Merge branch 'comfyanonymous:master' into master

comfy/ldm/ace/vae/music_vocoder.py

@@ -8,11 +8,7 @@ from typing import Callable, Tuple, List
 
 import numpy as np
 import torch.nn.functional as F
-from torch.nn.utils import weight_norm
 from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm
-# from diffusers.models.modeling_utils import ModelMixin
-# from diffusers.loaders import FromOriginalModelMixin
-# from diffusers.configuration_utils import ConfigMixin, register_to_config
 
 from .music_log_mel import LogMelSpectrogram
 
@@ -259,7 +255,7 @@ class ResBlock1(torch.nn.Module):
 
         self.convs1 = nn.ModuleList(
             [
-                weight_norm(
+                torch.nn.utils.parametrizations.weight_norm(
                     ops.Conv1d(
                         channels,
                         channels,
@@ -269,7 +265,7 @@ class ResBlock1(torch.nn.Module):
                         padding=get_padding(kernel_size, dilation[0]),
                     )
                 ),
-                weight_norm(
+                torch.nn.utils.parametrizations.weight_norm(
                     ops.Conv1d(
                         channels,
                         channels,
@@ -279,7 +275,7 @@ class ResBlock1(torch.nn.Module):
                         padding=get_padding(kernel_size, dilation[1]),
                     )
                 ),
-                weight_norm(
+                torch.nn.utils.parametrizations.weight_norm(
                     ops.Conv1d(
                         channels,
                         channels,
@@ -294,7 +290,7 @@ class ResBlock1(torch.nn.Module):
 
         self.convs2 = nn.ModuleList(
             [
-                weight_norm(
+                torch.nn.utils.parametrizations.weight_norm(
                     ops.Conv1d(
                         channels,
                         channels,
@@ -304,7 +300,7 @@ class ResBlock1(torch.nn.Module):
                         padding=get_padding(kernel_size, 1),
                     )
                 ),
-                weight_norm(
+                torch.nn.utils.parametrizations.weight_norm(
                     ops.Conv1d(
                         channels,
                         channels,
@@ -314,7 +310,7 @@ class ResBlock1(torch.nn.Module):
                         padding=get_padding(kernel_size, 1),
                     )
                 ),
-                weight_norm(
+                torch.nn.utils.parametrizations.weight_norm(
                     ops.Conv1d(
                         channels,
                         channels,
@@ -366,7 +362,7 @@ class HiFiGANGenerator(nn.Module):
             prod(upsample_rates) == hop_length
         ), f"hop_length must be {prod(upsample_rates)}"
 
-        self.conv_pre = weight_norm(
+        self.conv_pre = torch.nn.utils.parametrizations.weight_norm(
             ops.Conv1d(
                 num_mels,
                 upsample_initial_channel,
@@ -386,7 +382,7 @@ class HiFiGANGenerator(nn.Module):
         for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
             c_cur = upsample_initial_channel // (2 ** (i + 1))
             self.ups.append(
-                weight_norm(
+                torch.nn.utils.parametrizations.weight_norm(
                     ops.ConvTranspose1d(
                         upsample_initial_channel // (2**i),
                         upsample_initial_channel // (2 ** (i + 1)),
@@ -421,7 +417,7 @@ class HiFiGANGenerator(nn.Module):
                 self.resblocks.append(ResBlock1(ch, k, d))
 
         self.activation_post = post_activation()
-        self.conv_post = weight_norm(
+        self.conv_post = torch.nn.utils.parametrizations.weight_norm(
             ops.Conv1d(
                 ch,
                 1,

comfy/ldm/audio/autoencoder.py

@@ -75,16 +75,10 @@ class SnakeBeta(nn.Module):
         return x
 
 def WNConv1d(*args, **kwargs):
-    try:
+    return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs))
-        return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs))
-    except:
-        return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) #support pytorch 2.1 and older
 
 def WNConvTranspose1d(*args, **kwargs):
-    try:
+    return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs))
-        return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs))
-    except:
-        return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) #support pytorch 2.1 and older
 
 def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
     if activation == "elu":

comfy_api_nodes/nodes_kling.py

@@ -671,7 +671,6 @@ class KlingImage2VideoNode(KlingNodeBase):
                 negative_prompt=negative_prompt if negative_prompt else None,
                 cfg_scale=cfg_scale,
                 mode=KlingVideoGenMode(mode),
-                aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
                 duration=KlingVideoGenDuration(duration),
                 camera_control=camera_control,
             ),

comfy_extras/nodes_audio.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import av
 import torchaudio
 import torch
 import comfy.model_management
@@ -7,7 +8,6 @@ import folder_paths
 import os
 import io
 import json
-import struct
 import random
 import hashlib
 import node_helpers
@@ -90,60 +90,118 @@ class VAEDecodeAudio:
         return ({"waveform": audio, "sample_rate": 44100}, )
 
