Cleaner code.
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@ -8,6 +8,7 @@ from torch import Tensor, nn
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from .math import attention, rope
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import comfy.ops
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class EmbedND(nn.Module):
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def __init__(self, dim: int, theta: int, axes_dim: list):
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super().__init__()
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@ -174,20 +175,19 @@ class DoubleStreamBlock(nn.Module):
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txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
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# run actual attention
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q = torch.cat((txt_q, img_q), dim=2)
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k = torch.cat((txt_k, img_k), dim=2)
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v = torch.cat((txt_v, img_v), dim=2)
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attn = attention(torch.cat((txt_q, img_q), dim=2),
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torch.cat((txt_k, img_k), dim=2),
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torch.cat((txt_v, img_v), dim=2), pe=pe)
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attn = attention(q, k, v, pe=pe)
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txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
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# calculate the img bloks
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img = img + img_mod1.gate * self.img_attn.proj(img_attn)
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img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
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img += img_mod1.gate * self.img_attn.proj(img_attn)
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img += img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
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# calculate the txt bloks
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txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
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txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
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txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
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txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
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if txt.dtype == torch.float16:
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txt = txt.clip(-65504, 65504)
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@ -243,7 +243,7 @@ class SingleStreamBlock(nn.Module):
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attn = attention(q, k, v, pe=pe)
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# compute activation in mlp stream, cat again and run second linear layer
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output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
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x = x + mod.gate * output
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x += mod.gate * output
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if x.dtype == torch.float16:
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x = x.clip(-65504, 65504)
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return x
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