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
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. There is no workaround node — a Core ML model only accepts the resolution it was converted for.
Fix: convert a second model at the detailer's internal resolution and use it for the detailing pass, or run the detailer with a standard (non–Core ML) model.
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.