Upload fal_Qwen-Image-Edit-2511-Multiple-Angles-LoRA_0.txt with huggingface_hub

fal_Qwen-Image-Edit-2511-Multiple-Angles-LoRA_0.txt

@@ -15,7 +15,7 @@ image = pipe(image=input_image, prompt=prompt).images[0]
 
 ERROR: 
 Traceback (most recent call last):
-  File "/tmp/fal_Qwen-Image-Edit-2511-Multiple-Angles-LoRA_0tzdwhw.py", line 28, in <module>
+  File "/tmp/fal_Qwen-Image-Edit-2511-Multiple-Angles-LoRA_0WdcFVA.py", line 28, in <module>
     pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2511", dtype=torch.bfloat16, device_map="cuda")
   File "/tmp/.cache/uv/environments-v2/b071e5cd277f3d48/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 89, in _inner_fn
     return fn(*args, **kwargs)
@@ -44,4 +44,4 @@ Traceback (most recent call last):
     ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
   File "/tmp/.cache/uv/environments-v2/b071e5cd277f3d48/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 38.05 GiB. GPU 0 has a total capacity of 22.03 GiB of which 21.34 GiB is free. Including non-PyTorch memory, this process has 700.00 MiB memory in use. Of the allocated memory 494.18 MiB is allocated by PyTorch, and 19.82 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_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 38.05 GiB. GPU 0 has a total capacity of 22.03 GiB of which 6.40 GiB is free. Including non-PyTorch memory, this process has 15.63 GiB memory in use. Of the allocated memory 15.45 GiB is allocated by PyTorch, and 3.93 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)