Merge 2500eee072 into b08debceca

Signed-off-by: bigcat88 <bigcat88@icloud.com>

.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: "chill"
-  request_changes_workflow: false
+  profile: "assertive"
+  request_changes_workflow: true
   high_level_summary: false
   poem: false
   review_status: false
-  review_details: false
+  review_details: true
   commit_status: true
   collapse_walkthrough: true
   changed_files_summary: false
@@ -39,6 +39,14 @@ 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),
@@ -123,5 +131,10 @@ chat:
 
 knowledge_base:
   opt_out: false
+  code_guidelines:
+    enabled: true
+    filePatterns:
+      - files: "AGENTS.md"
+        applyTo: "**"
   learnings:
     scope: "auto"

comfy/sd1_clip.py

@@ -543,18 +543,24 @@ 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 and any leftover string, embedding can be None.
+        Returns a Tuple consisting of the embedding, the cleaned embedding name, 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, "{} {}".format(embedding_name[len(stripped):], leftover))
-        return (embed, leftover)
+                return (embed, embedding_name, "{} {}".format(embedding_name[len(stripped):], leftover))
+        return (embed, embedding_name, leftover)
 
     def pad_tokens(self, tokens, amount):
         if self.pad_left:
@@ -585,7 +591,7 @@ class SDTokenizer:
         tokens = []
         for weighted_segment, weight in parsed_weights:
             to_tokenize = unescape_important(weighted_segment)
-            split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize)
+            split = re.split(r'(?<=\s){}'.format(re.escape(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]))
@@ -595,7 +601,7 @@ 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, leftover = self._try_get_embedding(embedding_name)
+                    embed, embedding_name, 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

@@ -937,22 +937,41 @@ class BaseGenerate:
             return torch.argmax(logits, dim=-1, keepdim=True)
 
         # Sampling mode
-        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 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 temperature != 1.0:
             logits = logits / temperature
 
         if top_k > 0:
-            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
-            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+            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)
 
         if min_p > 0.0:
             probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)

comfy_api_nodes/util/request_logger.py

@@ -9,6 +9,7 @@ from typing import Any
 import folder_paths
 
 logger = logging.getLogger(__name__)
+_SENSITIVE_HEADERS = {"authorization", "x-api-key"}
 
 
 def get_log_directory():
@@ -73,6 +74,10 @@ def _format_data_for_logging(data: Any) -> str:
     return str(data)
 
 
+def _redact_headers(headers: dict) -> dict:
+    return {k: ("***" if k.lower() in _SENSITIVE_HEADERS else v) for k, v in headers.items()}
+
+
 def log_request_response(
     operation_id: str,
     request_method: str,
@@ -101,7 +106,7 @@ def log_request_response(
         log_content.append(f"Method: {request_method}")
         log_content.append(f"URL: {request_url}")
         if request_headers:
-            log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
+            log_content.append(f"Headers:\n{_format_data_for_logging(_redact_headers(request_headers))}")
         if request_params:
             log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
         if request_data is not None:

comfy_extras/nodes_color.py

@@ -16,23 +16,30 @@ class ColorToRGBInt(io.ComfyNode):
             ],
             outputs=[
                 io.Int.Output(display_name="rgb_int"),
-                io.Color.Output(display_name="hex")
+                io.Color.Output(display_name="hex"),
+                io.Float.Output(display_name="alpha"),
             ],
         )
 
     @classmethod
     def execute(cls, color: str) -> io.NodeOutput:
-        # expect format #RRGGBB
-        if len(color) != 7 or color[0] != "#":
-            raise ValueError("Color must be in format #RRGGBB")
+        # 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")
         try:
             int(color[1:], 16)
         except ValueError:
-            raise ValueError("Color must be in format #RRGGBB") from None
+            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]
+
         r, g, b = hex_to_rgb(color)
 
         rgb_int = r * 256 * 256 + g * 256 + b
-        return io.NodeOutput(rgb_int, color)
+        return io.NodeOutput(rgb_int, color, alpha)
 
 
 class ColorExtension(ComfyExtension):

nodes.py

@@ -2200,6 +2200,10 @@ EXTENSION_WEB_DIRS = {}
 # Dictionary of successfully loaded module names and associated directories.
 LOADED_MODULE_DIRS = {}
 
