57 lines
2.6 KiB
Python
57 lines
2.6 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from diffusers.models.normalization import RMSNorm
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from einops import rearrange
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class IPAFluxAttnProcessor2_0(nn.Module):
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"""Attention processor used typically in processing the SD3-like self-attention projections."""
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def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, timestep_range=None):
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super().__init__()
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self.hidden_size = hidden_size # 3072
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self.cross_attention_dim = cross_attention_dim # 4096
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self.scale = scale
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self.num_tokens = num_tokens
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self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
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self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
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self.norm_added_k = RMSNorm(128, eps=1e-5, elementwise_affine=False)
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self.norm_added_v = RMSNorm(128, eps=1e-5, elementwise_affine=False)
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self.timestep_range = timestep_range
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def __call__(
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self,
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num_heads,
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query,
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image_emb: torch.FloatTensor,
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t: torch.FloatTensor|torch.Tensor
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) -> torch.FloatTensor|torch.Tensor|None:
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# only apply IPA if timestep is within range
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if self.timestep_range is not None:
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if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
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return None
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# `ip-adapter` projections
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ip_hidden_states = image_emb
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ip_hidden_states_key_proj = self.to_k_ip(ip_hidden_states)
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ip_hidden_states_value_proj = self.to_v_ip(ip_hidden_states)
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ip_hidden_states_key_proj = rearrange(ip_hidden_states_key_proj, 'B L (H D) -> B H L D', H=num_heads)
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ip_hidden_states_value_proj = rearrange(ip_hidden_states_value_proj, 'B L (H D) -> B H L D', H=num_heads)
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ip_hidden_states_key_proj = self.norm_added_k(ip_hidden_states_key_proj)
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ip_hidden_states_value_proj = self.norm_added_v(ip_hidden_states_value_proj)
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ip_hidden_states = F.scaled_dot_product_attention(query.to(image_emb.device).to(image_emb.dtype),
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ip_hidden_states_key_proj,
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ip_hidden_states_value_proj,
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dropout_p=0.0, is_causal=False)
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ip_hidden_states = rearrange(ip_hidden_states, "B H L D -> B L (H D)", H=num_heads)
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ip_hidden_states = ip_hidden_states.to(query.dtype).to(query.device)
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return self.scale * ip_hidden_states
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