diff --git a/library/flux_train_utils.py b/library/flux_train_utils.py
index c96e4bb6..3247ecbe 100644
--- a/library/flux_train_utils.py
+++ b/library/flux_train_utils.py
@@ -169,11 +169,21 @@ def sample_image_inference(
 
     if seed is not None:
         torch.manual_seed(seed)
-        torch.cuda.manual_seed(seed)
+        if accelerator.device.type == "cuda":
+            torch.cuda.manual_seed(seed)
+        elif accelerator.device.type == "xpu":
+            torch.xpu.manual_seed(seed)
+        elif accelerator.device.type == "mps":
+            torch.mps.manual_seed(seed)
     else:
         # True random sample image generation
         torch.seed()
-        torch.cuda.seed()
+        if accelerator.device.type == "cuda":
+            torch.cuda.seed()
+        elif accelerator.device.type == "xpu":
+            torch.xpu.seed()
+        elif accelerator.device.type == "mps":
+            torch.mps.seed()
 
     if negative_prompt is None:
         negative_prompt = ""
@@ -474,6 +484,29 @@ def get_noisy_model_input_and_timesteps(
     bsz, h, w = latents.shape[0], latents.shape[-2], latents.shape[-1]
     assert bsz > 0, "Batch size not large enough"
     num_timesteps = noise_scheduler.config.num_train_timesteps
+    min_timestep = 0 if args.min_timestep is None else args.min_timestep
+    max_timestep = num_timesteps if args.max_timestep is None else args.max_timestep
+    if min_timestep > max_timestep:
+        min_timestep, max_timestep = max_timestep, min_timestep
+
+    if min_timestep == max_timestep:
+        # Deterministic timesteps (used by validation) need fully fixed noise.
+        timestep_value = float(max_timestep)
+        timesteps = torch.full((bsz,), timestep_value, device=device, dtype=dtype)
+        sigma_value = timestep_value / num_timesteps
+        sigmas = torch.full((bsz,), sigma_value, device=device, dtype=dtype)
+        sigmas = sigmas.view(-1, 1, 1, 1)
+        if args.ip_noise_gamma:
+            xi = torch.randn_like(latents, device=latents.device, dtype=dtype)
+            if args.ip_noise_gamma_random_strength:
+                ip_noise_gamma = torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma
+            else:
+                ip_noise_gamma = args.ip_noise_gamma
+            noisy_model_input = (1.0 - sigmas) * latents + sigmas * (noise + ip_noise_gamma * xi)
+        else:
+            noisy_model_input = (1.0 - sigmas) * latents + sigmas * noise
+        return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas
+
     if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid":
         # Simple random sigma-based noise sampling
         if args.timestep_sampling == "sigmoid":