diff --git a/examples/dreambooth/accelerate_config.yaml b/examples/dreambooth/accelerate_config.yaml
new file mode 100644
index 000000000000..30b1c1cb9ff0
--- /dev/null
+++ b/examples/dreambooth/accelerate_config.yaml
@@ -0,0 +1,14 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  deepspeed_config_file: ds_config_zero2.json
+  zero3_init_flag: false
+distributed_type: DEEPSPEED
+machine_rank: 0
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+use_cpu: false
\ No newline at end of file
diff --git a/examples/dreambooth/ds_config_zero2.json b/examples/dreambooth/ds_config_zero2.json
new file mode 100644
index 000000000000..41227d7011ea
--- /dev/null
+++ b/examples/dreambooth/ds_config_zero2.json
@@ -0,0 +1,38 @@
+{
+    "train_batch_size": 2,
+    "gradient_accumulation_steps": 1,
+    "gradient_clipping": 1.0,
+    "zero_optimization": {
+        "stage": 2,
+        "offload_optimizer": {
+            "device": "cpu"
+        },
+        "offload_param": {
+            "device": "cpu"
+        },
+        "overlap_comm": true,
+        "contiguous_gradients": true,
+        "reduce_bucket_size": 50000000,
+        "allgather_bucket_size": 50000000
+    },
+    "optimizer": {
+        "type": "AdamW",
+        "params": {
+            "lr": 1e-5,
+            "betas": [0.9, 0.999],
+            "eps": 1e-8,
+            "weight_decay": 1e-2
+        }
+    },
+    "scheduler": {
+        "type": "WarmupLR",
+        "params": {
+            "warmup_min_lr": 0,
+            "warmup_max_lr": 1e-5,
+            "warmup_num_steps": 100
+        }
+    },
+    "steps_per_print": 10,
+    "wall_clock_breakdown": false,
+    "communication_data_type": "fp16"
+}
\ No newline at end of file
diff --git a/examples/dreambooth/setup_training_env.sh b/examples/dreambooth/setup_training_env.sh
new file mode 100755
index 000000000000..a4d2752ab02d
--- /dev/null
+++ b/examples/dreambooth/setup_training_env.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+
+# Extend NCCL timeouts
+export NCCL_SOCKET_TIMEOUT=7200000
+export DEEPSPEED_TIMEOUT=7200000
+
+# Set CPU threading optimizations
+export OMP_NUM_THREADS=1
+export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb=512
+
+# Increase system shared memory limits
+sudo sysctl -w kernel.shmmax=85899345920
+sudo sysctl -w kernel.shmall=2097152
+
+# Enable NCCL debugging for diagnostics
+export NCCL_DEBUG=INFO
+
+# Optional: Set NCCL topology optimization 
+# Uncomment if needed after checking nvidia-smi topo -m
+# export NCCL_P2P_LEVEL=PHB
+
+# Persist changes to sysctl
+echo "kernel.shmmax=85899345920" | sudo tee -a /etc/sysctl.conf
+echo "kernel.shmall=2097152" | sudo tee -a /etc/sysctl.conf
+sudo sysctl -p
\ No newline at end of file
diff --git a/src/diffusers/utils/torch_utils.py b/src/diffusers/utils/torch_utils.py
index a1ab8cda431f..b9edd203d087 100644
--- a/src/diffusers/utils/torch_utils.py
+++ b/src/diffusers/utils/torch_utils.py
@@ -18,6 +18,7 @@
 import functools
 import os
 from typing import Callable, Dict, List, Optional, Tuple, Union
+import warnings
 
 from . import logging
 from .import_utils import is_torch_available, is_torch_npu_available, is_torch_version
@@ -222,8 +223,23 @@ def fourier_filter(x_in: "torch.Tensor", threshold: int, scale: int) -> "torch.T
         x = x.to(dtype=torch.float32)
 
     # FFT
-    x_freq = fftn(x, dim=(-2, -1))
-    x_freq = fftshift(x_freq, dim=(-2, -1))
+    # When running with torch.float16, PyTorch may emit a UserWarning about
+    # ComplexHalf (experimental) support when performing FFTs. This warning is
+    # noisy for users of the FreeU feature and doesn't change the behaviour of
+    # the algorithm here. We therefore locally suppress that specific warning
+    # around the FFT calls when the input dtype is float16.
+    if x.dtype == torch.float16:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                message="ComplexHalf support is experimental and many operators don't support it yet.*",
+                category=UserWarning,
+            )
+            x_freq = fftn(x, dim=(-2, -1))
+            x_freq = fftshift(x_freq, dim=(-2, -1))
+    else:
+        x_freq = fftn(x, dim=(-2, -1))
+        x_freq = fftshift(x_freq, dim=(-2, -1))
 
     B, C, H, W = x_freq.shape
     mask = torch.ones((B, C, H, W), device=x.device)
@@ -234,7 +250,16 @@ def fourier_filter(x_in: "torch.Tensor", threshold: int, scale: int) -> "torch.T
 
     # IFFT
     x_freq = ifftshift(x_freq, dim=(-2, -1))
-    x_filtered = ifftn(x_freq, dim=(-2, -1)).real
+    if x.dtype == torch.float16:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                message="ComplexHalf support is experimental and many operators don't support it yet.*",
+                category=UserWarning,
+            )
+            x_filtered = ifftn(x_freq, dim=(-2, -1)).real
+    else:
+        x_filtered = ifftn(x_freq, dim=(-2, -1)).real
 
     return x_filtered.to(dtype=x_in.dtype)