Error with Train your own stable diffusion model

<h3>Easy LoRA training tutorial – train your own Stable Diffusion model</h3>
https://andrewongai.gumroad.com/l/lora_training

I’m receiving error after training the model using the images provided by Andrew. Appreciate any suggestion to resolve the error.

Error :
<div>:28-323804 INFO     accelerate launch –num_cpu_threads_per_process=2 “./train_network.py”</div>
<div>                         –pretrained_model_name_or_path=”runwayml/stable-diffusion-v1-5″</div>
<div>                         –train_data_dir=”/content/drive/MyDrive/AI_PICS/training/AndyLauGanesh”</div>
<div>                         –resolution=”512,650″ –output_dir=”/content/drive/MyDrive/AI_PICS/Lora”</div>
<div>                         –network_alpha=”64″ –save_model_as=safetensors</div>
<div>                         –network_module=networks.lora –text_encoder_lr=5e-05 –unet_lr=0.0001</div>
<div>                         –network_dim=64 –output_name=”lastganeshhelp”</div>
<div>                         –lr_scheduler_num_cycles=”1″ –no_half_vae –learning_rate=”0.0001″</div>
<div>                         –lr_scheduler=”constant” –train_batch_size=”3″ –max_train_steps=”534″</div>
<div>                         –save_every_n_epochs=”1″ –mixed_precision=”bf16″ –save_precision=”bf16″</div>
<div>                         –seed=”1234″ –caption_extension=”.txt” –cache_latents</div>
<div>                         –optimizer_type=”AdamW” –max_data_loader_n_workers=”1″ –clip_skip=2</div>
<div>                         –bucket_reso_steps=64 –mem_eff_attn –xformers –bucket_no_upscale</div>
<div>                         –noise_offset=0.05</div>
<div>/usr/local/lib/python3.10/dist-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: ‘/usr/local/lib/python3.10/dist-packages/torchvision/image.so: undefined symbol: _ZN3c104cuda9SetDeviceEi’If you don’t plan on using image functionality from torchvision.io, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have libjpeg or libpng installed before building torchvision from source?</div>
<div>  warn(</div>
<div>The following values were not passed to accelerate launch and had defaults used instead:</div>
<div>--num_processes was set to a value of 0</div>
<div>--num_machines was set to a value of 1</div>
<div>--mixed_precision was set to a value of 'no'</div>
<div>--dynamo_backend was set to a value of 'no'</div>
<div>To avoid this warning pass in values for each of the problematic parameters or run accelerate config.</div>
<div>2024-01-02 03:39:44.970337: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered</div>
<div>2024-01-02 03:39:44.970504: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered</div>
<div>2024-01-02 03:39:45.161302: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered</div>
<div>2024-01-02 03:39:48.650217: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT</div>
<div>/usr/local/lib/python3.10/dist-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: ‘/usr/local/lib/python3.10/dist-packages/torchvision/image.so: undefined symbol: _ZN3c104cuda9SetDeviceEi’If you don’t plan on using image functionality from torchvision.io, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have libjpeg or libpng installed before building torchvision from source?</div>
<div>  warn(</div>
<div>prepare tokenizer</div>
<div>vocab.json: 100% 961k/961k [00:00<00:00, 14.7MB/s]</div>
<div>merges.txt: 100% 525k/525k [00:00<00:00, 14.5MB/s]</div>
<div>special_tokens_map.json: 100% 389/389 [00:00<00:00, 1.14MB/s]</div>
<div>tokenizer_config.json: 100% 905/905 [00:00<00:00, 1.64MB/s]</div>
<div>Using DreamBooth method.</div>
<div>prepare images.</div>
<div>found directory /content/drive/MyDrive/AI_PICS/training/AndyLauGanesh/100_AndyLauganesh contains 16 image files</div>
<div>1600 train images with repeating.</div>
