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fix: Use operations.LayerNorm for JoyImage norms

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Replace custom FP32LayerNorm with the standard affine-free operations.LayerNorm.
This commit is contained in:
huangfeice 2026-07-06 16:20:43 +08:00
parent 0eafd9cf0b
commit 3e42225399
1 changed files with 5 additions and 19 deletions
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24
comfy/ldm/joyimage/model.py
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@ -12,20 +12,6 @@ from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps
from comfy.ldm.modules.attention import optimized_attention from comfy.ldm.modules.attention import optimized_attention
class FP32LayerNorm(nn.Module):
def __init__(self, normalized_shape, eps: float = 1e-6):
super().__init__()
if isinstance(normalized_shape, int):
normalized_shape = (normalized_shape,)
self.normalized_shape = tuple(normalized_shape)
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
orig_dtype = x.dtype
out = F.layer_norm(x.float(), self.normalized_shape, None, None, self.eps)
return out.to(orig_dtype)
def _apply_rotary_emb( def _apply_rotary_emb(
xq: torch.Tensor, xq: torch.Tensor,
xk: torch.Tensor, xk: torch.Tensor,
@ -186,13 +172,13 @@ class JoyImageTransformerBlock(nn.Module):
mlp_hidden_dim = int(dim * mlp_width_ratio) mlp_hidden_dim = int(dim * mlp_width_ratio)
self.img_mod = JoyImageModulate(dim, factor=6, dtype=dtype, device=device) self.img_mod = JoyImageModulate(dim, factor=6, dtype=dtype, device=device)
self.img_norm1 = FP32LayerNorm(dim, eps=eps) self.img_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
self.img_norm2 = FP32LayerNorm(dim, eps=eps) self.img_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
self.img_mlp = JoyImageFeedForward(dim, inner_dim=mlp_hidden_dim, dtype=dtype, device=device, operations=operations) self.img_mlp = JoyImageFeedForward(dim, inner_dim=mlp_hidden_dim, dtype=dtype, device=device, operations=operations)
self.txt_mod = JoyImageModulate(dim, factor=6, dtype=dtype, device=device) self.txt_mod = JoyImageModulate(dim, factor=6, dtype=dtype, device=device)
self.txt_norm1 = FP32LayerNorm(dim, eps=eps) self.txt_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
self.txt_norm2 = FP32LayerNorm(dim, eps=eps) self.txt_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
self.txt_mlp = JoyImageFeedForward(dim, inner_dim=mlp_hidden_dim, dtype=dtype, device=device, operations=operations) self.txt_mlp = JoyImageFeedForward(dim, inner_dim=mlp_hidden_dim, dtype=dtype, device=device, operations=operations)
self.attn = JoyImageAttention( self.attn = JoyImageAttention(
@ -366,7 +352,7 @@ class JoyImageTransformer3DModel(nn.Module):
for _ in range(num_layers) for _ in range(num_layers)
]) ])
self.norm_out = FP32LayerNorm(hidden_size, eps=1e-6) self.norm_out = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.proj_out = operations.Linear( self.proj_out = operations.Linear(
hidden_size, hidden_size,
self.out_channels * math.prod(self.patch_size), self.out_channels * math.prod(self.patch_size),