Update README with header, introduction, and usage

README.md

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+---
+license: other
+license_name: qwen-research
+license_link: LICENSE
+tags:
+  - qwen
+  - prompt-rewriting
+  - text-to-image
+---
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/image2.1/logo.png" width="400"/>
+</p>
+<p align="center">
+    🤖 <a href="https://modelscope.cn/models/Qwen/Qwen-Image-2.1">ModelScope</a>&nbsp;&nbsp;|
+    &nbsp;&nbsp;🤗 <a href="https://huggingface.co/Qwen/Qwen-Image-2.1">HuggingFace</a>&nbsp;&nbsp;|
+    &nbsp;&nbsp;📑 <a href="https://qwen.ai/blog?id=qwen-image-2.1">Blog</a>&nbsp;&nbsp;|
+    &nbsp;&nbsp;🖥️ <a href="https://huggingface.co/spaces/Qwen/Qwen-Image-2.1">Demo</a>&nbsp;&nbsp;|
+    &nbsp;&nbsp;🫨 <a href="https://discord.gg/CV4E9rpNSD">Discord</a>
+</p>
+
+## 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.
+
+<p align="center">
+    <img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/image2.1/images/example-01.png" width="100%"/>
+</p>
+
+# Qwen-Image-2.1-PE-T2I
+
+Text-to-image **prompt rewriting model** for [Qwen-Image-2.1](https://huggingface.co/Qwen/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](https://github.com/QwenLM/Qwen-Image-2.1) and [Blog](https://qwen.ai/blog?id=qwen-image-2.1).
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install transformers>=5.4.0 torch>=2.4.0 accelerate pillow
+```
+
+### Usage with Transformers
+
+```python
+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
+
+```python
+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:
+
+```json
+{
+  "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](./LICENSE).