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from functools import partial
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import torch
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from ...util import default, instantiate_from_config
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class VanillaCFG:
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"""
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implements parallelized CFG
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"""
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def __init__(self, scale, dyn_thresh_config=None):
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scale_schedule = lambda scale, sigma: scale
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self.scale_schedule = partial(scale_schedule, scale)
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self.dyn_thresh = instantiate_from_config(
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default(
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dyn_thresh_config,
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{
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"target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
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},
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)
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)
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def __call__(self, x, sigma):
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x_u, x_c = x.chunk(2)
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scale_value = self.scale_schedule(sigma)
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x_pred = self.dyn_thresh(x_u, x_c, scale_value)
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return x_pred
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def prepare_inputs(self, x, s, c, uc):
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c_out = dict()
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for k in c:
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if k in ["vector", "crossattn", "concat", "control", 'control_vector', 'mask_x']:
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c_out[k] = torch.cat((uc[k], c[k]), 0)
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else:
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assert c[k] == uc[k]
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c_out[k] = c[k]
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return torch.cat([x] * 2), torch.cat([s] * 2), c_out
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class LinearCFG:
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def __init__(self, scale, scale_min=None, dyn_thresh_config=None):
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if scale_min is None:
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scale_min = scale
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scale_schedule = lambda scale, scale_min, sigma: (scale - scale_min) * sigma / 14.6146 + scale_min
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self.scale_schedule = partial(scale_schedule, scale, scale_min)
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self.dyn_thresh = instantiate_from_config(
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default(
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dyn_thresh_config,
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{
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"target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
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},
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)
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)
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def __call__(self, x, sigma):
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x_u, x_c = x.chunk(2)
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scale_value = self.scale_schedule(sigma)
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x_pred = self.dyn_thresh(x_u, x_c, scale_value)
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return x_pred
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def prepare_inputs(self, x, s, c, uc):
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c_out = dict()
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for k in c:
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if k in ["vector", "crossattn", "concat", "control", 'control_vector', 'mask_x']:
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c_out[k] = torch.cat((uc[k], c[k]), 0)
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else:
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assert c[k] == uc[k]
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c_out[k] = c[k]
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return torch.cat([x] * 2), torch.cat([s] * 2), c_out
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class IdentityGuider:
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def __call__(self, x, sigma):
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return x
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def prepare_inputs(self, x, s, c, uc):
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c_out = dict()
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for k in c:
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c_out[k] = c[k]
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return x, s, c_out
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