[Feat] DiffusionEngine Support async batch infer (#2729)

Signed-off-by: Semmer <semmer@live.cn>
Signed-off-by: jader <yjader@foxmail.com>
Signed-off-by: asukaqaq-s <1311722138@qq.com>
Co-authored-by: asukaqaq-s <1311722138@qq.com>
Co-authored-by: jader <yjader@foxmail.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>

recipes/Qwen/Qwen-Image.md

@@ -0,0 +1,130 @@
+# Qwen-Image for text-to-image serving with optional continuous batching on 1x A100 80GB
+
+## Summary
+
+- Vendor: Qwen
+- Model: `Qwen/Qwen-Image`
+- Task: Text-to-image generation
+- Mode: Online serving with optional step-wise continuous batching
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe when you want a known-good starting point for serving
+`Qwen/Qwen-Image` on a single 80 GB A100, validate the normal online-serving
+path, and optionally replay the benefit of step-wise continuous batching with
+the benchmark assets already bundled in this repository.
+
+## References
+
+- Upstream or canonical docs:
+  [`docs/user_guide/examples/online_serving/text_to_image.md`](../../docs/user_guide/examples/online_serving/text_to_image.md)
+- Related example under `examples/`:
+  [`examples/online_serving/text_to_image/README.md`](../../examples/online_serving/text_to_image/README.md)
+- Related benchmark:
+  [`benchmarks/diffusion/diffusion_benchmark_serving.py`](../../benchmarks/diffusion/diffusion_benchmark_serving.py)
+
+## Hardware Support
+
+This recipe currently documents one CUDA GPU serving configuration. Extend it
+with more hardware sections as community validation lands.
+
+## GPU
+
+### 1x A100 80GB
+
+#### Environment
+
+- OS: Linux
+- Python: 3.10+
+- Driver / runtime: NVIDIA CUDA environment with an A100 80 GB GPU
+- vLLM version: Match the repository requirements for your checkout
+- vLLM-Omni version or commit: Use the commit you are deploying from
+
+#### Command
+
+Start the baseline server:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni --port 8091
+```
+
+To enable the step-wise runtime without batching:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni \
+  --port 8091 \
+  --step-execution \
+  --max-num-seqs 1
+```
+
+To enable experimental compatible-request batching:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni \
+  --port 8091 \
+  --step-execution \
+  --max-num-seqs 8
+```
+
+You can also use the example launcher and pass the extra flags through:
+
+```bash
+bash examples/online_serving/text_to_image/run_server.sh --step-execution --max-num-seqs 8
+```
+
+#### Verification
+
+Run the existing client example after the server is ready:
+
+```bash
+python examples/online_serving/text_to_image/openai_chat_client.py \
+  --server http://localhost:8091 \
+  --prompt "A ceramic teapot on a wooden table" \
+  --output /tmp/qwen_image_recipe.png
+```
+
+For a direct API smoke test:
+
+```bash
+curl http://localhost:8091/v1/images/generations \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Qwen/Qwen-Image",
+    "prompt": "A ceramic teapot on a wooden table",
+    "size": "1024x1024",
+    "num_inference_steps": 20,
+    "seed": 42
+  }'
+```
+
+To replay the batching benefit with matched warmup:
+
+```bash
+python benchmarks/diffusion/diffusion_benchmark_serving.py \
+  --backend vllm-omni \
+  --dataset vbench \
+  --task t2i \
+  --model Qwen/Qwen-Image \
+  --num-prompts 10 \
+  --height 1024 \
+  --width 1024 \
+  --num-inference-steps 50 \
+  --seed 42 \
+  --port 8089 \
+  --max-concurrency 8 \
+  --warmup-requests 8 \
+  --warmup-concurrency 8 \
+  --warmup-num-inference-steps 3 \
+  --disable-tqdm
+```
+
+Run that once against `--max-num-seqs 1`, then rerun it against `--max-num-seqs 8`
+and compare the output JSON or terminal metrics.
+
+#### Notes
+
+- Memory usage: keep headroom for first-request compile and image decode overhead.
+- Key flags: `--step-execution` enables the step-wise runtime; `--max-num-seqs` controls how many compatible requests may stay active together.
+- Keep `--max-num-seqs 1` when you want the more conservative path, when traffic is mostly single-request, or when you are debugging correctness before measuring throughput.
+- Current batching is still shape-sensitive: different step progress can co-batch, but different resolutions do not yet co-batch.

recipes/README.md

@@ -25,6 +25,8 @@ recipes/
 
 ## Available Recipes
 
+- [`Qwen/Qwen-Image.md`](./Qwen/Qwen-Image.md): text-to-image serving recipe for
+  Qwen-Image on `1x A100 80GB`, including optional step-wise continuous batching replay
 - [`Qwen/Qwen3-Omni.md`](./Qwen/Qwen3-Omni.md): online serving recipe for
   multimodal chat on `1x A100 80GB`
 - [`Wan-AI/Wan2.2-I2V.md`](./Wan-AI/Wan2.2-I2V.md): image-to-video serving