Merge 75f6fad091 into affe881354

fix(clip_vision): update line 59 load_model_gpu(self.patcher) to load_models_gpu([self.patcher]) to match model_management.py (for plural function) and iterable argument requirement

comfy/clip_vision.py

@@ -56,7 +56,7 @@ class ClipVisionModel():
         return self.model.state_dict()
 
     def encode_image(self, image, crop=True):
-        comfy.model_management.load_model_gpu(self.patcher)
+        comfy.model_management.load_models_gpu([self.patcher])
         if self.model_type == "siglip2_vision_model":
             pixel_values = comfy.clip_model.siglip2_preprocess(image.to(self.load_device), size=self.image_size, patch_size=self.config.get("patch_size", 16), num_patches=self.config.get("num_patches", 256), mean=self.image_mean, std=self.image_std, crop=crop).float()
         else:

comfy/text_encoders/ace15.py

@@ -3,6 +3,7 @@ import comfy.text_encoders.llama
 from comfy import sd1_clip
 import torch
 import math
+import comfy.utils
 
 
 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):
         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):
         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]
@@ -54,8 +57,9 @@ def sample_manual_loop_no_classes(
         if eos_token_id is not None and eos_token_id < audio_start_id and min_tokens < step:
             eos_score = cfg_logits[:, eos_token_id].clone()
 
+        remove_logit_value = torch.finfo(cfg_logits.dtype).min
         # Only generate audio tokens
-        cfg_logits[:, :audio_start_id] = float('-inf')
+        cfg_logits[:, :audio_start_id] = remove_logit_value
 
         if eos_token_id is not None and eos_token_id < audio_start_id and min_tokens < step:
             cfg_logits[:, eos_token_id] = eos_score
@@ -63,7 +67,7 @@ def sample_manual_loop_no_classes(
         if top_k is not None and top_k > 0:
             top_k_vals, _ = torch.topk(cfg_logits, top_k)
             min_val = top_k_vals[..., -1, None]
-            cfg_logits[cfg_logits < min_val] = float('-inf')
+            cfg_logits[cfg_logits < min_val] = remove_logit_value
 
         if top_p is not None and top_p < 1.0:
             sorted_logits, sorted_indices = torch.sort(cfg_logits, descending=True)
@@ -72,7 +76,7 @@ def sample_manual_loop_no_classes(
             sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
             sorted_indices_to_remove[..., 0] = 0
             indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
-            cfg_logits[indices_to_remove] = float('-inf')
+            cfg_logits[indices_to_remove] = remove_logit_value
 
         if temperature > 0:
             cfg_logits = cfg_logits / temperature
@@ -90,6 +94,7 @@ def sample_manual_loop_no_classes(
         attention_mask = torch.cat([attention_mask, torch.ones((2, 1), device=device, dtype=attention_mask.dtype)], dim=1)
 
         output_audio_codes.append(token - audio_start_id)
+        progress_bar.update_absolute(step)
 
     return output_audio_codes