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Add scaled_dot_product_attention fallback to controlmodel_ipadapter.py for PyTorch v1 compatibility #2707

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Add scaled_dot_product_attention fallback to controlmodel_ipadapter.py for PyTorch v1 compatibility #2707

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90bc061
Add scaled_dot_product_attention fallback to controlmodel_ipadapter.p…
ochen1 Mar 29, 2024
34b964f
Merge for scaled_dot_product_attention backwards compatibility
ochen1 Mar 31, 2024
642cc4d
Merge branch 'main' of https://github.com/Mikubill/sd-webui-controlnet
ochen1 Mar 31, 2024
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33 changes: 31 additions & 2 deletions scripts/ipadapter/plugable_ipadapter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,35 @@
from .ipadapter_model import ImageEmbed, IPAdapterModel


def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None) -> torch.Tensor:
# Fallback implementation for PyTorch v1 compatibility (less efficient)
# Slightly modified from: https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
L, S = query.size(-2), key.size(-2)
scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
attn_bias = torch.zeros(L, S, dtype=query.dtype, device=query.device)
if is_causal:
assert attn_mask is None
temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
attn_bias.to(query.dtype)

if attn_mask is not None:
if attn_mask.dtype == torch.bool:
attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf"))
else:
attn_bias += attn_mask
attn_weight = query @ key.transpose(-2, -1) * scale_factor
attn_weight += attn_bias
attn_weight = torch.softmax(attn_weight, dim=-1)
attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
return attn_weight @ value

try:
scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
except AttributeError:
pass


def get_block(model, flag):
return {
"input": model.input_blocks,
Expand Down Expand Up @@ -30,7 +59,7 @@ def attn_forward_hacked(self, x, context=None, **kwargs):
(q, k, v),
)

out = torch.nn.functional.scaled_dot_product_attention(
out = scaled_dot_product_attention(
q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False
)
out = out.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
Expand Down Expand Up @@ -227,7 +256,7 @@ def forward(attn_blk, x, q):
ip_k = ip_k.to(dtype=q.dtype)
ip_v = ip_v.to(dtype=q.dtype)

ip_out = torch.nn.functional.scaled_dot_product_attention(
ip_out = scaled_dot_product_attention(
q, ip_k, ip_v, attn_mask=None, dropout_p=0.0, is_causal=False
)
ip_out = ip_out.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
Expand Down