aszc-dev
8f94f0eea5
Rewrite the README as a lean landing page and move depth into a docs/ folder. Correct the supported-model story and several stale facts, and answer the recurring questions from issue #21. - Convert-only is the supported path: suite-converted .mlpackage is the only supported input; drop coreml-community download guidance. - Remove all .mlmodelc / Xcode references — compilation was dropped and the loader handles .mlpackage only. - Fix compute-unit name (CPU_AND_NE, not CPU_AND_ANE) and the loader input name (coreml_name). - Document CoreMLSamplerAdvanced (previously undocumented). - Add docs/: hardware, nodes, conversion, workflows, faq, troubleshooting, limitations (with a support matrix). - Note conversion now lives in the coreml-diffusion package. - Remove dev scaffolding specs; ignore *.log, .DS_Store, .claude/.
2.7 KiB
Limitations & Support Matrix
Support matrix
| Feature | Status | Notes |
|---|---|---|
| SD1.5 | ✅ Full | ANE via SPLIT_EINSUM; the primary, fastest path |
| SDXL / SDXL Turbo | ⚠️ Partial | GPU only (no ANE), no speedup; possible quality loss vs source. Don't run Turbo at 1024² |
| SD2.1 | ❌ Unsupported | |
| Inpainting checkpoints (9-channel) | ❌ Unsupported | |
| ControlNet | ✅ Supported | Convert the checkpoint with controlnet_support = True |
| LoRA | ⚠️ Experimental | Inconsistent per-LoRA; baked at conversion, immutable afterward |
| LCM | ⚠️ Experimental | Hardcoded to LCM Dreamshaper v7 |
| SVD | ❌ Not supported | |
| AnimateDiff | ❌ Not supported | Motion modules need pre-conversion injection; not feasible today |
| IPAdapter | ❌ Not supported | Needs a real MODEL the Core ML wrapper can't provide |
| Core ML Adapter | ⚠️ Experimental | Works for many nodes; fails for merges/IPAdapter/etc. |
Fixed input/output shapes
A Core ML model is converted for one specific resolution and batch size. To work at a different size, re-convert with the new width/height (conversion is cheap and cached by name). This is also why detailers and latent-upscale workflows that rescale mid-graph break — see troubleshooting.
There is experimental support for flexible shapes via EnumeratedShapes, but it is much slower — user benchmarks show roughly 5× the per-iteration time on every run, not just the first. Fixed-shape models per resolution are the practical choice.
SDXL on the Neural Engine
SDXL and SDXL Turbo cannot run on the ANE — the dual-text-encoder UNet exceeds the
supported Neural Engine path. They run on the GPU at roughly MPS-equivalent speed,
so Core ML offers no speed advantage for SDXL, and converted output may look
degraded versus the safetensors original (an upstream conversion artifact). Use
ORIGINAL + CPU_AND_GPU. See hardware.
Experimental Core ML Adapter
The Adapter wraps a Core ML model to look like a standard ComfyUI MODEL, which
covers many standard and custom nodes. But it can't fully emulate a real model:
operations that need genuine MODEL internals — model merges, IPAdapter, some
LoRA flows, detailers without the size hook — generally won't work, and the model's
fixed input shapes aren't validated, so mismatches error at runtime. Prefer the
native Core ML Sampler when you don't need the MODEL type.
Prompt length
Core ML enforces a hard 77-token prompt limit with no auto-chunking. Split long prompts across multiple CLIP Text Encode nodes and merge with Conditioning (Combine).