Bubbliiiing
a1e6ea6335
* pai fuser sparse test * make pai fuser more clear * make pai fuser more clear * make pai fuser more clear * make pai fuser more clear * Update Readme * disable compile in rope for less error info * Fix sage attention backward bug * Fix checkpoint bugs * Fix Attention * Update predict and fast rope * Fix bug in Sparse * Fix bug in Sparse * Fix bug in loras load * Delete useless import * Fix bug in ui * Fix bug in ui
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import numpy as np
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import torch
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def cfg_skip():
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def decorator(func):
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def wrapper(self, x, *args, **kwargs):
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if self.cfg_skip_ratio is not None and self.current_steps >= self.num_inference_steps * (1 - self.cfg_skip_ratio):
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bs = len(x)
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bs_half = int(bs // 2)
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new_x = x[bs_half:]
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new_args = []
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for arg in args:
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if isinstance(arg, (torch.Tensor, list, tuple, np.ndarray)):
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new_args.append(arg[bs_half:])
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else:
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new_args.append(arg)
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new_kwargs = {}
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for key, content in kwargs.items():
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if isinstance(content, (torch.Tensor, list, tuple, np.ndarray)):
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new_kwargs[key] = content[bs_half:]
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else:
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new_kwargs[key] = content
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else:
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new_x = x
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new_args = args
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new_kwargs = kwargs
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result = func(self, new_x, *new_args, **new_kwargs)
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if self.cfg_skip_ratio is not None and self.current_steps >= self.num_inference_steps * (1 - self.cfg_skip_ratio):
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result = torch.cat([result, result], dim=0)
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return result
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return wrapper
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return decorator |