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Reduce the --save_interval to lower than --max_train_steps to save weights from intermediate steps.
--save_sample_prompt can be same as --instance_prompt to generate intermediate samples (saved along with weights in samples directory).
Logs
WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for: PyTorch 2.1.0+cu121 with CUDA 1201 (you have 2.1.0+cu118) Python 3.10.13 (you have 3.10.12) Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers) Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
2023-11-30 16:00:11.464430: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-11-30 16:00:11.464494: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-11-30 16:00:11.464533: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-11-30 16:00:13.133018: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
text_encoder/model.safetensors not found
/usr/local/lib/python3.10/dist-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.
warnings.warn(
You have disabled the safety checker for<class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered resultsin services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
Traceback (most recent call last):
File "/content/train_dreambooth.py", line 869, in<module>
main(args)
File "/content/train_dreambooth.py", line 487, in main
pipeline.enable_xformers_memory_efficient_attention()
File "/usr/local/lib/python3.10/dist-packages/diffusers/pipelines/pipeline_utils.py", line 1322, in enable_xformers_memory_efficient_attention
self.set_use_memory_efficient_attention_xformers(True, attention_op)
File "/usr/local/lib/python3.10/dist-packages/diffusers/pipelines/pipeline_utils.py", line 1347, in set_use_memory_efficient_attention_xformers
fn_recursive_set_mem_eff(module)
File "/usr/local/lib/python3.10/dist-packages/diffusers/pipelines/pipeline_utils.py", line 1338, in fn_recursive_set_mem_eff
module.set_use_memory_efficient_attention_xformers(valid, attention_op)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 219, in set_use_memory_efficient_attention_xformers
fn_recursive_set_mem_eff(module)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 215, in fn_recursive_set_mem_eff
fn_recursive_set_mem_eff(child)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 215, in fn_recursive_set_mem_eff
fn_recursive_set_mem_eff(child)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 215, in fn_recursive_set_mem_eff
fn_recursive_set_mem_eff(child)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 212, in fn_recursive_set_mem_eff
module.set_use_memory_efficient_attention_xformers(valid, attention_op)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 219, in set_use_memory_efficient_attention_xformers
fn_recursive_set_mem_eff(module)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 215, in fn_recursive_set_mem_eff
fn_recursive_set_mem_eff(child)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 215, in fn_recursive_set_mem_eff
fn_recursive_set_mem_eff(child)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 212, in fn_recursive_set_mem_eff
module.set_use_memory_efficient_attention_xformers(valid, attention_op)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/attention_processor.py", line 151, in set_use_memory_efficient_attention_xformers
raise e
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/attention_processor.py", line 145, in set_use_memory_efficient_attention_xformers
_ = xformers.ops.memory_efficient_attention(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/__init__.py", line 223, in memory_efficient_attention
return _memory_efficient_attention(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/__init__.py", line 321, in _memory_efficient_attention
return _memory_efficient_attention_forward(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/__init__.py", line 337, in _memory_efficient_attention_forward
op = _dispatch_fw(inp, False)
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/dispatch.py", line 120, in _dispatch_fw
return _run_priority_list(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/dispatch.py", line 63, in _run_priority_list
raise NotImplementedError(msg)
NotImplementedError: No operator found for`memory_efficient_attention_forward` with inputs:
query : shape=(1, 2, 1, 40) (torch.float32)
key : shape=(1, 2, 1, 40) (torch.float32)
value : shape=(1, 2, 1, 40) (torch.float32)
attn_bias :<class 'NoneType'>
p : 0.0
`decoderF` is not supported because:
xFormers wasn't build with CUDA support attn_bias type is <class 'NoneType'> operator wasn't built - see `python -m xformers.info`for more info
`flshattF@0.0.0` is not supported because:
xFormers wasn't build with CUDA support requires device with capability > (8, 0) but your GPU has capability (7, 5) (too old) dtype=torch.float32 (supported: {torch.float16, torch.bfloat16}) operator wasn't built - see `python -m xformers.info`for more info
`tritonflashattF` is not supported because:
xFormers wasn't build with CUDA support requires device with capability > (8, 0) but your GPU has capability (7, 5) (too old) dtype=torch.float32 (supported: {torch.float16, torch.bfloat16}) operator wasn't built - see `python -m xformers.info`for more info
triton is not available
requires GPU with sm80 minimum compute capacity, e.g., A100/H100/L4
Only work on pre-MLIR triton for now
`cutlassF` is not supported because:
xFormers wasn't build with CUDA support operator wasn't built - see `python -m xformers.info`for more info
`smallkF` is not supported because:
max(query.shape[-1] != value.shape[-1]) > 32
xFormers wasn't build with CUDA support operator wasn't built - see `python -m xformers.info`for more info
unsupported embed per head: 40
System Info
google colab
The text was updated successfully, but these errors were encountered:
It seems that you need to upgrade the pytorch version you are using in the install requirements. If you change the install requirements code block to this, it works fine for me.
Describe the bug
Hello,
![Screenshot 2023-11-30 220500](https://private-user-images.githubusercontent.com/145629415/286992431-c2421fcc-c046-45ec-a618-9025d71861ff.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MzAwMjgwMDcsIm5iZiI6MTczMDAyNzcwNywicGF0aCI6Ii8xNDU2Mjk0MTUvMjg2OTkyNDMxLWMyNDIxZmNjLWMwNDYtNDVlYy1hNjE4LTkwMjVkNzE4NjFmZi5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQxMDI3JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MTAyN1QxMTE1MDdaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT02NjJkYWYxNzAxZWM4ZjljMjQ0NjliZmFmN2ZlNTIyZjAzZTg5YTUzYTYwMjM3ZGNlM2UwZjVkNDZjNDk1N2I0JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.N6RK_oWnSNd5H0XjWBLYmE7g3wJqdimlTwvpiGa6Yv4)
whenever I hit the cell of training the model after uploading the images I'm getting this error
this is the error I get
Any solutions for this?
Reproduction
!python3 train_dreambooth.py
--pretrained_model_name_or_path=$MODEL_NAME
--pretrained_vae_name_or_path="stabilityai/sd-vae-ft-mse"
--output_dir=$OUTPUT_DIR
--revision="fp16"
--with_prior_preservation --prior_loss_weight=1.0
--seed=1337
--resolution=512
--train_batch_size=1
--train_text_encoder
--mixed_precision="fp16"
--use_8bit_adam
--gradient_accumulation_steps=1
--learning_rate=1e-6
--lr_scheduler="constant"
--lr_warmup_steps=0
--num_class_images=50
--sample_batch_size=4
--max_train_steps=1400
--save_interval=1400
--save_sample_prompt="photo of mumtahina person"
--concepts_list="concepts_list.json"
Reduce the
--save_interval
to lower than--max_train_steps
to save weights from intermediate steps.--save_sample_prompt
can be same as--instance_prompt
to generate intermediate samples (saved along with weights in samples directory).Logs
System Info
google colab
The text was updated successfully, but these errors were encountered: