Upload nvidia_ChronoEdit-14B-Diffusers-Paint-Brush-Lora_0.txt with huggingface_hub

nvidia_ChronoEdit-14B-Diffusers-Paint-Brush-Lora_0.txt

@@ -0,0 +1,47 @@
+```CODE: 
+import torch
+from diffusers import DiffusionPipeline
+from diffusers.utils import load_image
+
+# switch to "mps" for apple devices
+pipe = DiffusionPipeline.from_pretrained("nvidia/ChronoEdit-14B-Diffusers", dtype=torch.bfloat16, device_map="cuda")
+pipe.load_lora_weights("nvidia/ChronoEdit-14B-Diffusers-Paint-Brush-Lora")
+
+prompt = "Turn this cat into a dog"
+input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
+
+image = pipe(image=input_image, prompt=prompt).images[0]
+```
+
+ERROR: 
+Traceback (most recent call last):
+  File "/tmp/nvidia_ChronoEdit-14B-Diffusers-Paint-Brush-Lora_09Uo3RJ.py", line 28, in <module>
+    pipe = DiffusionPipeline.from_pretrained("nvidia/ChronoEdit-14B-Diffusers", dtype=torch.bfloat16, device_map="cuda")
+  File "/tmp/.cache/uv/environments-v2/6ca4846836ba659f/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+    return fn(*args, **kwargs)
+  File "/tmp/.cache/uv/environments-v2/6ca4846836ba659f/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-v2/6ca4846836ba659f/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-v2/6ca4846836ba659f/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+    return fn(*args, **kwargs)
+  File "/tmp/.cache/uv/environments-v2/6ca4846836ba659f/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1288, in from_pretrained
+    ) = cls._load_pretrained_model(
+        ~~~~~~~~~~~~~~~~~~~~~~~~~~^
+        model,
+        ^^^^^^
+    ...<13 lines>...
+        is_parallel_loading_enabled=is_parallel_loading_enabled,
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/tmp/.cache/uv/environments-v2/6ca4846836ba659f/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1537, in _load_pretrained_model
+    _caching_allocator_warmup(model, expanded_device_map, dtype, hf_quantizer)
+    ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/tmp/.cache/uv/environments-v2/6ca4846836ba659f/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 21.34 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_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)