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65 lines
2.0 KiB
65 lines
2.0 KiB
1 year ago
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import enum
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import torch
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import math
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import comfy.utils
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def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
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return abs(a*b) // math.gcd(a, b)
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class CONDRegular:
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def __init__(self, cond):
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self.cond = cond
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def _copy_with(self, cond):
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return self.__class__(cond)
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def process_cond(self, batch_size, device, **kwargs):
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return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device))
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def can_concat(self, other):
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if self.cond.shape != other.cond.shape:
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return False
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return True
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def concat(self, others):
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conds = [self.cond]
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for x in others:
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conds.append(x.cond)
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return torch.cat(conds)
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class CONDNoiseShape(CONDRegular):
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def process_cond(self, batch_size, device, area, **kwargs):
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data = self.cond[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
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return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device))
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class CONDCrossAttn(CONDRegular):
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def can_concat(self, other):
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s1 = self.cond.shape
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s2 = other.cond.shape
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if s1 != s2:
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if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
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return False
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mult_min = lcm(s1[1], s2[1])
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diff = mult_min // min(s1[1], s2[1])
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if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
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return False
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return True
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def concat(self, others):
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conds = [self.cond]
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crossattn_max_len = self.cond.shape[1]
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for x in others:
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c = x.cond
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crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
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conds.append(c)
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out = []
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for c in conds:
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if c.shape[1] < crossattn_max_len:
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c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
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out.append(c)
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return torch.cat(out)
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