From f207108643a2c44192986284cd87c0f5337d2bbf Mon Sep 17 00:00:00 2001
From: sayakpaul <spsayakpaul@gmail.com>
Date: Mon, 8 Dec 2025 18:13:38 +0530
Subject: [PATCH 1/7] disable progressbar in distributed.

---
 src/diffusers/pipelines/pipeline_utils.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py
index 392d5fb3feb4..8a67e3d09b54 100644
--- a/src/diffusers/pipelines/pipeline_utils.py
+++ b/src/diffusers/pipelines/pipeline_utils.py
@@ -1908,16 +1908,28 @@ def progress_bar(self, iterable=None, total=None):
                 f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
             )
 
+        progress_bar_config = dict(self._progress_bar_config)
+        if "disable" not in progress_bar_config:
+            progress_bar_config["disable"] = self._progress_bar_disabled_for_rank()
+
         if iterable is not None:
-            return tqdm(iterable, **self._progress_bar_config)
+            return tqdm(iterable, **progress_bar_config)
         elif total is not None:
-            return tqdm(total=total, **self._progress_bar_config)
+            return tqdm(total=total, **progress_bar_config)
         else:
             raise ValueError("Either `total` or `iterable` has to be defined.")
 
     def set_progress_bar_config(self, **kwargs):
         self._progress_bar_config = kwargs
 
+    def _progress_bar_disabled_for_rank(self):
+        if torch.distributed.is_available() and torch.distributed.is_initialized():
+            try:
+                return torch.distributed.get_rank() != 0
+            except (RuntimeError, ValueError):
+                return False
+        return False
+
     def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
         r"""
         Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this

From 97e380573c5c2ade17b98d1b2b8b35ecb22b2a5a Mon Sep 17 00:00:00 2001
From: sayakpaul <spsayakpaul@gmail.com>
Date: Mon, 8 Dec 2025 18:37:52 +0530
Subject: [PATCH 2/7] up

---
 src/diffusers/pipelines/pipeline_utils.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py
index 8a67e3d09b54..6f37208227db 100644
--- a/src/diffusers/pipelines/pipeline_utils.py
+++ b/src/diffusers/pipelines/pipeline_utils.py
@@ -982,7 +982,11 @@ def load_module(name, value):
         # 7. Load each module in the pipeline
         current_device_map = None
         _maybe_warn_for_wrong_component_in_quant_config(init_dict, quantization_config)
-        for name, (library_name, class_name) in logging.tqdm(init_dict.items(), desc="Loading pipeline components..."):
+        logging_tqdm_kwargs = {"desc": "Loading pipeline components..."}
+        if cls._progress_bar_disabled_for_rank():
+            logging_tqdm_kwargs["disable"] = True
+
+        for name, (library_name, class_name) in logging.tqdm(init_dict.items(), **logging_tqdm_kwargs):
             # 7.1 device_map shenanigans
             if final_device_map is not None:
                 if isinstance(final_device_map, dict) and len(final_device_map) > 0:
@@ -1922,7 +1926,8 @@ def progress_bar(self, iterable=None, total=None):
     def set_progress_bar_config(self, **kwargs):
         self._progress_bar_config = kwargs
 
-    def _progress_bar_disabled_for_rank(self):
+    @staticmethod
+    def _progress_bar_disabled_for_rank():
         if torch.distributed.is_available() and torch.distributed.is_initialized():
             try:
                 return torch.distributed.get_rank() != 0

From 8df3fbcc143610483a43aec48df30a26b3c025bc Mon Sep 17 00:00:00 2001
From: sayakpaul <spsayakpaul@gmail.com>
Date: Mon, 8 Dec 2025 18:47:26 +0530
Subject: [PATCH 3/7] up

---
 src/diffusers/models/modeling_utils.py    |  7 +++++--
 src/diffusers/pipelines/pipeline_utils.py | 15 +++------------
 src/diffusers/utils/torch_utils.py        | 17 +++++++++++++++++
 3 files changed, 25 insertions(+), 14 deletions(-)

diff --git a/src/diffusers/models/modeling_utils.py b/src/diffusers/models/modeling_utils.py
index 41da95d3a2a2..567fb424d4de 100644
--- a/src/diffusers/models/modeling_utils.py
+++ b/src/diffusers/models/modeling_utils.py
@@ -64,7 +64,7 @@
     load_or_create_model_card,
     populate_model_card,
 )
-from ..utils.torch_utils import empty_device_cache
+from ..utils.torch_utils import empty_device_cache, is_torch_dist_rank_zero
 from ._modeling_parallel import ContextParallelConfig, ContextParallelModelPlan, ParallelConfig
 from .model_loading_utils import (
     _caching_allocator_warmup,
@@ -1672,7 +1672,10 @@ def _load_pretrained_model(
         else:
             shard_files = resolved_model_file
             if len(resolved_model_file) > 1:
-                shard_files = logging.tqdm(resolved_model_file, desc="Loading checkpoint shards")
+                shard_tqdm_kwargs = {"desc": "Loading checkpoint shards"}
+                if not is_torch_dist_rank_zero():
+                    shard_tqdm_kwargs["disable"] = True
+                shard_files = logging.tqdm(resolved_model_file, **shard_tqdm_kwargs)
 
             for shard_file in shard_files:
                 offload_index, state_dict_index, _mismatched_keys, _error_msgs = load_fn(shard_file)
diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py
index 6f37208227db..de1d44df4d3c 100644
--- a/src/diffusers/pipelines/pipeline_utils.py
+++ b/src/diffusers/pipelines/pipeline_utils.py
@@ -68,7 +68,7 @@
     numpy_to_pil,
 )
 from ..utils.hub_utils import _check_legacy_sharding_variant_format, load_or_create_model_card, populate_model_card
-from ..utils.torch_utils import empty_device_cache, get_device, is_compiled_module
+from ..utils.torch_utils import empty_device_cache, get_device, is_compiled_module, is_torch_dist_rank_zero
 
 
 if is_torch_npu_available():
@@ -983,7 +983,7 @@ def load_module(name, value):
         current_device_map = None
         _maybe_warn_for_wrong_component_in_quant_config(init_dict, quantization_config)
         logging_tqdm_kwargs = {"desc": "Loading pipeline components..."}
-        if cls._progress_bar_disabled_for_rank():
+        if not is_torch_dist_rank_zero():
             logging_tqdm_kwargs["disable"] = True
 
         for name, (library_name, class_name) in logging.tqdm(init_dict.items(), **logging_tqdm_kwargs):
@@ -1914,7 +1914,7 @@ def progress_bar(self, iterable=None, total=None):
 
         progress_bar_config = dict(self._progress_bar_config)
         if "disable" not in progress_bar_config:
-            progress_bar_config["disable"] = self._progress_bar_disabled_for_rank()
+            progress_bar_config["disable"] = not is_torch_dist_rank_zero()
 
         if iterable is not None:
             return tqdm(iterable, **progress_bar_config)
@@ -1926,15 +1926,6 @@ def progress_bar(self, iterable=None, total=None):
     def set_progress_bar_config(self, **kwargs):
         self._progress_bar_config = kwargs
 
-    @staticmethod
-    def _progress_bar_disabled_for_rank():
-        if torch.distributed.is_available() and torch.distributed.is_initialized():
-            try:
-                return torch.distributed.get_rank() != 0
-            except (RuntimeError, ValueError):
-                return False
-        return False
-
     def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
         r"""
         Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this
diff --git a/src/diffusers/utils/torch_utils.py b/src/diffusers/utils/torch_utils.py
index 3b66fdadbef8..b8a61b9cdb1a 100644
--- a/src/diffusers/utils/torch_utils.py
+++ b/src/diffusers/utils/torch_utils.py
@@ -143,6 +143,23 @@ def backend_supports_training(device: str):
     return BACKEND_SUPPORTS_TRAINING[device]
 
 
+def is_torch_dist_rank_zero() -> bool:
+    if not is_torch_available():
+        return True
+
+    dist_module = getattr(torch, "distributed", None)
+    if dist_module is None or not dist_module.is_available():
+        return True
+
+    if not dist_module.is_initialized():
+        return True
+
+    try:
+        return dist_module.get_rank() == 0
+    except (RuntimeError, ValueError):
+        return True
+
+
 def randn_tensor(
     shape: Union[Tuple, List],
     generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,

From 94d671d03a2cdecac1a66285480a0b0af2f85cbc Mon Sep 17 00:00:00 2001
From: sayakpaul <spsayakpaul@gmail.com>
Date: Mon, 8 Dec 2025 19:01:22 +0530
Subject: [PATCH 4/7] up

---
 src/diffusers/models/model_loading_utils.py |  7 +++-
 src/diffusers/models/modeling_utils.py      |  9 ++----
 src/diffusers/pipelines/pipeline_utils.py   |  3 +-
 src/diffusers/utils/distributed_utils.py    | 36 +++++++++++++++++++++
 src/diffusers/utils/logging.py              | 23 ++++++++++++-
 src/diffusers/utils/torch_utils.py          | 17 ----------
 6 files changed, 69 insertions(+), 26 deletions(-)
 create mode 100644 src/diffusers/utils/distributed_utils.py

diff --git a/src/diffusers/models/model_loading_utils.py b/src/diffusers/models/model_loading_utils.py
index 8b48ba6b4873..08b3f0234f82 100644
--- a/src/diffusers/models/model_loading_utils.py
+++ b/src/diffusers/models/model_loading_utils.py
@@ -47,6 +47,7 @@
     is_torch_version,
     logging,
 )
+from ..utils.distributed_utils import is_torch_dist_rank_zero
 
 
 logger = logging.get_logger(__name__)
@@ -429,8 +430,12 @@ def _load_shard_files_with_threadpool(
         low_cpu_mem_usage=low_cpu_mem_usage,
     )
 
