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https://github.com/comfyanonymous/ComfyUI.git
synced 2025-09-13 13:05:07 +00:00
Made CosmosVideo work with optimized_attention_override
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@@ -176,6 +176,7 @@ class Attention(nn.Module):
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context=None,
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mask=None,
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rope_emb=None,
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transformer_options={},
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**kwargs,
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):
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"""
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@@ -184,7 +185,7 @@ class Attention(nn.Module):
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context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
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"""
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q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs)
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out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True)
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out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True, transformer_options=transformer_options)
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del q, k, v
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out = rearrange(out, " b n s c -> s b (n c)")
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return self.to_out(out)
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@@ -546,6 +547,7 @@ class VideoAttn(nn.Module):
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context: Optional[torch.Tensor] = None,
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crossattn_mask: Optional[torch.Tensor] = None,
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rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
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transformer_options: Optional[dict] = {},
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) -> torch.Tensor:
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"""
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Forward pass for video attention.
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@@ -571,6 +573,7 @@ class VideoAttn(nn.Module):
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context_M_B_D,
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crossattn_mask,
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rope_emb=rope_emb_L_1_1_D,
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transformer_options=transformer_options,
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)
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x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W)
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return x_T_H_W_B_D
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@@ -665,6 +668,7 @@ class DITBuildingBlock(nn.Module):
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crossattn_mask: Optional[torch.Tensor] = None,
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rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
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adaln_lora_B_3D: Optional[torch.Tensor] = None,
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transformer_options: Optional[dict] = {},
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) -> torch.Tensor:
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"""
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Forward pass for dynamically configured blocks with adaptive normalization.
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@@ -702,6 +706,7 @@ class DITBuildingBlock(nn.Module):
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adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),
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context=None,
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rope_emb_L_1_1_D=rope_emb_L_1_1_D,
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transformer_options=transformer_options,
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)
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elif self.block_type in ["cross_attn", "ca"]:
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x = x + gate_1_1_1_B_D * self.block(
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@@ -709,6 +714,7 @@ class DITBuildingBlock(nn.Module):
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context=crossattn_emb,
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crossattn_mask=crossattn_mask,
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rope_emb_L_1_1_D=rope_emb_L_1_1_D,
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transformer_options=transformer_options,
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)
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else:
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raise ValueError(f"Unknown block type: {self.block_type}")
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@@ -784,6 +790,7 @@ class GeneralDITTransformerBlock(nn.Module):
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crossattn_mask: Optional[torch.Tensor] = None,
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rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
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adaln_lora_B_3D: Optional[torch.Tensor] = None,
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transformer_options: Optional[dict] = {},
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) -> torch.Tensor:
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for block in self.blocks:
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x = block(
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@@ -793,5 +800,6 @@ class GeneralDITTransformerBlock(nn.Module):
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crossattn_mask,
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rope_emb_L_1_1_D=rope_emb_L_1_1_D,
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adaln_lora_B_3D=adaln_lora_B_3D,
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transformer_options=transformer_options,
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)
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return x
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@@ -520,6 +520,7 @@ class GeneralDIT(nn.Module):
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x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
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), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}"
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transformer_options = kwargs.get("transformer_options", {})
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for _, block in self.blocks.items():
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assert (
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self.blocks["block0"].x_format == block.x_format
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@@ -534,6 +535,7 @@ class GeneralDIT(nn.Module):
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crossattn_mask,
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rope_emb_L_1_1_D=rope_emb_L_1_1_D,
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adaln_lora_B_3D=adaln_lora_B_3D,
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transformer_options=transformer_options,
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)
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x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D")
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