+# Dictionary of import failure reasons keyed by module path.
+# Used to provide diagnostic information in the import summary.
+IMPORT_FAILED_REASONS: dict[str, str] = {}
+
 
 def get_module_name(module_path: str) -> str:
     """
@@ -2315,6 +2319,9 @@ async def load_custom_node(module_path: str, ignore=set(), module_parent="custom
             logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS or comfy_entrypoint (need one).")
             return False
     except Exception as e:
+        # Capture one-line failure reason for the import summary
+        error_msg = str(e).split('\n')[0][:100]  # First line, max 100 chars
+        IMPORT_FAILED_REASONS[module_path] = f"{type(e).__name__}: {error_msg}"
         logging.warning(traceback.format_exc())
         logging.warning(f"Cannot import {module_path} module for custom nodes: {e}")
         return False
@@ -2365,7 +2372,12 @@ async def init_external_custom_nodes():
             if n[2]:
                 import_message = ""
             else:
-                import_message = " (IMPORT FAILED)"
+                # Include failure reason if available
+                reason = IMPORT_FAILED_REASONS.get(n[1], "")
+                if reason:
+                    import_message = f" (IMPORT FAILED: {reason})"
+                else:
+                    import_message = " (IMPORT FAILED)"
             logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1]))
         logging.info("")
 

requirements.txt

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.45.20
 comfyui-workflow-templates==0.11.2
-comfyui-embedded-docs==0.5.6
+comfyui-embedded-docs==0.5.7
 torch
 torchsde
 torchvision

tests-unit/nodes_test/test_import_failure_reasons.py

@@ -0,0 +1,89 @@
+"""Tests for custom node import failure reason reporting."""
+
+import pytest
+import tempfile
+import os
+import shutil
+from unittest.mock import patch, MagicMock
+import asyncio
+
+
+class TestImportFailureReasons:
+    """Test that import failures include diagnostic information."""
+
+    def test_import_failure_reason_format(self):
+        """Test that failure reason is formatted correctly."""
+        # Simulate the formatting logic
+        exception = ImportError("No module named 'missing_dep'")
+        error_msg = str(exception).split('\n')[0][:100]
+        reason = f"{type(exception).__name__}: {error_msg}"
+
+        assert reason == "ImportError: No module named 'missing_dep'"
+
+    def test_import_failure_reason_truncation(self):
+        """Test that long error messages are truncated."""
+        long_msg = "a" * 200
+        exception = ValueError(long_msg)
+        error_msg = str(exception).split('\n')[0][:100]
+        reason = f"{type(exception).__name__}: {error_msg}"
+
+        # Should be truncated to 100 chars for the message part
+        assert len(error_msg) == 100
+        assert reason.startswith("ValueError: ")
+
+    def test_import_failure_reason_multiline(self):
+        """Test that only first line of error is used."""
+        multi_line_msg = "First line\nSecond line\nThird line"
+        exception = RuntimeError(multi_line_msg)
+        error_msg = str(exception).split('\n')[0][:100]
+        reason = f"{type(exception).__name__}: {error_msg}"
+
+        assert reason == "RuntimeError: First line"
+        assert "Second line" not in reason
+
+    def test_import_failure_reason_various_exceptions(self):
+        """Test formatting for various exception types."""
+        test_cases = [
+            (ModuleNotFoundError("No module named 'foo'"), "ModuleNotFoundError: No module named 'foo'"),
+            (SyntaxError("invalid syntax"), "SyntaxError: invalid syntax"),
+            (AttributeError("'NoneType' object has no attribute 'bar'"), "AttributeError: 'NoneType' object has no attribute 'bar'"),
+            (FileNotFoundError("[Errno 2] No such file"), "FileNotFoundError: [Errno 2] No such file"),
+        ]
+
+        for exception, expected in test_cases:
+            error_msg = str(exception).split('\n')[0][:100]
+            reason = f"{type(exception).__name__}: {error_msg}"
+            assert reason == expected, f"Failed for {type(exception).__name__}"
+
+
+class TestImportSummaryOutput:
+    """Test the import summary output format."""
+
+    def test_summary_message_with_reason(self):
+        """Test that summary includes reason when available."""
+        reason = "ImportError: No module named 'xyz'"
+        import_message = f" (IMPORT FAILED: {reason})"
+
+        assert import_message == " (IMPORT FAILED: ImportError: No module named 'xyz')"
+
+    def test_summary_message_without_reason(self):
+        """Test fallback when no reason is available."""
+        reason = ""
+        if reason:
+            import_message = f" (IMPORT FAILED: {reason})"
+        else:
+            import_message = " (IMPORT FAILED)"
+
+        assert import_message == " (IMPORT FAILED)"
+
+    def test_summary_format_string(self):
+        """Test the full summary line format."""
+        time_taken = 0.05
+        import_message = " (IMPORT FAILED: ImportError: missing module)"
+        module_path = "/path/to/custom_nodes/my_node"
+
+        summary_line = "{:6.1f} seconds{}: {}".format(time_taken, import_message, module_path)
+
+        assert "0.1 seconds" in summary_line
+        assert "(IMPORT FAILED: ImportError: missing module)" in summary_line
+        assert module_path in summary_line