Introduction

We are excited to open-source Qwen-Image-2.1, a unified text-to-image generation and image editing model in the Qwen family. With just 7B parameters in its visual generation component (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility.

Four key improvements define this release:

• Compact and Efficient — A lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost.
• Native Transparency, Unified Creation and Editing — Generate regular or transparent (RGBA) images from text, edit transparent layers, and extract subjects from photographs—all in one model.
• Versatile Editing — Support up to 10 reference images, specify local edits via circles, painted annotations, or separate masks, and preserve identity for people and products.
• Realistic Textures and Refined Aesthetics — Improved typography, portrait lighting, and fine details for more visually compelling results.

Qwen-Image-2.1-PE-T2I

Text-to-image prompt rewriting model for Qwen-Image-2.1. A fine-tuned Qwen3.5-VL 9B that turns a brief image request in any language into a detailed English prompt plus a recommended aspect ratio.

For more details, see the GitHub repo and Blog.

Quick Start

Installation

pip install transformers>=5.4.0 torch>=2.4.0 accelerate pillow

Usage with Transformers

import json
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Qwen/Qwen-Image-2.1-PE-T2I"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="auto"
).eval()

# Load the system prompt shipped with the model
import huggingface_hub
sys_prompt_path = huggingface_hub.hf_hub_download(model_id, "system_prompt.txt")
system_prompt = open(sys_prompt_path).read().strip()

user_prompt = "一只在雨中弹吉他的柯基"

text = tokenizer.apply_chat_template(
    [{"role": "system", "content": system_prompt},
     {"role": "user", "content": user_prompt}],
    tokenize=False, add_generation_prompt=True, enable_thinking=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs, max_new_tokens=16256,
        do_sample=True, temperature=1.0, top_p=0.95, top_k=20,
    )
gen = tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)

# Split thinking from the answer
thinking, _, answer = gen.partition("</think>")
result = json.loads(answer.strip())
print(result)
# {"rewritten_prompt": "<long detailed English prompt>", "wh_ratio": "16:9"}

Integration with Diffusers

import json
import torch
from diffusers import QwenImage21Pipeline

WH_RATIO_TO_SIZE = {
    "1:1": (2048, 2048), "4:3": (2400, 1792), "3:4": (1792, 2400),
    "3:2": (2528, 1696), "2:3": (1696, 2528), "16:9": (2752, 1536),
    "9:16": (1536, 2752),
}

# Assuming `result` from above
prompt = result["rewritten_prompt"]
width, height = WH_RATIO_TO_SIZE.get(result["wh_ratio"], (2048, 2048))

pipe = QwenImage21Pipeline.from_pretrained(
    "Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16
).to("cuda")

image = pipe(
    prompt=prompt,
    width=width, height=height,
    num_inference_steps=40,
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]

image.save("rewritten_t2i.png")

Output Format

The model outputs a JSON object after a <think> reasoning block:

{
  "rewritten_prompt": "<long detailed English prompt describing the finished image>",
  "wh_ratio": "16:9"
}

• rewritten_prompt — the expanded prompt to pass to the image generation model
• wh_ratio — the recommended aspect ratio for rendering

License

This model is licensed under the Qwen Research License Agreement.