Upload nvidia_ChronoEdit-14B-Diffusers_0.txt with huggingface_hub

nvidia_ChronoEdit-14B-Diffusers_0.txt

@@ -14,7 +14,7 @@ image = pipe(image=input_image, prompt=prompt).images[0]
 
 ERROR: 
 Traceback (most recent call last):
-  File "/tmp/nvidia_ChronoEdit-14B-Diffusers_0eCJFnV.py", line 28, in <module>
+  File "/tmp/nvidia_ChronoEdit-14B-Diffusers_0xIzDIp.py", line 28, in <module>
     pipe = DiffusionPipeline.from_pretrained("nvidia/ChronoEdit-14B-Diffusers", dtype=torch.bfloat16, device_map="cuda")
   File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
     return fn(*args, **kwargs)
@@ -43,4 +43,4 @@ Traceback (most recent call last):
     ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
   File "/tmp/.cache/uv/environments-v2/cc8e494e4eee1f68/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 30.54 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)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 30.54 GiB. GPU 0 has a total capacity of 22.03 GiB of which 19.49 GiB is free. Including non-PyTorch memory, this process has 2.54 GiB memory in use. Of the allocated memory 2.35 GiB is allocated by PyTorch, and 4.82 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)