build: prepare package for PyPI publishing

- complete project metadata: author (Adrian Szczepański), keywords,
  classifiers, Homepage/Issues URLs
- add MIT LICENSE and declare license-files
- adopt ruff (defaults + import sorting), wire lint into Tier 0 CI,
  apply initial formatting
- add Trusted Publishing (OIDC) workflow, triggered on GitHub Release

Verified: uv build + twine check pass; LICENSE bundled in wheel.

Author: aszc-dev

.github/workflows/publish-pypi.yml

@@ -0,0 +1,29 @@
+name: Publish to PyPI
+
+# Trusted Publishing (OIDC) — no API token stored. Configure a PyPI publisher
+# for this repo/workflow first (see https://docs.pypi.org/trusted-publishers/).
+# Fires when a GitHub Release is published; the release tag must match the
+# version in pyproject.toml (e.g. tag v0.1.0 -> version 0.1.0).
+on:
+  release:
+    types: [published]
+  workflow_dispatch:
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+    environment: pypi
+    permissions:
+      id-token: write # required for Trusted Publishing
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: astral-sh/setup-uv@v7
+        with:
+          enable-cache: true
+
+      - name: Build
+        run: uv build
+
+      - name: Publish
+        run: uv publish

.github/workflows/tier0.yml

@@ -22,5 +22,10 @@ jobs:
       - name: uv sync
         run: uv sync
 
+      - name: Lint (ruff)
+        run: |
+          uv run ruff check .
+          uv run ruff format --check .
+
       - name: Run Tier 0
         run: uv run pytest -m unit tests/ -v

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023-2026 Adrian Szczepański
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

coreml_diffusion/__init__.py

@@ -19,10 +19,11 @@
 
 because a saved workflow JSON references these strings verbatim.
 """
+
 from enum import Enum
 
-from coreml_diffusion.model_version import ModelVersion
 from coreml_diffusion.attention import ATTENTION_IMPLEMENTATIONS
+from coreml_diffusion.model_version import ModelVersion
 from coreml_diffusion.naming import (
     QUANT_NBITS_VALUES,
     compose_out_name,
@@ -43,7 +44,7 @@
 
 
 class Status(Enum):
-    VERIFIED = "verified"          # has a golden anchor + passing [M2-ANE] check
+    VERIFIED = "verified"  # has a golden anchor + passing [M2-ANE] check
     EXPERIMENTAL = "experimental"  # convertible, not yet anchored/verified
 
 
@@ -59,7 +60,7 @@ class Status(Enum):
     ModelVersion.SD15: Status.VERIFIED,
     ModelVersion.SDXL: Status.VERIFIED,
     ModelVersion.SDXL_REFINER: Status.EXPERIMENTAL,  # -> VERIFIED after a refiner golden anchor
-    ModelVersion.LCM: Status.EXPERIMENTAL,           # -> VERIFIED after E-LCM golden anchor
+    ModelVersion.LCM: Status.EXPERIMENTAL,  # -> VERIFIED after E-LCM golden anchor
 }
 
 

coreml_diffusion/cli.py

@@ -10,6 +10,7 @@
     coreml-diffusion convert --ckpt model.safetensors --model-version SD15 \\
         --out unet.mlpackage --height 512 --width 512 --attn-impl SPLIT_EINSUM
 """
+
 import argparse
 
 import coreml_diffusion
@@ -52,7 +53,9 @@ def build_parser():
     sub = parser.add_subparsers(dest="command", required=True)
 
