aszc-dev
1008f144ad
Docs described the pre-#67 suite: a removed standalone LCM converter node, a removed model_version input, a nonexistent CoreMLDetailerHookProvider node, and a deleted lcm/converter.py file. - drop LCM converter node docs; LCM checkpoints are auto-detected by the consolidated CoreMLConverter - remove model_version input and invented 512-768 resolution range - replace phantom detailer-hook fix with real workarounds - fix Python support claim (3.12+ per requires-python) - update LCM support-matrix row, drop stale line reference Refs #67, #68
54 lines
2.7 KiB
Markdown
54 lines
2.7 KiB
Markdown
# Limitations & Support Matrix
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## Support matrix
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| Feature | Status | Notes |
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|---|---|---|
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| SD1.5 | ✅ Full | ANE via `SPLIT_EINSUM`; the primary, fastest path |
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| SDXL / SDXL Turbo | ⚠️ Partial | GPU only (no ANE), no speedup; possible quality loss vs source. Don't run Turbo at 1024² |
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| SD2.1 | ❌ Unsupported | |
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| Inpainting checkpoints (9-channel) | ❌ Unsupported | |
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| ControlNet | ✅ Supported | Convert the checkpoint with `controlnet_support = True` |
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| LoRA | ⚠️ Experimental | Inconsistent per-LoRA; baked at conversion, immutable afterward |
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| LCM | ⚠️ Experimental | Full-distill LCM checkpoints auto-detected by the converter |
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| SVD | ❌ Not supported | |
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| AnimateDiff | ❌ Not supported | Motion modules need pre-conversion injection; not feasible today |
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| IPAdapter | ❌ Not supported | Needs a real `MODEL` the Core ML wrapper can't provide |
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| Core ML Adapter | ⚠️ Experimental | Works for many nodes; fails for merges/IPAdapter/etc. |
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## Fixed input/output shapes
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A Core ML model is converted for one specific resolution and batch size. To work
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at a different size, re-convert with the new width/height (conversion is cheap and
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cached by name). This is also why detailers and latent-upscale workflows that
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rescale mid-graph break — see [troubleshooting](troubleshooting.md).
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There is experimental support for flexible shapes via
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[EnumeratedShapes](https://apple.github.io/coremltools/docs-guides/source/flexible-inputs.html#select-from-predetermined-shapes),
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but it is **much slower** — user benchmarks show roughly **5×** the per-iteration
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time on every run, not just the first. Fixed-shape models per resolution are the
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practical choice.
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## SDXL on the Neural Engine
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SDXL and SDXL Turbo cannot run on the ANE — the dual-text-encoder UNet exceeds the
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supported Neural Engine path. They run on the GPU at roughly MPS-equivalent speed,
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so Core ML offers no speed advantage for SDXL, and converted output may look
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degraded versus the safetensors original (an upstream conversion artifact). Use
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`ORIGINAL` + `CPU_AND_GPU`. See [hardware](hardware.md).
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## Experimental Core ML Adapter
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The Adapter wraps a Core ML model to look like a standard ComfyUI `MODEL`, which
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covers many standard and custom nodes. But it can't fully emulate a real model:
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operations that need genuine `MODEL` internals — model merges, IPAdapter, some
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LoRA flows, detailers — generally won't work, and the model's
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fixed input shapes aren't validated, so mismatches error at runtime. Prefer the
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native Core ML Sampler when you don't need the `MODEL` type.
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## Prompt length
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Core ML enforces a hard 77-token prompt limit with no auto-chunking. Split long
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prompts across multiple CLIP Text Encode nodes and merge with Conditioning
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(Combine).
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