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Made Mochi work with optimized_attention_override

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Jedrzej Kosinski
2025-08-28 20:34:06 -07:00
parent ef894cdf08
commit 61b5c5fc75
1 changed files with 8 additions and 3 deletions
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11
comfy/ldm/genmo/joint_model/asymm_models_joint.py
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@@ -109,6 +109,7 @@ class AsymmetricAttention(nn.Module):
scale_x: torch.Tensor, # (B, dim_x), modulation for pre-RMSNorm.
scale_y: torch.Tensor, # (B, dim_y), modulation for pre-RMSNorm.
crop_y,
transformer_options={},
**rope_rotation,
) -> Tuple[torch.Tensor, torch.Tensor]:
rope_cos = rope_rotation.get("rope_cos")
@@ -143,7 +144,7 @@ class AsymmetricAttention(nn.Module):
xy = optimized_attention(q,
k,
v, self.num_heads, skip_reshape=True)
v, self.num_heads, skip_reshape=True, transformer_options=transformer_options)
x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1)
x = self.proj_x(x)
@@ -224,6 +225,7 @@ class AsymmetricJointBlock(nn.Module):
x: torch.Tensor,
c: torch.Tensor,
y: torch.Tensor,
transformer_options={},
**attn_kwargs,
):
"""Forward pass of a block.
@@ -256,6 +258,7 @@ class AsymmetricJointBlock(nn.Module):
y,
scale_x=scale_msa_x,
scale_y=scale_msa_y,
transformer_options=transformer_options,
**attn_kwargs,
)
@@ -524,10 +527,11 @@ class AsymmDiTJoint(nn.Module):
args["txt"],
rope_cos=args["rope_cos"],
rope_sin=args["rope_sin"],
crop_y=args["num_tokens"]
crop_y=args["num_tokens"],
transformer_options=args["transformer_options"]
)
return out
out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap})
out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens, "transformer_options": transformer_options}, {"original_block": block_wrap})
y_feat = out["txt"]
x = out["img"]
else:
@@ -538,6 +542,7 @@ class AsymmDiTJoint(nn.Module):
rope_cos=rope_cos,
rope_sin=rope_sin,
crop_y=num_tokens,
transformer_options=transformer_options,
) # (B, M, D), (B, L, D)
del y_feat # Final layers don't use dense text features.