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Fix lowvram issue with ltxv2 text encoder. (#11675)

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comfyanonymous 2026-01-06 14:33:03 -08:00 committed by GitHub
parent c3566c0d76
commit 023cf13721
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2 changed files with 4 additions and 2 deletions
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2
comfy/ldm/lightricks/embeddings_connector.py
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@ -276,7 +276,7 @@ class Embeddings1DConnector(nn.Module):
max(1024, hidden_states.shape[1]) / self.num_learnable_registers
)
learnable_registers = torch.tile(
self.learnable_registers, (num_registers_duplications, 1)
self.learnable_registers.to(hidden_states), (num_registers_duplications, 1)
)
hidden_states = torch.cat((hidden_states, learnable_registers[hidden_states.shape[1]:].unsqueeze(0).repeat(hidden_states.shape[0], 1, 1)), dim=1)

4
comfy/text_encoders/lt.py
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@ -86,17 +86,19 @@ class LTXAVTEModel(torch.nn.Module):
)
def set_clip_options(self, options):
self.execution_device = options.get("execution_device", self.execution_device)
self.gemma3_12b.set_clip_options(options)
def reset_clip_options(self):
self.gemma3_12b.reset_clip_options()
self.execution_device = None
def encode_token_weights(self, token_weight_pairs):
token_weight_pairs = token_weight_pairs["gemma3_12b"]
out, pooled, extra = self.gemma3_12b.encode_token_weights(token_weight_pairs)
out_device = out.device
out = out.movedim(1, -1).to(self.text_embedding_projection.weight.device)
out = out.movedim(1, -1).to(self.execution_device)
out = 8.0 * (out - out.mean(dim=(1, 2), keepdim=True)) / (out.amax(dim=(1, 2), keepdim=True) - out.amin(dim=(1, 2), keepdim=True) + 1e-6)
out = out.reshape((out.shape[0], out.shape[1], -1))
out = self.text_embedding_projection(out)