Upload nvidia_ChronoEdit-14B-Diffusers_0.txt with huggingface_hub

nvidia_ChronoEdit-14B-Diffusers_0.txt

@@ -14,7 +14,7 @@ image = pipe(image=input_image, prompt=prompt).images[0]
 
 ERROR: 
 Traceback (most recent call last):
-  File "/tmp/nvidia_ChronoEdit-14B-Diffusers_0YjewG1.py", line 28, in <module>
+  File "/tmp/nvidia_ChronoEdit-14B-Diffusers_0GqSGoi.py", line 28, in <module>
     pipe = DiffusionPipeline.from_pretrained("nvidia/ChronoEdit-14B-Diffusers", dtype=torch.bfloat16, device_map="cuda")
   File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
     return fn(*args, **kwargs)
@@ -26,30 +26,21 @@ Traceback (most recent call last):
     )
   File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 860, in load_sub_model
     loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
-  File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
-    return func(*args, **kwargs)
-  File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained
+  File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+    return fn(*args, **kwargs)
+  File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1288, in from_pretrained
     ) = cls._load_pretrained_model(
         ~~~~~~~~~~~~~~~~~~~~~~~~~~^
         model,
         ^^^^^^
-    ...<12 lines>...
-        weights_only=weights_only,
-        ^^^^^^^^^^^^^^^^^^^^^^^^^^
+    ...<13 lines>...
+        is_parallel_loading_enabled=is_parallel_loading_enabled,
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
     )
     ^
-  File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/transformers/modeling_utils.py", line 5468, in _load_pretrained_model
-    _error_msgs, disk_offload_index = load_shard_file(args)
-                                      ~~~~~~~~~~~~~~~^^^^^^
-  File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/transformers/modeling_utils.py", line 843, in load_shard_file
-    disk_offload_index = _load_state_dict_into_meta_model(
-        model,
-    ...<8 lines>...
-        device_mesh=device_mesh,
-    )
-  File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
-    return func(*args, **kwargs)
-  File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/transformers/modeling_utils.py", line 770, in _load_state_dict_into_meta_model
-    _load_parameter_into_model(model, param_name, param.to(param_device))
-                                                  ~~~~~~~~^^^^^^^^^^^^^^
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 26.00 MiB. GPU 0 has a total capacity of 22.03 GiB of which 19.12 MiB is free. Including non-PyTorch memory, this process has 22.01 GiB memory in use. Of the allocated memory 21.79 GiB is allocated by PyTorch, and 32.36 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+  File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1537, in _load_pretrained_model
+    _caching_allocator_warmup(model, expanded_device_map, dtype, hf_quantizer)
+    ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/diffusers/models/model_loading_utils.py", line 754, in _caching_allocator_warmup
+    _ = torch.empty(warmup_elems, dtype=dtype, device=device, requires_grad=False)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 30.54 GiB. GPU 0 has a total capacity of 22.03 GiB of which 21.84 GiB is free. Including non-PyTorch memory, this process has 186.00 MiB memory in use. Of the allocated memory 0 bytes is allocated by PyTorch, and 0 bytes is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)