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Configuration error
Configuration error
| from typing import Callable, Optional, Union | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| # from diffusers.utils | |
| from diffusers.utils import deprecate, logging | |
| from diffusers.utils.import_utils import is_xformers_available | |
| from diffusers.models.attention import FeedForward, AdaLayerNorm | |
| from diffusers.models.cross_attention import CrossAttention | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| if is_xformers_available(): | |
| import xformers | |
| import xformers.ops | |
| else: | |
| xformers = None | |
| class LoRALinearLayer(nn.Module): | |
| def __init__(self, in_features, out_features, rank=4, stride=1): | |
| super().__init__() | |
| if rank > min(in_features, out_features): | |
| Warning(f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}, reset to {min(in_features, out_features)//2}") | |
| rank = min(in_features, out_features)//2 | |
| self.down = nn.Conv1d(in_features, rank, bias=False, | |
| kernel_size=3, | |
| stride = stride, | |
| padding=1,) | |
| self.up = nn.Conv1d(rank, out_features, bias=False, | |
| kernel_size=3, | |
| padding=1,) | |
| nn.init.normal_(self.down.weight, std=1 / rank) | |
| # nn.init.zeros_(self.down.bias.data) | |
| nn.init.zeros_(self.up.weight) | |
| # nn.init.zeros_(self.up.bias.data) | |
| if stride > 1: | |
| self.skip = nn.AvgPool1d(kernel_size=3, stride=2, padding=1) | |
| def forward(self, hidden_states): | |
| orig_dtype = hidden_states.dtype | |
| dtype = self.down.weight.dtype | |
| down_hidden_states = self.down(hidden_states.to(dtype)) | |
| up_hidden_states = self.up(down_hidden_states) | |
| if hasattr(self, 'skip'): | |
| hidden_states=self.skip(hidden_states) | |
| return up_hidden_states.to(orig_dtype)+hidden_states | |
| class LoRACrossAttnProcessor(nn.Module): | |
| def __init__(self, hidden_size, cross_attention_dim=None, rank=4): | |
| super().__init__() | |
| self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank) | |
| self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) | |
| self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) | |
| self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank) | |
| def __call__( | |
| self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0 | |
| ): | |
| batch_size, sequence_length, _ = hidden_states.shape | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states) | |
| query = attn.head_to_batch_dim(query) | |
| encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states | |
| key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states) | |
| key = attn.head_to_batch_dim(key) | |
| value = attn.head_to_batch_dim(value) | |
| attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
| hidden_states = torch.bmm(attention_probs, value) | |
| hidden_states = attn.batch_to_head_dim(hidden_states) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| return hidden_states | |
| class LoRAXFormersCrossAttnProcessor(nn.Module): | |
| def __init__(self, hidden_size, cross_attention_dim, rank=4): | |
| super().__init__() | |
| self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank) | |
| self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) | |
| self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) | |
| self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank) | |
| def __call__( | |
| self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0 | |
| ): | |
| batch_size, sequence_length, _ = hidden_states.shape | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states) | |
| query = attn.head_to_batch_dim(query).contiguous() | |
| encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states | |
| key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states) | |
| key = attn.head_to_batch_dim(key).contiguous() | |
| value = attn.head_to_batch_dim(value).contiguous() | |
| hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) | |
| hidden_states = attn.batch_to_head_dim(hidden_states) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| return hidden_states | |