diff --git a/comfy/ldm/chroma_radiance/model.py b/comfy/ldm/chroma_radiance/model.py
index e643b4414629..70d1738890cb 100644
--- a/comfy/ldm/chroma_radiance/model.py
+++ b/comfy/ldm/chroma_radiance/model.py
@@ -37,7 +37,7 @@ class ChromaRadianceParams(ChromaParams):
     nerf_final_head_type: str
     # None means use the same dtype as the model.
     nerf_embedder_dtype: Optional[torch.dtype]
-
+    use_x0: bool
 
 class ChromaRadiance(Chroma):
     """
@@ -159,6 +159,9 @@ def __init__(self, image_model=None, final_layer=True, dtype=None, device=None,
         self.skip_dit = []
         self.lite = False
 
+        if params.use_x0:
+            self.register_buffer("__x0__", torch.tensor([]))
+
     @property
     def _nerf_final_layer(self) -> nn.Module:
         if self.params.nerf_final_head_type == "linear":
@@ -276,6 +279,12 @@ def radiance_get_override_params(self, overrides: dict) -> ChromaRadianceParams:
         params_dict |= overrides
         return params.__class__(**params_dict)
 
+    def _apply_x0_residual(self, predicted, noisy, timesteps):
+
+        # non zero during training to prevent 0 div
+        eps = 0.0
+        return (noisy - predicted) / (timesteps.view(-1,1,1,1) + eps)
+
     def _forward(
         self,
         x: Tensor,
@@ -316,4 +325,11 @@ def _forward(
             transformer_options,
             attn_mask=kwargs.get("attention_mask", None),
         )
-        return self.forward_nerf(img, img_out, params)[:, :, :h, :w]
+
+        out = self.forward_nerf(img, img_out, params)[:, :, :h, :w]
+
+        # If x0 variant → v-pred, just return this instead
+        if hasattr(self, "__x0__"):
+            out = self._apply_x0_residual(out, img, timestep)
+        return out
+
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 74c547427b43..19e6aa954259 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -257,6 +257,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                 dit_config["nerf_tile_size"] = 512
                 dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear"
                 dit_config["nerf_embedder_dtype"] = torch.float32
+            if "__x0__" in state_dict_keys: # x0 pred
+                dit_config["use_x0"] = True
         else:
             dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
             dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys
diff --git a/comfy/ops.py b/comfy/ops.py
index 35237c9f73e2..6f34d50fc051 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -22,7 +22,6 @@
 from comfy.cli_args import args, PerformanceFeature
 import comfy.float
 import comfy.rmsnorm
-import contextlib
 import json
 
 def run_every_op():
@@ -94,13 +93,6 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     else:
         offload_stream = None
 
-    if offload_stream is not None:
-        wf_context = offload_stream
-        if hasattr(wf_context, "as_context"):
-            wf_context = wf_context.as_context(offload_stream)
-    else:
-        wf_context = contextlib.nullcontext()
-
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
 
     weight_has_function = len(s.weight_function) > 0