docs: update Stable Diffusion 1.5 links

* fix(conversion): load .mlpackage instead of unloadable .mlmodelc

The native ct.models.MLModel runtime added in the diffusers conversion
path cannot load compiled .mlmodelc directories (no Manifest.json), so
the converter's compiled output failed at load with "A valid manifest
does not exist". Both converters now return the .mlpackage directly and
the loader lists only .mlpackage. Removes the now-dead coremlcompiler
wrappers.

* chore(release): bump version to 2.0.1

.github/workflows/publish.yml

@@ -0,0 +1,25 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'aszc-dev' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.github/workflows/tier0.yml

@@ -0,0 +1,27 @@
+name: Tier 0 — Unit (Linux)
+
+on:
+  push:
+    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.
+jobs:
+  unit:
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: astral-sh/setup-uv@v7
+        with:
+          enable-cache: true
+
+      - name: uv sync
+        run: uv sync --no-install-project
+
+      - name: Run Tier 0
+        run: uv run pytest -m unit tests/ -v

.github/workflows/tier1.yml

@@ -0,0 +1,23 @@
+name: Tier 1 — Smoke (macOS self-hosted)
+
+# macOS smoke tests run on the self-hosted Apple Silicon runner instead of
+# GitHub-hosted macOS (10x minute multiplier), which exhausts the included
+# Actions minutes too quickly.
+on:
+  push:
+    branches: [main]
+  pull_request:
+
+jobs:
+  smoke:
+    runs-on: [self-hosted, macOS, ARM64, coreml]
+    timeout-minutes: 20
+    steps:
+      - uses: actions/checkout@v4
+
+      # The self-hosted runner provides uv; no setup-uv action needed.
+      - 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

@@ -0,0 +1,134 @@
+name: Tier 2 — M2 / ANE (self-hosted)
+
+on:
+  pull_request:
+    # `labeled` fires when run-m2 is first added; `synchronize`/`reopened`
+    # re-run on every subsequent push while the label is present, so the
+    # result tracks the PR head instead of going stale. The `if` below keeps
+    # the run gated on the run-m2 label for all pull_request events.
+    types: [labeled, synchronize, reopened]
+  schedule:
+    # Nightly at 04:00 UTC (~05/06 in PL). Keeps the M2 path honest
+    # without burning the runner on every PR.
+    - cron: "0 4 * * *"
+  workflow_dispatch:
+
+jobs:
+  m2:
+    if: |
+      github.event_name == 'schedule' ||
+      github.event_name == 'workflow_dispatch' ||
+      (github.event_name == 'pull_request' &&
+       contains(github.event.pull_request.labels.*.name, 'run-m2'))
+    # Self-hosted Apple Silicon runner. Prerequisites: COMFY_DIR pointing at
+    # a runner-owned ComfyUI clone, plus a cached SD1.5 checkpoint.
+    runs-on: [self-hosted, macOS, ARM64, coreml]
+    timeout-minutes: 90
+    steps:
+      - uses: actions/checkout@v4
+
+      # 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
+      #     `comfy` uv group. Reproducible merge gate, immune to overnight drift.
+      - name: Resolve ComfyUI ref + mode
+        run: |
+          if [ "$GITHUB_EVENT_NAME" = "schedule" ]; then
+            echo "COMFY_MODE=latest" >> "$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"
+          fi
+
+      - name: Set up ComfyUI checkout
+        # COMFY_DIR is exported by the self-hosted runner's .env and MUST be a
+        # runner-owned ComfyUI clone (never your dev checkout — this step does
+        # git reset --hard and rewrites custom_nodes). Cloned on first run.
+        run: |
+          set -euo pipefail
+          if [ -z "${COMFY_DIR:-}" ]; then echo "COMFY_DIR unset"; exit 1; fi
+          # Init-in-place rather than `git clone`: COMFY_DIR may already hold the
+          # cached checkpoint (models/checkpoints) or converted .mlmodelc, and
+          # `git clone` refuses a non-empty target. init + fetch + `checkout -f`
+          # populates the ComfyUI tree while leaving untracked files (the
+          # checkpoint, the cached models) untouched — so setup order is free.
+          if [ ! -d "$COMFY_DIR/.git" ]; then
+            echo "initialising ComfyUI repo in $COMFY_DIR"
+            mkdir -p "$COMFY_DIR"
+            git -C "$COMFY_DIR" init -q
+          fi
+          git -C "$COMFY_DIR" remote get-url origin >/dev/null 2>&1 \
+            || git -C "$COMFY_DIR" remote add origin https://github.com/comfyanonymous/ComfyUI.git
+          git -C "$COMFY_DIR" fetch --quiet origin
+          if [ "$COMFY_MODE" = "latest" ]; then
+            git -C "$COMFY_DIR" checkout -f -B master origin/master
+          else
+            git -C "$COMFY_DIR" checkout -f "$COMFY_REF"
+          fi
+          COMFY_SHA="$(git -C "$COMFY_DIR" rev-parse HEAD)"
+          echo "COMFY_SHA=$COMFY_SHA" >> "$GITHUB_ENV"
+          echo "Tier 2 mode=$COMFY_MODE, ComfyUI \`$COMFY_SHA\`" >> "$GITHUB_STEP_SUMMARY"
+
+          # Point ComfyUI's custom-node loader at this checkout. Refresh the
+          # symlink only; refuse to clobber a real directory (guards against a
+          # COMFY_DIR that is accidentally a dev checkout).
+          NODE_LINK="$COMFY_DIR/custom_nodes/ComfyUI-CoreMLSuite"
+          if [ -e "$NODE_LINK" ] && [ ! -L "$NODE_LINK" ]; then
+            echo "ERROR: $NODE_LINK is a real directory, not a symlink."
+            echo "COMFY_DIR must be a runner-owned ComfyUI, not your dev checkout."
+            exit 1
+          fi
+          mkdir -p "$COMFY_DIR/custom_nodes"
+          ln -sfn "$GITHUB_WORKSPACE" "$NODE_LINK"
+
+      - name: Install dependencies
+        run: |
+          set -euo pipefail
+          if [ "$COMFY_MODE" = "latest" ]; then
+            # Node deps (our coremltools-9 toolchain), then ComfyUI's own
+            # requirements for the pulled SHA, capped by the toolchain ceiling.
+            uv sync
+            uv pip install -r "$COMFY_DIR/requirements.txt" \
+              -c constraints/comfy-ceiling.txt
+          else
+            # Pinned gate: the frozen group mirrors the known-good pinned SHA.
+            uv sync --group comfy
+          fi
+
+      - name: Start ComfyUI server (background)
+        run: |
+          cd "$COMFY_DIR"
+          nohup "$GITHUB_WORKSPACE/.venv/bin/python" main.py --port 8188 --cpu-vae > /tmp/comfyui-ci.log 2>&1 &
+          # Poll the HTTP endpoint for readiness — robust to startup-banner
+          # wording / colored-log changes in a floating-latest ComfyUI.
+          for _ in $(seq 1 90); do
+            if curl -sf -o /dev/null http://127.0.0.1:8188/system_stats; then
+              echo "comfy ready (ComfyUI ${COMFY_SHA:-unknown})"; exit 0
+            fi
+            sleep 2
+          done
+          echo "comfy failed to start"; tail -100 /tmp/comfyui-ci.log; exit 1
+
+      - name: Purge cached Core ML UNets (force fresh conversion)
+        # The converter skips when a model of the same name already exists. That
+        # cache key is conversion *parameters* only, not the conversion code or
+        # toolchain — so a stale model would let a conversion regression pass.
+        # Clear it so every Tier 2 run exercises the full convert -> compile ->
+        # sample path end to end.
+        run: |
+          rm -rf "$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).
+        run: uv run --no-sync pytest -m m2 tests/ -v
+
+      - name: Stop ComfyUI server
+        if: always()
+        run: pkill -f "main.py.*8188" || true

.gitignore

@@ -1,3 +1,5 @@
 playground/
-experiments/
 __pycache__/
+models/
+.venv/
+test_results/

README.md

@@ -2,14 +2,14 @@
 
 ## Overview
 
-Welcome! I've developed a set of custom nodes for ComfyUI that allows you to use Core ML models in your ComfyUI
-workflows.
+Welcome! In this repository you'll find a set of custom nodes for [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
+that allows you to use Core ML models in your ComfyUI workflows.
 These models are designed to leverage the Apple Neural Engine (ANE) on Apple Silicon (M1/M2) machines,
 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 models using
-[coremltools](https://github.com/apple/ml-stable-diffusion).
+[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)).
 
 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,
@@ -48,6 +48,8 @@ That's it! You're now ready to start enhancing your ComfyUI workflows with Core
 - **VAE**: Variational Autoencoder. A model that learns a latent representation of images. It's used as a prior in
   Stable Diffusion.
 - **Checkpoint**: A file that contains the weights of a model. It's used to load models in Stable Diffusion.
+- **LCM**: [Latent Consistency Model](https://latent-consistency-models.github.io/). A type of model designed to
+  generate images with as few steps as possible.
 
 > [!NOTE]
 > Note on Compute Units:
@@ -64,6 +66,12 @@ These custom nodes come with a host of features, including:
 - Support for ANE (Apple Neural Engine)
 - Support for CPU and GPU
 - Support for `mlmodelc` and `mlpackage` files
+- Support for SDXL models
+- Support for LCM models
+- Support for LoRAs
+- SD1.5 -> Core ML conversion
+- SDXL -> Core ML conversion
+- LCM -> Core ML conversion
 
 > [!NOTE]
 > Please note that using Core ML models can take a bit longer to load initially.
@@ -73,9 +81,43 @@ 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
 
-The installation process is simple!
+### Using ComfyUI-Manager
+
+The easiest way to install the custom nodes is to use the ComfyUI-Manager. You can find the installation instructions
+[here](https://github.com/ltdrdata/ComfyUI-Manager#installation). Once you've installed the ComfyUI-Manager, you can
+install the custom nodes by following these steps:
+
+- Open the ComfyUI-Manager by clicking the `Manager` button in the ComfyUI toolbar.
+- Click the `Install Custom Nodes` button.
+- Search for `Core ML` and click the `Install` button.
+- Restart ComfyUI.
+
+### Manual Installation
 
 1. Clone this repository into the custom_nodes directory of your ComfyUI. If you're not sure how to do this, you can
    download the repository as a zip file and extract it into the same directory.
@@ -121,10 +163,6 @@ node is a `coreml_model` object that can be used with the Core ML Sampler.
 - **Outputs**:
     - **coreml_model**: A Core ML model that can be used with the Core ML Sampler.
 
-> [!NOTE]  
-> Some models are designed to support ControlNet. If you're using such a model,
-> make sure to provide a ControlNet input; otherwise, the model will use random noise as ControlNet input.
-
 #### Core ML Sampler (`CoreMLSampler`)
 
 ![CoreMLSampler](./assets/sampler.png?raw=true)
@@ -143,6 +181,118 @@ resulting latent as you normally would in your workflow.
     - **LATENT**: The latent image output by the Core ML model. This can be decoded using a VAE Decoder or used as input
       to the next node in your workflow.
 
+#### Checkpoint Converter
+
+![CoreMLConverter](./assets/checkpoint_converter.png?raw=true)
+
+You can use this node to convert any **SD1.5** based checkpoint to a Core ML model. The converted model is stored in the
+`models/unet` directory and can be used with the `Core ML UNet Loader`. The conversion parameters are encoded in
+the node name, so if the model already exists, the node will not convert it again.
+
+- **Inputs**:
+    - **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.
+    - **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
+      SPLIT_EINSUM_V2 for better ANE support. Choose ORIGINAL for better GPU support.
+    - **compute_unit**: The hardware on which the model should run. This is used only when loading the model and doesn't
+      affect the conversion process.
+    - **controlnet_support**: For the model to support ControlNet, it must be converted with this option set to True.
+      The
+      default is False.
+    - **lora_params** [optional]: Optional LoRA names and weights. If provided, the model will be converted with LoRA(s)
+      baked in. More on loading LoRAs below.
+- **Outputs**:
+    - **coreml_model**: The converted Core ML model that can be used with Core ML Sampler.
+
+> [!NOTE]
+> Some models use a custom config .yaml file. If you're using such a model, you'll need to place the config file in the
+> `models/configs` directory. The config file should be named the same as the checkpoint file. For example, if the
+> checkpoint file is named `juggernaut_aftermath.safetensors`, the config file should be
+> named `juggernaut_aftermath.yaml`.
+> The config file will be automatically loaded during conversion.
+
+> [!NOTE]
+> For now, the converter relies heavilty on the model name to determine the conversion parameters. This means that if
+> you change the model name, the node will convert the model again. Other than that, if you find the name too long or
+> confusing, you can change it to anything you want.
+
+#### LoRA Loader
+
+![LoRALoader](./assets/lora_loader.png?raw=true)
+
+This node allows you to load LoRAs and bake them into a model. Since this is a workaround (as model weights can't be
+modified
+after conversion), there are a few caveats to keep in mind:
+
+- The LoRA weights and _strength_model_ parameter are baked into the model. This means that you can't change them
+  after conversion. This also means that you need to convert the model again if you want to change the LoRA weights.
+- Loading LoRA affects CLIP, which is not a part of Core ML workflow, so you'll need to load CLIP separately,
+  either using `CLIPLoader` or `CheckpointLoaderSimple`. (See [example workflows](#example-workflows) for more details.)
+- After conversion, if you want to load the model using `CoreMLUnetLoader`, you'll need to apply the same LoRAs to
+  CLIP manually. (See [example workflows](#example-workflows) for more details.)
+- The LoRA names are encoded in the model name. This means that if you change the name of the LoRA file,
+  you'll need to change the model name as well, or the node will convert the model again. (Model strength is not
+  encoded, so if you want to change it, you'll need to delete the converted model manually)
+- _strength_clip_ parameter only affects the CLIP model and is not baked into the converted model. This means that
+  you can change it after conversion.
+
+- **Inputs**:
+    - **lora_name**: The name of the LoRA to load.
+    - **strength_model**: The strength of the LoRA model.
+    - **strength_clip**: The strength of the LoRA CLIP.
+    - **lora_params** [optional]: Optional output from other LoRA Loaders.
+    - **clip**: The CLIP model to use with the LoRA. This can be either output of the
+      `CLIPLoader`/`CheckpointLoaderSimple` or other LoRA Loaders.
+- **Outputs**:
+    - **lora_params**: The LoRA parameters that can be passed to the Core ML Converter or other LoRA Loaders.
+    - **CLIP**: The CLIP model with LoRA applied.
+
+#### LCM Converter
+
+![LCMConverter](./assets/lcm_converter.png?raw=true)
+
+This node converts [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) model to Core
+ML. The converted model is stored in the `models/unet` directory and can be used with the Core ML UNet Loader. The
+conversion parameteres are encoded in the node name, so if the model already exists, the node will not convert it again.
+
+- **Inputs**:
+    - **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.
+    - **compute_unit**: The hardware on which the model should run. This is used only when loading the model and
+      doesn't affect the conversion process.
+    - **controlnet_support**: For the model to support ControlNet, it must be converted with this option set to True.
+      The default is False.
+
+> [!NOTE]
+> The conversion process can take a while, so please be patient.
+
+> [!NOTE]
+> When using the LCM model with Core ML Sampler, please set _sampler_name_ to `lcm` and _scheduler_ to `sgm_uniform`.
+
+#### Core ML Adapter (Experimental) (`CoreMLModelAdapter`)
+
+![CoreMLModelAdapter](./assets/adapter.png?raw=true)
+
+This node allows you to use a Core ML as a standard ComfyUI model. This is an experimental node and may not work with
+all models and nodes. Please use with caution and pay attention to the expected inputs of the model.
+
+- **Input**:
+    - **coreml_model**: The Core ML model to use as a ComfyUI model.
+- **Output**:
+    - **MODEL**: The Core ML model wrapped in a ComfyUI model.
+
+> [!NOTE]
+> While this approach allows you to use Core ML models with many ComfyUI nodes (both standard and custom), the
+> expected inputs of the model will not be checked, which may cause errors. Please make sure to use a model compatible
+> with the expected parameters.
+
 ### Example Workflows
 
 > [!NOTE]
@@ -157,8 +307,8 @@ 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/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).
+      [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).
       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).
@@ -166,7 +316,7 @@ 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/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors).
+      [here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/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).
@@ -180,19 +330,117 @@ being loaded using the standard ComfyUI nodes. Please refer to
 the [basic txt2img workflow](#basic-txt2img-with-core-ml-unet-loader) for more details on how to load the CLIP and VAE
 models.
 The ControlNet model used in this workflow is available
-[here](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_lineart.pth).
+[here](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/blob/main/diffusion_pytorch_model.fp16.safetensors).
 Once downloaded, place the model in the `models/controlnet` directory.
 ![coreml-unet+controlnet](./assets/unet+sampler+controlnet.png?raw=true)
 