 
-def create_vorbis_comment_block(comment_dict, last_block):
+def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None, quality="128k"):
-    vendor_string = b'ComfyUI'
-    vendor_length = len(vendor_string)
 
-    comments = []
+    filename_prefix += self.prefix_append
-    for key, value in comment_dict.items():
+    full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
-        comment = f"{key}={value}".encode('utf-8')
+    results: list[FileLocator] = []
-        comments.append(struct.pack('<I', len(comment)) + comment)
 
-    user_comment_list_length = len(comments)
+    # Prepare metadata dictionary
-    user_comments = b''.join(comments)
+    metadata = {}
+    if not args.disable_metadata:
+        if prompt is not None:
+            metadata["prompt"] = json.dumps(prompt)
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                metadata[x] = json.dumps(extra_pnginfo[x])
 
-    comment_data = struct.pack('<I', vendor_length) + vendor_string + struct.pack('<I', user_comment_list_length) + user_comments
+    # Opus supported sample rates
-    if last_block:
+    OPUS_RATES = [8000, 12000, 16000, 24000, 48000]
-        id = b'\x84'
-    else:
-        id = b'\x04'
-    comment_block = id + struct.pack('>I', len(comment_data))[1:] + comment_data
 
-    return comment_block
+    for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
+        filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+        file = f"{filename_with_batch_num}_{counter:05}_.{format}"
+        output_path = os.path.join(full_output_folder, file)
 
-def insert_or_replace_vorbis_comment(flac_io, comment_dict):
+        # Use original sample rate initially
-    if len(comment_dict) == 0:
+        sample_rate = audio["sample_rate"]
-        return flac_io
 
-    flac_io.seek(4)
+        # Handle Opus sample rate requirements
+        if format == "opus":
+            if sample_rate > 48000:
+                sample_rate = 48000
+            elif sample_rate not in OPUS_RATES:
+                # Find the next highest supported rate
+                for rate in sorted(OPUS_RATES):
+                    if rate > sample_rate:
+                        sample_rate = rate
+                        break
+                if sample_rate not in OPUS_RATES:  # Fallback if still not supported
+                    sample_rate = 48000
 
-    blocks = []
+            # Resample if necessary
-    last_block = False
+            if sample_rate != audio["sample_rate"]:
+                waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate)
 
-    while not last_block:
+        # Create in-memory WAV buffer
-        header = flac_io.read(4)
+        wav_buffer = io.BytesIO()
-        last_block = (header[0] & 0x80) != 0
+        torchaudio.save(wav_buffer, waveform, sample_rate, format="WAV")
-        block_type = header[0] & 0x7F
+        wav_buffer.seek(0)  # Rewind for reading
-        block_length = struct.unpack('>I', b'\x00' + header[1:])[0]
-        block_data = flac_io.read(block_length)
 
-        if block_type == 4 or block_type == 1:
+        # Use PyAV to convert and add metadata
-            pass
+        input_container = av.open(wav_buffer)
-        else:
-            header = bytes([(header[0] & (~0x80))]) + header[1:]
-            blocks.append(header + block_data)
 
-    blocks.append(create_vorbis_comment_block(comment_dict, last_block=True))
+        # Create output with specified format
+        output_buffer = io.BytesIO()
+        output_container = av.open(output_buffer, mode='w', format=format)
 
-    new_flac_io = io.BytesIO()
+        # Set metadata on the container
-    new_flac_io.write(b'fLaC')
+        for key, value in metadata.items():
-    for block in blocks:
+            output_container.metadata[key] = value
-        new_flac_io.write(block)
 
-    new_flac_io.write(flac_io.read())
+        # Set up the output stream with appropriate properties
-    return new_flac_io
+        input_container.streams.audio[0]
+        if format == "opus":
+            out_stream = output_container.add_stream("libopus", rate=sample_rate)
+            if quality == "64k":
+                out_stream.bit_rate = 64000
+            elif quality == "96k":
+                out_stream.bit_rate = 96000
+            elif quality == "128k":
+                out_stream.bit_rate = 128000
+            elif quality == "192k":
+                out_stream.bit_rate = 192000
+            elif quality == "320k":
+                out_stream.bit_rate = 320000
+        elif format == "mp3":
+            out_stream = output_container.add_stream("libmp3lame", rate=sample_rate)
+            if quality == "V0":
+                #TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
+                out_stream.codec_context.qscale = 1
+            elif quality == "128k":
+                out_stream.bit_rate = 128000
+            elif quality == "320k":
+                out_stream.bit_rate = 320000
+        else: #format == "flac":
+            out_stream = output_container.add_stream("flac", rate=sample_rate)
 
