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Running
on
Zero
| from einops import rearrange | |
| from diffusers.models.attention_processor import * | |
| # Copied from diffusers.models.attention_processor.JointAttnProcessor2_0 | |
| # The only modifications: reshape qkv by `num_views` | |
| class JointMVAttnProcessor2_0: | |
| """Attention processor used typically in processing the SD3-like self-attention projections.""" | |
| 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.FloatTensor, | |
| encoder_hidden_states: torch.FloatTensor = None, | |
| attention_mask: Optional[torch.FloatTensor] = None, | |
| num_views: Optional[int] = None, | |
| *args, | |
| **kwargs, | |
| ) -> torch.FloatTensor: | |
| residual = hidden_states | |
| batch_size = hidden_states.shape[0] | |
| # `sample` projections. | |
| query = attn.to_q(hidden_states) | |
| key = attn.to_k(hidden_states) | |
| value = attn.to_v(hidden_states) | |
| # Multi-view Self-Attn | |
| num_views = num_views if num_views is not None else kwargs.get("num_views", 1) | |
| key = rearrange(key, "(b v) n d -> b (v n) d", v=num_views) | |
| value = rearrange(value, "(b v) n d -> b (v n) d", v=num_views) | |
| query = rearrange(query, "(b v) n d -> b (v n) d", v=num_views) | |
| batch_size //= num_views | |
| 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) | |
| # `context` projections. | |
| if encoder_hidden_states is not None: | |
| encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states) | |
| encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) | |
| encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) | |
| encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view( | |
| batch_size, -1, attn.heads, head_dim | |
| ).transpose(1, 2) | |
| encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view( | |
| batch_size, -1, attn.heads, head_dim | |
| ).transpose(1, 2) | |
| encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view( | |
| batch_size, -1, attn.heads, head_dim | |
| ).transpose(1, 2) | |
| if attn.norm_added_q is not None: | |
| encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj) | |
| if attn.norm_added_k is not None: | |
| encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj) | |
| query = torch.cat([query, encoder_hidden_states_query_proj], dim=2) | |
| key = torch.cat([key, encoder_hidden_states_key_proj], dim=2) | |
| value = torch.cat([value, encoder_hidden_states_value_proj], dim=2) | |
| hidden_states = F.scaled_dot_product_attention(query, key, value, 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) | |
| if encoder_hidden_states is not None: | |
| # Split the attention outputs. | |
| hidden_states, encoder_hidden_states = ( | |
| hidden_states[:, : residual.shape[1]*num_views], | |
| hidden_states[:, residual.shape[1]*num_views :], | |
| ) | |
| if not attn.context_pre_only: | |
| encoder_hidden_states = attn.to_add_out(encoder_hidden_states) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| # Multi-view Self-Attn | |
| hidden_states = rearrange(hidden_states, "b (v n) d -> (b v) n d", v=num_views) | |
| batch_size *= num_views | |
| if encoder_hidden_states is not None: | |
| return hidden_states, encoder_hidden_states | |
| else: | |
| return hidden_states | |
| # Copied from diffusers.models.attention_processor.AttnProcessor2_0 | |
| # The only modifications: reshape qkv by `num_views` | |
| class MVAttnProcessor2_0: | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| 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, | |
| num_views: Optional[int] = None, | |
| *args, | |
| **kwargs, | |
| ) -> torch.Tensor: | |
| if len(args) > 0 or kwargs.get("scale", None) is not None: | |
| deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." | |
| deprecate("scale", "1.0.0", deprecation_message) | |
| 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 | |
| # Multi-view Self-Attn | |
| num_views = num_views if num_views is not None else kwargs.get("num_views", 1) | |
| key = rearrange(key, "(b v) n d -> b (v n) d", v=num_views) | |
| value = rearrange(value, "(b v) n d -> b (v n) d", v=num_views) | |
| query = rearrange(query, "(b v) n d -> b (v n) d", v=num_views) | |
| batch_size //= num_views | |
| 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) | |
| # 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) | |
| # Multi-view Self-Attn | |
| hidden_states = rearrange(hidden_states, "b (v n) d -> (b v) n d", v=num_views) | |
| batch_size *= num_views | |
| 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 | |