Merge 6a24746d14 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/apis/cambai.py

@@ -0,0 +1,48 @@
+from pydantic import BaseModel, Field
+
+
+class CambAITTSRequest(BaseModel):
+    text: str = Field(..., description="Text to convert to speech")
+    voice_id: int = Field(..., description="Voice ID for TTS")
+    language: str = Field(..., description="Language code (e.g., 'en-us')")
+    speech_model: str = Field(..., description="TTS model to use")
+    output_configuration: dict = Field(
+        default_factory=lambda: {"format": "wav"},
+        description="Output format configuration",
+    )
+
+
+class CambAITranslateRequest(BaseModel):
+    source_language: int = Field(..., description="Source language ID")
+    target_language: int = Field(..., description="Target language ID")
+    texts: list[str] = Field(..., description="Texts to translate")
+
+
+class CambAITaskResponse(BaseModel):
+    task_id: str = Field(..., description="Async task ID")
+
+
+class CambAIPollResult(BaseModel):
+    status: str = Field(..., description="Task status")
+    run_id: int | None = Field(None, description="Run ID for fetching results")
+
+
+class CambAITranslateResult(BaseModel):
+    texts: list[str] = Field(default_factory=list, description="Translated texts")
+
+
+class CambAIDialogueItem(BaseModel):
+    start: float = Field(..., description="Start time in seconds")
+    end: float = Field(..., description="End time in seconds")
+    text: str = Field(..., description="Dialogue text")
+    speaker: str = Field(..., description="Speaker identifier")
+
+
+class CambAIVoiceCloneResponse(BaseModel):
+    voice_id: int = Field(..., description="Cloned voice ID")
+
+
+class CambAITextToSoundRequest(BaseModel):
+    prompt: str = Field(..., description="Text prompt for sound generation")
+    audio_type: str = Field(..., description="Type of audio: 'sound' or 'music'")
+    duration: float = Field(..., description="Duration in seconds")

