Merge pull request #137 from invoke-ai/Typo-fix-for-float32

Fix

src/invoke_training/scripts/_experimental/lora_merge/merge_lora_into_sd_model.py

@@ -6,19 +6,19 @@
 import torch
 from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline
 
+
 # fmt: off
 # HACK(ryand): Import order matters, because invokeai contains circular imports.
 from invokeai.backend.model_manager import BaseModelType
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_patcher import ModelPatcher
 # fmt: on
-
 from invoke_training._shared.stable_diffusion.model_loading_utils import PipelineVersionEnum, load_pipeline
 
 
 # TODO(ryand): Consolidate multiple implementations of this function across the project.
 def str_to_dtype(dtype_str: Literal["float32", "float16", "bfloat16"]):
-    if dtype_str == "float23":
+    if dtype_str == "float32":
         return torch.float32
     elif dtype_str == "float16":
         return torch.float16
@@ -136,7 +136,7 @@ def merge_lora_into_sd_model(
 
 def parse_lora_model_arg(lora_model_arg: str) -> tuple[str, float]:
     """Parse a --lora-model argument into a tuple of the model path and weight."""
-    parts = lora_model_arg.split(":")
+    parts = lora_model_arg.split("::")
     if len(parts) == 1:
         return parts[0], 1.0
     elif len(parts) == 2:
@@ -171,8 +171,8 @@ def main():
         type=str,
         nargs="+",
         help="The path(s) to one or more LoRA models to merge into the base model. Model weights can be appended to "
-        "the path, separated by a colon (':'). E.g. 'path/to/lora_model:0.5'. The weight is optional and defaults to "
-        "1.0.",
+        "the path, separated by a double colon ('::'). E.g. 'path/to/lora_model::0.5'. The weight is optional and "
+        "defaults to 1.0.",
         required=True,
     )
     parser.add_argument(
@@ -192,6 +192,15 @@ def main():
 
     logging.basicConfig(level=logging.INFO)
     logger = logging.getLogger(__name__)
+
+    # Log the parsed arguments
+    logger.info(f"Base model: {args.base_model}")
+    logger.info(f"Base model variant: {args.base_model_variant}")
+    logger.info(f"Base model type: {args.base_model_type}")
+    logger.info(f"LoRA models: {args.lora_model}")
+    logger.info(f"Output directory: {args.output}")
+    logger.info(f"Save dtype: {args.save_dtype}")
+
     merge_lora_into_sd_model(
         logger=logger,
         base_model=args.base_model,