[lora] fix non-diffusers lora key handling for flux2

src/diffusers/loaders/lora_conversion_utils.py

@@ -2321,6 +2321,14 @@ def _convert_non_diffusers_flux2_lora_to_diffusers(state_dict):
     prefix = "diffusion_model."
     original_state_dict = {k[len(prefix) :]: v for k, v in state_dict.items()}
 
+    has_lora_down_up = any("lora_down" in k or "lora_up" in k for k in original_state_dict.keys())
+    if has_lora_down_up:
+        temp_state_dict = {}
+        for k, v in original_state_dict.items():
+            new_key = k.replace("lora_down", "lora_A").replace("lora_up", "lora_B")
+            temp_state_dict[new_key] = v
+        original_state_dict = temp_state_dict
+
     num_double_layers = 0
     num_single_layers = 0
     for key in original_state_dict.keys():
@@ -2337,13 +2345,15 @@ def _convert_non_diffusers_flux2_lora_to_diffusers(state_dict):
         attn_prefix = f"single_transformer_blocks.{sl}.attn"
 
         for lora_key in lora_keys:
-            converted_state_dict[f"{attn_prefix}.to_qkv_mlp_proj.{lora_key}.weight"] = original_state_dict.pop(
-                f"{single_block_prefix}.linear1.{lora_key}.weight"
-            )
+            linear1_key = f"{single_block_prefix}.linear1.{lora_key}.weight"
+            if linear1_key in original_state_dict:
+                converted_state_dict[f"{attn_prefix}.to_qkv_mlp_proj.{lora_key}.weight"] = original_state_dict.pop(
+                    linear1_key
+                )
 
-            converted_state_dict[f"{attn_prefix}.to_out.{lora_key}.weight"] = original_state_dict.pop(
-                f"{single_block_prefix}.linear2.{lora_key}.weight"
-            )
+            linear2_key = f"{single_block_prefix}.linear2.{lora_key}.weight"
+            if linear2_key in original_state_dict:
+                converted_state_dict[f"{attn_prefix}.to_out.{lora_key}.weight"] = original_state_dict.pop(linear2_key)
 
     for dl in range(num_double_layers):
         transformer_block_prefix = f"transformer_blocks.{dl}"
@@ -2352,6 +2362,10 @@ def _convert_non_diffusers_flux2_lora_to_diffusers(state_dict):
             for attn_type in attn_types:
                 attn_prefix = f"{transformer_block_prefix}.attn"
                 qkv_key = f"double_blocks.{dl}.{attn_type}.qkv.{lora_key}.weight"
+
+                if qkv_key not in original_state_dict:
+                    continue
+
                 fused_qkv_weight = original_state_dict.pop(qkv_key)
 
                 if lora_key == "lora_A":
@@ -2383,8 +2397,9 @@ def _convert_non_diffusers_flux2_lora_to_diffusers(state_dict):
         for org_proj, diff_proj in proj_mappings:
             for lora_key in lora_keys:
                 original_key = f"double_blocks.{dl}.{org_proj}.{lora_key}.weight"
-                diffusers_key = f"{transformer_block_prefix}.{diff_proj}.{lora_key}.weight"
-                converted_state_dict[diffusers_key] = original_state_dict.pop(original_key)
+                if original_key in original_state_dict:
+                    diffusers_key = f"{transformer_block_prefix}.{diff_proj}.{lora_key}.weight"
+                    converted_state_dict[diffusers_key] = original_state_dict.pop(original_key)
 
         mlp_mappings = [
             ("img_mlp.0", "ff.linear_in"),
@@ -2395,8 +2410,27 @@ def _convert_non_diffusers_flux2_lora_to_diffusers(state_dict):
         for org_mlp, diff_mlp in mlp_mappings:
             for lora_key in lora_keys:
                 original_key = f"double_blocks.{dl}.{org_mlp}.{lora_key}.weight"
-                diffusers_key = f"{transformer_block_prefix}.{diff_mlp}.{lora_key}.weight"
-                converted_state_dict[diffusers_key] = original_state_dict.pop(original_key)
+                if original_key in original_state_dict:
+                    diffusers_key = f"{transformer_block_prefix}.{diff_mlp}.{lora_key}.weight"
+                    converted_state_dict[diffusers_key] = original_state_dict.pop(original_key)
+
+    extra_mappings = {
+        "img_in": "x_embedder",
+        "txt_in": "context_embedder",
+        "time_in.in_layer": "time_guidance_embed.timestep_embedder.linear_1",
+        "time_in.out_layer": "time_guidance_embed.timestep_embedder.linear_2",
+        "final_layer.linear": "proj_out",
+        "final_layer.adaLN_modulation.1": "norm_out.linear",
+        "single_stream_modulation.lin": "single_stream_modulation.linear",
+        "double_stream_modulation_img.lin": "double_stream_modulation_img.linear",
+        "double_stream_modulation_txt.lin": "double_stream_modulation_txt.linear",
+    }
+
+    for org_key, diff_key in extra_mappings.items():
+        for lora_key in lora_keys:
+            original_key = f"{org_key}.{lora_key}.weight"
+            if original_key in original_state_dict:
+                converted_state_dict[f"{diff_key}.{lora_key}.weight"] = original_state_dict.pop(original_key)
 
     if len(original_state_dict) > 0:
         raise ValueError(f"`original_state_dict` should be empty at this point but has {original_state_dict.keys()=}.")