Upload Shakker-Labs_AWPortrait-Z_0.txt with huggingface_hub

Shakker-Labs_AWPortrait-Z_0.txt

@@ -12,7 +12,7 @@ image = pipe(prompt).images[0]
 
 ERROR: 
 Traceback (most recent call last):
-  File "/tmp/Shakker-Labs_AWPortrait-Z_0hlc5EE.py", line 27, in <module>
+  File "/tmp/Shakker-Labs_AWPortrait-Z_0bjUn4T.py", line 27, in <module>
     pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda")
   File "/tmp/.cache/uv/environments-v2/7c6f81e4bbdb69bc/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
     return fn(*args, **kwargs)
@@ -54,4 +54,4 @@ Traceback (most recent call last):
     ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
   File "/tmp/.cache/uv/environments-v2/7c6f81e4bbdb69bc/lib/python3.13/site-packages/accelerate/utils/modeling.py", line 343, in set_module_tensor_to_device
     new_value = value.to(device, non_blocking=non_blocking)
-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_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+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_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)