mirror of
https://github.com/ostris/ai-toolkit.git
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429 lines
17 KiB
Python
429 lines
17 KiB
Python
import sys
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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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import weakref
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from typing import Union, TYPE_CHECKING
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from transformers import T5EncoderModel, CLIPTextModel, CLIPTokenizer, T5Tokenizer, CLIPTextModelWithProjection
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from diffusers.models.embeddings import PixArtAlphaTextProjection
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from toolkit import train_tools
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from toolkit.paths import REPOS_ROOT
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from toolkit.prompt_utils import PromptEmbeds
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from diffusers import Transformer2DModel
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sys.path.append(REPOS_ROOT)
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from ipadapter.ip_adapter.attention_processor import AttnProcessor2_0
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if TYPE_CHECKING:
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from toolkit.stable_diffusion_model import StableDiffusion
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from toolkit.custom_adapter import CustomAdapter
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class TEAdapterCaptionProjection(nn.Module):
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def __init__(self, caption_channels, adapter: 'TEAdapter'):
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super().__init__()
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in_features = caption_channels
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self.adapter_ref: weakref.ref = weakref.ref(adapter)
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sd = adapter.sd_ref()
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self.parent_module_ref = weakref.ref(sd.unet.caption_projection)
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parent_module = self.parent_module_ref()
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self.linear_1 = nn.Linear(
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in_features=in_features,
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out_features=parent_module.linear_1.out_features,
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bias=True
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)
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self.linear_2 = nn.Linear(
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in_features=parent_module.linear_2.in_features,
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out_features=parent_module.linear_2.out_features,
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bias=True
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)
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# save the orig forward
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parent_module.linear_1.orig_forward = parent_module.linear_1.forward
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parent_module.linear_2.orig_forward = parent_module.linear_2.forward
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# replace original forward
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parent_module.orig_forward = parent_module.forward
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parent_module.forward = self.forward
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@property
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def is_active(self):
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return self.adapter_ref().is_active
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@property
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def unconditional_embeds(self):
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return self.adapter_ref().adapter_ref().unconditional_embeds
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@property
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def conditional_embeds(self):
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return self.adapter_ref().adapter_ref().conditional_embeds
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def forward(self, caption):
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if self.is_active and self.conditional_embeds is not None:
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adapter_hidden_states = self.conditional_embeds.text_embeds
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# check if we are doing unconditional
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if self.unconditional_embeds is not None and adapter_hidden_states.shape[0] != caption.shape[0]:
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# concat unconditional to match the hidden state batch size
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if self.unconditional_embeds.text_embeds.shape[0] == 1 and adapter_hidden_states.shape[0] != 1:
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unconditional = torch.cat([self.unconditional_embeds.text_embeds] * adapter_hidden_states.shape[0], dim=0)
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else:
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unconditional = self.unconditional_embeds.text_embeds
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adapter_hidden_states = torch.cat([unconditional, adapter_hidden_states], dim=0)
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hidden_states = self.linear_1(adapter_hidden_states)
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hidden_states = self.parent_module_ref().act_1(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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else:
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return self.parent_module_ref().orig_forward(caption)
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class TEAdapterAttnProcessor(nn.Module):
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r"""
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Attention processor for Custom TE for PyTorch 2.0.
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Args:
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hidden_size (`int`):
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The hidden size of the attention layer.
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cross_attention_dim (`int`):
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The number of channels in the `encoder_hidden_states`.
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scale (`float`, defaults to 1.0):
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the weight scale of image prompt.
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num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
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The context length of the image features.