     conv = sub.add_parser("convert", help="Convert a checkpoint's UNet to a .mlpackage")
-    conv.add_argument("--ckpt", required=True, help="Path to the source .safetensors checkpoint")
+    conv.add_argument(
+        "--ckpt", required=True, help="Path to the source .safetensors checkpoint"
+    )
     conv.add_argument(
         "--model-version",
         required=True,
@@ -62,9 +65,15 @@ def build_parser():
         help="Model architecture (verified: SD15, SDXL; experimental otherwise)",
     )
     conv.add_argument("--out", required=True, help="Output .mlpackage path to write")
-    conv.add_argument("--height", type=int, default=512, help="Target image height (default 512)")
-    conv.add_argument("--width", type=int, default=512, help="Target image width (default 512)")
-    conv.add_argument("--batch-size", type=int, default=1, help="Batch size (default 1)")
+    conv.add_argument(
+        "--height", type=int, default=512, help="Target image height (default 512)"
+    )
+    conv.add_argument(
+        "--width", type=int, default=512, help="Target image width (default 512)"
+    )
+    conv.add_argument(
+        "--batch-size", type=int, default=1, help="Batch size (default 1)"
+    )
     conv.add_argument(
         "--attn-impl",
         choices=coreml_diffusion.list_attention_impls(),
@@ -82,7 +91,9 @@ def build_parser():
         metavar="PATH[:STRENGTH]",
         help="LoRA to fuse before conversion; repeatable. STRENGTH defaults to 1.0",
     )
-    conv.add_argument("--config", default=None, help="Optional original-config YAML path")
+    conv.add_argument(
+        "--config", default=None, help="Optional original-config YAML path"
+    )
     conv.add_argument(
         "--quantize",
         choices=coreml_diffusion.list_quant_modes(),

coreml_diffusion/conversion/attention.py

@@ -19,7 +19,9 @@ def apply_attention_implementation(unet, attention_implementation):
         unet.set_attn_processor(SplitEinsumV2AttnProcessor())
         return unet
 
-    raise ValueError(f"Unsupported attention implementation: {attention_implementation}")
+    raise ValueError(
+        f"Unsupported attention implementation: {attention_implementation}"
+    )
 
 
 class SplitEinsumAttnProcessor:
@@ -80,14 +82,18 @@ def _attention_forward(
     input_ndim = hidden_states.ndim
     if input_ndim == 4:
         batch_size, channel, height, width = hidden_states.shape
-        hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+        hidden_states = hidden_states.view(
+            batch_size, channel, height * width
+        ).transpose(1, 2)
     else:
         batch_size, _, channel = hidden_states.shape
         height = None
         width = None
 
     batch_size, key_sequence_length, _ = (
-        hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        hidden_states.shape
+        if encoder_hidden_states is None
+        else encoder_hidden_states.shape
     )
 
     if attention_mask is not None:

coreml_diffusion/convert.py

@@ -8,6 +8,7 @@
 derives the trace timestep from an LCM scheduler) shares this single
 implementation instead of keeping a near-duplicate copy.
 """
+
 import gc
 import os
 import time
@@ -18,12 +19,12 @@
 from diffusers import UNet2DConditionModel
 
 from coreml_diffusion.attention import ATTENTION_IMPLEMENTATIONS
-from coreml_diffusion.model_version import ModelVersion
 from coreml_diffusion.conversion.attention import apply_attention_implementation
 from coreml_diffusion.conversion.shapes import conv2d_output_shape
 from coreml_diffusion.conversion.trace import prepare_unet_for_coreml_trace
 from coreml_diffusion.conversion.unet import CoreMLUNetWrapper
 from coreml_diffusion.logger import logger
+from coreml_diffusion.model_version import ModelVersion
 
 DEFAULT_TRACE_TIMESTEP = 999.0
 TEXT_TOKEN_SEQUENCE_LENGTH = 77
@@ -97,7 +98,9 @@ def convert_to_coreml(
     return coreml_model
 
 
-def get_sample_input(batch_size, encoder_hidden_states_shape, sample_shape, scheduler=None):
+def get_sample_input(
+    batch_size, encoder_hidden_states_shape, sample_shape, scheduler=None
+):
     """Build the example inputs used to JIT-trace the UNet.
 
     When ``scheduler`` is provided (the LCM path) the trace timestep is taken
@@ -106,7 +109,9 @@ def get_sample_input(batch_size, encoder_hidden_states_shape, sample_shape, sche
     graph — the random values are placeholders.
     """
     timestep_value = (
-        scheduler.timesteps[0].item() if scheduler is not None else DEFAULT_TRACE_TIMESTEP
+        scheduler.timesteps[0].item()
+        if scheduler is not None
+        else DEFAULT_TRACE_TIMESTEP
     )
     sample_unet_inputs = dict(
         [
@@ -144,7 +149,10 @@ def sdxl_inputs(sample_unet_inputs, ref_unet, model_version):
         time_ids_list = list(original_size + crops_coords_top_left + target_size)
 
     time_ids = torch.tensor(time_ids_list).repeat(batch_size, 1).to(torch.int64)
-    text_embeds_shape = (batch_size, get_sdxl_text_embeds_dim(ref_unet, len(time_ids_list)))
+    text_embeds_shape = (
+        batch_size,
+        get_sdxl_text_embeds_dim(ref_unet, len(time_ids_list)),
+    )
 
     return {
         "time_ids": time_ids,

coreml_diffusion/naming.py

@@ -5,6 +5,7 @@
 cache key: every workflow that references a converted .mlpackage depends
 on it staying byte-for-byte identical.
 """
+
 from typing import Iterable, Tuple
 
 ATTN_SUFFIX = {
@@ -52,7 +53,11 @@ def compose_out_name(
         )
     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 ""
+    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]
     quant_suffix = f"_q{quantize_nbits}" if quantize_nbits != "none" else ""

pyproject.toml

@@ -3,9 +3,31 @@ name = "coreml-diffusion"
 description = "Convert diffusion-model checkpoints (SD1.5/SDXL) to Core ML for Apple Neural Engine — framework-free, ComfyUI-independent."
 version = "0.1.0"
 license = "MIT"
+license-files = ["LICENSE"]
 requires-python = ">=3.12,<3.13"
 readme = "README.md"
-authors = [{ name = "aszc-dev" }]
+authors = [{ name = "Adrian Szczepański", email = "hi@aszc.dev" }]
+keywords = [
+    "coreml",
+    "core-ml",
+    "diffusion",
+    "stable-diffusion",
+    "sdxl",
+    "apple-neural-engine",
+    "ane",
+    "diffusers",
+    "comfyui",
+]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "Operating System :: MacOS",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.12",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Multimedia :: Graphics :: Graphics Conversion",
+    "Typing :: Typed",
+]
 dependencies = [
     "torch>=2.7,<2.8",
     "coremltools>=9,<10",
@@ -20,7 +42,9 @@ dependencies = [
 coreml-diffusion = "coreml_diffusion.cli:main"
 
 [project.urls]
+Homepage = "https://github.com/aszc-dev/coreml-diffusion"
 Repository = "https://github.com/aszc-dev/coreml-diffusion"
+Issues = "https://github.com/aszc-dev/coreml-diffusion/issues"
 
 [build-system]
 requires = ["hatchling"]
@@ -30,7 +54,17 @@ build-backend = "hatchling.build"
 packages = ["coreml_diffusion"]
 
 [dependency-groups]
-dev = ["pytest>=9.0.3"]
+dev = [
+    "pytest>=9.0.3",
+    "ruff>=0.14",
+]
+
+[tool.ruff]
+target-version = "py312"
+
+[tool.ruff.lint]
+# Ruff defaults (E, F) plus import sorting; ruff format does not sort imports.
+extend-select = ["I"]
 
 [tool.pytest.ini_options]
 markers = [

tests/conftest.py

@@ -4,6 +4,7 @@
 tier is requested via ``-m``) skips the other tiers at collection time so Tier 0
 on Linux never imports the Mac-only stack (coremltools) that Tier 1/2 pull in.
 """
+
 import pytest
 
 _TIER_BY_DIR = {