File size: 7,935 Bytes

---
license: apache-2.0
language:
  - en
  - zh
base_model:
  - Tongyi-MAI/Z-Image-Turbo
base_model_relation: quantized
pipeline_tag: text-to-image
library_name: diffusers
tags:
  - comfyui
  - quantization
  - nvfp4
  - txt2img
---

# Z-Image Turbo - NVFP4 Mixed-Precision

Surgical mixed-precision quantization of [Z-Image Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) (6B S3-DiT), generated with [`convert_to_quant`](https://github.com/silveroxides/convert_to_quant).

**Formats**: NVFP4 (baseline) + MXFP8 (sensitive layers) + BF16 (critical layers).  
**Size**: 4.84 GB (-58% vs BF16).  
**Inference**: ComfyUI + [`comfy-kitchen`](https://github.com/Comfy-Org/comfy-kitchen), Blackwell GPU (RTX 50xx / B100 / B200).

Also available: [MXFP8 uniform quantization](https://huggingface.co/InsecureErasure/Z-Image-Turbo-MXFP8) (6.23 GB, near-lossless).

![BF16 vs NFVP4](images/BF16-NVFP4-comp.png)
![NVFP4 vs NFVP4 plus rank 32 LoRA](images/NVFP4-LoRA-comp.png)

* **Prompt:**
```
A bust portrait of a woman in her mid-twenties with messy dark hair tied in a loose bun, wearing a worn denim jacket over a gray hoodie.
She is leaning her elbows on a washing machine, her chin resting on her folded hands. Behind her, a row of industrial dryers against a tiled wall,
with one dryer door hanging open. Above the dryers, a handwritten sign taped to the wall says 'OUT OF ORDER' in black marker,
with a small smiley face drawn on it. To her left, a plastic basket overflows with unfolded clothes. To her right, a vending machine glows green,
displaying 'SOAP $1.50' on a small digital screen. The light is cool and buzzing, like fluorescent tubes overhead. She looks tired but amused
with a faint smirk.
```
* **Sampler/Scheduler:** Euler/Simple
* **Steps:** 9
* **CFG:** 1.0
* **Shift:** 3.0
* **Seed:** 920698660737993
* **Resolution:** 1024 x 1536

## Strategy

Uses per-layer sensitivity analysis via [`quant_probe`](https://github.com/insecure-erasure/quant_probe) and the DiT quantization literature (PTQ4DiT, ViDiT-Q, SemanticDialect, SVDQuant) to maximize quality-per-byte:

- **~190 tensors → NVFP4** (4-bit E2M1): baseline for most attention + FF weights
- **~100 tensors → MXFP8** (8-bit E4M3 + E8M0): attention outputs, gate projections (w1), mid-block adaLN
- **~20 tensors → BF16**: last QKV, late adaLN modulations, refiner outputs
- **~110 tensors → BF16**: norms, biases, embeddings (auto-excluded by `--zimage`)

### MXFP8-protected layers

| Category | Blocks | Layers |
|---|---|---|
| Early attention outputs | 0, 1 | `attention.out` |
| Selected QKV projections | 10, 16, 26, 27, 28 | `attention.qkv` |
| Attention outputs | 3, 6, 9, 11–14, 19, 20, 26–29 | `attention.out` |
| Gate projections (w1) | 3–29 | `feed_forward.w1` |
| Mid-block modulations | 16–21 | `adaLN_modulation.0` |

### BF16-protected layers

| Category | Layers | Reason |
|---|---|---|
| Last QKV | `layers.29.attention.qkv` | Feeds directly into `final_layer` — no downstream compensation |
| Late modulations | `layers.(22–29).adaLN_modulation.0` | Controls scale/shift of features near output |
| Refiner attention outputs | `context_refiner.(0\|1).attention.out` | Only 2 refiner blocks — outputs have outsized impact |
| Selected refiner FF | `context_refiner.1.w2`, `noise_refiner.1.{qkv,out,w2}` | Critical single-block projections |
| Refiner up-projections | `noise_refiner.(0\|1).w3` | Noise refiner w3 expands features → direct output |

### Refiner sub-graphs

| Sub-graph | Block 0 | Block 1 |
|---|---|---|
| `context_refiner` | All MXFP8 (qkv, w1, w2, w3) | qkv + w1 + w3 MXFP8, out + w2 BF16 |
| `noise_refiner` | qkv + out + w1 + w2 MXFP8, w3 BF16 | qkv + out + w2 + w3 BF16, w1 MXFP8 |

