numz-ComfyUI-SeedVR2_VideoUpscaler/src/optimization/performance.py

"""
Performance optimization module for SeedVR2
Contains optimized tensor operations and video processing functions

Extracted from: seedvr2.py (lines 1633-1730)
"""

import torch
from typing import List


def optimized_channels_to_last(tensor):
    """🚀 Optimized replacement for rearrange(tensor, 'b c ... -> b ... c')
    Moves channels from position 1 to last position using PyTorch native operations.
    """
    if tensor.ndim == 3:  # [batch, channels, spatial]
        return tensor.permute(0, 2, 1)
    elif tensor.ndim == 4:  # [batch, channels, height, width]
        return tensor.permute(0, 2, 3, 1)
    elif tensor.ndim == 5:  # [batch, channels, depth, height, width]
        return tensor.permute(0, 2, 3, 4, 1)
    else:
        # Fallback for other dimensions - move channel (dim=1) to last
        dims = list(range(tensor.ndim))
        dims = [dims[0]] + dims[2:] + [dims[1]]  # [0, 2, 3, ..., 1]
        return tensor.permute(*dims)


def optimized_channels_to_second(tensor):
    """🚀 Optimized replacement for rearrange(tensor, 'b ... c -> b c ...')
    Moves channels from last position to position 1 using PyTorch native operations.
    """
    if tensor.ndim == 3:  # [batch, spatial, channels]
        return tensor.permute(0, 2, 1)
    elif tensor.ndim == 4:  # [batch, height, width, channels]
        return tensor.permute(0, 3, 1, 2)
    elif tensor.ndim == 5:  # [batch, depth, height, width, channels]
        return tensor.permute(0, 4, 1, 2, 3)
    else:
        # Fallback for other dimensions - move last dim to position 1
        dims = list(range(tensor.ndim))
        dims = [dims[0], dims[-1]] + dims[1:-1]  # [0, -1, 1, 2, ..., -2]
        return tensor.permute(*dims)


def optimized_video_rearrange(video_tensors: List[torch.Tensor]) -> List[torch.Tensor]:
    """
    🚀 OPTIMIZED version of video rearrangement
    Replaces slow loops with vectorized operations
    
    Transforms:
    - 3D: c h w -> t c h w (with t=1)  
    - 4D: c t h w -> t c h w
    
    Expected gains: 5-10x faster than naive loops
    
    Args:
        video_tensors: List of video tensors to rearrange
        
    Returns:
        List of rearranged tensors in t c h w format
        
    Raises:
        ValueError: If video tensor has invalid dimensions (not 3D or 4D)
    """
    if not video_tensors:
        return []
    
    # 🔍 Analyze dimensions to optimize processing
    videos_3d = []
    videos_4d = []
    indices_3d = []
    indices_4d = []
    
    for i, video in enumerate(video_tensors):
        if video.ndim == 3:
            videos_3d.append(video)
            indices_3d.append(i)
        elif video.ndim == 4:
            videos_4d.append(video)
            indices_4d.append(i)
        else:
            raise ValueError(f"Video tensor at index {i} has invalid dimensions: {video.ndim}. Expected 3D or 4D.")
    
    # 🎯 Prepare final result
    samples = [None] * len(video_tensors)
    
    # 🚀 BATCH PROCESSING for 3D videos (c h w -> 1 c h w)
    if videos_3d:
        # Stack + permute (faster than rearrange)
        # c h w -> c 1 h w -> 1 c h w
        batch_3d = torch.stack([v.unsqueeze(1) for v in videos_3d])  # [batch, c, 1, h, w]
        batch_3d = batch_3d.permute(0, 2, 1, 3, 4)  # [batch, 1, c, h, w]
        
        for i, idx in enumerate(indices_3d):
            samples[idx] = batch_3d[i]  # [1, c, h, w]
    
    # 🚀 BATCH PROCESSING for 4D videos (c t h w -> t c h w)  
    if videos_4d:
        # Check if all 4D videos have the same shape for maximum optimization
        shapes = [v.shape for v in videos_4d]
        if len(set(shapes)) == 1:
            # 🎯 MAXIMUM OPTIMIZATION: All shapes identical
            # Stack + permute in single operation
            batch_4d = torch.stack(videos_4d)  # [batch, c, t, h, w]
            batch_4d = batch_4d.permute(0, 2, 1, 3, 4)  # [batch, t, c, h, w]
            
            for i, idx in enumerate(indices_4d):
                samples[idx] = batch_4d[i]  # [t, c, h, w]
        else:
            # 🔄 FALLBACK: Different shapes, optimized individual processing
            for i, idx in enumerate(indices_4d):
                # Use permute instead of rearrange (faster)
                samples[idx] = videos_4d[i].permute(1, 0, 2, 3)  # c t h w -> t c h w
    
    return samples


def optimized_single_video_rearrange(video: torch.Tensor) -> torch.Tensor:
    """
    🚀 OPTIMIZED version for single video tensor
    Replaces rearrange() with native PyTorch operations
    
    Transforms:
    - 3D: c h w -> 1 c h w (add temporal dimension)
    - 4D: c t h w -> t c h w (permute dimensions)
    
    Expected gains: 2-5x faster than rearrange()
    
    Args:
        video: Input video tensor
        
    Returns:
        Rearranged tensor with temporal dimension first
    """
    if video.ndim == 3:
        # c h w -> 1 c h w (add temporal dimension t=1)
        return video.unsqueeze(0)
    else:  # ndim == 4
        # c t h w -> t c h w (permute channels and temporal)
        return video.permute(1, 0, 2, 3)


def optimized_sample_to_image_format(sample: torch.Tensor) -> torch.Tensor:
    """
    🚀 OPTIMIZED version to convert sample to image format
    Replaces rearrange() with native PyTorch operations
    
    Transforms:
    - 3D: c h w -> 1 h w c (add temporal dimension + permute to image format)
    - 4D: t c h w -> t h w c (permute to image format)
    
    Expected gains: 2-5x faster than rearrange()
    
    Args:
        sample: Input sample tensor
        
    Returns:
        Tensor in image format (channels last)
    """
    if sample.ndim == 3:
        # c h w -> 1 h w c (add temporal dimension then permute)
        return sample.unsqueeze(0).permute(0, 2, 3, 1)
    else:  # ndim == 4
        # t c h w -> t h w c (permute channels to last)
        return sample.permute(0, 2, 3, 1)