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Add progress bar to ace step. (#12242)
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comfyanonymous 2026-02-03 01:09:30 -08:00 committed by GitHub
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comfy/text_encoders/ace15.py
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@ -3,6 +3,7 @@ import comfy.text_encoders.llama
from comfy import sd1_clip from comfy import sd1_clip
import torch import torch
import math import math
import comfy.utils
def sample_manual_loop_no_classes( def sample_manual_loop_no_classes(
@ -42,6 +43,8 @@ def sample_manual_loop_no_classes(
for x in range(model_config.num_hidden_layers): for x in range(model_config.num_hidden_layers):
past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim], device=device, dtype=execution_dtype), torch.empty([embeds.shape[0], model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim], device=device, dtype=execution_dtype), 0)) past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim], device=device, dtype=execution_dtype), torch.empty([embeds.shape[0], model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim], device=device, dtype=execution_dtype), 0))
progress_bar = comfy.utils.ProgressBar(max_new_tokens)
for step in range(max_new_tokens): for step in range(max_new_tokens):
outputs = model.transformer(None, attention_mask, embeds=embeds.to(execution_dtype), num_tokens=num_tokens, intermediate_output=None, dtype=execution_dtype, embeds_info=embeds_info, past_key_values=past_key_values) outputs = model.transformer(None, attention_mask, embeds=embeds.to(execution_dtype), num_tokens=num_tokens, intermediate_output=None, dtype=execution_dtype, embeds_info=embeds_info, past_key_values=past_key_values)
next_token_logits = model.transformer.logits(outputs[0])[:, -1] next_token_logits = model.transformer.logits(outputs[0])[:, -1]
@ -90,6 +93,7 @@ def sample_manual_loop_no_classes(
attention_mask = torch.cat([attention_mask, torch.ones((2, 1), device=device, dtype=attention_mask.dtype)], dim=1) attention_mask = torch.cat([attention_mask, torch.ones((2, 1), device=device, dtype=attention_mask.dtype)], dim=1)
output_audio_codes.append(token - audio_start_id) output_audio_codes.append(token - audio_start_id)
progress_bar.update_absolute(step)
return output_audio_codes return output_audio_codes