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Running
on
Zero
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import math | |
| from diffusers.models.attention_processor import Attention | |
| from typing import Optional | |
| from diffusers.models.embeddings import apply_rotary_emb | |
| class FluxAttnProcessor2_0: | |
| """Attention processor used typically in processing the SD3-like self-attention projections.""" | |
| def __init__(self, train_seq_len=512 + 64 * 64): | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError( | |
| "FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." | |
| ) | |
| self.train_seq_len = train_seq_len | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.FloatTensor, | |
| encoder_hidden_states: torch.FloatTensor = None, | |
| attention_mask: Optional[torch.FloatTensor] = None, | |
| image_rotary_emb: Optional[torch.Tensor] = None, | |
| proportional_attention=False, | |
| ) -> torch.FloatTensor: | |
| batch_size, _, _ = ( | |
| hidden_states.shape | |
| if encoder_hidden_states is None | |
| else encoder_hidden_states.shape | |
| ) | |
| # `sample` projections. | |
| 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) | |
| # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states` | |
| if encoder_hidden_states is not None: | |
| # `context` projections. | |
| 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 | |
| ) | |
| # attention | |
| query = torch.cat([encoder_hidden_states_query_proj, query], dim=2) | |
| key = torch.cat([encoder_hidden_states_key_proj, key], dim=2) | |
| value = torch.cat([encoder_hidden_states_value_proj, value], dim=2) | |
| if image_rotary_emb is not None: | |
| query = apply_rotary_emb(query, image_rotary_emb) | |
| key = apply_rotary_emb(key, image_rotary_emb) | |
| if proportional_attention: | |
| attention_scale = math.sqrt( | |
| math.log(key.size(2), self.train_seq_len) / head_dim | |
| ) | |
| else: | |
| attention_scale = math.sqrt(1 / head_dim) | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, dropout_p=0.0, is_causal=False, scale=attention_scale | |
| ) | |
| 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: | |
| encoder_hidden_states, hidden_states = ( | |
| hidden_states[:, : encoder_hidden_states.shape[1]], | |
| hidden_states[:, encoder_hidden_states.shape[1] :], | |
| ) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| encoder_hidden_states = attn.to_add_out(encoder_hidden_states) | |
| return hidden_states, encoder_hidden_states | |
| else: | |
| return hidden_states | |
| class FluxAttnAdaptationProcessor2_0(nn.Module): | |
| """Attention processor used typically in processing the SD3-like self-attention projections.""" | |
| def __init__(self, rank=16, dim=3072, to_out=False, train_seq_len=512 + 64 * 64): | |
| super().__init__() | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError( | |
| "FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." | |
| ) | |
| self.to_q_a = nn.Linear(dim, rank, bias=False) | |
| self.to_q_b = nn.Linear(rank, dim, bias=False) | |
| self.to_q_b.weight.data = torch.zeros_like(self.to_q_b.weight.data) | |
| self.to_k_a = nn.Linear(dim, rank, bias=False) | |
| self.to_k_b = nn.Linear(rank, dim, bias=False) | |
| self.to_k_b.weight.data = torch.zeros_like(self.to_k_b.weight.data) | |
| self.to_v_a = nn.Linear(dim, rank, bias=False) | |
| self.to_v_b = nn.Linear(rank, dim, bias=False) | |
| self.to_v_b.weight.data = torch.zeros_like(self.to_v_b.weight.data) | |
| if to_out: | |
| self.to_out_a = nn.Linear(dim, rank, bias=False) | |
| self.to_out_b = nn.Linear(rank, dim, bias=False) | |
| self.to_out_b.weight.data = torch.zeros_like(self.to_out_b.weight.data) | |
| self.train_seq_len = train_seq_len | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.FloatTensor, | |
| encoder_hidden_states: torch.FloatTensor = None, | |
| attention_mask: Optional[torch.FloatTensor] = None, | |
| image_rotary_emb: Optional[torch.Tensor] = None, | |
| proportional_attention=False, | |
| ) -> torch.FloatTensor: | |
| batch_size, _, _ = ( | |
| hidden_states.shape | |
| if encoder_hidden_states is None | |
| else encoder_hidden_states.shape | |
| ) | |
| use_adaptation = True | |
| # `sample` projections. | |
| query = attn.to_q(hidden_states) | |
| key = attn.to_k(hidden_states) | |
| value = attn.to_v(hidden_states) | |
| if use_adaptation: | |
| query += self.to_q_b(self.to_q_a(hidden_states)) | |
| key += self.to_k_b(self.to_k_a(hidden_states)) | |
| value += self.to_v_b(self.to_v_a(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) | |
| # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states` | |
| if encoder_hidden_states is not None: | |
| # `context` projections. | |
| 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 | |
| ) | |
| # attention | |
| query = torch.cat([encoder_hidden_states_query_proj, query], dim=2) | |
| key = torch.cat([encoder_hidden_states_key_proj, key], dim=2) | |
| value = torch.cat([encoder_hidden_states_value_proj, value], dim=2) | |
| if image_rotary_emb is not None: | |
| query = apply_rotary_emb(query, image_rotary_emb) | |
| key = apply_rotary_emb(key, image_rotary_emb) | |
| if proportional_attention: | |
| attention_scale = math.sqrt( | |
| math.log(key.size(2), self.train_seq_len) / head_dim | |
| ) | |
| else: | |
| attention_scale = math.sqrt(1 / head_dim) | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, dropout_p=0.0, is_causal=False, scale=attention_scale | |
| ) | |
| 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: | |
| encoder_hidden_states, hidden_states = ( | |
| hidden_states[:, : encoder_hidden_states.shape[1]], | |
| hidden_states[:, encoder_hidden_states.shape[1] :], | |
| ) | |
| # linear proj | |
| hidden_states = ( | |
| ( | |
| attn.to_out[0](hidden_states) | |
| + self.to_out_b(self.to_out_a(hidden_states)) | |
| ) | |
| if use_adaptation | |
| else attn.to_out[0](hidden_states) | |
| ) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| encoder_hidden_states = attn.to_add_out(encoder_hidden_states) | |
| return hidden_states, encoder_hidden_states | |
| else: | |
| return hidden_states | |