docs: sync with converter consolidation

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

README.md

@@ -104,7 +104,7 @@ and no longer depends on that package: UNet conversion runs natively on
 `diffusers`' `UNet2DConditionModel`, the ANE attention path (`SPLIT_EINSUM`,
 `SPLIT_EINSUM_V2`) is reimplemented as standalone `diffusers` attention
 processors, and the toolchain tracks current ComfyUI (NumPy 2, Torch 2.7+,
-coremltools 9, Python 3.11/3.12). Conversion now lives in the separate
+coremltools 9, Python 3.12+). Conversion now lives in the separate
 [coreml-diffusion](https://github.com/aszc-dev/coreml-diffusion) package.
 
 ## Support

docs/conversion.md

@@ -3,8 +3,10 @@
 ## Conversion is the only supported path
 
 You always start from a Stable Diffusion checkpoint (`.safetensors` / `.ckpt`)
-and convert it with the **Convert Checkpoint to Core ML** (or **Convert LCM**)
-node. Pre-converted Core ML models from elsewhere are not supported, because:
+and convert it with the **Convert Checkpoint to Core ML** node. The model
+version (SD1.5, SDXL, SDXL refiner, full-distill LCM) is auto-detected from the
+checkpoint. Pre-converted Core ML models from elsewhere are not supported,
+because:
 
 - The suite uses its own input **dimensions**, **naming convention**, and
   **metadata**, all produced by the
@@ -23,7 +25,7 @@ or `coremlcompiler` dependency**.
 
 Conversion runs **once**, not on every queue. The converter encodes all
 conversion parameters into the output filename (via `coreml_diffusion.compose_out_name`,
-called in `coreml_suite/nodes.py:313`):
+called in `coreml_suite/nodes.py`):
 
 - checkpoint name, `batch_size`, `width`, `height`
 - `controlnet_support`, `attention_implementation`
@@ -46,7 +48,7 @@ it short-circuits to the cached file.)
 
 ## Quantization
 
-Both converter nodes accept an optional `quantize_nbits` dropdown that runs
+The converter node accepts an optional `quantize_nbits` dropdown that runs
 k-means weight palettization (`coremltools.optimize.coreml.palettize_weights`) on
 the UNet before saving.
 
@@ -89,7 +91,3 @@ The conversion engine was extracted into the standalone
 The nodes in this suite resolve ComfyUI paths and call into it; node names,
 inputs, and outputs are unchanged, so the split has effectively no user-facing
 impact beyond `pip install` pulling one more dependency.
-
-One detail: the LCM converter still imports `diffusers` directly (in
-`coreml_suite/lcm/converter.py`) to download the hardcoded LCM model from Hugging
-Face. This is an internal note, not something you need to act on.

docs/faq.md

@@ -67,8 +67,8 @@ avoids. Use the native sampler unless you specifically need a `MODEL` (e.g.
 
 ## Which Python versions work?
 
-Both 3.11 and 3.12. Older 3.12 install failures came from the now-removed
-`ml-stable-diffusion` build, not from this suite.
+Python 3.12 or newer (`requires-python >=3.12`). Older 3.12 install failures
+came from the now-removed `ml-stable-diffusion` build, not from this suite.
 
 ## Long prompts crash my workflow
 

docs/limitations.md

@@ -10,7 +10,7 @@
 | 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 |
+| LCM | ⚠️ Experimental | Full-distill LCM checkpoints auto-detected by the converter |
 | 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 |
@@ -42,7 +42,7 @@ degraded versus the safetensors original (an upstream conversion artifact). Use
 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
+LoRA flows, detailers — 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.
 

docs/nodes.md

@@ -11,7 +11,6 @@ All nodes live in the **Core ML Suite** category. Right-click the canvas →
 | Core ML Adapter (Experimental) | `CoreMLModelAdapter` | Wrap as a standard `MODEL` |
 | Load LoRA to use with Core ML | `Core ML LoRA Loader` | Bake LoRA(s) at conversion |
 | Convert Checkpoint to Core ML | `Core ML Converter` | Convert a checkpoint |
-| Convert LCM to Core ML | `Core ML LCM Converter` | Convert LCM Dreamshaper v7 |
 
 ---
 
@@ -122,14 +121,15 @@ only affects CLIP and can be changed freely. After conversion, when loading with
 
 ![Checkpoint Converter](../assets/checkpoint_converter.png?raw=true)
 
-Converts a SD1.5- or SDXL-based checkpoint from `models/checkpoints` to a Core ML
-`.mlpackage` in `models/unet`. The conversion parameters are encoded in the
-output name, so an already-converted model is reused instead of re-converted.
-See [conversion](conversion.md) for details.
+Converts a checkpoint from `models/checkpoints` to a Core ML `.mlpackage` in
+`models/unet`. The model version (SD1.5, SDXL, SDXL refiner, or full-distill
+LCM) is auto-detected from the checkpoint's architecture — there is no version
+dropdown. The conversion parameters are encoded in the output name, so an
+already-converted model is reused instead of re-converted. See
+[conversion](conversion.md) for details.
 
 - **Inputs**
   - `ckpt_name` — checkpoint in `models/checkpoints`.
-  - `model_version` — `SD15` or `SDXL` (list is discovered from `coreml-diffusion`).
   - `height`, `width` — target image size; any positive multiple of 8 (default
     512). The model's input size is fixed at these values.
   - `batch_size` — default 1; raise to convert a batch-capable model.
@@ -150,24 +150,8 @@ See [conversion](conversion.md) for details.
 > named like the checkpoint (e.g. `juggernaut.safetensors` →
 > `juggernaut.yaml`); it is loaded automatically during conversion.
 
----
-
-## Convert LCM to Core ML (`Core ML LCM Converter`)
-
-![LCM Converter](../assets/lcm_converter.png?raw=true)
-
-Converts [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)
-to Core ML in `models/unet`. As with the checkpoint converter, the parameters are
-encoded in the name and an existing model is reused.
-
-- **Inputs**
-  - `height`, `width` — 512–768, multiple of 8 (default 512).
-  - `batch_size` — default 1.
-  - `compute_unit` — used only when loading.
-  - `controlnet_support` — default `False`.
-- **Output**
-  - `coreml_model`.
-
 > [!NOTE]
-> When sampling an LCM model, set `sampler_name` to `lcm` and `scheduler` to
-> `sgm_uniform`. Conversion can take a while.
+> Full-distill LCM checkpoints (e.g.
+> [LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)) are
+> detected and converted like any other checkpoint. When sampling an LCM model,
+> set `sampler_name` to `lcm` and `scheduler` to `sgm_uniform`.

docs/troubleshooting.md

@@ -60,11 +60,11 @@ 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.
+fixed input shape. There is no workaround node — a Core ML model only accepts
+the resolution it was converted for.
 
-**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.
+**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`)
 

docs/workflows.md

@@ -77,8 +77,11 @@ passed through the same LoRA nodes used at conversion. Since `lora_name` and
 
 ## LCM conversion with ControlNet
 
-Convert [LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)
-with the LCM converter, then use it with or without ControlNet.
+Convert a full-distill LCM checkpoint (e.g.
+[LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)) with
+the standard **Convert Checkpoint to Core ML** node — the LCM architecture is
+auto-detected. Use it with or without ControlNet. When sampling, set
+`sampler_name` to `lcm` and `scheduler` to `sgm_uniform`.
 
 ![LCM + ControlNet](../assets/lcm+controlnet.png?raw=true)