diff --git a/backend/args.py b/backend/args.py
index 917f90e3..5057795e 100644
--- a/backend/args.py
+++ b/backend/args.py
@@ -162,10 +162,14 @@ class dynamic_args(metaclass=_DynamicArgsMeta):
     """Flux.2 Klein"""
     wan: bool = False
     """Wan 2.2"""
+    pid: bool = False
+    """PiD"""
     ref_latents: list["torch.Tensor"] = []
     """Reference Latent(s) for Flux Kontext / Qwen-Image-Edit / Flux.2 Klein"""
     concat_latent: "torch.Tensor" = None
     """Input Latent for Wan 2.2 I2V"""
+    lq_latent: tuple["torch.Tensor", "torch.Tensor"] = None
+    """lq_latent & degrade_sigma for PiD"""
     is_referencing: bool = False
     """Appending Reference Latent(s) (by. ImageStitch)"""
     ops: str = None
diff --git a/backend/loader.py b/backend/loader.py
index 9d841f3f..701219d8 100644
--- a/backend/loader.py
+++ b/backend/loader.py
@@ -867,13 +867,14 @@ def forge_loader(sd: os.PathLike, additional_state_dicts: list[os.PathLike] = No
     except AttributeError:
         raise ValueError("Failed to recognize model...") from None
 
-    repo_name = estimated_config.huggingface_repo
+    repo_name: str = estimated_config.huggingface_repo
 
     backend.args.dynamic_args.kontext = "kontext" in str(sd).lower()
     backend.args.dynamic_args.edit = "qwen" in str(sd).lower() and "edit" in str(sd).lower()
     backend.args.dynamic_args.nunchaku = getattr(estimated_config, "nunchaku", False)
     backend.args.dynamic_args.klein = "klein" in repo_name
     backend.args.dynamic_args.wan = "Wan" in repo_name
+    backend.args.dynamic_args.pid = "PiD" in repo_name
 
     if "xl" in repo_name and "rectified" in str(sd).lower():
         estimated_config.sampling_settings["RF"] = True
diff --git a/backend/nn/pixeldit/pid.py b/backend/nn/pixeldit/pid.py
index 6161f7af..c9321b68 100644
--- a/backend/nn/pixeldit/pid.py
+++ b/backend/nn/pixeldit/pid.py
@@ -4,6 +4,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from backend.args import dynamic_args
+
 from .model import PixDiT_T2I
 from .modules import precompute_freqs_cis_2d
 
@@ -178,7 +180,8 @@ class PidNet(PixDiT_T2I):
             return s
         return self.lq_proj.gate(s, pid_lq_features[out_idx], pid_degrade_sigma, out_idx)
 
-    def forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, lq_latent=None, degrade_sigma=None, **kwargs):
+    def forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, **kwargs):
+        lq_latent, degrade_sigma = dynamic_args.lq_latent
         assert lq_latent is not None
 
         expected_c = self.lq_proj.latent_channels