## Generation

```bash
#!/bin/bash
# NVFP4 baseline + MXFP8 for sensitive layers + BF16 at critical points.
# Refiners: block 0 fully MXFP8, block 1 outputs kept in BF16.
# Last QKV (layer 29), late adaLN (22-29), and refiner outputs in BF16.
# All main-trunk w1 (gate) projections in MXFP8.
convert_to_quant -i $1 \
  --nvfp4 --zimage --comfy_quant --save-quant-metadata \
  --custom-type mxfp8 \
  --custom-layers "layers\.(10|16|26)\.attention\.qkv\.weight|layers\.(27|28)\.attention\.qkv\.weight|layers\.(0|1)\.attention\.out\.weight|layers\.(3|6|9|11|12|13|14|19|20|26)\.attention\.out\.weight|layers\.(27|28|29)\.attention\.out\.weight|layers\.(3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26)\.feed_forward\.w1\.weight|layers\.(27|28|29)\.feed_forward\.w1\.weight|layers\.(16|17|18|19|20|21)\.adaLN_modulation\.0\.weight|context_refiner\.(0|1)\.attention\.qkv\.weight|context_refiner\.(0|1)\.feed_forward\.w1\.weight|context_refiner\.(0|1)\.feed_forward\.w2\.weight|context_refiner\.(0|1)\.feed_forward\.w3\.weight|noise_refiner\.(0)\.attention\.(qkv|out)\.weight|noise_refiner\.(0)\.feed_forward\.(w1|w2)\.weight|noise_refiner\.(1)\.feed_forward\.w1\.weight" \
  --exclude-layers "layers\.(29)\.attention\.qkv\.weight|layers\.(22|23|24|25|26)\.adaLN_modulation\.0\.weight|layers\.(27|28|29)\.adaLN_modulation\.0\.weight|context_refiner\.(0|1)\.attention\.out\.weight|context_refiner\.(1)\.feed_forward\.w2\.weight|noise_refiner\.(1)\.attention\.qkv\.weight|noise_refiner\.(1)\.attention\.out\.weight|noise_refiner\.(1)\.feed_forward\.w2\.weight|noise_refiner\.(0|1)\.feed_forward\.w3\.weight" \
  --num-iter 6000 --top-p 0.35 --calib-samples 8192 \
  --scale-optimization iterative --scale-refinement-rounds 2 \
  --extract-lora --lora-rank 32 \
  -o "${1%%.safetensors}-nvfp4.safetensors"
```

### Included files

| File | Description |
|---|---|
| `z_image_turbo_nvfp4.safetensors` | Quantized weights |
| `z_image_turbo_nvfp4_lora.safetensors` | Error-correction LoRA (rank 32) |

Use the LoRA with variable strength in ComfyUI for improved fidelity.

## Requirements

- **Inference**: CUDA 13.0+, PyTorch 2.10+, [`comfy-kitchen`](https://github.com/Comfy-Org/comfy-kitchen), Blackwell GPU (RTX 50xx / B100 / B200)
- **Generation**: `convert_to_quant >= 1.2.6`, `comfy-kitchen`

## Comparison

| | NVFP4 Mixed (this) | MXFP8 Uniform | Official NVFP4 |
| --- | --- | --- | --- |
| Size | 4.84 GB | 6.23 GB | 4.51 GB |
| Base format | NVFP4 (4-bit) | MXFP8 (8-bit) | NVFP4 (4-bit) |
| Custom layers | ~100 tensors → MXFP8 | None | None |
| BF16 exclusions | ~20 tensors | 8 patterns | Refiners fully BF16 |
| Learned rounding | ✅ 6000 iter | ❌ --simple | ❌ |
| LoRA | ✅ rank 32 | ❌ | ❌ |
| Refiner block 0 | MXFP8 | MXFP8 | BF16 |
| Late adaLN (22–29) | BF16 | BF16 | NVFP4 ⚠️ |
| Last QKV (layer 29) | BF16 | BF16 | NVFP4 ⚠️ |
| Quantization time¹ | ~60–90 min | ~5–10 min | N/A |

¹ Estimated on RTX 5060 (Blackwell) with `comfy-kitchen` CUDA kernels.

## Methodology

Layer sensitivity was analyzed using [`quant_probe`](https://github.com/insecure-erasure/quant_probe), which computes per-tensor excess kurtosis, dynamic range, and aspect ratio, then scores them against the model's own distribution to recommend `*KEEP*`, `FP8`, or `NVFP4`.

Recommendations were cross-referenced against the DiT quantization literature:

- **PTQ4DiT** (NeurIPS 2024) — salient channels in QKV + FFN, last blocks most affected
- **ViDiT-Q** (ICLR 2025) — metric-decoupled sensitivity: self-attention dominates visual quality
- **HTG** (2025) — channel-dependent outliers, severe in later blocks
- **SemanticDialect** (2026) — block-wise mixed-format validated for video DiTs
- **SVDQuant** (ICLR 2025) — low-rank branch absorbs 4-bit error, validated NVFP4

## Credits

- Quantization engine: [`convert_to_quant`](https://github.com/silveroxides/convert_to_quant) by silveroxides
- Z-Image Turbo model by [Tongyi-MAI](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo)
- ComfyUI integration via [`comfy-kitchen`](https://github.com/Comfy-Org/comfy-kitchen)
- Layer sensitivity analysis via [`quant_probe`](https://github.com/insecure-erasure/quant_probe)