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| from typing import Callable, List, Optional, Tuple, Union | |
| import torch | |
| import torch.nn.functional as F | |
| from diffusers.models.attention_processor import Attention | |
| from diffusers.utils import logging | |
| from diffusers.utils.import_utils import is_torch_npu_available, is_xformers_available | |
| from diffusers.utils.torch_utils import is_torch_version, maybe_allow_in_graph | |
| from einops import rearrange | |
| from torch import nn | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| class TripoSGAttnProcessor2_0: | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is | |
| used in the TripoSG model. It applies a s normalization layer and rotary embedding on query and key vector. | |
| """ | |
| def __init__(self): | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError( | |
| "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." | |
| ) | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.Tensor, | |
| encoder_hidden_states: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| temb: Optional[torch.Tensor] = None, | |
| image_rotary_emb: Optional[torch.Tensor] = None, | |
| ) -> torch.Tensor: | |
| from diffusers.models.embeddings import apply_rotary_emb | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view( | |
| batch_size, channel, height * width | |
| ).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape | |
| if encoder_hidden_states is None | |
| else encoder_hidden_states.shape | |
| ) | |
| if attention_mask is not None: | |
| attention_mask = attn.prepare_attention_mask( | |
| attention_mask, sequence_length, batch_size | |
| ) | |
| # scaled_dot_product_attention expects attention_mask shape to be | |
| # (batch, heads, source_length, target_length) | |
| attention_mask = attention_mask.view( | |
| batch_size, attn.heads, -1, attention_mask.shape[-1] | |
| ) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose( | |
| 1, 2 | |
| ) | |
| query = attn.to_q(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| elif attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states( | |
| encoder_hidden_states | |
| ) | |
| key = attn.to_k(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) | |
| # NOTE that pre-trained models split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim) | |
| # instead of .view(..., 3, attn.heads, dim). So we need to re-split here. | |
| if not attn.is_cross_attention: | |
| qkv = torch.cat((query, key, value), dim=-1) | |
| split_size = qkv.shape[-1] // attn.heads // 3 | |
| qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3) | |
| query, key, value = torch.split(qkv, split_size, dim=-1) | |
| else: | |
| kv = torch.cat((key, value), dim=-1) | |
| split_size = kv.shape[-1] // attn.heads // 2 | |
| kv = kv.view(batch_size, -1, attn.heads, split_size * 2) | |
| key, value = torch.split(kv, split_size, dim=-1) | |
| head_dim = key.shape[-1] | |
| query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| if attn.norm_q is not None: | |
| query = attn.norm_q(query) | |
| if attn.norm_k is not None: | |
| key = attn.norm_k(key) | |
| # Apply RoPE if needed | |
| if image_rotary_emb is not None: | |
| query = apply_rotary_emb(query, image_rotary_emb) | |
| if not attn.is_cross_attention: | |
| key = apply_rotary_emb(key, image_rotary_emb) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| # TODO: add support for attn.scale when we move to Torch 2.1 | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| hidden_states = hidden_states.transpose(1, 2).reshape( | |
| batch_size, -1, attn.heads * head_dim | |
| ) | |
| hidden_states = hidden_states.to(query.dtype) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape( | |
| batch_size, channel, height, width | |
| ) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| class FusedTripoSGAttnProcessor2_0: | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0) with fused | |
| projection layers. This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on | |
| query and key vector. | |
| """ | |
| def __init__(self): | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError( | |
| "FusedTripoSGAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." | |
| ) | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.Tensor, | |
| encoder_hidden_states: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| temb: Optional[torch.Tensor] = None, | |
| image_rotary_emb: Optional[torch.Tensor] = None, | |
| ) -> torch.Tensor: | |
| from diffusers.models.embeddings import apply_rotary_emb | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view( | |
| batch_size, channel, height * width | |
| ).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape | |
| if encoder_hidden_states is None | |
| else encoder_hidden_states.shape | |
| ) | |
| if attention_mask is not None: | |
| attention_mask = attn.prepare_attention_mask( | |
| attention_mask, sequence_length, batch_size | |
| ) | |
| # scaled_dot_product_attention expects attention_mask shape to be | |
| # (batch, heads, source_length, target_length) | |
| attention_mask = attention_mask.view( | |
| batch_size, attn.heads, -1, attention_mask.shape[-1] | |
| ) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose( | |
| 1, 2 | |
| ) | |
| # NOTE that pre-trained split heads first, then split qkv | |
| if encoder_hidden_states is None: | |
| qkv = attn.to_qkv(hidden_states) | |
| split_size = qkv.shape[-1] // attn.heads // 3 | |
| qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3) | |
| query, key, value = torch.split(qkv, split_size, dim=-1) | |
| else: | |
| if attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states( | |
| encoder_hidden_states | |
| ) | |
| query = attn.to_q(hidden_states) | |
| kv = attn.to_kv(encoder_hidden_states) | |
| split_size = kv.shape[-1] // attn.heads // 2 | |
| kv = kv.view(batch_size, -1, attn.heads, split_size * 2) | |
| key, value = torch.split(kv, split_size, dim=-1) | |
| head_dim = key.shape[-1] | |
| query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| if attn.norm_q is not None: | |
| query = attn.norm_q(query) | |
| if attn.norm_k is not None: | |
| key = attn.norm_k(key) | |
| # Apply RoPE if needed | |
| if image_rotary_emb is not None: | |
| query = apply_rotary_emb(query, image_rotary_emb) | |
| if not attn.is_cross_attention: | |
| key = apply_rotary_emb(key, image_rotary_emb) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| # TODO: add support for attn.scale when we move to Torch 2.1 | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| hidden_states = hidden_states.transpose(1, 2).reshape( | |
| batch_size, -1, attn.heads * head_dim | |
| ) | |
| hidden_states = hidden_states.to(query.dtype) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape( | |
| batch_size, channel, height, width | |
| ) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| class MIAttnProcessor2_0: | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is | |
| used in the MIDI model. It applies a normalization layer and rotary embedding on query and key vector. | |
| """ | |
| def __init__(self, use_mi: bool = True): | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError( | |
| "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." | |
| ) | |
| self.use_mi = use_mi | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.Tensor, | |
| encoder_hidden_states: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| temb: Optional[torch.Tensor] = None, | |
| image_rotary_emb: Optional[torch.Tensor] = None, | |
| num_instances: Optional[torch.IntTensor] = None, | |
| ) -> torch.Tensor: | |
| from diffusers.models.embeddings import apply_rotary_emb | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view( | |
| batch_size, channel, height * width | |
| ).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape | |
| if encoder_hidden_states is None | |
| else encoder_hidden_states.shape | |
| ) | |
| if attention_mask is not None: | |
| attention_mask = attn.prepare_attention_mask( | |
| attention_mask, sequence_length, batch_size | |
| ) | |
| # scaled_dot_product_attention expects attention_mask shape to be | |
| # (batch, heads, source_length, target_length) | |
| attention_mask = attention_mask.view( | |
| batch_size, attn.heads, -1, attention_mask.shape[-1] | |
| ) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose( | |
| 1, 2 | |
| ) | |
| query = attn.to_q(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| elif attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states( | |
| encoder_hidden_states | |
| ) | |
| key = attn.to_k(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) | |
| # NOTE that pre-trained models split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim) | |
| # instead of .view(..., 3, attn.heads, dim). So we need to re-split here. | |
| if not attn.is_cross_attention: | |
| qkv = torch.cat((query, key, value), dim=-1) | |
| split_size = qkv.shape[-1] // attn.heads // 3 | |
| qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3) | |
| query, key, value = torch.split(qkv, split_size, dim=-1) | |
| else: | |
| kv = torch.cat((key, value), dim=-1) | |
| split_size = kv.shape[-1] // attn.heads // 2 | |
| kv = kv.view(batch_size, -1, attn.heads, split_size * 2) | |
| key, value = torch.split(kv, split_size, dim=-1) | |
| head_dim = key.shape[-1] | |
| query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| if attn.norm_q is not None: | |
| query = attn.norm_q(query) | |
| if attn.norm_k is not None: | |
| key = attn.norm_k(key) | |
| # Apply RoPE if needed | |
| if image_rotary_emb is not None: | |
| query = apply_rotary_emb(query, image_rotary_emb) | |
| if not attn.is_cross_attention: | |
| key = apply_rotary_emb(key, image_rotary_emb) | |
| if self.use_mi and num_instances is not None: | |
| key = rearrange( | |
| key, "(b ni) h nt c -> b h (ni nt) c", ni=num_instances | |
| ).repeat_interleave(num_instances, dim=0) | |
| value = rearrange( | |
| value, "(b ni) h nt c -> b h (ni nt) c", ni=num_instances | |
| ).repeat_interleave(num_instances, dim=0) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, | |
| key, | |
| value, | |
| dropout_p=0.0, | |
| is_causal=False, | |
| ) | |
| else: | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, | |
| key, | |
| value, | |
| attn_mask=attention_mask, | |
| dropout_p=0.0, | |
| is_causal=False, | |
| ) | |
| hidden_states = hidden_states.transpose(1, 2).reshape( | |
| batch_size, -1, attn.heads * head_dim | |
| ) | |
| hidden_states = hidden_states.to(query.dtype) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape( | |
| batch_size, channel, height, width | |
| ) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |