README.md

---
license: apache-2.0
base_model: Tongyi-MAI/Z-Image-Turbo
tags:
  - core-ai
  - coreai
  - text-to-image
  - diffusion-transformer
  - on-device
  - macos
pipeline_tag: text-to-image
library_name: coreai
base_model_relation: quantized
---

Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's `coreai-torch` (LLMs: `coreai.llm.export`) into `.aimodel` bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol ([apple-silicon-llm-bench](https://github.com/john-rocky/apple-silicon-llm-bench), macOS 27 beta 26A5353q, 2026-06-11).

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# Z-Image-Turbo — Core AI (macOS)

Alibaba Tongyi-MAI **[Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo)**
(6B, Apache-2.0) — a Single-Stream Diffusion Transformer (S3-DiT) — converted to **Core AI**
and generating images entirely on the Mac GPU.

A Qwen3-4B text encoder conditions a 34-block DiT that denoises in 8 FlowMatchEuler steps
with classifier-free guidance; a 16-channel VAE decodes. Photoreal by default.

**One DiT graph covers 256², 512² and 1024², and any prompt length** — both the image-token
and caption axes are dynamic, at a ~5–9 % cost over static shapes.

## What's here

| file | role | size |
| --- | --- | --- |
| `zimage_dit_..._dyncap_dynimg_iofp32.aimodel` | the DiT — any resolution, any prompt length | 11 GB |
| `zimage_encoder_seq64_full_bf16_ids_iofp32.aimodel` | Qwen3 encoder → **penultimate** hidden; `embed_tokens` is *inside* the graph | 7.3 GB |
| `zimage_vae_{256,512,1024}_fp32.aimodel` | 16ch VAE decoder (per-size) | 189 MB each |
| `glue/` | RoPE tables + a `t_embedder` graph | 2.4 MB |
| `tokenizer/` | Qwen2 BPE | 15 MB |

**Weights are bf16; the graph boundaries are fp32** (`--io-fp32`). That is not a preference:
a Swift host cannot fill or read a bfloat16 `NDArray`, and bf16 is the only dtype this DiT is
numerically safe in. Casting at the boundary costs ~15 % per forward and *improves* fidelity
(PSNR 39.5 → 42.6 dB) because nothing rounds on the way in.

The `glue/` is what keeps a host from re-implementing the reference: `RopeEmbedder` is a
per-axis table lookup, so three tables reproduce it exactly at any resolution and prompt
length, and the timestep MLP ships as a 2 MB graph.

**bf16, not int8.** On this compute-bound graph weight-only int8 is *slower* than bf16
(2.35 vs 0.89 s/forward at 512²) because it dequantizes back to 16-bit and runs the same
matmul — it only wins on bandwidth-bound shapes and on footprint. bf16 is also what keeps
the port numerically near the fp32 reference.

## Speed & fidelity (M4 Max, vs the fp32 `diffusers` reference)

| | s/forward | denoise (8 steps, CFG = 16 forwards) | PSNR |
| --- | --- | --- | --- |
| 256² | 0.36 | 5.8 s | 35.6 dB |
| 512² | 1.12 | 17.9 s | **42.6 dB** |
| 1024² | 4.36 | 69.7 s | 42.3 dB |

Per-step velocity correlation vs the reference is ≥ 0.9997 at every step and both CFG
branches. PSNR is not comparable across prompts: a texture-heavy oil-painting prompt scores
27.7 dB while being visually indistinguishable from the reference.

## Usage

Run it in [CoreAIImageGen](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps/CoreAIImageGen)
(macOS): pick "Z-Image-Turbo 512" or "… 1024" → Download & Load → Generate. The host loop is
[`ZImagePipeline.swift`](https://github.com/john-rocky/coreai-model-zoo/blob/main/apps/CoreAIImageGen/Sources/ZImagePipeline.swift);
the Python twin is
[`conversion/zimage/pipeline_engine.py`](https://github.com/john-rocky/coreai-model-zoo/blob/main/conversion/zimage/pipeline_engine.py).
Both agree with the fp32 reference to ~42.6 dB.

The DiT graph takes host-prepped inputs (patchify, RoPE, pad masks) and returns the velocity;
the sampler loop lives on the host:

```python
# per step, for cond and uncond:
#   ins = build_native_inputs(rm, latent, cap)          # patchify + RoPE + pad masks
#   v   = dit(**ins, adaln=t_embedder(t * t_scale))     # Core AI graph
#   vel = unpatchify(v[:, :n_img])
# noise_pred = -(pos + guidance * (pos - neg))          # Z-Image CFG is NEGATED
# latent    += dsigma[s] * noise_pred                   # FlowMatchEuler
# image      = vae(latent)                              # unscale: z/0.3611 + 0.1159
```

Three details each cost a wrong image:

1. the DiT conditions on the encoder's **penultimate** hidden state (`hidden_states[-2]`);
2. the CFG is **negated**: `-(pos + g·(pos − neg))`, not `neg + g·(pos − neg)`;
3. captions are padded to a multiple of **32** with a *learned* pad token that is **real
   attention context** — `n_cap = round_up(L, 32)` must match, and cond/uncond generally
   have different `n_cap` (hence the dynamic caption axis).

## Notes

- **macOS only.** fp16 sends this DiT all-NaN at sampler step 2 (depth-driven, at every
  resolution); bf16 is exact — and `coreai-build compile` refuses a bf16 module, which iOS
  needs for graphs this size. Full analysis in the
  [port notes](https://github.com/john-rocky/coreai-model-zoo/blob/main/knowledge/zimage-port.md).
- The text-encoder graph is fixed at **64 chat-templated tokens** (≈ 35–40 words).
- `guidance=0` skips CFG — half the work, a different composition, still clean at 256².
- Weights are **not redistributed as source**: the graphs are produced from the original
  Apache-2.0 checkpoint by the conversion scripts in the zoo.

License: Apache-2.0 (inherited from Z-Image-Turbo).

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**More models in this format:** [Core AI Model Zoo](https://huggingface.co/collections/mlboydaisuke/core-ai-model-zoo-6a7ff330f753e8dcae04671a) — 75 models, each with the recipe that produced it.

**Want a different model on-device?** [Open a request](https://github.com/john-rocky/on-device-requests) — free, open weights only; the export and its measured numbers get published publicly.

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