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fix: regression in text generate with LTXAV model (#13170)

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Jukka Seppänen 2026-03-26 18:55:05 +02:00 committed by GitHub
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commit b0fd65e884
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1 changed files with 4 additions and 4 deletions
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comfy/text_encoders/lt.py
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@ -91,11 +91,11 @@ class Gemma3_12BModel(sd1_clip.SDClipModel):
self.dtypes.add(dtype)
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_12B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty):
tokens_only = [[t[0] for t in b] for b in tokens]
embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device)
comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)
return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106]) # 106 is <end_of_turn>
return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106], presence_penalty=presence_penalty) # 106 is <end_of_turn>
class DualLinearProjection(torch.nn.Module):
def __init__(self, in_dim, out_dim_video, out_dim_audio, dtype=None, device=None, operations=None):
@ -189,8 +189,8 @@ class LTXAVTEModel(torch.nn.Module):
return out.to(device=out_device, dtype=torch.float), pooled, extra
def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty):
return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty)
def load_sd(self, sd):
if "model.layers.47.self_attn.q_norm.weight" in sd: