Delete attention_processor.py

attention_processor.py

@@ -1,70 +0,0 @@
-from typing import Optional
-
-import torch
-import torch.nn as nn
-from diffusers.models import UNet2DConditionModel
-from diffusers.models.attention import Attention
-from diffusers.models.attention_processor import AttnProcessor2_0
-
-
-
-def add_imagedream_attn_processor(unet: UNet2DConditionModel) -> nn.Module:
-    attn_procs = {}
-    for key, attn_processor in unet.attn_processors.items():
-        if "attn1" in key:
-            attn_procs[key] = ImageDreamAttnProcessor2_0()
-        else:
-            attn_procs[key] = attn_processor
-    unet.set_attn_processor(attn_procs)
-    return unet
-
-
-class ImageDreamAttnProcessor2_0(AttnProcessor2_0):
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        temb: Optional[torch.Tensor] = None,
-        num_views: int = 1,
-        *args,
-        **kwargs,
-    ):
-        if num_views == 1:
-            return super().__call__(
-                attn=attn,
-                hidden_states=hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                attention_mask=attention_mask,
-                temb=temb,
-                *args,
-                **kwargs,
-            )
-
-        input_ndim = hidden_states.ndim
-        B = hidden_states.size(0)
-        if B % num_views:
-            raise ValueError(
-                f"`batch_size`(got {B}) must be a multiple of `num_views`(got {num_views})."
-            )
-        real_B = B // num_views
-        if input_ndim == 4:
-            H, W = hidden_states.shape[2:]
-            hidden_states = hidden_states.reshape(real_B, -1, H, W).transpose(1, 2)
-        else:
-            hidden_states = hidden_states.reshape(real_B, -1, hidden_states.size(-1))
-        hidden_states = super().__call__(
-            attn=attn,
-            hidden_states=hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            attention_mask=attention_mask,
-            temb=temb,
-            *args,
-            **kwargs,
-        )
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(B, -1, H, W)
-        else:
-            hidden_states = hidden_states.reshape(B, -1, hidden_states.size(-1))
-        return hidden_states