Files

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brenden 682afacd30 Add native self-hosted instance connection to fluxer_desktop

Trimmed monorepo checkout (fluxer_desktop + packages/voice_engine_v2 +
tools/ci) with a "Connect to a Different Server" menu item and popout
that lets the desktop app switch to any self-hosted Fluxer instance,
plus fixes for well-known discovery on single-domain self-hosted
deployments and a false-positive ERR_ABORTED on same-origin client
redirects during the switch. Defaults to chat.fluxr.chat and uses an
isolated userData directory from the official build.

2026-07-01 18:22:43 -04:00

README.md

Add native self-hosted instance connection to fluxer_desktop

2026-07-01 18:22:43 -04:00

selfie_segmenter_landscape.onnx

Add native self-hosted instance connection to fluxer_desktop

2026-07-01 18:22:43 -04:00

README.md

Bundled segmentation models

selfie_segmenter_landscape.onnx

Source: Google MediaPipe Selfie Segmenter (landscape), selfie_segmenter_landscape.tflite, downloaded from https://storage.googleapis.com/mediapipe-models/image_segmenter/selfie_segmenter_landscape/float16/latest/selfie_segmenter_landscape.tflite (sha256 490e9ea734313e0de10fa0cd9e3c6133e36ea4db2b7a49bde9ef019f72796b8e).
License: Apache License 2.0, per the official model card ("Model Card MediaPipe Selfie Segmentation", Google, 2021; https://storage.googleapis.com/mediapipe-assets/Model%20Card%20MediaPipe%20Selfie%20Segmentation.pdf). This is the Apache-licensed Selfie model, not the ToS-restricted Google Meet model (segm_full_v679.tflite), which must never be shipped.
Conversion: tf2onnx (python -m tf2onnx.convert --tflite selfie_segmenter_landscape.tflite --output model.onnx --opset 13), followed by graph surgery that replaces the single TFL_Convolution2DTransposeBias custom op with a standard ConvTranspose (weights transposed from [out, kh, kw, in] to [in, out, kh, kw], strides 2x2, no padding) wrapped in NHWC/NCHW transposes, and bumps the default opset domain to 14 for HardSwish.
Verification: output of the converted model matches the original tflite interpreter to a max abs diff of 8.7e-8 on random input. On a portrait test image the output is person confidence (1.0 on the subject, 0.0 in background corners), despite the output tensor name segment_back.
Signature: input input_1 [1, 144, 256, 3] f32 RGB scaled to 0..1, output segment_back [1, 144, 256, 1] f32 person confidence 0..1.
sha256: e8224061bba6031282bfd00cf23a2563fa11a1e28baae0a9052cef6b4e7f3321