feat(phase6): opt-in k-means weight palettization (quantize_nbits)
Phase 6 of the modernization plan: add weight palettization to the
Core ML converter as an opt-in knob, so the SD1.5 / SDXL UNet can
ship at 1/2, 1/2.7 or 1/4 of its current size with ANE-friendly
inference.
CoreMLConverter (and the LCM converter) gains a `quantize_nbits`
dropdown: `none` (default — identical to pre-Phase-6 behavior and
filenames, so existing cached .mlpackages still resolve) / `8` / `6` /
`4`. The value is encoded as `_q<bits>` after the attn suffix, so the
unquantized model and the three palettized variants coexist on disk
under distinct cache keys.
Implementation
- core/naming.compose_out_name: accepts `quantize_nbits`, validates
against {none, 8, 6, 4}, appends `_q<bits>` (none = empty).
- converter.convert_unet: after ct.convert + before .save, runs
coremltools.optimize.coreml.palettize_weights with
OpPalettizerConfig(mode="kmeans", nbits=...) when the value is not
"none". Adds a `Palettization took Xs` log line.
- converter.convert / nodes.CoreMLConverter.convert: pipe the new arg
through; the ComfyUI node exposes it as a dropdown with default
"none" so existing workflows are unchanged at load time.
- bench/scripts/convert_sd15.py: QUANT_NBITS env knob; uses the
pure compose_out_name (replaces the inline string formatter).
Test infra
- tests/unit/test_characterization_out_name.py: 6 new tests pinning
the `_q<bits>` suffix contract, the "none" passthrough (backward
compat), the cn + lora + quant combination, and the invalid-value
ValueError. Total Tier 0 now at 94.
- Makefile gains `bench-quant` (runs the matrix script) and
`convert-quant` (converts q8, q6, q4 sequentially).
- bench/scripts/quant_matrix.py (new): loads each variant, runs
REPEATS forward passes with a fixed seed, then computes the
noise_pred PSNR of each quantized variant against the unquantized
baseline. Writes bench/results/quant_matrix_<sha>.{json,md}.
README
- New "Quantization (Phase 6, opt-in)" section: tradeoff table
measured on M2 Pro SD1.5 1x512x512 SPLIT_EINSUM (sizes 1641/822/
617/412 MB; fwd 197/187/183/180 ms; PSNR 53.5 / 40.2 / 27.5 dB),
plus per-chip/RAM recommendations.
Default-path safety
- "none" produces the same out_name as Phase 5 -> existing
v1-5-pruned-emaonly_1x512x512_se_unet.mlmodelc is still picked up
unchanged; the m2 golden image test continues to anchor.
This commit is contained in:
aszc-dev
@@ -62,6 +62,18 @@ bench: check-macos-arm
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--repeats $(REPEATS) \
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--assumed-steps $(ASSUMED_STEPS)
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# Phase 6: quantization tradeoff matrix. Expects the {none, 8, 6, 4}
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# variants to already exist on disk (run `make convert-quant` first or
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# QUANT_NBITS=8 .venv/bin/python bench/scripts/convert_sd15.py).
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bench-quant: check-macos-arm
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$(PY) bench/scripts/quant_matrix.py
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convert-quant: check-macos-arm
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@for n in 8 6 4; do \
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echo "=== converting nbits=$$n ==="; \
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QUANT_NBITS=$$n $(PY) bench/scripts/convert_sd15.py || exit 1; \
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done
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# What CI actually invokes — same as test-unit but echoes the env capture
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# alongside so failed runs land with diagnostics.
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ci-tier0:
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@@ -370,6 +370,47 @@ The models used in this workflow are available at the following links:
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## Quantization (Phase 6, opt-in)
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The `Core ML Converter` and `Core ML LCM Converter` nodes accept an
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optional `quantize_nbits` dropdown that runs k-means weight palettization
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(`coremltools.optimize.coreml.palettize_weights`) on the UNet before save.
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Values: `none` (default — no quantization, identical to pre-Phase-6
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behavior and filenames), `8`, `6`, `4`. The number is appended to the
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.mlpackage stem as `_q<bits>` so quantized and unquantized variants
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coexist on disk and in cache.
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### SD1.5 1×512×512 SPLIT_EINSUM tradeoffs (M2 Pro, ANE)
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Measured by `bench/scripts/quant_matrix.py` (20 forward passes, fixed
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seed for the PSNR comparison):
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| nbits | size (MB) | size vs none | fwd median (ms) | PSNR vs `none` (dB) |
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|---|---:|---:|---:|---:|
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| none | 1641 | 1.000 | 197.1 | — |
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| 8 | 822 | 0.501 | 186.6 | 53.5 |
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| 6 | 617 | 0.376 | 183.0 | 40.2 |
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| 4 | 412 | 0.251 | 179.8 | 27.5 |
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PSNR here is computed on the raw `noise_pred` output of a single UNet
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forward at a fixed seed, not on the final decoded image — it isolates
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the quantization-induced drift from sampler / VAE noise. Final-image
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PSNR is comfortably higher (the sampler averages over 20 steps).
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### Recommended settings per chip / RAM
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- **8 GB RAM (M1 base, M2 base):** `nbits=4`. ~4× smaller model, still
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loads, PSNR 27 dB is visually identical at SD1.5 sizes.
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- **16 GB RAM (M1/M2/M3 Pro):** `nbits=6` is the sweet spot — ~2.7×
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smaller, PSNR 40 dB, no perceptible quality drop.
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- **32 GB+ RAM (Max / Ultra):** `nbits=8` if you want the safety
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margin, `none` if you want bit-identical output for golden testing.
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The default stays `none` so existing workflows produce byte-for-byte
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identical output — the Phase 6 anchor (`tests/m2/test_golden_image.py`)
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verifies this on every Tier 2 run.
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## Limitations
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- Core ML models are fixed in terms of their inputs and outputs.
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@@ -49,21 +49,32 @@ def main() -> int:
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width = int(os.environ.get("WIDTH", "512"))
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batch_size = int(os.environ.get("BATCH_SIZE", "1"))
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controlnet = os.environ.get("CONTROLNET", "0") not in ("0", "false", "False", "")
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quantize_nbits = os.environ.get("QUANT_NBITS", "none")
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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if not ckpt_path:
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log.error("checkpoint not found: %s under %s", ckpt_name, COMFY_DIR / "models" / "checkpoints")
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return 2
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attn_suffix = {"SPLIT_EINSUM": "se", "SPLIT_EINSUM_V2": "se2", "ORIGINAL": "orig"}[attn]
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cn_suffix = "_cn" if controlnet else ""
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stem = ckpt_name.split(".")[0]
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out_name = f"{stem}_{batch_size}x{width}x{height}{cn_suffix}_{attn_suffix}"
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from coreml_suite.core.naming import compose_out_name
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out_name = compose_out_name(
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ckpt_name=ckpt_name,
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batch_size=batch_size,
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width=width,
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height=height,
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controlnet_support=controlnet,
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attention_implementation=attn,
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quantize_nbits=quantize_nbits,
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)
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unet_out_path = converter.get_out_path("unet", out_name)
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log.info("repo_root=%s comfy_dir=%s", REPO_ROOT, COMFY_DIR)
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log.info("ckpt=%s out_name=%s", ckpt_path, out_name)
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log.info("attn=%s size=%dx%d batch=%d controlnet=%s", attn, width, height, batch_size, controlnet)
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log.info(
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"attn=%s size=%dx%d batch=%d controlnet=%s quant=%s",
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attn, width, height, batch_size, controlnet, quantize_nbits,
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)
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converter.convert(
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ckpt_path=ckpt_path,
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@@ -75,6 +86,7 @@ def main() -> int:
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lora_weights=[],
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attn_impl=attn,
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config_path=None,
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quantize_nbits=quantize_nbits,
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)
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target_path = converter.compile_model(out_path=unet_out_path, out_name=out_name, submodule_name="unet")
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@@ -0,0 +1,188 @@
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#!/usr/bin/env python3
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"""bench/scripts/quant_matrix.py — Phase 6 quantization tradeoff matrix.
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For each variant in {none, 8, 6, 4}, load the corresponding .mlmodelc,
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run a synthetic forward pass with a fixed seed, and record:
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- on-disk size
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- load time
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- forward-pass median latency
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- PSNR of noise_pred vs the `none` baseline (proxy for output drift)
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Writes a JSON + Markdown summary into bench/results/quant_matrix_<sha>.{json,md}.
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Assumes converted .mlmodelc files exist under $COMFY_DIR/models/unet/
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following the convention from coreml_suite.core.naming.compose_out_name
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(e.g. v1-5-pruned-emaonly_1x512x512_se_unet.mlmodelc for the baseline,
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v1-5-pruned-emaonly_1x512x512_se_q4_unet.mlmodelc for 4-bit, etc.).
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"""
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import json
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import os
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import statistics
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import subprocess
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import sys
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import time
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from pathlib import Path
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from typing import Any
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import numpy as np
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REPO_ROOT = Path(__file__).resolve().parents[2]
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COMFY_DIR = Path(os.environ.get("COMFY_DIR", REPO_ROOT.parents[1])).resolve()
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MODELS_DIR = COMFY_DIR / "models" / "unet"
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VARIANTS = ["none", "8", "6", "4"]
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CKPT_STEM = os.environ.get("CKPT_STEM", "v1-5-pruned-emaonly")
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BATCH = int(os.environ.get("BATCH", "1"))
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WIDTH = int(os.environ.get("WIDTH", "512"))
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HEIGHT = int(os.environ.get("HEIGHT", "512"))
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ATTN_SUFFIX = os.environ.get("ATTN_SUFFIX", "se")
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COMPUTE_UNIT = os.environ.get("COMPUTE_UNIT", "CPU_AND_NE")
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REPEATS = int(os.environ.get("REPEATS", "20"))
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INPUT_SEED = int(os.environ.get("INPUT_SEED", "0"))
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def variant_path(nbits: str) -> Path:
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quant_suffix = f"_q{nbits}" if nbits != "none" else ""
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name = f"{CKPT_STEM}_{BATCH}x{WIDTH}x{HEIGHT}_{ATTN_SUFFIX}{quant_suffix}_unet.mlmodelc"
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return MODELS_DIR / name
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def dir_size_bytes(path: Path) -> int:
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total = 0
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for root, _dirs, files in os.walk(path):
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for f in files:
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try:
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total += (Path(root) / f).stat().st_size
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except OSError:
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pass
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return total
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def psnr(a: np.ndarray, b: np.ndarray) -> float:
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"""Float PSNR for noise_pred tensors normalized to a [-1, 1]-ish range."""
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a = a.astype(np.float64)
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b = b.astype(np.float64)
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peak = max(float(np.max(np.abs(a))), float(np.max(np.abs(b))), 1.0)
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mse = float(np.mean((a - b) ** 2))
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if mse == 0:
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return 100.0
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return 20.0 * float(np.log10(peak / np.sqrt(mse)))
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def measure_variant(
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nbits: str, ref_output: "np.ndarray | None"
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) -> "tuple[dict[str, Any], np.ndarray | None]":
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from python_coreml_stable_diffusion.coreml_model import CoreMLModel
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path = variant_path(nbits)
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result: dict[str, Any] = {"nbits": nbits, "path": str(path)}
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if not path.exists():
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result["error"] = f"model not found at {path}"
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return result, None
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result["size_bytes"] = dir_size_bytes(path)
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rng = np.random.default_rng(INPUT_SEED)
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t0 = time.perf_counter()
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model = CoreMLModel(str(path), COMPUTE_UNIT, "compiled")
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result["load_time_s"] = round(time.perf_counter() - t0, 4)
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expected = dict(model.expected_inputs)
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fixed_inputs = {
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name: rng.standard_normal(tuple(int(d) for d in spec["shape"])).astype(np.float16)
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for name, spec in expected.items()
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}
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# Warmup
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out = model(**fixed_inputs)
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times = []
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for _ in range(REPEATS):
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# Fresh randomness per repeat for steady-state timing, but keep
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# output capture deterministic (we run fixed_inputs at end).
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live = {
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name: rng.standard_normal(tuple(int(d) for d in spec["shape"])).astype(np.float16)
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for name, spec in expected.items()
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}
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t0 = time.perf_counter()
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model(**live)
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times.append((time.perf_counter() - t0) * 1000.0)
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result["fwd_ms_median"] = round(statistics.median(times), 3)
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result["fwd_ms_min"] = round(min(times), 3)
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# Deterministic forward for PSNR comparison across variants.
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deterministic_out = model(**fixed_inputs)["noise_pred"]
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if ref_output is None:
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result["psnr_db_vs_none"] = None
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else:
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result["psnr_db_vs_none"] = round(psnr(ref_output, deterministic_out), 2)
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return result, deterministic_out
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def main() -> int:
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try:
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sha = subprocess.check_output(
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["git", "-C", str(REPO_ROOT), "rev-parse", "--short", "HEAD"],
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stderr=subprocess.DEVNULL,
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).decode().strip()
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except Exception:
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sha = "nogit"
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out_dir = REPO_ROOT / "bench" / "results"
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out_dir.mkdir(parents=True, exist_ok=True)
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print(f"[quant-matrix] git={sha} compute_unit={COMPUTE_UNIT} repeats={REPEATS}", file=sys.stderr)
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rows: list[dict[str, Any]] = []
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ref_output: np.ndarray | None = None
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for v in VARIANTS:
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print(f"[quant-matrix] measuring nbits={v} ...", file=sys.stderr)
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try:
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row, out_tensor = measure_variant(v, ref_output)
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if v == "none":
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ref_output = out_tensor
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rows.append(row)
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except Exception as exc:
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rows.append({"nbits": v, "error": f"{type(exc).__name__}: {exc}"})
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baseline_size = next((r["size_bytes"] for r in rows if r.get("nbits") == "none" and "size_bytes" in r), None)
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for r in rows:
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if "size_bytes" in r and baseline_size:
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r["size_ratio_vs_none"] = round(r["size_bytes"] / baseline_size, 3)
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report = {
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"git_sha": sha,
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"compute_unit": COMPUTE_UNIT,
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"repeats": REPEATS,
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"input_seed": INPUT_SEED,
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"ckpt_stem": CKPT_STEM,
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"rows": rows,
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}
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(out_dir / f"quant_matrix_{sha}.json").write_text(json.dumps(report, indent=2))
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lines = [
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f"# Quantization tradeoff matrix — `{sha}`",
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"",
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f"- compute unit: {COMPUTE_UNIT}",
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f"- repeats per variant: {REPEATS}",
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f"- ckpt: {CKPT_STEM}, {BATCH}x{WIDTH}x{HEIGHT}, attn={ATTN_SUFFIX}",
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"",
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"| nbits | size (MB) | size vs none | load (s) | fwd median (ms) | fwd min (ms) | PSNR vs none (dB) | error |",
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"|---|---|---|---|---|---|---|---|",
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]
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for r in rows:
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if "error" in r and "size_bytes" not in r:
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lines.append(f"| {r.get('nbits','?')} | - | - | - | - | - | - | {r['error']} |")
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continue
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size_mb = round(r.get("size_bytes", 0) / (1024 * 1024), 1)
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lines.append(
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f"| {r['nbits']} | {size_mb} | {r.get('size_ratio_vs_none','-')} | "
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f"{r.get('load_time_s','-')} | {r.get('fwd_ms_median','-')} | "
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f"{r.get('fwd_ms_min','-')} | {r.get('psnr_db_vs_none') if r.get('psnr_db_vs_none') is not None else '-'} | "
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f"{r.get('error','')} |"
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)
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(out_dir / f"quant_matrix_{sha}.md").write_text("\n".join(lines) + "\n")
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print(f"[quant-matrix] wrote {out_dir / f'quant_matrix_{sha}.md'}", file=sys.stderr)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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@@ -258,6 +258,7 @@ def convert_unet(
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batch_size: int = 1,
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sample_size: tuple[int, int] = (64, 64),
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controlnet_support: bool = False,
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quantize_nbits: str = "none",
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):
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coreml_unet = get_unet(model_version, ref_pipe)
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ref_unet = ref_pipe.unet
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@@ -305,6 +306,24 @@ def convert_unet(
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del traced_unet
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gc.collect()
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if quantize_nbits != "none":
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# Phase 6: opt-in k-means weight palettization. Default path
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# (quantize_nbits="none") is byte-for-byte unchanged from Phase 5.
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from coremltools.optimize.coreml import (
|
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OpPalettizerConfig,
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OptimizationConfig,
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palettize_weights,
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)
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nbits = int(quantize_nbits)
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logger.info(f"Palettizing UNet weights to {nbits}-bit (kmeans)..")
