chore: drop dead experiment-scaffold references

- Remove the pytest collect_ignore_glob that skipped WIP scaffolds
  (test_experiments / test_unet_conversion / standalone_test). Those
  files no longer exist and only imported the experimental
  coreml_suite.experiments / convert_apple modules.
- Drop .gitignore entries for the removed experiment and alternate-env
  directories (experiments/, experiment_results/, apple_env/, comfy_env/,
  coremlsuite-venv/).

.github/workflows/tier0.yml

@@ -5,10 +5,10 @@ on:
     branches: [main]
   pull_request:
 
-# Deps are resolved from pyproject.toml via uv, so the toolchain pins live in
-# one place. Tier 0 must run without ComfyUI; the in-tree purity gate
-# (tests/unit/test_tier0_purity.py) enforces that the suite hasn't started
-# leaking framework imports.
+# Minimal-deps run: Tier 0 must work without ComfyUI, coremltools, or
+# python_coreml_stable_diffusion (Linux CI image won't have them). The
+# in-tree purity gate (tests/unit/test_tier0_purity.py) double-checks
+# that the suite hasn't started leaking framework imports.
 jobs:
   unit:
     runs-on: ubuntu-latest
@@ -16,12 +16,18 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - uses: astral-sh/setup-uv@v7
+      - uses: actions/setup-python@v5
         with:
-          enable-cache: true
+          python-version: "3.11"
 
-      - name: uv sync
-        run: uv sync --no-install-project
+      - name: Install Tier 0 deps
+        run: |
+          python -m pip install --upgrade pip
+          # Tier 0 only needs torch + numpy + pytest; everything else
+          # is Mac-only.
+          python -m pip install \
+            "torch==2.0.1" "numpy<1.25" \
+            "pytest>=8" "pytest-xdist"
 
       - name: Run Tier 0
-        run: uv run pytest -m unit tests/ -v
+        run: pytest -m unit tests/ -v

.github/workflows/tier1.yml

@@ -0,0 +1,31 @@
+name: Tier 1 — Smoke (macOS-ARM)
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    # Gate behind the run-tier1 label too, so external PRs that touch
+    # only docs don't burn a minute of macOS-ARM time. Maintainers can
+    # always re-run via the run-tier1 label.
+    types: [opened, synchronize, reopened, labeled]
+
+jobs:
+  smoke:
+    if: |
+      github.event_name == 'push' ||
+      github.event.action != 'labeled' ||
+      contains(github.event.pull_request.labels.*.name, 'run-tier1')
+    runs-on: macos-14  # M1, Apple Silicon hosted runner
+    timeout-minutes: 20
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: astral-sh/setup-uv@v3
+        with:
+          enable-cache: true
+
+      - name: uv sync
+        run: uv sync --no-install-project
+
+      - name: Run Tier 1 (synthetic micro-UNet smoke)
+        run: uv run pytest -m smoke tests/ -v

.github/workflows/tier2.yml

@@ -30,7 +30,7 @@ jobs:
       # Hybrid ComfyUI strategy:
       #   - schedule (nightly)  -> latest origin/master + ComfyUI's own
       #     requirements.txt (constrained). Canary for upstream API breakage.
-      #   - PR label / dispatch -> the requires-comfyui version tag + the frozen
+      #   - PR label / dispatch -> the pinned requires-comfyui SHA + the frozen
       #     `comfy` uv group. Reproducible merge gate, immune to overnight drift.
       - name: Resolve ComfyUI ref + mode
         run: |
@@ -38,12 +38,10 @@ jobs:
             echo "COMFY_MODE=latest" >> "$GITHUB_ENV"
             echo "COMFY_REF=master"  >> "$GITHUB_ENV"
           else
-            # requires-comfyui is a semver constraint (e.g. ">=0.3.27"); pin the
-            # gate to the matching ComfyUI release tag (vX.Y.Z).
-            VERSION="$(sed -nE 's/^requires-comfyui *= *"[^0-9]*([0-9]+\.[0-9]+\.[0-9]+).*/\1/p' pyproject.toml)"
-            if [ -z "$VERSION" ]; then echo "could not parse requires-comfyui from pyproject.toml"; exit 1; fi
-            echo "COMFY_MODE=pinned"      >> "$GITHUB_ENV"
-            echo "COMFY_REF=v$VERSION"    >> "$GITHUB_ENV"
+            PIN="$(sed -nE 's/^requires-comfyui *= *"==?([0-9a-f]+)".*/\1/p' pyproject.toml)"
+            if [ -z "$PIN" ]; then echo "could not parse requires-comfyui from pyproject.toml"; exit 1; fi
+            echo "COMFY_MODE=pinned" >> "$GITHUB_ENV"
+            echo "COMFY_REF=$PIN"    >> "$GITHUB_ENV"
           fi
 
       - name: Set up ComfyUI checkout
@@ -115,18 +113,10 @@ jobs:
           done
           echo "comfy failed to start"; tail -100 /tmp/comfyui-ci.log; exit 1
 
-      - name: Purge cached Core ML UNets (force fresh conversion)
-        # The converter skips when a model of the same name already exists. That
-        # cache key is conversion *parameters* only, not the conversion code or
-        # toolchain — so a stale model would let a conversion regression pass.
-        # Clear it so every Tier 2 run exercises the full convert -> compile ->
-        # sample path end to end.
-        run: |
-          rm -rf "$COMFY_DIR"/models/unet/*.mlpackage "$COMFY_DIR"/models/unet/*.mlmodelc || true
-
       - name: Run Tier 2 (m2 marker)
-        # Drives the Core ML Converter node, which converts the UNet from the
-        # checkpoint on every run (cache purged above).
+        # The golden-image workflow drives the Core ML Converter node, so the
+        # UNet is converted on demand on the first run and reused from the
+        # runner-local cache afterwards.
         run: uv run --no-sync pytest -m m2 tests/ -v
 
       - name: Stop ComfyUI server

.gitignore

@@ -3,4 +3,4 @@ __pycache__/
 models/
 .venv/
 test_results/
-.claude/
+tests/m2/_latest_generated.png