comfy_api_nodes/nodes_cambai.py

@@ -0,0 +1,467 @@
+import os
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.cambai import (
+    CambAIDialogueItem,
+    CambAIPollResult,
+    CambAITaskResponse,
+    CambAITextToSoundRequest,
+    CambAITranslateRequest,
+    CambAITranslateResult,
+    CambAITTSRequest,
+    CambAIVoiceCloneResponse,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_bytes_to_audio_input,
+    audio_ndarray_to_bytesio,
+    audio_tensor_to_contiguous_ndarray,
+    poll_op,
+    sync_op,
+    sync_op_raw,
+    validate_string,
+)
+
+CAMBAI_API_BASE = "https://client.camb.ai/apis"
+CAMBAI_VOICE = "CAMBAI_VOICE"
+CAMBAI_GENDER_MAP = {"male": 0, "female": 1, "other": 2, "prefer not to say": 9}
+
+
+def _cambai_endpoint(route: str, method: str = "GET") -> ApiEndpoint:
+    api_key = os.environ.get("CAMBAI_API_KEY", "")
+    return ApiEndpoint(
+        path=f"{CAMBAI_API_BASE}/{route}",
+        method=method,
+        headers={"x-api-key": api_key},
+    )
+
+CAMBAI_LANGUAGES_TTS = [
+    "en-us", "es-es", "fr-fr", "de-de", "it-it", "pt-br",
+    "zh-cn", "ja-jp", "ko-kr", "ar-sa", "hi-in", "ru-ru",
+    "nl-nl", "pl-pl", "tr-tr", "sv-se",
+]
+
+CAMBAI_LANGUAGE_MAP = {
+    "English": 1, "Spanish": 54, "French": 76, "German": 31,
+    "Italian": 83, "Portuguese": 112, "Chinese": 139, "Japanese": 88,
+    "Korean": 93, "Arabic": 4, "Hindi": 73, "Russian": 116,
+    "Dutch": 103, "Polish": 110, "Turkish": 133, "Swedish": 125,
+}
+
+CAMBAI_TRANSCRIPTION_LANGUAGE_MAP = {
+    "English": 1, "Spanish": 54, "French": 76, "German": 31,
+    "Italian": 83, "Portuguese": 112, "Chinese": 139, "Japanese": 88,
+    "Korean": 93, "Arabic": 4, "Hindi": 73, "Russian": 116,
+}
+
+
+class CambAIVoiceSelector(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="CambAIVoiceSelector",
+            display_name="CAMB AI Voice Selector",
+            category="api node/audio/CAMB AI",
+            description="Select a CAMB AI voice by ID for text-to-speech generation.",
+            inputs=[
+                IO.Int.Input(
+                    "voice_id",
+                    default=147320,
+                    min=1,
+                    max=999999999,
+                    tooltip="Voice ID to use for CAMB AI TTS.",
+                ),
+            ],
+            outputs=[
+                IO.Custom(CAMBAI_VOICE).Output(display_name="voice"),
+            ],
+            is_api_node=False,
+        )
+
+    @classmethod
+    def execute(cls, voice_id: int) -> IO.NodeOutput:
+        return IO.NodeOutput(voice_id)
+
+
+class CambAIVoiceClone(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="CambAIVoiceClone",
+            display_name="CAMB AI Voice Clone",
+            category="api node/audio/CAMB AI",
+            description="Create a custom cloned voice from an audio sample.",
+            inputs=[
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Audio sample of the voice to clone.",
+                ),
+                IO.String.Input(
+                    "voice_name",
+                    default="My Custom Voice",
+                    tooltip="Name for the cloned voice.",
+                ),
+                IO.Combo.Input(
+                    "gender",
+                    options=["male", "female", "other", "prefer not to say"],
+                    default="male",
+                    tooltip="Gender of the voice to clone.",
+                ),
+            ],
+            outputs=[
+                IO.Custom(CAMBAI_VOICE).Output(display_name="voice"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        audio: Input.Audio,
+        voice_name: str,
+        gender: str,
+    ) -> IO.NodeOutput:
+        audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+        audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, audio["sample_rate"], "wav", "pcm_s16le")
+
+        response = await sync_op(
+            cls,
+            _cambai_endpoint("create-custom-voice", "POST"),
+            response_model=CambAIVoiceCloneResponse,
+            data=None,
+            files={
+                "voice_name": (None, voice_name),
+                "gender": (None, str(CAMBAI_GENDER_MAP[gender])),
+                "file": ("voice.wav", audio_bytes_io.getvalue(), "audio/wav"),
+            },
+            content_type="multipart/form-data",
+        )
+        return IO.NodeOutput(response.voice_id)
+
+
+class CambAITextToSpeech(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="CambAITextToSpeech",
+            display_name="CAMB AI Text to Speech",
+            category="api node/audio/CAMB AI",
+            description="Convert text to speech using CAMB AI TTS models.",
+            inputs=[
+                IO.Custom(CAMBAI_VOICE).Input(
+                    "voice",
+                    tooltip="Voice to use for speech synthesis. Connect from Voice Selector or Voice Clone.",
+                ),
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="The text to convert to speech.",
+                ),
+                IO.Combo.Input(
+                    "language",
+                    options=CAMBAI_LANGUAGES_TTS,
+                    default="en-us",
+                    tooltip="Language for speech synthesis.",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["mars-flash", "mars-pro", "mars-instruct"],
+                    default="mars-flash",
+                    tooltip="TTS model to use.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        voice: int,
+        text: str,
+        language: str,
+        model: str,
+    ) -> IO.NodeOutput:
+        validate_string(text, min_length=1)
+        request = CambAITTSRequest(
+            text=text,
+            voice_id=voice,
+            language=language,
+            speech_model=model,
+        )
+        response = await sync_op_raw(
+            cls,
+            _cambai_endpoint("tts-stream", "POST"),
+            data=request,
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class CambAITranslation(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="CambAITranslation",
+            display_name="CAMB AI Translation",
+            category="api node/text/CAMB AI",
+            description="Translate text between languages using CAMB AI.",
+            inputs=[
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text to translate.",
+                ),
+                IO.Combo.Input(
+                    "source_language",