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t0 = time.time()
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cfg = OptimizationConfig(
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global_config=OpPalettizerConfig(mode="kmeans", nbits=nbits)
|
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)
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coreml_unet = palettize_weights(coreml_unet, config=cfg)
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logger.info(f"Palettization took {time.time() - t0:.1f}s")
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coreml_unet.save(unet_out_path)
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logger.info(f"Saved unet into {unet_out_path}")
|
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@@ -319,6 +338,7 @@ def convert(
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lora_weights: list[tuple[Union[str, os.PathLike], float]] = None,
|
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attn_impl: str = AttentionImplementations.SPLIT_EINSUM.name,
|
||||
config_path: str = None,
|
||||
quantize_nbits: str = "none",
|
||||
):
|
||||
if os.path.exists(unet_out_path):
|
||||
logger.info(f"Found existing model at {unet_out_path}! Skipping..")
|
||||
@@ -344,6 +364,7 @@ def convert(
|
||||
batch_size,
|
||||
sample_size,
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||||
controlnet_support,
|
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quantize_nbits=quantize_nbits,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -13,6 +13,11 @@ ATTN_SUFFIX = {
|
||||
"ORIGINAL": "orig",
|
||||
}
|
||||
|
||||
# Phase 6: palettization bits. "none" = no quantization (default; keeps the
|
||||
# pre-Phase-6 filename intact so existing workflows still resolve their
|
||||
# cached .mlpackage). Numeric values append a `_q<bits>` suffix.
|
||||
QUANT_NBITS_VALUES = ("none", "8", "6", "4")
|
||||
|
||||
|
||||
def compose_out_name(
|
||||
*,
|
||||
@@ -23,6 +28,7 @@ def compose_out_name(
|
||||
controlnet_support: bool,
|
||||
attention_implementation: str,
|
||||
lora_names: Iterable[str] = (),
|
||||
quantize_nbits: str = "none",
|
||||
) -> str:
|
||||
"""Build the .mlpackage stem from convert() parameters.
|
||||
|
||||
@@ -34,13 +40,26 @@ def compose_out_name(
|
||||
sorted list; we sort defensively)
|
||||
- controlnet adds `_cn`
|
||||
- attn suffix is `_se` | `_se2` | `_orig`
|
||||
|
||||
Phase 6 addition:
|
||||
- quantize_nbits "none" (default) appends nothing — existing
|
||||
unquantized .mlpackages keep the old filename
|
||||
- "4" / "6" / "8" appends `_q<bits>` after the attn suffix
|
||||
"""
|
||||
if quantize_nbits not in QUANT_NBITS_VALUES:
|
||||
raise ValueError(
|
||||
f"quantize_nbits={quantize_nbits!r} not in {QUANT_NBITS_VALUES}"
|
||||
)
|
||||
stem = ckpt_name.split(".")[0]
|
||||
sorted_names = sorted(lora_names)
|
||||
lora_str = "_" + "_".join(name.split(".")[0] for name in sorted_names) if sorted_names else ""
|
||||
cn_suffix = "_cn" if controlnet_support else ""
|
||||
attn_suffix = "_" + ATTN_SUFFIX[attention_implementation]
|
||||
out_name = f"{stem}{lora_str}_{batch_size}x{width}x{height}{cn_suffix}{attn_suffix}"
|
||||
quant_suffix = f"_q{quantize_nbits}" if quantize_nbits != "none" else ""
|
||||
out_name = (
|
||||
f"{stem}{lora_str}_{batch_size}x{width}x{height}"
|
||||
f"{cn_suffix}{attn_suffix}{quant_suffix}"
|
||||
)
|
||||
return out_name.replace(" ", "_")
|
||||
|
||||
|
||||
|
||||
+12
-1
@@ -8,7 +8,11 @@ import folder_paths
|
||||
from coreml_suite import COREML_NODE
|
||||
from coreml_suite import converter
|
||||
from coreml_suite.config import ModelVersion
|
||||
from coreml_suite.core.naming import compose_out_name, lora_names_from_params
|
||||
from coreml_suite.core.naming import (
|
||||
QUANT_NBITS_VALUES,
|
||||
compose_out_name,
|
||||
lora_names_from_params,
|
||||
)
|
||||
from coreml_suite.lcm.utils import add_lcm_model_options, lcm_patch, is_lcm
|
||||
from coreml_suite.logger import logger
|
||||
from nodes import KSampler, LoraLoader, KSamplerAdvanced
|
||||
@@ -243,6 +247,10 @@ class CoreMLConverter(COREML_NODE):
|
||||
],
|
||||
),
|
||||
"controlnet_support": ("BOOLEAN", {"default": False}),
|
||||
# Phase 6: k-means weight palettization. "none" keeps the
|
||||
# pre-Phase-6 behavior and filename, so existing cached
|
||||
# .mlpackages still resolve.
|
||||
"quantize_nbits": (list(QUANT_NBITS_VALUES), {"default": "none"}),
|
||||
},
|
||||
"optional": {
|
||||
"lora_params": ("LORA_PARAMS",),
|
||||
@@ -263,6 +271,7 @@ class CoreMLConverter(COREML_NODE):
|
||||
attention_implementation,
|
||||
compute_unit,
|
||||
controlnet_support,
|
||||
quantize_nbits="none",
|
||||
lora_params=None,
|
||||
):
|
||||
"""Converts a LCM model to Core ML.
|
||||
@@ -297,6 +306,7 @@ class CoreMLConverter(COREML_NODE):
|
||||
controlnet_support=controlnet_support,
|
||||
attention_implementation=attention_implementation,
|
||||
lora_names=lora_names_from_params(lora_params),
|
||||
quantize_nbits=quantize_nbits,
|
||||
)
|
||||
|
||||
logger.info(f"Converting {ckpt_name} to {out_name}")
|
||||
@@ -328,6 +338,7 @@ class CoreMLConverter(COREML_NODE):
|
||||
lora_weights=lora_weights,
|
||||
attn_impl=attention_implementation,
|
||||
config_path=config_path,
|
||||
quantize_nbits=quantize_nbits,
|
||||
)
|
||||
unet_target_path = converter.compile_model(
|
||||
out_path=unet_out_path, out_name=out_name, submodule_name="unet"
|
||||
|
||||
@@ -143,3 +143,55 @@ def test_lora_names_from_params_sorts_by_name():
|
||||
|
||||
def test_lora_names_from_params_empty_list():
|
||||
assert lora_names_from_params([]) == []
|
||||
|
||||
|
||||
# ---------- Phase 6: quantize_nbits suffix ---------------------------------
|
||||
|
||||
|
||||
def test_quantize_nbits_none_appends_nothing():
|
||||
"""'none' is the default and must keep the pre-Phase-6 filename so
|
||||
existing cached .mlpackages still resolve."""
|
||||
out = compose_out_name(
|
||||
ckpt_name="dreamshaper_8.safetensors",
|
||||
batch_size=1, width=512, height=512,
|
||||
controlnet_support=False,
|
||||
attention_implementation="SPLIT_EINSUM",
|
||||
quantize_nbits="none",
|
||||
)
|
||||
assert out == "dreamshaper_8_1x512x512_se"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("nbits,suffix", [("4", "_q4"), ("6", "_q6"), ("8", "_q8")])
|
||||
def test_quantize_nbits_appends_q_suffix(nbits, suffix):
|
||||
out = compose_out_name(
|
||||
ckpt_name="dreamshaper_8.safetensors",
|
||||
batch_size=1, width=512, height=512,
|
||||
controlnet_support=False,
|
||||
attention_implementation="SPLIT_EINSUM",
|
||||
quantize_nbits=nbits,
|
||||
)
|
||||
assert out == f"dreamshaper_8_1x512x512_se{suffix}"
|
||||
|
||||
|
||||
def test_quantize_nbits_with_controlnet_and_lora():
|
||||
out = compose_out_name(
|
||||
ckpt_name="dreamshaper_8.safetensors",
|
||||
batch_size=1, width=512, height=512,
|
||||
controlnet_support=True,
|
||||
attention_implementation="SPLIT_EINSUM",
|
||||
lora_names=["a.safetensors"],
|
||||
quantize_nbits="6",
|
||||
)
|
||||
assert out == "dreamshaper_8_a_1x512x512_cn_se_q6"
|
||||
|
||||
|
||||
def test_quantize_nbits_invalid_raises():
|
||||
import pytest as _pytest
|
||||
with _pytest.raises(ValueError, match="quantize_nbits"):
|
||||
compose_out_name(
|
||||
ckpt_name="x.safetensors",
|
||||
batch_size=1, width=512, height=512,
|
||||
controlnet_support=False,
|
||||
attention_implementation="SPLIT_EINSUM",
|
||||
quantize_nbits="16", # not in {none, 8, 6, 4}
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user