 
+        # Copy frames from input to output
+        for frame in input_container.decode(audio=0):
+            frame.pts = None  # Let PyAV handle timestamps
+            output_container.mux(out_stream.encode(frame))
+
+        # Flush encoder
+        output_container.mux(out_stream.encode(None))
+
+        # Close containers
+        output_container.close()
+        input_container.close()
+
+        # Write the output to file
+        output_buffer.seek(0)
+        with open(output_path, 'wb') as f:
+            f.write(output_buffer.getbuffer())
+
+        results.append({
+            "filename": file,
+            "subfolder": subfolder,
+            "type": self.type
+        })
+        counter += 1
+
+    return { "ui": { "audio": results } }
+
 class SaveAudio:
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
@@ -153,50 +211,70 @@ class SaveAudio:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "audio": ("AUDIO", ),
-                              "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})},
+                            "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
+                            },
                 "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                 }
 
     RETURN_TYPES = ()
-    FUNCTION = "save_audio"
+    FUNCTION = "save_flac"
 
     OUTPUT_NODE = True
 
     CATEGORY = "audio"
 
-    def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+    def save_flac(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None):
-        filename_prefix += self.prefix_append
+        return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo)
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
-        results: list[FileLocator] = []
 
-        metadata = {}
+class SaveAudioMP3:
-        if not args.disable_metadata:
+    def __init__(self):
-            if prompt is not None:
+        self.output_dir = folder_paths.get_output_directory()
-                metadata["prompt"] = json.dumps(prompt)
+        self.type = "output"
-            if extra_pnginfo is not None:
+        self.prefix_append = ""
-                for x in extra_pnginfo:
-                    metadata[x] = json.dumps(extra_pnginfo[x])
 
-        for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
+    @classmethod
-            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+    def INPUT_TYPES(s):
-            file = f"{filename_with_batch_num}_{counter:05}_.flac"
+        return {"required": { "audio": ("AUDIO", ),
+                            "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
+                            "quality": (["V0", "128k", "320k"], {"default": "V0"}),
+                            },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
 
-            buff = io.BytesIO()
+    RETURN_TYPES = ()
-            torchaudio.save(buff, waveform, audio["sample_rate"], format="FLAC")
+    FUNCTION = "save_mp3"
 
-            buff = insert_or_replace_vorbis_comment(buff, metadata)
+    OUTPUT_NODE = True
 
-            with open(os.path.join(full_output_folder, file), 'wb') as f:
+    CATEGORY = "audio"
-                f.write(buff.getbuffer())
 
-            results.append({
+    def save_mp3(self, audio, filename_prefix="ComfyUI", format="mp3", prompt=None, extra_pnginfo=None, quality="128k"):
-                "filename": file,
+        return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality)
-                "subfolder": subfolder,
-                "type": self.type
-            })
-            counter += 1
 
-        return { "ui": { "audio": results } }
+class SaveAudioOpus:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "audio": ("AUDIO", ),
+                            "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
+                            "quality": (["64k", "96k", "128k", "192k", "320k"], {"default": "128k"}),
+                            },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_opus"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "audio"
+
+    def save_opus(self, audio, filename_prefix="ComfyUI", format="opus", prompt=None, extra_pnginfo=None, quality="V3"):
+        return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality)
 
 class PreviewAudio(SaveAudio):
     def __init__(self):
@@ -248,7 +326,20 @@ NODE_CLASS_MAPPINGS = {
     "VAEEncodeAudio": VAEEncodeAudio,
     "VAEDecodeAudio": VAEDecodeAudio,
     "SaveAudio": SaveAudio,
+    "SaveAudioMP3": SaveAudioMP3,
+    "SaveAudioOpus": SaveAudioOpus,
     "LoadAudio": LoadAudio,
     "PreviewAudio": PreviewAudio,
     "ConditioningStableAudio": ConditioningStableAudio,
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "EmptyLatentAudio": "Empty Latent Audio",
+    "VAEEncodeAudio": "VAE Encode Audio",
+    "VAEDecodeAudio": "VAE Decode Audio",
+    "PreviewAudio": "Preview Audio",
+    "LoadAudio": "Load Audio",
+    "SaveAudio": "Save Audio (FLAC)",
+    "SaveAudioMP3": "Save Audio (MP3)",
+    "SaveAudioOpus": "Save Audio (Opus)",
+}

comfy_extras/nodes_load_3d.py

@@ -2,6 +2,10 @@ import nodes
 import folder_paths
 import os
 
+from comfy.comfy_types import IO
+from comfy_api.input_impl import VideoFromFile
+
+
 def normalize_path(path):
     return path.replace('\\', '/')
 
@@ -21,8 +25,8 @@ class Load3D():
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
         }}
 
-    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA")
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO)
-    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info")
+    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info", "recording_video")
 
     FUNCTION = "process"
     EXPERIMENTAL = True
@@ -41,7 +45,14 @@ class Load3D():
         normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
         lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path)
 
-        return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info']
+        video = None
+
+        if image['recording'] != "":
+            recording_video_path = folder_paths.get_annotated_filepath(image['recording'])
+
+            video = VideoFromFile(recording_video_path)
+
+        return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info'], video
 
 class Load3DAnimation():
     @classmethod
@@ -59,8 +70,8 @@ class Load3DAnimation():
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
         }}
 
-    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA")
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO)
-    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info")
+    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info", "recording_video")
 
     FUNCTION = "process"
     EXPERIMENTAL = True
@@ -77,7 +88,14 @@ class Load3DAnimation():
         ignore_image, output_mask = load_image_node.load_image(image=mask_path)
         normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
 
-        return output_image, output_mask, model_file, normal_image, image['camera_info']
+        video = None
+
+        if image['recording'] != "":
+            recording_video_path = folder_paths.get_annotated_filepath(image['recording'])
+
+            video = VideoFromFile(recording_video_path)
+
+        return output_image, output_mask, model_file, normal_image, image['camera_info'], video
 
