mirror of
https://github.com/vladmandic/sdnext.git
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282 lines
11 KiB
Python
282 lines
11 KiB
Python
# Copyright 2023 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from diffusers import StableDiffusionXLPipeline
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from dataclasses import dataclass
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import torch
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import torch.nn as nn
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from torch.nn import functional as nnf
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from diffusers.models import attention_processor # pylint: disable=ungrouped-imports
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import einops
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T = torch.Tensor
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@dataclass(frozen=True)
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class StyleAlignedArgs:
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share_group_norm: bool = True
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share_layer_norm: bool = True
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share_attention: bool = True
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adain_queries: bool = True
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adain_keys: bool = True
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adain_values: bool = False
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full_attention_share: bool = False
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shared_score_scale: float = 1.
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shared_score_shift: float = 0.
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only_self_level: float = 0.
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def expand_first(feat: T, scale=1.,) -> T:
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b = feat.shape[0]
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feat_style = torch.stack((feat[0], feat[b // 2])).unsqueeze(1)
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if scale == 1:
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feat_style = feat_style.expand(2, b // 2, *feat.shape[1:])
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else:
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feat_style = feat_style.repeat(1, b // 2, 1, 1, 1)
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feat_style = torch.cat([feat_style[:, :1], scale * feat_style[:, 1:]], dim=1)
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return feat_style.reshape(*feat.shape)
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def concat_first(feat: T, dim=2, scale=1.) -> T:
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feat_style = expand_first(feat, scale=scale)
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return torch.cat((feat, feat_style), dim=dim)
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def calc_mean_std(feat, eps: float = 1e-5) -> tuple[T, T]:
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feat_std = (feat.var(dim=-2, keepdims=True) + eps).sqrt()
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feat_mean = feat.mean(dim=-2, keepdims=True)
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return feat_mean, feat_std
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def adain(feat: T) -> T:
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feat_mean, feat_std = calc_mean_std(feat)
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feat_style_mean = expand_first(feat_mean)
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feat_style_std = expand_first(feat_std)
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feat = (feat - feat_mean) / feat_std
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feat = feat * feat_style_std + feat_style_mean
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return feat
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class DefaultAttentionProcessor(nn.Module):
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def __init__(self):
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super().__init__()
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self.processor = attention_processor.AttnProcessor2_0()
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def __call__(self, attn: attention_processor.Attention, hidden_states, encoder_hidden_states=None,
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attention_mask=None, **kwargs):
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return self.processor(attn, hidden_states, encoder_hidden_states, attention_mask)
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class SharedAttentionProcessor(DefaultAttentionProcessor):
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def shifted_scaled_dot_product_attention(self, attn: attention_processor.Attention, query: T, key: T, value: T) -> T:
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logits = torch.einsum('bhqd,bhkd->bhqk', query, key) * attn.scale
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logits[:, :, :, query.shape[2]:] += self.shared_score_shift
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probs = logits.softmax(-1)
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return torch.einsum('bhqk,bhkd->bhqd', probs, value)
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def shared_call( # pylint: disable=unused-argument
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self,
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attn: attention_processor.Attention,
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hidden_states,
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encoder_hidden_states=None,
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attention_mask=None,
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**kwargs
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):
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residual = hidden_states
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input_ndim = hidden_states.ndim
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if input_ndim == 4:
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batch_size, channel, height, width = hidden_states.shape
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hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
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)
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if attention_mask is not None:
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attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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# scaled_dot_product_attention expects attention_mask shape to be
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# (batch, heads, source_length, target_length)
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attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
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if attn.group_norm is not None:
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
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query = attn.to_q(hidden_states)
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key = attn.to_k(hidden_states)
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value = attn.to_v(hidden_states)
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inner_dim = key.shape[-1]
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head_dim = inner_dim // attn.heads
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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# if self.step >= self.start_inject:
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if self.adain_queries:
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query = adain(query)
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if self.adain_keys:
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key = adain(key)
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if self.adain_values:
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value = adain(value)
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if self.share_attention:
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key = concat_first(key, -2, scale=self.shared_score_scale)
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value = concat_first(value, -2)
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if self.shared_score_shift != 0:
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hidden_states = self.shifted_scaled_dot_product_attention(attn, query, key, value,)
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else:
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hidden_states = nnf.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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else:
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hidden_states = nnf.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 = adain(hidden_states)
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hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
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hidden_states = hidden_states.to(query.dtype)
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# linear proj
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hidden_states = attn.to_out[0](hidden_states)
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# dropout
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hidden_states = attn.to_out[1](hidden_states)
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if input_ndim == 4:
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hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
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if attn.residual_connection:
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hidden_states = hidden_states + residual
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hidden_states = hidden_states / attn.rescale_output_factor
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return hidden_states
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def __call__(self, attn: attention_processor.Attention, hidden_states, encoder_hidden_states=None,
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attention_mask=None, **kwargs):
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if self.full_attention_share:
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_b, n, _d = hidden_states.shape
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hidden_states = einops.rearrange(hidden_states, '(k b) n d -> k (b n) d', k=2)
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hidden_states = super().__call__(attn, hidden_states, encoder_hidden_states=encoder_hidden_states,
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attention_mask=attention_mask, **kwargs)
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hidden_states = einops.rearrange(hidden_states, 'k (b n) d -> (k b) n d', n=n)
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else:
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hidden_states = self.shared_call(attn, hidden_states, hidden_states, attention_mask, **kwargs)
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return hidden_states
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def __init__(self, style_aligned_args: StyleAlignedArgs):
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super().__init__()
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self.share_attention = style_aligned_args.share_attention
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self.adain_queries = style_aligned_args.adain_queries
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self.adain_keys = style_aligned_args.adain_keys
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self.adain_values = style_aligned_args.adain_values
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self.full_attention_share = style_aligned_args.full_attention_share
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self.shared_score_scale = style_aligned_args.shared_score_scale
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self.shared_score_shift = style_aligned_args.shared_score_shift
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def _get_switch_vec(total_num_layers, level):
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if level <= 0:
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return torch.zeros(total_num_layers, dtype=torch.bool)
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if level >= 1:
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return torch.ones(total_num_layers, dtype=torch.bool)
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to_flip = level > .5
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if to_flip:
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level = 1 - level
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num_switch = int(level * total_num_layers)
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vec = torch.arange(total_num_layers)
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vec = vec % (total_num_layers // num_switch)
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vec = vec == 0
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if to_flip:
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vec = ~vec
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return vec
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def init_attention_processors(pipeline: StableDiffusionXLPipeline, style_aligned_args: StyleAlignedArgs | None = None):
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attn_procs = {}
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unet = pipeline.unet
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number_of_self, number_of_cross = 0, 0
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num_self_layers = len([name for name in unet.attn_processors.keys() if 'attn1' in name])
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if style_aligned_args is None:
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only_self_vec = _get_switch_vec(num_self_layers, 1)
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else:
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only_self_vec = _get_switch_vec(num_self_layers, style_aligned_args.only_self_level)
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for i, name in enumerate(unet.attn_processors.keys()):
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is_self_attention = 'attn1' in name
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if is_self_attention:
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number_of_self += 1
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if style_aligned_args is None or only_self_vec[i // 2]:
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attn_procs[name] = DefaultAttentionProcessor()
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else:
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attn_procs[name] = SharedAttentionProcessor(style_aligned_args)
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else:
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number_of_cross += 1
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attn_procs[name] = DefaultAttentionProcessor()
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unet.set_attn_processor(attn_procs)
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def register_shared_norm(pipeline: StableDiffusionXLPipeline,
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share_group_norm: bool = True,
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share_layer_norm: bool = True,
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):
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def register_norm_forward(norm_layer: nn.GroupNorm | nn.LayerNorm) -> nn.GroupNorm | nn.LayerNorm:
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if not hasattr(norm_layer, 'orig_forward'):
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setattr(norm_layer, 'orig_forward', norm_layer.forward) # noqa
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orig_forward = norm_layer.orig_forward
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def forward_(hidden_states: T) -> T:
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n = hidden_states.shape[-2]
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hidden_states = concat_first(hidden_states, dim=-2)
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hidden_states = orig_forward(hidden_states)
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return hidden_states[..., :n, :]
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norm_layer.forward = forward_
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return norm_layer
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def get_norm_layers(pipeline_, norm_layers_: dict[str, list[nn.GroupNorm | nn.LayerNorm]]):
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if isinstance(pipeline_, nn.LayerNorm) and share_layer_norm:
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norm_layers_['layer'].append(pipeline_)
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if isinstance(pipeline_, nn.GroupNorm) and share_group_norm:
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norm_layers_['group'].append(pipeline_)
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else:
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for layer in pipeline_.children():
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get_norm_layers(layer, norm_layers_)
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norm_layers = {'group': [], 'layer': []}
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get_norm_layers(pipeline.unet, norm_layers)
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return [register_norm_forward(layer) for layer in norm_layers['group']] + [register_norm_forward(layer) for layer in
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norm_layers['layer']]
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class Handler:
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def register(self, style_aligned_args: StyleAlignedArgs):
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self.norm_layers = register_shared_norm(self.pipeline, style_aligned_args.share_group_norm,
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style_aligned_args.share_layer_norm)
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init_attention_processors(self.pipeline, style_aligned_args)
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def remove(self):
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for layer in self.norm_layers:
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layer.forward = layer.orig_forward
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self.norm_layers = []
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init_attention_processors(self.pipeline, None)
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def __init__(self, pipeline: StableDiffusionXLPipeline):
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self.pipeline = pipeline
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self.norm_layers = []
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