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""" | |
Copyright (c) Meta Platforms, Inc. and affiliates. | |
All rights reserved. | |
This source code is licensed under the license found in the | |
LICENSE file in the root directory of this source tree. | |
""" | |
from copy import deepcopy | |
import numpy as np | |
import torch | |
import torch.nn as nn | |
# A wrapper model for Classifier-free guidance **SAMPLING** only | |
# https://arxiv.org/abs/2207.12598 | |
class ClassifierFreeSampleModel(nn.Module): | |
def __init__(self, model): | |
super().__init__() | |
self.model = model # model is the actual model to run | |
self.nfeats = self.model.nfeats | |
self.cond_mode = self.model.cond_mode | |
self.add_frame_cond = self.model.add_frame_cond | |
if self.add_frame_cond is not None: | |
if self.model.resume_trans is not None: | |
self.transformer = self.model.transformer | |
self.tokenizer = self.model.tokenizer | |
self.step = self.model.step | |
def forward(self, x, timesteps, y=None): | |
out = self.model(x, timesteps, y, cond_drop_prob=0.0) | |
out_uncond = self.model(x, timesteps, y, cond_drop_prob=1.0) | |
return out_uncond + (y["scale"].view(-1, 1, 1) * (out - out_uncond)) | |