Enhance custom model loading for TTS and VC by adding support for user-specified model folders, improving error handling, and refining fallback mechanisms to Hugging Face models.

FL_Image_Randomizer.py

@@ -0,0 +1,111 @@
+import os
+import numpy as np
+import torch
+from PIL import Image, ImageOps
+import cv2
+import random
+
+class FL_ImageRandomizer:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mode": (["Image", "Video"], {"default": "Image"}),
+                "directory_path": ("STRING", {"default": ""}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "search_subdirectories": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "PATH", "IMAGE", "STRING")
+    RETURN_NAMES = ("image_batch", "selected_path", "image_list", "filename")
+    OUTPUT_IS_LIST = (False, False, True, False)
+    FUNCTION = "select_media"
+    CATEGORY = "🏵️Fill Nodes/Image"
+
+    def select_media(self, mode, directory_path, seed, search_subdirectories=False):
+        if not directory_path:
+            raise ValueError("Directory path is not provided.")
+
+        if mode == "Image":
+            image_tensor, selected_path = self.select_image_data(directory_path, seed, search_subdirectories)
+            filename = os.path.basename(selected_path)
+            return (image_tensor, selected_path, [image_tensor], filename)
+        else:  # Video mode
+            frames_tensor, selected_path = self.select_video_data(directory_path, seed, search_subdirectories)
+            filename = os.path.basename(selected_path)
+            return (frames_tensor, selected_path, [frames_tensor], filename) # Video frames are already a batch, but we wrap in list for consistency
+
+    def select_image_data(self, directory_path, seed, search_subdirectories=False):
+        images = self.load_files(directory_path, search_subdirectories, file_type="image")
+        if not images:
+            raise ValueError("No images found in the specified directory.")
+
+        num_images = len(images)
+        selected_index = seed % num_images
+
+        selected_image_path = images[selected_index]
+
+        image = Image.open(selected_image_path)
+        image = ImageOps.exif_transpose(image)
+        image = image.convert("RGB")
+        image_np = np.array(image).astype(np.float32) / 255.0
+        image_tensor = torch.from_numpy(image_np)[None,]
+
+        return image_tensor, selected_image_path
+
+    def select_video_data(self, directory_path, seed, search_subdirectories=False):
+        videos = self.load_files(directory_path, search_subdirectories, file_type="video")
+        if not videos:
+            raise ValueError("No videos found in the specified directory.")
+
+        num_videos = len(videos)
+        selected_index = seed % num_videos
+
+        selected_video_path = videos[selected_index]
+
+        cap = cv2.VideoCapture(selected_video_path)
+        if not cap.isOpened():
+            raise ValueError(f"Could not open video file: {selected_video_path}")
+
+        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+        if frame_count <= 0:
+            raise ValueError(f"No frames found in video: {selected_video_path}")
+
+        frames = []
+        success = True
+        while success:
+            success, frame = cap.read()
+            if success:
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                frame_np = np.array(frame).astype(np.float32) / 255.0
+                frames.append(frame_np)
+
+        cap.release()
+
+        if not frames:
+            raise ValueError(f"Failed to extract frames from video: {selected_video_path}")
+
+        frames_tensor = torch.from_numpy(np.stack(frames))
+
+        return frames_tensor, selected_video_path
+
+    def load_files(self, directory, search_subdirectories=False, file_type="image"):
+        if file_type == "image":
+            supported_formats = ["jpg", "jpeg", "png", "bmp", "gif", "webp"]
+        else:  # video
+            supported_formats = ["mp4", "avi", "mov", "mkv", "wmv", "webm"]
+
+        file_paths = []
+
+        if search_subdirectories:
+            for root, _, files in os.walk(directory):
+                for f in files:
+                    if f.split('.')[-1].lower() in supported_formats:
+                        file_paths.append(os.path.join(root, f))
+        else:
+            file_paths = sorted([os.path.join(directory, f) for f in os.listdir(directory)
+                                if os.path.isfile(os.path.join(directory, f)) and f.split('.')[-1].lower() in supported_formats])
+
+        return sorted(file_paths)

README.md

@@ -1,5 +1,12 @@
 # ComfyUI_Fill-ChatterBox
 
+If you enjoy this project, consider supporting me on Patreon!
+<p align="left">
+  <a href="https://www.patreon.com/c/Machinedelusions">
+    <img src="assets/Patreon.png" width="150px" alt="Patreon">
+  </a>
+</p>
+
 A custom node extension for ComfyUI that adds text-to-speech (TTS) and voice conversion (VC) capabilities using the Chatterbox library.
 Supports a MAXIMUM of 40 seconds. Iv tried removing this limitation, but the model falls apart really badly with anything longer than that, so it remains.
 
@@ -18,6 +25,12 @@ Supports a MAXIMUM of 40 seconds. Iv tried removing this limitation, but the mod
    pip install -r ComfyUI_Fill-ChatterBox/requirements.txt
    ```
 
+3. (Optional) Install watermarking support:
+   ```bash
+   pip install resemble-perth
+   ```
+   **Note**: The `resemble-perth` package may have compatibility issues with Python 3.12+. If you encounter import errors, the nodes will still function without watermarking.
+
 
 ## Usage
 
@@ -33,7 +46,15 @@ Supports a MAXIMUM of 40 seconds. Iv tried removing this limitation, but the mod
 
 ## Change Log
 
+### 6/24/2025
+- Added seed parameter to both TTS and VC nodes for reproducible generation
+- Seed range: 0 to 4,294,967,295 (32-bit integer)
+- Enables consistent audio output for debugging and workflow control
+- Made Perth watermarking optional to fix Python 3.12+ compatibility issues
+- Nodes now function without watermarking if resemble-perth import fails
+
 ### 5/31/2025
 - Added Persistent model loading, and loading bar functionality
 - Added Mac support (needs to be tested so HMU)
 - removed the chatterbox-tts library and implemented native inference code.
+