Cross Attention maps

[danielajisafe]

Hello,

Thank you so much for your great work and codebase!

I would appreciate your clarifications on a few items.

1. From within TextToVideoSDPipelineCall.py, at this line, the attention maps from the temporal layers seem to be empty, by approximately using this code block

for name, module in self.unet.named_modules():
                module_name = type(module).__name__
                if module_name == "Attention" and "attn2" in name:
                
                #  --- First set
                    if "temp_attentions" in name:
                         print(name) # replace .0 with [0]
                         extracted_attention_map = module.processor.cross_attention_map
                         if extracted_attention_map!=None:
                             print(extracted_attention_map.shape)
                    else:
                        #  --- Second set
                        ...

- First set

down_blocks[0].temp_attentions[0].transformer_blocks[0].attn2 down_blocks[0].temp_attentions[1].transformer_blocks[0].attn2 down_blocks[1].temp_attentions[0].transformer_blocks[0].attn2 down_blocks[1].temp_attentions[1].transformer_blocks[0].attn2 down_blocks[2].temp_attentions[0].transformer_blocks[0].attn2 down_blocks[2].temp_attentions[1].transformer_blocks[0].attn2 up_blocks[1].temp_attentions[0].transformer_blocks[0].attn2 up_blocks[1].temp_attentions[1].transformer_blocks[0].attn2 up_blocks[1].temp_attentions[2].transformer_blocks[0].attn2 up_blocks[2].temp_attentions[0].transformer_blocks[0].attn2 up_blocks[2].temp_attentions[1].transformer_blocks[0].attn2 up_blocks[2].temp_attentions[2].transformer_blocks[0].attn2 up_blocks[3].temp_attentions[0].transformer_blocks[0].attn2 up_blocks[3].temp_attentions[1].transformer_blocks[0].attn2 up_blocks[3].temp_attentions[2].transformer_blocks[0].attn2 mid_block.temp_attentions[0].transformer_blocks[0].attn2

while only .attentions layers and the transformer_in layer in the second set have cross attention maps.

- Second set

transformer_in.transformer_blocks[0].attn2 torch.Size([64, 64, 24, 24]) down_blocks[0].attentions[0].transformer_blocks[0].attn2 torch.Size([120, 64, 64, 77]) down_blocks[0].attentions[1].transformer_blocks[0].attn2 torch.Size([120, 64, 64, 77]) down_blocks[1].attentions[0].transformer_blocks[0].attn2 torch.Size([240, 32, 32, 77]) down_blocks[1].attentions[1].transformer_blocks[0].attn2 torch.Size([240, 32, 32, 77]) down_blocks[2].attentions[0].transformer_blocks[0].attn2 torch.Size([480, 16, 16, 77]) down_blocks[2].attentions[1].transformer_blocks[0].attn2 torch.Size([480, 16, 16, 77]) up_blocks[1].attentions[0].transformer_blocks[0].attn2 torch.Size([480, 16, 16, 77]) up_blocks[1].attentions[1].transformer_blocks[0].attn2 torch.Size([480, 16, 16, 77]) up_blocks[1].attentions[2].transformer_blocks[0].attn2 torch.Size([480, 16, 16, 77]) up_blocks[2].attentions[0].transformer_blocks[0].attn2 torch.Size([240, 32, 32, 77]) up_blocks[2].attentions[1].transformer_blocks[0].attn2 torch.Size([240, 32, 32, 77]) up_blocks[2].attentions[2].transformer_blocks[0].attn2 torch.Size([240, 32, 32, 77]) up_blocks[3].attentions[0].transformer_blocks[0].attn2 torch.Size([120, 64, 64, 77]) up_blocks[3].attentions[1].transformer_blocks[0].attn2 torch.Size([120, 64, 64, 77]) up_blocks[3].attentions[2].transformer_blocks[0].attn2 torch.Size([120, 64, 64, 77]) mid_block.attentions[0].transformer_blocks[0].attn2 torch.Size([480, 8, 8, 77])

2. If one should assume that the second set is the spatial attention maps, it does not align with modules listed in the supplemental document (page 1, screenshot included), particularly the transformer_in.transformer_blocks[0].attn2 with size 64, 64, 24, 24 suggesting its temporal (not spatial as mentioned in supplemental) with 24 frames and mid_block.attentions[0].transformer_blocks[0].attn2 with size 480, 8, 8, 77 suggesting its the spatial attention map (not temporal) with 77 tokens.

Your kind clarification would be very helpful. Thanks