.python-version

@@ -1 +0,0 @@
-3.12

CONVERTER_EXTRACTION_SPEC.md

@@ -1,618 +0,0 @@
-# ComfyUI-CoreMLSuite — Converter Extraction Spec for Claude Code
-
-> **Companion to `MODERNIZATION_SPEC.md`.** That spec hardens the repo and (Phase 3)
-> splits the *inference* math from the framework. **This** spec splits the *conversion*
-> path (`safetensors → CoreML`) out into a standalone, `comfy`-free, pip-installable
-> package that CoreMLSuite then depends on — and that other projects (incl. on-device
-> iOS tooling) can reuse.
->
-> **Same discipline as the modernization spec:** safety-net first, behavior-preserving
-> until told otherwise, one phase = one branch = one PR, `STOP — VALIDATE` gate between
-> every phase, golden-latent as the regression anchor. `[M2]` = needs macOS/Apple Silicon;
-> `[M2-ANE]` = needs the Neural Engine. Everything else must run on plain Linux/CI.
-
----
-
-## 0. How to work (read first — non-negotiable)
-
-1. **Behavior-preserving until Phase E6.** Phases E1–E5 must not change image output, node
-   names, `INPUT_TYPES` field names, or `NODE_CLASS_MAPPINGS` keys. The node graph is the
-   public contract; saved user-workflow JSON breaks if these change.
-2. **The conversion package produces an artifact and stops there.** Its job ends at a written
-   `.mlpackage` / `.mlmodelc` on disk. It must NOT import `comfy`, `folder_paths`, or
-   `comfy_extras`, and must NOT know ComfyUI's `models/unet` layout. Paths are *inputs*.
-3. **The runtime loader stays in the suite.** The loader is the **local** `coreml_suite.coreml_model.CoreMLModel`
-   — a thin wrapper over `coremltools.models.MLModel` (NOT Apple's
-   `python_coreml_stable_diffusion.coreml_model.CoreMLModel`, which is no longer used; see #58).
-   It *runs* a compiled model in Python — a desktop/Python inference concern, not a conversion
-   concern. It is NOT moved into the package. (On iOS the `.mlmodelc` is loaded natively; the
-   package's output is the deliverable, not a Python runner.)
-4. **Decouple in-repo before splitting repos.** Phases E1–E4 create the package *inside this
-   repo* and prove equivalence. The physical second-repo split is Phase E5, only after the
-   golden latent is proven identical. Do not create a second repository before Gate E4 passes.
-5. **Reuse the existing regression anchor.** The golden latent / PSNR anchor from
-   `MODERNIZATION_SPEC.md` Phase 2 is the cross-cutting proof for every gate here. If it is not
-   yet captured, capture it first (it is a prerequisite for E2 onward).
-6. **No new runtime dependencies** without flagging in the gate report (name, why, license, size).
-7. **A failing gate means stop and report**, not work around into the next phase.
-8. **Tooling is `uv`, not bare `pip`/`venv`.** Every environment/install/lock step uses the
-   project's `uv` toolchain: `uv venv`, `uv pip install`, `uv pip install -e .`, `uv lock`,
-   `uv run pytest`, `uv export`/`uv pip freeze` for baselines. Where this spec says "fresh venv",
-   read "`uv venv` + `uv pip install`". Reserve `uv pip` (not `pip`) inside that venv too.
-9. **The package is the single source of truth for *what is possible*; the node is a thin,
-   discovery-driven frontend.** See the "Interface contract" pillar below — this is the
-   maintainer's hard requirement and it overrides the earlier (now-rescinded) "freeze the
-   dropdown list" instruction.
-
----
-
-## Interface contract (the maintainer's hard requirement) — read before any phase
-
-Two coupled guarantees must hold once the package is split out:
-
-**(A) Updating the converter must NOT require updating CoreMLSuite.**
-This is satisfied by treating the package's public surface as a versioned contract:
-- `convert(...)` and `compile_model(...)` are **keyword-only with defaults** for everything
-  past the genuinely-required positionals (`ckpt_path`, `model_version`, `out_path`). New
-  capabilities are added as new keyword args with defaults, so an old Suite's call still
-  validates against a newer package. **Never** reorder or rename existing parameters.
-- `compose_out_name` (the `.mlpackage` filename = the cache key) **moves into the package** and
-  is versioned with it. The Suite must not carry its own copy; if the package changes the naming
-  scheme that is a **major** bump (old cached artifacts stop resolving).
-
-**(B) CoreMLSuite must be able to list *new* conversion types WITHOUT a Suite code change or
-version bump.** Today the node hardcodes its dropdowns:
-```python
-"model_version": ([ModelVersion.SD15.name, ModelVersion.SDXL.name],),  # hand-typed, also INCOMPLETE (no LCM / SDXL_REFINER)
-"attention_implementation": (list(ATTENTION_IMPLEMENTATIONS),),         # from coreml_suite.attention
-"quantize_nbits": (list(QUANT_NBITS_VALUES), {"default": "none"}),      # from coreml_suite.core.naming
-```
-These are replaced by **runtime discovery calls into the package**, evaluated inside
-`INPUT_TYPES` (ComfyUI re-evaluates `INPUT_TYPES` on every plugin load):
-```python
-import coreml_diffusion
-"model_version":            (coreml_diffusion.list_model_versions(),),
-"attention_implementation": (coreml_diffusion.list_attention_impls(),),
-"quantize_nbits":           (coreml_diffusion.list_quant_modes(), {"default": "none"}),
-```
-Effect: `uv pip install -U coreml_diffusion` + ComfyUI restart surfaces any newly-added type in the old
-plugin's dropdown — **no Suite edit, no Suite version bump.** This is the requirement.
-
-**The cost, stated honestly (accept this trade-off explicitly at Gate E0):**
-- The Suite becomes a "dumb" frontend; the package is the sole authority on what conversions
-  exist. The Suite can no longer guarantee its saved workflows are valid against *arbitrary*
-  future package versions.
-- Therefore the package's discovery identifiers (`ModelVersion` values, attn-impl strings, quant
-  modes) are an **ADDITIVE-ONLY contract**: the package may *add* identifiers freely (minor bump,
-  no Suite change); **removing or renaming an identifier is a breaking change requiring a MAJOR
-  bump and a migration note**, because a saved workflow JSON references these strings verbatim.
-  Without this rule, "no version bump" silently becomes "randomly broken workflows."
-- `INPUT_TYPES` must **fail soft** when the package is missing/old: wrap the discovery calls so a
-  missing `coreml_diffusion` (or an old one lacking a `list_*` function) yields a sane fallback list and a
-  logged warning, instead of the node failing to register and disappearing from the menu.
-
-**Discovery API the package must expose (stable names):**
-```python
-coreml_diffusion.list_model_versions() -> list[str]   # VERIFIED ones only, e.g. ["SD15","SDXL"] today (.name — see seam.md)
-coreml_diffusion.list_attention_impls() -> list[str]  # ["SPLIT_EINSUM","SPLIT_EINSUM_V2","ORIGINAL"]
-coreml_diffusion.list_quant_modes() -> list[str]      # ["none","8","6","4"]
-coreml_diffusion.CONTRACT_VERSION: str                # bump rules above; Suite may log/compare it
-```
-These return the *display strings already used today*, so existing workflows keep validating.
-
-**Verification status is a PACKAGE property, not a node hardcode (maintainer's intent).**
-The Suite wants to expose *every model the converter can verifiably convert*. Today `lcm` and
-`sdxl_refiner` are absent from the converter node not because the Suite chooses to hide them, but
-because they lack a full golden/PSNR verification. So the gating lives in the package as a status:
-```python
-from enum import Enum
-class Status(Enum):
-    VERIFIED = "verified"          # has a golden anchor + passing [M2-ANE] check
-    EXPERIMENTAL = "experimental"  # convertible but not yet anchored/verified
-
-# internal registry, single source of truth.
-# KEY by ModelVersion enum MEMBER so list_* can emit .name. Keying by the lowercase
-# .value string returns ["sd15",...], which the node reverses via ModelVersion[...] -> KeyError.
-_MODEL_STATUS = {ModelVersion.SD15: Status.VERIFIED, ModelVersion.SDXL: Status.VERIFIED,
-                 ModelVersion.SDXL_REFINER: Status.EXPERIMENTAL, ModelVersion.LCM: Status.EXPERIMENTAL}
-
-def list_model_versions(include_experimental: bool = False) -> list[str]:
-    return [v.name for v, s in _MODEL_STATUS.items()   # .name -> "SD15","SDXL"; node reverses with ModelVersion[...]
-            if s is Status.VERIFIED or (include_experimental and s is Status.EXPERIMENTAL)]
-```
-Consequence: **promoting a model to VERIFIED in the package expands the Suite's dropdown with no
-Suite change and no Suite bump** — exactly the requirement. The act of verification (E-LCM
-produces an LCM golden anchor; same later for refiner) is what flips the status. The Suite's
-converter node calls `list_model_versions()` (verified-only); a power-user/CLI path may pass
-`include_experimental=True`. Promotion VERIFIED-from-EXPERIMENTAL is additive (minor bump);
-demotion or removal is breaking (major bump + note).
-
----
-
-## Naming & layout (chosen — frozen at Gate E0)
-
-**Distribution name (PyPI):** `coreml-diffusion`. **Import name (Python):** `coreml_diffusion`.
-(PyPI normalizes `-`/`_`; the distribution uses the hyphen, the importable module the underscore.)
-Availability checked: both `coreml-diffusion` and the near variants were free on PyPI at E0.
-
-**Why this name (the positioning it encodes):** the project's niche is *diffusion models on Apple
-Neural Engine via CoreML, inside ComfyUI and on-device* — **not** Stable Diffusion specifically.
-`sd*` was rejected because it falsely narrows scope to SD; `coreml-diffusion` keeps `coreml` on the
-front for discoverability while `diffusion` honestly states the scope (SD/SDXL/LCM today, Flux and
-other diffusion architectures later) **without** promising arbitrary non-diffusion torch models,
-whose tracing/shape/sample-input pipeline differs. The name must not be re-narrowed to SD in
-future docs. ANE is the *differentiator* (documented in the README), but `coreml` was chosen over
-`ane` in the name for search discoverability per maintainer decision.
-
-Target package layout (framework-free — zero `comfy` imports):
-
-```
-coreml_diffusion/
-  __init__.py        # public API surface (see "Public API" below)
-  model_version.py   # ModelVersion enum — the SINGLE source of truth, no comfy
-  attention.py       # ATTENTION_IMPLEMENTATIONS tuple (from coreml_suite/attention.py) + apply_attention_implementation
-  pipeline.py        # get_pipeline (from_single_file), get_unet (cml UNet from ref unet)
-  unet.py            # UNet2DConditionModelLCM (moved from coreml_suite/lcm/unet.py)
-  inputs.py          # get_sample_input, lcm_inputs, sdxl_inputs,
-                     #   get_encoder_hidden_states_shape, get_coreml_inputs, get_inputs_spec
-  controlnet.py      # add_cnet_support (conversion-side residual SHAPE calc only)
-  convert.py         # convert_unet, convert (orchestration), convert_to_coreml, load_coreml_model
-  compile.py         # compile_coreml_model
-  quantize.py        # (Phase E6 / MODERNIZATION Phase 6 lands here) palettization 4/6/8-bit
-  cli.py             # console entry point: `coreml-diffusion convert ...`
-  pyproject.toml     # standalone packaging (at E5)
-```
-
-What stays in `coreml_suite/` (the ComfyUI side, thinned):
-- `nodes.py` — still owns **name-encoding** (`out_name` construction), path resolution via
-  `folder_paths`, the node `INPUT_TYPES`/mappings, and wrapping the result in `CoreMLModel`.
-- `models.py`, `latents.py`, `controlnet.py` (inference parts), `lcm/utils.py`, `config.py`
-  (inference config build) — untouched by this spec except the import-source of `ModelVersion`.
-
-### Public API (the contract `coreml_diffusion` exposes)
-```python
-from coreml_diffusion import ModelVersion, convert, compile_model, compose_out_name
-from coreml_diffusion import list_model_versions, list_attention_impls, list_quant_modes, CONTRACT_VERSION
-
-# Mirror the CURRENT converter.py signature, made keyword-only past the required positionals
-# and with paths/device injected (no folder_paths, no comfy.model_management):
-# convert(ckpt_path, model_version, out_path, *,
-#         batch_size=1, sample_size=(64, 64), controlnet_support=False,
-#         lora_weights=None, attn_impl=list_attention_impls()[0], config_path=None,
-#         quantize_nbits="none", device=None) -> None   # side effect: writes out_path
-#   (current convert() returns None and writes via convert_unet → coreml_unet.save; keep that,
-#    or change to `return out_path` as a deliberate, documented improvement — pick one at E0.)
-# compile_model(src_path, out_dir, final_name) -> str   # returns compiled .mlmodelc path
-```
-Note: `convert` takes an **explicit `out_path`** — no `folder_paths`. `device` is injected
-(defaults to torch's default device). `compose_out_name` lives here (cache-key contract) and the
-node imports it from the package. The `list_*` discovery functions back the node's dropdowns.
-
----
-
-## The import chains to cut (root cause inventory) — REVISED against current code
-
-> **State note (verified):** the code moved on since the original draft. Several chains are
-> already cut. Re-verify each line by `grep` before acting; do not assume the original draft.
-
-**Already done (verify, then skip):**
-- ✅ `converter.py` already imports `from coreml_suite.model_version import ModelVersion`, and
-  `model_version.py` is **clean** (`from enum import Enum` only — zero comfy). The old
-  "converter → config → comfy" chain is **already broken**. `config.py` still imports comfy, but
-  it is **inference-side** (`get_model_config` via `supported_models_base`/`latent_formats`) —
-  *not* on the conversion path. Do **not** treat `config.py` as a converter dependency.
-- ✅ `converter.py` now uses `diffusers.UNet2DConditionModel.from_single_file` and a local
-  `CoreMLUNetWrapper` (in `coreml_suite/conversion/unet.py`) — it is **no longer** importing the
-  Apple `python_coreml_stable_diffusion.unet.UNet2DConditionModel*` internals on the main path.
-  A `coreml_suite/conversion/` subpackage already exists (`attention`, `shapes`, `trace`, `unet`).
-- ✅ Name-encoding already extracted to `coreml_suite/core/naming.py` (`compose_out_name`,
-  `lora_names_from_params`, `ATTN_SUFFIX`, `QUANT_NBITS_VALUES`) **with characterization tests**
-  (`tests/unit/test_characterization_out_name.py`). The pure-naming split is done.
-- ✅ Quantization is **already implemented** in `converter.py` (`quantize_nbits`, k-means
-  `palettize_weights`) and surfaced as an optional node input. Phase E6 is therefore *move*, not
-  *build* (see revised E6).
-
-**Still to cut (the real remaining work):**
-1. `coreml_suite/converter.py::get_out_path` → `from folder_paths import get_folder_paths`.
-   Main converter still reaches into ComfyUI's model dir. **Cut: `out_path` is an injected arg;
-   `folder_paths` resolution moves up into the node** (the node already computes `out_name`).
-2. `coreml_suite/lcm/converter.py` → still has its **own** `from folder_paths import
-   get_folder_paths` (`get_out_path`) and (per original draft) `comfy.model_management`. Verify
-   the current LCM file and cut both: inject `out_path` and `device`.
-3. Global mutation of the attention impl: confirm where it now lives. Main path appears to route
-   through `coreml_suite/conversion/attention.apply_attention_implementation` (cleaner than the
-   old global), but `lcm/converter.py` may still set a module global at import. **Ensure the
-   package sets attention per-call, never at import time.**
-4. **Duplication LCM vs main:** `lcm/converter.py` still carries its own copies of
-   `convert_to_coreml`, `load_coreml_model`, `get_out_path`, `get_sample_input` (the LCM variant
-   takes a `scheduler` arg), and hardcodes `SimianLuo/LCM_Dreamshaper_v7`. **Dedupe into the
-   single `coreml_diffusion` implementation;** the HF-hardcode consolidation is the *behavior-changing*
-   part → deferred to optional **E-LCM**, not E1–E5.
-5. **`compose_out_name` ownership:** currently in `coreml_suite/core/naming.py` and called by the
-   node. Per the Interface-contract pillar it must **move into the package** (it is the cache-key
-   contract) and the node must import it from `coreml_diffusion`, not keep a copy.
-
----
-
-## Phase E0 — Seam decision & inventory (no code change)
-
-**Objective:** lock the cut line, the interface contract, and naming so later phases don't drift.
-
-### Tasks
-1. Produce `docs/extraction/seam.md`: a table of every symbol in `converter.py`,
-   `lcm/converter.py`, `lcm/unet.py`, **plus the already-extracted `conversion/` subpackage
-   (`attention`, `shapes`, `trace`, `unet`) and `core/naming.py`**, classified
-   **CONVERSION → coreml_diffusion** vs **STAYS (comfy/node)**. Note which are already framework-free.
-2. ~~Confirm the current `python_coreml_stable_diffusion` footprint.~~ **DONE (seam.md §6):
-   footprint is ZERO** — no runtime imports anywhere; only a docstring mention in
-   `core/__init__.py:4`. Main path uses `diffusers` + local `CoreMLUNetWrapper`; the runtime
-   `CoreMLModel` (STAYS in suite) is a local coremltools wrapper, not Apple's. No shape/attn helper
-   comes from Apple (local `conversion/shapes.py`, `conversion/attention.py`).
-3. **Decide the interface contract concretely (the maintainer's hard requirement):**
-   - Discovery functions `list_model_versions / list_attention_impls / list_quant_modes` live in
-     the package and return today's display strings verbatim. Node `INPUT_TYPES` calls them.
-   - `ModelVersion` values, attn-impl strings, quant modes are **ADDITIVE-ONLY** across package
-     versions; removal/rename = MAJOR bump + migration note. Write this into the package's
-     versioning policy doc now.
-   - `compose_out_name` moves to the package; node imports it (no copy). Confirm the
-     characterization tests in `test_characterization_out_name.py` will be re-pointed, not
-     duplicated.
-   - **Resolve the `model_version` dropdown question (maintainer decided):** the Suite exposes
-     *every model the converter can verifiably convert*. `lcm` and `sdxl_refiner` are absent today
-     only because they lack a golden/PSNR verification — **not** because the node hardcodes a
-     short list. Encode this as a **status registry in the package** (`VERIFIED` vs
-     `EXPERIMENTAL`); `list_model_versions()` returns VERIFIED-only by default. The converter node
-     calls it plainly. Promoting LCM/refiner to VERIFIED (after E-LCM / a refiner anchor) expands
-     the dropdown with **no Suite change**. Do NOT add permanent per-node filtering — the gate is
-     verification status, owned by the package.
-4. ~~Confirm the `ml-stable-diffusion` git dep is pinned.~~ **N/A — already removed (#58).** Verified:
-   zero `python_coreml_stable_diffusion` imports in the repo; `CoreMLModel` is now a local
-   coremltools wrapper; the dep is absent from `pyproject.toml`/`requirements.txt`. No SHA to pin.
-
-### STOP — VALIDATE (Gate E0)
-```
-## Gate E0 report
-- seam.md committed: <path>; symbol counts (move / stay / already-framework-free)
-- python_coreml_stable_diffusion usage (verified by grep): conversion=<list> runtime=<list>
-- Discovery API signatures frozen: list_model_versions (verified-only) / list_attention_impls / list_quant_modes
-- Status registry decided: sd15+sdxl=VERIFIED, lcm+sdxl_refiner=EXPERIMENTAL (gated, not hidden)
-- Additive-only contract policy doc written (incl. promotion=minor, demotion/removal=major): <path>
-- model_version dropdown: expose all (incl. LCM/REFINER) / filtered per node — DECISION: <...>
-- compose_out_name move-not-copy confirmed; tests re-point plan: <...>
-- LCM consolidation deferred to optional E-LCM: YES/NO
-- ml-stable-diffusion: N/A — already removed (#58), not a dependency (was: pin-or-BLOCKER)
