Upload Fast-Neural-Style-OpenVINO: ONNX, OpenVINO IR, Intel NPU blob, card and manifest

.gitattributes

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README.md

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+---
+license: bsd-3-clause
+base_model: litert-community/Fast-Neural-Style-LiteRT
+base_model_relation: quantized
+library_name: openvino
+pipeline_tag: image-to-image
+tags:
+- openvino
+- onnx
+- intel-npu
+---
+
+# Fast-Neural-Style-OpenVINO
+
+Fast-Neural-Style for Intel NPUs, GPUs and CPUs: style_mosaic_fp16 from [litert-community/Fast-Neural-Style-LiteRT](https://huggingface.co/litert-community/Fast-Neural-Style-LiteRT/tree/1a697a4af21a3a133b710d1ec8e2d7a7bd918594) as ONNX (tf2onnx 1.17.0, opset 17), OpenVINO IR with fp32 weights, and a precompiled Intel NPU blob. On the NPU of a Core Ultra 9 386H the blob runs in 7.08 ms per run, numpy input to numpy output.
+
+Scripts, raw records, and the same table for all 52 graphs of LiteRT-Models: https://github.com/john-rocky/windows-zoo
+
+## Files
+
+| file | bytes | sha256 |
+|---|---|---|
+| `style_mosaic_fp16.onnx` | 6,945,235 | `fa49f475fc42ee4beffd7fb0f619cbf70f799de371c23bb29ba39bc02acda642` |
+| `openvino/style_mosaic_fp16.xml` | 522,432 | `1ecf9b6943c0ecc5828d9876be0f691a3da3a4f0d3bde6b3774cf2bbe7f478e1` |
+| `openvino/style_mosaic_fp16.bin` | 6,717,452 | `fd7528cf319d9f69017a65622029b3b6388d241f57c4a7161293d028e7c648ce` |
+| `npu/style_mosaic_fp16.blob` | 5,857,409 | `135725019b95b4761e738bbc45ffc968785bbfb06dfc845a71fa6d71085c6d04` |
+
+The NPU blob was compiled with OpenVINO 2026.4.1, Intel NPU driver 32.0.100.4841 (OpenVINO reports NPU_DRIVER_VERSION 1004841, NPU_COMPILER_VERSION 524292). If `import_model` refuses a blob on another driver or OpenVINO version, compile the IR instead (the commented line in the snippet).
+
+## How it was measured
+
+MSI Stealth 16 AI+ (Core Ultra 9 386H, RTX 5080 Laptop GPU at a 95 W power limit, Windows 11 build 26200.9711, on AC power), 2026-11-06 to 2026-11-07. On the laptop, each row is one process: load, one run, 5 warmup runs, then the median of 30 timed runs (2 warmup + 10 timed runs when the first run takes more than 3 s), numpy input to numpy output, every runtime at its default settings, one process at a time, each started after the CPU, the RTX and the Intel GPU had gone idle (the thresholds are in METHOD.md of the GitHub repo). Output check: each output against the CPU output in fp32 on the same input (ONNX Runtime's CPU EP); PASS = cosine ≥ 0.99, no NaN or Inf, and top-1 agreement ≥ 0.99 for class scores. The Snapdragon rows are Qualcomm AI Hub profile jobs of the same ONNX files (their own timer).
+
+## Speed and output check
+
+| processor | runtime | file | ms | load s | output check |
+|---|---|---|---|---|---|
+| Intel AI Boost NPU | OpenVINO 2026.4.1 | `npu/` blob (`import_model`) | 7.08 | 0.01 | PASS (cos ≥ 0.9999) |
+| Intel AI Boost NPU | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx`, compiled when the session loads | 7.37 | 5.86 | PASS (cos ≥ 0.9999) |
+| Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx` | 3.92 | 1.14 | PASS (cos ≥ 0.9999) |
+| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | fp16 copy of the `.onnx` (not in this repo) | 4.75 | 1.26 | PASS (cos ≥ 0.9999) |
+| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | 6.10 | 1.22 | PASS (cos ≥ 0.9999) |
+| Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | 33.59 | 0.13 | reference |
+| Snapdragon X2 Elite NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 7.61 | 0.39 | not compared (AI Hub profile job, 2026-10-30) |
+| Snapdragon X2 Elite NPU | Qualcomm AI Hub, ONNX Runtime QNN EP | `.onnx`, compiled when it loads | 10.72 (2 layers on the CPU) | 2.17 | not compared (AI Hub profile job, 2026-10-30) |
+| Galaxy S26 NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 6.62 | 0.16 | not compared (AI Hub profile job, 2026-10-30) |
+
+The ONNX file against the source `.tflite` (LiteRT interpreter on the CPU, the same input): PASS, cosine ≥ 0.9999 for every output.
+
+## Run it on the Intel NPU
+
+Download this repo, then run the snippets from its folder:
+
+```sh
+pip install huggingface_hub
+hf download mlboydaisuke/Fast-Neural-Style-OpenVINO --local-dir Fast-Neural-Style-OpenVINO
+cd Fast-Neural-Style-OpenVINO
+```
+
+OpenVINO (`pip install openvino==2026.4.1`):
+
+```python
+import numpy as np
+import openvino as ov
+
+core = ov.Core()
+if "NPU" not in core.available_devices:
+    raise SystemExit(f"no Intel NPU visible to OpenVINO: {core.available_devices}")
+with open("npu/style_mosaic_fp16.blob", "rb") as f:
+    compiled = core.import_model(f.read(), "NPU")  # precompiled: no compile at load
+# or compile the IR on this machine: compiled = core.compile_model("openvino/style_mosaic_fp16.xml", "NPU")
+# replace the zeros with your preprocessed input (float32, the shapes printed by compiled.inputs)
+feeds = {i.get_any_name(): np.zeros(list(i.shape), np.float32) for i in compiled.inputs}
+outputs = compiled(feeds)
+print({o.get_any_name(): outputs[o].shape for o in compiled.outputs})
+```
+
+ONNX Runtime with the OpenVINO EP (`pip install onnxruntime==1.31.1 onnxruntime-ep-openvino==1.8.1`):
+
+```python
+import numpy as np
+import onnxruntime as ort
+import onnxruntime_ep_openvino as ov_ep
+
+ort.register_execution_provider_library(ov_ep.get_ep_name(), ov_ep.get_library_path())
+npu = [d for d in ort.get_ep_devices()
+       if d.ep_name == ov_ep.get_ep_name() and d.ep_metadata.get("ov_device") == "NPU"]
+if not npu:
+    raise SystemExit("no Intel NPU visible to the OpenVINO EP")
+so = ort.SessionOptions()
+so.add_provider_for_devices(npu, {})
+sess = ort.InferenceSession("style_mosaic_fp16.onnx", sess_options=so)  # compiles for the NPU here
+# replace the zeros with your preprocessed input (float32)
+feeds = {i.name: np.zeros(i.shape, np.float32) for i in sess.get_inputs()}
+outputs = sess.run(None, feeds)
+print([o.shape for o in outputs])
+```
+
+## Licence
+
+BSD-3-Clause, the licence of [litert-community/Fast-Neural-Style-LiteRT](https://huggingface.co/litert-community/Fast-Neural-Style-LiteRT). The source card names the upstream model as `pytorch/examples` (not a Hugging Face repo).

manifest.json

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+{
+ "source_repo": "litert-community/Fast-Neural-Style-LiteRT",
+ "source_revision": "1a697a4af21a3a133b710d1ec8e2d7a7bd918594",
+ "graphs": {
+  "style_mosaic_fp16": {
+   "from": "style_mosaic_fp16",
+   "ir": {
+    "openvino": "2026.4.1-22982-e213a147257-releases/2026/4",
+    "compress_to_fp16": false
+   },
+   "blob_compiled_with": {
+    "openvino": "2026.4.1-22982-e213a147257-releases/2026/4",
+    "FULL_DEVICE_NAME": "Intel(R) AI Boost",
+    "NPU_DRIVER_VERSION": "1004841",
+    "NPU_COMPILER_VERSION": "524292",
+    "NPU_COMPILER_TYPE": "CompilerType.PREFER_PLUGIN",
+    "INFERENCE_PRECISION_HINT": "<Type: 'float16'>"
+   },
+   "files": {
+    "style_mosaic_fp16.onnx": {
+     "bytes": 6945235,
+     "sha256": "fa49f475fc42ee4beffd7fb0f619cbf70f799de371c23bb29ba39bc02acda642"
+    },
+    "openvino/style_mosaic_fp16.xml": {
+     "bytes": 522432,
+     "sha256": "1ecf9b6943c0ecc5828d9876be0f691a3da3a4f0d3bde6b3774cf2bbe7f478e1"
+    },
+    "openvino/style_mosaic_fp16.bin": {
+     "bytes": 6717452,
+     "sha256": "fd7528cf319d9f69017a65622029b3b6388d241f57c4a7161293d028e7c648ce"
+    },
+    "npu/style_mosaic_fp16.blob": {
+     "bytes": 5857409,
+     "sha256": "135725019b95b4761e738bbc45ffc968785bbfb06dfc845a71fa6d71085c6d04"
+    }
+   }
+  }
+ }
+}

npu/style_mosaic_fp16.blob

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openvino/style_mosaic_fp16.bin

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style_mosaic_fp16.onnx

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