import typing as tp import torch from einops import rearrange from torch import nn from torch.nn import functional as F from x_transformers import ContinuousTransformerWrapper, Encoder from .blocks import FourierFeatures from .transformer import ContinuousTransformer class DiffusionTransformer(nn.Module): def __init__(self, io_channels=32, patch_size=1, embed_dim=768, cond_token_dim=0, project_cond_tokens=True, global_cond_dim=0, project_global_cond=True, input_concat_dim=0, prepend_cond_dim=0, depth=12, num_heads=8, transformer_type: tp.Literal["x-transformers", "continuous_transformer"] = "x-transformers", global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend", **kwargs): super().__init__() self.cond_token_dim = cond_token_dim # Timestep embeddings timestep_features_dim = 256 self.timestep_features = FourierFeatures(1, timestep_features_dim) self.to_timestep_embed = nn.Sequential( nn.Linear(timestep_features_dim, embed_dim, bias=True), nn.SiLU(), nn.Linear(embed_dim, embed_dim, bias=True), ) if cond_token_dim > 0: # Conditioning tokens cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim self.to_cond_embed = nn.Sequential( nn.Linear(cond_token_dim, cond_embed_dim, bias=False), nn.SiLU(), nn.Linear(cond_embed_dim, cond_embed_dim, bias=False) ) else: cond_embed_dim = 0 if global_cond_dim > 0: # Global conditioning global_embed_dim = global_cond_dim if not project_global_cond else embed_dim self.to_global_embed = nn.Sequential( nn.Linear(global_cond_dim, global_embed_dim, bias=False), nn.SiLU(), nn.Linear(global_embed_dim, global_embed_dim, bias=False) ) if prepend_cond_dim > 0: # Prepend conditioning self.to_prepend_embed = nn.Sequential( nn.Linear(prepend_cond_dim, embed_dim, bias=False), nn.SiLU(), nn.Linear(embed_dim, embed_dim, bias=False) ) self.input_concat_dim = input_concat_dim dim_in = io_channels + self.input_concat_dim self.patch_size = patch_size # Transformer self.transformer_type = transformer_type self.global_cond_type = global_cond_type if self.transformer_type == "x-transformers": self.transformer = ContinuousTransformerWrapper( dim_in=dim_in * patch_size, dim_out=io_channels * patch_size, max_seq_len=0, #Not relevant without absolute positional embeds attn_layers = Encoder( dim=embed_dim, depth=depth, heads=num_heads, attn_flash = True, cross_attend = cond_token_dim > 0, dim_context=None if cond_embed_dim == 0 else cond_embed_dim, zero_init_branch_output=True, use_abs_pos_emb = False, rotary_pos_emb=True, ff_swish = True, ff_glu = True, **kwargs ) ) elif self.transformer_type == "continuous_transformer": global_dim = None if self.global_cond_type == "adaLN": # The global conditioning is projected to the embed_dim already at this point global_dim = embed_dim self.transformer = ContinuousTransformer( dim=embed_dim, depth=depth, dim_heads=embed_dim // num_heads, dim_in=dim_in * patch_size, dim_out=io_channels * patch_size, cross_attend = cond_token_dim > 0, cond_token_dim = cond_embed_dim, global_cond_dim=global_dim, **kwargs ) else: raise ValueError(f"Unknown transformer type: {self.transformer_type}") self.preprocess_conv = nn.Conv1d(dim_in, dim_in, 1, bias=False) nn.init.zeros_(self.preprocess_conv.weight) self.postprocess_conv = nn.Conv1d(io_channels, io_channels, 1, bias=False) nn.init.zeros_(self.postprocess_conv.weight) def _forward( self, x, t, mask=None, cross_attn_cond=None, cross_attn_cond_mask=None, input_concat_cond=None, global_embed=None, prepend_cond=None, prepend_cond_mask=None, return_info=False, **kwargs): if cross_attn_cond is not None: cross_attn_cond = self.to_cond_embed(cross_attn_cond) if global_embed is not None: # Project the global conditioning to the embedding dimension global_embed = self.to_global_embed(global_embed) prepend_inputs = None prepend_mask = None prepend_length = 0 if prepend_cond is not None: # Project the prepend conditioning to the embedding dimension prepend_cond = self.to_prepend_embed(prepend_cond) prepend_inputs = prepend_cond if prepend_cond_mask is not None: prepend_mask = prepend_cond_mask if input_concat_cond is not None: # Interpolate input_concat_cond to the same length as x if input_concat_cond.shape[2] != x.shape[2]: input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest') x = torch.cat([x, input_concat_cond], dim=1) # Get the batch of timestep embeddings timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None])) # (b, embed_dim) # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists if global_embed is not None: global_embed = global_embed + timestep_embed else: global_embed = timestep_embed # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer if self.global_cond_type == "prepend": if prepend_inputs is None: # Prepend inputs are just the global embed, and the mask is all ones prepend_inputs = global_embed.unsqueeze(1) prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool) else: # Prepend inputs are the prepend conditioning + the global embed prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1) prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1) prepend_length = prepend_inputs.shape[1] x = self.preprocess_conv(x) + x x = rearrange(x, "b c t -> b t