From e8d3f1cca5b613e6fe6e289293ec557e448adf2a Mon Sep 17 00:00:00 2001 From: kabachuha Date: Thu, 12 Jun 2025 15:30:30 +0300 Subject: [PATCH] fixup when context lens are none --- nodes.py | 3 --- wanvideo/modules/model.py | 10 +++++++--- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/nodes.py b/nodes.py index 4b5647c..853bc05 100644 --- a/nodes.py +++ b/nodes.py @@ -3064,11 +3064,8 @@ class WanVideoSampler: negative_embeds = negative_embeds * len(positive_embeds) if use_nag: - print("Nag triggered!") nag_negative_prompt_embeds = negative_embeds - print(positive_embeds.shape) positive_embeds = torch.cat([positive_embeds, nag_negative_prompt_embeds], dim=0) - print(positive_embeds.shape) if not batched_cfg: #cond diff --git a/wanvideo/modules/model.py b/wanvideo/modules/model.py index 19f8526..49ef653 100644 --- a/wanvideo/modules/model.py +++ b/wanvideo/modules/model.py @@ -380,8 +380,12 @@ class WanT2VCrossAttention(WanSelfAttention): batch_size = b context_positive = context[:batch_size] context_negative = context[batch_size:] - context_lens_positive = context_lens[:batch_size] - context_lens_negative = context_lens[batch_size:] + if context_lens is not None: + context_lens_positive = context_lens[:batch_size] + context_lens_negative = context_lens[batch_size:] + else: + context_lens_positive = None + context_lens_negative = None k_positive = self.norm_k(self.k(context_positive)).view(batch_size, -1, n, d) v_positive = self.v(context_positive).view(batch_size, -1, n, d) @@ -408,7 +412,7 @@ class WanT2VCrossAttention(WanSelfAttention): x_text = hidden_states_guidance * self.nag_alpha + hidden_states_positive * (1 - self.nag_alpha) - print("Nag applied in apply_guidance!") + #print("Nag applied in apply_guidance!") else: k = self.norm_k(self.k(context)).view(b, -1, n, d) v = self.v(context).view(b, -1, n, d)