+#### Checkpoint conversion
+
+This workflow uses the Checkpoint Converter to convert the checkpoint file. See
+[Checkpoint Converter](#checkpoint-converter) description for more details.
+
+![checkpoint-converter](./assets/basic_conversion.png?raw=true)
+
+#### Checkpoint conversion with LoRA
+
+This workflow uses the Checkpoint Converter to convert the checkpoint file with LoRA. See
+[LoRA Loader](#lora-loader) description to read more about the caveats of using LoRA.
+
+![checkpoint-converter+lora](./assets/conversion+lora.png?raw=true)
+
+#### LCM LoRA conversion
+
+Please note that you can use multiple LoRAs with the same model. To do this, you'll need to use multiple LoRA Loaders.
+> [!IMPORTANT]
+> In this example, the model is passed through the adapter and `ModelSamplingDiscrete` nodes to a standard ComfyUI's
+> KSampler (not Core ML Sampler). ModelSamplingDiscrete needs to be used to sample models with LCM LoRAs properly.
+
+![multiple-loras](./assets/conversion+lcm_lora.png?raw=true)
+
+#### Loader with LoRAs
+
+This workflow uses the Core ML UNet Loader to load a model with LoRAs. The CLIP must be loaded separately and passed
+through the same LoRA nodes as during conversion. See [LoRA Loader](#lora-loader) description to read more about the
+caveats of using LoRA. Since _lora_name_ and _strength_model_ are baked into the model, it is not necessary to pass
+them as inputs to the loader.
+> [!IMPORTANT]
+> In this example, the model is passed through the adapter and `ModelSamplingDiscrete` nodes to a standard ComfyUI's
+> KSampler (not Core ML Sampler). ModelSamplingDiscrete needs to be used to sample models with LCM LoRAs properly.
+
+![loader+lora](./assets/loader+lcm_lora.png?raw=true)
+
+#### LCM conversion with ControlNet
+
+This workflow uses LCM converter to
+convert [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)
+model to Core ML. The converted model can then be used with or without ControlNet to generate images.
+![lcm+controlnet](./assets/lcm+controlnet.png?raw=true)
+
+#### SDXL Base + Refiner conversion
+
+This is a basic workflow for SDXL. You add LoRAs and ControlNets the same way as in the previous examples.
+You can also skip the refiner step.
+
+The models used in this workflow are available at the following links:
+
+- [Base model + text_encoder (clip) + text_encoder_2 (clip2)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+- [Refiner model](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)
+- [VAE](https://huggingface.co/stabilityai/sdxl-vae)
+
+> [!IMPORTANT]
+> SDXL on ANE is not supported. If loading of the model gets stuck, please try using CPU_AND_GPU or CPU_ONLY.  
+> For best results, use ORIGINAL attention implementation.
+
+![sdxl](./assets/sdxl_conversion.png?raw=true)
+
+## Quantization (opt-in)
+
+The `Core ML Converter` and `Core ML LCM Converter` nodes accept an
+optional `quantize_nbits` dropdown that runs k-means weight palettization
+(`coremltools.optimize.coreml.palettize_weights`) on the UNet before save.
+
+Values: `none` (default — no quantization, identical to unquantized
+behavior and filenames), `8`, `6`, `4`. The number is appended to the
+.mlpackage stem as `_q<bits>` so quantized and unquantized variants
+coexist on disk and in cache.
+
+### SD1.5 1×512×512 SPLIT_EINSUM tradeoffs (M2 Pro, ANE)
+
+Measured with 20 UNet forward passes at a fixed seed for the PSNR
+comparison:
+
+| nbits | size (MB) | size vs none | fwd median (ms) | PSNR vs `none` (dB) |
+|---|---:|---:|---:|---:|
+| none | 1641 | 1.000 | 197.1 | — |
+| 8    |  822 | 0.501 | 186.6 | 53.5 |
+| 6    |  617 | 0.376 | 183.0 | 40.2 |
+| 4    |  412 | 0.251 | 179.8 | 27.5 |
+
+PSNR here is computed on the raw `noise_pred` output of a single UNet
+forward at a fixed seed, not on the final decoded image — it isolates
+the quantization-induced drift from sampler / VAE noise. Final-image
+PSNR is comfortably higher (the sampler averages over 20 steps).
+
+### Recommended settings per chip / RAM
+
+- **8 GB RAM (M1 base, M2 base):** `nbits=4`. ~4× smaller model, still
+  loads, PSNR 27 dB is visually identical at SD1.5 sizes.
+- **16 GB RAM (M1/M2/M3 Pro):** `nbits=6` is the sweet spot — ~2.7×
+  smaller, PSNR 40 dB, no perceptible quality drop.
+- **32 GB+ RAM (Max / Ultra):** `nbits=8` if you want the safety
+  margin, `none` if you want bit-identical output for golden testing.
+
+The default stays `none` so existing workflows produce byte-for-byte
+identical output.
+
 ## 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 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.
-- For now, only Stable Diffusion v1.5 is supported.
-- LoRA is not supported yet.
+  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).
+- SD2.1 models are not supported.
 
 [^1]:
 Unless [EnumeratedShapes](https://apple.github.io/coremltools/docs-guides/source/flexible-inputs.html#select-from-predetermined-shapes)

__init__.py

@@ -3,13 +3,33 @@ import sys
 
 sys.path.append(os.path.dirname(__file__))
 
-from coreml_suite import CoreMLLoaderUNet, CoreMLSampler
+from coreml_suite.nodes import (
+    CoreMLLoaderUNet,
+    CoreMLSampler,
+    CoreMLSamplerAdvanced,
+    CoreMLModelAdapter,
+    CoreMLConverter,
+    COREML_LOAD_LORA,
+)
+from coreml_suite.lcm import (
+    COREML_CONVERT_LCM,
+)
 
 NODE_CLASS_MAPPINGS = {
     "CoreMLUNetLoader": CoreMLLoaderUNet,
     "CoreMLSampler": CoreMLSampler,
+    "CoreMLSamplerAdvanced": CoreMLSamplerAdvanced,
+    "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",
     "CoreMLSampler": "Core ML Sampler",
+    "CoreMLSamplerAdvanced": "Core ML Sampler (Advanced)",
+    "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",
 }

conftest.py

@@ -0,0 +1,4 @@
+"""Top-level conftest: prevent pytest from importing the repo-root
+__init__.py (the ComfyUI custom-node entry point pulls in comfy + nodes,
+which breaks the Tier-0 'no-framework' promise)."""
+collect_ignore = ["__init__.py"]

constraints/comfy-ceiling.txt

@@ -0,0 +1,18 @@
+# Toolchain ceiling for installing a floating-latest ComfyUI's requirements.txt
+# in the Tier 2 nightly canary (.github/workflows/tier2.yml, latest mode).
+#
+# ComfyUI's requirements.txt requests bare `torch`/`torchvision`/`torchaudio`
+# and `numpy>=1.25.0`, which would float past the versions coremltools 9 /
+# apple-ml-stable-diffusion have been validated against.
+# These constraints cap the resolution so the canary keeps testing the same
+# toolchain the suite actually ships.
+#
+# If upstream ComfyUI ever hard-requires something beyond these bounds, the
+# install FAILS — and that failure is the signal we want: it means the host
+# outgrew the pinned toolchain and coremltools / ml-stable-diffusion need a
+# deliberate bump, not a silent float.
+torch>=2.7,<2.8
+torchvision>=0.22,<0.23
+torchaudio>=2.7,<2.8
+numpy>=1.25,<2
+coremltools>=9,<10

coreml_suite/__init__.py

@@ -1,4 +1,2 @@
-from coreml_suite.loaders import CoreMLLoaderUNet
-from coreml_suite.samplers import CoreMLSampler
-
-__all__ = ["CoreMLLoaderUNet", "CoreMLSampler"]
+class COREML_NODE:
+    CATEGORY = "Core ML Suite"

coreml_suite/attention.py

@@ -0,0 +1,5 @@
+ATTENTION_IMPLEMENTATIONS = (
+    "SPLIT_EINSUM",
+    "SPLIT_EINSUM_V2",
+    "ORIGINAL",
+)

coreml_suite/config.py

@@ -0,0 +1,115 @@
+import torch
+
+from comfy import supported_models_base
+from comfy import latent_formats
+from comfy.model_detection import convert_config
+
+from coreml_suite.model_version import ModelVersion
+
+
+config_map = {
+    ModelVersion.SD15: {
+        "use_checkpoint": False,
+        "image_size": 32,
+        "out_channels": 4,
+        "use_spatial_transformer": True,
+        "legacy": False,
+        "adm_in_channels": None,
+        "dtype": torch.float16,
+        "in_channels": 4,
+        "model_channels": 320,
+        "num_res_blocks": 2,
+        "attention_resolutions": [1, 2, 4],
+        "transformer_depth": [1, 1, 1, 0],
+        "channel_mult": [1, 2, 4, 4],
+        "transformer_depth_middle": 1,
+        "use_linear_in_transformer": False,
+        "context_dim": 768,
+        "num_heads": 8,
+        "disable_unet_model_creation": True,
+    },
+    ModelVersion.SDXL: {
+        "use_checkpoint": False,
+        "image_size": 32,
+        "out_channels": 4,
+        "use_spatial_transformer": True,
+        "legacy": False,
+        "num_classes": "sequential",
+        "adm_in_channels": 2816,
+        "dtype": torch.float16,
+        "in_channels": 4,
+        "model_channels": 320,
+        "num_res_blocks": 2,
+        "attention_resolutions": [2, 4],
+        "transformer_depth": [0, 2, 10],
+        "channel_mult": [1, 2, 4],
+        "transformer_depth_middle": 10,
+        "use_linear_in_transformer": True,
+        "context_dim": 2048,
+        "num_head_channels": 64,
+        "disable_unet_model_creation": True,
+    },
+    ModelVersion.SDXL_REFINER: {
+        "use_checkpoint": False,
+        "image_size": 32,
+        "out_channels": 4,
+        "use_spatial_transformer": True,
+        "legacy": False,
+        "num_classes": "sequential",
+        "adm_in_channels": 2560,
+        "dtype": torch.float16,
+        "in_channels": 4,
+        "model_channels": 384,
+        "num_res_blocks": 2,
+        "attention_resolutions": [2, 4],
+        "transformer_depth": [0, 4, 4, 0],
+        "channel_mult": [1, 2, 4, 4],
+        "transformer_depth_middle": 4,
+        "use_linear_in_transformer": True,
+        "context_dim": 1280,
+        "num_head_channels": 64,
+        "disable_unet_model_creation": True,
+    },
+}
+
+latent_format_map = {
+    ModelVersion.SD15: latent_formats.SD15,
+    ModelVersion.SDXL: latent_formats.SDXL,
+    ModelVersion.SDXL_REFINER: latent_formats.SDXL,
+}
+
+
+def get_model_config(model_version: ModelVersion):
+    unet_config = convert_config(config_map[model_version])
+    config = supported_models_base.BASE(unet_config)
+    config.latent_format = latent_format_map[model_version]()
+    return config
+
+
+def unet_config_from_diffusers_unet(state_dict):
+    match = {}
+    attention_resolutions = []
+
+    attn_res = 1
+    for i in range(5):
+        k = "down_blocks.{}.attentions.1.transformer_blocks.0.attn2.to_k.weight".format(
+            i
+        )
+        if k in state_dict:
+            match["context_dim"] = state_dict[k].shape[1]
+            attention_resolutions.append(attn_res)
+        attn_res *= 2
+
+    match["attention_resolutions"] = attention_resolutions
+
+    match["model_channels"] = state_dict["conv_in.weight"].shape[0]
+    match["in_channels"] = state_dict["conv_in.weight"].shape[1]
+    match["adm_in_channels"] = None
+    if "class_embedding.linear_1.weight" in state_dict:
+        match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[
+            1
+        ]
+    elif "add_embedding.linear_1.weight" in state_dict:
+        match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
+
+    print(match)

coreml_suite/controlnet.py

@@ -0,0 +1,14 @@
+"""Compatibility shim — re-exports from coreml_suite.core.controlnet."""
+from coreml_suite.core.controlnet import (
+    chunk_control,
+    expand_inputs,
+    extract_residual_kwargs,
+    no_control,
+)
+
+__all__ = [
+    "chunk_control",
+    "expand_inputs",
+    "extract_residual_kwargs",
+    "no_control",
+]

coreml_suite/conversion/__init__.py

@@ -0,0 +1,9 @@
+"""Core ML conversion helpers.
+
+The conversion approach originates from Apple's ml-stable-diffusion
+(https://github.com/apple/ml-stable-diffusion). This implementation has since
+diverged: it runs natively on diffusers' UNet2DConditionModel with its own
+SPLIT_EINSUM / SPLIT_EINSUM_V2 attention processors and no longer depends on
+that package. The intent is to keep iterating on these methods independently
+while tracking current tooling.
+"""

coreml_suite/conversion/attention.py

@@ -0,0 +1,239 @@
+import logging
+
+import torch
+
+logger = logging.getLogger(__name__)
+
+CHUNK_SIZE = 512
+
+
+def apply_attention_implementation(unet, attention_implementation):
+    if attention_implementation == "ORIGINAL":
+        return unet
+
+    if attention_implementation == "SPLIT_EINSUM":
+        unet.set_attn_processor(SplitEinsumAttnProcessor())
+        return unet
+
+    if attention_implementation == "SPLIT_EINSUM_V2":
+        unet.set_attn_processor(SplitEinsumV2AttnProcessor())
+        return unet
+
+    raise ValueError(f"Unsupported attention implementation: {attention_implementation}")
+
+
+class SplitEinsumAttnProcessor:
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        *args,
+        **kwargs,
+    ):
+        return _attention_forward(
+            attn,
+            hidden_states,
+            encoder_hidden_states,
+            attention_mask,
+            temb,
+            split_einsum,
+        )
+
+
+class SplitEinsumV2AttnProcessor:
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        *args,
+        **kwargs,
+    ):
+        return _attention_forward(
+            attn,
+            hidden_states,
+            encoder_hidden_states,
+            attention_mask,
+            temb,
+            split_einsum_v2,
+        )
+
+
+def _attention_forward(
+    attn,
+    hidden_states,
+    encoder_hidden_states,
+    attention_mask,
+    temb,
+    attention_fn,
+):
+    residual = hidden_states
+
+    if attn.spatial_norm is not None:
+        hidden_states = attn.spatial_norm(hidden_states, temb)
+
+    input_ndim = hidden_states.ndim
+    if input_ndim == 4:
+        batch_size, channel, height, width = hidden_states.shape
+        hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+    else:
+        batch_size, _, channel = hidden_states.shape
+        height = None
+        width = None
+
+    batch_size, key_sequence_length, _ = (
+        hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+    )
+
+    if attention_mask is not None:
+        attention_mask = attn.prepare_attention_mask(
+            attention_mask,
+            key_sequence_length,
+            batch_size,
+        )
+        attention_mask = _prepare_split_einsum_mask(
+            attention_mask,
+            batch_size,
+            attn.heads,
+            key_sequence_length,
+        )
+
+    if attn.group_norm is not None:
+        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+    query = attn.to_q(hidden_states)
+
+    if encoder_hidden_states is None:
+        encoder_hidden_states = hidden_states
+    elif attn.norm_cross:
+        encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+    key = attn.to_k(encoder_hidden_states)
+    value = attn.to_v(encoder_hidden_states)
+
+    batch_size = query.shape[0]
+    dim_head = attn.inner_kv_dim // attn.heads
+
+    query = _linear_projection_to_bchw(query)
+    key = _linear_projection_to_bchw(key)
+    value = _linear_projection_to_bchw(value)
+
+    hidden_states = attention_fn(
+        query,
+        key,
+        value,
+        attention_mask,
+        attn.heads,
+        dim_head,
+    )
+    hidden_states = hidden_states.squeeze(2).transpose(1, 2)
+    hidden_states = hidden_states.reshape(batch_size, -1, attn.inner_dim)
+
+    hidden_states = attn.to_out[0](hidden_states)
+    hidden_states = attn.to_out[1](hidden_states)
+
+    if input_ndim == 4:
+        hidden_states = hidden_states.transpose(-1, -2).reshape(
+            batch_size,
+            channel,
+            height,
+            width,
+        )
+
+    if attn.residual_connection:
+        hidden_states = hidden_states + residual
+
+    hidden_states = hidden_states / attn.rescale_output_factor
+    return hidden_states
+
+
+def split_einsum(q, k, v, mask, heads, dim_head):
+    q_heads = _split_heads(q, heads, dim_head)
+    k = k.transpose(1, 3)
+    k_heads = [
+        k[:, :, :, head_idx * dim_head : (head_idx + 1) * dim_head]
+        for head_idx in range(heads)
+    ]
+    v_heads = _split_heads(v, heads, dim_head)
+
+    weights = [
+        torch.einsum("bchq,bkhc->bkhq", query, key) * (dim_head**-0.5)
+        for query, key in zip(q_heads, k_heads)
+    ]
+    if mask is not None:
+        weights = [weight + mask for weight in weights]
+
+    weights = [weight.softmax(dim=1) for weight in weights]
+    outputs = [
+        torch.einsum("bkhq,bchk->bchq", weight, value)
+        for weight, value in zip(weights, v_heads)
+    ]
+    return torch.cat(outputs, dim=1)
+
+
+def split_einsum_v2(q, k, v, mask, heads, dim_head):
+    query_length = q.size(3)
+    num_chunks = query_length // CHUNK_SIZE
+    if num_chunks == 0:
+        logger.info(
+            "SPLIT_EINSUM_V2 query sequence is shorter than %s; using SPLIT_EINSUM.",
+            CHUNK_SIZE,
+        )
+        return split_einsum(q, k, v, mask, heads, dim_head)
+
+    q_heads = _split_heads(q, heads, dim_head)
+    q_chunks = [
+        [
+            head[..., chunk_idx * CHUNK_SIZE : (chunk_idx + 1) * CHUNK_SIZE]
+            for chunk_idx in range(num_chunks)
+        ]
+        for head in q_heads
+    ]
+
+    k = k.transpose(1, 3)
+    k_heads = [
+        k[:, :, :, head_idx * dim_head : (head_idx + 1) * dim_head]
+        for head_idx in range(heads)
+    ]
+    v_heads = _split_heads(v, heads, dim_head)
+
+    head_outputs = []
+    for query_chunks, key, value in zip(q_chunks, k_heads, v_heads):
+        chunk_outputs = []
+        for query_chunk in query_chunks:
+            weights = torch.einsum("bchq,bkhc->bkhq", query_chunk, key)
+            weights = weights * (dim_head**-0.5)
+            if mask is not None:
+                weights = weights + mask
+            weights = weights.softmax(dim=1)
+            chunk_outputs.append(torch.einsum("bkhq,bchk->bchq", weights, value))
+        head_outputs.append(torch.cat(chunk_outputs, dim=3))
+
+    return torch.cat(head_outputs, dim=1)
+
+
+def _split_heads(x, heads, dim_head):
+    return [
+        x[:, head_idx * dim_head : (head_idx + 1) * dim_head, :, :]
+        for head_idx in range(heads)
+    ]
+
+
+def _linear_projection_to_bchw(x):
+    return x.transpose(1, 2).unsqueeze(2)
+
+
+def _prepare_split_einsum_mask(mask, batch_size, heads, key_sequence_length):
+    if mask.ndim == 2:
+        mask = mask[:, None, :]
+    if mask.shape[0] == batch_size * heads:
+        mask = mask.reshape(batch_size, heads, -1, key_sequence_length)
+        mask = mask[:, 0]
+    if mask.ndim == 3:
+        mask = mask[:, :, None, None]
+    return mask

coreml_suite/conversion/shapes.py

@@ -0,0 +1,20 @@
+def conv2d_output_shape(height, width, conv):
+    """Return the spatial output shape for a torch.nn.Conv2d-like module."""
+    kernel_h, kernel_w = _pair(conv.kernel_size)
+    stride_h, stride_w = _pair(conv.stride)
+    pad_h, pad_w = _pair(conv.padding)
+    dilation_h, dilation_w = _pair(conv.dilation)
+
+    out_h = _conv_output_dim(height, kernel_h, stride_h, pad_h, dilation_h)
+    out_w = _conv_output_dim(width, kernel_w, stride_w, pad_w, dilation_w)
+    return out_h, out_w
+
+
+def _conv_output_dim(size, kernel, stride, padding, dilation):
+    return ((size + (2 * padding) - (dilation * (kernel - 1)) - 1) // stride) + 1
+
+
+def _pair(value):
+    if isinstance(value, tuple):
+        return value
+    return value, value

coreml_suite/conversion/trace.py

@@ -0,0 +1,61 @@
+from types import MethodType
+
+from diffusers.models.transformers.transformer_2d import Transformer2DModel
+
+
+def prepare_unet_for_coreml_trace(unet):
+    for module in unet.modules():
+        if isinstance(module, Transformer2DModel):
+            module._operate_on_continuous_inputs = MethodType(
+                _operate_on_continuous_inputs,
+                module,
+            )
+            module._get_output_for_continuous_inputs = MethodType(
+                _get_output_for_continuous_inputs,
+                module,
+            )
+    return unet
+
+
+def _operate_on_continuous_inputs(self, hidden_states):
+    hidden_states = self.norm(hidden_states)
+
+    if not self.use_linear_projection:
+        hidden_states = self.proj_in(hidden_states)
+        inner_dim = self.inner_dim
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+    else:
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+        hidden_states = self.proj_in(hidden_states)
+
+    return hidden_states, inner_dim
+
+
+def _get_output_for_continuous_inputs(
+    self,
+    hidden_states,
+    residual,
+    batch_size,
+    height,
+    width,
+    inner_dim,
+):
+    if not self.use_linear_projection:
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size,
+            inner_dim,
+            height,
+            width,
+        )
+        hidden_states = self.proj_out(hidden_states)
+    else:
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size,
+            inner_dim,
+            height,
+            width,
+        )
+
+    return hidden_states + residual

coreml_suite/conversion/unet.py

@@ -0,0 +1,54 @@
+import torch
+
+
+class CoreMLUNetWrapper(torch.nn.Module):
+    """Adapt diffusers UNet inputs to CoreMLSuite's stable Core ML contract."""
+
+    def __init__(self, unet, model_version):
+        super().__init__()
+        self.unet = unet
+        self.model_version = model_version
+
+    def forward(self, sample, timestep, encoder_hidden_states, *extra_inputs):
+        input_index = 0
+        timestep_cond = None
+        if self._is_lcm:
+            timestep_cond = extra_inputs[input_index]
+            input_index += 1
+
+        added_cond_kwargs = None
+        if self._is_sdxl:
+            time_ids = extra_inputs[input_index]
+            text_embeds = extra_inputs[input_index + 1]
+            input_index += 2
+            added_cond_kwargs = {
+                "time_ids": time_ids,
+                "text_embeds": text_embeds,
+            }
+
+        additional_residuals = extra_inputs[input_index:]
+        down_residuals = None
+        mid_residual = None
+        if additional_residuals:
+            down_residuals = tuple(additional_residuals[:-1])
+            mid_residual = additional_residuals[-1]
+
+        outputs = self.unet(
+            sample,
+            timestep,
+            encoder_hidden_states=encoder_hidden_states,
+            timestep_cond=timestep_cond,
+            added_cond_kwargs=added_cond_kwargs,
+            down_block_additional_residuals=down_residuals,
+            mid_block_additional_residual=mid_residual,
+            return_dict=False,
+        )
+        return outputs[0]
+
+    @property
+    def _is_lcm(self):
+        return self.model_version.name == "LCM"
+
+    @property
+    def _is_sdxl(self):
+        return self.model_version.name in {"SDXL", "SDXL_REFINER"}

coreml_suite/converter.py

@@ -0,0 +1,322 @@
+import gc
+import os
+import time
+
+import coremltools as ct
+import numpy as np
+import torch
+from diffusers import UNet2DConditionModel
+
+from coreml_suite.attention import ATTENTION_IMPLEMENTATIONS
+from coreml_suite.conversion.attention import apply_attention_implementation
+from coreml_suite.conversion.shapes import conv2d_output_shape
+from coreml_suite.conversion.trace import prepare_unet_for_coreml_trace
+from coreml_suite.conversion.unet import CoreMLUNetWrapper
+from coreml_suite.logger import logger
+from coreml_suite.model_version import ModelVersion
+
+DEFAULT_TRACE_TIMESTEP = 999.0
+TEXT_TOKEN_SEQUENCE_LENGTH = 77
+
+
+def get_unet(model_version: ModelVersion, ref_unet, attention_implementation):
+    ref_unet = prepare_unet_for_coreml_trace(ref_unet)
+    unet = apply_attention_implementation(
+        ref_unet.eval(),
+        attention_implementation,
+    )
+    return CoreMLUNetWrapper(unet, model_version)
+
+
+def get_encoder_hidden_states_shape(ref_unet, batch_size):
+    encoder_hidden_states_shape = (
+        batch_size,
+        TEXT_TOKEN_SEQUENCE_LENGTH,
+        ref_unet.config.cross_attention_dim,
+    )
+
+    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):
+    from folder_paths import get_folder_paths
+
+    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 get_sample_input(batch_size, encoder_hidden_states_shape, sample_shape):
+    sample_unet_inputs = dict(
+        [
+            ("sample", torch.rand(*sample_shape)),
+            (
+                "timestep",
+                torch.tensor([DEFAULT_TRACE_TIMESTEP] * 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_unet, model_version):
+    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 = model_version == ModelVersion.SDXL_REFINER
+
+    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, get_sdxl_text_embeds_dim(ref_unet, len(time_ids_list)))
+
+    return {
+        "time_ids": time_ids,
+        "text_embeds": torch.randn(*text_embeds_shape).to(torch.float32),
+    }
+
+
+def get_sdxl_text_embeds_dim(ref_unet, time_ids_dim):
+    projection_dim = ref_unet.config.projection_class_embeddings_input_dim
+    time_embed_dim = ref_unet.config.addition_time_embed_dim
+    return projection_dim - (time_ids_dim * time_embed_dim)
+
+
+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):
+    additional_residuals_shapes = []
+
+    batch_size = sample_shape[0]
+    h, w = sample_shape[2:]
+
+    # conv_in
+    out_h, out_w = 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 = 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_unet,
+    model_version: ModelVersion,
+    unet_out_path: str,
+    batch_size: int = 1,
+    sample_size: tuple[int, int] = (64, 64),
+    controlnet_support: bool = False,
+    attention_implementation: str = ATTENTION_IMPLEMENTATIONS[0],
+    quantize_nbits: str = "none",
+):
+    coreml_unet = get_unet(model_version, ref_unet, attention_implementation)
+
+    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, batch_size)
+
+    sample_inputs = get_sample_input(
+        batch_size, encoder_hidden_states_shape, sample_shape
+    )
+
+    if model_version == ModelVersion.LCM:
+        sample_inputs |= lcm_inputs(sample_inputs)
+
+    if model_version in {ModelVersion.SDXL, ModelVersion.SDXL_REFINER}:
+        sample_inputs |= sdxl_inputs(sample_inputs, ref_unet, model_version)
+
+    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[str | os.PathLike, float]] = None,
+    attn_impl: str = ATTENTION_IMPLEMENTATIONS[0],
+    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
+
+    if attn_impl not in ATTENTION_IMPLEMENTATIONS:
+        raise ValueError(
+            f"Unsupported attention implementation {attn_impl!r}. "
+            f"Expected one of {ATTENTION_IMPLEMENTATIONS}."
+        )
+    ref_unet = load_unet(ckpt_path, config_path)
+
+    for i, lora_weight in enumerate(lora_weights or []):
+        lora_path, strength = lora_weight
+        adapter_name = f"lora_{i}"
+        ref_unet.load_lora_adapter(lora_path, adapter_name=adapter_name)
+        ref_unet.set_adapters([adapter_name], weights=[strength])
+        ref_unet.fuse_lora()
+
+    convert_unet(
+        ref_unet,
+        model_version,
+        unet_out_path,
+        batch_size,
+        sample_size,
+        controlnet_support,
+        attention_implementation=attn_impl,
+        quantize_nbits=quantize_nbits,
+    )
+
+
+def load_unet(ckpt_path, config_path):
+    return UNet2DConditionModel.from_single_file(
+        ckpt_path,
+        original_config=config_path,
+    )

coreml_suite/core/__init__.py

@@ -0,0 +1,10 @@
+"""Framework-free pure-logic core of ComfyUI-CoreMLSuite.
+
+Modules under this package must NOT import `comfy`, `coremltools`,
+`python_coreml_stable_diffusion`, `folder_paths`, `nodes`, or any other
+ComfyUI / Apple runtime. Only `numpy` and `torch` are allowed.
+
+The thin adapters in `coreml_suite.{latents,controlnet,models}` keep the
+old public import paths working so `coreml_suite/nodes.py` and downstream
+ComfyUI workflows are unchanged.
+"""

coreml_suite/core/controlnet.py

@@ -0,0 +1,67 @@
+"""Pure helpers around the ControlNet residual inputs of the Core ML UNet.
+
+Re-exported by coreml_suite.controlnet. Characterization tests cover
+shapes, dtype (fp16), and zero-fill fallback.
+"""
+from itertools import chain
+from math import ceil
+
+import numpy as np
+import torch
+
+from coreml_suite.core.latents import chunk_batch
+
+
+def expand_inputs(inputs):
+    expanded = inputs.copy()
+    for k, v in inputs.items():
+        if isinstance(v, np.ndarray):
+            expanded[k] = np.concatenate([v] * 2) if v.shape[0] == 1 else v
+        elif isinstance(v, torch.Tensor):
+            expanded[k] = torch.cat([v] * 2) if v.shape[0] == 1 else v
+        elif isinstance(v, list):
+            expanded[k] = v * 2 if len(v) == 1 else v
+        elif isinstance(v, dict):
+            expand_inputs(v)
+    return expanded
+
+
+def extract_residual_kwargs(expected_inputs, control):
+    if "additional_residual_0" not in expected_inputs.keys():
+        return {}
+    if control is None:
+        return no_control(expected_inputs)
+
+    residual_kwargs = {
+        "additional_residual_{}".format(i): r.cpu().numpy().astype(np.float16)
+        for i, r in enumerate(chain(control["output"], control["middle"]))
+    }
+    return residual_kwargs
+
+
+def no_control(expected_inputs):
+    shapes_dict = {
+        k: v["shape"] for k, v in expected_inputs.items() if k.startswith("additional")
+    }
+    residual_kwargs = {
+        k: torch.zeros(*shape).cpu().numpy().astype(dtype=np.float16)
+        for k, shape in shapes_dict.items()
+    }
+    return residual_kwargs
+
+
+def chunk_control(cn, target_size):
+    if cn is None:
+        return [None] * target_size
+
+    num_chunks = ceil(cn["output"][0].shape[0] / target_size)
+
+    out = [{"output": [], "middle": []} for _ in range(num_chunks)]
+
+    for k, v in cn.items():
+        for i, x in enumerate(v):
+            chunks = chunk_batch(x, (target_size, *x.shape[1:]))
+            for j, chunk in enumerate(chunks):
+                out[j][k].append(chunk)
+
+    return out

coreml_suite/core/inputs.py

@@ -0,0 +1,111 @@
+"""Pure transform from torch sampler inputs to Core ML UNet kwargs.
+
+Characterization tests cover SD1.5 / SDXL base / SDXL refiner / LCM
+variants and the chunked-batch fan-out.
+"""
+import numpy as np
+import torch
+
+from coreml_suite.core.controlnet import extract_residual_kwargs, chunk_control
+from coreml_suite.core.latents import chunk_batch
+
+
+class CoreMLInputs:
+    def __init__(self, x, t, context, control, **kwargs):
+        self.x = x
+        self.t = t
+        self.context = context
+        self.control = control
+        self.time_ids = kwargs.get("time_ids")
+        self.text_embeds = kwargs.get("text_embeds")
+        self.ts_cond = kwargs.get("timestep_cond")
+
+    def coreml_kwargs(self, expected_inputs):
+        sample = self.x.cpu().numpy().astype(np.float16)
+
+        context = self.context.cpu().numpy().astype(np.float16)
+
+        t = self.t.cpu().numpy().astype(np.float16)
+
+        model_input_kwargs = {
+            "sample": sample,
+            "encoder_hidden_states": context,
+            "timestep": t,
+        }
+        residual_kwargs = extract_residual_kwargs(expected_inputs, self.control)
+        model_input_kwargs |= residual_kwargs
+
+        # LCM
+        if self.ts_cond is not None:
+            model_input_kwargs["timestep_cond"] = (
+                self.ts_cond.cpu().numpy().astype(np.float16)
+            )
+
+        # SDXL
+        if "text_embeds" in expected_inputs:
+            model_input_kwargs["text_embeds"] = (
+                self.text_embeds.cpu().numpy().astype(np.float16)
+            )
+        if "time_ids" in expected_inputs:
+            model_input_kwargs["time_ids"] = (
+                self.time_ids.cpu().numpy().astype(np.float16)
+            )
+
+        return model_input_kwargs
+
+    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"]
+
+        chunked_x = chunk_batch(self.x, sample_shape)
+        ts = list(torch.full((len(chunked_x), timestep_shape[0]), self.t[0]))
+        chunked_context = chunk_batch(self.context, context_shape)
+
+        chunked_control = [None] * len(chunked_x)
+        if self.control is not None:
+            chunked_control = chunk_control(self.control, sample_shape[0])
+
+        chunked_ts_cond = [None] * len(chunked_x)
+        if self.ts_cond is not None:
+            ts_cond_shape = expected_inputs["timestep_cond"]["shape"]
+            chunked_ts_cond = chunk_batch(self.ts_cond, ts_cond_shape)
+
+        chunked_time_ids = [None] * len(chunked_x)
+        if expected_inputs.get("time_ids") is not None:
+            time_ids_shape = expected_inputs["time_ids"]["shape"]
+            if self.time_ids is None:
+                self.time_ids = torch.zeros(len(chunked_x), *time_ids_shape[1:]).to(
+                    self.x.device
+                )
+            chunked_time_ids = chunk_batch(self.time_ids, time_ids_shape)
+
+        chunked_text_embeds = [None] * len(chunked_x)
+        if expected_inputs.get("text_embeds") is not None:
+            text_embeds_shape = expected_inputs["text_embeds"]["shape"]
+            if self.text_embeds is None:
+                self.text_embeds = torch.zeros(
+                    len(chunked_x), *text_embeds_shape[1:]
+                ).to(self.x.device)
+            chunked_text_embeds = chunk_batch(self.text_embeds, text_embeds_shape)
+
+        return [
+            CoreMLInputs(
+                x,
+                t,
+                context,
+                control,
+                timestep_cond=ts_cond,
+                time_ids=time_ids,
+                text_embeds=text_embeds,
+            )
+            for x, t, context, control, ts_cond, time_ids, text_embeds in zip(
+                chunked_x,
+                ts,
+                chunked_context,
+                chunked_control,
+                chunked_ts_cond,
+                chunked_time_ids,
+                chunked_text_embeds,
+            )
+        ]

coreml_suite/core/latents.py

@@ -0,0 +1,42 @@
+"""Pure batch-chunking helpers for Core ML's fixed-shape UNet inputs.
+
+Re-exported by coreml_suite.latents. Characterization tests cover the
+contract (padding-zero regions, truncation in merge_chunks,
+identity-passthrough when shape already matches).
+"""
+import torch
+
+
+def chunk_batch(input_tensor, target_shape):
+    if input_tensor.shape == target_shape:
+        return [input_tensor]
+
+    batch_size = input_tensor.shape[0]
+    target_batch_size = target_shape[0]
+
+    num_chunks = batch_size // target_batch_size
+    if num_chunks == 0:
+        padding = torch.zeros(target_batch_size - batch_size, *target_shape[1:]).to(
+            input_tensor.device
+        )
+        return [torch.cat((input_tensor, padding), dim=0)]
+
+    mod = batch_size % target_batch_size
+    if mod != 0:
+        chunks = list(torch.chunk(input_tensor[:-mod], num_chunks))
+        padding = torch.zeros(target_batch_size - mod, *target_shape[1:]).to(
+            input_tensor.device
+        )
+        padded = torch.cat((input_tensor[-mod:], padding), dim=0)
+        chunks.append(padded)
+        return chunks
+
+    chunks = list(torch.chunk(input_tensor, num_chunks))
+    return chunks
+
+
+def merge_chunks(chunks, orig_shape):
+    merged = torch.cat(chunks, dim=0)
+    if merged.shape == orig_shape:
+        return merged
+    return merged[: orig_shape[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/core/sdxl.py

@@ -0,0 +1,91 @@
+"""Pure SDXL detection + time_ids/text_embeds assembly.
+
+The framework-coupled adapter `add_sdxl_model_options` lives in models.py
+and delegates the math here. Characterization tests cover base (len 6) vs
+refiner (len 5) and the closure free-vars produced by
+`sdxl_model_function_wrapper`.
+"""
+import torch
+
+
+def is_sdxl(coreml_model):
+    return (
+        "time_ids" in coreml_model.expected_inputs
+        and "text_embeds" in coreml_model.expected_inputs
+    )
+
+
+def is_sdxl_base(coreml_model):
+    return (
+        is_sdxl(coreml_model)
+        and coreml_model.expected_inputs["time_ids"]["shape"][1] == 6
+    )
+
+
+def is_sdxl_refiner(coreml_model):
+    return (
+        is_sdxl(coreml_model)
+        and coreml_model.expected_inputs["time_ids"]["shape"][1] == 5
+    )
+
+
+def build_sdxl_time_ids(pos_dict, neg_dict, *, is_base: bool, is_refiner: bool):
+    """Compose the (2, N) time_ids tensor for the SDXL Core ML UNet.
+
+    - base: N=6  -> [h, w, crop_h, crop_w, target_h, target_w]
+    - refiner: N=5 -> [h, w, crop_h, crop_w, aesthetic_score]
+    - neither: N=4 -> [h, w, crop_h, crop_w] (edge case kept for parity)
+    """
+    pos_time_ids = [
+        pos_dict.get("height", 768),
+        pos_dict.get("width", 768),
+        pos_dict.get("crop_h", 0),
+        pos_dict.get("crop_w", 0),
+    ]
+    neg_time_ids = [
+        neg_dict.get("height", 768),
+        neg_dict.get("width", 768),
+        neg_dict.get("crop_h", 0),
+        neg_dict.get("crop_w", 0),
+    ]
+
+    if is_base:
+        pos_time_ids += [
+            pos_dict.get("target_height", 768),
+            pos_dict.get("target_width", 768),
+        ]
+        neg_time_ids += [
+            neg_dict.get("target_height", 768),
+            neg_dict.get("target_width", 768),
+        ]
+
+    if is_refiner:
+        pos_time_ids += [pos_dict.get("aesthetic_score", 6)]
+        neg_time_ids += [neg_dict.get("aesthetic_score", 2.5)]
+
+    return torch.tensor([pos_time_ids, neg_time_ids])
+
+
+def build_sdxl_text_embeds(pos_pooled, neg_pooled):
+    """Concat pos then neg along the batch dim. Locked contract."""
+    return torch.cat((pos_pooled, neg_pooled))
+
+
+def sdxl_model_function_wrapper(time_ids, text_embeds, refiner=False):
+    def wrapper(model_function, params):
+        x = params["input"]
+        t = params["timestep"]
+        c = params["c"]
+
+        context = c.get("c_crossattn")
+
+        if context is None:
+            return torch.zeros_like(x)
+
+        if refiner and context is not None:
+            # converted refiner accepts only g clip
+            c["c_crossattn"] = context[:, :, 768:]
+
+        return model_function(x, t, **c, time_ids=time_ids, text_embeds=text_embeds)
+
+    return wrapper

coreml_suite/coreml_model.py

@@ -0,0 +1,42 @@
+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/latents.py

@@ -0,0 +1,4 @@
+"""Compatibility shim — re-exports from coreml_suite.core.latents."""
+from coreml_suite.core.latents import chunk_batch, merge_chunks
+
+__all__ = ["chunk_batch", "merge_chunks"]

coreml_suite/lcm/__init__.py

@@ -0,0 +1,3 @@
+from .nodes import COREML_CONVERT_LCM
+
+__all__ = ["COREML_CONVERT_LCM"]

coreml_suite/lcm/converter.py

@@ -0,0 +1,259 @@
+import os
+import logging
+import time
+import gc
+
+import numpy as np
+import torch
+from diffusers import UNet2DConditionModel, LCMScheduler
+from diffusers.loaders import LoraLoaderMixin
+
+from coreml_suite.conversion.attention import apply_attention_implementation
+from coreml_suite.conversion.shapes import conv2d_output_shape
+from coreml_suite.conversion.unet import CoreMLUNetWrapper
+from coreml_suite.model_version import ModelVersion
+
+import coremltools as ct
+
+logging.basicConfig()
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+MODEL_VERSION = "SimianLuo/LCM_Dreamshaper_v7"
+MODEL_NAME = MODEL_VERSION.split("/")[-1] + "_4k"
+TEXT_TOKEN_SEQUENCE_LENGTH = 77
+
+
+def get_unets():
+    ref_unet = UNet2DConditionModel.from_pretrained(
+        MODEL_VERSION,
+        subfolder="unet",
+        device_map=None,
+        low_cpu_mem_usage=False,
+    )
+
+    cml_unet = CoreMLUNetWrapper(
+        apply_attention_implementation(ref_unet.eval(), "SPLIT_EINSUM"),
+        ModelVersion.LCM,
+    )
+
+    return cml_unet, ref_unet
+
+
+def get_encoder_hidden_states_shape(unet_config, batch_size):
+    encoder_hidden_states_shape = (
+        batch_size,
+        TEXT_TOKEN_SEQUENCE_LENGTH,
+        unet_config.cross_attention_dim,
+    )
+
+    return encoder_hidden_states_shape
+
+
+def get_scheduler():
+    from comfy.model_management import get_torch_device
+
+    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):
+    from folder_paths import get_folder_paths
+
+    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 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):
+    additional_residuals_shapes = []
+
+    batch_size = sample_shape[0]
+    h, w = sample_shape[2:]
+
+    # conv_in
+    out_h, out_w = 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 = 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}")
+
+
+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)

coreml_suite/lcm/nodes.py

@@ -0,0 +1,70 @@
+import os
+
+from coremltools import ComputeUnit
+
+from coreml_suite import COREML_NODE
+from coreml_suite.coreml_model import CoreMLModel
+
+
+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.
+        """
+        from coreml_suite.lcm import converter as lcm_converter
+
+        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,
+            )
+
+        return (CoreMLModel(out_path, compute_unit),)

coreml_suite/lcm/unet.py

@@ -0,0 +1,98 @@
+from diffusers import UNet2DConditionModel
+from diffusers.models.embeddings import 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
+
+    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/lcm/utils.py

@@ -0,0 +1,73 @@
+import torch
+
+from comfy.model_management import get_torch_device
+from comfy_extras.nodes_model_advanced import ModelSamplingDiscreteDistilled, LCM
+
+
+def is_lcm(coreml_model):
+    return "timestep_cond" in coreml_model.expected_inputs
+
+
+def get_w_embedding(w, embedding_dim=512, dtype=torch.float32):
+    assert len(w.shape) == 1
+    w = w * 1000.0
+
+    half_dim = embedding_dim // 2
+    emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+    emb = w.to(dtype)[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0, 1))
+    assert emb.shape == (w.shape[0], embedding_dim)
+    return emb
+
+
+def model_function_wrapper(w_embedding):
+    def wrapper(model_function, params):
+        x = params["input"]
+        t = params["timestep"]
+        c = params["c"]
+
+        context = c.get("c_crossattn")
+
+        if context is None:
+            return torch.zeros_like(x)
+
+        return model_function(x, t, **c, timestep_cond=w_embedding)
+
+    return wrapper
+
+
+def lcm_patch(model):
+    m = model.clone()
+    sampling_type = LCM
+    sampling_base = ModelSamplingDiscreteDistilled
+
+    class ModelSamplingAdvanced(sampling_base, sampling_type):
+        pass
+
+    model_sampling = ModelSamplingAdvanced()
+    m.add_object_patch("model_sampling", model_sampling)
+
+    return m
+
+
+def add_lcm_model_options(model_patcher, cfg, latent_image):
+    mp = model_patcher.clone()
+
+    latent = latent_image["samples"].to(get_torch_device())
+    batch_size = latent.shape[0]
+    dtype = latent.dtype
+    device = get_torch_device()
+
+    w = torch.tensor(cfg).repeat(batch_size)
+    w_embedding = get_w_embedding(w, embedding_dim=256).to(device=device, dtype=dtype)
+
+    model_options = {
+        "model_function_wrapper": model_function_wrapper(w_embedding),
+        "sampler_cfg_function": lambda x: x["cond"].to(device),
+    }
+    mp.model_options |= model_options
+
+    return mp

coreml_suite/loaders.py

@@ -1,84 +0,0 @@
-import os.path
-
-from coremltools import ComputeUnit
-from python_coreml_stable_diffusion.coreml_model import CoreMLModel
-
-import folder_paths
-
-from coreml_suite.logger import logger
-
-
-class CoreMLLoader:
-    PACKAGE_DIRNAME = ""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "coreml_name": (list(s.coreml_filenames().keys()),),
-                "compute_unit": (
-                    [
-                        ComputeUnit.CPU_AND_NE.name,
-                        ComputeUnit.CPU_AND_GPU.name,
-                        ComputeUnit.ALL.name,
-                        ComputeUnit.CPU_ONLY.name,
-                    ],
-                ),
-            }
-        }
-
-    FUNCTION = "load"
-    CATEGORY = "Core ML Suite"
-
-    @classmethod
-    def coreml_filenames(cls):
-        extensions = (".mlmodelc", ".mlpackage")
-        all_paths = folder_paths.get_filename_list_(cls.PACKAGE_DIRNAME)[1]
-        coreml_paths = folder_paths.filter_files_extensions(all_paths, extensions)
-
-        return {os.path.split(p)[-1]: p for p in coreml_paths}
-
-    def load(self, coreml_name, compute_unit):
-        logger.info(f"Loading {coreml_name} to {compute_unit}")
-
-        coreml_path = self.coreml_filenames()[coreml_name]
-
-        sources = "compiled" if coreml_name.endswith(".mlmodelc") else "packages"
-
-        return self._load(coreml_path, compute_unit, sources)
-
-    def _load(self, coreml_path, compute_unit, sources):
-        return (CoreMLModel(coreml_path, compute_unit, sources),)
-
-
-class CoreMLLoaderCkpt(CoreMLLoader):
-    PACKAGE_DIRNAME = "checkpoints"
-    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
-
-    def load(self, coreml_name, compute_unit):
-        # TODO: Implement this
-        pass
-
-
-class CoreMLLoaderTextEncoder(CoreMLLoader):
-    PACKAGE_DIRNAME = "clip"
-    RETURN_TYPES = ("CLIP",)
-
-    def load(self, coreml_name, compute_unit):
-        # TODO: Implement this
-        pass
-
-
-class CoreMLLoaderUNet(CoreMLLoader):
-    PACKAGE_DIRNAME = "unet"
-    RETURN_TYPES = ("COREML_UNET",)
-    RETURN_NAMES = ("coreml_model",)
-
-
-class CoreMLLoaderVAE(CoreMLLoader):
-    PACKAGE_DIRNAME = "vae"
-    RETURN_TYPES = ("VAE",)
-
-    def load(self, coreml_name, compute_unit):
-        # TODO: Implement this
-        pass

coreml_suite/model_version.py

@@ -0,0 +1,8 @@
+from enum import Enum
+
+
+class ModelVersion(Enum):
+    SD15 = "sd15"
+    SDXL = "sdxl"
+    SDXL_REFINER = "sdxl_refiner"
+    LCM = "lcm"

coreml_suite/models.py

@@ -1,63 +1,147 @@
-import numpy as np
+"""Framework-coupled glue between Core ML UNets and ComfyUI's sampler stack.
+
+Pure math (CoreMLInputs, SDXL detection, time_ids/text_embeds assembly,
+sdxl_model_function_wrapper) lives in coreml_suite.core.*.
+This module is what touches comfy.*: model_base, ModelPatcher, the
+diffusion_model wrapper, and the maintainer-facing add_sdxl_model_options
+adapter.
+"""
 import torch
 
+from comfy import model_base
+from comfy.model_management import get_torch_device
+from comfy.model_patcher import ModelPatcher
 
-from comfy import supported_models_base
-from comfy.latent_formats import SD15
-from comfy.model_base import BaseModel
+from coreml_suite.config import get_model_config, ModelVersion
+from coreml_suite.core.inputs import CoreMLInputs
+from coreml_suite.core.latents import merge_chunks
+from coreml_suite.core.sdxl import (
+    build_sdxl_text_embeds,
+    build_sdxl_time_ids,
+    is_sdxl,
+    is_sdxl_base,
+    is_sdxl_refiner,
+    sdxl_model_function_wrapper,
+)
+from coreml_suite.lcm.utils import is_lcm
+from coreml_suite.logger import logger
 
-from coreml_suite.utils import expand_inputs, extract_residual_kwargs
+__all__ = [
+    "CoreMLInputs",
+    "CoreMLModelWrapper",
+    "CoreMLModelWrapperLCM",
+    "add_sdxl_model_options",
+    "get_latent_image",
+    "get_model_patcher",
+    "is_sdxl",
+    "is_sdxl_base",
+    "is_sdxl_refiner",
+    "sdxl_model_function_wrapper",
+]
 
 
-def get_model_config():
-    # TODO: This is a dummy model config, but it should be enough to
-    #  get the model to load - implement a proper model config
-    model_config = supported_models_base.BASE({})
-    model_config.latent_format = SD15()
-    model_config.unet_config = {
-        "disable_unet_model_creation": True,
-        "num_res_blocks": 2,
-        "attention_resolutions": [1, 2, 4],
-        "channel_mult": [1, 2, 4, 4],
-        "transformer_depth": [1, 1, 1, 0],
+class CoreMLModelWrapper:
+    def __init__(self, coreml_model):
+        self.coreml_model = coreml_model
+        self.dtype = torch.float16
+
+    def __call__(self, x, t, context, control, transformer_options=None, **kwargs):
+        inputs = CoreMLInputs(x, t, context, control, **kwargs)
+        input_list = inputs.chunks(self.expected_inputs)
+
+        chunked_out = [
+            self.get_torch_outputs(
+                self.coreml_model(**input_kwargs.coreml_kwargs(self.expected_inputs)),
+                x.device,
+            )
+            for input_kwargs in input_list
+        ]
+        merged_out = merge_chunks(chunked_out, x.shape)
+
+        return merged_out
+
+    @staticmethod
+    def get_torch_outputs(model_output, device):
+        return torch.from_numpy(model_output["noise_pred"]).to(device)
+
+    @property
+    def expected_inputs(self):
+        return self.coreml_model.expected_inputs
+
+    @property
+    def is_lcm(self):
+        return is_lcm(self.coreml_model)
+
+    @property
+    def is_sdxl_base(self):
+        return is_sdxl_base(self.coreml_model)
+
+    @property
+    def is_sdxl_refiner(self):
+        return is_sdxl_refiner(self.coreml_model)
+
+    @property
+    def config(self):
+        if self.is_sdxl_base:
+            return get_model_config(ModelVersion.SDXL)
+
+        if self.is_sdxl_refiner:
+            return get_model_config(ModelVersion.SDXL_REFINER)
+
+        return get_model_config(ModelVersion.SD15)
+
+
+class CoreMLModelWrapperLCM(CoreMLModelWrapper):
+    def __init__(self, coreml_model):
+        super().__init__(coreml_model)
+        self.config = None
+
+
+def add_sdxl_model_options(model_patcher, positive, negative):
+    mp = model_patcher.clone()
+
+    pos_dict = positive[0][1]
+    neg_dict = negative[0][1]
+
+    is_base = model_patcher.model.diffusion_model.is_sdxl_base
+    is_refiner = model_patcher.model.diffusion_model.is_sdxl_refiner
+
+    time_ids = build_sdxl_time_ids(
+        pos_dict, neg_dict, is_base=is_base, is_refiner=is_refiner
+    )
+    text_embeds = build_sdxl_text_embeds(
+        pos_dict["pooled_output"], neg_dict["pooled_output"]
+    )
+
+    mp.model_options |= {
+        "model_function_wrapper": sdxl_model_function_wrapper(
+            time_ids, text_embeds, is_refiner
+        ),
     }
-    return model_config
+    return mp
 
 
-class CoreMLModelWrapper(BaseModel):
-    def __init__(self, model_config, coreml_model):
-        super().__init__(model_config)
-        self.diffusion_model = coreml_model
+def get_latent_image(coreml_model, latent_image):
+    if latent_image is not None:
+        return latent_image
 
-    def apply_model(
-        self,
-        x,
-        t,
-        c_concat=None,
-        c_crossattn=None,
-        c_adm=None,
-        control=None,
-        transformer_options={},
-    ):
-        sample = x.cpu().numpy().astype(np.float16)
+    logger.warning("No latent image provided, using empty tensor.")
+    expected = coreml_model.expected_inputs["sample"]["shape"]
+    batch_size = max(expected[0] // 2, 1)
+    latent_image = {"samples": torch.zeros(batch_size, *expected[1:])}
+    return latent_image
 
-        context = c_crossattn.cpu().numpy().astype(np.float16)
-        context = context.transpose(0, 2, 1)[:, :, None, :]
 
-        t = t.cpu().numpy().astype(np.float16)
+def get_model_patcher(coreml_model):
+    wrapped_model = CoreMLModelWrapper(coreml_model)
 
-        model_input_kwargs = {
-            "sample": sample,
-            "encoder_hidden_states": context,
-            "timestep": t,
-        }
-        residual_kwargs = extract_residual_kwargs(self.diffusion_model, control)
-        model_input_kwargs |= residual_kwargs
-        model_input_kwargs = expand_inputs(model_input_kwargs)
+    if wrapped_model.is_sdxl_base:
+        model = model_base.SDXL(wrapped_model.config, device=get_torch_device())
+    elif wrapped_model.is_sdxl_refiner:
+        model = model_base.SDXLRefiner(wrapped_model.config, device=get_torch_device())
+    else:
+        model = model_base.BaseModel(wrapped_model.config, device=get_torch_device())
 
-        np_out = self.diffusion_model(**model_input_kwargs)["noise_pred"]
-        return torch.from_numpy(np_out).to(x.device)
-
-    def get_dtype(self):
-        # Hardcoding torch-compatible dtype (used for memory allocation)
-        return torch.float16
+    model.diffusion_model = wrapped_model
+    model_patcher = ModelPatcher(model, get_torch_device(), None)
+    return model_patcher

coreml_suite/nodes.py

@@ -0,0 +1,370 @@
+import os
+
+from coremltools import ComputeUnit
+
+import folder_paths
+from coreml_suite import COREML_NODE
+from coreml_suite.attention import ATTENTION_IMPLEMENTATIONS
+from coreml_suite.coreml_model import CoreMLModel
+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 coreml_suite.model_version import ModelVersion
+from nodes import KSampler, LoraLoader, KSamplerAdvanced
+
+from coreml_suite.models import (
+    add_sdxl_model_options,
+    is_sdxl,
+    get_model_patcher,
+    get_latent_image,
+)
+
+
+class CoreMLSampler(COREML_NODE, KSampler):
+    @classmethod
+    def INPUT_TYPES(s):
+        old_required = KSampler.INPUT_TYPES()["required"].copy()
+        old_required.pop("model")
+        old_required.pop("negative")
+        old_required.pop("latent_image")
+        new_required = {"coreml_model": ("COREML_UNET",)}
+        return {
+            "required": new_required | old_required,
+            "optional": {"negative": ("CONDITIONING",), "latent_image": ("LATENT",)},
+        }
+
+    def sample(
+        self,
+        coreml_model,
+        seed,
+        steps,
+        cfg,
+        sampler_name,
+        scheduler,
+        positive,
+        negative=None,
+        latent_image=None,
+        denoise=1.0,
+    ):
+        model_patcher = get_model_patcher(coreml_model)
+        latent_image = get_latent_image(coreml_model, latent_image)
+
+        if is_lcm(coreml_model):
+            negative = [[None, {}]]
+            positive[0][1]["control_apply_to_uncond"] = False
+            model_patcher = add_lcm_model_options(model_patcher, cfg, latent_image)
+            model_patcher = lcm_patch(model_patcher)
+        else:
+            assert (
+                negative is not None
+            ), "Negative conditioning is optional only for LCM models."
+
+        if is_sdxl(coreml_model):
+            model_patcher = add_sdxl_model_options(model_patcher, positive, negative)
+
+        return super().sample(
+            model_patcher,
+            seed,
+            steps,
+            cfg,
+            sampler_name,
+            scheduler,
+            positive,
+            negative,
+            latent_image,
+            denoise,
+        )
+
+
+class CoreMLSamplerAdvanced(COREML_NODE, KSamplerAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        old_required = KSamplerAdvanced.INPUT_TYPES()["required"].copy()
+        old_required.pop("model")
+        old_required.pop("negative")
+        old_required.pop("latent_image")
+        new_required = {"coreml_model": ("COREML_UNET",)}
+        return {
+            "required": new_required | old_required,
+            "optional": {"negative": ("CONDITIONING",), "latent_image": ("LATENT",)},
+        }
+
+    def sample(
+        self,
+        coreml_model,
+        add_noise,
+        noise_seed,
+        steps,
+        cfg,
+        sampler_name,
+        scheduler,
+        positive,
+        start_at_step,
+        end_at_step,
+        return_with_leftover_noise,
+        negative=None,
+        latent_image=None,
+        denoise=1.0,
+    ):
+        model_patcher = get_model_patcher(coreml_model)
+        latent_image = get_latent_image(coreml_model, latent_image)
+
+        if is_lcm(coreml_model):
+            negative = [[None, {}]]
+            positive[0][1]["control_apply_to_uncond"] = False
+            model_patcher = add_lcm_model_options(model_patcher, cfg, latent_image)
+            model_patcher = lcm_patch(model_patcher)
+        else:
+            assert (
+                negative is not None
+            ), "Negative conditioning is optional only for LCM models."
+
+        if is_sdxl(coreml_model):
+            model_patcher = add_sdxl_model_options(model_patcher, positive, negative)
+
+        return super().sample(
+            model_patcher,
+            add_noise,
+            noise_seed,
+            steps,
+            cfg,
+            sampler_name,
+            scheduler,
+            positive,
+            negative,
+            latent_image,
+            start_at_step,
+            end_at_step,
+            return_with_leftover_noise,
+            denoise,
+        )
+
+
+class CoreMLLoader(COREML_NODE):
+    PACKAGE_DIRNAME = ""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "coreml_name": (list(s.coreml_filenames().keys()),),
+                "compute_unit": (
+                    [
+                        ComputeUnit.CPU_AND_NE.name,
+                        ComputeUnit.CPU_AND_GPU.name,
+                        ComputeUnit.ALL.name,
+                        ComputeUnit.CPU_ONLY.name,
+                    ],
+                ),
+            }
+        }
+
+    FUNCTION = "load"
+
+    @classmethod
+    def coreml_filenames(cls):
+        extensions = (".mlpackage",)
+        all_paths = folder_paths.get_filename_list_(cls.PACKAGE_DIRNAME)[1]
+        coreml_paths = folder_paths.filter_files_extensions(all_paths, extensions)
+
+        return {os.path.split(p)[-1]: p for p in coreml_paths}
+
+    def load(self, coreml_name, compute_unit):
+        logger.info(f"Loading {coreml_name} to {compute_unit}")
+
+        coreml_path = self.coreml_filenames()[coreml_name]
+
+        return (CoreMLModel(coreml_path, compute_unit),)
+
+
+class CoreMLLoaderUNet(CoreMLLoader):
+    PACKAGE_DIRNAME = "unet"
+    RETURN_TYPES = ("COREML_UNET",)
+    RETURN_NAMES = ("coreml_model",)
+
+
+class CoreMLModelAdapter(COREML_NODE):
+    """
+    Adapter Node to use CoreML models as Comfy models. This is an experimental
+    feature and may not work as expected.
+    """
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "coreml_model": ("COREML_UNET",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+
+    FUNCTION = "wrap"
+    CATEGORY = "Core ML Suite"
+
+    def wrap(self, coreml_model):
+        model_patcher = get_model_patcher(coreml_model)
+        return (model_patcher,)
+
+
+class CoreMLConverter(COREML_NODE):
+    """Converts a LCM model to Core ML."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
+                "model_version": (
+                    [
+                        ModelVersion.SD15.name,
+                        ModelVersion.SDXL.name,
+                    ],
+                ),
+                "height": ("INT", {"default": 512, "min": 8, "step": 8}),
+                "width": ("INT", {"default": 512, "min": 8, "step": 8}),
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
+                "attention_implementation": (
+                    list(ATTENTION_IMPLEMENTATIONS),
+                ),
+                "compute_unit": (
+                    [
+                        ComputeUnit.CPU_AND_NE.name,
+                        ComputeUnit.CPU_AND_GPU.name,
+                        ComputeUnit.ALL.name,
+                        ComputeUnit.CPU_ONLY.name,
+                    ],
+                ),
+                "controlnet_support": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                # k-means weight palettization. Kept optional so workflows
+                # that omit it still validate — ComfyUI rejects a prompt that
+                # omits any `required` input. When omitted it defaults to
+                # "none", identical to unquantized behavior and filename, so
+                # existing cached .mlpackages still resolve.
+                "quantize_nbits": (list(QUANT_NBITS_VALUES), {"default": "none"}),
+                "lora_params": ("LORA_PARAMS",),
+            },
+        }
+
+    RETURN_TYPES = ("COREML_UNET",)
+    RETURN_NAMES = ("coreml_model",)
+    FUNCTION = "convert"
+
+    def convert(
+        self,
+        ckpt_name,
+        model_version,
+        height,
+        width,
+        batch_size,
+        attention_implementation,
+        compute_unit,
+        controlnet_support,
+        quantize_nbits="none",
+        lora_params=None,
+    ):
+        """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.
+        """
+        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])
+        lora_weights = [(self.lora_path(lora[0]), lora[1]) for lora in lora_params]
+
+        h = height
+        w = width
+        sample_size = (h // 8, w // 8)
+        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=lora_names_from_params(lora_params),
+            quantize_nbits=quantize_nbits,
+        )
+
+        logger.info(f"Converting {ckpt_name} to {out_name}")
+        logger.info(f"Batch size: {batch_size}")
+        logger.info(f"Width: {w}, Height: {h}")
+        logger.info(f"ControlNet support: {controlnet_support}")
+        logger.info(f"Attention implementation: {attention_implementation}")
+
+        if lora_params:
+            logger.info(f"LoRAs used:")
+            for lora_param in lora_params:
+                logger.info(f"  {lora_param[0]} - strength: {lora_param[1]}")
+
+        from coreml_suite import converter
+
+        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"
+        config_path = folder_paths.get_full_path("configs", config_filename)
+        if config_path:
+            logger.info(f"Using config file {config_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,
+            lora_weights=lora_weights,
+            attn_impl=attention_implementation,
+            config_path=config_path,
+            quantize_nbits=quantize_nbits,
+        )
+        return (CoreMLModel(unet_out_path, compute_unit),)
+
+    @staticmethod
+    def lora_path(lora_name):
+        return folder_paths.get_full_path("loras", lora_name)
+
+
+class COREML_LOAD_LORA(COREML_NODE, LoraLoader):
+    @classmethod
+    def INPUT_TYPES(s):
+        required = LoraLoader.INPUT_TYPES()["required"].copy()
+        required.pop("model")
+        return {
+            "required": required,
+            "optional": {"lora_params": ("LORA_PARAMS",)},
+        }
+
+    RETURN_TYPES = ("CLIP", "LORA_PARAMS")
+    RETURN_NAMES = ("CLIP", "lora_params")
+
+    def load_lora(
+        self, clip, lora_name, strength_model, strength_clip, lora_params=None
+    ):
+        _, lora_clip = super().load_lora(
+            None, clip, lora_name, strength_model, strength_clip
+        )
+
+        lora_params = lora_params or {}
+        lora_params[lora_name] = (strength_model, strength_clip)
+
+        return lora_clip, lora_params

coreml_suite/samplers.py

@@ -1,74 +0,0 @@
-import torch
-from torchvision.transforms.functional import resize
-
-from comfy.model_management import get_torch_device
-from comfy.model_patcher import ModelPatcher
-from coreml_suite.logger import logger
-from nodes import KSampler
-
-from coreml_suite.models import CoreMLModelWrapper, get_model_config
-
-
-def reshape_latent_image(latent_image, target_shape):
-    if latent_image is None:
-        logger.warning("No latent image provided, using zeros.")
-        return {"samples": torch.zeros(target_shape)}
-
-    if latent_image["samples"].shape == target_shape:
-        return latent_image
-
-    logger.warning(
-        "Latent image shape does not match model input shape,"
-        " resizing to match models expected input shape."
-    )
-    resized = resize(latent_image["samples"], target_shape[-2:])
-    return {"samples": resized}
-
-
-class CoreMLSampler(KSampler):
-    @classmethod
-    def INPUT_TYPES(s):
-        old_required = KSampler.INPUT_TYPES()["required"].copy()
-        old_required.pop("model")
-        old_required.pop("latent_image")
-        new_required = {"coreml_model": ("COREML_UNET",)}
-        return {
-            "required": new_required | old_required,
-            "optional": {"latent_image": ("LATENT",)},
-        }
-
-    CATEGORY = "Core ML Suite"
-
-    def sample(
-        self,
-        coreml_model,
-        seed,
-        steps,
-        cfg,
-        sampler_name,
-        scheduler,
-        positive,
-        negative,
-        latent_image=None,
-        denoise=1.0,
-    ):
-        sample_shape = coreml_model.expected_inputs["sample"]["shape"]
-        latent_image = reshape_latent_image(latent_image, sample_shape)
-        latent_image["samples"] = latent_image["samples"][0:1]
-
-        model_config = get_model_config()
-        wrapped_model = CoreMLModelWrapper(model_config, coreml_model)
-        model = ModelPatcher(wrapped_model, get_torch_device(), None)
-
-        return super().sample(
-            model,
-            seed,
-            steps,
-            cfg,
-            sampler_name,
-            scheduler,
-            positive,
-            negative,
-            latent_image,
-            denoise,
-        )

coreml_suite/utils.py

@@ -1,62 +0,0 @@
-from itertools import chain
-
-import numpy as np
-import torch
-
-from coreml_suite.logger import logger
-
-
-def expand_inputs(inputs):
-    expanded = inputs.copy()
-    for k, v in inputs.items():
-        if isinstance(v, np.ndarray):
-            expanded[k] = np.concatenate([v] * 2) if v.shape[0] == 1 else v
-        elif isinstance(v, torch.Tensor):
-            expanded[k] = torch.cat([v] * 2) if v.shape[0] == 1 else v
-        elif isinstance(v, list):
-            expanded[k] = v * 2 if len(v) == 1 else v
-        elif isinstance(v, dict):
-            expand_inputs(v)
-    return expanded
-
-
-def extract_residual_kwargs(model, control):
-    if "additional_residual_0" not in model.expected_inputs.keys():
-        return {}
-    if control is None:
-        return no_control(model)
-
-    residual_kwargs = {
-        "additional_residual_{}".format(i): r.cpu().numpy().astype(np.float16)
-        for i, r in enumerate(chain(control["output"], control["middle"]))
-    }
-    return residual_kwargs
-
-
-def no_control(model):
-    # Dirty hack to get the expected input shape when doing partial ControlNet
-    # 0.18215 is the latent scale factor (IDK, it kinda works)
-    # TODO: Find a better way to do this or tweak the values
-
-    logger.warning(
-        "No ControlNet input, despite the model supports it. "
-        "Using random noise as ControlNet residuals. "
-        "For better results, please use a ControlNet or a model "
-        "that does not support ControlNet."
-    )
-    residuals_names = [
-        name
-        for name in model.expected_inputs.keys()
-        if name.startswith("additional_residual")
-    ]
-    residual_kwargs = {
-        "additional_residual_{}".format(i): 0.18215
-        * torch.randn(
-            *model.expected_inputs["additional_residual_{}".format(i)]["shape"]
-        )
-        .cpu()
-        .numpy()
-        .astype(dtype=np.float16)
-        for i in range(len(residuals_names))
-    }
-    return residual_kwargs

pyproject.toml

@@ -0,0 +1,56 @@
+[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 = "2.0.2"
+license = "MIT"
+requires-python = ">=3.12,<3.13"
+packages = [{ include = "coreml_suite" }]
+dependencies = [
+    "torch>=2.7,<2.8",
+    "coremltools>=9,<10",
+    "numpy>=2,<3",
+    "diffusers>=0.30",
+    "peft>=0.13",
+    "omegaconf>=2.3",
+    "transformers>=4.44",
+]
+
+[project.urls]
+Repository = "https://github.com/aszc-dev/ComfyUI-CoreMLSuite"
+
+[tool.comfy]
+PublisherId = "aszc-dev"
+DisplayName = "ComfyUI-CoreMLSuite"
+Icon = ""
+requires-comfyui = ">=0.3.27"
+
+[dependency-groups]
+dev = [
+    "pillow>=12.2.0",
+    "psutil>=7.2.2",
+    "pytest>=9.0.3",
+]
+comfy = [
+    "comfyui-frontend-package==1.14.6",
+    "torchvision",
+    "torchaudio",
+    "torchsde",
+    "einops",
+    "tokenizers>=0.13.3",
+    "safetensors>=0.4.2",
+    "aiohttp>=3.11.8",
+    "yarl>=1.18.0",
+    "kornia>=0.7.1",
+    "spandrel",
+    "soundfile",
+    "sentencepiece",
+]
+
+[tool.pytest.ini_options]
+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)",
+]
+testpaths = ["tests"]
+addopts = ["--import-mode=importlib", "--confcutdir=tests"]

requirements.txt

@@ -1,2 +1,7 @@
-git+https://github.com/apple/ml-stable-diffusion.git
-coremltools
+torch>=2.7,<2.8
+coremltools>=9,<10
+numpy>=2,<3
+diffusers>=0.30
+peft>=0.13
+omegaconf>=2.3
+transformers>=4.44

tests/conftest.py

@@ -0,0 +1,61 @@
+"""Pytest bootstrap for ComfyUI-CoreMLSuite tests.
+
+- Adds the ComfyUI checkout to sys.path so the framework-coupled modules
+  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.
+"""
+import sys
+from pathlib import Path
+
+import pytest
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+COMFY_DIR = REPO_ROOT.parents[1]
+
+for p in (str(COMFY_DIR), str(REPO_ROOT)):
+    if p not in sys.path:
+        sys.path.insert(0, p)
+
+
+_TIER_BY_DIR = {
+    "tests/unit": "unit",
+    "tests/m2": "m2",
+    "tests/integration": "m2",
+    "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.
+_TIER_DIRS = {
+    "unit": ("/tests/unit/",),
+    "m2": ("/tests/m2/", "/tests/integration/"),
+    "smoke": ("/tests/smoke/",),
+}
+
+
+def pytest_ignore_collect(collection_path, config):
+    expr = config.option.markexpr
+    if expr not in _TIER_DIRS:
+        return None
+    allowed = _TIER_DIRS[expr]
+    rel = str(collection_path).replace("\\", "/")
+    if "/tests/" not in rel:
+        return None
+    # Always allow tests/ root + the tier's own dirs.
+    if rel.endswith("/tests"):
+        return None
+    if any(frag in rel + "/" for frag in allowed):
+        return None
+    return True
+
+
+def pytest_collection_modifyitems(config, items):
+    for item in items:
+        path = str(item.fspath).replace("\\", "/")
+        for fragment, marker in _TIER_BY_DIR.items():
+            if f"/{fragment}/" in path:
+                item.add_marker(getattr(pytest.mark, marker))
+                break