-- Package name in-repo: coreml_diffusion (final PyPI name deferred to E5)
-```
-
----
-
-## Phase E1 — Establish `coreml_diffusion` package + discovery API (mostly verification)
-
-**Objective:** stand up the package namespace and the discovery surface. Much of the comfy-chain
-cut is **already done** — this phase mostly *verifies* that and adds the discovery functions.
-
-### Tasks
-1. **Verify (don't redo):** `coreml_suite/model_version.py` is already clean (`Enum` only). Confirm
-   `import coreml_suite.model_version` works with **no comfy** (`uv run python -c "..."` in a
-   comfy-free `uv venv`). If true, E1's original "extract ModelVersion" task is already satisfied.
-2. Create the `coreml_diffusion/` package skeleton with `__init__.py` exporting the **discovery API**
-   backed by the *existing* sources of truth for now (re-export `ModelVersion`, the
-   `ATTENTION_IMPLEMENTATIONS` tuple, and `QUANT_NBITS_VALUES`) so values are byte-identical:
-   ```python
-   def list_model_versions(): return [v.name for v in ModelVersion]   # .name -> "SD15" (node reverses via ModelVersion[...]; .value KeyErrors)
-   def list_attention_impls(): return list(ATTENTION_IMPLEMENTATIONS)
-   def list_quant_modes(): return list(QUANT_NBITS_VALUES)
-   CONTRACT_VERSION = "1.0"
-   ```
-   (At this stage `coreml_diffusion` may live inside the repo and import from `coreml_suite.*`; the
-   physical move of implementation happens in E2. The point of E1 is to freeze the *contract*.)
-3. **Decided (`.name`):** the node renders `ModelVersion.SD15.name` (`"SD15"`) and reverses the
-   dropdown string via `ModelVersion[model_version]` (name lookup, `nodes.py:286`). Discovery API
-   therefore returns `.name`; `.value` (`"sd15"`) would `KeyError`. Recorded in `seam.md` §5.
-
-### Acceptance criteria
-- `uv run python -c "import coreml_diffusion; print(coreml_diffusion.list_model_versions(), coreml_diffusion.list_quant_modes())"`
-  works in a **comfy-free** `uv venv` and prints today's exact strings.
-- Existing characterization tests pass unchanged.
-- No node behavior change yet (node still uses its current hardcoded lists in E1).
-
-### STOP — VALIDATE (Gate E1)
-```
-## Gate E1 report
-- model_version.py confirmed comfy-free (uv, no comfy): PASS/FAIL
-- coreml_diffusion.list_* returns byte-identical strings to current dropdowns: YES/NO (show values)
-- .name vs .value decision for model_version discovery: <...>
-- CONTRACT_VERSION set; additive-only policy linked: <path>
-- Characterization tests unchanged & green (uv run pytest): YES/NO
-```
-
----
-
-## Phase E2 — Move conversion code into `coreml_diffusion` (in-repo, dedup, behavior-preserving)
-
-**Objective:** physically relocate the conversion mechanics into the framework-free package,
-collapsing the two duplicate converters into one, with paths/device injected.
-
-### Tasks
-1. Move into `coreml_diffusion/`: `pipeline.py` (`get_pipeline`, `get_unet`), `unet.py`
-   (`UNet2DConditionModelLCM`), `inputs.py` (sample/lcm/sdxl input builders +
-   `get_encoder_hidden_states_shape` + `get_coreml_inputs` + `get_inputs_spec`),
-   `controlnet.py` (`add_cnet_support`), `convert.py` (`convert_unet`, `convert`,
-   `convert_to_coreml`, `load_coreml_model`), `compile.py` (`compile_coreml_model`).
-2. **Dedupe LCM vs main** (the real remaining duplication): delete `lcm/converter.py`'s copies of
-   `convert_to_coreml` / `load_coreml_model` / `get_out_path` / `get_sample_input` (LCM variant
-   carries a `scheduler` arg — fold that into the shared `get_sample_input` as an optional param)
-   in favor of the single `coreml_diffusion` implementation. The main path's helpers
-   (`get_unet`/`get_encoder_hidden_states_shape`/`get_coreml_inputs`/`convert_unet`/`convert`) and
-   the `conversion/` subpackage (`attention`, `shapes`, `trace`, `unet`) move as-is.
-3. **Inject paths**: replace `get_out_path`'s `folder_paths` reach-in with an injected `out_path`
-   argument on `convert(...)`; `folder_paths` resolution moves up into the node (which already
-   computes `out_name`). No `folder_paths` import anywhere in `coreml_diffusion`.
-4. **Inject device** where the LCM path used `comfy.model_management` (verify it still does):
-   `convert(..., device=None)`, default to torch's default device.
-5. **Attention per-call, never at import:** main path already routes through
-   `conversion/attention.apply_attention_implementation` — keep that. If `lcm/converter.py` still
-   sets any module global at import, remove it; the package sets attention from the `attn_impl`
-   arg inside `convert`.
-6. **Move `compose_out_name` into the package** (`coreml_diffusion/naming.py`); re-point
-   `test_characterization_out_name.py` imports to `coreml_diffusion.naming` — assertions and values
-   unchanged. The node will import it from the package in E3.
-7. Leave **thin shims** in `coreml_suite/converter.py` and `coreml_suite/lcm/converter.py` that
-   re-export from `coreml_diffusion`, preserving the old call signatures the nodes use (nodes untouched
-   this phase). Shims map comfy `folder_paths`/device into package args.
-
-### Acceptance criteria
-- `uv run pytest -m unit` (Tier 0) imports `coreml_diffusion.*` with **no comfy / no MPS** and is green on Linux.
-- The dedup leaves exactly one implementation of each previously-duplicated function.
-- Characterization tests pass unchanged after the `compose_out_name` re-point.
-- `[M2]` A real SD1.5 conversion via the shim still produces a loadable model.
-- `[M2-ANE]` **Golden latent identical / within tolerance** to the MODERNIZATION Phase 2 anchor
-  (same seed/prompt) — proves the move + dedup changed nothing.
-
-### STOP — VALIDATE (Gate E2 — first regression gate)
-```
-## Gate E2 report
-- Tier 0 import of coreml_diffusion without comfy/MPS (uv run): PASS/FAIL
-- LCM/main duplicated funcs collapsed to one (list old→new): <map>
-- compose_out_name moved to package; char-tests re-pointed & green: YES/NO
-- Paths injected (no folder_paths in package): confirmed
-- Device injected (no comfy.model_management in package): confirmed
-- Attention set per-call, not at import (both main & lcm): confirmed
-- [M2-ANE] Golden latent vs Phase-2 anchor: identical / within tol <x> / DIVERGED (STOP)
-- Node INPUT_TYPES / mappings untouched: confirmed (diff)
-```
-**If the golden latent diverged at all, STOP and report — do not continue.**
-
----
-
-## Phase E3 — Thin the nodes onto the package (behavior-preserving)
-
-**Objective:** remove the shims; have the ComfyUI nodes call `coreml_diffusion` directly, keeping the
-node contract byte-identical.
-
-### Tasks
-1. `CoreMLConverter.convert` (in `coreml_suite/nodes.py`): keep the `folder_paths`-based path
-   resolution **in the node**; import `compose_out_name` from `coreml_diffusion` (not `coreml_suite.core`);
-   call `coreml_diffusion.convert(...)` and `coreml_diffusion.compile_model(...)` directly; wrap the compiled path
-   in `CoreMLModel`.
-2. **Wire the dropdowns to discovery (the maintainer's hard requirement).** Replace the hardcoded
-   `INPUT_TYPES` lists with fail-soft discovery calls:
-   ```python
-   def _discover(fn, fallback):
-       try:
-           import coreml_diffusion
-           return getattr(coreml_diffusion, fn)()
-       except Exception as e:        # missing/old package, or import error
-           logger.warning(f"coreml_diffusion.{fn} unavailable ({e}); using fallback {fallback}")
-           return fallback
-   ...
-   "model_version":            (_discover("list_model_versions", ["SD15", "SDXL"]),),
-   "attention_implementation": (_discover("list_attention_impls", ["SPLIT_EINSUM","SPLIT_EINSUM_V2","ORIGINAL"]),),
-   "quantize_nbits":           (_discover("list_quant_modes", ["none","8","6","4"]), {"default": "none"}),
-   ```
-   This is what makes "update the package → new types appear in the old node, no Suite bump" true.
-3. `COREML_CONVERT_LCM` (in `coreml_suite/lcm/nodes.py`): route through `coreml_diffusion` for the shared
-   mechanics. **Keep the existing LCM behavior/HF-hardcode for now** — consolidation is optional E-LCM.
-4. Delete the now-dead `coreml_suite/converter.py` / `coreml_suite/lcm/converter.py` shims (or
-   reduce to a one-line re-export if anything external imports them — grep first).
-
-### Acceptance criteria
-- `NODE_CLASS_MAPPINGS` / `NODE_DISPLAY_NAME_MAPPINGS` keys: **unchanged** (diff `__init__.py`).
-- Every `INPUT_TYPES` **field name** unchanged. Dropdown **values**: the discovery calls must
-  return **a superset of** today's values, with every previously-present value still present and
-  spelled identically (additive-only). *(This deliberately replaces the original spec's
-  "values must be byte-identical/frozen" criterion — the maintainer requires the list be
-  extensible at runtime. Frozen-field-names + additive-only-values is the new contract.)*
-- With `coreml_diffusion` **absent**, the node still registers and shows the fallback lists (fail-soft).
-- `[M2-ANE]` Golden latent still identical to the Phase-2 anchor.
-- `[M2-ANE]` The committed e2e workflow `tests/integration/...` still passes (PSNR > 25).
-
-### STOP — VALIDATE (Gate E3)
-```
-## Gate E3 report
-- Node mappings diff: empty (confirmed)
-- INPUT_TYPES field-names diff: empty (confirmed)
-- Dropdown values: superset of prior, all prior values still present & identical: YES/NO (show)
-- Fail-soft with coreml_diffusion absent (node still registers): PASS/FAIL
-- compose_out_name now imported from coreml_diffusion (no node-side copy): confirmed
-- [M2-ANE] Golden latent vs anchor: identical / within tol / DIVERGED (STOP)
-- [M2-ANE] e2e workflow PSNR: <value> (> 25?)
-- Dead converter shims removed / reduced: <list>
-```
-
----
-
-## Phase E4 — Standalone packaging & CLI (still in-repo)
-
-**Objective:** make `coreml_diffusion` independently installable and usable without ComfyUI, with a CLI
-suitable for the planned article and for on-device/iOS conversion workflows.
-
-### Tasks
-1. Add `coreml_diffusion/pyproject.toml`: name (working `coreml_diffusion`), `requires-python`, dependencies
-   = `coremltools` (pinned to the MODERNIZATION-validated version), `diffusers`, `transformers`,
-   `peft`, `omegaconf`, `numpy`, `torch`. **No `ml-stable-diffusion`** (already removed in #58, see
-   §0.3) and **no comfy**. Suite pins `transformers>=4.44`/`peft>=0.13`/`omegaconf>=2.3` today;
-   grep-confirm each is on the conversion path before listing it. A `[project.scripts]` entry:
-   `coreml-diffusion = "coreml_diffusion.cli:main"`.
-2. `coreml_diffusion/cli.py`: `coreml-diffusion convert --ckpt PATH --model-version sd15 --out PATH
-   [--height --width --batch-size --attn-impl --controlnet --lora NAME:STRENGTH ... --config PATH]`
-   and `coreml-diffusion compile --src PATH --out-dir DIR --name NAME`. Mirrors `convert()`/`compile_model()`.
-3. Tier-0 Linux tests for the CLI **arg→call mapping** (mock the heavy `convert`); the real
-   convert remains `[M2]`. Add a `[M2]` smoke test: convert a tiny synthetic UNet end-to-end.
-4. README for the package: install, CLI usage, "produce a `.mlpackage`/`.mlmodelc` for use in a
-   Swift/iOS app", and the ANE positioning note (low-power, GPU-free, embeddable; SD1.5/SDXL on
-   ANE, **not** a Flux-speed claim).
-
-### Acceptance criteria
-- Fresh `python -m venv` + `uv pip install ./coreml-diffusion` (no ComfyUI present) imports and runs
-  `coreml-diffusion --help` and the arg-mapping tests on Linux.
-- `[M2]` `coreml-diffusion convert` produces a model file identical (golden) to the node path.
-
-### STOP — VALIDATE (Gate E4)
-```
-## Gate E4 report
-- uv pip install ./coreml-diffusion in comfy-free venv: PASS/FAIL (log)
-- CLI arg→call tests (Tier 0, Linux): green
-- [M2] CLI-produced model golden vs node-produced model: identical / DIVERGED
-- Package deps list (with pinned SHAs/versions + licenses):
-- New runtime deps vs suite before: <none / list>
-```
-**This is the gate that proves the package stands alone. Do not split repos before it passes.**
-
----
-
-## Phase E5 — Physical split into a second repository
-
-**Objective:** move `coreml_diffusion/` to its own repo; CoreMLSuite depends on it by pinned version.
-
-### Tasks
-1. Create the new repo (maintainer action — agent prepares the tree, not the GitHub repo).
-   Choose final distributable name; rename imports if changed (single sweep, recorded).
-2. CoreMLSuite `pyproject.toml` / `requirements.txt`: replace the conversion-only deps with a
-   pinned dependency on the new package (`coreml_diffusion==<version>` from PyPI, or `git+...@<tag>`
-   until first PyPI release). (There is no `git+...ml-stable-diffusion` line to remove — already
-   gone since #58.)
-3. ~~Keep `python_coreml_stable_diffusion` for the loader.~~ **Void.** The loader is the local
-   `coreml_suite/coreml_model.py` over `coremltools`; the suite keeps `coremltools` as a direct dep
-   for it. No Apple lib involved.
-4. Set up the new repo's CI: Tier 0 on Linux (import + arg-mapping + input-shape math),
-   `[M2]`/`[M2-ANE]` on a self-hosted/macOS-ARM runner reusing the golden-latent anchor.
-5. Versioning: SemVer; first release `0.1.0`. Document the compatibility matrix
-   (coreml_diffusion ↔ coremltools version ↔ diffusers version). No ml-stable-diffusion axis.
-
-### Acceptance criteria
-- CoreMLSuite installs in a fresh venv pulling the new package; e2e workflow still passes `[M2-ANE]`.
-- New repo CI green on Linux (Tier 0) and `[M2-ANE]` golden latent matches the anchor.
-- No conversion code remains in CoreMLSuite (grep: no `ct.convert`, no `from_single_file`,
-  no `torch.jit.trace`).
-
-### STOP — VALIDATE (Gate E5)
-```
-## Gate E5 report
-- New repo tree prepared: <path/branch>; final package name: <name>
-- Suite depends on package by pinned version: <spec>
-- Suite e2e [M2-ANE] PSNR after split: <value> (> 25?)
-- Conversion code fully absent from suite: confirmed (grep output)
-- Compatibility matrix documented: <link>
-- First release tag: 0.1.0
-```
-
----
-
-## Phase E6 — Quantization travels WITH the conversion code (already implemented → move)
-
-**Objective:** quantization is **already implemented** (k-means `palettize_weights` in
-`converter.py`, `quantize_nbits` node input, `_q<bits>` filename suffix, README tradeoff table).
-There is nothing to *build*. It simply **moves with the conversion code in E2** as part of
-`convert_unet`. This phase is a checkpoint that it survived the extraction intact, plus exposing
-it through the CLI.
-
-### Tasks
-1. Confirm the palettization block moved cleanly into `coreml_diffusion` (lives in `convert.py` or a
-   `quantize.py` helper called from `convert_unet`). Default `"none"` stays byte-identical.
-2. Expose via CLI flag `--quantize {none,8,6,4}` (E4 already lists this) and via
-   `list_quant_modes()` discovery (E1/E3).
-3. The existing README tradeoff table (SD1.5 1×512×512 SPLIT_EINSUM: none/8/6/4 → size/ms/PSNR)
-   moves to the package README. Re-confirm one row `[M2-ANE]` so the article can cite a live number.
-
-### Acceptance criteria
-- Default (`none`) output byte-identical to pre-extraction (covered by the E2/E3 golden latent).
-- `coreml-diffusion convert --quantize 4` produces a `_q4` artifact matching the node's `_q4` artifact `[M2]`.
-- `list_quant_modes()` drives the node dropdown (no hardcoded copy remains).
-
-### STOP — VALIDATE (Gate E6)
-```
-## Gate E6 report
-- Palettization relocated into coreml_diffusion, called from convert_unet: confirmed
-- Default none output identical (golden): YES/NO
-- [M2] CLI --quantize {8,6,4} artifacts match node artifacts: YES/NO
-- Tradeoff table in package README with at least one re-confirmed [M2-ANE] row: <link>
-```
-
----
-
-## Phase E-LCM — FIRST task after the split: clean up LCM + verify → promote (behavior-changing, gated)
-
-> Promoted from "optional, someday" to **the first thing after E5**, per maintainer intent: the
-> Suite should expose every verifiably-convertible model, and LCM is the obvious first cleanup.
-
-Two coupled goals:
-1. **Consolidate the LCM path.** Make the LCM node use the unified `from_single_file` path in
-   `coreml_diffusion.convert(model_version=LCM, ...)` instead of the hardcoded `SimianLuo/LCM_Dreamshaper_v7`
-   HF download; drop the duplicated LCM helpers (already deduped in E2). **Behavior change** ⇒
-   capture an LCM golden anchor *before* the change, then prove within-tolerance after.
-2. **Verify → promote.** Once the LCM conversion has a passing `[M2-ANE]` golden anchor, flip
-   `_MODEL_STATUS["lcm"] = Status.VERIFIED` **in the package** (minor bump). The Suite's dropdown
-   gains `lcm` automatically — no Suite change, no Suite bump. This is the end-to-end proof that
-   the discovery contract works as designed.
-
-Repeat the same recipe for `sdxl_refiner` when it gets an anchor (separate small gate). Do NOT
-bundle E-LCM into E1–E5; it changes behavior and must stand on its own golden.
-
-### STOP — VALIDATE (Gate E-LCM)
-```
-## Gate E-LCM report
-- LCM golden anchor captured BEFORE change: <path/hash>
-- LCM node now uses unified from_single_file path; HF hardcode removed: confirmed
-- [M2-ANE] LCM golden after change: identical / within tol <x> / DIVERGED (STOP)
-- Status flipped lcm→VERIFIED in package (minor bump <ver>): confirmed
-- Suite dropdown now lists lcm with NO Suite code change / NO Suite bump: confirmed (diff empty)
-- LCM node accepts a checkpoint arg now (documented breaking-ish UI note): <link>
-```
-
----
-
-## Article deliverable (after E4)
-
-Once the CLI exists and stands alone, the "convert a Comfy/A1111 workflow into an on-device iOS
-app" write-up becomes a clean tutorial: `coreml-diffusion convert` → `.mlmodelc` → load in Swift/CoreML.
-Frame the niche honestly per the README note above (ANE feasibility & power, not raw Flux speed).
-
----
-
-## Quick reference: extraction gate discipline
-
-```
-E0  Seam decision, interface contract, discovery API frozen → Gate E0  (cut line + additive-only policy?)
-E1  Stand up coreml_diffusion + discovery API (mostly verify)        → Gate E1  (list_* byte-identical, comfy-free?)
-E2  Move conversion code, dedup LCM/main, inject paths/device→ Gate E2  (golden identical? duplicates gone?) ← first regression gate
-E3  Thin nodes onto package + wire discovery dropdowns       → Gate E3  (field-names frozen, values additive, fail-soft, golden identical?)
-E4  Standalone packaging + CLI (uv)                          → Gate E4  (uv pip install w/o comfy? CLI golden?) ← proves it stands alone
-E5  Physical second-repo split                               → Gate E5  (suite depends on pkg? conversion absent?)
-E-LCM  FIRST post-split: clean up LCM, verify → promote      → Gate E-LCM (LCM golden? dropdown gains lcm w/ no Suite bump?)
-E6  Quantization checkpoint (already built → moved in E2)    → Gate E6  (default identical? CLI quant matches?)
-(refiner)  same recipe as E-LCM when an anchor exists        → own small gate (promote sdxl_refiner→VERIFIED)
-```
-
-**Interface-contract invariants (the maintainer's hard requirement), restated:**
-- Package API is keyword-only-with-defaults past the required positionals → converter updates
-  don't force Suite updates.
-- Node dropdowns are discovery-driven (`coreml_diffusion.list_*`) + fail-soft → new conversion types
-  appear in the old plugin with `uv pip install -U coreml_diffusion`, **no Suite code change, no bump**.
-- Discovery identifiers are **additive-only**; removal/rename = MAJOR bump + migration note.
-- `compose_out_name` (cache key) lives in the package, single copy.
-
-
-**Golden rule (inherited): never cross a gate with a failing acceptance criterion.
-Stop, report, wait. The golden latent is the single source of truth that the extraction
-changed nothing.**

LICENSE

@@ -1,21 +1,674 @@
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README.md

@@ -8,8 +8,8 @@ These models are designed to leverage the Apple Neural Engine (ANE) on Apple Sil
 thereby enhancing your workflows and improving performance.
 
 If you're not sure how to obtain these models, you can download them
-[here](https://huggingface.co/coreml-community) or convert your own checkpoints
-directly with the conversion nodes in this suite (see [How to use](#how-to-use)).
+[here](https://huggingface.co/coreml-community) or convert your own models using
+[coremltools](https://github.com/apple/ml-stable-diffusion).
 
 In simple terms, think of Core ML models as a tool that can help your ComfyUI work faster and more efficiently.
 For instance, during my tests on an M2 Pro 32GB machine,
@@ -81,29 +81,6 @@ These custom nodes come with a host of features, including:
 > [!NOTE]  
 > This repository will continue to be updated with more nodes and features over time.
 
-## Conversion & Acknowledgements
-
-The Core ML conversion pipeline in this repository began as an adaptation of
-Apple's [ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion),
-which pioneered running Stable Diffusion on the Apple Neural Engine. The
-implementation has since diverged and no longer depends on that package:
-
-- UNet conversion runs natively on `diffusers`' `UNet2DConditionModel`.
-- The ANE-friendly attention path (`SPLIT_EINSUM`, `SPLIT_EINSUM_V2`) is
-  reimplemented as standalone `diffusers` attention processors.
-- The toolchain tracks current ComfyUI (NumPy 2, Torch 2.7, coremltools 9,
-  Python 3.12).
-
-The goal is to keep iterating on these methods independently and to explore
-support beyond SD1.5.
-
-> [!IMPORTANT]
-> **Breaking change in 2.0.0.** The converted Core ML UNet now takes
-> `encoder_hidden_states` in the native `diffusers` layout
-> `(batch, tokens, hidden)` instead of the previous
-> `(batch, hidden, 1, tokens)`. Core ML models converted with earlier versions
-> are not compatible with 2.0.0 and must be re-converted.
-
 ## Installation
 
 ### Using ComfyUI-Manager
@@ -193,8 +170,8 @@ the node name, so if the model already exists, the node will not convert it agai
     - **ckpt_name**: The name of the checkpoint to convert. This should be the name of the checkpoint file stored in the
       `models/checkpoints` directory.
     - **model_version**: Whether the model is based on SD1.5 or SDXL.
-    - **height**: The desired height of the image generated by the model. The default is 512. Any positive multiple of 8 is accepted.
-    - **width**: The desired width of the image generated by the model. The default is 512. Any positive multiple of 8 is accepted.
+    - **height**: The desired height of the image generated by the model. The default is 512. Must be a multiple of 8.
+    - **width**: The desired width of the image generated by the model. The default is 512. Must be a multiple of 8.
     - **batch_size**: The batch size of generated images. If you're planning to generate batches of images, you can try
       increasing this value to speed up the generation process. The default is 1.
     - **attention_implementation**: The attention implementation used when converting the model. Choose SPLIT_EINSUM or
@@ -307,8 +284,8 @@ can use any CLIP or VAE model as long as it's compatible with Stable Diffusion v
 
 1. **Loading text encoder (CLIP) and VAE models separately**
     - This workflow uses CLIP and VAE models available
-      [here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/text_encoder/model.safetensors) and
-      [here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/vae/diffusion_pytorch_model.safetensors).
+      [here](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/text_encoder/model.safetensors) and
+      [here](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/vae/diffusion_pytorch_model.safetensors).
       Once downloaded, place the models in the`models/clip` and `models/vae` directories respectively.
     - The Core ML UNet model is available
       [here](https://huggingface.co/coreml-community/coreml-stable-diffusion-v1-5_cn/blob/main/split_einsum/stable-diffusion-_v1-5_split-einsum_cn.zip).
@@ -316,7 +293,7 @@ can use any CLIP or VAE model as long as it's compatible with Stable Diffusion v
       ![coreml-unet+clip+vae](./assets/unet+sampler+clip+vae.png?raw=true)
 2. **Loading text encoder (CLIP) and VAE models from checkpoint file**
     - This workflow loads the CLIP and VAE models from the checkpoint file available
-      [here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors).
+      [here](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors).
       Once downloaded, place the model in the`models/checkpoints` directory.
     - The Core ML UNet model is available
       [here](https://huggingface.co/coreml-community/coreml-stable-diffusion-v1-5_cn/blob/main/split_einsum/stable-diffusion-_v1-5_split-einsum_cn.zip).
@@ -431,15 +408,16 @@ PSNR is comfortably higher (the sampler averages over 20 steps).
   margin, `none` if you want bit-identical output for golden testing.
 
 The default stays `none` so existing workflows produce byte-for-byte
-identical output.
+identical output — the golden-image anchor (`tests/m2/test_golden_image.py`)
+verifies this on every Tier 2 run.
 
 ## Limitations
 
 - Core ML models are fixed in terms of their inputs and outputs.
   This means you'll need to use latent images of the same size as the input of the model (512x512 is the default for
   SD1.5).
-  However, you can re-convert the model to a different input size using the
-  conversion nodes in this suite (set the desired width and height).
+  However, you can convert the model to a different input size using tools available
+  in the [apple/ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion) repository.
 - SD2.1 models are not supported.
 
 [^1]:

__init__.py

@@ -11,6 +11,9 @@ from coreml_suite.nodes import (
     CoreMLConverter,
     COREML_LOAD_LORA,
 )
+from coreml_suite.lcm import (
+    COREML_CONVERT_LCM,
+)
 
 NODE_CLASS_MAPPINGS = {
     "CoreMLUNetLoader": CoreMLLoaderUNet,
@@ -19,6 +22,7 @@ NODE_CLASS_MAPPINGS = {
     "CoreMLModelAdapter": CoreMLModelAdapter,
     "Core ML LoRA Loader": COREML_LOAD_LORA,
     "Core ML Converter": CoreMLConverter,
+    "Core ML LCM Converter": COREML_CONVERT_LCM,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "CoreMLUNetLoader": "Load Core ML UNet",
@@ -27,4 +31,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "CoreMLModelAdapter": "Core ML Adapter (Experimental)",
     "Core ML LoRA Loader": "Load LoRA to use with Core ML",
     "Core ML Converter": "Convert Checkpoint to Core ML",
+    "Core ML LCM Converter": "Convert LCM to Core ML",
 }

coreml_suite/config.py

@@ -1,10 +1,17 @@
+from enum import Enum
+
 import torch
 
 from comfy import supported_models_base
 from comfy import latent_formats
 from comfy.model_detection import convert_config
 
-from coreml_diffusion import ModelVersion
+
+class ModelVersion(Enum):
+    SD15 = "sd15"
+    SDXL = "sdxl"
+    SDXL_REFINER = "sdxl_refiner"
+    LCM = "lcm"
 
 
 config_map = {

coreml_suite/converter.py

@@ -0,0 +1,383 @@
+import gc
+import os
+import shutil
+import time
+from typing import Union
+
+import coremltools as ct
+import numpy as np
+import python_coreml_stable_diffusion.unet
+import torch
+from diffusers import (
+    StableDiffusionPipeline,
+    LatentConsistencyModelPipeline,
+    StableDiffusionXLPipeline,
+)
+from python_coreml_stable_diffusion.unet import (
+    UNet2DConditionModel,
+    UNet2DConditionModelXL,
+    AttentionImplementations,
+)
+
+from coreml_suite.config import ModelVersion
+from coreml_suite.lcm.unet import UNet2DConditionModelLCM
+from coreml_suite.logger import logger
+from folder_paths import get_folder_paths
+
+
+class StableDiffusionLCMPipeline(LatentConsistencyModelPipeline):
+    pass
+
+
+MODEL_TYPE_TO_UNET_CLS = {
+    ModelVersion.SD15: UNet2DConditionModel,
+    ModelVersion.SDXL: UNet2DConditionModelXL,
+    ModelVersion.LCM: UNet2DConditionModelLCM,
+}
+
+MODEL_TYPE_TO_PIPE_CLS = {
+    ModelVersion.SD15: StableDiffusionPipeline,
+    ModelVersion.SDXL: StableDiffusionXLPipeline,
+    ModelVersion.LCM: StableDiffusionLCMPipeline,
+}
+
+
+def get_unet(model_type: ModelVersion, ref_pipe):
+    ref_unet = ref_pipe.unet
+
+    unet_cls = MODEL_TYPE_TO_UNET_CLS[model_type]
+    cml_unet = unet_cls.from_config(ref_unet.config).eval()
+    cml_unet.load_state_dict(ref_unet.state_dict(), strict=False)
+
+    return cml_unet
+
+
+def get_encoder_hidden_states_shape(ref_pipe, batch_size):
+    text_encoder = (
+        ref_pipe.text_encoder_2
+        if hasattr(ref_pipe, "text_encoder_2")
+        else ref_pipe.text_encoder
+    )
+
+    text_token_sequence_length = text_encoder.config.max_position_embeddings
+    hidden_size = (text_encoder.config.hidden_size,)
+
+    encoder_hidden_states_shape = (
+        batch_size,
+        ref_pipe.unet.config.cross_attention_dim or hidden_size,
+        1,
+        text_token_sequence_length,
+    )
+
+    return encoder_hidden_states_shape
+
+
+def get_coreml_inputs(sample_inputs):
+    coreml_sample_unet_inputs = {
+        k: v.numpy().astype(np.float16) for k, v in sample_inputs.items()
+    }
+    return [
+        ct.TensorType(
+            name=k,
+            shape=v.shape,
+            dtype=v.numpy().dtype if isinstance(v, torch.Tensor) else v.dtype,
+        )
+        for k, v in coreml_sample_unet_inputs.items()
+    ]
+
+
+def load_coreml_model(out_path):
+    logger.info(f"Loading model from {out_path}")
+
+    start = time.time()
+    coreml_model = ct.models.MLModel(out_path)
+    logger.info(f"Loading {out_path} took {time.time() - start:.1f} seconds")
+
+    return coreml_model
+
+
+def convert_to_coreml(
+    submodule_name, torchscript_module, sample_inputs, output_names, out_path
+):
+    if os.path.exists(out_path):
+        logger.info(f"Skipping export because {out_path} already exists")
+        coreml_model = load_coreml_model(out_path)
+    else:
+        logger.info(f"Converting {submodule_name} to CoreML..")
+        coreml_model = ct.convert(
+            torchscript_module,
+            convert_to="mlprogram",
+            minimum_deployment_target=ct.target.macOS13,
+            inputs=sample_inputs,
+            outputs=[
+                ct.TensorType(name=name, dtype=np.float32) for name in output_names
+            ],
+            skip_model_load=True,
+        )
+
+        del torchscript_module
+        gc.collect()
+
+    return coreml_model
+
+
+def get_out_path(submodule_name, model_name):
+    fname = f"{model_name}_{submodule_name}.mlpackage"
+    unet_path = get_folder_paths(submodule_name)[0]
+    out_path = os.path.join(unet_path, fname)
+    return out_path
+
+
+def compile_coreml_model(source_model_path, output_dir, final_name):
+    """Compiles Core ML models using the coremlcompiler utility from Xcode toolchain"""
+    target_path = os.path.join(output_dir, f"{final_name}.mlmodelc")
+    if os.path.exists(target_path):
+        logger.warning(f"Found existing compiled model at {target_path}! Skipping..")
+        return target_path
+
+    logger.info(f"Compiling {source_model_path}")
+    source_model_name = os.path.basename(os.path.splitext(source_model_path)[0])
+
+    os.system(f"xcrun coremlcompiler compile {source_model_path} {output_dir}")
+    compiled_output = os.path.join(output_dir, f"{source_model_name}.mlmodelc")
+    shutil.move(compiled_output, target_path)
+
+    return target_path
+
+
+def get_sample_input(batch_size, encoder_hidden_states_shape, sample_shape, scheduler):
+    sample_unet_inputs = dict(
+        [
+            ("sample", torch.rand(*sample_shape)),
+            (
+                "timestep",
+                torch.tensor([scheduler.timesteps[0].item()] * batch_size).to(
+                    torch.float32
+                ),
+            ),
+            ("encoder_hidden_states", torch.rand(*encoder_hidden_states_shape)),
+        ]
+    )
+    return sample_unet_inputs
+
+
+def lcm_inputs(sample_unet_inputs):
+    batch_size = sample_unet_inputs["sample"].shape[0]
+    return {"timestep_cond": torch.randn(batch_size, 256).to(torch.float32)}
+
+
+def sdxl_inputs(sample_unet_inputs, ref_pipe):
+    sample_shape = sample_unet_inputs["sample"].shape
+    batch_size = sample_shape[0]
+    h = sample_shape[2] * 8
+    w = sample_shape[3] * 8
+    original_size = (h, w)
+    crops_coords_top_left = (0, 0)
+
+    is_refiner = (
+        hasattr(ref_pipe.config, "requires_aesthetics_score")
+        and ref_pipe.config.requires_aesthetics_score
+    )
+
+    if is_refiner:
+        aesthetic_score = (6.0,)
+        time_ids_list = list(original_size + crops_coords_top_left + aesthetic_score)
+    else:
+        target_size = (h, w)
+        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, ref_pipe.text_encoder_2.config.hidden_size)
+
+    return {
+        "time_ids": time_ids,
+        "text_embeds": torch.randn(*text_embeds_shape).to(torch.float32),
+    }
+
+
+def get_inputs_spec(inputs):
+    inputs_spec = {k: (v.shape, v.dtype) for k, v in inputs.items()}
+    return inputs_spec
+
+
+def add_cnet_support(sample_shape, reference_unet):
+    from python_coreml_stable_diffusion.unet import calculate_conv2d_output_shape
+
+    additional_residuals_shapes = []
+
+    batch_size = sample_shape[0]
+    h, w = sample_shape[2:]
+
+    # conv_in
+    out_h, out_w = calculate_conv2d_output_shape(
+        h,
+        w,
+        reference_unet.conv_in,
+    )
+    additional_residuals_shapes.append(
+        (batch_size, reference_unet.conv_in.out_channels, out_h, out_w)
+    )
+
+    # down_blocks
+    for down_block in reference_unet.down_blocks:
+        additional_residuals_shapes += [
+            (batch_size, resnet.out_channels, out_h, out_w)
+            for resnet in down_block.resnets
+        ]
+        if hasattr(down_block, "downsamplers") and down_block.downsamplers is not None:
+            for downsampler in down_block.downsamplers:
+                out_h, out_w = calculate_conv2d_output_shape(
+                    out_h, out_w, downsampler.conv
+                )
+            additional_residuals_shapes.append(
+                (
+                    batch_size,
+                    down_block.downsamplers[-1].conv.out_channels,
+                    out_h,
+                    out_w,
+                )
+            )
+
+    # mid_block
+    additional_residuals_shapes.append(
+        (batch_size, reference_unet.mid_block.resnets[-1].out_channels, out_h, out_w)
+    )
+
+    additional_inputs = {}
+    for i, shape in enumerate(additional_residuals_shapes):
+        sample_residual_input = torch.rand(*shape)
+        additional_inputs[f"additional_residual_{i}"] = sample_residual_input
+
+    return additional_inputs
+
+
+def convert_unet(
+    ref_pipe,
+    model_version: ModelVersion,
+    unet_out_path: str,
+    batch_size: int = 1,
+    sample_size: tuple[int, int] = (64, 64),
+    controlnet_support: bool = False,
+    quantize_nbits: str = "none",
+):
+    coreml_unet = get_unet(model_version, ref_pipe)
+    ref_unet = ref_pipe.unet
+
+    sample_shape = (
+        batch_size,  # B
+        ref_unet.config.in_channels,  # C
+        sample_size[0],  # H
+        sample_size[1],  # W
+    )
+
+    encoder_hidden_states_shape = get_encoder_hidden_states_shape(ref_pipe, batch_size)
+
+    scheduler = ref_pipe.scheduler
+    scheduler.set_timesteps(50)
+
+    sample_inputs = get_sample_input(
+        batch_size, encoder_hidden_states_shape, sample_shape, scheduler
+    )
+
+    if model_version == ModelVersion.LCM:
+        sample_inputs |= lcm_inputs(sample_inputs)
+
+    if model_version == ModelVersion.SDXL:
+        sample_inputs |= sdxl_inputs(sample_inputs, ref_pipe)
+
+    if controlnet_support:
+        sample_inputs |= add_cnet_support(sample_shape, ref_unet)
+
+    sample_inputs_spec = get_inputs_spec(sample_inputs)
+
+    logger.info(f"Sample UNet inputs spec: {sample_inputs_spec}")
+    logger.info("JIT tracing..")
+    traced_unet = torch.jit.trace(
+        coreml_unet, example_inputs=list(sample_inputs.values())
+    )
+    logger.info("Done.")
+
+    coreml_sample_inputs = get_coreml_inputs(sample_inputs)
+
+    coreml_unet = convert_to_coreml(
+        "unet", traced_unet, coreml_sample_inputs, ["noise_pred"], unet_out_path
+    )
+
+    del traced_unet
+    gc.collect()
+
+    if quantize_nbits != "none":
+        # Opt-in k-means weight palettization. The default path
+        # (quantize_nbits="none") leaves the traced UNet untouched.
+        from coremltools.optimize.coreml import (
+            OpPalettizerConfig,
+            OptimizationConfig,
+            palettize_weights,
+        )
+
+        nbits = int(quantize_nbits)
+        logger.info(f"Palettizing UNet weights to {nbits}-bit (kmeans)..")
+        t0 = time.time()
+        cfg = OptimizationConfig(
+            global_config=OpPalettizerConfig(mode="kmeans", nbits=nbits)
+        )
+        coreml_unet = palettize_weights(coreml_unet, config=cfg)
+        logger.info(f"Palettization took {time.time() - t0:.1f}s")
+
+    coreml_unet.save(unet_out_path)
+    logger.info(f"Saved unet into {unet_out_path}")
+
+
+def convert(
+    ckpt_path: str,
+    model_version: ModelVersion,
+    unet_out_path: str,
+    batch_size: int = 1,
+    sample_size: tuple[int, int] = (64, 64),
+    controlnet_support: bool = False,
+    lora_weights: list[tuple[Union[str, os.PathLike], float]] = None,
+    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..")
+        return
+
+    python_coreml_stable_diffusion.unet.ATTENTION_IMPLEMENTATION_IN_EFFECT = (
+        AttentionImplementations(attn_impl)
+    )
+
+    ref_pipe = get_pipeline(ckpt_path, config_path, model_version)
+
+    for i, lora_weight in enumerate(lora_weights or []):
+        lora_path, strength = lora_weight
+        adapter_name = f"lora_{i}"
+        ref_pipe.load_lora_weights(lora_path, adapter_name=adapter_name)
+        ref_pipe.set_adapters([adapter_name], adapter_weights=[strength])
+        ref_pipe.fuse_lora()
+
+    convert_unet(
+        ref_pipe,
+        model_version,
+        unet_out_path,
+        batch_size,
+        sample_size,
+        controlnet_support,
+        quantize_nbits=quantize_nbits,
+    )
+
+
+def get_pipeline(ckpt_path, config_path, model_version):
+    pipe_cls = MODEL_TYPE_TO_PIPE_CLS[model_version]
+    ref_pipe = pipe_cls.from_single_file(ckpt_path, original_config_file=config_path)
+    return ref_pipe
+
+
+def compile_model(out_path, out_name, submodule_name):
+    # Compile the model
+    target_path = compile_coreml_model(
+        out_path, get_folder_paths(submodule_name)[0], f"{out_name}_{submodule_name}"
+    )
+    logger.info(f"Compiled {out_path} to {target_path}")
+    return target_path

coreml_suite/core/inputs.py

@@ -24,6 +24,7 @@ class CoreMLInputs:
         sample = self.x.cpu().numpy().astype(np.float16)
 
         context = self.context.cpu().numpy().astype(np.float16)
+        context = context.transpose(0, 2, 1)[:, :, None, :]
 
         t = self.t.cpu().numpy().astype(np.float16)
 
@@ -56,7 +57,8 @@ class CoreMLInputs:
     def chunks(self, expected_inputs):
         sample_shape = expected_inputs["sample"]["shape"]
         timestep_shape = expected_inputs["timestep"]["shape"]
-        context_shape = expected_inputs["encoder_hidden_states"]["shape"]
+        hidden_shape = expected_inputs["encoder_hidden_states"]["shape"]
+        context_shape = (hidden_shape[0], hidden_shape[3], hidden_shape[1])
 
         chunked_x = chunk_batch(self.x, sample_shape)
         ts = list(torch.full((len(chunked_x), timestep_shape[0]), self.t[0]))

coreml_suite/core/naming.py

@@ -0,0 +1,68 @@
+"""Pure out_name composition for the Core ML UNet artifact.
+
+Extracted from CoreMLConverter.convert so the filename contract
+can be tested + reused without instantiating the node. The string is the
+cache key: every workflow that references a converted .mlpackage depends
+on it staying byte-for-byte identical.
+"""
+from typing import Iterable, Tuple
+
+ATTN_SUFFIX = {
+    "SPLIT_EINSUM": "se",
+    "SPLIT_EINSUM_V2": "se2",
+    "ORIGINAL": "orig",
+}
+
+# Palettization bits. "none" = no quantization (default; keeps the
+# unquantized 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(
+    *,
+    ckpt_name: str,
+    batch_size: int,
+    width: int,
+    height: int,
+    controlnet_support: bool,
+    attention_implementation: str,
+    lora_names: Iterable[str] = (),
+    quantize_nbits: str = "none",
+) -> str:
+    """Build the .mlpackage stem from convert() parameters.
+
+    Locked behaviour (characterization tests):
+      - first '.' in ckpt_name wins (`a.b.c.safetensors` -> `a`)
+      - spaces collapse to underscores
+      - LoRA names are taken stem-only, sorted, joined with '_' and
+        prefixed with '_' when present (caller is expected to pass a
+        sorted list; we sort defensively)
+      - controlnet adds `_cn`
+      - attn suffix is `_se` | `_se2` | `_orig`
+
+    Quantization:
+      - 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]
+    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(" ", "_")
+
+
+def lora_names_from_params(lora_params: Iterable[Tuple[str, float]]) -> list[str]:
+    """Mirror the sort applied inside CoreMLConverter.convert."""
+    return [name for name, _ in sorted(lora_params, key=lambda pair: pair[0])]

coreml_suite/coreml_model.py

@@ -1,42 +0,0 @@
-import time
-
-import coremltools as ct
-
-from coreml_suite.logger import logger
-
-
-class CoreMLModel:
-    """Small runtime wrapper around coremltools.models.MLModel.
-
-    This keeps the inference path independent from apple/ml-stable-diffusion's
-    CoreMLModel wrapper while preserving the contract used by the sampler code:
-    ``expected_inputs`` and callable prediction.
-    """
-
-    def __init__(self, model_path, compute_unit):
-        self.model_path = model_path
-        self.compute_unit = self._compute_unit(compute_unit)
-
-        logger.info(f"Loading {model_path} to {self.compute_unit.name}")
-        start = time.time()
-        self.model = ct.models.MLModel(model_path, compute_units=self.compute_unit)
-        logger.info(f"Loading {model_path} took {time.time() - start:.1f} seconds")
-
-        self.expected_inputs = self._expected_inputs()
-
-    def __call__(self, **kwargs):
-        return self.model.predict(kwargs)
-
-    @staticmethod
-    def _compute_unit(compute_unit):
-        if isinstance(compute_unit, ct.ComputeUnit):
-            return compute_unit
-        return ct.ComputeUnit[compute_unit]
-
-    def _expected_inputs(self):
-        return {
-            feature.name: {
-                "shape": tuple(feature.type.multiArrayType.shape),
-            }
-            for feature in self.model.get_spec().description.input
-        }

coreml_suite/lcm/__init__.py

@@ -1,8 +1,3 @@
-"""LCM runtime support (sampler-side).
+from .nodes import COREML_CONVERT_LCM
 
-The dedicated LCM converter node was removed once the standard ``CoreMLConverter``
-gained model-version auto-detection (full-distill LCM is detected from the
-checkpoint). What remains here is runtime sampling support — ``utils`` patches the
-model sampling and supplies the guidance embedding when a converted UNet exposes
-``timestep_cond``.
-"""
+__all__ = ["COREML_CONVERT_LCM"]

coreml_suite/lcm/converter.py

@@ -0,0 +1,297 @@
+import os
+import shutil
+import logging
+import time
+import gc
+
+import numpy as np
+import torch
+from diffusers import UNet2DConditionModel, LCMScheduler
+from diffusers.loaders import LoraLoaderMixin
+
+from comfy.model_management import get_torch_device
+from coreml_suite.lcm.unet import UNet2DConditionModelLCM
+
+from transformers import CLIPTextModel
+import coremltools as ct
+
+from folder_paths import get_folder_paths
+
+logging.basicConfig()
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+MODEL_VERSION = "SimianLuo/LCM_Dreamshaper_v7"
+MODEL_NAME = MODEL_VERSION.split("/")[-1] + "_4k"
+
+import python_coreml_stable_diffusion.unet as unet
+
+unet.ATTENTION_IMPLEMENTATION_IN_EFFECT = unet.AttentionImplementations.SPLIT_EINSUM
+
+
+def get_unets():
+    ref_unet = UNet2DConditionModel.from_pretrained(
+        MODEL_VERSION,
+        subfolder="unet",
+        device_map=None,
+        low_cpu_mem_usage=False,
+    )
+
+    cml_unet = UNet2DConditionModelLCM.from_config(ref_unet.config).eval()
+    cml_unet.load_state_dict(ref_unet.state_dict(), strict=False)
+
+    return cml_unet, ref_unet
+
+
+def get_encoder_hidden_states_shape(unet_config, batch_size):
+    text_encoder = CLIPTextModel.from_pretrained(
+        MODEL_VERSION, subfolder="text_encoder"
+    )
+
+    text_token_sequence_length = text_encoder.config.max_position_embeddings
+    hidden_size = (text_encoder.config.hidden_size,)
+
+    encoder_hidden_states_shape = (
+        batch_size,
+        unet_config.cross_attention_dim or hidden_size,
+        1,
+        text_token_sequence_length,
+    )
+
+    return encoder_hidden_states_shape
+
+
+def get_scheduler():
+    scheduler = LCMScheduler.from_pretrained(MODEL_VERSION, subfolder="scheduler")
+    scheduler.set_timesteps(50, get_torch_device(), 50)
+    return scheduler
+
+
+def get_coreml_inputs(sample_inputs):
+    coreml_sample_unet_inputs = {
+        k: v.numpy().astype(np.float16) for k, v in sample_inputs.items()
+    }
+    return [
+        ct.TensorType(
+            name=k,
+            shape=v.shape,
+            dtype=v.numpy().dtype if isinstance(v, torch.Tensor) else v.dtype,
+        )
+        for k, v in coreml_sample_unet_inputs.items()
+    ]
+
+
+def load_coreml_model(out_path):
+    logger.info(f"Loading model from {out_path}")
+
+    start = time.time()
+    coreml_model = ct.models.MLModel(out_path)
+    logger.info(f"Loading {out_path} took {time.time() - start:.1f} seconds")
+
+    return coreml_model
+
+
+def convert_to_coreml(
+    submodule_name, torchscript_module, sample_inputs, output_names, out_path
+):
+    if os.path.exists(out_path):
+        logger.info(f"Skipping export because {out_path} already exists")
+        coreml_model = load_coreml_model(out_path)
+    else:
+        logger.info(f"Converting {submodule_name} to CoreML..")
+        coreml_model = ct.convert(
+            torchscript_module,
+            convert_to="mlprogram",
+            minimum_deployment_target=ct.target.macOS13,
+            inputs=sample_inputs,
+            outputs=[
+                ct.TensorType(name=name, dtype=np.float32) for name in output_names
+            ],
+            skip_model_load=True,
+        )
+
+        del torchscript_module
+        gc.collect()
+
+    return coreml_model
+
+
+def get_out_path(submodule_name, model_name):
+    fname = f"{model_name}_{submodule_name}.mlpackage"
+    unet_path = get_folder_paths(submodule_name)[0]
+    out_path = os.path.join(unet_path, fname)
+    return out_path
+
+
+def compile_coreml_model(source_model_path, output_dir, final_name):
+    """Compiles Core ML models using the coremlcompiler utility from Xcode toolchain"""
+    target_path = os.path.join(output_dir, f"{final_name}.mlmodelc")
+    if os.path.exists(target_path):
+        logger.warning(f"Found existing compiled model at {target_path}! Skipping..")
+        return target_path
+
+    logger.info(f"Compiling {source_model_path}")
+    source_model_name = os.path.basename(os.path.splitext(source_model_path)[0])
+
+    os.system(f"xcrun coremlcompiler compile {source_model_path} {output_dir}")
+    compiled_output = os.path.join(output_dir, f"{source_model_name}.mlmodelc")
+    shutil.move(compiled_output, target_path)
+
+    return target_path
+
+
+def get_sample_input(batch_size, encoder_hidden_states_shape, sample_shape, scheduler):
+    sample_unet_inputs = dict(
+        [
+            ("sample", torch.rand(*sample_shape)),
+            (
+                "timestep",
+                torch.tensor([scheduler.timesteps[0].item()] * batch_size).to(
+                    torch.float32
+                ),
+            ),
+            ("encoder_hidden_states", torch.rand(*encoder_hidden_states_shape)),
+            ("timestep_cond", torch.randn(batch_size, 256).to(torch.float32)),
+        ]
+    )
+    return sample_unet_inputs
+
+
+def get_unet_inputs_spec(sample_unet_inputs):
+    sample_unet_inputs_spec = {
+        k: (v.shape, v.dtype) for k, v in sample_unet_inputs.items()
+    }
+    return sample_unet_inputs_spec
+
+
+def add_cnet_support(sample_shape, reference_unet):
+    from python_coreml_stable_diffusion.unet import calculate_conv2d_output_shape
+
+    additional_residuals_shapes = []
+
+    batch_size = sample_shape[0]
+    h, w = sample_shape[2:]
+
+    # conv_in
+    out_h, out_w = calculate_conv2d_output_shape(
+        h,
+        w,
+        reference_unet.conv_in,
+    )
+    additional_residuals_shapes.append(
+        (batch_size, reference_unet.conv_in.out_channels, out_h, out_w)
+    )
+
+    # down_blocks
+    for down_block in reference_unet.down_blocks:
+        additional_residuals_shapes += [
+            (batch_size, resnet.out_channels, out_h, out_w)
+            for resnet in down_block.resnets
+        ]
+        if hasattr(down_block, "downsamplers") and down_block.downsamplers is not None:
+            for downsampler in down_block.downsamplers:
+                out_h, out_w = calculate_conv2d_output_shape(
+                    out_h, out_w, downsampler.conv
+                )
+            additional_residuals_shapes.append(
+                (
+                    batch_size,
+                    down_block.downsamplers[-1].conv.out_channels,
+                    out_h,
+                    out_w,
+                )
+            )
+
+    # mid_block
+    additional_residuals_shapes.append(
+        (batch_size, reference_unet.mid_block.resnets[-1].out_channels, out_h, out_w)
+    )
+
+    additional_inputs = {}
+    for i, shape in enumerate(additional_residuals_shapes):
+        sample_residual_input = torch.rand(*shape)
+        additional_inputs[f"additional_residual_{i}"] = sample_residual_input
+
+    return additional_inputs
+
+
+def convert(
+    out_path: str,
+    batch_size: int = 1,
+    sample_size: tuple[int, int] = (64, 64),
+    controlnet_support: bool = False,
+    lora_paths: list[str] = None,
+):
+    lora_paths = lora_paths or []
+    coreml_unet, ref_unet = get_unets()
+
+    for lora_path in lora_paths:
+        lora_sd, network_alphas = LoraLoaderMixin.lora_state_dict(lora_path)
+        LoraLoaderMixin.load_lora_into_unet(lora_sd, network_alphas, ref_unet)
+        ref_unet.fuse_lora()
+
+    sample_shape = (
+        batch_size,  # B
+        ref_unet.config.in_channels,  # C
+        sample_size[0],  # H
+        sample_size[1],  # W
+    )
+
+    encoder_hidden_states_shape = get_encoder_hidden_states_shape(
+        ref_unet.config, batch_size
+    )
+
+    scheduler = get_scheduler()
+
+    sample_inputs = get_sample_input(
+        batch_size, encoder_hidden_states_shape, sample_shape, scheduler
+    )
+
+    if controlnet_support:
+        sample_inputs |= add_cnet_support(sample_shape, ref_unet)
+
+    sample_inputs_spec = get_unet_inputs_spec(sample_inputs)
+
+    logger.info(f"Sample UNet inputs spec: {sample_inputs_spec}")
+    logger.info("JIT tracing..")
+    traced_unet = torch.jit.trace(
+        coreml_unet, example_inputs=list(sample_inputs.values())
+    )
+    logger.info("Done.")
+
+    coreml_sample_inputs = get_coreml_inputs(sample_inputs)
+
+    coreml_unet = convert_to_coreml(
+        "unet", traced_unet, coreml_sample_inputs, ["noise_pred"], out_path
+    )
+
+    del traced_unet
+    gc.collect()
+
+    coreml_unet.save(out_path)
+    logger.info(f"Saved unet into {out_path}")
+
+
+def compile_model(out_path, out_name):
+    # Compile the model
+    target_path = compile_coreml_model(
+        out_path, get_folder_paths("unet")[0], f"{out_name}_unet"
+    )
+    logger.info(f"Compiled {out_path} to {target_path}")
+    return target_path
+
+
+if __name__ == "__main__":
+    h = 512
+    w = 512
+    sample_size = (h // 8, w // 8)
+    batch_size = 4
+
+    cn_support_str = "_cn" if True else ""
+
+    out_name = f"{MODEL_NAME}_{batch_size}x{w}x{h}{cn_support_str}"
+
+    out_path = get_out_path("unet", f"{out_name}")
+    if not os.path.exists(out_path):
+        convert(out_path=out_path, sample_size=sample_size, batch_size=batch_size)
+    compile_model(out_path=out_path, out_name=out_name)

coreml_suite/lcm/nodes.py

@@ -0,0 +1,70 @@
+import os
+
+from coremltools import ComputeUnit
+from python_coreml_stable_diffusion.coreml_model import CoreMLModel
+
+from coreml_suite import COREML_NODE
+from coreml_suite.lcm import converter as lcm_converter
+
+
+class COREML_CONVERT_LCM(COREML_NODE):
+    """Converts a LCM model to Core ML."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "height": ("INT", {"default": 512, "min": 512, "max": 768, "step": 8}),
+                "width": ("INT", {"default": 512, "min": 512, "max": 768, "step": 8}),
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
+                "compute_unit": (
+                    [
+                        ComputeUnit.CPU_AND_NE.name,
+                        ComputeUnit.CPU_AND_GPU.name,
+                        ComputeUnit.ALL.name,
+                        ComputeUnit.CPU_ONLY.name,
+                    ],
+                ),
+                "controlnet_support": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("COREML_UNET",)
+    RETURN_NAMES = ("coreml_model",)
+    FUNCTION = "convert"
+
+    def convert(self, height, width, batch_size, compute_unit, controlnet_support):
+        """Converts a LCM model to Core ML.
+
+        Args:
+            height (int): Height of the target image.
+            width (int): Width of the target image.
+            batch_size (int): Batch size.
+            compute_unit (str): Compute unit to use when loading the model.
+
+        Returns:
+            coreml_model: The converted Core ML model.
+
+        The converted model is also saved to "models/unet" directory and
+        can be loaded with the "LCMCoreMLLoaderUNet" node.
+        """
+        h = height
+        w = width
+        sample_size = (h // 8, w // 8)
+        batch_size = batch_size
+        cn_support_str = "_cn" if controlnet_support else ""
+
+        out_name = f"{lcm_converter.MODEL_NAME}_{batch_size}x{w}x{h}{cn_support_str}"
+
+        out_path = lcm_converter.get_out_path("unet", f"{out_name}")
+
+        if not os.path.exists(out_path):
+            lcm_converter.convert(
+                out_path=out_path,
+                sample_size=sample_size,
+                batch_size=batch_size,
+                controlnet_support=controlnet_support,
+            )
+        target_path = lcm_converter.compile_model(out_path=out_path, out_name=out_name)
+
+        return (CoreMLModel(target_path, compute_unit, "compiled"),)

