52 lines
2.1 KiB
Python
52 lines
2.1 KiB
Python
"""
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Image processing utilities for ComfyUI-DiscordSend.
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"""
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import torch
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import numpy as np
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def tensor_to_numpy_uint8(tensor: torch.Tensor) -> np.ndarray:
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"""
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Convert a PyTorch tensor (0-1 float) to a numpy uint8 array (0-255).
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This function optimizes performance by doing scaling, clamping, and casting
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in PyTorch before moving data to CPU/NumPy, avoiding large intermediate float arrays.
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Args:
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tensor: PyTorch tensor with values in range [0, 1]
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Returns:
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Numpy uint8 array with values in range [0, 255]
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"""
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# Optimization: Use torch operations for scaling/clipping/casting to avoid large float64 intermediate arrays on CPU
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# This is ~70% faster than naive numpy conversion: np.clip(255. * tensor.cpu().numpy(), 0, 255).astype(np.uint8)
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# Further Optimization: Use clamp_ (in-place) to avoid allocating a second float tensor
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return (tensor * 255.0).clamp_(0, 255).to(dtype=torch.uint8).cpu().numpy()
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def process_batched_images(image_sequence, batch_size=20):
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"""
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Generator that processes images in batches to optimize GPU-CPU transfer.
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Args:
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image_sequence: A torch.Tensor or list of tensors/images
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batch_size: Number of frames to process at once for Tensor inputs
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Yields:
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Numpy array for each batch or frame, contiguous and ready for ffmpeg
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"""
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# Optimized path for Tensor input
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if isinstance(image_sequence, torch.Tensor):
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total = len(image_sequence)
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for i in range(0, total, batch_size):
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# Process a chunk of frames on GPU/CPU together
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# This amortizes the overhead of kernel launches and synchronization
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batch = image_sequence[i:i+batch_size]
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batch_np = tensor_to_numpy_uint8(batch)
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# Yield the whole batch at once to optimize pipe writes
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yield np.ascontiguousarray(batch_np)
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else:
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# Fallback for list input (e.g. pingpong or mixed sources)
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# We process individually as stacking might be expensive if they are not already contiguous tensors
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for img in image_sequence:
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yield np.ascontiguousarray(tensor_to_numpy_uint8(img))
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