from ttts.diffusion.ldm.modules.diffusionmodules.util import ( conv_nd, linear, normalization, zero_module, timestep_embedding, ) from ttts.diffusion.ldm.modules.attention import SpatialTransformer from ttts.diffusion.ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock, Upsample, convert_module_to_f16, convert_module_to_f32 from ttts.diffusion.ldm.util import exists import torch as th from einops import rearrange, repeat import torch import torch.nn as nn import torch.nn.functional as F from torch import autocast from ttts.diffusion.cldm.cond_emb import CLIP from ttts.utils.utils import normalization, AttentionBlock def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) class BaseModel(nn.Module): """ The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param attention_resolutions: a collection of downsample rates at which attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x downsampling, attention will be used. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this model will be class-conditional with `num_classes` classes. :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use a fixed channel width per attention head. :param num_heads_upsample: works with num_heads to set a different number of heads for upsampling. Deprecated. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=1, num_classes=None, use_checkpoint=False, use_fp16=False, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, # custom transformer support transformer_depth=1, # custom transformer support context_dim=None, # custom transformer support n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model legacy=True, disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, use_linear_in_transformer=False, ): super().__init__() if use_spatial_transformer: assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' if context_dim is not None: assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' from omegaconf.listconfig import ListConfig if type(context_dim) == ListConfig: context_dim = list(context_dim) if num_heads_upsample == -1: num_heads_upsample = num_heads if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels if isinstance(num_res_blocks, int): self.num_res_blocks = len(channel_mult) * [num_res_blocks] else: if len(num_res_blocks) != len(channel_mult): raise ValueError("provide num_res_blocks either as an int (globally constant) or " "as a list/tuple (per-level) with the same length as channel_mult") self.num_res_blocks = num_res_blocks if disable_self_attentions is not None: # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not assert len(disable_self_attentions) == len(channel_mult) if num_attention_blocks is not None: assert len(num_attention_blocks) == len(self.num_res_blocks) assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " f"This option has LESS priority than attention_resolutions {attention_resolutions}, " f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " f"attention will still not be set.") self.attention_resolutions = attention_resolutions self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = th.float16 if use_fp16 else th.float32 self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.predict_codebook_ids = n_embed is not None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) if self.num_classes is not None: if isinstance(self.num_classes, int): self.label_emb = nn.Embedding(num_classes, time_embed_dim) elif self.num_classes == "continuous": print("setting up linear c_adm embedding layer") self.label_emb = nn.Linear(1, time_embed_dim) else: raise ValueError() self.blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for nr in range(self.num_res_blocks[level]): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = mult * model_channels if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint ) ) self.blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) # if level != len(channel_mult) - 1: out_ch = ch self.blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ) ) ch = out_ch input_block_chans.append(ch) # ds *= 2 self._feature_size += ch self.out = nn.Sequential( normalization(ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), ) self.hint_converter = nn.Conv1d(1024,model_channels,3,padding=1) def convert_to_fp16(self): """ Convert the torso of the model to float16. """ self.blocks.apply(convert_module_to_f16) # self.input_blocks.apply(convert_module_to_f16) # self.middle_block.apply(convert_module_to_f16) # self.output_blocks.apply(convert_module_to_f16) def convert_to_fp32(self): """ Convert the torso of the model to float32. """ self.blocks.apply(convert_module_to_f32) # self.input_blocks.apply(convert_module_to_f32) # self.middle_block.apply(convert_module_to_f32) # self.output_blocks.apply(convert_module_to_f32) def forward(self, x, timesteps=None, context=None, hint=None, control=None, **kwargs): hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) # guided_hint = self.input_hint_block(hint, emb, context) hint = self.hint_converter(hint) # context = self.context_proj(context).unsqueeze(-1) # scale, shift = torch.chunk(context, 2, dim = 1) # hint = hint*(1+scale)+shift h = x.type(self.dtype) flag=0 for module in self.blocks: if flag==0: h = module(h, emb, context, control.pop(0)) h += hint flag=1 else: h = module(h, emb, context, control.pop(0)) hs.append(h) h = h.type(x.dtype) return self.out(h) class ReferenceNet(BaseModel): def forward(self, x, timesteps=None, context=None, **kwargs): hs = [] control = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) h = x.type(self.dtype) for module in self.blocks: h,refer = module(h, emb, context,return_refer=True) hs.append(h) control.append(refer) h = h.type(x.dtype) # h = self.out(h) return control TACOTRON_MEL_MAX = 5.5451774444795624753378569716654 TACOTRON_MEL_MIN = -16.118095650958319788125940182791 # TACOTRON_MEL_MIN = -11.512925464970228420089957273422 CVEC_MAX = 5.5451774444795624753378569716654 CVEC_MIN = -5.5451774444795624753378569716654 def denormalize_tacotron_mel(norm_mel): return norm_mel/0.18215 def normalize_tacotron_mel(mel): mel = torch.clamp(mel, min=-TACOTRON_MEL_MAX) return mel*0.18215 def denormalize_cvec(norm_mel): return norm_mel/0.11111 def normalize_cvec(mel): return mel*0.11111 class AA_diffusion(nn.Module): def __init__(self, config, *args, **kwargs): super().__init__(*args, **kwargs) self.refer_enc = CLIP(**config['clip']) self.refer_model = ReferenceNet(**config['refer_diffusion']) self.base_model = BaseModel(**config['base_diffusion']) print("base model params:", count_parameters(self.base_model)) self.unconditioned_percentage = 0.1 # self.control_model = instantiate_from_config(control_stage_config) # self.refer_model = instantiate_from_config(refer_config) self.control_scales = [1.0] * 13 # self.unconditioned_embedding = nn.Parameter(torch.randn(1,100,1)) self.unconditioned_cat_embedding = nn.Parameter(torch.randn(1,1024,1)) def get_uncond_batch(self, code_emb): unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device) # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance. if self.training and self.unconditioned_percentage > 0: unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1), device=code_emb.device) < self.unconditioned_percentage code_emb = torch.where(unconditioned_batches, self.unconditioned_cat_embedding.repeat(code_emb.shape[0], 1, 1), code_emb) return code_emb def forward(self, x, t, hint, refer, conditioning_free=False): if conditioning_free: hint = self.unconditioned_cat_embedding.repeat(x.shape[0], 1, x.shape[-1]) else: if self.training: hint = self.get_uncond_batch(hint) hint = F.interpolate(hint, size=x.shape[-1], mode='nearest') refer_cross = self.refer_enc(refer) refer_self = self.refer_model(refer, timesteps = t, context = refer_cross) eps = self.base_model(x, timesteps=t, context=refer_cross, hint=hint, control=refer_self) return eps