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| from typing import Optional | |
| import torch | |
| import torch.nn.functional as F | |
| from diffusers.models.attention import Attention | |
| from diffusers.models.embeddings import apply_rotary_emb | |
| from einops import rearrange, repeat | |
| class HunyuanAttnProcessor2_0: | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). 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("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: | |
| 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) | |
| inner_dim = key.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| 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 LazyKVCompressionProcessor2_0: | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is | |
| used in the KVCompression 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: | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| batch_size, channel, num_frames, height, width = hidden_states.shape | |
| hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c", f=num_frames, h=height, w=width) | |
| 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) | |
| key = rearrange(key, "b (f h w) c -> (b f) c h w", f=num_frames, h=height, w=width) | |
| key = attn.k_compression(key) | |
| key_shape = key.size() | |
| key = rearrange(key, "(b f) c h w -> b (f h w) c", f=num_frames) | |
| value = rearrange(value, "b (f h w) c -> (b f) c h w", f=num_frames, h=height, w=width) | |
| value = attn.v_compression(value) | |
| value = rearrange(value, "(b f) c h w -> b (f h w) c", f=num_frames) | |
| inner_dim = key.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| 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: | |
| compression_image_rotary_emb = ( | |
| rearrange(image_rotary_emb[0], "(f h w) c -> f c h w", f=num_frames, h=height, w=width), | |
| rearrange(image_rotary_emb[1], "(f h w) c -> f c h w", f=num_frames, h=height, w=width), | |
| ) | |
| compression_image_rotary_emb = ( | |
| F.interpolate(compression_image_rotary_emb[0], size=key_shape[-2:], mode='bilinear'), | |
| F.interpolate(compression_image_rotary_emb[1], size=key_shape[-2:], mode='bilinear') | |
| ) | |
| compression_image_rotary_emb = ( | |
| rearrange(compression_image_rotary_emb[0], "f c h w -> (f h w) c"), | |
| rearrange(compression_image_rotary_emb[1], "f c h w -> (f h w) c"), | |
| ) | |
| query = apply_rotary_emb(query, image_rotary_emb) | |
| if not attn.is_cross_attention: | |
| key = apply_rotary_emb(key, compression_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 attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| class EasyAnimateAttnProcessor2_0: | |
| def __init__(self): | |
| pass | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.Tensor, | |
| encoder_hidden_states: torch.Tensor, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| image_rotary_emb: Optional[torch.Tensor] = None, | |
| attn2: Attention = None, | |
| ) -> torch.Tensor: | |
| text_seq_length = encoder_hidden_states.size(1) | |
| 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) | |
| attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) | |
| if attn2 is None: | |
| hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1) | |
| query = attn.to_q(hidden_states) | |
| key = attn.to_k(hidden_states) | |
| value = attn.to_v(hidden_states) | |
| inner_dim = key.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| 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) | |
| if attn2 is not None: | |
| query_txt = attn2.to_q(encoder_hidden_states) | |
| key_txt = attn2.to_k(encoder_hidden_states) | |
| value_txt = attn2.to_v(encoder_hidden_states) | |
| inner_dim = key_txt.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| query_txt = query_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key_txt = key_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value_txt = value_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| if attn2.norm_q is not None: | |
| query_txt = attn2.norm_q(query_txt) | |
| if attn2.norm_k is not None: | |
| key_txt = attn2.norm_k(key_txt) | |
| query = torch.cat([query_txt, query], dim=2) | |
| key = torch.cat([key_txt, key], dim=2) | |
| value = torch.cat([value_txt, value], dim=2) | |
| # Apply RoPE if needed | |
| if image_rotary_emb is not None: | |
| query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb) | |
| if not attn.is_cross_attention: | |
| key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb) | |
| 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) | |
| if attn2 is None: | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| encoder_hidden_states, hidden_states = hidden_states.split( | |
| [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1 | |
| ) | |
| else: | |
| encoder_hidden_states, hidden_states = hidden_states.split( | |
| [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1 | |
| ) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| encoder_hidden_states = attn2.to_out[0](encoder_hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| encoder_hidden_states = attn2.to_out[1](encoder_hidden_states) | |
| return hidden_states, encoder_hidden_states | |
| try: | |
| from flash_attn import flash_attn_func, flash_attn_varlen_func | |
