Upload black-forest-labs_FLUX.2-dev_1.txt with huggingface_hub

black-forest-labs_FLUX.2-dev_1.txt

@@ -14,7 +14,7 @@ image = pipe(image=input_image, prompt=prompt).images[0]
 
 ERROR: 
 Traceback (most recent call last):
-  File "/tmp/black-forest-labs_FLUX.2-dev_1OQuO5p.py", line 28, in <module>
+  File "/tmp/black-forest-labs_FLUX.2-dev_1DTEmOR.py", line 28, in <module>
     pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.2-dev", dtype=torch.bfloat16, device_map="cuda")
   File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
     return fn(*args, **kwargs)
@@ -26,30 +26,21 @@ Traceback (most recent call last):
     )
   File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 876, in load_sub_model
     loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
-  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
-    return func(*args, **kwargs)
-  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained
+  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+    return fn(*args, **kwargs)
+  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1296, in from_pretrained
     ) = cls._load_pretrained_model(
         ~~~~~~~~~~~~~~~~~~~~~~~~~~^
         model,
         ^^^^^^
-    ...<12 lines>...
-        weights_only=weights_only,
-        ^^^^^^^^^^^^^^^^^^^^^^^^^^
+    ...<13 lines>...
+        is_parallel_loading_enabled=is_parallel_loading_enabled,
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
     )
     ^
-  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/transformers/modeling_utils.py", line 5468, in _load_pretrained_model
-    _error_msgs, disk_offload_index = load_shard_file(args)
-                                      ~~~~~~~~~~~~~~~^^^^^^
-  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/transformers/modeling_utils.py", line 843, in load_shard_file
-    disk_offload_index = _load_state_dict_into_meta_model(
-        model,
-    ...<8 lines>...
-        device_mesh=device_mesh,
-    )
-  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
-    return func(*args, **kwargs)
-  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/transformers/modeling_utils.py", line 770, in _load_state_dict_into_meta_model
-    _load_parameter_into_model(model, param_name, param.to(param_device))
-                                                  ~~~~~~~~^^^^^^^^^^^^^^
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 640.00 MiB. GPU 0 has a total capacity of 22.03 GiB of which 585.12 MiB is free. Including non-PyTorch memory, this process has 21.46 GiB memory in use. Of the allocated memory 20.89 GiB is allocated by PyTorch, and 389.18 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)
+  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1635, in _load_pretrained_model
+    _caching_allocator_warmup(model, expanded_device_map, dtype, hf_quantizer)
+    ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/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 60.02 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)