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adapter
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"""
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def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, adapter=None,
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adapter_hidden_size=None, layer_name=None):
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super().__init__()
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self.layer_name = layer_name
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
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self.adapter_ref: weakref.ref = weakref.ref(adapter)
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self.hidden_size = hidden_size
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self.adapter_hidden_size = adapter_hidden_size
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self.cross_attention_dim = cross_attention_dim
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self.scale = scale
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self.num_tokens = num_tokens
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self.to_k_adapter = nn.Linear(adapter_hidden_size, hidden_size, bias=False)
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self.to_v_adapter = nn.Linear(adapter_hidden_size, hidden_size, bias=False)
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@property
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def is_active(self):
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return self.adapter_ref().is_active
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@property
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def unconditional_embeds(self):
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return self.adapter_ref().adapter_ref().unconditional_embeds
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@property
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def conditional_embeds(self):
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return self.adapter_ref().adapter_ref().conditional_embeds
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def __call__(
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self,
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attn,
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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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temb=None,
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):
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is_active = self.adapter_ref().is_active
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residual = hidden_states
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if attn.spatial_norm is not None:
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hidden_states = attn.spatial_norm(hidden_states, temb)
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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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# will be none if disabled
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if encoder_hidden_states is None:
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encoder_hidden_states = hidden_states
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elif attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
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# only use one TE or the other. If our adapter is active only use ours
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if self.is_active and self.conditional_embeds is not None:
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adapter_hidden_states = self.conditional_embeds.text_embeds
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# check if we are doing unconditional
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if self.unconditional_embeds is not None and adapter_hidden_states.shape[0] != encoder_hidden_states.shape[0]:
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# concat unconditional to match the hidden state batch size
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if self.unconditional_embeds.text_embeds.shape[0] == 1 and adapter_hidden_states.shape[0] != 1:
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unconditional = torch.cat([self.unconditional_embeds.text_embeds] * adapter_hidden_states.shape[0], dim=0)
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else:
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unconditional = self.unconditional_embeds.text_embeds
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adapter_hidden_states = torch.cat([unconditional, adapter_hidden_states], dim=0)
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# for ip-adapter
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key = self.to_k_adapter(adapter_hidden_states)
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value = self.to_v_adapter(adapter_hidden_states)
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else:
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key = attn.to_k(encoder_hidden_states)
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value = attn.to_v(encoder_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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try:
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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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except RuntimeError:
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raise RuntimeError(f"key shape: {key.shape}, value shape: {value.shape}")
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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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# TODO: add support for attn.scale when we move to Torch 2.1
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hidden_states = F.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 = 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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class TEAdapter(torch.nn.Module):
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def __init__(
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self,
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adapter: 'CustomAdapter',
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sd: 'StableDiffusion',
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te: Union[T5EncoderModel],
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tokenizer: CLIPTokenizer
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):
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super(TEAdapter, self).__init__()
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self.adapter_ref: weakref.ref = weakref.ref(adapter)
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self.sd_ref: weakref.ref = weakref.ref(sd)
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self.te_ref: weakref.ref = weakref.ref(te)
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self.tokenizer_ref: weakref.ref = weakref.ref(tokenizer)
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self.adapter_modules = []
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self.caption_projection = None
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self.embeds_store = []
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is_pixart = sd.is_pixart
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if self.adapter_ref().config.text_encoder_arch == "t5":
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self.token_size = self.te_ref().config.d_model
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else:
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self.token_size = self.te_ref().config.hidden_size
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# add text projection if is sdxl
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self.text_projection = None
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if sd.is_xl:
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clip_with_projection: CLIPTextModelWithProjection = sd.text_encoder[0]
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self.text_projection = nn.Linear(te.config.hidden_size, clip_with_projection.config.projection_dim, bias=False)
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# init adapter modules
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attn_procs = {}
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unet_sd = sd.unet.state_dict()
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attn_dict_map = {
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}
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module_idx = 0
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# init adapter modules
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attn_procs = {}
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unet_sd = sd.unet.state_dict()
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attn_processor_keys = []
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if is_pixart:
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transformer: Transformer2DModel = sd.unet
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for i, module in transformer.transformer_blocks.named_children():
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attn_processor_keys.append(f"transformer_blocks.{i}.attn1")
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# cross attention
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attn_processor_keys.append(f"transformer_blocks.{i}.attn2")
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else:
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attn_processor_keys = list(sd.unet.attn_processors.keys())
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attn_processor_names = []
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blocks = []
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transformer_blocks = []
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for name in attn_processor_keys:
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cross_attention_dim = None if name.endswith("attn1.processor") or name.endswith("attn.1") or name.endswith("attn1") else \
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sd.unet.config['cross_attention_dim']
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if name.startswith("mid_block"):
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hidden_size = sd.unet.config['block_out_channels'][-1]
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elif name.startswith("up_blocks"):
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block_id = int(name[len("up_blocks.")])
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hidden_size = list(reversed(sd.unet.config['block_out_channels']))[block_id]
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elif name.startswith("down_blocks"):
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block_id = int(name[len("down_blocks.")])
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hidden_size = sd.unet.config['block_out_channels'][block_id]
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elif name.startswith("transformer"):
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hidden_size = sd.unet.config['cross_attention_dim']
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else:
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# they didnt have this, but would lead to undefined below
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raise ValueError(f"unknown attn processor name: {name}")
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if cross_attention_dim is None:
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attn_procs[name] = AttnProcessor2_0()
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else:
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layer_name = name.split(".processor")[0]
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to_k_adapter = unet_sd[layer_name + ".to_k.weight"]
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to_v_adapter = unet_sd[layer_name + ".to_v.weight"]
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# add zero padding to the adapter
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if to_k_adapter.shape[1] < self.token_size:
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to_k_adapter = torch.cat([
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to_k_adapter,
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torch.randn(to_k_adapter.shape[0], self.token_size - to_k_adapter.shape[1]).to(
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to_k_adapter.device, dtype=to_k_adapter.dtype) * 0.01
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],
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dim=1
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)
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to_v_adapter = torch.cat([
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to_v_adapter,
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torch.randn(to_v_adapter.shape[0], self.token_size - to_v_adapter.shape[1]).to(
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to_k_adapter.device, dtype=to_k_adapter.dtype) * 0.01
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],
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dim=1
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)
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elif to_k_adapter.shape[1] > self.token_size:
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to_k_adapter = to_k_adapter[:, :self.token_size]
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to_v_adapter = to_v_adapter[:, :self.token_size]
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else:
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to_k_adapter = to_k_adapter
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to_v_adapter = to_v_adapter
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# todo resize to the TE hidden size
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weights = {
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"to_k_adapter.weight": to_k_adapter,
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"to_v_adapter.weight": to_v_adapter,
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}
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if self.sd_ref().is_pixart:
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# pixart is much more sensitive
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weights = {
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"to_k_adapter.weight": weights["to_k_adapter.weight"] * 0.01,
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"to_v_adapter.weight": weights["to_v_adapter.weight"] * 0.01,
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}
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attn_procs[name] = TEAdapterAttnProcessor(
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hidden_size=hidden_size,
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cross_attention_dim=cross_attention_dim,
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scale=1.0,
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num_tokens=self.adapter_ref().config.num_tokens,
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adapter=self,
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adapter_hidden_size=self.token_size,
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layer_name=layer_name
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)
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attn_procs[name].load_state_dict(weights)
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self.adapter_modules.append(attn_procs[name])
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if self.sd_ref().is_pixart:
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# we have to set them ourselves
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transformer: Transformer2DModel = sd.unet
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for i, module in transformer.transformer_blocks.named_children():
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module.attn1.processor = attn_procs[f"transformer_blocks.{i}.attn1"]
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module.attn2.processor = attn_procs[f"transformer_blocks.{i}.attn2"]
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self.adapter_modules = torch.nn.ModuleList(
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[
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transformer.transformer_blocks[i].attn2.processor for i in
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range(len(transformer.transformer_blocks))
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])
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self.caption_projection = TEAdapterCaptionProjection(
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caption_channels=self.token_size,
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adapter=self,
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)
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else:
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sd.unet.set_attn_processor(attn_procs)
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self.adapter_modules = torch.nn.ModuleList(sd.unet.attn_processors.values())
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# make a getter to see if is active
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@property
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def is_active(self):
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return self.adapter_ref().is_active
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def encode_text(self, text):
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te: T5EncoderModel = self.te_ref()
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tokenizer: T5Tokenizer = self.tokenizer_ref()
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# input_ids = tokenizer(
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# text,
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# max_length=77,
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# padding="max_length",
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# truncation=True,
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# return_tensors="pt",
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# ).input_ids.to(te.device)
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# outputs = te(input_ids=input_ids)
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# outputs = outputs.last_hidden_state
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embeds, attention_mask = train_tools.encode_prompts_pixart(
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tokenizer,
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te,
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text,
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truncate=True,
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max_length=self.adapter_ref().config.num_tokens,
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)
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attn_mask_float = attention_mask.to(embeds.device, dtype=embeds.dtype)
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if self.text_projection is not None:
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# pool the output of embeds ignoring 0 in the attention mask
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pooled_output = embeds * attn_mask_float.unsqueeze(-1)
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# reduce along dim 1 while maintaining batch and dim 2
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pooled_output_sum = pooled_output.sum(dim=1)
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attn_mask_sum = attn_mask_float.sum(dim=1).unsqueeze(-1)
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pooled_output = pooled_output_sum / attn_mask_sum
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pooled_embeds = self.text_projection(pooled_output)
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t5_embeds = PromptEmbeds(
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(embeds, pooled_embeds),
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attention_mask=attention_mask,
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).detach()
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else:
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t5_embeds = PromptEmbeds(
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embeds,
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attention_mask=attention_mask,
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).detach()
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return t5_embeds
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def forward(self, input):
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return input
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