Choice of `lora_target_modules`

Hi DiffSynth team, many thanks for open-sourcing this great repo. I have a question regarding the lora_target_modules choice when finetuning the QWenImageEdit series. For example for img_mlp, why only lora finetuning the img_mlp.net.2 although there is also a linear layer in img_mlp.net.0.proj? Also why isn't the proj_out being finetuned? Any specific reason to keep those component frozen?

class QwenFeedForward(nn.Module):
    def __init__(
        self,
        dim: int,
        dim_out: Optional[int] = None,
        dropout: float = 0.0,
    ):
        super().__init__()
        inner_dim = int(dim * 4)
        self.net = nn.ModuleList([])
        self.net.append(ApproximateGELU(dim, inner_dim))
        self.net.append(nn.Dropout(dropout))
        self.net.append(nn.Linear(inner_dim, dim_out))

    def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
        for module in self.net:
            hidden_states = module(hidden_states)
        return hidden_states

Start by locating the QWenImageEdit LoRA configuration and the QwenFeedForward implementation shown in the issue. Trace how lora_target_modules selects layers, including img_mlp.net.2, img_mlp.net.0.proj, and proj_out. Done means the repository documents the rationale for the selected and frozen components.