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replace duplicate code with a function

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AUTOMATIC 2022-10-11 11:09:51 +03:00
parent 5e2627a1a6
commit 948533950c
2 changed files with 29 additions and 38 deletions
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23
modules/hypernetwork.py
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@ -64,21 +64,26 @@ def load_hypernetwork(filename):
shared.loaded_hypernetwork = None
def apply_hypernetwork(hypernetwork, context):
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
if hypernetwork_layers is None:
return context, context
context_k = hypernetwork_layers[0](context)
context_v = hypernetwork_layers[1](context)
return context_k, context_v
def attention_CrossAttention_forward(self, x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
hypernetwork = shared.loaded_hypernetwork
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
if hypernetwork_layers is not None:
k = self.to_k(hypernetwork_layers[0](context))
v = self.to_v(hypernetwork_layers[1](context))
else:
k = self.to_k(context)
v = self.to_v(context)
context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context)
k = self.to_k(context_k)
v = self.to_v(context_v)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))

44
modules/sd_hijack_optimizations.py
View file

@ -8,7 +8,8 @@ from torch import einsum
from ldm.util import default
from einops import rearrange
from modules import shared
from modules import shared, hypernetwork
if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
try:
@ -26,16 +27,10 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
q_in = self.to_q(x)
context = default(context, x)
hypernetwork = shared.loaded_hypernetwork
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
if hypernetwork_layers is not None:
k_in = self.to_k(hypernetwork_layers[0](context))
v_in = self.to_v(hypernetwork_layers[1](context))
else:
k_in = self.to_k(context)
v_in = self.to_v(context)
del context, x
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
del context, context_k, context_v, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
@ -59,22 +54,16 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
return self.to_out(r2)
# taken from https://github.com/Doggettx/stable-diffusion
# taken from https://github.com/Doggettx/stable-diffusion and modified
def split_cross_attention_forward(self, x, context=None, mask=None):
h = self.heads
q_in = self.to_q(x)
context = default(context, x)
hypernetwork = shared.loaded_hypernetwork
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
if hypernetwork_layers is not None:
k_in = self.to_k(hypernetwork_layers[0](context))
v_in = self.to_v(hypernetwork_layers[1](context))
else:
k_in = self.to_k(context)
v_in = self.to_v(context)
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
k_in *= self.scale
@ -130,14 +119,11 @@ def xformers_attention_forward(self, x, context=None, mask=None):
h = self.heads
q_in = self.to_q(x)
context = default(context, x)
hypernetwork = shared.loaded_hypernetwork
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
if hypernetwork_layers is not None:
k_in = self.to_k(hypernetwork_layers[0](context))
v_in = self.to_v(hypernetwork_layers[1](context))
else:
k_in = self.to_k(context)
v_in = self.to_v(context)
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)