[Doc] Add Stable-Diffusion-3.5 recipe for 1x RTX A6000 48GB (#2645) (#4052)

Signed-off-by: leo.yang <leo.yang.engineer@gmail.com>

recipes/README.md

@@ -50,6 +50,7 @@ recipes/
 | [`Tencent/HunyuanImage-3.0-Instruct.md`](./Tencent/HunyuanImage-3.0-Instruct.md) | DiT-only text-to-image serving and benchmark, including ModelOpt mixed FP8/NVFP4 | 4x H100/H800 80GB / 2x B200 |
 | [`Wan-AI/Wan2.2-I2V.md`](./Wan-AI/Wan2.2-I2V.md) | Image-to-video serving (Wan2.2 14B) | 8x Ascend NPU (A2/A3) |
 | [`Wan-AI/Wan2.2-S2V.md`](./Wan-AI/Wan2.2-S2V.md) | Speech-to-video serving (Wan2.2 14B) | 2x A100/H100 80GB |
+| [`StabilityAI/Stable-Diffusion-3.5.md`](./StabilityAI/Stable-Diffusion-3.5.md) | Text-to-image serving (SD 3.5-medium and SD 3.5-large) | 1x RTX A6000 48GB |
 | [`zai-org/GLM-TTS.md`](./zai-org/GLM-TTS.md) | Online serving for Chinese/English zero-shot voice-cloned TTS | 1x A40 48GB |
 | [`GLM/GLM-Image.md`](./GLM/GLM-Image.md) | Online serving for image generation | 1x A800 80GB / 2x A800 80GB |
 |

recipes/StabilityAI/Stable-Diffusion-3.5.md

@@ -0,0 +1,139 @@
+# Stable Diffusion 3.5
+
+> Text-to-image serving with Stability AI's SD 3.5 family
+
+## Summary
+
+- Vendor: Stability AI
+- Model: `stabilityai/stable-diffusion-3.5-medium`, `stabilityai/stable-diffusion-3.5-large`
+- Task: Text-to-image generation
+- Mode: Online serving with the OpenAI-compatible images API
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe when you want a lightweight, fast text-to-image model that
+fits on a single consumer or workstation GPU. SD 3.5-medium requires only
+~17 GiB VRAM and generates 1024×1024 images in ~4 seconds, making it one of
+the most accessible diffusion models in vLLM-Omni. SD 3.5-large produces
+noticeably sharper images at the cost of ~34 GiB VRAM and ~21 seconds per
+image.
+
+## References
+
+- Model (medium): <https://huggingface.co/stabilityai/stable-diffusion-3.5-medium>
+- Model (large): <https://huggingface.co/stabilityai/stable-diffusion-3.5-large>
+- Related example under `examples/`:
+  [`examples/online_serving/text_to_image/README.md`](../../examples/online_serving/text_to_image/README.md)
+- User guide:
+  [`docs/user_guide/examples/online_serving/text_to_image.md`](../../docs/user_guide/examples/online_serving/text_to_image.md)
+
+## Hardware Support
+
+This recipe documents two CUDA GPU configurations on a single RTX A6000 48 GB,
+one for each model variant. Extend it with more hardware sections as community
+validation lands.
+
+## GPU
+
+### 1x NVIDIA RTX A6000 48GB — stable-diffusion-3.5-medium
+
+#### Environment
+
+- OS: Ubuntu 22.04
+- Python: 3.12
+- Driver / runtime: NVIDIA driver 580.126.09, CUDA 13.0
+- vLLM version: 0.22.0
+- vLLM-Omni version or commit: 0.22.0rc2.dev8+g41a795b5d
+
+#### Command
+
+```bash
+vllm serve stabilityai/stable-diffusion-3.5-medium --omni --port 8091
+```
+
+#### Verification
+
+```bash
+curl -X POST http://localhost:8091/v1/images/generations \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "stabilityai/stable-diffusion-3.5-medium",
+    "prompt": "a cute cat sitting on a windowsill, highly detailed, sharp focus",
+    "negative_prompt": "blurry, out of focus",
+    "guidance_scale": 4.5,
+    "n": 1,
+    "size": "1024x1024"
+  }' | python3 -c "
+import json, base64, sys
+data = json.load(sys.stdin)
+with open('output.png', 'wb') as f:
+    f.write(base64.b64decode(data['data'][0]['b64_json']))
+print('saved output.png')
+"
+```
+
+Or use the example client:
+
+```bash
+python examples/online_serving/text_to_image/openai_chat_client.py \
+  --server http://localhost:8091 \
+  --prompt "a cute cat sitting on a windowsill" \
+  --output output.png
+```
+
+#### Notes
+
+- Memory usage: Model weights ~15.6 GiB, peak VRAM ~17.0 GiB (no CFG), ~20.9 GiB with `guidance_scale > 1` (CFG enabled).
+- Generation time: ~4 seconds without CFG, ~8 seconds with `guidance_scale=4.5`.
+- Model loading time: ~55 seconds (including weight download on first run).
+- `negative_prompt` requires `guidance_scale > 1` to take effect; SD3 defaults to 1.0 (no CFG).
+
+### 1x NVIDIA RTX A6000 48GB — stable-diffusion-3.5-large
+
+#### Environment
+
+- OS: Ubuntu 22.04
+- Python: 3.12
+- Driver / runtime: NVIDIA driver 580.126.09, CUDA 13.0
+- vLLM version: 0.22.0
+- vLLM-Omni version or commit: 0.22.0rc2.dev8+g41a795b5d
+
+#### Command
+
+```bash
+vllm serve stabilityai/stable-diffusion-3.5-large --omni --port 8091
+```
+
+> **Note**: `stable-diffusion-3.5-large` is a gated model. Accept the license
+> on [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-3.5-large)
+> and set `HF_TOKEN` before running.
+
+#### Verification
+
+```bash
+curl -X POST http://localhost:8091/v1/images/generations \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "stabilityai/stable-diffusion-3.5-large",
+    "prompt": "a cute cat sitting on a windowsill, highly detailed, sharp focus",
+    "negative_prompt": "blurry, out of focus",
+    "guidance_scale": 4.5,
+    "n": 1,
+    "size": "1024x1024"
+  }' | python3 -c "
+import json, base64, sys
+data = json.load(sys.stdin)
+with open('output.png', 'wb') as f:
+    f.write(base64.b64decode(data['data'][0]['b64_json']))
+print('saved output.png')
+"
+```
+
+#### Notes
+
+- Memory usage: Peak VRAM ~31.6 GiB at 1024×1024, ~45.5 GiB at 2048×2048 with `guidance_scale=4.5`.
+- Generation time: ~19 seconds at 1024×1024, ~102 seconds at 2048×2048.
+- Image quality is noticeably better than medium, with sharper details.
+- `negative_prompt` requires `guidance_scale > 1` to take effect; SD3 defaults to 1.0 (no CFG).
+- 2048×2048 fits within 48 GB but leaves little headroom (~45.5 GiB used out of 49 GiB).