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import inspect
import math
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
from diffusers.image_processor import IPAdapterMaskProcessor
from diffusers.utils import deprecate, is_torch_xla_available, logging
from diffusers.utils.import_utils import is_torch_npu_available, is_torch_xla_version, is_xformers_available
from diffusers.utils.torch_utils import is_torch_version, maybe_allow_in_graph
from diffusers.models.attention_processor import (
Attention
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
if is_torch_npu_available():
import torch_npu
if is_xformers_available():
import xformers
import xformers.ops
else:
xformers = None
if is_torch_xla_available():
# flash attention pallas kernel is introduced in the torch_xla 2.3 release.
if is_torch_xla_version(">", "2.2"):
from torch_xla.experimental.custom_kernel import flash_attention
from torch_xla.runtime import is_spmd
XLA_AVAILABLE = True
else:
XLA_AVAILABLE = False
class JointAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self, scale=4, attn_mask=None, neg_prompt_length=0, maps=None):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("JointAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
self.attn_mask = attn_mask
self.neg_prompt_length = neg_prompt_length
self.scale = scale
self.maps = maps
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states
batch_size = hidden_states.shape[0]
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)
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, encoder_hidden_states_key_proj[:,:,-self.neg_prompt_length:]], dim=2)
value = torch.cat([value, encoder_hidden_states_value_proj, encoder_hidden_states_value_proj[:,:,-self.neg_prompt_length:]], dim=2)
value[:,:,-self.neg_prompt_length:] *= -self.scale
# pos_map = torch.einsum('bhqd,bhkd->bhqk', query[:,:,:-encoder_hidden_states.shape[1]], key[:,:,-encoder_hidden_states.shape[1]-self.neg_prompt_length:-self.neg_prompt_length])
# neg_map = torch.einsum('bhqd,bhkd->bhqk', query[:,:,:-encoder_hidden_states.shape[1]], key[:,:,-self.neg_prompt_length:])
# self.maps.append([pos_map.detach().cpu(), neg_map.detach().cpu()])
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False, attn_mask=self.attn_mask.to(query.dtype))
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:
hidden_states, encoder_hidden_states = (
hidden_states[:, : residual.shape[1]],
hidden_states[:, residual.shape[1] :],
)
if not attn.context_pre_only:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
hidden_states = attn.to_out[0](hidden_states)
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None:
return hidden_states, encoder_hidden_states
else:
return hidden_states
class FluxAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self, scale=4, attn_mask=None, neg_prompt_length=0):
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.attn_mask = attn_mask
self.neg_prompt_length = neg_prompt_length
self.scale = scale
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:
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)
# print(encoder_hidden_states_key_proj.shape, encoder_hidden_states_value_proj.shape, encoder_hidden_states_query_proj.shape)
# attention
query = torch.cat([query, encoder_hidden_states_query_proj, encoder_hidden_states_query_proj[:,:,-self.neg_prompt_length:]], dim=2)
key = torch.cat([key, encoder_hidden_states_key_proj, encoder_hidden_states_key_proj[:,:,-self.neg_prompt_length:]], dim=2)
value = torch.cat([value, encoder_hidden_states_value_proj, encoder_hidden_states_value_proj[:,:,-self.neg_prompt_length:]], dim=2)
value[:,:,-self.neg_prompt_length:] *= -self.scale # negative prompt
if self.image_rotary_emb is not None:
from diffusers.models.embeddings import apply_rotary_emb
# print(query.shape, self.image_rotary_emb[0].shape)
query = apply_rotary_emb(query, self.image_rotary_emb)
key = apply_rotary_emb(key, self.image_rotary_emb)
if encoder_hidden_states is not None:
query = query[:,:,:-self.neg_prompt_length]
if self.attn_mask is not None:
self.attn_mask = self.attn_mask.to(query.dtype)
# print(query.device, key.device, value.device, self.attn_mask.device if self.attn_mask is not None else None)
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=self.attn_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)
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
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