---
license: other
library_name: sglang
pipeline_tag: image-to-image
base_model: Qwen/Qwen-Image-Edit
tags:
- sglang
- diffusion
- qwen-image-edit
- modelopt
- fp8
---

# Qwen Image Edit ModelOpt FP8 SGLang Transformer

This repository contains a SGLang-ready ModelOpt FP8 transformer override for [`Qwen/Qwen-Image-Edit`](https://huggingface.co/Qwen/Qwen-Image-Edit).
It only replaces the transformer weights; tokenizer, image encoder, scheduler, VAE, and other non-transformer components are loaded from the original base model.

The checkpoint is intended for SGLang Diffusion with the Qwen Image Edit FP8 support from [sgl-project/sglang#23155](https://github.com/sgl-project/sglang/pull/23155).

## Usage

Use your own input image, or download the validation input image from this repository:

```bash
huggingface-cli download BBuf/Qwen-Image-Edit-ModelOpt-FP8-SGLang \
  validation/assets/qwen_image_edit_input.png \
  --local-dir /tmp/qwen-image-edit-fp8
```

```bash
sglang generate \
  --backend=sglang \
  --model-id=Qwen-Image-Edit \
  --model-path Qwen/Qwen-Image-Edit \
  --transformer-path BBuf/Qwen-Image-Edit-ModelOpt-FP8-SGLang \
  --prompt "A clean product photo of a small ceramic teapot on a wooden table, soft daylight, sharp details." \
  --image-path /tmp/qwen-image-edit-fp8/validation/assets/qwen_image_edit_input.png \
  --width=512 \
  --height=512 \
  --num-inference-steps=8 \
  --guidance-scale=4.0 \
  --seed=42 \
  --num-gpus=1 \
  --dit-cpu-offload false \
  --dit-layerwise-offload false \
  --warmup \
  --save-output
```

## H100 Validation Snapshot

Validation was run on one H100 GPU using rank0 with `--backend=sglang`. The FP8 image below is from the fixed checkpoint after keeping the validated sensitive Qwen Image fallback tensors in BF16.

Artifacts:

- Validation tree: [`validation/`](https://huggingface.co/BBuf/Qwen-Image-Edit-ModelOpt-FP8-SGLang/tree/main/validation)
- Input image: [`validation/assets/qwen_image_edit_input.png`](https://huggingface.co/BBuf/Qwen-Image-Edit-ModelOpt-FP8-SGLang/resolve/main/validation/assets/qwen_image_edit_input.png)
- BF16 command: [`validation/commands/bf16_qwen_image_edit_512_8_benchmark.sh`](https://huggingface.co/BBuf/Qwen-Image-Edit-ModelOpt-FP8-SGLang/blob/main/validation/commands/bf16_qwen_image_edit_512_8_benchmark.sh)
- FP8 command: [`validation/commands/fp8_fixed_qwen_image_edit_512_8_benchmark.sh`](https://huggingface.co/BBuf/Qwen-Image-Edit-ModelOpt-FP8-SGLang/blob/main/validation/commands/fp8_fixed_qwen_image_edit_512_8_benchmark.sh)
- Benchmark comparison: [`qwen_image_edit_bf16_vs_fp8_fixed_512_8_compare.md`](https://huggingface.co/BBuf/Qwen-Image-Edit-ModelOpt-FP8-SGLang/blob/main/validation/benchmark/qwen_image_edit_bf16_vs_fp8_fixed_512_8_compare.md)

| Input | BF16, 512x512, 8 steps | FP8 fixed, 512x512, 8 steps |
|---|---|---|
| ![Input image](https://huggingface.co/BBuf/Qwen-Image-Edit-ModelOpt-FP8-SGLang/resolve/main/validation/assets/qwen_image_edit_input.png) | ![BF16 edit output](https://huggingface.co/BBuf/Qwen-Image-Edit-ModelOpt-FP8-SGLang/resolve/main/validation/images/qwen_image_edit_bf16_512_8.png) | ![FP8 fixed edit output](https://huggingface.co/BBuf/Qwen-Image-Edit-ModelOpt-FP8-SGLang/resolve/main/validation/images/qwen_image_edit_fp8_fixed_512_8.png) |

Benchmark, warmup excluded:

| Metric | BF16 | FP8 fixed | Delta | Speedup |
|---|---:|---:|---:|---:|
| E2E latency | 6.792 s | 6.085 s | -0.707 s (-10.4%) | 1.12x |
| Denoising stage | 5.204 s | 4.524 s | -0.680 s (-13.1%) | 1.15x |
| Decoding stage | 154.77 ms | 121.06 ms | -33.72 ms (-21.8%) | 1.28x |
| Image encoding | 1.316 s | 1.328 s | +0.011 s (+0.9%) | 0.99x |
| Image VAE encoding | 100.62 ms | 94.93 ms | -5.69 ms (-5.7%) | 1.06x |

Notes:

- Validation prompt: `A clean product photo of a small ceramic teapot on a wooden table, soft daylight, sharp details.`
- Validation settings: `512x512`, `8` inference steps, `guidance_scale=4.0`, `seed=42`, `--dit-cpu-offload false`, `--dit-layerwise-offload false`, `--warmup`.

## Conversion Notes

The checkpoint was converted from a NVIDIA ModelOpt FP8 export with SGLang's `build_modelopt_fp8_transformer` tool.
Most linear weights are FP8. The validated fallback set keeps numerically sensitive tensors in BF16, including the Qwen Image image-MLP output projection family needed for normal image quality.