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Running
on
Zero
File size: 3,608 Bytes
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import torch
import math
import comfy.utils
class CONDRegular:
def __init__(self, cond):
self.cond = cond
def _copy_with(self, cond):
return self.__class__(cond)
def process_cond(self, batch_size, device, **kwargs):
return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device))
def can_concat(self, other):
if self.cond.shape != other.cond.shape:
return False
return True
def concat(self, others):
conds = [self.cond]
for x in others:
conds.append(x.cond)
return torch.cat(conds)
def size(self):
return list(self.cond.size())
class CONDNoiseShape(CONDRegular):
def process_cond(self, batch_size, device, area, **kwargs):
data = self.cond
if area is not None:
dims = len(area) // 2
for i in range(dims):
data = data.narrow(i + 2, area[i + dims], area[i])
return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device))
class CONDCrossAttn(CONDRegular):
def can_concat(self, other):
s1 = self.cond.shape
s2 = other.cond.shape
if s1 != s2:
if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
return False
mult_min = math.lcm(s1[1], s2[1])
diff = mult_min // min(s1[1], s2[1])
if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
return False
return True
def concat(self, others):
conds = [self.cond]
crossattn_max_len = self.cond.shape[1]
for x in others:
c = x.cond
crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])
conds.append(c)
out = []
for c in conds:
if c.shape[1] < crossattn_max_len:
c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
out.append(c)
return torch.cat(out)
class CONDConstant(CONDRegular):
def __init__(self, cond):
self.cond = cond
def process_cond(self, batch_size, device, **kwargs):
return self._copy_with(self.cond)
def can_concat(self, other):
if self.cond != other.cond:
return False
return True
def concat(self, others):
return self.cond
def size(self):
return [1]
class CONDList(CONDRegular):
def __init__(self, cond):
self.cond = cond
def process_cond(self, batch_size, device, **kwargs):
out = []
for c in self.cond:
out.append(comfy.utils.repeat_to_batch_size(c, batch_size).to(device))
return self._copy_with(out)
def can_concat(self, other):
if len(self.cond) != len(other.cond):
return False
for i in range(len(self.cond)):
if self.cond[i].shape != other.cond[i].shape:
return False
return True
def concat(self, others):
out = []
for i in range(len(self.cond)):
o = [self.cond[i]]
for x in others:
o.append(x.cond[i])
out.append(torch.cat(o))
return out
def size(self): # hackish implementation to make the mem estimation work
o = 0
c = 1
for c in self.cond:
size = c.size()
o += math.prod(size)
if len(size) > 1:
c = size[1]
return [1, c, o // c]
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