google-labs-jules[bot]
7cf1b45545
- Implements `process_batched_images` generator in `nodes/video_node.py` to process video frames in batches (default 20), significantly reducing GPU-CPU synchronization overhead. - Optimizes `tensor_to_numpy_uint8` in `shared/media/image_processing.py` to use in-place operations (`.clamp_()`), saving memory allocations for large tensors. - Reduces performance bottlenecks in video encoding pipelines.
25 lines
965 B
Python
25 lines
965 B
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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