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| import torch | |
| from diffusers import ConfigMixin, ModelMixin | |
| from einops import rearrange | |
| from torch import nn | |
| class AudioProjModel(ModelMixin, ConfigMixin): | |
| def __init__( | |
| self, | |
| seq_len=5, | |
| blocks=12, # add a new parameter blocks | |
| channels=768, # add a new parameter channels | |
| intermediate_dim=512, | |
| output_dim=768, | |
| context_tokens=32, | |
| ): | |
| super().__init__() | |
| self.seq_len = seq_len | |
| self.blocks = blocks | |
| self.channels = channels | |
| self.input_dim = seq_len * blocks * channels # update input_dim to be the product of blocks and channels. | |
| self.intermediate_dim = intermediate_dim | |
| self.context_tokens = context_tokens | |
| self.output_dim = output_dim | |
| # define multiple linear layers | |
| self.proj1 = nn.Linear(self.input_dim, intermediate_dim) | |
| self.proj2 = nn.Linear(intermediate_dim, intermediate_dim) | |
| self.proj3 = nn.Linear(intermediate_dim, context_tokens * output_dim) | |
| self.norm = nn.LayerNorm(output_dim) | |
| def forward(self, audio_embeds): | |
| video_length = audio_embeds.shape[1] | |
| audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c") | |
| batch_size, window_size, blocks, channels = audio_embeds.shape | |
| audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels) | |
| audio_embeds = torch.relu(self.proj1(audio_embeds)) | |
| audio_embeds = torch.relu(self.proj2(audio_embeds)) | |
| context_tokens = self.proj3(audio_embeds).reshape(batch_size, self.context_tokens, self.output_dim) | |
| context_tokens = self.norm(context_tokens) | |
| context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length) | |
| return context_tokens | |