Upload Tongyi-MAI_Z-Image-Turbo_0.txt with huggingface_hub
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Tongyi-MAI_Z-Image-Turbo_0.txt
CHANGED
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@@ -11,7 +11,7 @@ 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-
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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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@@ -40,4 +40,4 @@ 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/diffusers/models/model_loading_utils.py", line 751, in _caching_allocator_warmup
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_ = torch.empty(warmup_elems, dtype=dtype, device=device, requires_grad=False)
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torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 11.46 GiB. GPU 0 has a total capacity of 22.03 GiB of which 6.
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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_0QnLco9.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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~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/models/model_loading_utils.py", line 751, in _caching_allocator_warmup
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_ = torch.empty(warmup_elems, dtype=dtype, device=device, requires_grad=False)
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torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 11.46 GiB. GPU 0 has a total capacity of 22.03 GiB of which 6.53 GiB is free. Including non-PyTorch memory, this process has 15.50 GiB memory in use. Of the allocated memory 15.30 GiB is allocated by PyTorch, and 17.75 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)
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