pass options to cross attention class

ldm_patched/ldm/modules/attention.py

@@ -385,7 +385,7 @@ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.
 
  self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
 
- def forward(self, x, context=None, value=None, mask=None):
+ def forward(self, x, context=None, value=None, mask=None, transformer_options=None):
  q = self.to_q(x)
  context = default(context, x)
  k = self.to_k(context)
@@ -504,7 +504,7 @@ def _forward(self, x, context=None, transformer_options={}):
  n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
  n = self.attn1.to_out(n)
  else:
- n = self.attn1(n, context=context_attn1, value=value_attn1)
+ n = self.attn1(n, context=context_attn1, value=value_attn1, transformer_options=extra_options)
 
  if "attn1_output_patch" in transformer_patches:
  patch = transformer_patches["attn1_output_patch"]
@@ -544,7 +544,7 @@ def _forward(self, x, context=None, transformer_options={}):
  n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
  n = self.attn2.to_out(n)
  else:
- n = self.attn2(n, context=context_attn2, value=value_attn2)
+ n = self.attn2(n, context=context_attn2, value=value_attn2, transformer_options=extra_options)
 
  if "attn2_output_patch" in transformer_patches:
  patch = transformer_patches["attn2_output_patch"]