Support other lora formats

src/maxdiffusion/generate_sdxl.py

@@ -225,6 +225,7 @@ def run(config):
       pipeline.unet, None, config, checkpoint_loader.mesh, weights_init_fn, False
   )
 
+  # load unet params from orbax checkpoint
   unet_params = load_params_from_path(
       config, checkpoint_loader.checkpoint_manager, unboxed_abstract_state.params, "unet_state"
   )
@@ -253,14 +254,14 @@ def run(config):
   vae_state, vae_state_shardings = checkpoint_loader.create_vae_state(
       pipeline, params, checkpoint_item_name="vae_state", is_training=False
   )
-  text_encoder_state, text_encoder_state_shardings = checkpoint_loader.create_text_encoder_state(
-      pipeline, params, checkpoint_item_name="text_encoder_state", is_training=False
-  )
-
-  text_encoder_2_state, text_encoder_2_state_shardings = checkpoint_loader.create_text_encoder_2_state(
-      pipeline, params, checkpoint_item_name="text_encoder_2_state", is_training=False
-  )
+  with nn.intercept_methods(lora_interceptor):
+    text_encoder_state, text_encoder_state_shardings = checkpoint_loader.create_text_encoder_state(
+        pipeline, params, checkpoint_item_name="text_encoder_state", is_training=False
+    )
 
+    text_encoder_2_state, text_encoder_2_state_shardings = checkpoint_loader.create_text_encoder_2_state(
+        pipeline, params, checkpoint_item_name="text_encoder_2_state", is_training=False
+    )
   states = {}
   state_shardings = {}
 

src/maxdiffusion/loaders/lora_pipeline.py

@@ -121,21 +121,35 @@ def _get_lora_layer(cls, module_path, module, rank, network_alphas):
 
   def rename_for_interceptor(params_keys, network_alphas):
     new_params_keys = []
+    new_network_alphas = {}
     for layer_lora in params_keys:
       if "lora" in layer_lora:
         new_layer_lora = layer_lora[: layer_lora.index("lora")]
         if new_layer_lora not in new_params_keys:
           new_params_keys.append(new_layer_lora)
           network_alpha = network_alphas[layer_lora]
-          del network_alphas[layer_lora]
-          network_alphas[new_layer_lora] = network_alpha
-    return new_params_keys, network_alphas
+          new_network_alphas[new_layer_lora] = network_alpha
+    return new_params_keys, new_network_alphas
 
   @classmethod
   def make_lora_interceptor(cls, params, rank, network_alphas):
     # Only unet interceptor supported for now.
+    network_alphas_for_interceptor = {}
+
     unet_lora_keys = flax.traverse_util.flatten_dict(params["unet"]).keys()
-    unet_lora_keys, network_alphas = cls.rename_for_interceptor(unet_lora_keys, network_alphas)
+    lora_keys, unet_alphas = cls.rename_for_interceptor(unet_lora_keys, network_alphas)
+    network_alphas_for_interceptor.update(unet_alphas)
+
+    text_encoder_keys = flax.traverse_util.flatten_dict(params["text_encoder"]).keys()
+    text_encoder_keys, text_encoder_alphas = cls.rename_for_interceptor(text_encoder_keys, network_alphas)
+    lora_keys.extend(text_encoder_keys)
+    network_alphas_for_interceptor.update(text_encoder_alphas)
+
+    if "text_encoder_2" in params.keys():
+      text_encoder_2_keys = flax.traverse_util.flatten_dict(params["text_encoder_2"]).keys()
+      text_encoder_2_keys, text_encoder_2_alphas = cls.rename_for_interceptor(text_encoder_2_keys, network_alphas)
+      lora_keys.extend(text_encoder_2_keys)
+      network_alphas_for_interceptor.update(text_encoder_2_alphas)
 
     def _intercept(next_fn, args, kwargs, context):
       mod = context.module
@@ -146,8 +160,8 @@ def _intercept(next_fn, args, kwargs, context):
       h = next_fn(*args, **kwargs)
       if context.method_name == "__call__":
         module_path = context.module.path
-        if module_path in unet_lora_keys:
-          lora_layer = cls._get_lora_layer(module_path, context.module, rank, network_alphas)
+        if module_path in lora_keys:
+          lora_layer = cls._get_lora_layer(module_path, context.module, rank, network_alphas_for_interceptor)
           return lora_layer(h, *args, **kwargs)
       return h
 

src/maxdiffusion/maxdiffusion_utils.py

@@ -49,11 +49,13 @@ def _noop_interceptor(next_fn, args, kwargs, context):
   if len(lora_config["lora_model_name_or_path"]) > 0:
     # For now only first lora supported. In the future, they will be merged
     # before being loaded.
+    # TODO - merge LoRAs here.
     params, rank, network_alphas = pipeline.load_lora_weights(
         lora_config["lora_model_name_or_path"][0],
         weight_name=lora_config["weight_name"][0],
         params=params,
         adapter_name=lora_config["adapter_name"][0],
+        unet_config=pipeline.unet.config,
     )
     interceptor = pipeline.make_lora_interceptor(params, rank, network_alphas)
 

src/maxdiffusion/models/modeling_flax_pytorch_utils.py

@@ -137,7 +137,15 @@ def create_flax_params_from_pytorch_state(
   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
     network_alpha_value = get_network_alpha_value(pt_key, network_alphas)
-    renamed_pt_key = rename_key(pt_key)
+
+    # rename text encoders fc1 lora layers.
+    pt_key = pt_key.replace("lora_linear_layer", "lora")
+
+    # only rename the unet keys, text encoders are already correct.
+    if "unet" in pt_key:
+      renamed_pt_key = rename_key(pt_key)
+    else:
+      renamed_pt_key = pt_key
     pt_tuple_key = tuple(renamed_pt_key.split("."))
     # conv
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
@@ -147,13 +155,24 @@ def create_flax_params_from_pytorch_state(
       flax_tensor = pt_tensor
     else:
       flax_key_list = [*pt_tuple_key]
-      for rename_from, rename_to in (
-          ("to_k_lora", ("to_k", "lora")),
-          ("to_q_lora", ("to_q", "lora")),
-          ("to_v_lora", ("to_v", "lora")),
-          ("to_out_lora", ("to_out_0", "lora")),
-          ("weight", "kernel"),
-      ):
+      if "text_encoder" in pt_tuple_key or "text_encoder_2" in pt_tuple_key:
+        rename_from_to = (
+            ("to_k_lora", ("k_proj", "lora")),
+            ("to_q_lora", ("q_proj", "lora")),
+            ("to_v_lora", ("v_proj", "lora")),
+            ("to_out_lora", ("out_proj", "lora")),
+            ("weight", "kernel"),
+        )
+      # the unet
+      else:
+        rename_from_to = (
+            ("to_k_lora", ("to_k", "lora")),
+            ("to_q_lora", ("to_q", "lora")),
+            ("to_v_lora", ("to_v", "lora")),
+            ("to_out_lora", ("to_out_0", "lora")),
+            ("weight", "kernel"),
+        )
+      for rename_from, rename_to in rename_from_to:
         tmp = []
         for s in flax_key_list:
           if s == rename_from: