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Delete PERFORMANCE.md
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# Apple Silicon + Cross-Platform Performance
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End-to-end measurements from commit `f65aeae` on a MacBook Pro M3 Max
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against `hacksider/Deep-Live-Cam` upstream `main@64d3f06`, same hardware,
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same camera, same source/target faces.
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| Mode | Upstream `main` | This fork | Delta |
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|---|---|---|---|
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| Face swap only | **<5 FPS** | **>20 FPS** | ~5× |
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| Face swap + GFPGAN enhancer | **<2 FPS** | **>10 FPS** | ~5× |
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| Camera resolution | 640×480 default | **960×540 MJPEG** | wider FoV |
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| Camera frame rate | 15–30 fps (backend default) | **60 fps negotiated + measured** | up to 2–4× |
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The gap is cumulative — no single change accounts for it. Each section
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below describes one contributor, in rough order of impact on the
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live-video pipeline.
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## 1. Camera capture negotiation — `modules/video_capture.py`
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Upstream calls `cv2.VideoCapture(device_index)` with no hints and
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accepts whatever the camera defaults to. On most webcams that means
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640×480 YUV at 15–30 fps, and `CAP_PROP_FPS` lies on DirectShow.
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This fork:
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- Requests `MJPG` fourcc to bypass USB bandwidth limits on uncompressed
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YUV at high resolutions.
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- Requests 960×540 @ 60 fps up front.
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- Reads back the camera's actual resolution via `CAP_PROP_FRAME_WIDTH/HEIGHT`.
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- **Empirically measures FPS** by timing 30 frames after 10 warmup
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reads (`_measure_fps`) instead of trusting `CAP_PROP_FPS`. Costs
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~0.5–1 s at startup; gives ground-truth numbers that downstream
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code (detection cadence, enhancer throttle) tunes against.
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- Windows path tries `CAP_MSMF` before `CAP_DSHOW` (DirectShow often
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caps at 30 fps even when the camera supports 60).
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This single change is why the resolution / FoV / FPS look different between
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upstream and the fork before any ML work starts.
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## 2. CoreML graph rewrites — `modules/onnx_optimize.py`
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CoreML EP silently falls back to CPU for ops it doesn't support,
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creating partition boundaries with CPU↔ANE round-trips between each.
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Three pre-load rewrites eliminate the fallbacks:
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### 2a. `Pad(mode=reflect)` → `Slice + Concat` (inswapper_128)
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Verified on this machine:
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| Config | Partitions | inswapper latency |
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|---|---|---|
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| Original ONNX, ORT 1.24.4 | **14** | 55.3 ms |
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| Rewritten, ORT 1.24.4 (this pass) | **1** | 27.4 ms |
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| Original ONNX, ORT 1.26 (main) | **1** | 27.2 ms |
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The third row uses ORT built from `main` at `fb13eb3edd` which contains
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[microsoft/onnxruntime#28073](https://github.com/microsoft/onnxruntime/pull/28073)
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— native MIL `pad(mode="reflect")` under `MLProgram`. Once ORT ≥ 1.26 is the
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floor, this pass can be deleted. See `REVIEW_TODOS.md` for the cleanup note.
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### 2b. `Shape → Gather` folded to constants (det_10g)
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Dynamic shape chains for FPN upsample target sizes forced parts of the
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face detector onto CPU. When the input shape is known at load time we
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run ONNX shape inference and replace the chains with `int64` constants.
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Measured: **21 ms → 4 ms** on the detection model.
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### 2c. `Split(axis=1, 2 outputs)` → `Slice` pairs (GFPGAN)
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CoreML EP doesn't support `Split`. GFPGAN's SFT modulation layers use
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channel-wise splits everywhere, forcing partition boundaries. Rewriting
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each 2-way Split as two Slices eliminates the fallbacks.
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Measured: **155 ms → 89 ms** on GFPGAN. This is the single largest
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contributor to the "GFPGAN enabled" row in the headline table.
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All three rewrites cache to disk with a `_coreml` suffix so the cost is
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paid once per model per machine.