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

@@ -0,0 +1,182 @@
+{
+  "3": {
+    "inputs": {
+      "seed": 0,
+      "steps": 20,
+      "cfg": 8,
+      "sampler_name": "dpmpp_2m",
+      "scheduler": "karras",
+      "denoise": 1,
+      "model": [
+        "4",
+        0
+      ],
+      "positive": [
+        "6",
+        0
+      ],
+      "negative": [
+        "7",
+        0
+      ],
+      "latent_image": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "KSampler",
+    "_meta": {
+      "title": "KSampler"
+    }
+  },
+  "4": {
+    "inputs": {
+      "ckpt_name": "dreamshaper_8.safetensors"
+    },
+    "class_type": "CheckpointLoaderSimple",
+    "_meta": {
+      "title": "Load Checkpoint"
+    }
+  },
+  "5": {
+    "inputs": {
+      "width": 512,
+      "height": 512,
+      "batch_size": 1
+    },
+    "class_type": "EmptyLatentImage",
+    "_meta": {
+      "title": "Empty Latent Image"
+    }
+  },
+  "6": {
+    "inputs": {
+      "text": "beautiful scenery nature glass bottle landscape, purple galaxy bottle",
+      "clip": [
+        "4",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "7": {
+    "inputs": {
+      "text": "text, watermark",
+      "clip": [
+        "4",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "8": {
+    "inputs": {
+      "samples": [
+        "3",
+        0
+      ],
+      "vae": [
+        "4",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "9": {
+    "inputs": {
+      "filename_prefix": "E2E-1.5-MPS",
+      "images": [
+        "8",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "Save Image"
+    }
+  },
+  "10": {
+    "inputs": {
+      "ckpt_name": "dreamshaper_8.safetensors",
+      "model_version": "SD15",
+      "height": 512,
+      "width": 512,
+      "batch_size": 1,
+      "attention_implementation": "SPLIT_EINSUM",
+      "compute_unit": "CPU_AND_NE",
+      "controlnet_support": false
+    },
+    "class_type": "Core ML Converter",
+    "_meta": {
+      "title": "Convert Checkpoint to Core ML"
+    }
+  },
+  "11": {
+    "inputs": {
+      "seed": 0,
+      "steps": 20,
+      "cfg": 8,
+      "sampler_name": "dpmpp_2m",
+      "scheduler": "karras",
+      "denoise": 1,
+      "coreml_model": [
+        "10",
+        0
+      ],
+      "positive": [
+        "6",
+        0
+      ],
+      "negative": [
+        "7",
+        0
+      ],
+      "latent_image": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "CoreMLSampler",
+    "_meta": {
+      "title": "Core ML Sampler"
+    }
+  },
+  "13": {
+    "inputs": {
+      "samples": [
+        "11",
+        0
+      ],
+      "vae": [
+        "4",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "14": {
+    "inputs": {
+      "filename_prefix": "E2E-1.5-CoreML",
+      "images": [
+        "13",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "Save Image"
+    }
+  }
+}

tests/m2/goldens/sd15_seed42.sha256

@@ -0,0 +1 @@
+e89344e544d4edfbd3ebe9a1c78dadb2729f53549666052b74ac7308f326f4fc

tests/m2/test_golden_image.py

@@ -0,0 +1,170 @@
+"""[M2-ANE] golden-image anchor.
+
+Runs the e2e SD1.5 + CoreML workflow against a local ComfyUI server, fetches
+the generated PNG, and asserts both:
+  - byte-identical SHA256 against the stored golden, OR
+  - PSNR >= GOLDEN_PSNR_MIN_DB against the stored golden PNG.
+
+The hash is the strict gate (a refactor that doesn't touch the math
+should hit it). PSNR is the soft gate that tolerates the drift a
+toolchain bump injects through different MIL graphs / kernel selection
+/ fp accumulation order — anything below the threshold is treated as a
+regression.
+
+The 20 dB default absorbs Apple Neural Engine run-to-run nondeterminism:
+the same model and seed can drift several dB between runs as the 20
+sampling steps amplify tiny per-step UNet differences (kernel selection /
+fp accumulation order). Same-scene ANE outputs have been observed at
+~23 dB, so 20 leaves margin while still catching gross regressions — a
+broken image lands far lower. Bump it up for pure-refactor PRs that must
+not change math; down for toolchain bumps.
+
+Skips entirely on non-Apple-Silicon hosts or when the server / converted
+model is missing, so the unit lane on Linux still passes.
+
+The first run with no golden writes one and fails so it's reviewed before
+being committed.
+"""
+import hashlib
+import json
+import os
+import platform
+import shutil
+import time
+import urllib.error
+import urllib.request
+from pathlib import Path
+
+import numpy as np
+import pytest
+from PIL import Image
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+COMFY_DIR = Path(os.environ.get("COMFY_DIR", REPO_ROOT.parents[1])).resolve()
+COMFY_HOST = os.environ.get("COMFY_HOST", "localhost")
+COMFY_PORT = int(os.environ.get("COMFY_PORT", "8188"))
+COMFY_URL = f"http://{COMFY_HOST}:{COMFY_PORT}"
+
+CKPT_NAME = os.environ.get("CKPT_NAME", "v1-5-pruned-emaonly.safetensors")
+WORKFLOW_PATH = (
+    REPO_ROOT / "tests" / "integration" / "workflows" / "e2e-1.5-basic-conversion.json"
+)
+GOLDEN_DIR = Path(__file__).parent / "goldens"
+GOLDEN_HASH_PATH = GOLDEN_DIR / "sd15_seed42.sha256"
+GOLDEN_PNG_PATH = GOLDEN_DIR / "sd15_seed42.png"
+GOLDEN_PSNR_MIN_DB = float(os.environ.get("GOLDEN_PSNR_MIN_DB", "20"))
+SEED = 42
+
+
+def _server_reachable() -> bool:
+    try:
+        with urllib.request.urlopen(f"{COMFY_URL}/prompt", timeout=3) as r:
+            return r.status == 200
+    except (urllib.error.URLError, urllib.error.HTTPError, ConnectionError):
+        return False
+
+
+@pytest.fixture(scope="module")
+def comfy_server():
+    if platform.machine() != "arm64":
+        pytest.skip("requires Apple Silicon")
+    if not _server_reachable():
+        pytest.skip(f"ComfyUI server not reachable at {COMFY_URL}")
+    return COMFY_URL
+
+
+def _http_post_json(path: str, payload: dict) -> dict:
+    data = json.dumps(payload).encode("utf-8")
+    req = urllib.request.Request(
+        f"{COMFY_URL}{path}", data=data,
+        headers={"Content-Type": "application/json"}, method="POST",
+    )
+    with urllib.request.urlopen(req, timeout=300) as r:
+        return json.loads(r.read().decode())
+
+
+def _http_get_json(path: str, timeout: int = 300) -> dict:
+    """ComfyUI runs UNet inference on its single asyncio loop, so GET /prompt
+    blocks while the queued prompt is executing. Use a generous timeout."""
+    with urllib.request.urlopen(f"{COMFY_URL}{path}", timeout=timeout) as r:
+        return json.loads(r.read().decode())
+
+
+def _drain_queue(timeout_s: int = 600) -> None:
+    deadline = time.time() + timeout_s
+    while time.time() < deadline:
+        try:
+            q = _http_get_json("/prompt")
+        except (urllib.error.URLError, TimeoutError):
+            # Transient block while server executes; retry until our overall
+            # deadline expires.
+            continue
+        if q.get("exec_info", {}).get("queue_remaining", -1) == 0:
+            return
+        time.sleep(2)
+    raise TimeoutError(f"queue did not drain within {timeout_s}s")
+
+
+def _post_workflow_and_collect_png() -> bytes:
+    workflow = json.loads(WORKFLOW_PATH.read_text())
+    for nid in ("4", "10"):
+        if nid in workflow:
+            workflow[nid]["inputs"]["ckpt_name"] = CKPT_NAME
+    for nid in ("3", "11"):
+        if nid in workflow and "seed" in workflow[nid].get("inputs", {}):
+            workflow[nid]["inputs"]["seed"] = SEED
+    # Drop the MPS reference branch — only the Core ML pipeline is needed here.
+    for nid in ("3", "8", "9"):
+        workflow.pop(nid, None)
+
+    _http_post_json("/prompt", {"prompt": workflow})
+    _drain_queue()
+
+    comfy_out = COMFY_DIR / "output"
+    matches = sorted(comfy_out.glob("E2E-1.5-CoreML_*.png"), reverse=True)
+    if not matches:
+        raise FileNotFoundError(f"no Core ML image under {comfy_out}")
+    return matches[0].read_bytes()
+
+
+def _psnr(a: np.ndarray, b: np.ndarray) -> float:
+    mse = float(np.mean((a.astype(np.float64) - b.astype(np.float64)) ** 2))
+    if mse == 0:
+        return 100.0
+    return 20.0 * float(np.log10(255.0 / np.sqrt(mse)))
+
+
+def test_sd15_seed42_image_matches_golden(comfy_server):
+    GOLDEN_DIR.mkdir(parents=True, exist_ok=True)
+    png_bytes = _post_workflow_and_collect_png()
+    sha = hashlib.sha256(png_bytes).hexdigest()
+
+    if not GOLDEN_HASH_PATH.exists() or not GOLDEN_PNG_PATH.exists():
+        GOLDEN_HASH_PATH.write_text(sha + "\n")
+        # Persist the PNG too for visual diffing + PSNR.
+        tmp_path = Path(__file__).parent / "_latest_generated.png"
+        tmp_path.write_bytes(png_bytes)
+        shutil.copy2(tmp_path, GOLDEN_PNG_PATH)
+        pytest.fail(
+            f"No golden present; wrote {GOLDEN_HASH_PATH.name} and "
+            f"{GOLDEN_PNG_PATH.name}. Review the image and re-run."
+        )
+
+    expected_hash = GOLDEN_HASH_PATH.read_text().strip()
+    if sha == expected_hash:
+        return
+
+    # Hash drift: fall back to PSNR to distinguish a refactor-safe rounding
+    # change from a real regression.
+    a = np.array(Image.open(GOLDEN_PNG_PATH).convert("RGB"))
+    b_path = Path(__file__).parent / "_latest_generated.png"
+    b_path.write_bytes(png_bytes)
+    b = np.array(Image.open(b_path).convert("RGB"))
+    if a.shape != b.shape:
+        pytest.fail(f"shape mismatch: golden={a.shape} actual={b.shape}")
+    psnr_db = _psnr(a, b)
+    assert psnr_db >= GOLDEN_PSNR_MIN_DB, (
+        f"hash drifted (got {sha[:12]}.., expected {expected_hash[:12]}..) and "
+        f"PSNR {psnr_db:.2f} dB < {GOLDEN_PSNR_MIN_DB} dB threshold; "
+        f"diff PNG at {b_path}"
+    )

tests/smoke/test_split_einsum_attention.py

@@ -0,0 +1,41 @@
+import platform
+
+import pytest
+import torch
+from diffusers.models.attention_processor import Attention, AttnProcessor
+
+from coreml_suite.conversion.attention import (
+    SplitEinsumAttnProcessor,
+    SplitEinsumV2AttnProcessor,
+)
+
+
+pytestmark = pytest.mark.skipif(
+    platform.system() != "Darwin" or platform.machine() != "arm64",
+    reason="Tier 1 requires macOS on Apple Silicon",
+)
+
+
+@pytest.mark.parametrize(
+    "processor",
+    [
+        SplitEinsumAttnProcessor(),
+        SplitEinsumV2AttnProcessor(),
+    ],
+)
+def test_split_einsum_processor_matches_diffusers_attention(processor):
+    torch.manual_seed(0)
+    reference = Attention(query_dim=32, heads=4, dim_head=8, dropout=0.0)
+    reference.set_processor(AttnProcessor())
+
+    candidate = Attention(query_dim=32, heads=4, dim_head=8, dropout=0.0)
+    candidate.load_state_dict(reference.state_dict())
+    candidate.set_processor(processor)
+
+    hidden_states = torch.randn(2, 17, 32)
+    encoder_hidden_states = torch.randn(2, 11, 32)
+
+    expected = reference(hidden_states, encoder_hidden_states=encoder_hidden_states)
+    actual = candidate(hidden_states, encoder_hidden_states=encoder_hidden_states)
+
+    assert torch.allclose(actual, expected, atol=1e-5)

tests/smoke/test_synthetic_unet.py

@@ -0,0 +1,138 @@
+"""Tier 1 smoke: convert a synthetic micro-UNet through coremltools and load
+it back with CoreMLSuite's runtime CoreMLModel wrapper.
+
+Purpose: catch API breakage in coremltools *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 CoreMLSuite's 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
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+import torch.nn as nn
+
+from coreml_suite.conversion.unet import CoreMLUNetWrapper
+
+
+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, 4, 64)  # native diffusers encoder_hidden_states (batch, tokens, hidden)
+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 CoreMLSuite's runtime 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,
+        timestep_cond=None,
+        added_cond_kwargs=None,
+        down_block_additional_residuals=None,
+        mid_block_additional_residual=None,
+        return_dict=True,
+    ):
+        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.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 = CoreMLUNetWrapper(
+        TinyUNet().eval(),
+        SimpleNamespace(name="SD15"),
+    )
+    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 coreml_suite.coreml_model import CoreMLModel
+
+    model = CoreMLModel(str(tiny_mlpackage), "CPU_ONLY")
+
+    # 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/test_latents.py

@@ -1,19 +0,0 @@
-import pytest
-
-import torch
-
-from coreml_suite.samplers import reshape_latent_image
-
-
-def test_fix_latents_no_latent_image():
-    reshaped = reshape_latent_image(None, (2, 4, 64, 64))
-    assert reshaped["samples"].shape == (2, 4, 64, 64)
-
-
-@pytest.mark.parametrize(
-    "latent_shape", [(2, 4, 64, 64), (2, 4, 128, 128), (2, 4, 32, 32), (2, 4, 128, 64)]
-)
-def test_reshape_latents(latent_shape):
-    latent_image = {"samples": torch.zeros(latent_shape)}
-    reshaped = reshape_latent_image(latent_image, (2, 4, 64, 64))
-    assert reshaped["samples"].shape == (2, 4, 64, 64)

tests/unit/test_characterization_controlnet.py

@@ -0,0 +1,186 @@
+"""Characterization tests for coreml_suite.controlnet.
+
+Locks shapes + dtypes + zero-fill behavior of expand_inputs / no_control /
+extract_residual_kwargs / chunk_control. These pure helpers feed the Core ML
+UNet's additional_residual_N inputs; any drift here silently breaks
+ControlNet-based workflows.
+"""
+import numpy as np
+import pytest
+import torch
+
+from coreml_suite.core.controlnet import (
+    chunk_control,
+    expand_inputs,
+    extract_residual_kwargs,
+    no_control,
+)
+
+
+@pytest.fixture(autouse=True)
+def _deterministic_seed():
+    torch.manual_seed(0)
+    np.random.seed(0)
+
+
+SD15_RESIDUAL_SPEC = {
+    "additional_residual_0": {"shape": (2, 320, 64, 64)},
+    "additional_residual_1": {"shape": (2, 640, 32, 32)},
+    "additional_residual_2": {"shape": (2, 1280, 8, 8)},
+}
+NON_RESIDUAL_SPEC = {
+    "sample": {"shape": (2, 4, 64, 64)},
+    "encoder_hidden_states": {"shape": (2, 77, 768)},
+}
+
+
+# ---------- expand_inputs ----------------------------------------------------
+
+
+def test_expand_inputs_doubles_singleton_numpy():
+    inputs = {"a": np.ones((1, 4), dtype=np.float32)}
+    out = expand_inputs(inputs)
+    assert out["a"].shape == (2, 4)
+    assert np.array_equal(out["a"], np.ones((2, 4)))
+
+
+def test_expand_inputs_doubles_singleton_torch():
+    inputs = {"a": torch.ones(1, 4)}
+    out = expand_inputs(inputs)
+    assert out["a"].shape == (2, 4)
+    assert torch.equal(out["a"], torch.ones(2, 4))
+
+
+def test_expand_inputs_doubles_singleton_list():
+    inputs = {"a": [42]}
+    out = expand_inputs(inputs)
+    assert out["a"] == [42, 42]
+
+
+def test_expand_inputs_skips_already_batched():
+    """batch > 1 inputs are returned unchanged (same object identity)."""
+    arr = np.ones((2, 4), dtype=np.float32)
+    tensor = torch.ones(3, 4)
+    lst = [1, 2]
+    out = expand_inputs({"a": arr, "b": tensor, "c": lst})
+    assert out["a"] is arr
+    assert out["b"] is tensor
+    assert out["c"] is lst
+
+
+def test_expand_inputs_preserves_unknown_value_types():
+    # Strings/None pass through untouched — locks current permissive contract.
+    inputs = {"s": "hello", "none": None, "int": 7}
+    out = expand_inputs(inputs)
+    assert out == {"s": "hello", "none": None, "int": 7}
+
+
+# ---------- no_control -------------------------------------------------------
+
+
+def test_no_control_returns_zero_fp16_for_residuals():
+    out = no_control({**SD15_RESIDUAL_SPEC, **NON_RESIDUAL_SPEC})
+    # Only additional_residual_* keys are produced.
+    assert set(out.keys()) == set(SD15_RESIDUAL_SPEC.keys())
+    for key, spec in SD15_RESIDUAL_SPEC.items():
+        arr = out[key]
+        assert arr.shape == spec["shape"]
+        assert arr.dtype == np.float16
+        assert np.all(arr == 0)
+
+
+def test_no_control_returns_empty_when_no_residuals():
+    out = no_control(NON_RESIDUAL_SPEC)
+    assert out == {}
+
+
+# ---------- extract_residual_kwargs -----------------------------------------
+
+
+def test_extract_residual_kwargs_empty_when_model_has_no_residual_inputs():
+    out = extract_residual_kwargs(NON_RESIDUAL_SPEC, control={"output": [], "middle": []})
+    assert out == {}
+
+
+def test_extract_residual_kwargs_none_control_returns_no_control_shapes():
+    out = extract_residual_kwargs(SD15_RESIDUAL_SPEC, control=None)
+    assert set(out.keys()) == set(SD15_RESIDUAL_SPEC.keys())
+    for key, spec in SD15_RESIDUAL_SPEC.items():
+        assert out[key].shape == spec["shape"]
+        assert out[key].dtype == np.float16
+        assert np.all(out[key] == 0)
+
+
+def test_extract_residual_kwargs_flattens_output_then_middle_and_casts_fp16():
+    """output residuals come first (indexed 0..N-1), then middle residuals
+    (indexed N..M-1). Values come out of CPU as fp16 numpy arrays."""
+    control = {
+        "output": [torch.ones(2, 320, 64, 64) * 0.5, torch.ones(2, 640, 32, 32) * 2.0],
+        "middle": [torch.ones(2, 1280, 8, 8) * -1.0],
+    }
+    out = extract_residual_kwargs(SD15_RESIDUAL_SPEC, control)
+    assert set(out.keys()) == {"additional_residual_0", "additional_residual_1", "additional_residual_2"}
+    assert out["additional_residual_0"].shape == (2, 320, 64, 64)
+    assert out["additional_residual_1"].shape == (2, 640, 32, 32)
+    assert out["additional_residual_2"].shape == (2, 1280, 8, 8)
+    for arr in out.values():
+        assert arr.dtype == np.float16
+    # Locked order: index 0 == first output residual (0.5), index 2 == middle (-1.0).
+    assert np.allclose(out["additional_residual_0"], 0.5)
+    assert np.allclose(out["additional_residual_1"], 2.0)
+    assert np.allclose(out["additional_residual_2"], -1.0)
+
+
+# ---------- chunk_control ----------------------------------------------------
+
+
+def test_chunk_control_none_returns_list_of_nones_with_length_target():
+    """`no_control` path: when there's no control, you get [None] * target_size
+    (NOT [None, None] regardless of target — this is the contract today)."""
+    assert chunk_control(None, 1) == [None]
+    assert chunk_control(None, 2) == [None, None]
+    assert chunk_control(None, 4) == [None, None, None, None]
+
+
+@pytest.mark.parametrize(
+    "batch,target,expected_chunks",
+    [(1, 2, 1), (2, 2, 1), (3, 2, 2), (4, 2, 2), (5, 3, 2), (9, 4, 3)],
+)
+def test_chunk_control_shapes_after_chunking(batch, target, expected_chunks):
+    cn = {
+        "output": [
+            torch.randn(batch, 320, 64, 64),
+            torch.randn(batch, 640, 32, 32),
+        ],
+        "middle": [torch.randn(batch, 1280, 8, 8)],
+    }
+    chunks = chunk_control(cn, target)
+    assert len(chunks) == expected_chunks
+    for c in chunks:
+        assert c["output"][0].shape == (target, 320, 64, 64)
+        assert c["output"][1].shape == (target, 640, 32, 32)
+        assert c["middle"][0].shape == (target, 1280, 8, 8)
+
+
+def test_chunk_control_preserves_keys_order():
+    """Output dicts contain exactly {"output", "middle"} in that order."""
+    cn = {
+        "output": [torch.zeros(2, 4, 4, 4)],
+        "middle": [torch.zeros(2, 4, 4, 4)],
+    }
+    chunks = chunk_control(cn, 2)
+    assert list(chunks[0].keys()) == ["output", "middle"]
+
+
+def test_chunk_control_zero_pads_remainder():
+    """A batch=3, target=2 split puts the third row alongside a zero row."""
+    cn = {
+        "output": [torch.arange(3 * 4).reshape(3, 1, 2, 2).float()],
+        "middle": [torch.arange(3 * 4).reshape(3, 1, 2, 2).float()],
+    }
+    chunks = chunk_control(cn, 2)
+    assert len(chunks) == 2
+    last_out = chunks[-1]["output"][0]
+    # First row is the original third row; second row is padding zeros.
+    assert torch.equal(last_out[0], cn["output"][0][2])
+    assert torch.equal(last_out[1], torch.zeros(1, 2, 2))

