Spaces:
Running
Running
| 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 | |