Convert alphas for embedders for sd-scripts to ai toolkit conversion (huggingface#12332)

* Convert alphas for embedders for sd-scripts to ai toolkit conversion

* Add kohya embedders conversion test

* Apply style fixes

---------

Co-authored-by: Sayak Paul <[email protected]>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

Author: 3 people

src/diffusers/loaders/lora_conversion_utils.py

@@ -558,70 +558,62 @@ def assign_remaining_weights(assignments, source):
                     ait_sd[target_key] = value
 
         if any("guidance_in" in k for k in sds_sd):
-            assign_remaining_weights(
-                [
-                    (
-                        "time_text_embed.guidance_embedder.linear_1.{lora_key}.weight",
-                        "lora_unet_guidance_in_in_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                    (
-                        "time_text_embed.guidance_embedder.linear_2.{lora_key}.weight",
-                        "lora_unet_guidance_in_out_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                ],
+            _convert_to_ai_toolkit(
                 sds_sd,
+                ait_sd,
+                "lora_unet_guidance_in_in_layer",
+                "time_text_embed.guidance_embedder.linear_1",
+            )
+
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                "lora_unet_guidance_in_out_layer",
+                "time_text_embed.guidance_embedder.linear_2",
             )
 
         if any("img_in" in k for k in sds_sd):
-            assign_remaining_weights(
-                [
-                    ("x_embedder.{lora_key}.weight", "lora_unet_img_in.{orig_lora_key}.weight", None),
-                ],
+            _convert_to_ai_toolkit(
                 sds_sd,
+                ait_sd,
+                "lora_unet_img_in",
+                "x_embedder",
             )
 
         if any("txt_in" in k for k in sds_sd):
-            assign_remaining_weights(
-                [
-                    ("context_embedder.{lora_key}.weight", "lora_unet_txt_in.{orig_lora_key}.weight", None),
-                ],
+            _convert_to_ai_toolkit(
                 sds_sd,
+                ait_sd,
+                "lora_unet_txt_in",
+                "context_embedder",
             )
 
         if any("time_in" in k for k in sds_sd):
-            assign_remaining_weights(
-                [
-                    (
-                        "time_text_embed.timestep_embedder.linear_1.{lora_key}.weight",
-                        "lora_unet_time_in_in_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                    (
-                        "time_text_embed.timestep_embedder.linear_2.{lora_key}.weight",
-                        "lora_unet_time_in_out_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                ],
+            _convert_to_ai_toolkit(
                 sds_sd,
+                ait_sd,
+                "lora_unet_time_in_in_layer",
+                "time_text_embed.timestep_embedder.linear_1",
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                "lora_unet_time_in_out_layer",
+                "time_text_embed.timestep_embedder.linear_2",
             )
 
         if any("vector_in" in k for k in sds_sd):
-            assign_remaining_weights(
-                [
-                    (
-                        "time_text_embed.text_embedder.linear_1.{lora_key}.weight",
-                        "lora_unet_vector_in_in_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                    (
-                        "time_text_embed.text_embedder.linear_2.{lora_key}.weight",
-                        "lora_unet_vector_in_out_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                ],
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                "lora_unet_vector_in_in_layer",
+                "time_text_embed.text_embedder.linear_1",
+            )
+            _convert_to_ai_toolkit(
                 sds_sd,
+                ait_sd,
+                "lora_unet_vector_in_out_layer",
+                "time_text_embed.text_embedder.linear_2",
             )
 
         if any("final_layer" in k for k in sds_sd):

tests/lora/test_lora_layers_flux.py

@@ -907,6 +907,13 @@ def test_flux_kohya_with_text_encoder(self):
 
         assert max_diff < 1e-3
 
+    def test_flux_kohya_embedders_conversion(self):
+        """Test that embedders load without throwing errors"""
+        self.pipeline.load_lora_weights("rockerBOO/flux-bpo-po-lora")
+        self.pipeline.unload_lora_weights()
+
+        assert True
+
     def test_flux_xlabs(self):
         self.pipeline.load_lora_weights("XLabs-AI/flux-lora-collection", weight_name="disney_lora.safetensors")
         self.pipeline.fuse_lora()