diff --git a/modules/processing_callbacks.py b/modules/processing_callbacks.py
index 3c0a81848..a491dc7e1 100644
--- a/modules/processing_callbacks.py
+++ b/modules/processing_callbacks.py
@@ -75,7 +75,7 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict = No
             time.sleep(0.1)
     if latents is None:
         return kwargs
-    elif shared.opts.nan_skip:
+    elif (getattr(p, 'nan_skip', None) if (p is not None and getattr(p, 'nan_skip', None) is not None) else shared.opts.nan_skip):
         assert not torch.isnan(latents[..., 0, 0]).all(), f'NaN detected at step {step}: Skipping...'
     if p is None:
         return kwargs
@@ -93,7 +93,7 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict = No
             debug_callback(f"Callback: IP Adapter scales={ip_adapter_scales}")
             pipe.set_ip_adapter_scale(ip_adapter_scales)
     if step != getattr(pipe, 'num_timesteps', 0):
-        kwargs = processing_correction.correction_callback(p, timestep, kwargs, initial=step == 0)
+        kwargs = processing_correction.correction_callback(p, timestep, kwargs, pipe=pipe, initial=step == 0)
     kwargs = prompt_callback(step, kwargs)  # monkey patch for diffusers callback issues
 
     if step == 0:
@@ -164,13 +164,27 @@ def diffusers_callback(pipe, step: int = 0, timestep: int = 0, kwargs: dict = No
             shared.state.current_latent = kwargs['latents']
             shared.state.current_noise_pred = current_noise_pred
 
+        # Video latent preview: extract middle frame from 5D [B,C,T,H,W] to 4D [B,C,H,W]
+        if shared.state.current_latent is not None and shared.state.current_latent.ndim == 5:
+            _b, _c, t, _h, _w = shared.state.current_latent.shape
+            shared.state.current_latent = shared.state.current_latent[:, :, t // 2, :, :]
+
         if hasattr(pipe, "scheduler") and hasattr(pipe.scheduler, "sigmas") and hasattr(pipe.scheduler, "step_index") and pipe.scheduler.step_index is not None:
             try:
                 shared.state.current_sigma = pipe.scheduler.sigmas[pipe.scheduler.step_index-1]
                 shared.state.current_sigma_next = pipe.scheduler.sigmas[pipe.scheduler.step_index]
-                if (shared.opts.schedulers_sigma_adjust != 1.0) and (timestep > 1000 * shared.opts.schedulers_sigma_adjust_min) and (timestep < 1000 * shared.opts.schedulers_sigma_adjust_max):
-                    pipe.scheduler.sigmas[pipe.scheduler.step_index+1] = pipe.scheduler.sigmas[pipe.scheduler.step_index+1] * shared.opts.schedulers_sigma_adjust
-                    p.extra_generation_params["Sigma adjust"] = shared.opts.schedulers_sigma_adjust
+                _sigma_adjust = getattr(p, 'schedulers_sigma_adjust', None) if p is not None else None
+                if _sigma_adjust is None:
+                    _sigma_adjust = shared.opts.schedulers_sigma_adjust
+                _sigma_adjust_min = getattr(p, 'schedulers_sigma_adjust_min', None) if p is not None else None
+                if _sigma_adjust_min is None:
+                    _sigma_adjust_min = shared.opts.schedulers_sigma_adjust_min
+                _sigma_adjust_max = getattr(p, 'schedulers_sigma_adjust_max', None) if p is not None else None
+                if _sigma_adjust_max is None:
+                    _sigma_adjust_max = shared.opts.schedulers_sigma_adjust_max
+                if (_sigma_adjust != 1.0) and (timestep > 1000 * _sigma_adjust_min) and (timestep < 1000 * _sigma_adjust_max):
+                    pipe.scheduler.sigmas[pipe.scheduler.step_index+1] = pipe.scheduler.sigmas[pipe.scheduler.step_index+1] * _sigma_adjust
+                    p.extra_generation_params["Sigma adjust"] = _sigma_adjust
             except Exception:
                 pass
     except Exception as e: