diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index 68fd73d0b..03fd59e3d 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -37,11 +37,41 @@ class SDXL(LatentFormat):
 class SD_X4(LatentFormat):
     def __init__(self):
         self.scale_factor = 0.08333
+        self.latent_rgb_factors = [
+            [-0.2340, -0.3863, -0.3257],
+            [ 0.0994,  0.0885, -0.0908],
+            [-0.2833, -0.2349, -0.3741],
+            [ 0.2523, -0.0055, -0.1651]
+        ]
 
 class SC_Prior(LatentFormat):
     def __init__(self):
         self.scale_factor = 1.0
+        self.latent_rgb_factors = [
+            [-0.0326, -0.0204, -0.0127],
+            [-0.1592, -0.0427,  0.0216],
+            [ 0.0873,  0.0638, -0.0020],
+            [-0.0602,  0.0442,  0.1304],
+            [ 0.0800, -0.0313, -0.1796],
+            [-0.0810, -0.0638, -0.1581],
+            [ 0.1791,  0.1180,  0.0967],
+            [ 0.0740,  0.1416,  0.0432],
+            [-0.1745, -0.1888, -0.1373],
+            [ 0.2412,  0.1577,  0.0928],
+            [ 0.1908,  0.0998,  0.0682],
+            [ 0.0209,  0.0365, -0.0092],
+            [ 0.0448, -0.0650, -0.1728],
+            [-0.1658, -0.1045, -0.1308],
+            [ 0.0542,  0.1545,  0.1325],
+            [-0.0352, -0.1672, -0.2541]
+        ]
 
 class SC_B(LatentFormat):
     def __init__(self):
         self.scale_factor = 1.0
+        self.latent_rgb_factors = [
+            [ 0.1121,  0.2006,  0.1023],
+            [-0.2093, -0.0222, -0.0195],
+            [-0.3087, -0.1535,  0.0366],
+            [ 0.0290, -0.1574, -0.4078]
+        ]
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 2ce9b1583..33915f6bf 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -473,15 +473,11 @@ class StableCascade_B(BaseModel):
 
         clip_text_pooled = kwargs["pooled_output"]
         if clip_text_pooled is not None:
-            out['clip_text_pooled'] = conds.CONDRegular(clip_text_pooled)
+            out['clip'] = conds.CONDRegular(clip_text_pooled)
 
         #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched
         prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device))
 
         out["effnet"] = conds.CONDRegular(prior)
         out["sca"] = conds.CONDRegular(torch.zeros((1,)))
-
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            out['clip'] = conds.CONDCrossAttn(cross_attn)
         return out
diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index ae42d81f2..97e91a01d 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -150,10 +150,10 @@ class StableCascadeSampling(ModelSamplingDiscrete):
         self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
 
         #This part is just for compatibility with some schedulers in the codebase
-        self.num_timesteps = 1000
+        self.num_timesteps = 10000
         sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
         for x in range(self.num_timesteps):
-            t = x / self.num_timesteps
+            t = (x + 1) / self.num_timesteps
             sigmas[x] = self.sigma(t)
 
         self.set_sigmas(sigmas)
diff --git a/comfy_extras/nodes/nodes_model_advanced.py b/comfy_extras/nodes/nodes_model_advanced.py
index 5abbef206..07f82fc99 100644
--- a/comfy_extras/nodes/nodes_model_advanced.py
+++ b/comfy_extras/nodes/nodes_model_advanced.py
@@ -16,6 +16,10 @@ class LCM(comfy.model_sampling.EPS):
 
         return c_out * x0 + c_skip * model_input
 
+class X0(comfy.model_sampling.EPS):
+    def calculate_denoised(self, sigma, model_output, model_input):
+        return model_output
+
 class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete):
     original_timesteps = 50
 
@@ -67,7 +71,7 @@ class ModelSamplingDiscrete:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["eps", "v_prediction", "lcm"],),
+                              "sampling": (["eps", "v_prediction", "lcm", "x0"],),
                               "zsnr": ("BOOLEAN", {"default": False}),
                               }}
 
@@ -87,6 +91,8 @@ class ModelSamplingDiscrete:
         elif sampling == "lcm":
             sampling_type = LCM
             sampling_base = ModelSamplingDiscreteDistilled
+        elif sampling == "x0":
+            sampling_type = X0
 
         class ModelSamplingAdvanced(sampling_base, sampling_type):
             pass