---
title: Pruna-Qwen-Image-2.1 — 5/8-step Qwen-Image-2.1
emoji: ⚡
colorFrom: gray
colorTo: blue
sdk: gradio
sdk_version: 6.28.0
python_version: "3.12"
app_file: app.py
pinned: false
license: other
license_name: qwen-research
short_description: 5/8-step distilled Qwen-Image-2.1 T2I + editing
models:
  - PrunaAI/Pruna-Qwen-Image-2.1
  - Qwen/Qwen-Image-2.1
tags:
  - text-to-image
  - image-editing
  - distillation
---

# Pruna-Qwen-Image-2.1 — few-step Qwen-Image-2.1

A DMD-distillation **LoRA** from Pruna that runs [Qwen-Image-2.1](https://huggingface.co/Qwen/Qwen-Image-2.1)
in **5 or 8 sampling steps with no classifier-free guidance**, against the base model's 40 — the same
pipeline, a fraction of the time. One model does both tasks, exactly as the base does: leave the
reference slots empty for **text-to-image**, or attach one to three reference images for
**editing / composition / style transfer**.

- **8-step adapter** — recommended default, higher quality.
- **5-step adapter** — fastest; the card notes visibly lower quality.
- Each adapter is trained on its own sigma schedule, so the app loads exactly one at a time and
  swaps it when you change the Steps setting.

The distillation recipe is reproduced exactly as published on the
[model card](https://huggingface.co/PrunaAI/Pruna-Qwen-Image-2.1): the card's raw sigma nodes are
passed as `sigmas=` on a scheduler rebuilt with `use_dynamic_shifting=False, shift=1.0,
shift_terminal=None` (the stock Qwen-Image-2.1 scheduler ships `use_dynamic_shifting=true` and
`shift_terminal=0.02`, which would shift the given sigmas a second time and stretch the terminal
node), `true_cfg_scale=1.0` with no negative prompt, LoRA strength 1.0, and condition images encoded
at 1024-area (`output_resolution=1024`). Trained at 1K resolution, so the size menu offers 1024-area
buckets for every aspect ratio.

> **Known limits (from the card).** This is v0.1 — quality is **below the 40-step base model**; short
> or vague text-to-image prompts give weaker results (write detailed ones); other step counts,
> schedules, or CFG values are not supported.

## Performance notes

- The 5-step and 8-step adapters are loaded together at startup (336 MB each) with distinct PEFT
  adapter names and switched with `set_adapters` — only one is ever active on the transformer.
- No VAE tiling: the pipeline encodes reference images through the same VAE, and tiled reference
  latents wreck edits (duplicated subjects, wrong scale).
- `Qwen3VLVisionPatchEmbed.forward` is rebound to an equivalent matmul: cuDNN has no usable bf16
  Conv3d kernel for that shape and it costs ~30 s **per reference image**; the matmul is bitwise
  equal and ~0.8 ms.

## License

The base model is released under the **Qwen RESEARCH LICENSE AGREEMENT — Non-Commercial: research or
evaluation purposes only**. This distilled derivative and this demo inherit that restriction.
Commercial use requires a separate license from Alibaba (`model-business@notice.qwencloud.com`).

> Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) 2026 Hangzhou Tongyi
> Laboratory Technology Co., Ltd. All Rights Reserved.