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More code reuse in wan.

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Fix bug when changing the compute dtype on wan.
This commit is contained in:
comfyanonymous 2025-02-26 05:22:29 -05:00
parent 0844998db3
commit fa62287f1f
2 changed files with 6 additions and 23 deletions
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27
comfy/ldm/wan/model.py
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@ -9,9 +9,11 @@ from einops import repeat
from comfy.ldm.modules.attention import optimized_attention from comfy.ldm.modules.attention import optimized_attention
from comfy.ldm.flux.layers import EmbedND from comfy.ldm.flux.layers import EmbedND
from comfy.ldm.flux.math import apply_rope from comfy.ldm.flux.math import apply_rope
from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm
import comfy.ldm.common_dit import comfy.ldm.common_dit
import comfy.model_management import comfy.model_management
def sinusoidal_embedding_1d(dim, position): def sinusoidal_embedding_1d(dim, position):
# preprocess # preprocess
assert dim % 2 == 0 assert dim % 2 == 0
@ -25,25 +27,6 @@ def sinusoidal_embedding_1d(dim, position):
return x return x
class WanRMSNorm(nn.Module):
def __init__(self, dim, eps=1e-5, device=None, dtype=None):
super().__init__()
self.dim = dim
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim, device=device, dtype=dtype))
def forward(self, x):
r"""
Args:
x(Tensor): Shape [B, L, C]
"""
return self._norm(x.float()).type_as(x) * comfy.model_management.cast_to(self.weight, dtype=x.dtype, device=x.device)
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
class WanSelfAttention(nn.Module): class WanSelfAttention(nn.Module):
def __init__(self, def __init__(self,
@ -66,8 +49,8 @@ class WanSelfAttention(nn.Module):
self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) 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.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.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
self.norm_q = WanRMSNorm(dim, eps=eps, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() 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 = WanRMSNorm(dim, eps=eps, 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()
def forward(self, x, freqs): def forward(self, x, freqs):
r""" r"""
@ -131,7 +114,7 @@ class WanI2VCrossAttention(WanSelfAttention):
self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) 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.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.alpha = nn.Parameter(torch.zeros((1, )))
self.norm_k_img = WanRMSNorm(dim, eps=eps, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() 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()
def forward(self, x, context): def forward(self, x, context):
r""" r"""

2
comfy/model_patcher.py
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@ -639,7 +639,7 @@ class ModelPatcher:
mem_counter += module_mem mem_counter += module_mem
load_completely.append((module_mem, n, m, params)) load_completely.append((module_mem, n, m, params))
if cast_weight: if cast_weight and hasattr(m, "comfy_cast_weights"):
m.prev_comfy_cast_weights = m.comfy_cast_weights m.prev_comfy_cast_weights = m.comfy_cast_weights
m.comfy_cast_weights = True m.comfy_cast_weights = True