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_1tKRE1v.py", line 28, in <module>
+  File "/tmp/black-forest-labs_FLUX.2-dev_135Owr3.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 89, in _inner_fn
     return fn(*args, **kwargs)
@@ -26,21 +26,42 @@ 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/huggingface_hub/utils/_validators.py", line 89, 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,
-        ^^^^^^
-    ...<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 4109, in from_pretrained
+    load_info = cls._load_pretrained_model(model, state_dict, checkpoint_files, load_config)
+  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/transformers/modeling_utils.py", line 4231, in _load_pretrained_model
+    convert_and_load_state_dict_in_model(
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
+        model=model,
+        ^^^^^^^^^^^^
+    ...<4 lines>...
+        disk_offload_index=disk_offload_index,
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/transformers/core_model_loading.py", line 1217, in convert_and_load_state_dict_in_model
+    realized_value, conversion_errors = mapping.convert(
+                                        ~~~~~~~~~~~~~~~^
+        first_param_name,
+        ^^^^^^^^^^^^^^^^^
+    ...<4 lines>...
+        conversion_errors=conversion_errors,
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
     )
     ^
-  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_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/transformers/core_model_loading.py", line 696, in convert
+    collected_tensors = self.materialize_tensors()
+  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/transformers/core_model_loading.py", line 671, in materialize_tensors
+    tensors = [future.result() for future in tensors]
+               ~~~~~~~~~~~~~^^
+  File "/usr/lib/python3.13/concurrent/futures/_base.py", line 456, in result
+    return self.__get_result()
+           ~~~~~~~~~~~~~~~~~^^
+  File "/usr/lib/python3.13/concurrent/futures/_base.py", line 401, in __get_result
+    raise self._exception
+  File "/usr/lib/python3.13/concurrent/futures/thread.py", line 59, in run
+    result = self.fn(*self.args, **self.kwargs)
+  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/transformers/core_model_loading.py", line 818, in _job
+    return _materialize_copy(tensor, device, dtype)
+  File "/tmp/.cache/uv/environments-v2/d49ad6c613615895/lib/python3.13/site-packages/transformers/core_model_loading.py", line 807, in _materialize_copy
+    tensor = tensor.to(device=device, dtype=dtype)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 320.00 MiB. GPU 0 has a total capacity of 22.03 GiB of which 287.12 MiB is free. Including non-PyTorch memory, this process has 21.75 GiB memory in use. Of the allocated memory 21.45 GiB is allocated by PyTorch, and 120.15 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)