Correct spelling 'token_weight_pars_t5' to 'token_weight_pairs_t5' (#4200)
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@ -52,9 +52,9 @@ class FluxClipModel(torch.nn.Module):
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def encode_token_weights(self, token_weight_pairs):
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token_weight_pairs_l = token_weight_pairs["l"]
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token_weight_pars_t5 = token_weight_pairs["t5xxl"]
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token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
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t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
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t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
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l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
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return t5_out, l_pooled
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@ -81,7 +81,7 @@ class SD3ClipModel(torch.nn.Module):
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def encode_token_weights(self, token_weight_pairs):
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token_weight_pairs_l = token_weight_pairs["l"]
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token_weight_pairs_g = token_weight_pairs["g"]
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token_weight_pars_t5 = token_weight_pairs["t5xxl"]
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token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
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lg_out = None
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pooled = None
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out = None
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@ -108,7 +108,7 @@ class SD3ClipModel(torch.nn.Module):
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pooled = torch.cat((l_pooled, g_pooled), dim=-1)
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if self.t5xxl is not None:
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t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
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t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
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if lg_out is not None:
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out = torch.cat([lg_out, t5_out], dim=-2)
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else:
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