tests/unit/test_characterization_inputs.py

@@ -0,0 +1,228 @@
+"""Characterization tests for coreml_suite.models.CoreMLInputs.
+
+Locks the shape transforms applied by chunks() and coreml_kwargs() for the
+four model variants the suite supports: SD1.5, LCM (SD1.5 + timestep_cond),
+SDXL base (time_ids len 6), and SDXL refiner (time_ids len 5).
+
+These contracts feed the Core ML UNet at runtime; if a refactor silently
+re-shapes them, generation breaks.
+"""
+import numpy as np
+import pytest
+import torch
+
+from coreml_suite.core.inputs import CoreMLInputs
+
+
+@pytest.fixture(autouse=True)
+def _deterministic_seed():
+    torch.manual_seed(0)
+    np.random.seed(0)
+
+
+# ---------- expected_inputs fixtures (mirror real model expectations) -------
+
+SD15_EXPECTED = {
+    "sample": {"shape": (2, 4, 64, 64)},
+    "timestep": {"shape": (2,)},
+    "encoder_hidden_states": {"shape": (2, 77, 768)},
+}
+
+SD15_WITH_CN = {
+    **SD15_EXPECTED,
+    "additional_residual_0": {"shape": (2, 320, 64, 64)},
+    "additional_residual_1": {"shape": (2, 640, 32, 32)},
+}
+
+LCM_EXPECTED = {
+    **SD15_EXPECTED,
+    "timestep_cond": {"shape": (2, 256)},
+}
+
+SDXL_BASE_EXPECTED = {
+    "sample": {"shape": (2, 4, 128, 128)},
+    "timestep": {"shape": (2,)},
+    "encoder_hidden_states": {"shape": (2, 77, 2048)},
+    "time_ids": {"shape": (2, 6)},
+    "text_embeds": {"shape": (2, 1280)},
+}
+
+SDXL_REFINER_EXPECTED = {
+    "sample": {"shape": (2, 4, 128, 128)},
+    "timestep": {"shape": (2,)},
+    "encoder_hidden_states": {"shape": (2, 77, 1280)},
+    "time_ids": {"shape": (2, 5)},
+    "text_embeds": {"shape": (2, 1280)},
+}
+
+
+def _sd15_inputs(batch=1, with_control=False, with_ts_cond=False):
+    x = torch.randn(batch, 4, 64, 64)
+    t = torch.full((batch,), 999.0)
+    context = torch.randn(batch, 77, 768)
+    control = None
+    if with_control:
+        control = {
+            "output": [torch.randn(batch, 320, 64, 64), torch.randn(batch, 640, 32, 32)],
+            "middle": [],
+        }
+    kwargs = {}
+    if with_ts_cond:
+        kwargs["timestep_cond"] = torch.randn(batch, 256)
+    return CoreMLInputs(x, t, context, control, **kwargs)
+
+
+def _sdxl_inputs(batch=1, refiner=False):
+    x = torch.randn(batch, 4, 128, 128)
+    t = torch.full((batch,), 999.0)
+    ctx_dim = 1280 if refiner else 2048
+    context = torch.randn(batch, 77, ctx_dim)
+    time_ids_dim = 5 if refiner else 6
+    time_ids = torch.randn(batch, time_ids_dim)
+    text_embeds = torch.randn(batch, 1280)
+    return CoreMLInputs(
+        x, t, context, control=None, time_ids=time_ids, text_embeds=text_embeds
+    )
+
+
+# ---------- coreml_kwargs ---------------------------------------------------
+
+
+def test_coreml_kwargs_sd15_shapes_and_fp16():
+    out = _sd15_inputs(batch=1).coreml_kwargs(SD15_EXPECTED)
+    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)
+    assert out["encoder_hidden_states"].dtype == np.float16
+    assert out["timestep"].shape == (1,)
+    assert out["timestep"].dtype == np.float16
+
+
+def test_coreml_kwargs_sd15_with_controlnet_emits_residuals():
+    inputs = _sd15_inputs(batch=1, with_control=True)
+    out = inputs.coreml_kwargs(SD15_WITH_CN)
+    assert "additional_residual_0" in out
+    assert "additional_residual_1" in out
+    assert out["additional_residual_0"].shape == (1, 320, 64, 64)
+    assert out["additional_residual_1"].shape == (1, 640, 32, 32)
+
+
+def test_coreml_kwargs_sd15_without_controlnet_zero_fills_residuals():
+    inputs = _sd15_inputs(batch=1, with_control=False)
+    out = inputs.coreml_kwargs(SD15_WITH_CN)
+    assert np.all(out["additional_residual_0"] == 0)
+    assert np.all(out["additional_residual_1"] == 0)
+
+
+def test_coreml_kwargs_lcm_adds_timestep_cond():
+    inputs = _sd15_inputs(batch=1, with_ts_cond=True)
+    out = inputs.coreml_kwargs(LCM_EXPECTED)
+    assert "timestep_cond" in out
+    assert out["timestep_cond"].shape == (1, 256)
+    assert out["timestep_cond"].dtype == np.float16
+
+
+def test_coreml_kwargs_lcm_skips_timestep_cond_when_not_provided():
+    """timestep_cond is only forwarded when the input supplied one — even if
+    the model's expected_inputs lists it."""
+    inputs = _sd15_inputs(batch=1, with_ts_cond=False)
+    out = inputs.coreml_kwargs(LCM_EXPECTED)
+    assert "timestep_cond" not in out
+
+
+def test_coreml_kwargs_sdxl_base_emits_time_ids_and_text_embeds():
+    out = _sdxl_inputs(batch=1, refiner=False).coreml_kwargs(SDXL_BASE_EXPECTED)
+    assert out["time_ids"].shape == (1, 6)
+    assert out["text_embeds"].shape == (1, 1280)
+    assert out["time_ids"].dtype == np.float16
+    assert out["text_embeds"].dtype == np.float16
+
+
+def test_coreml_kwargs_sdxl_refiner_uses_len5_time_ids():
+    out = _sdxl_inputs(batch=1, refiner=True).coreml_kwargs(SDXL_REFINER_EXPECTED)
+    assert out["time_ids"].shape == (1, 5)
+
+
+# ---------- chunks ----------------------------------------------------------
+
+
+def test_chunks_sd15_pad_to_batch2_returns_one_chunk():
+    chunked = _sd15_inputs(batch=1).chunks(SD15_EXPECTED)
+    assert len(chunked) == 1
+    c = chunked[0]
+    assert c.x.shape == (2, 4, 64, 64)
+    assert c.t.shape == (2,)
+    # context shape: (b, seq, dim) padded along batch dim.
+    assert c.context.shape == (2, 77, 768)
+    assert c.control is None
+    assert c.ts_cond is None
+    assert c.time_ids is None
+    assert c.text_embeds is None
+
+
+def test_chunks_sd15_with_controlnet_chunks_residuals_too():
+    chunked = _sd15_inputs(batch=1, with_control=True).chunks(SD15_EXPECTED)
+    assert len(chunked) == 1
+    cn = chunked[0].control
+    assert cn is not None
+    assert cn["output"][0].shape == (2, 320, 64, 64)
+    assert cn["output"][1].shape == (2, 640, 32, 32)
+
+
+def test_chunks_lcm_carries_timestep_cond_per_chunk():
+    chunked = _sd15_inputs(batch=1, with_ts_cond=True).chunks(LCM_EXPECTED)
+    assert len(chunked) == 1
+    assert chunked[0].ts_cond is not None
+    assert chunked[0].ts_cond.shape == (2, 256)
+
+
+def test_chunks_sdxl_base_propagates_time_ids_and_text_embeds():
+    chunked = _sdxl_inputs(batch=1, refiner=False).chunks(SDXL_BASE_EXPECTED)
+    assert len(chunked) == 1
+    c = chunked[0]
+    assert c.time_ids is not None and c.time_ids.shape == (2, 6)
+    assert c.text_embeds is not None and c.text_embeds.shape == (2, 1280)
+
+
+def test_chunks_sdxl_refiner_uses_len5_time_ids():
+    chunked = _sdxl_inputs(batch=1, refiner=True).chunks(SDXL_REFINER_EXPECTED)
+    assert chunked[0].time_ids.shape == (2, 5)
+
+
+def test_chunks_sdxl_synthesizes_zero_time_ids_when_caller_omits():
+    """If the model expects time_ids but caller passed nothing, the suite
+    fabricates a zero-filled tensor. Lock that fallback."""
+    x = torch.randn(1, 4, 128, 128)
+    t = torch.full((1,), 999.0)
+    context = torch.randn(1, 77, 2048)
+    inputs = CoreMLInputs(x, t, context, control=None)
+    chunked = inputs.chunks(SDXL_BASE_EXPECTED)
+    assert chunked[0].time_ids.shape == (2, 6)
+    assert torch.equal(chunked[0].time_ids, torch.zeros(2, 6))
+    assert chunked[0].text_embeds.shape == (2, 1280)
+    assert torch.equal(chunked[0].text_embeds, torch.zeros(2, 1280))
+
+
+def test_chunks_splits_batch_into_multiple_target2_chunks():
+    """batch=5 with target_batch=2 -> 3 chunks (last padded)."""
+    chunked = _sd15_inputs(batch=5).chunks(SD15_EXPECTED)
+    assert len(chunked) == 3
+    for c in chunked:
+        assert c.x.shape == (2, 4, 64, 64)
+        assert c.context.shape == (2, 77, 768)
+    # Last chunk's second batch row is the zero-pad.
+    assert torch.equal(chunked[-1].x[1], torch.zeros(4, 64, 64))
+
+
+def test_chunks_timestep_is_broadcast_from_first_value():
+    """t is rebuilt from t[0] across all chunks: locks current behavior that
+    discards any per-row timestep variation."""
+    x = torch.randn(2, 4, 64, 64)
+    t = torch.tensor([42.0, 99.0])  # the second value will be lost
+    context = torch.randn(2, 77, 768)
+    inputs = CoreMLInputs(x, t, context, control=None)
+    chunked = inputs.chunks(SD15_EXPECTED)
+    assert chunked[0].t.shape == (2,)
+    assert torch.equal(chunked[0].t, torch.full((2,), 42.0))

tests/unit/test_characterization_latents.py

@@ -0,0 +1,118 @@
+"""Characterization tests for coreml_suite.latents.
+
+Locks the *current* behavior of chunk_batch / merge_chunks — including the
+zero-pad regions and the truncation in merge — so a refactor
+cannot silently shift either contract.
+"""
+import pytest
+import torch
+
+from coreml_suite.core.latents import chunk_batch, merge_chunks
+
+
+@pytest.fixture(autouse=True)
+def _deterministic_seed():
+    torch.manual_seed(0)
+
+
+def _const_tensor(batch, *rest):
+    return torch.arange(batch * 4 * 8 * 8, dtype=torch.float32).reshape(batch, 4, 8, 8)
+
+
+# ---------- chunk_batch ------------------------------------------------------
+
+
+def test_chunk_batch_passthrough_when_shape_matches():
+    x = _const_tensor(2)
+    out = chunk_batch(x, (2, 4, 8, 8))
+    assert len(out) == 1
+    # passthrough: the same object identity is returned (no copy).
+    assert out[0] is x
+
+
+def test_chunk_batch_pads_single_chunk_when_input_smaller():
+    """batch=1, target=2 -> one padded chunk; the second row is exact zero."""
+    x = _const_tensor(1)
+    out = chunk_batch(x, (2, 4, 8, 8))
+    assert len(out) == 1
+    assert out[0].shape == (2, 4, 8, 8)
+    assert torch.equal(out[0][0], x[0])
+    assert torch.equal(out[0][1], torch.zeros(4, 8, 8))
+
+
+def test_chunk_batch_splits_exact_multiple():
+    """batch=4, target=2 -> two chunks, no padding."""
+    x = _const_tensor(4)
+    out = chunk_batch(x, (2, 4, 8, 8))
+    assert len(out) == 2
+    assert out[0].shape == (2, 4, 8, 8)
+    assert out[1].shape == (2, 4, 8, 8)
+    assert torch.equal(out[0], x[:2])
+    assert torch.equal(out[1], x[2:])
+
+
+def test_chunk_batch_pads_remainder_chunk():
+    """batch=5, target=2 -> chunks=[x[0:2], x[2:4]] then [x[4], 0]."""
+    x = _const_tensor(5)
+    out = chunk_batch(x, (2, 4, 8, 8))
+    assert len(out) == 3
+    assert torch.equal(out[0], x[0:2])
+    assert torch.equal(out[1], x[2:4])
+    last = out[-1]
+    assert last.shape == (2, 4, 8, 8)
+    assert torch.equal(last[0], x[4])
+    # The remainder row is zero-padded; lock that exact contract.
+    assert torch.equal(last[1], torch.zeros(4, 8, 8))
+    assert last[1].sum() == 0
+
+
+@pytest.mark.parametrize(
+    "batch_size,target,expected_chunks",
+    [
+        (1, 4, 1),
+        (3, 2, 2),
+        (5, 3, 2),
+        (9, 4, 3),
+    ],
+)
+def test_chunk_batch_pad_region_is_zero(batch_size, target, expected_chunks):
+    x = _const_tensor(batch_size)
+    out = chunk_batch(x, (target, 4, 8, 8))
+    assert len(out) == expected_chunks
+    mod = batch_size % target
+    if mod == 0 and batch_size >= target:
+        return
+    last = out[-1]
+    pad_rows = target - (mod if (mod != 0 and batch_size >= target) else batch_size)
+    pad_region = last[-pad_rows:]
+    assert torch.equal(pad_region, torch.zeros_like(pad_region))
+
+
+# ---------- merge_chunks -----------------------------------------------------
+
+
+def test_merge_chunks_exact_concat():
+    x = _const_tensor(4)
+    chunks = chunk_batch(x, (2, 4, 8, 8))
+    merged = merge_chunks(chunks, x.shape)
+    assert merged.shape == x.shape
+    assert torch.equal(merged, x)
+
+
+def test_merge_chunks_truncates_padding():
+    """Round-trip with a padded last chunk drops the pad rows."""
+    x = _const_tensor(5)
+    chunks = chunk_batch(x, (2, 4, 8, 8))
+    merged = merge_chunks(chunks, x.shape)
+    assert merged.shape == x.shape
+    assert torch.equal(merged, x)
+
+
+def test_merge_chunks_singleton_returns_equal_copy_when_shape_matches():
+    """A singleton chunk list still goes through torch.cat, so we get a new
+    tensor equal to the input — locked here because a refactor might be tempted
+    to short-circuit and accidentally return the same object."""
+    x = _const_tensor(2)
+    out = merge_chunks([x], x.shape)
+    assert torch.equal(out, x)
+    assert out is not x

