diff --git a/recipes/README.md b/recipes/README.md
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@@ -42,6 +42,7 @@ recipes/
 | -------- | ------ | ---------- |
 | [`Baidu/ERNIE-Image.md`](./Baidu/ERNIE-Image.md) | Text-to-image online serving (ERNIE-Image 8B) | 1x or 2x RTX 4090 24GB |
 | [`Bagel/BAGEL-7B-MoT.md`](./Bagel/BAGEL-7B-MoT.md) | Text-to-image with shared online/offline examples | 1x A100 80GB / 2x CUDA GPUs |
+| [`black-forest-labs/FLUX.1-schnell.md`](./black-forest-labs/FLUX.1-schnell.md) | Offline text-to-image generation with CPU offload | 1x NVIDIA GeForce RTX 5090 32GB |
 | [`Boogu/Boogu-Image.md`](./Boogu/Boogu-Image.md) | Text-to-image online serving (Boogu-Image-0.1-Base) | 1x A100/H100 40GB+ |
 | [`BosonAI/Higgs-Audio-V3-TTS.md`](./BosonAI/Higgs-Audio-V3-TTS.md) | Online + offline multilingual TTS with voice cloning | 1x H100 80GB |
 | [`ByteDance/Lance.md`](./ByteDance/Lance.md) | Unified AR+diffusion: text/img/video gen + understanding (Lance 3B) | 1x B300 / A100 80GB |
diff --git a/recipes/black-forest-labs/FLUX.1-schnell.md b/recipes/black-forest-labs/FLUX.1-schnell.md
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+# FLUX.1-schnell
+
+> Offline text-to-image generation on one NVIDIA GeForce RTX 5090 32 GB
+
+## Summary
+
+- Vendor: Black Forest Labs
+- Model: `black-forest-labs/FLUX.1-schnell`
+- Task: Text-to-image generation
+- Mode: Offline inference with the shared text-to-image runner
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe as a personally validated starting point for generating a
+1024x1024 image with FLUX.1-schnell on one NVIDIA GeForce RTX 5090. The tested
+configuration uses BF16, four denoising steps and model-level CPU offload.
+
+This recipe covers single-image generation. It does not attempt to optimize
+throughput with quantization or cache acceleration.
+
+## References
+
+- Model card: <https://huggingface.co/black-forest-labs/FLUX.1-schnell>
+- Canonical offline text-to-image guide:
+  [`docs/user_guide/examples/offline_inference/text_to_image.md`](../../docs/user_guide/examples/offline_inference/text_to_image.md)
+- Shared runnable example:
+  [`examples/offline_inference/text_to_image`](../../examples/offline_inference/text_to_image)
+- CPU offload guide:
+  [`docs/user_guide/diffusion/cpu_offload.md`](../../docs/user_guide/diffusion/cpu_offload.md)
+- Community recipe tracker:
+  [#2645](https://github.com/vllm-project/vllm-omni/issues/2645)
+
+## Hardware Support
+
+## GPU
+
+### 1x NVIDIA GeForce RTX 5090 32 GB
+
+This configuration was personally validated end to end on September 9, 2026.
+
+#### Environment
+
+- OS: Ubuntu 22.04.3 x86_64
+- Python: 3.12.7
+- GPU: 1x NVIDIA GeForce RTX 5090, 32,607 MiB reported by `nvidia-smi`
+- Host RAM: approximately 62 GiB (64 GB class)
+- NVIDIA driver: 595.71.05; CUDA 13.2 reported by `nvidia-smi`
+- PyTorch: 2.13.0+cu130; PyTorch CUDA runtime: 13.0
+- vLLM: 0.28.0
+- vLLM-Omni: `0.28.1.dev112+ga31690041`
+- Diffusers: 0.40.0
+- Transformers: 5.14.1
+
+#### Command
+
+Run the shared example from the vLLM-Omni repository root in an environment
+with vLLM-Omni installed:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model black-forest-labs/FLUX.1-schnell \
+  --prompt 'A red fox standing in fresh snow, cinematic photography' \
+  --height 1024 --width 1024 \
+  --num-inference-steps 4 --guidance-scale 0 --seed 42 \
+  --enable-cpu-offload \
+  --output flux-schnell-rtx5090.png
+```
+
+The command uses the public model ID for portability. The measured runs used
+already downloaded FLUX.1-schnell weights; initial Hub download and
+authentication were not part of the inference validation.
