Upload black-forest-labs_FLUX.1-schnell_1.txt with huggingface_hub

black-forest-labs_FLUX.1-schnell_1.txt

@@ -11,21 +11,21 @@ image = pipe(prompt).images[0]
 
 ERROR: 
 Traceback (most recent call last):
-  File "/tmp/black-forest-labs_FLUX.1-schnell_1qNfa5e.py", line 27, in <module>
+  File "/tmp/black-forest-labs_FLUX.1-schnell_1kkmqql.py", line 27, in <module>
     pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", dtype=torch.bfloat16, device_map="cuda")
   File "/tmp/.cache/uv/environments-v2/e32bfabdbd53d898/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/e32bfabdbd53d898/lib/python3.13/site-packages/diffusers/pipelines/pipeline_utils.py", line 1025, in from_pretrained
+  File "/tmp/.cache/uv/environments-v2/e32bfabdbd53d898/lib/python3.13/site-packages/diffusers/pipelines/pipeline_utils.py", line 1021, in from_pretrained
     loaded_sub_model = load_sub_model(
         library_name=library_name,
     ...<21 lines>...
         quantization_config=quantization_config,
     )
-  File "/tmp/.cache/uv/environments-v2/e32bfabdbd53d898/lib/python3.13/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 860, in load_sub_model
+  File "/tmp/.cache/uv/environments-v2/e32bfabdbd53d898/lib/python3.13/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 876, in load_sub_model
     loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
   File "/tmp/.cache/uv/environments-v2/e32bfabdbd53d898/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/e32bfabdbd53d898/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1288, in from_pretrained
+  File "/tmp/.cache/uv/environments-v2/e32bfabdbd53d898/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1296, in from_pretrained
     ) = cls._load_pretrained_model(
         ~~~~~~~~~~~~~~~~~~~~~~~~~~^
         model,
@@ -35,9 +35,9 @@ Traceback (most recent call last):
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
     )
     ^
-  File "/tmp/.cache/uv/environments-v2/e32bfabdbd53d898/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1537, in _load_pretrained_model
+  File "/tmp/.cache/uv/environments-v2/e32bfabdbd53d898/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/e32bfabdbd53d898/lib/python3.13/site-packages/diffusers/models/model_loading_utils.py", line 754, in _caching_allocator_warmup
+  File "/tmp/.cache/uv/environments-v2/e32bfabdbd53d898/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 22.15 GiB. GPU 0 has a total capacity of 22.03 GiB of which 21.51 GiB is free. Including non-PyTorch memory, this process has 524.00 MiB memory in use. Of the allocated memory 320.87 MiB is allocated by PyTorch, and 17.13 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 22.15 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)