 class Preview3D():
     @classmethod

comfy_extras/nodes_string.py

@@ -0,0 +1,322 @@
+import re
+
+from comfy.comfy_types.node_typing import IO
+
+class StringConcatenate():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string_a": (IO.STRING, {"multiline": True}),
+                "string_b": (IO.STRING, {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string_a, string_b, **kwargs):
+        return string_a + string_b,
+
+class StringSubstring():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "start": (IO.INT, {}),
+                "end": (IO.INT, {}),
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, start, end, **kwargs):
+        return string[start:end],
+
+class StringLength():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES = (IO.INT,)
+    RETURN_NAMES = ("length",)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, **kwargs):
+        length = len(string)
+
+        return length,
+
+class CaseConverter():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "mode": (IO.COMBO, {"options": ["UPPERCASE", "lowercase", "Capitalize", "Title Case"]})
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, mode, **kwargs):
+        if mode == "UPPERCASE":
+            result = string.upper()
+        elif mode == "lowercase":
+            result = string.lower()
+        elif mode == "Capitalize":
+            result = string.capitalize()
+        elif mode == "Title Case":
+            result = string.title()
+        else:
+            result = string
+
+        return result,
+
+
+class StringTrim():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "mode": (IO.COMBO, {"options": ["Both", "Left", "Right"]})
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, mode, **kwargs):
+        if mode == "Both":
+            result = string.strip()
+        elif mode == "Left":
+            result = string.lstrip()
+        elif mode == "Right":
+            result = string.rstrip()
+        else:
+            result = string
+
+        return result,
+
+class StringReplace():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "find": (IO.STRING, {"multiline": True}),
+                "replace": (IO.STRING, {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, find, replace, **kwargs):
+        result = string.replace(find, replace)
+        return result,
+
+
+class StringContains():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "substring": (IO.STRING, {"multiline": True}),
+                "case_sensitive": (IO.BOOLEAN, {"default": True})
+            }
+        }
+
+    RETURN_TYPES = (IO.BOOLEAN,)
+    RETURN_NAMES = ("contains",)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, substring, case_sensitive, **kwargs):
+        if case_sensitive:
+            contains = substring in string
+        else:
+            contains = substring.lower() in string.lower()
+
+        return contains,
+
+
+class StringCompare():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string_a": (IO.STRING, {"multiline": True}),
+                "string_b": (IO.STRING, {"multiline": True}),
+                "mode": (IO.COMBO, {"options": ["Starts With", "Ends With", "Equal"]}),
+                "case_sensitive": (IO.BOOLEAN, {"default": True})
+            }
+        }
+
+    RETURN_TYPES = (IO.BOOLEAN,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string_a, string_b, mode, case_sensitive, **kwargs):
+        if case_sensitive:
+            a = string_a
+            b = string_b
+        else:
+            a = string_a.lower()
+            b = string_b.lower()
+
+        if mode == "Equal":
+            return a == b,
+        elif mode == "Starts With":
+            return a.startswith(b),
+        elif mode == "Ends With":
+            return a.endswith(b),
+
+class RegexMatch():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "regex_pattern": (IO.STRING, {"multiline": True}),
+                "case_insensitive": (IO.BOOLEAN, {"default": True}),
+                "multiline": (IO.BOOLEAN, {"default": False}),
+                "dotall": (IO.BOOLEAN, {"default": False})
+            }
+        }
+
+    RETURN_TYPES = (IO.BOOLEAN,)
+    RETURN_NAMES = ("matches",)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, regex_pattern, case_insensitive, multiline, dotall, **kwargs):
+        flags = 0
+
+        if case_insensitive:
+            flags |= re.IGNORECASE
+        if multiline:
+            flags |= re.MULTILINE
+        if dotall:
+            flags |= re.DOTALL
+
+        try:
+            match = re.search(regex_pattern, string, flags)
+            result = match is not None
+
+        except re.error:
+            result = False
+
+        return result,
+
+
+class RegexExtract():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "regex_pattern": (IO.STRING, {"multiline": True}),
+                "mode": (IO.COMBO, {"options": ["First Match", "All Matches", "First Group", "All Groups"]}),
+                "case_insensitive": (IO.BOOLEAN, {"default": True}),
+                "multiline": (IO.BOOLEAN, {"default": False}),
+                "dotall": (IO.BOOLEAN, {"default": False}),
+                "group_index": (IO.INT, {"default": 1, "min": 0, "max": 100})
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, regex_pattern, mode, case_insensitive, multiline, dotall, group_index, **kwargs):
+        join_delimiter = "\n"
+
+        flags = 0
+        if case_insensitive:
+            flags |= re.IGNORECASE
+        if multiline:
+            flags |= re.MULTILINE
+        if dotall:
+            flags |= re.DOTALL
+
+        try:
+            if mode == "First Match":
+                match = re.search(regex_pattern, string, flags)
+                if match:
+                    result = match.group(0)
+                else:
+                    result = ""
+
+            elif mode == "All Matches":
+                matches = re.findall(regex_pattern, string, flags)
+                if matches:
+                    if isinstance(matches[0], tuple):
+                        result = join_delimiter.join([m[0] for m in matches])
+                    else:
+                        result = join_delimiter.join(matches)
+                else:
+                    result = ""
+
+            elif mode == "First Group":
+                match = re.search(regex_pattern, string, flags)
+                if match and len(match.groups()) >= group_index:
+                    result = match.group(group_index)
+                else:
+                    result = ""
+
+            elif mode == "All Groups":
+                matches = re.finditer(regex_pattern, string, flags)
+                results = []
+                for match in matches:
+                    if match.groups() and len(match.groups()) >= group_index:
+                        results.append(match.group(group_index))
+                result = join_delimiter.join(results)
+            else:
+                result = ""
+
+        except re.error:
+            result = ""
+
+        return result,
+
+NODE_CLASS_MAPPINGS = {
+    "StringConcatenate": StringConcatenate,
+    "StringSubstring": StringSubstring,
+    "StringLength": StringLength,
+    "CaseConverter": CaseConverter,
+    "StringTrim": StringTrim,
+    "StringReplace": StringReplace,
+    "StringContains": StringContains,
+    "StringCompare": StringCompare,
+    "RegexMatch": RegexMatch,
+    "RegexExtract": RegexExtract
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "StringConcatenate": "Concatenate",
+    "StringSubstring": "Substring",
+    "StringLength": "Length",
+    "CaseConverter": "Case Converter",
+    "StringTrim": "Trim",
+    "StringReplace": "Replace",
+    "StringContains": "Contains",
+    "StringCompare": "Compare",
+    "RegexMatch": "Regex Match",
+    "RegexExtract": "Regex Extract"
+}

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.33"
+__version__ = "0.3.34"

nodes.py

@@ -2263,6 +2263,7 @@ def init_builtin_extra_nodes():
         "nodes_fresca.py",
         "nodes_preview_any.py",
         "nodes_ace.py",
+        "nodes_string.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.33"
+version = "0.3.34"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.18.10
+comfyui-frontend-package==1.19.9
 comfyui-workflow-templates==0.1.14
 torch
 torchsde