Update REAMDE.md (Conversion and LoRA)

README.md

@@ -143,6 +143,76 @@ resulting latent as you normally would in your workflow.
     - **LATENT**: The latent image output by the Core ML model. This can be decoded using a VAE Decoder or used as input
       to the next node in your workflow.
 
+#### Checkpoint Converter
+
+![CoreMLConverter](./assets/checkpoint_converter.png?raw=true)
+
+You can use this node to convert any **SD1.5** based checkpoint to a Core ML model. The converted model is stored in the
+`models/unet` directory and can be used with the `Core ML UNet Loader`. The conversion parameters are encoded in
+the node name, so if the model already exists, the node will not convert it again.
+
+- **Inputs**:
+    - **ckpt_name**: The name of the checkpoint to convert. This should be the name of the checkpoint file stored in the
+      `models/checkpoints` directory.
+    - **height**: The desired height of the image generated by the model. The default is 512. Must be a multiple of 8.
+    - **width**: The desired width of the image generated by the model. The default is 512. Must be a multiple of 8.
+    - **batch_size**: The batch size of generated images. If you're planning to generate batches of images, you can try
+      increasing this value to speed up the generation process. The default is 1.
+    - **attention_implementation**: The attention implementation used when converting the model. Choose SPLIT_EINSUM or
+      SPLIT_EINSUM_V2 for better ANE support. Choose ORIGINAL for better GPU support.
+    - **compute_unit**: The hardware on which the model should run. This is used only when loading the model and doesn't
+      affect the conversion process.
+    - **controlnet_support**: For the model to support ControlNet, it must be converted with this option set to True.
+      The
+      default is False.
+    - **lora_params** [optional]: Optional LoRA names and weights. If provided, the model will be converted with LoRA(s)
+      baked in. More on loading LoRAs below.
+- **Outputs**:
+    - **coreml_model**: The converted Core ML model that can be used with Core ML Sampler.
+
+> [!NOTE]
+> Some models use a custom config .yaml file. If you're using such a model, you'll need to place the config file in the
+> `models/configs` directory. The config file should be named the same as the checkpoint file. For example, if the
+> checkpoint file is named `juggernaut_aftermath.safetensors`, the config file should be
+> named `juggernaut_aftermath.yaml`.
+> The config file will be automatically loaded during conversion.
+
+> [!NOTE]
+> For now, the converter relies heavilty on the model name to determine the conversion parameters. This means that if
+> you change the model name, the node will convert the model again. Other than that, if you find the name too long or
+> confusing, you can change it to anything you want.
+
+#### LoRA Loader
+
+![LoRALoader](./assets/lora_loader.png?raw=true)
+
+This node allows you to load LoRAs and bake them into a model. Since this is a workaround (as model weights can't be
+modified
+after conversion), there are a few caveats to keep in mind:
+
+- The LoRA weights and _strength_model_ parameter are baked into the model. This means that you can't change them
+  after conversion. This also means that you need to convert the model again if you want to change the LoRA weights.
+- Loading LoRA affects CLIP, which is not a part of Core ML workflow, so you'll need to load CLIP separately,
+  either using `CLIPLoader` or `CheckpointLoaderSimple`. (See [example workflows](#example-workflows) for more details.)
+- After conversion, if you want to load the model using `CoreMLUnetLoader`, you'll need to apply the same LoRAs to
+  CLIP manually. (See [example workflows](#example-workflows) for more details.)
+- The LoRA names are encoded in the model name. This means that if you change the name of the LoRA file,
+  you'll need to change the model name as well, or the node will convert the model again. (Model strength is not
+  encoded, so if you want to change it, you'll need to delete the converted model manually)
+- _strength_clip_ parameter only affects the CLIP model and is not baked into the converted model. This means that
+  you can change it after conversion.
+
+- **Inputs**:
+    - **lora_name**: The name of the LoRA to load.
+    - **strength_model**: The strength of the LoRA model.
+    - **strength_clip**: The strength of the LoRA CLIP.
+    - **lora_params** [optional]: Optional output from other LoRA Loaders.
+    - **clip**: The CLIP model to use with the LoRA. This can be either output of the
+      `CLIPLoader`/`CheckpointLoaderSimple` or other LoRA Loaders.
+- **Outputs**:
+    - **lora_params**: The LoRA parameters that can be passed to the Core ML Converter or other LoRA Loaders.
+    - **CLIP**: The CLIP model with LoRA applied.
+
 #### LCM Converter
 
 ![LCMConverter](./assets/lcm_converter.png?raw=true)
@@ -225,6 +295,41 @@ The ControlNet model used in this workflow is available
 Once downloaded, place the model in the `models/controlnet` directory.
 ![coreml-unet+controlnet](./assets/unet+sampler+controlnet.png?raw=true)
 
+#### Checkpoint conversion
+
+This workflow uses the Checkpoint Converter to convert the checkpoint file. See
+[Checkpoint Converter](#checkpoint-converter) description for more details.
+
+![checkpoint-converter](./assets/basic_conversion.png?raw=true)
+
+#### Checkpoint conversion with LoRA
+
+This workflow uses the Checkpoint Converter to convert the checkpoint file with LoRA. See
+[LoRA Loader](#lora-loader) description to read more about the caveats of using LoRA.
+
+![checkpoint-converter+lora](./assets/conversion+lora.png?raw=true)
+
+#### LCM LoRA conversion
+
+Please note that you can use multiple LoRAs with the same model. To do this, you'll need to use multiple LoRA Loaders.
+> [!IMPORTANT]
+> In this example, the model is passed through the adapter and `ModelSamplingDiscrete` nodes to a standard ComfyUI's
+> KSampler (not Core ML Sampler). ModelSamplingDiscrete needs to be used to sample models with LCM LoRAs properly.
+
+![multiple-loras](./assets/conversion+lcm_lora.png?raw=true)
+
+#### Loader with LoRAs
+
+This workflow uses the Core ML UNet Loader to load a model with LoRAs. The CLIP must be loaded separately and passed
+through the same LoRA nodes as during conversion. See [LoRA Loader](#lora-loader) description to read more about the
+caveats of using LoRA. Since _lora_name_ and _strength_model_ are baked into the model, it is not necessary to pass
+them as inputs to the loader.
+> [!IMPORTANT]
+> In this example, the model is passed through the adapter and `ModelSamplingDiscrete` nodes to a standard ComfyUI's
+> KSampler (not Core ML Sampler). ModelSamplingDiscrete needs to be used to sample models with LCM LoRAs properly.
+
+![loader+lora](./assets/loader+lcm_lora.png?raw=true)
+
 #### LCM conversion with ControlNet
 
 This workflow uses LCM converter to
@@ -240,7 +345,6 @@ model to Core ML. The converted model can then be used with or without ControlNe
   However, you can convert the model to a different input size using tools available
   in the [apple/ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion) repository.
 - For now, only Stable Diffusion v1.5 is supported.
-- LoRA is not supported yet.
 
 [^1]:
 Unless [EnumeratedShapes](https://apple.github.io/coremltools/docs-guides/source/flexible-inputs.html#select-from-predetermined-shapes)