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import torch |
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import torch.nn.functional as F |
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from typing import Optional |
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from diffusers.models.attention_processor import Attention |
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class WanAttnProcessor2_0: |
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def __init__(self, num_img_tokens: int = 257): |
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self.num_img_tokens = num_img_tokens |
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if not hasattr(F, "scaled_dot_product_attention"): |
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raise ImportError( |
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"WanAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.") |
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def __call__( |
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self, |
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attn: Attention, |
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hidden_states: torch.Tensor, |
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encoder_hidden_states: Optional[torch.Tensor] = None, |
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attention_mask: Optional[torch.Tensor] = None, |
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rotary_emb: Optional[torch.Tensor] = None, |
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) -> torch.Tensor: |
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encoder_hidden_states_img = None |
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if attn.add_k_proj is not None: |
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encoder_hidden_states_img = encoder_hidden_states[:, |
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:self.num_img_tokens] |
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encoder_hidden_states = encoder_hidden_states[:, |
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self.num_img_tokens:] |
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if encoder_hidden_states is None: |
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encoder_hidden_states = hidden_states |
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query = attn.to_q(hidden_states) |
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key = attn.to_k(encoder_hidden_states) |
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value = attn.to_v(encoder_hidden_states) |
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if attn.norm_q is not None: |
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query = attn.norm_q(query) |
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if attn.norm_k is not None: |
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key = attn.norm_k(key) |
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query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2) |
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key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2) |
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value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2) |
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if rotary_emb is not None: |
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def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor): |
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x_rotated = torch.view_as_complex( |
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hidden_states.to(torch.float64).unflatten(3, (-1, 2))) |
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x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4) |
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return x_out.type_as(hidden_states) |
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query = apply_rotary_emb(query, rotary_emb) |
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key = apply_rotary_emb(key, rotary_emb) |
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hidden_states_img = None |
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if encoder_hidden_states_img is not None: |
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key_img = attn.add_k_proj(encoder_hidden_states_img) |
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key_img = attn.norm_added_k(key_img) |
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value_img = attn.add_v_proj(encoder_hidden_states_img) |
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key_img = key_img.unflatten(2, (attn.heads, -1)).transpose(1, 2) |
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value_img = value_img.unflatten( |
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2, (attn.heads, -1)).transpose(1, 2) |
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hidden_states_img = F.scaled_dot_product_attention( |
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query, key_img, value_img, attn_mask=None, dropout_p=0.0, is_causal=False |
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) |
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hidden_states_img = hidden_states_img.transpose(1, 2).flatten(2, 3) |
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hidden_states_img = hidden_states_img.type_as(query) |
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hidden_states = F.scaled_dot_product_attention( |
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False |
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) |
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hidden_states = hidden_states.transpose(1, 2).flatten(2, 3) |
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hidden_states = hidden_states.type_as(query) |
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if hidden_states_img is not None: |
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hidden_states = hidden_states + hidden_states_img |
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hidden_states = attn.to_out[0](hidden_states) |
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hidden_states = attn.to_out[1](hidden_states) |
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return hidden_states |
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