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https://github.com/ostris/ai-toolkit.git
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Added single value adapter training
This commit is contained in:
Jaret Burkett
402
toolkit/models/single_value_adapter.py
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402
toolkit/models/single_value_adapter.py
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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 diffusers import Transformer2DModel
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from transformers import T5EncoderModel, CLIPTextModel, CLIPTokenizer, T5Tokenizer, CLIPVisionModelWithProjection
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from toolkit.paths import REPOS_ROOT
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sys.path.append(REPOS_ROOT)
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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 AttnProcessor2_0(torch.nn.Module):
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r"""
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Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
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"""
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def __init__(
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self,
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hidden_size=None,
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cross_attention_dim=None,
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):
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super().__init__()
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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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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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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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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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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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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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# 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 SingleValueAdapterAttnProcessor(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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adapter
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"""
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def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, adapter=None,
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adapter_hidden_size=None, has_bias=False, **kwargs):
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super().__init__()
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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.to_k_adapter = nn.Linear(adapter_hidden_size, hidden_size, bias=has_bias)
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self.to_v_adapter = nn.Linear(adapter_hidden_size, hidden_size, bias=has_bias)
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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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# return False
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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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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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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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# 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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# 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
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if adapter_hidden_states.shape[0] < batch_size:
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# doing cfg
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adapter_hidden_states = torch.cat([
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self.unconditional_embeds,
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adapter_hidden_states
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], dim=0)
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# needs to be shape (batch, 1, 1)
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if len(adapter_hidden_states.shape) == 2:
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adapter_hidden_states = adapter_hidden_states.unsqueeze(1)
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# conditional_batch_size = adapter_hidden_states.shape[0]
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# conditional_query = query
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# for ip-adapter
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vd_key = self.to_k_adapter(adapter_hidden_states)
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vd_value = self.to_v_adapter(adapter_hidden_states)
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vd_key = vd_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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vd_value = vd_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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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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vd_hidden_states = F.scaled_dot_product_attention(
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query, vd_key, vd_value, attn_mask=None, dropout_p=0.0, is_causal=False
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)
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vd_hidden_states = vd_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
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vd_hidden_states = vd_hidden_states.to(query.dtype)
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hidden_states = hidden_states + self.scale * vd_hidden_states
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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 SingleValueAdapter(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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num_values: int = 1,
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):
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super(SingleValueAdapter, self).__init__()
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is_pixart = sd.is_pixart
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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.token_size = num_values
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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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for name in attn_processor_keys:
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cross_attention_dim = None if name.endswith("attn1.processor") or name.endswith("attn.1") else 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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# if is_pixart:
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# to_k_bias = unet_sd[layer_name + ".to_k.bias"]
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# to_v_bias = unet_sd[layer_name + ".to_v.bias"]
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# else:
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# to_k_bias = None
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# to_v_bias = None
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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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# if is_pixart:
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# to_k_bias = torch.cat([
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# to_k_bias,
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# torch.zeros(self.token_size - to_k_adapter.shape[1]).to(
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# to_k_adapter.device, dtype=to_k_adapter.dtype)
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# ],
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# dim=0
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# )
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# to_v_bias = torch.cat([
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# to_v_bias,
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# torch.zeros(self.token_size - to_v_adapter.shape[1]).to(
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# to_k_adapter.device, dtype=to_k_adapter.dtype)
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# ],
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# dim=0
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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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# if is_pixart:
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# to_k_bias = to_k_bias[:self.token_size]
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# to_v_bias = to_v_bias[: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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# if is_pixart:
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# to_k_bias = to_k_bias
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# to_v_bias = to_v_bias
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weights = {
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"to_k_adapter.weight": to_k_adapter * 0.01,
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"to_v_adapter.weight": to_v_adapter * 0.01,
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}
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# if is_pixart:
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# weights["to_k_adapter.bias"] = to_k_bias
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# weights["to_v_adapter.bias"] = to_v_bias
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attn_procs[name] = SingleValueAdapterAttnProcessor(
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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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adapter=self,
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adapter_hidden_size=self.token_size,
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has_bias=False,
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)
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attn_procs[name].load_state_dict(weights)
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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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transformer.transformer_blocks[i].attn1.processor for i in range(len(transformer.transformer_blocks))
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] + [
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transformer.transformer_blocks[i].attn2.processor for i in range(len(transformer.transformer_blocks))
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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 forward(self, input):
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return input
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