Free all old model patchers, gc.collect(), then free tensors.

.coderabbit.yaml

@@ -4,12 +4,12 @@ early_access: false
 tone_instructions: "Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code."
 
 reviews:
-  profile: "assertive"
-  request_changes_workflow: true
+  profile: "chill"
+  request_changes_workflow: false
   high_level_summary: false
   poem: false
   review_status: false
-  review_details: true
+  review_details: false
   commit_status: true
   collapse_walkthrough: true
   changed_files_summary: false
@@ -39,14 +39,6 @@ reviews:
     - path: "**"
       instructions: |
         IMPORTANT: Only comment on issues directly introduced by this PR's code changes.
-        Treat AGENTS.md as mandatory repository policy, not optional style guidance.
-        Flag PR changes that violate AGENTS.md even when the code is otherwise functional.
-        In particular, enforce architecture boundaries, dtype/device/memory rules,
-        interface contracts, import style, no unnecessary try/except blocks, no inline
-        imports, no outbound internet paths in core ComfyUI, and narrow scoped fixes.
-        Prefer direct findings over suggestions when a rule is violated. Only ignore
-        AGENTS.md when it clearly conflicts with a newer explicit maintainer instruction
-        in the PR.
         Do NOT flag pre-existing issues in code that was merely moved, re-indented,
         de-indented, or reformatted without logic changes. If code appears in the diff
         only due to whitespace or structural reformatting (e.g., removing a `with:` block),
@@ -131,10 +123,5 @@ chat:
 
 knowledge_base:
   opt_out: false
-  code_guidelines:
-    enabled: true
-    filePatterns:
-      - files: "AGENTS.md"
-        applyTo: "**"
   learnings:
     scope: "auto"

CLAUDE.md

@@ -1 +0,0 @@
-AGENTS.md

comfy/sd1_clip.py

@@ -543,24 +543,18 @@ class SDTokenizer:
     def _try_get_embedding(self, embedding_name:str):
         '''
         Takes a potential embedding name and tries to retrieve it.
-        Returns a Tuple consisting of the embedding, the cleaned embedding name, and any leftover string, embedding can be None.
+        Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
         '''
         split_embed = embedding_name.split()
         embedding_name = split_embed[0]
         leftover = ' '.join(split_embed[1:])
-
-        match = re.search(r'[<\[]', embedding_name)
-        if match is not None:
-            leftover = embedding_name[match.start():] + (" " + leftover if leftover else "")
-            embedding_name = embedding_name[:match.start()]
-
         embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
         if embed is None:
             stripped = embedding_name.strip(',')
             if len(stripped) < len(embedding_name):
                 embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
-                return (embed, embedding_name, "{} {}".format(embedding_name[len(stripped):], leftover))
-        return (embed, embedding_name, leftover)
+                return (embed, "{} {}".format(embedding_name[len(stripped):], leftover))
+        return (embed, leftover)
 
     def pad_tokens(self, tokens, amount):
         if self.pad_left:
@@ -591,7 +585,7 @@ class SDTokenizer:
         tokens = []
         for weighted_segment, weight in parsed_weights:
             to_tokenize = unescape_important(weighted_segment)
-            split = re.split(r'(?<=\s){}'.format(re.escape(self.embedding_identifier)), to_tokenize)
+            split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize)
             to_tokenize = [split[0]]
             for i in range(1, len(split)):
                 to_tokenize.append("{}{}".format(self.embedding_identifier, split[i]))
@@ -601,7 +595,7 @@ class SDTokenizer:
                 # if we find an embedding, deal with the embedding
                 if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
                     embedding_name = word[len(self.embedding_identifier):].strip('\n')
-                    embed, embedding_name, leftover = self._try_get_embedding(embedding_name)
+                    embed, leftover = self._try_get_embedding(embedding_name)
                     if embed is None:
                         logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
                     else:

comfy/text_encoders/llama.py

@@ -937,41 +937,22 @@ class BaseGenerate:
             return torch.argmax(logits, dim=-1, keepdim=True)
 
         # Sampling mode
-        if len(token_history) > 0 and (repetition_penalty != 1.0 or (presence_penalty is not None and presence_penalty != 0.0)):
-            token_ids = torch.tensor(list(set(token_history)), device=logits.device)
-            token_logits = logits[:, token_ids]
-            if repetition_penalty != 1.0:
-                token_logits = torch.where(token_logits < 0, token_logits * repetition_penalty, token_logits / repetition_penalty)
-            if presence_penalty is not None and presence_penalty != 0.0:
-                token_logits = token_logits - presence_penalty
-            logits[:, token_ids] = token_logits
+        if repetition_penalty != 1.0:
+            for i in range(logits.shape[0]):
+                for token_id in set(token_history):
+                    logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
+
+        if presence_penalty is not None and presence_penalty != 0.0:
+            for i in range(logits.shape[0]):
+                for token_id in set(token_history):
+                    logits[i, token_id] -= presence_penalty
 
         if temperature != 1.0:
             logits = logits / temperature
 
         if top_k > 0:
-            top_k = min(top_k, logits.shape[-1])
-            logits, top_indices = torch.topk(logits, top_k)
-
-            if min_p > 0.0:
-                probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)
-                top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True)
-                min_threshold = min_p * top_probs
-                indices_to_remove = probs_before_filter < min_threshold
-                logits[indices_to_remove] = torch.finfo(logits.dtype).min
-
-            if top_p < 1.0:
-                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
-                cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
-                sorted_indices_to_remove = cumulative_probs > top_p
-                sorted_indices_to_remove[..., 0] = False
-                indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
-                indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)
-                logits[indices_to_remove] = torch.finfo(logits.dtype).min
-
-            probs = torch.nn.functional.softmax(logits, dim=-1)
-            next_token = torch.multinomial(probs, num_samples=1, generator=generator)
-            return top_indices.gather(1, next_token)
+            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+            logits[indices_to_remove] = torch.finfo(logits.dtype).min
 
         if min_p > 0.0:
             probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)

comfy_execution/caching.py

@@ -1,5 +1,6 @@
 import asyncio
 import bisect
+import gc
 import itertools
 import psutil
 import time
@@ -528,6 +529,38 @@ class RAMPressureCache(LRUCache):
         if psutil.virtual_memory().available >= target:
             return
 
+        def remove_cache_key(key):
+            del self.cache[key]
+            self.used_generation.pop(key, None)
+            self.timestamps.pop(key, None)
+            self.children.pop(key, None)
+
+        def has_old_model_patcher(outputs):
+            if outputs is None:
+                return False
+            for output in outputs:
+                if isinstance(output, (list, tuple)):
+                    if has_old_model_patcher(output):
+                        return True
+                elif isinstance(output, ModelPatcher):
+                    return True
+            return False
+
+        old_modelpatcher_keys = []
+        for key, cache_entry in self.cache.items():
+            if self.used_generation[key] == self.generation:
+                continue
+            if has_old_model_patcher(cache_entry.outputs):
+                old_modelpatcher_keys.append(key)
+
+        for key in old_modelpatcher_keys:
+            remove_cache_key(key)
+
+        if old_modelpatcher_keys:
+            gc.collect()
+            if psutil.virtual_memory().available >= target:
+                return
+
         clean_list = []
 
         for key, cache_entry in self.cache.items():
@@ -545,19 +578,17 @@ class RAMPressureCache(LRUCache):
                         scan_list_for_ram_usage(output)
                     elif isinstance(output, torch.Tensor) and output.device.type == 'cpu':
                         ram_usage += output.numel() * output.element_size()
-                    elif isinstance(output, ModelPatcher) and self.used_generation[key] != self.generation:
-                        #old ModelPatchers are the first to go
-                        ram_usage = 1e30
             scan_list_for_ram_usage(cache_entry.outputs)
 
             oom_score *= ram_usage
             #In the case where we have no information on the node ram usage at all,
             #break OOM score ties on the last touch timestamp (pure LRU)
-            bisect.insort(clean_list, (oom_score, self.timestamps[key], key))
+            bisect.insort(clean_list, (oom_score, self.timestamps[key], ram_usage, key))
 
-        while psutil.virtual_memory().available < target and clean_list:
-            _, _, key = clean_list.pop()
-            del self.cache[key]
-            self.used_generation.pop(key, None)
-            self.timestamps.pop(key, None)
-            self.children.pop(key, None)
+        to_free = target - psutil.virtual_memory().available
+        while to_free > 0 and clean_list:
+            _, _, ram_usage, key = clean_list.pop()
+            remove_cache_key(key)
+            to_free -= ram_usage
+
+        gc.collect()

comfy_extras/nodes_color.py

@@ -16,30 +16,23 @@ class ColorToRGBInt(io.ComfyNode):
             ],
             outputs=[
                 io.Int.Output(display_name="rgb_int"),
-                io.Color.Output(display_name="hex"),
-                io.Float.Output(display_name="alpha"),
+                io.Color.Output(display_name="hex")
             ],
         )
 
     @classmethod
     def execute(cls, color: str) -> io.NodeOutput:
-        # expect format #RRGGBB or #RRGGBBAA
-        if len(color) not in (7, 9) or color[0] != "#":
-            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA")
+        # expect format #RRGGBB
+        if len(color) != 7 or color[0] != "#":
+            raise ValueError("Color must be in format #RRGGBB")
         try:
             int(color[1:], 16)
         except ValueError:
-            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA") from None
-
-        alpha = 1.0
-        if len(color) == 9:
-            alpha = int(color[7:9], 16) / 255.0
-            color = color[:7]
-
+            raise ValueError("Color must be in format #RRGGBB") from None
         r, g, b = hex_to_rgb(color)
 
         rgb_int = r * 256 * 256 + g * 256 + b
-        return io.NodeOutput(rgb_int, color, alpha)
+        return io.NodeOutput(rgb_int, color)
 
 
 class ColorExtension(ComfyExtension):

main.py

@@ -314,7 +314,7 @@ def prompt_worker(q, server_instance):
     cache_ram = 0
     cache_ram_inactive = 0
     if not args.cache_classic and not args.cache_none and args.cache_lru <= 0:
-        cache_ram = min(10.0, max(2.0, comfy.model_management.total_ram * 0.10 / 1024.0))
+        cache_ram = min(10.0, max(1.5, comfy.model_management.total_ram * 0.05 / 1024.0))
         cache_ram_inactive = min(96.0, comfy.model_management.total_ram / 1024.0)
         if len(args.cache_ram) > 0:
             cache_ram = args.cache_ram[0]