diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py
index 504b79ede..10eb68d73 100644
--- a/comfy/ldm/modules/diffusionmodules/openaimodel.py
+++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py
@@ -633,6 +633,10 @@ class UNetModel(nn.Module):
                     h = p(h, transformer_options)
 
             hs.append(h)
+            if "input_block_patch_after_skip" in transformer_patches:
+                patch = transformer_patches["input_block_patch_after_skip"]
+                for p in patch:
+                    h = p(h, transformer_options)
 
         transformer_options["block"] = ("middle", 0)
         h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 023684331..a3cffc3be 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -37,7 +37,7 @@ class ModelPatcher:
         return size
 
     def clone(self):
-        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device)
+        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)
         n.patches = {}
         for k in self.patches:
             n.patches[k] = self.patches[k][:]
@@ -99,6 +99,9 @@ class ModelPatcher:
     def set_model_input_block_patch(self, patch):
         self.set_model_patch(patch, "input_block_patch")
 
+    def set_model_input_block_patch_after_skip(self, patch):
+        self.set_model_patch(patch, "input_block_patch_after_skip")
+
     def set_model_output_block_patch(self, patch):
         self.set_model_patch(patch, "output_block_patch")
 
diff --git a/comfy/utils.py b/comfy/utils.py
index 1985012e0..f4c0ab419 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -258,7 +258,7 @@ def set_attr(obj, attr, value):
     for name in attrs[:-1]:
         obj = getattr(obj, name)
     prev = getattr(obj, attrs[-1])
-    setattr(obj, attrs[-1], torch.nn.Parameter(value))
+    setattr(obj, attrs[-1], torch.nn.Parameter(value, requires_grad=False))
     del prev
 
 def copy_to_param(obj, attr, value):
diff --git a/comfy_extras/nodes_model_downscale.py b/comfy_extras/nodes_model_downscale.py
new file mode 100644
index 000000000..8850d0948
--- /dev/null
+++ b/comfy_extras/nodes_model_downscale.py
@@ -0,0 +1,49 @@
+import torch
+
+class PatchModelAddDownscale:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}),
+                              "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}),
+                              "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                              "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}),
+                              "downscale_after_skip": ("BOOLEAN", {"default": True}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+
+    def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip):
+        sigma_start = model.model.model_sampling.percent_to_sigma(start_percent).item()
+        sigma_end = model.model.model_sampling.percent_to_sigma(end_percent).item()
+
+        def input_block_patch(h, transformer_options):
+            if transformer_options["block"][1] == block_number:
+                sigma = transformer_options["sigmas"][0].item()
+                if sigma <= sigma_start and sigma >= sigma_end:
+                    h = torch.nn.functional.interpolate(h, scale_factor=(1.0 / downscale_factor), mode="bicubic", align_corners=False)
+            return h
+
+        def output_block_patch(h, hsp, transformer_options):
+            if h.shape[2] != hsp.shape[2]:
+                h = torch.nn.functional.interpolate(h, size=(hsp.shape[2], hsp.shape[3]), mode="bicubic", align_corners=False)
+            return h, hsp
+
+        m = model.clone()
+        if downscale_after_skip:
+            m.set_model_input_block_patch_after_skip(input_block_patch)
+        else:
+            m.set_model_input_block_patch(input_block_patch)
+        m.set_model_output_block_patch(output_block_patch)
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "PatchModelAddDownscale": PatchModelAddDownscale,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    # Sampling
+    "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)",
+}
diff --git a/nodes.py b/nodes.py
index e8cfb5e6a..f9d2d7f6c 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1799,6 +1799,7 @@ def init_custom_nodes():
         "nodes_custom_sampler.py",
         "nodes_hypertile.py",
         "nodes_model_advanced.py",
+        "nodes_model_downscale.py",
     ]
 
     for node_file in extras_files: