Upload Kwai-Kolors_CoTyle_0.txt with huggingface_hub

Kwai-Kolors_CoTyle_0.txt

@@ -0,0 +1,52 @@
+```CODE: 
+import torch
+from diffusers import DiffusionPipeline
+
+# switch to "mps" for apple devices
+pipe = DiffusionPipeline.from_pretrained("Kwai-Kolors/CoTyle", dtype=torch.bfloat16, device_map="cuda")
+
+prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
+image = pipe(prompt).images[0]
+```
+
+ERROR: 
+Traceback (most recent call last):
+  File "/tmp/Kwai-Kolors_CoTyle_0efUEbW.py", line 27, in <module>
+    pipe = DiffusionPipeline.from_pretrained("Kwai-Kolors/CoTyle", dtype=torch.bfloat16, device_map="cuda")
+  File "/tmp/.cache/uv/environments-v2/5a55ee875dab4467/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+    return fn(*args, **kwargs)
+  File "/tmp/.cache/uv/environments-v2/5a55ee875dab4467/lib/python3.13/site-packages/diffusers/pipelines/pipeline_utils.py", line 1025, in from_pretrained
+    loaded_sub_model = load_sub_model(
+        library_name=library_name,
+    ...<21 lines>...
+        quantization_config=quantization_config,
+    )
+  File "/tmp/.cache/uv/environments-v2/5a55ee875dab4467/lib/python3.13/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 860, in load_sub_model
+    loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
+  File "/tmp/.cache/uv/environments-v2/5a55ee875dab4467/lib/python3.13/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
+    return func(*args, **kwargs)
+  File "/tmp/.cache/uv/environments-v2/5a55ee875dab4467/lib/python3.13/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained
+    ) = cls._load_pretrained_model(
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~^
+        model,
+        ^^^^^^
+    ...<12 lines>...
+        weights_only=weights_only,
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/tmp/.cache/uv/environments-v2/5a55ee875dab4467/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-v2/5a55ee875dab4467/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-v2/5a55ee875dab4467/lib/python3.13/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
+    return func(*args, **kwargs)
+  File "/tmp/.cache/uv/environments-v2/5a55ee875dab4467/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 260.00 MiB. GPU 0 has a total capacity of 22.03 GiB of which 183.12 MiB is free. Including non-PyTorch memory, this process has 21.85 GiB memory in use. Of the allocated memory 21.56 GiB is allocated by PyTorch, and 112.29 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)