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/.
3.2 KiB
Troubleshooting
Expected shape … got … / latent size mismatch
The most common error. A Core ML model has fixed input dimensions — a model converted for 512×512 expects a 64×64 latent and rejects any other size (batch size is handled and doesn't matter; only width/height are fixed).
Fix: set your Empty Latent (or upstream latent) to exactly the resolution the model was converted for, or re-convert at the size you want.
Old .mlmodelc model, or metadata.json not found
This suite no longer produces or loads .mlmodelc; the loader lists .mlpackage
only. Models from an older version (or downloaded community models) with a
.mlmodelc structure won't load.
Fix: re-convert the checkpoint with Convert Checkpoint to Core ML. No
Xcode or coremlcompiler is required — that dependency was removed.
Prompt too long (Expected size 154 but got 77, or a crash)
Core ML enforces a hard 77-token prompt limit and does not auto-chunk like A1111/ComfyUI.
Fix: split the prompt across multiple CLIP Text Encode nodes and merge them with Conditioning (Combine).
cannot import name 'ModelSamplingDiscreteLCM'
A ComfyUI refactor renamed this symbol.
Fix: update the suite (fixed in PR #29) and re-run
pip install -r requirements.txt.
LoRA loader ImportError
peft became a required dependency.
Fix: pip install -r requirements.txt. This recurs after ComfyUI-Manager
updates if requirements aren't reinstalled.
ControlNet has no effect
ControlNet support is baked at conversion. If the checkpoint was converted with
controlnet_support = False, ControlNet does nothing.
Fix: re-convert with controlnet_support = True. The ControlNet model itself
needs no conversion, and .fp16.safetensors vs .safetensors makes no
difference.
LoRAs produce garbage
LoRA support is inconsistent — some work, some don't, with no firm rule. Test per-LoRA. For some LCM-LoRA setups, routing through the Core ML Adapter is more reliable than the basic loader path. Remember weights are baked at conversion and can't be changed afterward.
FaceDetailer / detailers error on size
Detailers rescale latents internally (e.g. 512 → 1024), which breaks the model's fixed input shape.
Fix: use the CoreMLDetailerHookProvider node to pin the detailer's internal
size to the model's converted resolution. Note it only offers preset sizes, so
non-standard resolutions may not be selectable.
Inpainting checkpoint errors (tensor size 9 vs 4)
SD1.5 inpainting checkpoints use a 9-channel input and are not supported. This error is expected, not a bug.
Errors mentioning python_coreml_stable_diffusion or ml-stable-diffusion
You're on a stale install. That dependency was removed; old install scripts tried
pip install git+…/ml-stable-diffusion.git, which fails on modern Python.
Fix: reinstall the current suite (pip install -r requirements.txt, which
pulls coreml-diffusion from PyPI).
all input tensors must be on the same device (mps:0 and cpu) / ControlNet residual shape (2,…) vs (1,…)
Old bugs that have been fixed.
Fix: update to the latest version.