+                    options=list(CAMBAI_LANGUAGE_MAP.keys()),
+                    default="English",
+                    tooltip="Source language.",
+                ),
+                IO.Combo.Input(
+                    "target_language",
+                    options=list(CAMBAI_LANGUAGE_MAP.keys()),
+                    default="Spanish",
+                    tooltip="Target language.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="text"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        text: str,
+        source_language: str,
+        target_language: str,
+    ) -> IO.NodeOutput:
+        validate_string(text, min_length=1)
+        src_id = CAMBAI_LANGUAGE_MAP[source_language]
+        tgt_id = CAMBAI_LANGUAGE_MAP[target_language]
+
+        request = CambAITranslateRequest(
+            source_language=src_id,
+            target_language=tgt_id,
+            texts=[text],
+        )
+        response = await sync_op(
+            cls,
+            _cambai_endpoint("translate", "POST"),
+            response_model=CambAITaskResponse,
+            data=request,
+        )
+
+        poll_result = await poll_op(
+            cls,
+            _cambai_endpoint(f"translate/{response.task_id}"),
+            response_model=CambAIPollResult,
+            status_extractor=lambda x: x.status,
+        )
+
+        if not poll_result.run_id:
+            raise ValueError("No run_id returned from CAMB AI translation task.")
+
+        result = await sync_op(
+            cls,
+            _cambai_endpoint(f"translation-result/{poll_result.run_id}"),
+            response_model=CambAITranslateResult,
+        )
+
+        if result.texts and len(result.texts) > 0:
+            return IO.NodeOutput(result.texts[0])
+        return IO.NodeOutput("")
+
+
+class CambAITranscription(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="CambAITranscription",
+            display_name="CAMB AI Transcription",
+            category="api node/audio/CAMB AI",
+            description="Transcribe audio to text using CAMB AI.",
+            inputs=[
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Audio to transcribe.",
+                ),
+                IO.Combo.Input(
+                    "language",
+                    options=list(CAMBAI_TRANSCRIPTION_LANGUAGE_MAP.keys()),
+                    default="English",
+                    tooltip="Language of the audio.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="text"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        audio: Input.Audio,
+        language: str,
+    ) -> IO.NodeOutput:
+        lang_id = CAMBAI_TRANSCRIPTION_LANGUAGE_MAP[language]
+        audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+        audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, audio["sample_rate"], "wav", "pcm_s16le")
+
+        response = await sync_op(
+            cls,
+            _cambai_endpoint("transcribe", "POST"),
+            response_model=CambAITaskResponse,
+            data=None,
+            files={
+                "language": (None, str(lang_id)),
+                "media_file": ("audio.wav", audio_bytes_io.getvalue(), "audio/wav"),
+            },
+            content_type="multipart/form-data",
+        )
+
+        poll_result = await poll_op(
+            cls,
+            _cambai_endpoint(f"transcribe/{response.task_id}"),
+            response_model=CambAIPollResult,
+            status_extractor=lambda x: x.status,
+        )
+
+        if not poll_result.run_id:
+            raise ValueError("No run_id returned from CAMB AI transcription task.")
+
+        result_raw = await sync_op_raw(
+            cls,
+            _cambai_endpoint(f"transcription-result/{poll_result.run_id}"),
+        )
+
+        transcript = result_raw.get("transcript", [])
+        dialogues = [CambAIDialogueItem(**item) for item in transcript]
+        text = " ".join(item.text for item in dialogues)
+        return IO.NodeOutput(text)
+
+
+class CambAITextToSound(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="CambAITextToSound",
+            display_name="CAMB AI Text to Sound",
+            category="api node/audio/CAMB AI",
+            description="Generate sound effects or music from a text description using CAMB AI.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the sound to generate.",
+                ),
+                IO.Combo.Input(
+                    "audio_type",
+                    options=["sound", "music"],
+                    default="sound",
+                    tooltip="Type of audio to generate.",
+                ),
+                IO.Float.Input(
+                    "duration",
+                    default=5.0,
+                    min=0.5,
+                    max=30.0,
+                    step=0.5,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Duration of generated audio in seconds.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        audio_type: str,
+        duration: float,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1)
+        request = CambAITextToSoundRequest(
+            prompt=prompt,
+            audio_type=audio_type,
+            duration=duration,
+        )
+        response = await sync_op(
+            cls,
+            _cambai_endpoint("text-to-sound", "POST"),
+            response_model=CambAITaskResponse,
+            data=request,
+        )
+
+        poll_result = await poll_op(
+            cls,
+            _cambai_endpoint(f"text-to-sound/{response.task_id}"),
+            response_model=CambAIPollResult,
+            status_extractor=lambda x: x.status,
+        )
+
+        if not poll_result.run_id:
+            raise ValueError("No run_id returned from CAMB AI text-to-sound task.")
+
+        audio_bytes = await sync_op_raw(
+            cls,
+            _cambai_endpoint(f"text-to-sound-result/{poll_result.run_id}"),
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(audio_bytes))
+
+
+class CambAIExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            CambAIVoiceSelector,
+            CambAIVoiceClone,
+            CambAITextToSpeech,
+            CambAITranslation,
+            CambAITranscription,
+            CambAITextToSound,
+        ]
+
+
+async def comfy_entrypoint() -> CambAIExtension:
+    return CambAIExtension()

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):

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