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## 3. Pipeline overlap — `modules/processors/frame/core.py`, `face_swapper.py`
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Face detection and face swap both use the Neural Engine. Running them
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serially leaves the ANE idle during the detection half. The fork:
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- Overlaps detection N+1 with swap N via a thread pool. Adds one frame
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of latency; doubles ANE utilization.
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- Skips `landmark_2d_106` when only `face_swapper` is active (landmarks
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are unused unless mouth-mask or interpolation is on).
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- Parallelizes landmark + recognition post-detection when both are
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needed.
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- Routes `det_10g` to GPU (Metal) so ANE stays free for the swap model.
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The one-frame detection lag is a known trade-off — acceptable for
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video frame rates where the face barely moves frame-to-frame. Flagged
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as a quality risk on fast motion / scene cuts in `REVIEW_TODOS.md`.
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## 4. GFPGAN-specific — `modules/processors/frame/face_enhancer.py`, `_onnx_enhancer.py`
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- Temporal cache: in live mode, run GFPGAN inference every Nth frame
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and reuse the enhancement; interpolate the affine paste-back each
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frame. Essentially free interpolation between inferences.
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- Pre-computed FFHQ 512 landmark template (avoids per-frame matrix solve).
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- Session created once under `create_onnx_session` with the same
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`ModelFormat=MLProgram + MLComputeUnits=ALL` config as the swap
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model — previously GFPGAN fell back to CPU-only.
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## 5. Paste-back optimization — `modules/processors/frame/face_swapper.py`
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`_fast_paste_back` replaces insightface's `paste_back` which operates
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on the full frame:
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- Computes face bbox from the affine matrix directly (no warp-and-scan
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of a white mask).
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- Runs erosion, blur, and blend on the face bbox only.
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- GPU path (CUDA) keeps mask arithmetic on GPU end-to-end
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(`torch.nn.functional.max_pool2d` / `avg_pool2d` for erode + blur).
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- Writes in-place into `target_img` to avoid a full-frame copy.
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## 6. Platform routing — `modules/platform_info.py`
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Single source of truth for OS / accelerator detection. Consumed by
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capture backend selection, provider config for `face_swapper` and
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`face_enhancer`, and a one-line startup banner confirming which code
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path the app took.
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## 7. Windows CUDA path (not exercised in M3 Max numbers)
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Not contributing to the Apple Silicon table but included in the same
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commit:
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- CUDA graphs via `enable_cuda_graph=1` + `io_binding` with
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pre-allocated `OrtValue` buffers. Replays the recorded kernel launch
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sequence each frame with near-zero CPU overhead. Requires static
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input shape — inswapper is always `1×3×128×128 + 1×512`.
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- FP16 model auto-selected on GPUs with Tensor Cores (Turing+),
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falls back to FP32 on older GPUs where FP16 can produce NaN.
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- DLL discovery fix for NVIDIA CUDA/cuDNN from pip-installed `torch`
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and `nvidia-*` wheels.
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Enables 1080p @ 60 FPS on NVIDIA hardware (measured separately, not in
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the table above).
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## Reproducing the measurements
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Cold run (kill all deep-live-cam processes, no active CoreML cache):
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```bash
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cd /path/to/Deep-Live-Cam
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.venv/bin/python run.py
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# Look for:
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# [platform] ... -> confirms accelerator selection
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# [VideoCapturer] 960x540 @ 60fps (reported=...)
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# Partitions: 1 -> from CoreML EP verbose logs
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```
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For the CoreML partition / inswapper latency numbers specifically, see
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the standalone test at `/Users/max/Development/onnxruntime_test/test_pad_reflect.py`
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— runs the model with and without the graph rewrite on any installed ORT.
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## Future cleanup
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When ORT floor ≥ 1.26.0 lands (microsoft/onnxruntime#28073):
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- Delete `_decompose_reflect_pad` in `modules/onnx_optimize.py`.
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- Drop the `TODO(ort>=1.26)` markers.
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- Update `requirements.txt`.
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Does not change runtime performance — native MIL reflect matches the
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Slice+Concat rewrite to within noise (27.2 vs 27.4 ms in the table
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above). Purely a code-deletion cleanup.
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