diff --git a/comfy_extras/v3/nodes_stable_cascade.py b/comfy_extras/v3/nodes_stable_cascade.py
deleted file mode 100644
index af2893641..000000000
--- a/comfy_extras/v3/nodes_stable_cascade.py
+++ /dev/null
@@ -1,218 +0,0 @@
-"""
-    This file is part of ComfyUI.
-    Copyright (C) 2024 Stability AI
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-"""
-
-import torch
-import nodes
-import comfy.utils
-
-from comfy_api.v3 import io
-
-
-class StableCascade_EmptyLatentImage_V3(io.ComfyNodeV3):
-    @classmethod
-    def DEFINE_SCHEMA(cls):
-        return io.SchemaV3(
-            node_id="StableCascade_EmptyLatentImage_V3",
-            category="latent/stable_cascade",
-            inputs=[
-                io.Int.Input(
-                    "width",
-                    default=1024,
-                    min=256,
-                    max=nodes.MAX_RESOLUTION, step=8,
-                ),
-                io.Int.Input(
-                    "height",
-                    default=1024,
-                    min=256,
-                    max=nodes.MAX_RESOLUTION,
-                    step=8,
-                ),
-                io.Int.Input(
-                    "compression",
-                    default=42,
-                    min=4,
-                    max=128,
-                    step=1,
-                ),
-                io.Int.Input(
-                    "batch_size",
-                    default=1,
-                    min=1,
-                    max=4096,
-                ),
-            ],
-            outputs=[
-                io.Latent.Output(
-                    "stage_c",
-                    display_name="stage_c",
-                ),
-                io.Latent.Output(
-                    "stage_b",
-                    display_name="stage_b",
-                ),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, width, height, compression, batch_size=1):
-        c_latent = torch.zeros([batch_size, 16, height // compression, width // compression])
-        b_latent = torch.zeros([batch_size, 4, height // 4, width // 4])
-        return ({
-            "samples": c_latent,
-        }, {
-            "samples": b_latent,
-        })
-
-
-class StableCascade_StageC_VAEEncode_V3(io.ComfyNodeV3):
-    @classmethod
-    def DEFINE_SCHEMA(cls):
-        return io.SchemaV3(
-            node_id="StableCascade_StageC_VAEEncode_V3",
-            category="latent/stable_cascade",
-            inputs=[
-                io.Image.Input(
-                    "image",
-                ),
-                io.Vae.Input(
-                    "vae",
-                ),
-                io.Int.Input(
-                    "compression",
-                    default=42,
-                    min=4,
-                    max=128,
-                    step=1,
-                ),
-            ],
-            outputs=[
-                io.Latent.Output(
-                    "stage_c",
-                    display_name="stage_c",
-                ),
-                io.Latent.Output(
-                    "stage_b",
-                    display_name="stage_b",
-                ),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, image, vae, compression):
-        width = image.shape[-2]
-        height = image.shape[-3]
-        out_width = (width // compression) * vae.downscale_ratio
-        out_height = (height // compression) * vae.downscale_ratio
-
-        s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1)
-
-        c_latent = vae.encode(s[:,:,:,:3])
-        b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2])
-        return ({
-            "samples": c_latent,
-        }, {
-            "samples": b_latent,
-        })
-
-
-class StableCascade_StageB_Conditioning_V3(io.ComfyNodeV3):
-    @classmethod
-    def DEFINE_SCHEMA(cls):
-        return io.SchemaV3(
-            node_id="StableCascade_StageB_Conditioning_V3",
-            category="conditioning/stable_cascade",
-            inputs=[
-                io.Conditioning.Input(
-                    "conditioning",
-                ),
-                io.Latent.Input(
-                    "stage_c",
-                ),
-            ],
-            outputs=[
-              io.Conditioning.Output(
-                  "CONDITIONING",
-              ),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, conditioning, stage_c):
-        c = []
-        for t in conditioning:
-            d = t[1].copy()
-            d['stable_cascade_prior'] = stage_c['samples']
-            n = [t[0], d]
-            c.append(n)
-        return (c, )
-
-
-class StableCascade_SuperResolutionControlnet_V3(io.ComfyNodeV3):
-    @classmethod
-    def DEFINE_SCHEMA(cls):
-        return io.SchemaV3(
-            node_id="StableCascade_SuperResolutionControlnet_V3",
-            category="_for_testing/stable_cascade",
-            is_experimental=True,
-            inputs=[
-                io.Image.Input(
-                    "image",
-                ),
-                io.Vae.Input(
-                    "vae",
-                ),
-            ],
-            outputs=[
-                io.Image.Output(
-                    "controlnet_input",
-                    display_name="controlnet_input",
-                ),
-                io.Latent.Output(
-                    "stage_c",
-                    display_name="stage_c",
-                ),
-                io.Latent.Output(
-                    "stage_b",
-                    display_name="stage_b",
-                ),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, image, vae):
-        width = image.shape[-2]
-        height = image.shape[-3]
-        batch_size = image.shape[0]
-        controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1)
-
-        c_latent = torch.zeros([batch_size, 16, height // 16, width // 16])
-        b_latent = torch.zeros([batch_size, 4, height // 2, width // 2])
-        return (controlnet_input, {
-            "samples": c_latent,
-        }, {
-            "samples": b_latent,
-        })
-
-
-NODES_LIST: list[type[io.ComfyNodeV3]] = [
-    StableCascade_EmptyLatentImage_V3,
-    StableCascade_StageB_Conditioning_V3,
-    StableCascade_StageC_VAEEncode_V3,
-    StableCascade_SuperResolutionControlnet_V3,
-]
diff --git a/nodes.py b/nodes.py
index c1806c646..4e4dae917 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2302,7 +2302,6 @@ def init_builtin_extra_nodes():
         "v3/nodes_images.py",
         "v3/nodes_mask.py",
         "v3/nodes_webcam.py",
-        "v3/nodes_stable_cascade.py",
     ]
 
     import_failed = []