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https://github.com/invoke-ai/InvokeAI
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Delete IP-Adapter copies of AttnProcessor and AttnProcessor2_0, which were unmodified from diffusers.
This commit is contained in:
Ryan Dick
parent
23fdf0156f
commit
7703bf2ca1
@ -6,79 +6,24 @@
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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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class AttnProcessor(nn.Module):
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r"""
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Default processor for performing attention-related computations.
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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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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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from diffusers.models.attention_processor import AttnProcessor as DiffusersAttnProcessor
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from diffusers.models.attention_processor import (
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AttnProcessor2_0 as DiffusersAttnProcessor2_0,
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)
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attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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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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# Create versions of AttnProcessor and AttnProcessor2_0 that are sub-classes of nn.Module. This is required for
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# IP-Adapter state_dict loading.
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class AttnProcessor(DiffusersAttnProcessor, nn.Module):
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def __init__(self):
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DiffusersAttnProcessor.__init__(self)
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nn.Module.__init__(self)
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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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query = attn.head_to_batch_dim(query)
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key = attn.head_to_batch_dim(key)
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value = attn.head_to_batch_dim(value)
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attention_probs = attn.get_attention_scores(query, key, attention_mask)
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hidden_states = torch.bmm(attention_probs, value)
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hidden_states = attn.batch_to_head_dim(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 AttnProcessor2_0(DiffusersAttnProcessor2_0, nn.Module):
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def __init__(self):
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DiffusersAttnProcessor2_0.__init__(self)
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nn.Module.__init__(self)
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class IPAttnProcessor(nn.Module):
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@ -186,95 +131,6 @@ class IPAttnProcessor(nn.Module):
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return hidden_states
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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 IPAttnProcessor2_0(torch.nn.Module):
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r"""
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Attention processor for IP-Adapater for PyTorch 2.0.
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@ -4,8 +4,6 @@
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from typing import List
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import torch
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from PIL import Image
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from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
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# FIXME: Getting errors when trying to use PyTorch 2.0 versions of IPAttnProcessor and AttnProcessor
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# so for now falling back to the default versions
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@ -14,6 +12,9 @@ from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
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# from .attention_processor import IPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor
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# else:
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# from .attention_processor import IPAttnProcessor, AttnProcessor
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from PIL import Image
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from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
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from .attention_processor import AttnProcessor, IPAttnProcessor
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from .resampler import Resampler
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