| from flash_attn.bert_padding import pad_input, unpad_input | |
| except: | |
| print("Flash Attention is not installed. Please install with `pip install flash-attn`, if you want to use SWA.") | |
| class EasyAnimateSWAttnProcessor2_0: | |
| def __init__(self, window_size=1024): | |
| self.window_size = window_size | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.Tensor, | |
| encoder_hidden_states: torch.Tensor, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| image_rotary_emb: Optional[torch.Tensor] = None, | |
| num_frames: int = None, | |
| height: int = None, | |
| width: int = None, | |
| attn2: Attention = None, | |
| ) -> torch.Tensor: | |
| text_seq_length = encoder_hidden_states.size(1) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| if attn2 is None: | |
| hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1) | |
| query = attn.to_q(hidden_states) | |
| key = attn.to_k(hidden_states) | |
| value = attn.to_v(hidden_states) | |
| inner_dim = key.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| 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) | |
| if attn.norm_q is not None: | |
| query = attn.norm_q(query) | |
| if attn.norm_k is not None: | |
| key = attn.norm_k(key) | |
| if attn2 is not None: | |
| query_txt = attn2.to_q(encoder_hidden_states) | |
| key_txt = attn2.to_k(encoder_hidden_states) | |
| value_txt = attn2.to_v(encoder_hidden_states) | |
| inner_dim = key_txt.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| query_txt = query_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key_txt = key_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value_txt = value_txt.view(batch_size, -1, attn.heads, head_dim) | |
| if attn2.norm_q is not None: | |
| query_txt = attn2.norm_q(query_txt) | |
| if attn2.norm_k is not None: | |
| key_txt = attn2.norm_k(key_txt) | |
| query = torch.cat([query_txt, query], dim=2) | |
| key = torch.cat([key_txt, key], dim=2) | |
| value = torch.cat([value_txt, value], dim=1) | |
| # Apply RoPE if needed | |
| if image_rotary_emb is not None: | |
| query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb) | |
| if not attn.is_cross_attention: | |
| key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb) | |
| query = query.transpose(1, 2).to(value) | |
| key = key.transpose(1, 2).to(value) | |
| interval = max((query.size(1) - text_seq_length) // (self.window_size - text_seq_length), 1) | |
| cross_key = torch.cat([key[:, :text_seq_length], key[:, text_seq_length::interval]], dim=1) | |
| cross_val = torch.cat([value[:, :text_seq_length], value[:, text_seq_length::interval]], dim=1) | |
| cross_hidden_states = flash_attn_func(query, cross_key, cross_val, dropout_p=0.0, causal=False) | |
| # Split and rearrange to six directions | |
| querys = torch.tensor_split(query[:, text_seq_length:], 6, 2) | |
| keys = torch.tensor_split(key[:, text_seq_length:], 6, 2) | |
| values = torch.tensor_split(value[:, text_seq_length:], 6, 2) | |
| new_querys = [querys[0]] | |
| new_keys = [keys[0]] | |
| new_values = [values[0]] | |
| for index, mode in enumerate( | |
| [ | |
| "bs (f h w) hn hd -> bs (f w h) hn hd", | |
| "bs (f h w) hn hd -> bs (h f w) hn hd", | |
| "bs (f h w) hn hd -> bs (h w f) hn hd", | |
| "bs (f h w) hn hd -> bs (w f h) hn hd", | |
| "bs (f h w) hn hd -> bs (w h f) hn hd" | |
| ] | |
| ): | |
| new_querys.append(rearrange(querys[index + 1], mode, f=num_frames, h=height, w=width)) | |
| new_keys.append(rearrange(keys[index + 1], mode, f=num_frames, h=height, w=width)) | |
| new_values.append(rearrange(values[index + 1], mode, f=num_frames, h=height, w=width)) | |
| query = torch.cat(new_querys, dim=2) | |
| key = torch.cat(new_keys, dim=2) | |
| value = torch.cat(new_values, dim=2) | |
| # apply attention | |
| hidden_states = flash_attn_func(query, key, value, dropout_p=0.0, causal=False, window_size=(self.window_size, self.window_size)) | |
| hidden_states = torch.tensor_split(hidden_states, 6, 2) | |
| new_hidden_states = [hidden_states[0]] | |
| for index, mode in enumerate( | |
| [ | |
| "bs (f w h) hn hd -> bs (f h w) hn hd", | |
| "bs (h f w) hn hd -> bs (f h w) hn hd", | |
| "bs (h w f) hn hd -> bs (f h w) hn hd", | |
| "bs (w f h) hn hd -> bs (f h w) hn hd", | |
| "bs (w h f) hn hd -> bs (f h w) hn hd" | |
| ] | |
| ): | |
| new_hidden_states.append(rearrange(hidden_states[index + 1], mode, f=num_frames, h=height, w=width)) | |
| hidden_states = torch.cat([cross_hidden_states[:, :text_seq_length], torch.cat(new_hidden_states, dim=2)], dim=1) + cross_hidden_states | |
| hidden_states = hidden_states.reshape(batch_size, -1, attn.heads * head_dim) | |
| if attn2 is None: | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| encoder_hidden_states, hidden_states = hidden_states.split( | |
| [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1 | |
| ) | |
| else: | |
| encoder_hidden_states, hidden_states = hidden_states.split( | |
| [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1 | |
| ) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| encoder_hidden_states = attn2.to_out[0](encoder_hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| encoder_hidden_states = attn2.to_out[1](encoder_hidden_states) | |
| return hidden_states, encoder_hidden_states |