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

@@ -0,0 +1,127 @@
+"""Characterization tests for the SDXL options math.
+
+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.
+"""
+import inspect
+
+import pytest
+import torch
+
+from coreml_suite.core.sdxl import (
+    build_sdxl_text_embeds,
+    build_sdxl_time_ids,
+    sdxl_model_function_wrapper,
+)
+
+
+@pytest.fixture(autouse=True)
+def _deterministic_seed():
+    torch.manual_seed(0)
+
+
+# ---------- build_sdxl_time_ids: base (len 6) -------------------------------
+
+
+def test_build_time_ids_base_defaults():
+    out = build_sdxl_time_ids({}, {}, is_base=True, is_refiner=False)
+    expected = torch.tensor([[768, 768, 0, 0, 768, 768], [768, 768, 0, 0, 768, 768]])
+    assert out.shape == (2, 6)
+    assert torch.equal(out, expected)
+
+
+def test_build_time_ids_base_respects_overrides():
+    pos = {"height": 1024, "width": 512, "crop_h": 8, "crop_w": 4,
+           "target_height": 1024, "target_width": 1024}
+    neg = {"height": 256, "width": 256, "crop_h": 0, "crop_w": 0,
+           "target_height": 256, "target_width": 256}
+    out = build_sdxl_time_ids(pos, neg, is_base=True, is_refiner=False)
+    expected = torch.tensor([[1024, 512, 8, 4, 1024, 1024], [256, 256, 0, 0, 256, 256]])
+    assert torch.equal(out, expected)
+
+
+# ---------- build_sdxl_time_ids: refiner (len 5) ----------------------------
+
+
+def test_build_time_ids_refiner_defaults():
+    out = build_sdxl_time_ids({}, {}, is_base=False, is_refiner=True)
+    expected = torch.tensor([[768, 768, 0, 0, 6.0], [768, 768, 0, 0, 2.5]])
+    assert out.shape == (2, 5)
+    assert torch.equal(out, expected)
+
+
+def test_build_time_ids_refiner_respects_aesthetic_score():
+    pos = {"aesthetic_score": 8.5}
+    neg = {"aesthetic_score": 1.5}
+    out = build_sdxl_time_ids(pos, neg, is_base=False, is_refiner=True)
+    expected = torch.tensor([[768, 768, 0, 0, 8.5], [768, 768, 0, 0, 1.5]])
+    assert torch.equal(out, expected)
+
+
+# ---------- build_sdxl_time_ids: edge case ----------------------------------
+
+
+def test_build_time_ids_neither_base_nor_refiner_returns_len4():
+    out = build_sdxl_time_ids({}, {}, is_base=False, is_refiner=False)
+    assert out.shape == (2, 4)
+
+
+# ---------- build_sdxl_text_embeds ------------------------------------------
+
+
+def test_text_embeds_concat_pos_then_neg():
+    pos = torch.full((1, 1280), 1.0)
+    neg = torch.full((1, 1280), -1.0)
+    out = build_sdxl_text_embeds(pos, neg)
+    assert out.shape == (2, 1280)
+    assert torch.equal(out[0], pos[0])
+    assert torch.equal(out[1], neg[0])
+
+
+# ---------- sdxl_model_function_wrapper closure -----------------------------
+
+
+def test_wrapper_captures_time_ids_text_embeds_refiner_via_closure():
+    time_ids = torch.zeros(2, 6)
+    text_embeds = torch.zeros(2, 1280)
+    wrapper = sdxl_model_function_wrapper(time_ids, text_embeds, refiner=False)
+    closure = inspect.getclosurevars(wrapper).nonlocals
+    assert closure["time_ids"] is time_ids
+    assert closure["text_embeds"] is text_embeds
+    assert closure["refiner"] is False
+
+
+def test_wrapper_returns_zero_when_context_missing():
+    """When c_crossattn is None the wrapper short-circuits to zeros_like(x).
+    Locked here because the refactor mustn't change this default."""
+    wrapper = sdxl_model_function_wrapper(torch.zeros(2, 6), torch.zeros(2, 1280))
+    x = torch.randn(2, 4, 16, 16)
+    out = wrapper(
+        model_function=lambda *a, **kw: pytest.fail("model_function must not run"),
+        params={"input": x, "timestep": torch.zeros(2), "c": {}},
+    )
+    assert torch.equal(out, torch.zeros_like(x))
+
+
+def test_wrapper_refiner_truncates_context_to_g_clip():
+    """refiner=True slices c_crossattn[:, :, 768:] before forwarding."""
+    captured = {}
+
+    def fake_model(x, t, **c):
+        captured["context_shape"] = c["c_crossattn"].shape
+        captured["time_ids_shape"] = c["time_ids"].shape
+        return x
+
+    wrapper = sdxl_model_function_wrapper(
+        torch.zeros(2, 5), torch.zeros(2, 1280), refiner=True
+    )
+    x = torch.randn(2, 4, 16, 16)
+    context = torch.randn(2, 77, 2048)  # 768 + 1280 dims
+    wrapper(
+        model_function=fake_model,
+        params={"input": x, "timestep": torch.zeros(2), "c": {"c_crossattn": context}},
+    )
+    assert captured["context_shape"] == (2, 77, 1280)
+    assert captured["time_ids_shape"] == (2, 5)

tests/unit/test_chunks.py

@@ -0,0 +1,122 @@
+"""Smoke tests for the pure batch-chunking helpers in coreml_suite.core.
+
+Uses torch.device('cpu') instead of comfy.model_management.get_torch_device
+so Tier 0 runs without ComfyUI.
+"""
+import pytest
+import torch
+
+from coreml_suite.core.controlnet import chunk_control
+from coreml_suite.core.inputs import CoreMLInputs
+from coreml_suite.core.latents import chunk_batch, merge_chunks
+
+
+CPU = torch.device("cpu")
+
+
+@pytest.fixture
+def expected_inputs():
+    return {
+        "sample": {"shape": (2, 4, 64, 64)},
+        "timestep": {"shape": (2,)},
+        "timestep_cond": {"shape": (2, 256)},
+        "encoder_hidden_states": {"shape": (2, 77, 768)},
+        "additional_residual_0": {"shape": (2, 320, 64, 64)},
+        "additional_residual_1": {"shape": (2, 640, 32, 32)},
+    }
+
+
+@pytest.mark.parametrize("batch_size", [1, 2, 4, 5, 9])
+def test_batch_chunking(batch_size):
+    latent_image = torch.randn(batch_size, 4, 64, 64).to(CPU)
+    target_shape = (4, 4, 64, 64)
+
+    chunked = chunk_batch(latent_image, target_shape)
+
+    for chunk in chunked:
+        assert chunk.shape == target_shape
+
+    if batch_size % target_shape[0] != 0:
+        assert chunked[-1][batch_size % target_shape[0] :].sum() == 0
+
+
+@pytest.mark.parametrize("batch_size", [1, 2, 4, 5, 9])
+def test_merge_chunks(batch_size):
+    input_tensor = torch.randn(batch_size, 4, 64, 64).to(CPU)
+    target_shape = (4, 4, 64, 64)
+    chunked = chunk_batch(input_tensor, target_shape)
+
+    merged = merge_chunks(chunked, input_tensor.shape)
+
+    assert merged.shape == input_tensor.shape
+    assert torch.equal(input_tensor, merged)
+
+
+@pytest.fixture
+def inputs():
+    x = torch.randn(1, 4, 64, 64).to(CPU)
+    t = torch.randn([1]).to(CPU)
+    c_crossattn = torch.randn(1, 77, 768).to(CPU)
+    control = {
+        "output": [
+            torch.randn(1, 320, 64, 64).to(CPU),
+            torch.randn(1, 640, 32, 32).to(CPU),
+        ],
+    }
+    timestep_cond = torch.randn(1, 256).to(CPU)
+
+    return CoreMLInputs(x, t, c_crossattn, control, timestep_cond=timestep_cond)
+
+
+@pytest.mark.parametrize(
+    "b, target_size, num_chunks",
+    [
+        (1, 2, 1),
+        (1, 1, 1),
+        (2, 2, 1),
+        (3, 2, 2),
+        (4, 2, 2),
+        (5, 3, 2),
+        (9, 4, 3),
+    ],
+)
+def test_chunking_controlnet(b, target_size, num_chunks):
+    cn = {
+        "output": [
+            torch.randn(b, 320, 64, 64).to(CPU),
+            torch.randn(b, 640, 32, 32).to(CPU),
+        ],
+        "middle": [
+            torch.randn(b, 1280, 8, 8).to(CPU),
+        ],
+    }
+
+    chunked = chunk_control(cn, target_size)
+
+    assert len(chunked) == num_chunks
+    for chunk in chunked:
+        assert chunk["output"][0].shape == (target_size, 320, 64, 64)
+        assert chunk["output"][1].shape == (target_size, 640, 32, 32)
+        assert chunk["middle"][0].shape == (target_size, 1280, 8, 8)
+
+
+def test_chunking_no_control():
+    cn = None
+    target_size = 2
+
+    chunked = chunk_control(cn, target_size)
+
+    assert chunked == [None, None]
+
+
+def test_chunking_inputs(expected_inputs, inputs):
+    chunked = inputs.chunks(expected_inputs)
+
+    assert len(chunked) == 1
+
+    assert chunked[0].x.shape == (2, 4, 64, 64)
+    assert chunked[0].t.shape == (2,)
+    assert chunked[0].context.shape == (2, 77, 768)
+    assert chunked[0].control["output"][0].shape == (2, 320, 64, 64)
+    assert chunked[0].control["output"][1].shape == (2, 640, 32, 32)
+    assert chunked[0].ts_cond.shape == (2, 256)

tests/unit/test_controlnet.py

@@ -0,0 +1,16 @@
+from coreml_suite.controlnet import no_control
+
+
+def test_no_control():
+    expected_inputs = {
+        "additional_residual_0": {"shape": (2, 2, 2)},
+        "additional_residual_1": {"shape": (2, 4, 4)},
+        "additional_residual_2": {"shape": (2, 8, 8)},
+    }
+
+    residual_kwargs = no_control(expected_inputs)
+
+    assert len(residual_kwargs) == 3
+    assert residual_kwargs["additional_residual_0"].shape == (2, 2, 2)
+    assert residual_kwargs["additional_residual_1"].shape == (2, 4, 4)
+    assert residual_kwargs["additional_residual_2"].shape == (2, 8, 8)

tests/unit/test_conversion_helpers.py

@@ -0,0 +1,183 @@
+from types import SimpleNamespace
+
+import torch
+
+from coreml_suite.conversion.attention import (
+    SplitEinsumAttnProcessor,
+    SplitEinsumV2AttnProcessor,
+    apply_attention_implementation,
+    split_einsum,
+    split_einsum_v2,
+)
+from coreml_suite.conversion.shapes import conv2d_output_shape
+from coreml_suite.conversion.unet import CoreMLUNetWrapper
+
+
+class RecordingUNet(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.call = None
+
+    def forward(
+        self,
+        sample,
+        timestep,
+        encoder_hidden_states,
+        timestep_cond=None,
+        added_cond_kwargs=None,
+        down_block_additional_residuals=None,
+        mid_block_additional_residual=None,
+        return_dict=True,
+        **kwargs,
+    ):
+        self.call = {
+            "sample": sample,
+            "timestep": timestep,
+            "encoder_hidden_states": encoder_hidden_states,
+            "timestep_cond": timestep_cond,
+            "added_cond_kwargs": added_cond_kwargs,
+            "down_block_additional_residuals": down_block_additional_residuals,
+            "mid_block_additional_residual": mid_block_additional_residual,
+            "return_dict": return_dict,
+        }
+        return (sample + 1,)
+
+
+def test_conv2d_output_shape_matches_torch_conv2d_contract():
+    conv = torch.nn.Conv2d(
+        4,
+        8,
+        kernel_size=(3, 5),
+        stride=(2, 3),
+        padding=(1, 2),
+        dilation=(1, 2),
+    )
+
+    assert conv2d_output_shape(17, 19, conv) == (9, 5)
+
+
+def test_unet_wrapper_passes_context_through_for_sd15():
+    unet = RecordingUNet()
+    wrapper = CoreMLUNetWrapper(unet, SimpleNamespace(name="SD15"))
+
+    sample = torch.randn(2, 4, 8, 8)
+    timestep = torch.randn(2)
+    context = torch.randn(2, 77, 768)
+
+    out = wrapper(sample, timestep, context)
+
+    assert torch.equal(out, sample + 1)
+    assert unet.call["encoder_hidden_states"] is context
+    assert unet.call["return_dict"] is False
+
+
+def test_unet_wrapper_routes_lcm_sdxl_and_controlnet_inputs():
+    unet = RecordingUNet()
+    wrapper = CoreMLUNetWrapper(unet, SimpleNamespace(name="LCM"))
+
+    sample = torch.randn(1, 4, 8, 8)
+    timestep = torch.randn(1)
+    context = torch.randn(1, 77, 768)
+    timestep_cond = torch.randn(1, 256)
+    down_residual = torch.randn(1, 320, 8, 8)
+    mid_residual = torch.randn(1, 1280, 1, 1)
+
+    wrapper(sample, timestep, context, timestep_cond, down_residual, mid_residual)
+
+    assert unet.call["timestep_cond"] is timestep_cond
+    assert len(unet.call["down_block_additional_residuals"]) == 1
+    assert unet.call["down_block_additional_residuals"][0] is down_residual
+    assert unet.call["mid_block_additional_residual"] is mid_residual
+
+
+def test_unet_wrapper_routes_sdxl_added_conditioning():
+    unet = RecordingUNet()
+    wrapper = CoreMLUNetWrapper(unet, SimpleNamespace(name="SDXL"))
+
+    sample = torch.randn(1, 4, 8, 8)
+    timestep = torch.randn(1)
+    context = torch.randn(1, 77, 2048)
+    time_ids = torch.randn(1, 6)
+    text_embeds = torch.randn(1, 1280)
+
+    wrapper(sample, timestep, context, time_ids, text_embeds)
+
+    assert unet.call["added_cond_kwargs"]["time_ids"] is time_ids
+    assert unet.call["added_cond_kwargs"]["text_embeds"] is text_embeds
+
+
+def test_split_einsum_matches_original_attention_math():
+    torch.manual_seed(0)
+    batch = 2
+    heads = 3
+    dim_head = 4
+    sequence = 16
+    channels = heads * dim_head
+    q = torch.randn(batch, channels, 1, sequence)
+    k = torch.randn(batch, channels, 1, sequence)
+    v = torch.randn(batch, channels, 1, sequence)
+
+    expected = _original_attention(q, k, v, None, heads, dim_head)
+
+    # split-einsum reorders the float32 reductions vs the reference, so equality
+    # only holds up to rounding; the drift exceeds allclose's default atol on
+    # some BLAS backends (e.g. Linux x86 CI).
+    assert torch.allclose(split_einsum(q, k, v, None, heads, dim_head), expected, atol=1e-6)
+    assert torch.allclose(split_einsum_v2(q, k, v, None, heads, dim_head), expected, atol=1e-6)
+
+
+def test_split_einsum_v2_chunked_path_matches_original_attention_math():
+    torch.manual_seed(0)
+    batch = 1
+    heads = 2
+    dim_head = 2
+    sequence = 512
+    channels = heads * dim_head
+    q = torch.randn(batch, channels, 1, sequence)
+    k = torch.randn(batch, channels, 1, sequence)
+    v = torch.randn(batch, channels, 1, sequence)
+
+    expected = _original_attention(q, k, v, None, heads, dim_head)
+
+    assert torch.allclose(
+        split_einsum_v2(q, k, v, None, heads, dim_head),
+        expected,
+        atol=1e-6,
+    )
+
+
+def test_apply_attention_implementation_sets_split_processors():
+    unet = RecordingProcessorUNet()
+
+    assert apply_attention_implementation(unet, "ORIGINAL") is unet
+    assert unet.processor is None
+
+    apply_attention_implementation(unet, "SPLIT_EINSUM")
+    assert isinstance(unet.processor, SplitEinsumAttnProcessor)
+
+    apply_attention_implementation(unet, "SPLIT_EINSUM_V2")
+    assert isinstance(unet.processor, SplitEinsumV2AttnProcessor)
+
+
+class RecordingProcessorUNet:
+    def __init__(self):
+        self.processor = None
+
+    def set_attn_processor(self, processor):
+        self.processor = processor
+
+
+def _original_attention(q, k, v, mask, heads, dim_head):
+    batch = q.size(0)
+    mh_q = q.view(batch, heads, dim_head, -1)
+    mh_k = k.view(batch, heads, dim_head, -1)
+    mh_v = v.view(batch, heads, dim_head, -1)
+
+    weights = torch.einsum("bhcq,bhck->bhqk", mh_q, mh_k)
+    weights = weights * (dim_head**-0.5)
+    if mask is not None:
+        weights = weights + mask
+    weights = weights.softmax(dim=3)
+
+    attn = torch.einsum("bhqk,bhck->bhcq", weights, mh_v)
+    return attn.contiguous().view(batch, heads * dim_head, 1, -1)

tests/unit/test_tier0_purity.py

@@ -0,0 +1,43 @@
+"""Gate: prove the Tier-0 lane is framework-free.
+
+In a pure `pytest -m unit` run, none of the banned runtime modules
+(comfy, coremltools, python_coreml_stable_diffusion, folder_paths,
+nodes, comfy_extras, diffusers, diffusionkit) may be in sys.modules
+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.
+"""
+import sys
+
+import pytest
+
+BANNED_ROOTS = {
+    "comfy",
+    "comfy_extras",
+    "coremltools",
+    "python_coreml_stable_diffusion",
+    "folder_paths",
+    "nodes",
+    "diffusers",
+    "diffusionkit",
+}
+
+
+def test_no_framework_modules_loaded_by_unit_tier(request):
+    markexpr = request.config.option.markexpr
+    if markexpr != "unit":
+        pytest.skip(
+            "purity gate only meaningful in a pure `-m unit` run "
+            f"(got markexpr={markexpr!r}); other tiers are expected to "
+            "import comfy/coremltools."
+        )
+    loaded = {name for name in sys.modules if name.split(".")[0] in BANNED_ROOTS}
+    assert not loaded, (
+        f"Tier-0 leakage: these framework modules are in sys.modules after "
+        f"collecting tests/unit/: {sorted(loaded)}. Pure-core promise broken."
+    )