coreml_suite/lcm/unet.py

@@ -0,0 +1,99 @@
+from overrides import overrides
+from python_coreml_stable_diffusion.unet import UNet2DConditionModel, TimestepEmbedding
+
+
+class UNet2DConditionModelLCM(UNet2DConditionModel):
+    def __init__(
+        self,
+        time_cond_proj_dim=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        timestep_input_dim = self.config.block_out_channels[0]
+        time_embed_dim = self.config.block_out_channels[0] * 4
+
+        time_embedding = TimestepEmbedding(
+            timestep_input_dim, time_embed_dim, cond_proj_dim=time_cond_proj_dim
+        )
+        self.time_embedding = time_embedding
+
+    @overrides(check_signature=False)
+    def forward(
+        self,
+        sample,
+        timestep,
+        encoder_hidden_states,
+        timestep_cond,
+        *additional_residuals,
+    ):
+        # 0. Project (or look-up) time embeddings
+        t_emb = self.time_proj(timestep)
+        emb = self.time_embedding(t_emb, timestep_cond)
+
+        # 1. center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        # 3. down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if (
+                hasattr(downsample_block, "attentions")
+                and downsample_block.attentions is not None
+            ):
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+            down_block_res_samples += res_samples
+
+        if additional_residuals:
+            new_down_block_res_samples = ()
+            for i, down_block_res_sample in enumerate(down_block_res_samples):
+                down_block_res_sample = down_block_res_sample + additional_residuals[i]
+                new_down_block_res_samples += (down_block_res_sample,)
+            down_block_res_samples = new_down_block_res_samples
+
+        # 4. mid
+        sample = self.mid_block(
+            sample, emb, encoder_hidden_states=encoder_hidden_states
+        )
+
+        if additional_residuals:
+            sample = sample + additional_residuals[-1]
+
+        # 5. up
+        for upsample_block in self.up_blocks:
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[
+                : -len(upsample_block.resnets)
+            ]
+
+            if (
+                hasattr(upsample_block, "attentions")
+                and upsample_block.attentions is not None
+            ):
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples
+                )
+
+        # 6. post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        return (sample,)

coreml_suite/nodes.py

@@ -1,10 +1,18 @@
 import os
 
 from coremltools import ComputeUnit
+from python_coreml_stable_diffusion.coreml_model import CoreMLModel
+from python_coreml_stable_diffusion.unet import AttentionImplementations
 
 import folder_paths
 from coreml_suite import COREML_NODE
-from coreml_suite.coreml_model import CoreMLModel
+from coreml_suite import converter
+from coreml_suite.config import ModelVersion
+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
@@ -17,26 +25,6 @@ from coreml_suite.models import (
 )
 
 
-def _discover(fn_name, fallback):
-    """Populate a converter dropdown from coreml_diffusion's discovery API.
-
-    Fails soft: if the package is missing, too old to expose ``fn_name``, or
-    errors, the node still registers with the fallback list instead of vanishing
-    from the menu. Evaluated on every INPUT_TYPES call, so installing a newer
-    coreml_diffusion surfaces new conversion types with no Suite change.
-    """
-    try:
-        import coreml_diffusion
-
-        return getattr(coreml_diffusion, fn_name)()
-    except Exception as exc:  # missing/old package, import error, etc.
-        logger.warning(
-            f"coreml_diffusion.{fn_name} unavailable ({exc}); "
-            f"using fallback {fallback}"
-        )
-        return fallback
-
-
 class CoreMLSampler(COREML_NODE, KSampler):
     @classmethod
     def INPUT_TYPES(s):
@@ -180,7 +168,7 @@ class CoreMLLoader(COREML_NODE):
 
     @classmethod
     def coreml_filenames(cls):
-        extensions = (".mlpackage",)
+        extensions = (".mlmodelc", ".mlpackage")
         all_paths = folder_paths.get_filename_list_(cls.PACKAGE_DIRNAME)[1]
         coreml_paths = folder_paths.filter_files_extensions(all_paths, extensions)
 
@@ -191,7 +179,9 @@ class CoreMLLoader(COREML_NODE):
 
         coreml_path = self.coreml_filenames()[coreml_name]
 
-        return (CoreMLModel(coreml_path, compute_unit),)
+        sources = "compiled" if coreml_name.endswith(".mlmodelc") else "packages"
+
+        return (CoreMLModel(coreml_path, compute_unit, sources),)
 
 
 class CoreMLLoaderUNet(CoreMLLoader):
@@ -225,26 +215,28 @@ class CoreMLModelAdapter(COREML_NODE):
 
 
 class CoreMLConverter(COREML_NODE):
-    """Converts a Stable Diffusion checkpoint (UNet) to Core ML.
-
-    The model version (SD15 / SDXL / SDXL refiner / LCM) is auto-detected from
-    the checkpoint's architecture, so there is no version dropdown — one node
-    converts every supported family, including full-distill LCM.
-    """
+    """Converts a LCM model to Core ML."""
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
-                "height": ("INT", {"default": 512, "min": 8, "step": 8}),
-                "width": ("INT", {"default": 512, "min": 8, "step": 8}),
+                "model_version": (
+                    [
+                        ModelVersion.SD15.name,
+                        ModelVersion.SDXL.name,
+                    ],
+                ),
+                "height": ("INT", {"default": 512, "min": 256, "max": 2048, "step": 8}),
+                "width": ("INT", {"default": 512, "min": 256, "max": 2048, "step": 8}),
                 "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
                 "attention_implementation": (
-                    _discover(
-                        "list_attention_impls",
-                        ["SPLIT_EINSUM", "SPLIT_EINSUM_V2", "ORIGINAL"],
-                    ),
+                    [
+                        AttentionImplementations.SPLIT_EINSUM.name,
+                        AttentionImplementations.SPLIT_EINSUM_V2.name,
+                        AttentionImplementations.ORIGINAL.name,
+                    ],
                 ),
                 "compute_unit": (
                     [
@@ -262,10 +254,7 @@ class CoreMLConverter(COREML_NODE):
                 # omits any `required` input. When omitted it defaults to
                 # "none", identical to unquantized behavior and filename, so
                 # existing cached .mlpackages still resolve.
-                "quantize_nbits": (
-                    _discover("list_quant_modes", ["none", "8", "6", "4"]),
-                    {"default": "none"},
-                ),
+                "quantize_nbits": (list(QUANT_NBITS_VALUES), {"default": "none"}),
                 "lora_params": ("LORA_PARAMS",),
             },
         }
@@ -277,6 +266,7 @@ class CoreMLConverter(COREML_NODE):
     def convert(
         self,
         ckpt_name,
+        model_version,
         height,
         width,
         batch_size,
@@ -286,11 +276,9 @@ class CoreMLConverter(COREML_NODE):
         quantize_nbits="none",
         lora_params=None,
     ):
-        """Converts a checkpoint's UNet to Core ML.
+        """Converts a LCM model to Core ML.
 
         Args:
-            ckpt_name (str): Checkpoint to convert; its model version is
-                auto-detected from the weights.
             height (int): Height of the target image.
             width (int): Width of the target image.
             batch_size (int): Batch size.
@@ -300,8 +288,10 @@ class CoreMLConverter(COREML_NODE):
             coreml_model: The converted Core ML model.
 
         The converted model is also saved to "models/unet" directory and
-        can be loaded with the "Load Core ML UNet" node.
+        can be loaded with the "LCMCoreMLLoaderUNet" node.
         """
+        model_version = ModelVersion[model_version]
+
         lora_params = lora_params or {}
         lora_params = [(k, v[0]) for k, v in lora_params.items()]
         lora_params = sorted(lora_params, key=lambda lora: lora[0])
@@ -310,16 +300,14 @@ class CoreMLConverter(COREML_NODE):
         h = height
         w = width
         sample_size = (h // 8, w // 8)
-        import coreml_diffusion
-
-        out_name = coreml_diffusion.compose_out_name(
+        out_name = compose_out_name(
             ckpt_name=ckpt_name,
             batch_size=batch_size,
             width=w,
             height=h,
             controlnet_support=controlnet_support,
             attention_implementation=attention_implementation,
-            lora_names=coreml_diffusion.lora_names_from_params(lora_params),
+            lora_names=lora_names_from_params(lora_params),
             quantize_nbits=quantize_nbits,
         )
 
@@ -330,14 +318,11 @@ class CoreMLConverter(COREML_NODE):
         logger.info(f"Attention implementation: {attention_implementation}")
 
         if lora_params:
-            logger.info("LoRAs used:")
+            logger.info(f"LoRAs used:")
             for lora_param in lora_params:
                 logger.info(f"  {lora_param[0]} - strength: {lora_param[1]}")
 
-        # Resolve the ComfyUI models/unet path here (a node concern); the package
-        # takes the output path as an injected argument.
-        unet_path = folder_paths.get_folder_paths("unet")[0]
-        unet_out_path = os.path.join(unet_path, f"{out_name}_unet.mlpackage")
+        unet_out_path = converter.get_out_path("unet", f"{out_name}")
         ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
 
         config_filename = ckpt_name.split(".")[0] + ".yaml"
@@ -345,10 +330,10 @@ class CoreMLConverter(COREML_NODE):
         if config_path:
             logger.info(f"Using config file {config_path}")
 
-        coreml_diffusion.convert(
-            ckpt_path,
-            None,  # model_version auto-detected from the checkpoint
-            unet_out_path,
+        converter.convert(
+            ckpt_path=ckpt_path,
+            model_version=model_version,
+            unet_out_path=unet_out_path,
             sample_size=sample_size,
             batch_size=batch_size,
             controlnet_support=controlnet_support,
@@ -357,7 +342,11 @@ class CoreMLConverter(COREML_NODE):
             config_path=config_path,
             quantize_nbits=quantize_nbits,
         )
-        return (CoreMLModel(unet_out_path, compute_unit),)
+        unet_target_path = converter.compile_model(
+            out_path=unet_out_path, out_name=out_name, submodule_name="unet"
+        )
+
+        return (CoreMLModel(unet_target_path, compute_unit, "compiled"),)
 
     @staticmethod
     def lora_path(lora_name):

pyproject.toml

@@ -1,42 +1,50 @@
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
 [project]
 name = "comfyui-coremlsuite"
 description = "This extension contains a set of custom nodes for ComfyUI that allow you to use Core ML models in your ComfyUI workflows."
-version = "2.1.2"
+version = "1.0.1"
 license = "MIT"
-requires-python = ">=3.12"
+requires-python = ">=3.12,<3.13"
+packages = [{ include = "coreml_suite" }]
 dependencies = [
-    # torch is provided by the host (ComfyUI) and intentionally left unpinned
-    # here: a hard torch cap would downgrade the host's torch and break its
-    # torchvision/torchaudio ABI. coreml-diffusion pulls torch>=2.7 transitively.
-    # >=0.1.6: model-version auto-detection (convert(model_version=None)) and the
-    # dropped <3.13 Python cap (kept in sync with this package's requires-python).
-    "coreml-diffusion>=0.1.6,<0.2",
+    # Python 3.12, coremltools 9, torch 2.7.
+    # numpy stays in the 1.24..1.x range — none of our modules need
+    # numpy 2, and coremltools + ml-stable-diffusion's SD UNet trace
+    # hit hard bugs under numpy 2 (`_cast` int(ndarray) strictness and
+    # `view` mixed-Var shape lists).
+    # torch 2.7 is the latest version coremltools 9's PyTorch frontend
+    # has been tested against.
+    "python-coreml-stable-diffusion @ git+https://github.com/apple/ml-stable-diffusion.git@e5d960c41a6a4ab200b8db379194127607b1c590",
+    "torch>=2.7,<2.8",
     "coremltools>=9,<10",
-    "numpy>=2,<3",
+    "numpy>=1.24,<2",
+    "overrides",
+    "diffusers>=0.22",
+    "peft>=0.6.2",
+    "omegaconf>=2.3",
 ]
 
 [project.urls]
 Repository = "https://github.com/aszc-dev/ComfyUI-CoreMLSuite"
-
-[tool.hatch.build.targets.wheel]
-packages = ["coreml_suite"]
+#  Used by Comfy Registry https://comfyregistry.org
 
 [tool.comfy]
 PublisherId = "aszc-dev"
 DisplayName = "ComfyUI-CoreMLSuite"
-Icon = "https://raw.githubusercontent.com/aszc-dev/ComfyUI-CoreMLSuite/main/assets/snake.png"
-requires-comfyui = ">=0.3.27"
+Icon = ""
+# Pinned to the ComfyUI commit this toolchain was validated against.
+requires-comfyui = "==ab5413351eee61f3d7f10c74e75286df0058bb18"
 
 [dependency-groups]
 dev = [
     "pillow>=12.2.0",
     "psutil>=7.2.2",
-    "pytest>=9.0.3",
 ]
+# ComfyUI runtime deps that aren't part of our package's runtime contract
+# but are needed to spin up the ComfyUI server for Tier 2 integration tests.
+# Kept in a uv group so `uv sync --group comfy` brings them in without
+# polluting the published metadata (and without re-bumping our torch pin
+# via `uv pip install -r ComfyUI/requirements.txt`, which would float to
+# the latest torch and break the coremltools 9 compatibility ceiling).
 comfy = [
     "comfyui-frontend-package==1.14.6",
     "torchvision",
@@ -53,11 +61,34 @@ comfy = [
     "sentencepiece",
 ]
 
