Upload black-forest-labs_FLUX.1-dev_1.txt with huggingface_hub

black-forest-labs_FLUX.1-dev_1.txt

@@ -11,7 +11,7 @@ image = pipe(prompt).images[0]
 
 ERROR: 
 Traceback (most recent call last):
-  File "/tmp/black-forest-labs_FLUX.1-dev_1FgqgWI.py", line 27, in <module>
+  File "/tmp/black-forest-labs_FLUX.1-dev_1fuwDki.py", line 27, in <module>
     pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
   File "/tmp/.cache/uv/environments-v2/b90b3a1935bc74f7/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/b90b3a1935bc74f7/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 22.17 GiB. GPU 0 has a total capacity of 22.03 GiB of which 21.05 GiB is free. Including non-PyTorch memory, this process has 998.00 MiB memory in use. Of the allocated memory 792.19 MiB is allocated by PyTorch, and 19.81 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 22.17 GiB. GPU 0 has a total capacity of 22.03 GiB of which 3.63 GiB is free. Including non-PyTorch memory, this process has 18.39 GiB memory in use. Of the allocated memory 18.05 GiB is allocated by PyTorch, and 160.35 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)