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Upload Tongyi-MAI_Z-Image-Turbo_0.txt with huggingface_hub

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  1. Tongyi-MAI_Z-Image-Turbo_0.txt +4 -4
Tongyi-MAI_Z-Image-Turbo_0.txt CHANGED
@@ -11,9 +11,9 @@ image = pipe(prompt).images[0]
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  ERROR:
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  Traceback (most recent call last):
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- File "/tmp/Tongyi-MAI_Z-Image-Turbo_0icXyvq.py", line 27, in <module>
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  pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda")
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- File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
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  return fn(*args, **kwargs)
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  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/pipelines/pipeline_utils.py", line 1021, in from_pretrained
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  loaded_sub_model = load_sub_model(
@@ -23,7 +23,7 @@ Traceback (most recent call last):
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  )
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  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 876, in load_sub_model
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  loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
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- File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
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  return fn(*args, **kwargs)
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  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1296, in from_pretrained
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  ) = cls._load_pretrained_model(
@@ -53,4 +53,4 @@ Traceback (most recent call last):
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  ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/accelerate/utils/modeling.py", line 343, in set_module_tensor_to_device
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  new_value = value.to(device, non_blocking=non_blocking)
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- torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 58.00 MiB. GPU 0 has a total capacity of 22.03 GiB of which 17.12 MiB is free. Including non-PyTorch memory, this process has 22.01 GiB memory in use. Of the allocated memory 21.72 GiB is allocated by PyTorch, and 111.56 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
 
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  ERROR:
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  Traceback (most recent call last):
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+ File "/tmp/Tongyi-MAI_Z-Image-Turbo_0YeR1su.py", line 27, in <module>
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  pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda")
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+ File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 89, in _inner_fn
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  return fn(*args, **kwargs)
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  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/pipelines/pipeline_utils.py", line 1021, in from_pretrained
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  loaded_sub_model = load_sub_model(
 
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  )
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  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 876, in load_sub_model
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  loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
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+ File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 89, in _inner_fn
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  return fn(*args, **kwargs)
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  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1296, in from_pretrained
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  ) = cls._load_pretrained_model(
 
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  ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/accelerate/utils/modeling.py", line 343, in set_module_tensor_to_device
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  new_value = value.to(device, non_blocking=non_blocking)
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+ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 150.00 MiB. GPU 0 has a total capacity of 22.03 GiB of which 113.12 MiB is free. Including non-PyTorch memory, this process has 21.92 GiB memory in use. Of the allocated memory 21.61 GiB is allocated by PyTorch, and 123.28 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)