Upload code/src/diffusion_model/Processor.py with huggingface_hub

code/src/diffusion_model/Processor.py

@@ -0,0 +1,120 @@
+import torch
+import math
+import torch.nn.functional as F
+DIFFUSION_LAYERS = [
+    'down_blocks[0].attentions[0].transformer_blocks[0].attn1',  # 0
+    'down_blocks[0].attentions[0].transformer_blocks[0].attn2',  # 1
+    'down_blocks[0].attentions[1].transformer_blocks[0].attn1',  # 2
+    'down_blocks[0].attentions[1].transformer_blocks[0].attn2',  # 3
+    'down_blocks[1].attentions[0].transformer_blocks[0].attn1',  # 4
+    'down_blocks[1].attentions[0].transformer_blocks[0].attn2',  # 5
+    'down_blocks[1].attentions[1].transformer_blocks[0].attn1',  # 6
+    'down_blocks[1].attentions[1].transformer_blocks[0].attn2',  # 7
+    'down_blocks[2].attentions[0].transformer_blocks[0].attn1',  # 8
+    'down_blocks[2].attentions[0].transformer_blocks[0].attn2',  # 9
+    'down_blocks[2].attentions[1].transformer_blocks[0].attn1',  # 10
+    'down_blocks[2].attentions[1].transformer_blocks[0].attn2',  # 11
+
+    'mid_block.attentions[0].transformer_blocks[0].attn1',
+    'mid_block.attentions[0].transformer_blocks[0].attn2',
+
+    'up_blocks[1].attentions[0].transformer_blocks[0].attn1',  # -18
+    "up_blocks[1].attentions[0].transformer_blocks[0].attn2",  # -17
+    'up_blocks[1].attentions[1].transformer_blocks[0].attn1',  # -16
+    "up_blocks[1].attentions[1].transformer_blocks[0].attn2",  # -15
+    'up_blocks[1].attentions[2].transformer_blocks[0].attn1',  # -14
+    "up_blocks[1].attentions[2].transformer_blocks[0].attn2",  # -13
+    'up_blocks[2].attentions[0].transformer_blocks[0].attn1',  # -12
+    "up_blocks[2].attentions[0].transformer_blocks[0].attn2",  # -11
+    'up_blocks[2].attentions[1].transformer_blocks[0].attn1',  # -10
+    "up_blocks[2].attentions[1].transformer_blocks[0].attn2",  # -9
+    'up_blocks[2].attentions[2].transformer_blocks[0].attn1',  # -8
+    'up_blocks[2].attentions[2].transformer_blocks[0].attn2',  # -7
+    "up_blocks[3].attentions[0].transformer_blocks[0].attn1",  # -6
+    'up_blocks[3].attentions[0].transformer_blocks[0].attn2',  # -5
+    "up_blocks[3].attentions[1].transformer_blocks[0].attn1",  # -4
+    'up_blocks[3].attentions[1].transformer_blocks[0].attn2',  # -3
+    "up_blocks[3].attentions[2].transformer_blocks[0].attn1",  # -2
+    'up_blocks[3].attentions[2].transformer_blocks[0].attn2',  # -1
+]
+
+
+class AttnProcessorForCallBack:
+    def __init__(self, model, layer):
+        self.model = model
+        self.layer = layer
+
+    def __call__(
+        self,
+        attn,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+        query = attn.to_q(hidden_states)
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        # ! add code 
+        h=w=int(math.sqrt(query.shape[1]))
+        low_res_query=query.clone().transpose(-2,-1).view(batch_size, -1, h,w)
+        low_res_key=key.clone().transpose(-2,-1).view(batch_size, -1,h,w)
+        low_res_query_ds = F.interpolate(low_res_query, size=(35, 35), mode='bilinear', align_corners=False)
+        low_res_key_ds = F.interpolate(low_res_key, size=(35, 35), mode='bilinear', align_corners=False)
+        low_res_query_ds=low_res_query_ds.flatten(start_dim=-2).transpose(-2,-1)
+        low_res_key_ds=low_res_key_ds.flatten(start_dim=-2).transpose(-2,-1)
+        low_res_query_ds = attn.head_to_batch_dim(low_res_query_ds)
+        low_res_key_ds = attn.head_to_batch_dim(low_res_key_ds)
+        low_res_attention_probs = attn.get_attention_scores(low_res_query_ds, low_res_key_ds, attention_mask)
+        # ! add code 
+        query = attn.head_to_batch_dim(query)
+        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)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        head_size = attn.heads
+        batch_size, q_len, v_len = attention_probs.shape
+        attention_probs = attention_probs.reshape(batch_size // head_size, head_size, q_len, v_len)
+        self.model.attention_maps[self.layer] = low_res_attention_probs
+        return hidden_states