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| from typing import Optional | |
| from diffusers.models.attention_processor import Attention | |
| import torch | |
| import torch.nn.functional as F | |
| class FluxSageAttnProcessor2_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("FluxAttnProcessor2_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, | |
| image_rotary_emb: Optional[torch.Tensor] = None, | |
| ) -> torch.FloatTensor: | |
| from sageattention import sageattn | |
| 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: | |
| from diffusers.models.embeddings import apply_rotary_emb | |
| query = apply_rotary_emb(query, image_rotary_emb) | |
| key = apply_rotary_emb(key, image_rotary_emb) | |
| hidden_states = sageattn(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: | |
| 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 |