c") extra_args = {} if self.global_cond_type == "adaLN": extra_args["global_cond"] = global_embed if self.patch_size > 1: x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size) if self.transformer_type == "x-transformers": output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs) elif self.transformer_type == "continuous_transformer": output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs) if return_info: output, info = output elif self.transformer_type == "mm_transformer": output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs) output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:] if self.patch_size > 1: output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size) output = self.postprocess_conv(output) + output if return_info: return output, info return output def forward( self, x, t, cross_attn_cond=None, cross_attn_cond_mask=None, negative_cross_attn_cond=None, negative_cross_attn_mask=None, input_concat_cond=None, global_embed=None, negative_global_embed=None, prepend_cond=None, prepend_cond_mask=None, cfg_scale=1.0, cfg_dropout_prob=0.0, causal=False, scale_phi=0.0, mask=None, return_info=False, **kwargs): assert causal == False, "Causal mode is not supported for DiffusionTransformer" if cross_attn_cond_mask is not None: cross_attn_cond_mask = cross_attn_cond_mask.bool() cross_attn_cond_mask = None # Temporarily disabling conditioning masks due to kernel issue for flash attention if prepend_cond_mask is not None: prepend_cond_mask = prepend_cond_mask.bool() # CFG dropout if cfg_dropout_prob > 0.0: if cross_attn_cond is not None: null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device) dropout_mask = torch.bernoulli(torch.full((cross_attn_cond.shape[0], 1, 1), cfg_dropout_prob, device=cross_attn_cond.device)).to(torch.bool) cross_attn_cond = torch.where(dropout_mask, null_embed, cross_attn_cond) if prepend_cond is not None: null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device) dropout_mask = torch.bernoulli(torch.full((prepend_cond.shape[0], 1, 1), cfg_dropout_prob, device=prepend_cond.device)).to(torch.bool) prepend_cond = torch.where(dropout_mask, null_embed, prepend_cond) if cfg_scale != 1.0 and (cross_attn_cond is not None or prepend_cond is not None): # Classifier-free guidance # Concatenate conditioned and unconditioned inputs on the batch dimension batch_inputs = torch.cat([x, x], dim=0) batch_timestep = torch.cat([t, t], dim=0) if global_embed is not None: batch_global_cond = torch.cat([global_embed, global_embed], dim=0) else: batch_global_cond = None if input_concat_cond is not None: batch_input_concat_cond = torch.cat([input_concat_cond, input_concat_cond], dim=0) else: batch_input_concat_cond = None batch_cond = None batch_cond_masks = None # Handle CFG for cross-attention conditioning if cross_attn_cond is not None: null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device) # For negative cross-attention conditioning, replace the null embed with the negative cross-attention conditioning if negative_cross_attn_cond is not None: # If there's a negative cross-attention mask, set the masked tokens to the null embed if negative_cross_attn_mask is not None: negative_cross_attn_mask = negative_cross_attn_mask.to(torch.bool).unsqueeze(2) negative_cross_attn_cond = torch.where(negative_cross_attn_mask, negative_cross_attn_cond, null_embed) batch_cond = torch.cat([cross_attn_cond, negative_cross_attn_cond], dim=0) else: batch_cond = torch.cat([cross_attn_cond, null_embed], dim=0) if cross_attn_cond_mask is not None: batch_cond_masks = torch.cat([cross_attn_cond_mask, cross_attn_cond_mask], dim=0) batch_prepend_cond = None batch_prepend_cond_mask = None if prepend_cond is not None: null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device) batch_prepend_cond = torch.cat([prepend_cond, null_embed], dim=0) if prepend_cond_mask is not None: batch_prepend_cond_mask = torch.cat([prepend_cond_mask, prepend_cond_mask], dim=0) if mask is not None: batch_masks = torch.cat([mask, mask], dim=0) else: batch_masks = None batch_output = self._forward( batch_inputs, batch_timestep, cross_attn_cond=batch_cond, cross_attn_cond_mask=batch_cond_masks, mask = batch_masks, input_concat_cond=batch_input_concat_cond, global_embed = batch_global_cond, prepend_cond = batch_prepend_cond, prepend_cond_mask = batch_prepend_cond_mask, return_info = return_info, **kwargs) if return_info: batch_output, info = batch_output cond_output, uncond_output = torch.chunk(batch_output, 2, dim=0) cfg_output = uncond_output + (cond_output - uncond_output) * cfg_scale # CFG Rescale if scale_phi != 0.0: cond_out_std = cond_output.std(dim=1, keepdim=True) out_cfg_std = cfg_output.std(dim=1, keepdim=True) output = scale_phi * (cfg_output * (cond_out_std/out_cfg_std)) + (1-scale_phi) * cfg_output else: output = cfg_output if return_info: return output, info return output else: return self._forward( x, t, cross_attn_cond=cross_attn_cond, cross_attn_cond_mask=cross_attn_cond_mask, input_concat_cond=input_concat_cond, global_embed=global_embed, prepend_cond=prepend_cond, prepend_cond_mask=prepend_cond_mask, mask=mask, return_info=return_info, **kwargs )