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

Tongyi-MAI_Z-Image-Turbo_0.txt

@@ -11,7 +11,7 @@ image = pipe(prompt).images[0]
 
 ERROR: 
 Traceback (most recent call last):
-  File "/tmp/Tongyi-MAI_Z-Image-Turbo_0QnLco9.py", line 27, in <module>
+  File "/tmp/Tongyi-MAI_Z-Image-Turbo_0e8Vhdm.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/aa292f16bb0b222a/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
     return fn(*args, **kwargs)
@@ -35,9 +35,22 @@ Traceback (most recent call last):
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
     )
     ^
-  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1635, in _load_pretrained_model
-    _caching_allocator_warmup(model, expanded_device_map, dtype, hf_quantizer)
-    ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/models/model_loading_utils.py", line 751, in _caching_allocator_warmup
-    _ = torch.empty(warmup_elems, dtype=dtype, device=device, requires_grad=False)
-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)
+  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1678, in _load_pretrained_model
+    offload_index, state_dict_index, _mismatched_keys, _error_msgs = load_fn(shard_file)
+                                                                     ~~~~~~~^^^^^^^^^^^^
+  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/models/model_loading_utils.py", line 367, in _load_shard_file
+    offload_index, state_dict_index = load_model_dict_into_meta(
+                                      ~~~~~~~~~~~~~~~~~~~~~~~~~^
+        model,
+        ^^^^^^
+    ...<9 lines>...
+        state_dict_folder=state_dict_folder,
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/lib/python3.13/site-packages/diffusers/models/model_loading_utils.py", line 307, in load_model_dict_into_meta
+    set_module_tensor_to_device(model, param_name, param_device, value=param, **set_module_kwargs)
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/tmp/.cache/uv/environments-v2/aa292f16bb0b222a/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)