+
+#### Verification
+
+Verify that the runner succeeded and inspect the saved image:
+
+```bash
+python - <<'PY'
+import hashlib
+from pathlib import Path
+
+from PIL import Image, ImageStat
+
+path = Path("flux-schnell-rtx5090.png")
+with Image.open(path) as image:
+    image.load()
+    assert image.format == "PNG"
+    assert image.size == (1024, 1024)
+    rgb = image.convert("RGB")
+    stats = ImageStat.Stat(rgb)
+    assert max(stats.stddev) > 1, "Image is nearly uniform"
+    print(f"format={image.format}")
+    print(f"size={image.width}x{image.height}")
+    print(f"mode={image.mode}")
+    print(f"bytes={path.stat().st_size}")
+    print("rgb_mean=" + ",".join(f"{value:.3f}" for value in stats.mean))
+    print(f"rgb_extrema={rgb.getextrema()}")
+
+print(f"sha256={hashlib.sha256(path.read_bytes()).hexdigest()}")
+PY
+```
+
+The repeated experiment completed 36 image requests (6 additional warmups and
+30 measured outputs) with successful process exits. Every PNG passed decoding,
+1024x1024 dimension and non-uniform pixel checks. SHA-256 hashes were recorded
+for the artifacts; matching those hashes is not required across environments.
+These checks establish file integrity, not a quantitative image-quality score.
+
+#### Performance
+
+The experiment used three independent processes. Each process initialized the
+model once, generated two warmup images, then generated ten measured images
+sequentially. All 30 measured images used BF16, CPU offload, 1024x1024, four
+steps, guidance scale 0 and TP=1, without concurrent requests.
+
+| Generation measurement | Result |
+| --- | ---: |
+| Measured images | 30 |
+| Mean latency | 25.8411 seconds |
+| P95 latency (nearest-rank) | 26.1446 seconds |
+
+| Startup / resource measurement | Maximum |
+| --- | ---: |
+| Model initialization (seconds) | 64.73 |
+| Full-lifetime RAM PSS peak (MiB) | 49306 |
+| Full-lifetime device GPU memory peak (MiB) | 28121 |
+| Measured PyTorch reserved GPU memory peak (MiB) | 27360 |
+
+Latency measures only `Omni.generate()`, excluding image saving and
+verification. The two warmup images per process are excluded. P95 uses the
+nearest-rank method over the 30 measured images. Model initialization includes
+the engine's built-in dummy warmup; weights were already downloaded and OS file
+caches were not cleared.
+
+RAM and whole-device GPU memory were sampled throughout each process lifetime
+at a target interval of 200 ms. RAM is the maximum simultaneous sum of PSS for
+the inference process and its workers, read from Linux `smaps_rollup`.
+Whole-device GPU memory is the maximum reported by `nvidia-smi`. Both are
+sampled peaks and may miss shorter spikes.
+
+The PyTorch value is the maximum worker-reported `peak_memory_mb` across the
+30 measured requests. Despite the field name, its unit is MiB; it measures the
+PyTorch allocator's reserved-memory high-water mark rather than whole-device
+usage.
+
+#### Notes
+
+- `--enable-cpu-offload` swaps the transformer and text encoders between host
+  and device memory at phase boundaries. It reduces GPU residency but adds
+  transfer overhead and requires sufficient host RAM. The minimum host RAM
+  requirement was not measured.
+- CPU offload does not imply CPU-only computation. The tested model-level
+  offload backend keeps the VAE on the GPU.
+- Only offline single-image generation on one GPU with BF16, 1024x1024,
+  four steps and CPU offload was validated. Online serving, FP8, other
+  resolutions, concurrency, execution without offload and smaller host RAM
+  configurations are outside this recipe's validated scope.
+- The upstream revision of the downloaded weights was not recorded, so the
+  measurements do not establish exact checkpoint reproducibility.