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Everything was good in black-forest-labs_FLUX.1-Kontext-dev_0.txt
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```CODE:
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import torch
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from diffusers import DiffusionPipeline
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from diffusers.utils import load_image
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# switch to "mps" for apple devices
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda")
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prompt = "Turn this cat into a dog"
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input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
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image = pipe(image=input_image, prompt=prompt).images[0]
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```
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ERROR:
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Traceback (most recent call last):
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File "/tmp/black-forest-labs_FLUX.1-Kontext-dev_1rIMizY.py", line 26, in <module>
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda")
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File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
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return fn(*args, **kwargs)
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File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/diffusers/pipelines/pipeline_utils.py", line 1025, in from_pretrained
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loaded_sub_model = load_sub_model(
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library_name=library_name,
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...<21 lines>...
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quantization_config=quantization_config,
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)
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File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 860, in load_sub_model
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loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
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File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/transformers/tokenization_utils_base.py", line 2097, in from_pretrained
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return cls._from_pretrained(
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~~~~~~~~~~~~~~~~~~~~^
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resolved_vocab_files,
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^^^^^^^^^^^^^^^^^^^^^
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...<9 lines>...
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**kwargs,
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^^^^^^^^^
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)
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^
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File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/transformers/tokenization_utils_base.py", line 2343, in _from_pretrained
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tokenizer = cls(*init_inputs, **init_kwargs)
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File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/transformers/models/t5/tokenization_t5_fast.py", line 119, in __init__
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super().__init__(
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~~~~~~~~~~~~~~~~^
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vocab_file=vocab_file,
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^^^^^^^^^^^^^^^^^^^^^^
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...<7 lines>...
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**kwargs,
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^^^^^^^^^
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)
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^
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File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/transformers/tokenization_utils_fast.py", line 108, in __init__
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raise ValueError(
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...<2 lines>...
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)
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ValueError: Cannot instantiate this tokenizer from a slow version. If it's based on sentencepiece, make sure you have sentencepiece installed.
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Everything was good in black-forest-labs_FLUX.1-dev_0.txt
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```CODE:
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import torch
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from diffusers import DiffusionPipeline
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# switch to "mps" for apple devices
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
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prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
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image = pipe(prompt).images[0]
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```
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ERROR:
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Traceback (most recent call last):
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File "/tmp/black-forest-labs_FLUX.1-dev_1jIfTh2.py", line 25, in <module>
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
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File "/tmp/.cache/uv/environments-v2/b90b3a1935bc74f7/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
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return fn(*args, **kwargs)
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File "/tmp/.cache/uv/environments-v2/b90b3a1935bc74f7/lib/python3.13/site-packages/diffusers/pipelines/pipeline_utils.py", line 1025, in from_pretrained
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loaded_sub_model = load_sub_model(
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library_name=library_name,
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...<21 lines>...
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quantization_config=quantization_config,
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)
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File "/tmp/.cache/uv/environments-v2/b90b3a1935bc74f7/lib/python3.13/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 860, in load_sub_model
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loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
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File "/tmp/.cache/uv/environments-v2/b90b3a1935bc74f7/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
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return fn(*args, **kwargs)
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File "/tmp/.cache/uv/environments-v2/b90b3a1935bc74f7/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1288, in from_pretrained
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) = cls._load_pretrained_model(
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~~~~~~~~~~~~~~~~~~~~~~~~~~^
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model,
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^^^^^^
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...<13 lines>...
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is_parallel_loading_enabled=is_parallel_loading_enabled,
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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)
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^
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File "/tmp/.cache/uv/environments-v2/b90b3a1935bc74f7/lib/python3.13/site-packages/diffusers/models/modeling_utils.py", line 1537, in _load_pretrained_model
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_caching_allocator_warmup(model, expanded_device_map, dtype, hf_quantizer)
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~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "/tmp/.cache/uv/environments-v2/b90b3a1935bc74f7/lib/python3.13/site-packages/diffusers/models/model_loading_utils.py", line 754, in _caching_allocator_warmup
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_ = torch.empty(warmup_elems, dtype=dtype, device=device, requires_grad=False)
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torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 22.17 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)
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