+    tqdm_kwargs = {"total": len(shard_files), "desc": "Loading checkpoint shards"}
+    if not is_torch_dist_rank_zero():
+        tqdm_kwargs["disable"] = True
+
     with ThreadPoolExecutor(max_workers=num_workers) as executor:
-        with logging.tqdm(total=len(shard_files), desc="Loading checkpoint shards") as pbar:
+        with logging.tqdm(**tqdm_kwargs) as pbar:
             futures = [executor.submit(load_one, shard_file) for shard_file in shard_files]
             for future in as_completed(futures):
                 result = future.result()
diff --git a/src/diffusers/models/modeling_utils.py b/src/diffusers/models/modeling_utils.py
index 567fb424d4de..ba5b93605f50 100644
--- a/src/diffusers/models/modeling_utils.py
+++ b/src/diffusers/models/modeling_utils.py
@@ -59,12 +59,9 @@
     is_torch_version,
     logging,
 )
-from ..utils.hub_utils import (
-    PushToHubMixin,
-    load_or_create_model_card,
-    populate_model_card,
-)
-from ..utils.torch_utils import empty_device_cache, is_torch_dist_rank_zero
+from ..utils.distributed_utils import is_torch_dist_rank_zero
+from ..utils.hub_utils import PushToHubMixin, load_or_create_model_card, populate_model_card
+from ..utils.torch_utils import empty_device_cache
 from ._modeling_parallel import ContextParallelConfig, ContextParallelModelPlan, ParallelConfig
 from .model_loading_utils import (
     _caching_allocator_warmup,
diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py
index de1d44df4d3c..5c4ac8a6554a 100644
--- a/src/diffusers/pipelines/pipeline_utils.py
+++ b/src/diffusers/pipelines/pipeline_utils.py
@@ -67,8 +67,9 @@
     logging,
     numpy_to_pil,
 )
+from ..utils.distributed_utils import is_torch_dist_rank_zero
 from ..utils.hub_utils import _check_legacy_sharding_variant_format, load_or_create_model_card, populate_model_card
-from ..utils.torch_utils import empty_device_cache, get_device, is_compiled_module, is_torch_dist_rank_zero
+from ..utils.torch_utils import empty_device_cache, get_device, is_compiled_module
 
 
 if is_torch_npu_available():
diff --git a/src/diffusers/utils/distributed_utils.py b/src/diffusers/utils/distributed_utils.py
new file mode 100644
index 000000000000..239b7b26200d
--- /dev/null
+++ b/src/diffusers/utils/distributed_utils.py
@@ -0,0 +1,36 @@
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+try:
+    import torch
+except ImportError:
+    torch = None
+
+
+def is_torch_dist_rank_zero() -> bool:
+    if torch is None:
+        return True
+
+    dist_module = getattr(torch, "distributed", None)
+    if dist_module is None or not dist_module.is_available():
+        return True
+
+    if not dist_module.is_initialized():
+        return True
+
+    try:
+        return dist_module.get_rank() == 0
+    except (RuntimeError, ValueError):
+        return True
diff --git a/src/diffusers/utils/logging.py b/src/diffusers/utils/logging.py
index 2ad6d3a47607..48a5d900af56 100644
--- a/src/diffusers/utils/logging.py
+++ b/src/diffusers/utils/logging.py
@@ -32,6 +32,8 @@
 
 from tqdm import auto as tqdm_lib
 
+from .distributed_utils import is_torch_dist_rank_zero
+
 
 _lock = threading.Lock()
 _default_handler: Optional[logging.Handler] = None
@@ -47,6 +49,22 @@
 _default_log_level = logging.WARNING
 
 _tqdm_active = True
+_rank_zero_filter = None
+
+
+class _RankZeroFilter(logging.Filter):
+    def filter(self, record):
+        return is_torch_dist_rank_zero()
+
+
+def _ensure_rank_zero_filter(logger: logging.Logger) -> None:
+    global _rank_zero_filter
+
+    if _rank_zero_filter is None:
+        _rank_zero_filter = _RankZeroFilter()
+
+    if not any(isinstance(f, _RankZeroFilter) for f in logger.filters):
+        logger.addFilter(_rank_zero_filter)
 
 
 def _get_default_logging_level() -> int:
@@ -90,6 +108,7 @@ def _configure_library_root_logger() -> None:
         library_root_logger.addHandler(_default_handler)
         library_root_logger.setLevel(_get_default_logging_level())
         library_root_logger.propagate = False
+        _ensure_rank_zero_filter(library_root_logger)
 
 
 def _reset_library_root_logger() -> None:
@@ -120,7 +139,9 @@ def get_logger(name: Optional[str] = None) -> logging.Logger:
         name = _get_library_name()
 
     _configure_library_root_logger()
-    return logging.getLogger(name)
+    logger = logging.getLogger(name)
+    _ensure_rank_zero_filter(logger)
+    return logger
 
 
 def get_verbosity() -> int:
diff --git a/src/diffusers/utils/torch_utils.py b/src/diffusers/utils/torch_utils.py
index b8a61b9cdb1a..3b66fdadbef8 100644
--- a/src/diffusers/utils/torch_utils.py
+++ b/src/diffusers/utils/torch_utils.py
@@ -143,23 +143,6 @@ def backend_supports_training(device: str):
     return BACKEND_SUPPORTS_TRAINING[device]
 
 
-def is_torch_dist_rank_zero() -> bool:
-    if not is_torch_available():
-        return True
-
-    dist_module = getattr(torch, "distributed", None)
-    if dist_module is None or not dist_module.is_available():
-        return True
-
-    if not dist_module.is_initialized():
-        return True
-
-    try:
-        return dist_module.get_rank() == 0
-    except (RuntimeError, ValueError):
-        return True
-
-
 def randn_tensor(
     shape: Union[Tuple, List],
     generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,

From 2529fdf6af4de622066a07aeb204048da00bb727 Mon Sep 17 00:00:00 2001
From: sayakpaul <spsayakpaul@gmail.com>
Date: Tue, 9 Dec 2025 08:31:10 +0530
Subject: [PATCH 5/7] up

---
 src/diffusers/utils/distributed_utils.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/src/diffusers/utils/distributed_utils.py b/src/diffusers/utils/distributed_utils.py
index 239b7b26200d..154ecab3a5d0 100644
--- a/src/diffusers/utils/distributed_utils.py
+++ b/src/diffusers/utils/distributed_utils.py
@@ -13,16 +13,15 @@
 # limitations under the License.
 
 
-try:
-    import torch
-except ImportError:
-    torch = None
+from .import_utils import is_torch_available
 
 
 def is_torch_dist_rank_zero() -> bool:
-    if torch is None:
+    if not is_torch_available():
         return True
 
+    import torch
+
     dist_module = getattr(torch, "distributed", None)
     if dist_module is None or not dist_module.is_available():
         return True

From 8193f38e1970ce1f695230fe785e3ea1b43f7766 Mon Sep 17 00:00:00 2001
From: sayakpaul <spsayakpaul@gmail.com>
Date: Tue, 9 Dec 2025 08:37:06 +0530
Subject: [PATCH 6/7] up

---
 src/diffusers/utils/distributed_utils.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/diffusers/utils/distributed_utils.py b/src/diffusers/utils/distributed_utils.py
index 154ecab3a5d0..239b7b26200d 100644
--- a/src/diffusers/utils/distributed_utils.py
+++ b/src/diffusers/utils/distributed_utils.py
@@ -13,15 +13,16 @@
 # limitations under the License.
 
 
-from .import_utils import is_torch_available
+try:
+    import torch
+except ImportError:
+    torch = None
 
 
 def is_torch_dist_rank_zero() -> bool:
-    if not is_torch_available():
+    if torch is None:
         return True
 
-    import torch
-
     dist_module = getattr(torch, "distributed", None)
     if dist_module is None or not dist_module.is_available():
         return True

From 086a770174b8eb1b550dd9f2c7b8a1bc996d7a89 Mon Sep 17 00:00:00 2001
From: sayakpaul <spsayakpaul@gmail.com>
Date: Wed, 10 Dec 2025 08:45:42 +0530
Subject: [PATCH 7/7] up

---
 src/diffusers/utils/logging.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/diffusers/utils/logging.py b/src/diffusers/utils/logging.py
index 48a5d900af56..80e108e4a6ff 100644
--- a/src/diffusers/utils/logging.py
+++ b/src/diffusers/utils/logging.py
@@ -54,7 +54,8 @@
 
 class _RankZeroFilter(logging.Filter):
     def filter(self, record):
-        return is_torch_dist_rank_zero()
+        # Always allow rank-zero logs, but keep debug-level messages from all ranks for troubleshooting.
+        return is_torch_dist_rank_zero() or record.levelno <= logging.DEBUG
 
 
 def _ensure_rank_zero_filter(logger: logging.Logger) -> None: