Genera cleanup/documentation.

invokeai/backend/lora/conversions/flux_diffusers_lora_conversion_utils.py

@@ -202,6 +202,7 @@ def _group_by_layer(state_dict: Dict[str, torch.Tensor]) -> dict[str, dict[str,
     """Groups the keys in the state dict by layer."""
     layer_dict: dict[str, dict[str, torch.Tensor]] = {}
     for key in state_dict:
+        # Split the 'lora_A.weight' or 'lora_B.weight' suffix from the layer name.
         parts = key.rsplit(".", maxsplit=2)
         layer_name = parts[0]
         key_name = ".".join(parts[1:])

invokeai/backend/lora/conversions/flux_kohya_lora_conversion_utils.py

@@ -23,10 +23,7 @@ def is_state_dict_likely_in_flux_kohya_format(state_dict: Dict[str, Any]) -> boo
     This is intended to be a high-precision detector, but it is not guaranteed to have perfect precision. (A
     perfect-precision detector would require checking all keys against a whitelist and verifying tensor shapes.)
     """
-    for k in state_dict.keys():
-        if not re.match(FLUX_KOHYA_KEY_REGEX, k):
-            return False
-    return True
+    return all(re.match(FLUX_KOHYA_KEY_REGEX, k) for k in state_dict.keys())
 
 
 def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAModelRaw:

tests/backend/lora/conversions/lora_state_dicts/flux_lora_diffusers_format.py

@@ -1,3 +1,6 @@
+# A sample state dict in the Diffusers FLUX LoRA format.
+# These keys are based on the LoRA model here:
+# https://civitai.com/models/200255/hands-xl-sd-15-flux1-dev?modelVersionId=781855
 state_dict_keys = [
     "transformer.single_transformer_blocks.0.attn.to_k.lora_A.weight",
     "transformer.single_transformer_blocks.0.attn.to_k.lora_B.weight",

tests/backend/lora/conversions/lora_state_dicts/flux_lora_kohya_format.py

@@ -1,3 +1,6 @@
+# A sample state dict in the Kohya FLUX LoRA format.
+# These keys are based on the LoRA model here:
+# https://civitai.com/models/159333/pokemon-trainer-sprite-pixelart?modelVersionId=779247
 state_dict_keys = [
     "lora_unet_double_blocks_0_img_attn_proj.alpha",
     "lora_unet_double_blocks_0_img_attn_proj.lora_down.weight",