README: sync Quick Start with latest GitHub (separate Qwen-Image-Edit FastAPI service, MeiGen-AI/GenEvolve as MODEL_PATH, install order)

README.md

@@ -111,27 +111,56 @@ For a user request, the agent samples a multi-turn trajectory of tool calls befo
 
 ## 🚀 Quick Start
 
-The deployed checkpoint is the **student policy** — it consumes a user prompt and returns a JSON `gen_prompt + reference_images` program through a `<think>/<tool_call>/<answer>` loop. The end-to-end runtime (vLLM/SGLang server + agent loop + tools + Qwen/Nano renderers) lives in the [GitHub repo](https://github.com/Ephemeral182/GenEvolve).
+The deployed checkpoint is the **student policy** — it consumes a user prompt and returns a JSON `gen_prompt + reference_images` program through a `<think>/<tool_call>/<answer>` loop. The end-to-end runtime (vLLM serving + agent loop + tools + Qwen/Nano renderers) lives in the [GitHub repo](https://github.com/Ephemeral182/GenEvolve); the snippet below mirrors its installation and usage.
+
+### 1. Install the main GenEvolve runtime
 
 ```bash
 git clone https://github.com/Ephemeral182/GenEvolve.git
 cd GenEvolve
+
 conda create -n genevolve python=3.11 -y && conda activate genevolve
+pip install -U pip setuptools wheel packaging psutil ninja
 pip install torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128
-pip install --no-build-isolation -r requirements.txt && pip install -e .
+pip install --no-build-isolation -r requirements.txt
+pip install -e .
+```
+
+Qwen-Image-Edit rendering runs as a **separate FastAPI service** (kept out of the vLLM environment to avoid CUDA/diffusers conflicts). Set up that service from the GitHub README when you want to use `--backend qwen-image-edit-service`.
 
-# Serve the policy (TP/DP knobs scale across GPUs)
+### 2. Serve the agent policy
+
+```bash
+# Single GPU / single replica.
+MODEL_PATH=MeiGen-AI/GenEvolve PORT=8000 TP=1 DP=1 bash scripts/serve_vllm.sh
+
+# Higher throughput on one 8-GPU node (8 replicas, 1 GPU each).
 MODEL_PATH=MeiGen-AI/GenEvolve PORT=8000 TP=1 DP=8 bash scripts/serve_vllm.sh
+```
 
-# End-to-end example (Nano backend)
+`TP` shards one model replica across multiple GPUs; `DP` launches multiple replicas; total GPU usage is `TP × DP`.
+
+### 3. End-to-end example
+
+```bash
 export SERPER_API_KEY=<your_key>      # required for search / image_search
-export GOOGLE_API_KEY=<your_key>      # only for the Nano Banana Pro backend
+export GOOGLE_API_KEY=<your_key>      # only for --backend nano-banana-pro
+
+# Nano Banana Pro renderer
 python examples/quickstart.py \
     --backend nano-banana-pro \
     --base-url http://localhost:8000/v1 \
     --model GenEvolve \
     --prompt "A 1990s travel-magazine cover of two backpackers in front of the Eiffel Tower at golden hour, the title \"PARIS\" in bold serif." \
     --output paris.png
+
+# Qwen-Image-Edit renderer (point at your Qwen-Image-Edit FastAPI service)
+python examples/quickstart.py \
+    --backend qwen-image-edit-service \
+    --service-url http://your-qwen-service:8001 \
+    --base-url http://localhost:8000/v1 \
+    --model GenEvolve \
+    --output paris_qwen.png
 ```
 
 The agent's final `<answer>` is a JSON object: