Change lumina to native RMSNorm. (#7935)

Author: comfyanonymous

comfy/ldm/lumina/model.py

@@ -8,7 +8,7 @@
 import torch.nn.functional as F
 import comfy.ldm.common_dit
 
-from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, RMSNorm
+from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder
 from comfy.ldm.modules.attention import optimized_attention_masked
 from comfy.ldm.flux.layers import EmbedND
 
@@ -64,8 +64,8 @@ def __init__(
         )
 
         if qk_norm:
-            self.q_norm = RMSNorm(self.head_dim, elementwise_affine=True, **operation_settings)
-            self.k_norm = RMSNorm(self.head_dim, elementwise_affine=True, **operation_settings)
+            self.q_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+            self.k_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         else:
             self.q_norm = self.k_norm = nn.Identity()
 
@@ -242,11 +242,11 @@ def __init__(
             operation_settings=operation_settings,
         )
         self.layer_id = layer_id
-        self.attention_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
-        self.ffn_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+        self.attention_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.ffn_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
 
-        self.attention_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
-        self.ffn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+        self.attention_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.ffn_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
 
         self.modulation = modulation
         if modulation:
@@ -431,7 +431,7 @@ def __init__(
 
         self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings)
         self.cap_embedder = nn.Sequential(
-            RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, **operation_settings),
+            operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
             operation_settings.get("operations").Linear(
                 cap_feat_dim,
                 dim,
@@ -457,7 +457,7 @@ def __init__(
                 for layer_id in range(n_layers)
             ]
         )
-        self.norm_final = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+        self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings)
 
         assert (dim // n_heads) == sum(axes_dims)