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Everything was good in black-forest-labs_FLUX.1-Kontext-dev_0.txt
```CODE:
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda")
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/black-forest-labs_FLUX.1-Kontext-dev_1rIMizY.py", line 26, in <module>
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda")
File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/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/ca96a1cdc2ecd6c7/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/ca96a1cdc2ecd6c7/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/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/transformers/tokenization_utils_base.py", line 2097, in from_pretrained
return cls._from_pretrained(
~~~~~~~~~~~~~~~~~~~~^
resolved_vocab_files,
^^^^^^^^^^^^^^^^^^^^^
...<9 lines>...
**kwargs,
^^^^^^^^^
)
^
File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/transformers/tokenization_utils_base.py", line 2343, in _from_pretrained
tokenizer = cls(*init_inputs, **init_kwargs)
File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/transformers/models/t5/tokenization_t5_fast.py", line 119, in __init__
super().__init__(
~~~~~~~~~~~~~~~~^
vocab_file=vocab_file,
^^^^^^^^^^^^^^^^^^^^^^
...<7 lines>...
**kwargs,
^^^^^^^^^
)
^
File "/tmp/.cache/uv/environments-v2/ca96a1cdc2ecd6c7/lib/python3.13/site-packages/transformers/tokenization_utils_fast.py", line 108, in __init__
raise ValueError(
...<2 lines>...
)
ValueError: Cannot instantiate this tokenizer from a slow version. If it's based on sentencepiece, make sure you have sentencepiece installed.
Everything was good in black-forest-labs_FLUX.1-dev_0.txt
```CODE:
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", 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/black-forest-labs_FLUX.1-dev_1jIfTh2.py", line 25, in <module>
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
File "/tmp/.cache/uv/environments-v2/b90b3a1935bc74f7/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/b90b3a1935bc74f7/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/b90b3a1935bc74f7/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/b90b3a1935bc74f7/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/b90b3a1935bc74f7/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/b90b3a1935bc74f7/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/b90b3a1935bc74f7/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 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)