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images_to_video_path.py

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+import os
+import uuid
+import subprocess
+import tempfile
+import torch
+import numpy as np
+from PIL import Image
+
+class ImagesListToVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "frames_per_second": ("FLOAT", {"default": 30, "min": 1, "max": 120, "step": 1}),
+            }
+        }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("video_path",)
+    FUNCTION = "images_to_video"
+    CATEGORY = "Bjornulf"
+
+    def images_to_video(self, images, frames_per_second=30):
+        # Create the output directory if it doesn't exist
+        output_dir = os.path.join("Bjornulf", "images_to_video")
+        os.makedirs(output_dir, exist_ok=True)
+
+        # Generate a unique filename for the video
+        video_filename = f"video_{uuid.uuid4().hex}.mp4"
+        video_path = os.path.join(output_dir, video_filename)
+
+        # Create a temporary directory to store image files
+        with tempfile.TemporaryDirectory() as temp_dir:
+            # Save each image as a PNG file in the temporary directory
+            for i, img in enumerate(images):
+                # Convert the image to the correct format
+                img_np = self.convert_to_numpy(img)
+                
+                # Ensure the image is in RGB format
+                if img_np.shape[-1] != 3:
+                    img_np = self.convert_to_rgb(img_np)
+                
+                # Convert to PIL Image
+                img_pil = Image.fromarray(img_np)
+                img_path = os.path.join(temp_dir, f"frame_{i:05d}.png")
+                img_pil.save(img_path)
+
+            # Use FFmpeg to create a video from the image sequence
+            ffmpeg_cmd = [
+                "ffmpeg",
+                "-framerate", str(frames_per_second),
+                "-i", os.path.join(temp_dir, "frame_%05d.png"),
+                "-c:v", "libx264",
+                "-pix_fmt", "yuv420p",
+                "-crf", "23",
+                "-y",  # Overwrite output file if it exists
+                video_path
+            ]
+
+            try:
+                subprocess.run(ffmpeg_cmd, check=True, capture_output=True, text=True)
+            except subprocess.CalledProcessError as e:
+                print(f"FFmpeg error: {e.stderr}")
+                return ("",)  # Return empty string if video creation fails
+
+        return (video_path,)
+
+    def convert_to_numpy(self, img):
+        if isinstance(img, torch.Tensor):
+            img = img.cpu().numpy()
+        if img.dtype == np.uint8:
+            return img
+        elif img.dtype == np.float32 or img.dtype == np.float64:
+            return (img * 255).astype(np.uint8)
+        else:
+            raise ValueError(f"Unsupported data type: {img.dtype}")
+
+    def convert_to_rgb(self, img):
+        if img.shape[-1] == 1:  # Grayscale
+            return np.repeat(img, 3, axis=-1)
+        elif img.shape[-1] == 768:  # Latent space representation
+            # This is a placeholder. You might need a more sophisticated method to convert latent space to RGB
+            img = img.reshape((-1, 3))  # Reshape to (H*W, 3)
+            img = (img - img.min()) / (img.max() - img.min())  # Normalize to [0, 1]
+            img = (img * 255).astype(np.uint8)
+            return img.reshape((img.shape[0], -1, 3))  # Reshape back to (H, W, 3)
+        elif len(img.shape) == 2:  # 2D array
+            return np.stack([img, img, img], axis=-1)
+        else:
+            raise ValueError(f"Unsupported image shape: {img.shape}")