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

black-forest-labs_FLUX.1-schnell_1.txt

@@ -11,7 +11,7 @@ image = pipe(prompt).images[0]
 
 ERROR: 
 Traceback (most recent call last):
-  File "/tmp/black-forest-labs_FLUX.1-schnell_1pkLed1.py", line 27, in <module>
+  File "/tmp/black-forest-labs_FLUX.1-schnell_1qNfa5e.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)
@@ -40,4 +40,4 @@ Traceback (most recent call last):
     ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
   File "/tmp/.cache/uv/environments-v2/e32bfabdbd53d898/lib/python3.13/site-packages/diffusers/models/model_loading_utils.py", line 754, 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 21.06 GiB is free. Including non-PyTorch memory, this process has 994.00 MiB memory in use. Of the allocated memory 790.44 MiB is allocated by PyTorch, and 17.56 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 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)