Upload stabilityai_stable-diffusion-3.5-large_1.txt with huggingface_hub

stabilityai_stable-diffusion-3.5-large_1.txt

@@ -11,7 +11,7 @@ image = pipe(prompt).images[0]
 
 ERROR: 
 Traceback (most recent call last):
-  File "/tmp/stabilityai_stable-diffusion-3.5-large_1YP2Gj0.py", line 27, in <module>
+  File "/tmp/stabilityai_stable-diffusion-3.5-large_1LkVgr6.py", line 27, in <module>
     pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", dtype=torch.bfloat16, device_map="cuda")
   File "/tmp/.cache/uv/environments-v2/b4c16b19a8353fbb/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/b4c16b19a8353fbb/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 15.17 GiB. GPU 0 has a total capacity of 22.03 GiB of which 3.77 GiB is free. Including non-PyTorch memory, this process has 18.25 GiB memory in use. Of the allocated memory 18.06 GiB is allocated by PyTorch, and 17.48 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 15.17 GiB. GPU 0 has a total capacity of 22.03 GiB of which 705.12 MiB is free. Including non-PyTorch memory, this process has 21.34 GiB memory in use. Of the allocated memory 21.10 GiB is allocated by PyTorch, and 53.37 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)