/ scheduler/`) with the
+transformer stored at bf16. Loaded by the Swift/MLX port; int8/int4 are produced at load time
+(correct resident footprint — a q4 pipeline ≈ 6 GB fits a 16 GB Mac).
+
+## Parity (Swift port vs PyTorch goldens, fp32/CPU stream)
+- Full 6.15B S3-DiT: cosine **≥0.9999999** (both aligned + padded token cases)
+- FLUX.1-dev AE decode: **118 dB** · Qwen3-4B encoder: token ids **exact**, features cosine **1.0000000**
+- Full pipeline e2e: **105–108 dB** (256²/CPU)
+
+## Use (Swift / MLXEngine)
+
+```swift
+import MLXZImage
+import MLXToolKit
+let package = ZImageTurboT2IPackage(configuration: .turbo(quant: .int4, snapshotPath: "<this repo dir>"))
+try await package.load()
+let r = try await package.run(T2IRequest(prompt: "a lighthouse at dusk, photorealistic",
+                                         width: 1024, height: 1024, seed: 42)) as! T2IResponse
+```
+
+Code: https://github.com/xocialize/z-image-swift