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Implemented EasyCache for aura_flow

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Jedrzej Kosinski 2025-08-22 00:11:43 -07:00
parent e1b94339b7
commit 73ca950662
1 changed files with 8 additions and 0 deletions
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8
comfy/ldm/aura/mmdit.py
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@ -9,6 +9,7 @@ import torch.nn.functional as F
from comfy.ldm.modules.attention import optimized_attention from comfy.ldm.modules.attention import optimized_attention
import comfy.ops import comfy.ops
import comfy.patcher_extension
import comfy.ldm.common_dit import comfy.ldm.common_dit
def modulate(x, shift, scale): def modulate(x, shift, scale):
@ -436,6 +437,13 @@ class MMDiT(nn.Module):
return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1]) return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
def forward(self, x, timestep, context, transformer_options={}, **kwargs): def forward(self, x, timestep, context, transformer_options={}, **kwargs):
return comfy.patcher_extension.WrapperExecutor.new_class_executor(
self._forward,
self,
comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
).execute(x, timestep, context, transformer_options, **kwargs)
def _forward(self, x, timestep, context, transformer_options={}, **kwargs):
patches_replace = transformer_options.get("patches_replace", {}) patches_replace = transformer_options.get("patches_replace", {})
# patchify x, add PE # patchify x, add PE
b, c, h, w = x.shape b, c, h, w = x.shape