[pull] master from comfyanonymous:master

comfy/ldm/genmo/joint_model/asymm_models_joint.py

@@ -13,7 +13,6 @@
 from .layers import (
     FeedForward,
     PatchEmbed,
-    RMSNorm,
     TimestepEmbedder,
 )
 
@@ -90,10 +89,10 @@ def __init__(
 
         # Query and key normalization for stability.
         assert qk_norm
-        self.q_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype)
-        self.k_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype)
-        self.q_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype)
-        self.k_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype)
+        self.q_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype)
+        self.k_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype)
+        self.q_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype)
+        self.k_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype)
 
         # Output layers. y features go back down from dim_x -> dim_y.
         self.proj_x = operations.Linear(dim_x, dim_x, bias=out_bias, device=device, dtype=dtype)

comfy/ldm/genmo/joint_model/layers.py

@@ -151,14 +151,3 @@ def forward(self, x):
 
         x = self.norm(x)
         return x
-
-
-class RMSNorm(torch.nn.Module):
-    def __init__(self, hidden_size, eps=1e-5, device=None, dtype=None):
-        super().__init__()
-        self.eps = eps
-        self.weight = torch.nn.Parameter(torch.empty(hidden_size, device=device, dtype=dtype))
-        self.register_parameter("bias", None)
-
-    def forward(self, x):
-        return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)

comfy/ldm/wan/model.py

@@ -9,7 +9,6 @@
 from comfy.ldm.modules.attention import optimized_attention
 from comfy.ldm.flux.layers import EmbedND
 from comfy.ldm.flux.math import apply_rope
-from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm
 import comfy.ldm.common_dit
 import comfy.model_management
 
@@ -49,8 +48,8 @@ def __init__(self,
         self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.norm_q = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
-        self.norm_k = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+        self.norm_q = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+        self.norm_k = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
 
     def forward(self, x, freqs):
         r"""
@@ -114,7 +113,7 @@ def __init__(self,
         self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.v_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         # self.alpha = nn.Parameter(torch.zeros((1, )))
-        self.norm_k_img = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+        self.norm_k_img = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
 
     def forward(self, x, context, context_img_len):
         r"""