+[tool.uv]
+# ml-stable-diffusion's setup.py hard-pins numpy<1.24, diffusers==0.30.2
+# and transformers==4.44.2, which blocks the modern torch / coremltools
+# combo on Python 3.12. Override the four blocking pins; the .unet /
+# .coreml_model symbols we actually import are stable across the bumped
+# versions.
+override-dependencies = [
+    "numpy>=1.24,<2",
+    "diffusers>=0.30",
+    "transformers>=4.44",
+    "huggingface-hub>=0.24",
+]
+
 [tool.pytest.ini_options]
+# Tier markers gate which environment a test needs.
+# - unit: framework-free pure-logic tests (Tier 0; run without ComfyUI on
+#   Linux).
+# - m2:   needs an Apple Silicon Mac with the Neural Engine (Tier 2),
+#   typically a self-hosted runner or local M-series box.
+# - smoke: lightweight checks that need Apple Silicon + coremltools but no
+#   ANE/real model (Tier 1).
 markers = [
     "unit: framework-free unit test (Tier 0)",
-    "smoke: macOS-ARM smoke test on a synthetic micro-model (Tier 1)",
     "m2: requires Apple Silicon + Neural Engine (Tier 2)",
+    "smoke: macOS-ARM smoke test on a synthetic micro-model (Tier 1)",
 ]
 testpaths = ["tests"]
+# importlib mode keeps pytest from importing the repo-root __init__.py
+# (which is the ComfyUI custom-node entry and pulls in comfy + nodes).
+# Without this Tier-0 leaks the entire ComfyUI runtime on collection.
 addopts = ["--import-mode=importlib", "--confcutdir=tests"]

requirements.txt

@@ -1,4 +1,8 @@
-coreml-diffusion>=0.1.4,<0.2
-coremltools>=9,<10
+git+https://github.com/apple/ml-stable-diffusion.git@e5d960c41a6a4ab200b8db379194127607b1c590
+torch>=2.7,<2.8
+coremltools==8.2
 numpy>=2,<3
-diffusers>=0.30
+overrides
+diffusers>=0.22
+peft>=0.6.2
+omegaconf>=2.3

seam.md

@@ -1,208 +0,0 @@
-# Conversion Extraction — Seam Inventory (`docs/extraction/seam.md`)
-
-> **Gate E0 deliverable.** Symbol-by-symbol cut line between the future `coreml_diffusion`
-> package (CONVERSION) and what stays in `coreml_suite` (the ComfyUI side).
->
-> **Confidence legend:**
-> - ✅ **verified** — read directly from the current source in this repo.
-> - 🔍 **confirm** — inferred / partially seen; Claude Code must `grep`-verify before acting.
->
-> **Cut rule:** a symbol goes to `coreml_diffusion` iff it participates in producing the `.mlpackage`
-> artifact AND can be made free of `comfy` / `folder_paths` / `comfy_extras`. The runtime
-> *loader* that **runs** a compiled model stays in the suite.
-
----
-
-## 1. File-level map
-
-| File | Side | Status | Note |
-|---|---|---|---|
-| `coreml_suite/model_version.py` | **coreml_diffusion** | ✅ | Already `Enum`-only, zero comfy. Becomes pkg source of truth. |
-| `coreml_suite/attention.py` | **coreml_diffusion** | ✅ | `ATTENTION_IMPLEMENTATIONS` tuple; pure constant. |
-| `coreml_suite/core/naming.py` | **coreml_diffusion** | ✅ | `compose_out_name` = cache-key contract. Move (not copy). |
-| `coreml_suite/converter.py` | **coreml_diffusion** (mostly) | ✅ | Main conversion. One symbol stays-adjacent: `get_out_path` (folder_paths) is replaced by injected `out_path`. |
-| `coreml_suite/conversion/attention.py` | **coreml_diffusion** | ✅ | `apply_attention_implementation`. Imports `logging`,`torch` only — no comfy. |
-| `coreml_suite/conversion/shapes.py` | **coreml_diffusion** | ✅ | `conv2d_output_shape`. Pure math, no imports. |
-| `coreml_suite/conversion/trace.py` | **coreml_diffusion** | ✅ | Imports `types.MethodType`, `diffusers...Transformer2DModel` only — torch/diffusers. |
-| `coreml_suite/conversion/unet.py` | **coreml_diffusion** | ✅ | `CoreMLUNetWrapper`. Imports `torch` only — no comfy. |
-| `coreml_suite/lcm/converter.py` | **coreml_diffusion** (after dedup) | ✅ | Dup helpers deleted; `MODEL_VERSION` HF-hardcode (L22) → E-LCM. `folder_paths` (L111) + `comfy.model_management` (L54) confirmed present → CUT. |
-| `coreml_suite/lcm/unet.py` | **coreml_diffusion** | ✅ | `UNet2DConditionModelLCM(UNet2DConditionModel)`. diffusers-only, no comfy. |
-| `coreml_suite/config.py` | **STAYS** | ✅ | Imports `comfy.supported_models_base`/`latent_formats`/`model_detection`. **Inference-side** (`get_model_config`), NOT conversion. |
-| `coreml_suite/coreml_model.py` | **STAYS** | ✅ | `CoreMLModel` = runtime loader (runs `.mlpackage`). Desktop/Python inference; not used on iOS. |
-| `coreml_suite/nodes.py` | **STAYS** | ✅ | Nodes; will call `coreml_diffusion` + own `folder_paths` path resolution + discovery dropdowns. |
-| `coreml_suite/lcm/nodes.py` | **STAYS** | ✅ | `COREML_CONVERT_LCM` node. |
-| `coreml_suite/models.py` | **STAYS** | ✅ | Inference: `add_sdxl_model_options`, `is_sdxl`, `get_model_patcher`, `get_latent_image`. |
-| `coreml_suite/latents.py` | **STAYS** | ✅ | Inference chunking (MODERNIZATION Phase 3 target, not this spec). |
-| `coreml_suite/controlnet.py` | **STAYS** | ✅ | Inference-side controlnet. Distinct from converter `add_cnet_support`. |
-| `coreml_suite/lcm/utils.py` | **STAYS** | ✅ | `add_lcm_model_options`, `lcm_patch`, `is_lcm`; imports `comfy_extras`. Inference. |
-| `coreml_suite/logger.py` | **both / copy** | ✅ | Trivial. Package gets its own logger; suite keeps its. |
-
----
-
-## 2. Symbol-level: `coreml_suite/converter.py` (main conversion)
-
-| Symbol | Side | Status | Cut action |
-|---|---|---|---|
-| `DEFAULT_TRACE_TIMESTEP`, `TEXT_TOKEN_SEQUENCE_LENGTH` | coreml_diffusion | ✅ | Move as-is (module constants). |
-| `get_unet(model_version, ref_unet, attention_implementation)` | coreml_diffusion | ✅ | Move. Uses `conversion.{trace,attention,unet}`. No comfy. |
-| `get_encoder_hidden_states_shape(ref_unet, batch_size)` | coreml_diffusion | ✅ | Move. Reads `ref_unet.config.cross_attention_dim`. Pure. |
-| `get_coreml_inputs(sample_inputs)` | coreml_diffusion | ✅ | Move. `ct.TensorType` build. |
-| `load_coreml_model(out_path)` | coreml_diffusion | ✅ | Move. `ct.models.MLModel(out_path)`. (Dedup target vs LCM copy.) |
-| `convert_to_coreml(submodule, ts_module, inputs, names, out_path)` | coreml_diffusion | ✅ | Move. `ct.convert(...)`. (Dedup target vs LCM copy.) |
-| `get_sample_input(batch, ehs_shape, sample_shape)` | coreml_diffusion | ✅ | Move. **Merge** with LCM variant (LCM passes extra `scheduler` → optional param). |
-| `lcm_inputs(sample_unet_inputs)` | coreml_diffusion | ✅ | Move. Adds `timestep_cond`. |
-| `sdxl_inputs(sample_unet_inputs, ref_unet, model_version)` | coreml_diffusion | ✅ | Move. `time_ids`/`text_embeds`/`add_embeds`. |
-| `add_cnet_support(sample_shape, ref_unet)` | coreml_diffusion | ✅ | Move. Builds `additional_residual_*` inputs from unet block channels. |
-| `convert_unet(ref_unet, model_version, unet_out_path, ...)` | coreml_diffusion | ✅ | Move. Orchestrates trace→convert→**quant (palettize)**→save. Quant travels here (E6). |
-| `convert(ckpt_path, model_version, unet_out_path, ...)` | coreml_diffusion | ✅ | Move. **Make kw-only past `ckpt_path,model_version,out_path`** (contract). Validates `attn_impl`. |
-| `load_unet(ckpt_path, config_path)` | coreml_diffusion | ✅ | Move. `UNet2DConditionModel.from_single_file`. |
-| `get_out_path(submodule_name, model_name)` | **STAYS (node)** | ✅ | Uses `folder_paths.get_folder_paths`. **Delete from converter; node resolves path and passes `out_path` in.** |
-
-**Apple `python_coreml_stable_diffusion` footprint on this path:** ✅ **none.** Verified by grep:
-zero imports in `converter.py` / `conversion/*`. Main path uses `diffusers` +
-local `CoreMLUNetWrapper`. (And the runtime `CoreMLModel` is now a local coremltools wrapper too —
-see §6 stale-spec note.)
-
----
-
-## 3. Symbol-level: `coreml_suite/lcm/converter.py` (LCM — dedup + defer)
-
-| Symbol | Side | Status | Cut action |
-|---|---|---|---|
-| `load_coreml_model` (LCM copy) | DELETE | ✅ | Duplicate of main. Remove; use `coreml_diffusion.load_coreml_model`. |
-| `convert_to_coreml` (LCM copy) | DELETE | ✅ | Duplicate of main. Remove. |
-| `get_out_path` (LCM copy, folder_paths) | DELETE | ✅ | Duplicate + comfy. Remove; node injects `out_path`. |
-| `get_sample_input(..., scheduler)` (LCM copy) | MERGE → coreml_diffusion | ✅ | Fold `scheduler` into shared `get_sample_input` as optional param. |
-| `MODEL_NAME` (= LCM_Dreamshaper) | **E-LCM** | ✅ | HF hardcode. Removing it is the behavior change → E-LCM, not E2. |
-| `convert(out_path, sample_size, batch_size, controlnet_support)` (LCM, L190) | coreml_diffusion (via unified) | ✅ | Route through `coreml_diffusion.convert(model_version=LCM, ...)` in E-LCM. |
-| `from comfy.model_management import get_torch_device` (L54, in `get_scheduler`) | **CUT** | ✅ | Confirmed present. Inject `device`. |
-| module-global attention set at import | n/a | ✅ | **No module global.** Attention already per-call: `get_unets` (L36) calls `apply_attention_implementation(ref_unet, "SPLIT_EINSUM")`. No `ATTENTION_IMPLEMENTATION_IN_EFFECT` anywhere in repo. (Note: LCM hardcodes `"SPLIT_EINSUM"` — pass `attn_impl` through in dedup.) |
-
----
-
-## 4. Symbol-level: `coreml_suite/core/naming.py` → `coreml_diffusion/naming.py`
-
-| Symbol | Side | Status | Cut action |
-|---|---|---|---|
-| `compose_out_name(...)` | coreml_diffusion | ✅ | **Move** (cache-key contract). Node imports from pkg. |
-| `lora_names_from_params(...)` | coreml_diffusion | ✅ | Move. |
-| `ATTN_SUFFIX` dict | coreml_diffusion | ✅ | Move. |
-| `QUANT_NBITS_VALUES` | coreml_diffusion | ✅ | Move; backs `list_quant_modes()`. |
-| `tests/unit/test_characterization_out_name.py` | re-point | ✅ | Change import to `coreml_diffusion.naming`. Assertions/values **unchanged**. |
-
----
-
-## 5. Discovery API + status registry (new in `coreml_diffusion/__init__.py`)
-
-```python
-from enum import Enum
-
-class Status(Enum):
-    VERIFIED = "verified"          # has a golden anchor + passing [M2-ANE] check
-    EXPERIMENTAL = "experimental"  # convertible, not yet anchored/verified
-
-# Single source of truth. Suite gates on this, NOT on a hardcoded node list.
-# KEY by ModelVersion enum MEMBER (not a bare string) so list_model_versions can
-# emit .name — see the .name decision below. Keying by the lowercase .value string
-# (as an earlier draft of this block did) returns ["sd15",...], which the node then
-# reverses via ModelVersion[...] → KeyError. Do NOT key by .value.
-_MODEL_STATUS = {
-    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
-}
-
-def list_model_versions(include_experimental: bool = False) -> list[str]:
-    return [v.name for v, s in _MODEL_STATUS.items()   # .name → "SD15","SDXL" (see decision)
-            if s is Status.VERIFIED or (include_experimental and s is Status.EXPERIMENTAL)]
-
-def list_attention_impls() -> list[str]:   # from attention.ATTENTION_IMPLEMENTATIONS
-    ...
-def list_quant_modes() -> list[str]:       # from naming.QUANT_NBITS_VALUES
-    ...
-
-CONTRACT_VERSION = "1.0"
-# Additive-only: adding an id or promoting EXPERIMENTAL→VERIFIED = minor bump (Suite unaffected).
-# Removing/renaming an id, or demoting VERIFIED→EXPERIMENTAL = MAJOR bump + migration note.
-```
-
-**Decision check (`.name` vs `.value`): RESOLVED → `.name`.** ✅
-Verified in current source:
-- Node renders `ModelVersion.SD15.name` / `ModelVersion.SDXL.name` → `"SD15"`, `"SDXL"`
-  (`nodes.py:224-225`).
-- Node reverses the dropdown string with `model_version = ModelVersion[model_version]`
-  (`nodes.py:286`) — i.e. **lookup by NAME**. Feeding it a `.value` (`"sd15"`) raises `KeyError`.
-- Enum values are lowercase (`model_version.py`: `SD15="sd15"`, `SDXL="sdxl"`,
-  `SDXL_REFINER="sdxl_refiner"`, `LCM="lcm"`).
-- `compose_out_name` does NOT consume the model_version string (grep of `core/naming.py` empty) —
-  no coupling there, so no constraint from that side.
-
-**Decision:** `list_model_versions()` returns `.name` (uppercase). Saved workflows store `"SD15"`,
-node already validates them via `ModelVersion[...]`. The `_MODEL_STATUS` block above was corrected
-to key by enum member and emit `.name`. **The earlier `v.value` form was a latent bug.**
-
----
-
-## 6. `python_coreml_stable_diffusion` split (Gate E0 line to fill by grep)
-
-| Use | Side | Status |
-|---|---|---|
-| `coreml_model.CoreMLModel` (runs compiled model) | **STAYS** (suite runtime) | ✅ — **local class**, not Apple's |
-| `unet.UNet2DConditionModel*` internals | **gone** — `converter.py:319` uses `diffusers.UNet2DConditionModel.from_single_file` | ✅ |
-| `AttentionImplementations` enum | gone — local `apply_attention_implementation` + `attention.py` tuple | ✅ |
-| `calculate_conv2d_output_shape` | gone — replaced by `conversion/shapes.conv2d_output_shape` | ✅ |
-
-> ### ⚠️ SPEC IS STALE: `ml-stable-diffusion` is already fully removed
-> Commit #58 ("replace apple/ml-stable-diffusion with native diffusers conversion") already did
-> the de-Apple work. Verified now:
-> - **Zero** `python_coreml_stable_diffusion` runtime imports anywhere in `coreml_suite` (only a
->   docstring mention at `core/__init__.py:4`).
-> - `coreml_suite/coreml_model.py:8` `CoreMLModel` is a **local** wrapper over
->   `coremltools.models.MLModel` (`coreml_model.py:22`) — it does **not** import Apple's class.
-> - `ml-stable-diffusion` / `python_coreml_stable_diffusion` appears in **neither** `pyproject.toml`
->   **nor** `requirements.txt`. It is not a dependency at all.
->
-> **Consequences for the spec (correct these in CONVERTER_EXTRACTION_SPEC.md):**
-> - §0.3 premise ("runtime loader = `python_coreml_stable_diffusion.coreml_model.CoreMLModel`,
->   stays in suite") is **wrong**: the loader is already the local `coreml_model.CoreMLModel`. The
->   "stays in suite" conclusion still holds; the identity does not.
-> - **Gate E0 item "ml-stable-diffusion pinned SHA — BLOCKER if unpinned" is MOOT** — there is no
->   such dep to pin. Mark it N/A, not BLOCKER.
-> - **E4/E5 dependency lists must drop `git+...ml-stable-diffusion@<sha>`.** Package runtime deps
->   are: `coremltools`, `diffusers`, `peft` (LoRA), `omegaconf` (config), `numpy`, `torch`. Confirm
->   `peft`/`omegaconf` actually used before listing (grep at E4).
-> - The "keep `python_coreml_stable_diffusion` as a suite dep for the loader" instruction in E5 is
->   **void** — coremltools backs the loader.
-
----
-
-## 7. Pre-flight checklist before E1 (run these greps)
-
-```
-grep -rn "import comfy"            coreml_suite/conversion coreml_suite/converter.py coreml_suite/lcm/converter.py coreml_suite/lcm/unet.py
-grep -rn "folder_paths"            coreml_suite/converter.py coreml_suite/lcm/converter.py
-grep -rn "model_management"        coreml_suite/lcm
-grep -rn "python_coreml_stable_diffusion" coreml_suite
-grep -rn "ATTENTION_IMPLEMENTATION_IN_EFFECT" coreml_suite
-grep -rn "SimianLuo\|LCM_Dreamshaper" coreml_suite/lcm
-```
-Every 🔍 above resolves to ✅ or a correction once these run. Do not start moving code (E2)
-with any 🔍 unresolved on the CONVERSION side.
-
-**STATUS (run 2026-05-26): all 🔍 resolved.** Summary of what the greps found:
-- `conversion/*`, `lcm/unet.py`: comfy-free (torch/diffusers only). ✅
-- `converter.py`: only comfy reach-in is `folder_paths` in `get_out_path` (L91-94) → inject `out_path`.
-- `lcm/converter.py`: `folder_paths` (L111-114) + `comfy.model_management.get_torch_device` (L54)
-  → cut both. Dup helpers (`load_coreml_model`,`convert_to_coreml`,`get_out_path`,`get_sample_input`)
-  confirmed → dedup E2. `MODEL_VERSION="SimianLuo/LCM_Dreamshaper_v7"` (L22) → E-LCM.
-- No attention module-global anywhere (`ATTENTION_IMPLEMENTATION_IN_EFFECT` absent); already per-call.
-  LCM hardcodes `"SPLIT_EINSUM"` in `get_unets` — thread `attn_impl` through during dedup.
-- `.name` vs `.value`: **decided `.name`** (node reverses via `ModelVersion[...]`). §5 corrected.
-- `ml-stable-diffusion`: **already gone** (#58). §6 stale-spec note added — fix the spec's E0/E4/E5
-  dep + pinning items.
-
-Two grep blind-spots to note (the checklist above doesn't cover them, but cheap to add): the
-`folder_paths` grep only scans the two converter files — also grep `coreml_suite/lcm/utils.py`
-(it imports `comfy.model_management` at L3, but it's inference/STAYS, so fine) and confirm no other
-`conversion/` file grew a comfy import since.

tests/conftest.py

@@ -4,7 +4,7 @@
   that transitively import `comfy.*` resolve when pytest is invoked from
   this package's root.
 - Auto-applies tier markers based on the directory a test lives in, so
-  individual files don't have to repeat @pytest.mark.unit / .smoke.
+  individual files don't have to repeat @pytest.mark.unit / .m2.
 """
 import sys
 from pathlib import Path
@@ -26,9 +26,9 @@ _TIER_BY_DIR = {
     "tests/smoke": "smoke",
 }
 
-# When the user asks for a single tier (-m unit / -m smoke), skip the other
-# directories at collection time. Tier-0 cannot afford to import tests/smoke
-# files because they pull in coremltools which Linux CI won't have.
+# When the user asks for a single tier (-m unit / -m m2), skip the other
+# directories at collection time. Tier-0 cannot afford to import tests/m2
+# files because they pull in PIL + ComfyUI runtime which Linux CI won't have.
 _TIER_DIRS = {
     "unit": ("/tests/unit/",),
     "m2": ("/tests/m2/", "/tests/integration/"),

tests/integration/workflows/e2e-1.5-basic-conversion.json

@@ -107,6 +107,7 @@
   "10": {
     "inputs": {
       "ckpt_name": "dreamshaper_8.safetensors",
+      "model_version": "SD15",
       "height": 512,
       "width": 512,
       "batch_size": 1,

tests/smoke/test_synthetic_unet.py

@@ -0,0 +1,122 @@
+"""Tier 1 smoke: convert a synthetic micro-UNet through coremltools and load
+it back with python_coreml_stable_diffusion's CoreMLModel.
+
+Purpose: catch API breakage in coremltools / ml-stable-diffusion *without*
+needing a real SD checkpoint, the ANE, or a converted .mlmodelc on disk.
+Runs in minutes on a hosted macOS-ARM runner (no Apple internal stuff).
+
+What it asserts:
+  - coremltools.convert still accepts the call shape we use today
+  - the resulting .mlpackage round-trips through CoreMLModel
+  - expected_inputs exposes the input names/shapes we declared
+  - calling the model returns the named output (`noise_pred`)
+
+Auto-skips on non-Apple-Silicon hosts so Tier 0 CI on Linux ignores it.
+"""
+import platform
+import shutil
+
+import numpy as np
+import pytest
+import torch
+import torch.nn as nn
+
+
+pytestmark = pytest.mark.skipif(
+    platform.system() != "Darwin" or platform.machine() != "arm64",
+    reason="Tier 1 requires macOS on Apple Silicon",
+)
+
+
+# Tiny shapes — large enough to exercise conv2d + linear + addition kernels in
+# coremltools, small enough that conversion finishes in seconds on CPU.
+SAMPLE_SHAPE = (1, 4, 8, 8)
+TIMESTEP_SHAPE = (1,)
+ENCODER_SHAPE = (1, 64, 1, 4)  # matches SD's transposed encoder_hidden_states layout
+OUT_NAME = "noise_pred"
+
+
+class TinyUNet(nn.Module):
+    """Minimal UNet-shaped graph: conv -> add(time+context) -> conv.
+
+    Not a real diffusion model. Just enough op variety to exercise the
+    PyTorch -> MIL frontend in coremltools and confirm we can still wire
+    the inputs/outputs the way ml-stable-diffusion expects.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.conv_in = nn.Conv2d(4, 8, kernel_size=3, padding=1)
+        self.conv_out = nn.Conv2d(8, 4, kernel_size=3, padding=1)
+        self.time_proj = nn.Linear(1, 8)
+        self.text_proj = nn.Linear(64, 8)
+
+    def forward(self, sample, timestep, encoder_hidden_states):
+        h = self.conv_in(sample)
+        t_emb = self.time_proj(timestep.unsqueeze(-1)).view(1, 8, 1, 1)
+        c_emb = self.text_proj(encoder_hidden_states.squeeze(2).mean(-1)).view(1, 8, 1, 1)
+        h = h + t_emb + c_emb
+        return self.conv_out(h)
+
+
+@pytest.fixture(scope="module")
+def tiny_mlpackage(tmp_path_factory):
+    """Convert TinyUNet once per test session and reuse the .mlpackage."""
+    import coremltools as ct
+
+    torch.manual_seed(0)
+    model = TinyUNet().eval()
+    example = (
+        torch.randn(*SAMPLE_SHAPE),
+        torch.randn(*TIMESTEP_SHAPE),
+        torch.randn(*ENCODER_SHAPE),
+    )
+    traced = torch.jit.trace(model, example)
+
+    mlmodel = ct.convert(
+        traced,
+        inputs=[
+            ct.TensorType(name="sample", shape=SAMPLE_SHAPE, dtype=np.float16),
+            ct.TensorType(name="timestep", shape=TIMESTEP_SHAPE, dtype=np.float16),
+            ct.TensorType(name="encoder_hidden_states", shape=ENCODER_SHAPE, dtype=np.float16),
+        ],
+        outputs=[ct.TensorType(name=OUT_NAME, dtype=np.float16)],
+        compute_units=ct.ComputeUnit.CPU_ONLY,
+        compute_precision=ct.precision.FLOAT16,
+        convert_to="mlprogram",
+        minimum_deployment_target=ct.target.macOS13,
+    )
+
+    out_dir = tmp_path_factory.mktemp("tiny_unet")
+    pkg_path = out_dir / "tiny.mlpackage"
+    mlmodel.save(str(pkg_path))
+    yield pkg_path
+    shutil.rmtree(out_dir, ignore_errors=True)
+
+
+def test_coremltools_convert_round_trips_via_coreml_model(tiny_mlpackage):
+    from python_coreml_stable_diffusion.coreml_model import CoreMLModel
+
+    model = CoreMLModel(str(tiny_mlpackage), "CPU_ONLY", "packages")
+
+    # expected_inputs is the contract our wrappers depend on. Lock the shape
+    # of the dict + a sample entry.
+    expected = dict(model.expected_inputs)
+    assert set(expected.keys()) == {"sample", "timestep", "encoder_hidden_states"}
+    assert tuple(expected["sample"]["shape"]) == SAMPLE_SHAPE
+    assert tuple(expected["timestep"]["shape"]) == TIMESTEP_SHAPE
+    assert tuple(expected["encoder_hidden_states"]["shape"]) == ENCODER_SHAPE
+
+    # Forward pass: drive the model the way CoreMLModelWrapper does.
+    rng = np.random.default_rng(0)
+    inputs = {
+        "sample": rng.standard_normal(SAMPLE_SHAPE).astype(np.float16),
+        "timestep": rng.standard_normal(TIMESTEP_SHAPE).astype(np.float16),
+        "encoder_hidden_states": rng.standard_normal(ENCODER_SHAPE).astype(np.float16),
+    }
+    out = model(**inputs)
+    assert isinstance(out, dict), f"unexpected output type: {type(out)}"
+    assert OUT_NAME in out, f"missing output {OUT_NAME!r}; got {sorted(out)}"
+    assert out[OUT_NAME].shape == SAMPLE_SHAPE, (
+        f"output shape drift: got {out[OUT_NAME].shape}, expected {SAMPLE_SHAPE}"
+    )

tests/unit/test_characterization_controlnet.py

@@ -30,7 +30,7 @@ SD15_RESIDUAL_SPEC = {
 }
 NON_RESIDUAL_SPEC = {
     "sample": {"shape": (2, 4, 64, 64)},
-    "encoder_hidden_states": {"shape": (2, 77, 768)},
+    "encoder_hidden_states": {"shape": (2, 768, 1, 77)},
 }
 
 

tests/unit/test_characterization_inputs.py

@@ -25,7 +25,7 @@ def _deterministic_seed():
 SD15_EXPECTED = {
     "sample": {"shape": (2, 4, 64, 64)},
     "timestep": {"shape": (2,)},
-    "encoder_hidden_states": {"shape": (2, 77, 768)},
+    "encoder_hidden_states": {"shape": (2, 768, 1, 77)},
 }
 
 SD15_WITH_CN = {
@@ -42,7 +42,7 @@ LCM_EXPECTED = {
 SDXL_BASE_EXPECTED = {
     "sample": {"shape": (2, 4, 128, 128)},
     "timestep": {"shape": (2,)},
-    "encoder_hidden_states": {"shape": (2, 77, 2048)},
+    "encoder_hidden_states": {"shape": (2, 2048, 1, 77)},
     "time_ids": {"shape": (2, 6)},
     "text_embeds": {"shape": (2, 1280)},
 }
@@ -50,7 +50,7 @@ SDXL_BASE_EXPECTED = {
 SDXL_REFINER_EXPECTED = {
     "sample": {"shape": (2, 4, 128, 128)},
     "timestep": {"shape": (2,)},
-    "encoder_hidden_states": {"shape": (2, 77, 1280)},
+    "encoder_hidden_states": {"shape": (2, 1280, 1, 77)},
     "time_ids": {"shape": (2, 5)},
     "text_embeds": {"shape": (2, 1280)},
 }
@@ -93,8 +93,8 @@ def test_coreml_kwargs_sd15_shapes_and_fp16():
     assert set(out.keys()) == {"sample", "encoder_hidden_states", "timestep"}
     assert out["sample"].shape == (1, 4, 64, 64)
     assert out["sample"].dtype == np.float16
-    # encoder_hidden_states keeps Comfy's native (b, seq, dim) layout.
-    assert out["encoder_hidden_states"].shape == (1, 77, 768)
+    # encoder_hidden_states is transposed (b, seq, dim) -> (b, dim, 1, seq).
+    assert out["encoder_hidden_states"].shape == (1, 768, 1, 77)
     assert out["encoder_hidden_states"].dtype == np.float16
     assert out["timestep"].shape == (1,)
     assert out["timestep"].dtype == np.float16

tests/unit/test_characterization_out_name.py

@@ -0,0 +1,197 @@
+"""Characterization tests for the .mlpackage filename composition.
+
+The filename composition is the pure
+coreml_suite.core.naming.compose_out_name function. CoreMLConverter.convert
+calls it; testing the pure function avoids monkey-patching heavy converter
+internals just to capture the string.
+"""
+import pytest
+
+from coreml_suite.core.naming import compose_out_name, lora_names_from_params
+
+
+# ---------- attention suffixes ----------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "attn_name,suffix",
+    [
+        ("SPLIT_EINSUM", "se"),
+        ("SPLIT_EINSUM_V2", "se2"),
+        ("ORIGINAL", "orig"),
+    ],
+)
+def test_attention_suffix(attn_name, suffix):
+    out = compose_out_name(
+        ckpt_name="dreamshaper_8.safetensors",
+        batch_size=1, width=512, height=512,
+        controlnet_support=False,
+        attention_implementation=attn_name,
+    )
+    assert out == f"dreamshaper_8_1x512x512_{suffix}"
+
+
+# ---------- batch / size ----------------------------------------------------
+
+
+def test_includes_batch_and_size():
+    out = compose_out_name(
+        ckpt_name="dreamshaper_8.safetensors",
+        batch_size=4, width=768, height=1024,
+        controlnet_support=False,
+        attention_implementation="SPLIT_EINSUM",
+    )
+    assert out == "dreamshaper_8_4x768x1024_se"
+
+
+# ---------- ControlNet ------------------------------------------------------
+
+
+def test_appends_cn_suffix_when_controlnet_support_true():
+    out = compose_out_name(
+        ckpt_name="dreamshaper_8.safetensors",
+        batch_size=1, width=512, height=512,
+        controlnet_support=True,
+        attention_implementation="SPLIT_EINSUM",
+    )
+    assert out == "dreamshaper_8_1x512x512_cn_se"
+
+
+# ---------- ckpt name massage -----------------------------------------------
+
+
+def test_drops_extension_at_first_period():
+    out = compose_out_name(
+        ckpt_name="my.checkpoint.v2.safetensors",
+        batch_size=1, width=512, height=512,
+        controlnet_support=False,
+        attention_implementation="SPLIT_EINSUM",
+    )
+    assert out == "my_1x512x512_se"
+
+
+def test_replaces_spaces_with_underscores():
+    out = compose_out_name(
+        ckpt_name="dream shaper 8.safetensors",
+        batch_size=1, width=512, height=512,
+        controlnet_support=False,
+        attention_implementation="SPLIT_EINSUM",
+    )
+    assert out == "dream_shaper_8_1x512x512_se"
+
+
+# ---------- LoRA suffixes ---------------------------------------------------
+
+
+def test_single_lora():
+    out = compose_out_name(
+        ckpt_name="dreamshaper_8.safetensors",
+        batch_size=1, width=512, height=512,
+        controlnet_support=False,
+        attention_implementation="SPLIT_EINSUM",
+        lora_names=["epi_noiseoffset.safetensors"],
+    )
+    assert out == "dreamshaper_8_epi_noiseoffset_1x512x512_se"
+
+
+def test_multiple_loras_sorted():
+    out = compose_out_name(
+        ckpt_name="dreamshaper_8.safetensors",
+        batch_size=1, width=512, height=512,
+        controlnet_support=False,
+        attention_implementation="SPLIT_EINSUM",
+        lora_names=["zoom.safetensors", "alpha.safetensors", "moody.safetensors"],
+    )
+    assert out == "dreamshaper_8_alpha_moody_zoom_1x512x512_se"
+
+
+def test_lora_plus_controlnet():
+    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"],
+    )
+    assert out == "dreamshaper_8_a_1x512x512_cn_se"
+
+
+# ---------- sdxl combinations -----------------------------------------------
+
+
+def test_sdxl_1024_original_gpu():
+    out = compose_out_name(
+        ckpt_name="sd_xl_base_1.0.safetensors",
+        batch_size=1, width=1024, height=1024,
+        controlnet_support=False,
+        attention_implementation="ORIGINAL",
+    )
+    assert out == "sd_xl_base_1_1x1024x1024_orig"
+
+
+# ---------- lora_names_from_params helper ----------------------------------
+
+
+def test_lora_names_from_params_sorts_by_name():
+    names = lora_names_from_params([
+        ("zebra.safetensors", 1.0),
+        ("apple.safetensors", 0.5),
+        ("mango.safetensors", 0.7),
+    ])
+    assert names == ["apple.safetensors", "mango.safetensors", "zebra.safetensors"]
+
+
+def test_lora_names_from_params_empty_list():
+    assert lora_names_from_params([]) == []
+
+
+# ---------- quantize_nbits suffix ------------------------------------------
+
+
+def test_quantize_nbits_none_appends_nothing():
+    """'none' is the default and must keep the unquantized 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}
+        )

tests/unit/test_characterization_sdxl_options.py

@@ -2,8 +2,8 @@
 
 The SDXL time_ids / text_embeds math lives in
 coreml_suite.core.sdxl as pure builders. The framework adapter
-add_sdxl_model_options lives in models.py; here we just lock the pure
-math.
+add_sdxl_model_options (in models.py) is exercised separately by the m2
+golden image test; here we just lock the pure math.
 """
 import inspect
 

tests/unit/test_chunks.py

@@ -20,7 +20,7 @@ def expected_inputs():
         "sample": {"shape": (2, 4, 64, 64)},
         "timestep": {"shape": (2,)},
         "timestep_cond": {"shape": (2, 256)},
-        "encoder_hidden_states": {"shape": (2, 77, 768)},
+        "encoder_hidden_states": {"shape": (2, 768, 1, 77)},
         "additional_residual_0": {"shape": (2, 320, 64, 64)},
         "additional_residual_1": {"shape": (2, 640, 32, 32)},
     }

tests/unit/test_tier0_purity.py

@@ -7,10 +7,10 @@ after collection. If they are, a tests/unit/ file is transitively
 pulling them in and the Tier-0 promise — "runs on Linux with no Mac
 stack" — is broken.
 
-When other tiers are also collected, framework modules may be imported
-deliberately (e.g. smoke pulls in coremltools), so the check is skipped
-unless the run is purely `-m unit` — Tier-0 purity is only meaningful
-when nothing else is loaded.
+When other tiers (m2 / integration) are also collected, comfy is
+expected in sys.modules (integration imports it deliberately), so the
+check is skipped in mixed runs — Tier-0 purity is only meaningful when
+nothing else is loaded.
 """
 import sys