<div>0 reg images.</div>
<div>no regularization images / 正則化画像が見つかりませんでした</div>
<div>[Dataset 0]</div>
<div>  batch_size: 3</div>
<div>  resolution: (512, 650)</div>
<div>  enable_bucket: False</div>
<div></div>
<div>  [Subset 0 of Dataset 0]</div>
<div>    image_dir: “/content/drive/MyDrive/AI_PICS/training/AndyLauGanesh/100_AndyLauganesh”</div>
<div>    image_count: 16</div>
<div>    num_repeats: 100</div>
<div>    shuffle_caption: False</div>
<div>    keep_tokens: 0</div>
<div>    caption_dropout_rate: 0.0</div>
<div>    caption_dropout_every_n_epoches: 0</div>
<div>    caption_tag_dropout_rate: 0.0</div>
<div>    caption_prefix: None</div>
<div>    caption_suffix: None</div>
<div>    color_aug: False</div>
<div>    flip_aug: False</div>
<div>    face_crop_aug_range: None</div>
<div>    random_crop: False</div>
<div>    token_warmup_min: 1,</div>
<div>    token_warmup_step: 0,</div>
<div>    is_reg: False</div>
<div>    class_tokens: AndyLauganesh</div>
<div>    caption_extension: .txt</div>
<div></div>
<div></div>
<div>[Dataset 0]</div>
<div>loading image sizes.</div>
<div>100% 16/16 [00:00<00:00, 449.34it/s]</div>
<div>prepare dataset</div>
<div>preparing accelerator</div>
<div>loading model for process 0/1</div>
<div>load Diffusers pretrained models: runwayml/stable-diffusion-v1-5</div>
<div>model_index.json: 100% 541/541 [00:00<00:00, 1.85MB/s]</div>
<div>Fetching 9 files:   0% 0/9 [00:00<?, ?it/s]</div>
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<div>text_encoder/config.json:   0% 0.00/617 [00:00<?, ?B/s]</div>
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<div>text_encoder/config.json: 100% 617/617 [00:00<00:00, 466kB/s]</div>
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<div>unet/config.json: 100% 743/743 [00:00<00:00, 406kB/s]</div>
<div>scheduler/scheduler_config.json: 100% 308/308 [00:00<00:00, 828kB/s]</div>
<div></div>
<div>(…)ature_extractor/preprocessor_config.json: 100% 342/342 [00:00<00:00, 918kB/s]</div>
<div>Fetching 9 files:  11% 1/9 [00:01<00:08,  1.02s/it]</div>
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<div>Fetching 9 files: 100% 9/9 [00:57<00:00,  6.34s/it]</div>
<div>Loading pipeline components…: 100% 5/5 [00:01<00:00,  2.73it/s]</div>
<div>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 results in 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 .</div>
<div>UNet2DConditionModel: 64, 8, 768, False, False</div>
<div>U-Net converted to original U-Net</div>
<div>Enable memory efficient attention for U-Net</div>
<div>Traceback (most recent call last):</div>
<div>  File “/content/kohya_ss/./train_network.py”, line 990, in <module></div>
<div>    trainer.train(args)</div>
<div>  File “/content/kohya_ss/./train_network.py”, line 222, in train</div>
<div>    vae.set_use_memory_efficient_attention_xformers(args.xformers)</div>
<div>  File “/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py”, line 263, in set_use_memory_efficient_attention_xformers</div>
<div>    fn_recursive_set_mem_eff(module)</div>
<div>  File “/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py”, line 259, in fn_recursive_set_mem_eff</div>
<div>    fn_recursive_set_mem_eff(child)</div>
<div>  File “/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py”, line 259, in fn_recursive_set_mem_eff</div>
<div>    fn_recursive_set_mem_eff(child)</div>
<div>  File “/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py”, line 259, in fn_recursive_set_mem_eff</div>
<div>    fn_recursive_set_mem_eff(child)</div>
<div>  File “/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py”, line 256, in fn_recursive_set_mem_eff</div>
<div>    module.set_use_memory_efficient_attention_xformers(valid, attention_op)</div>
<div>  File “/usr/local/lib/python3.10/dist-packages/diffusers/models/attention_processor.py”, line 255, in set_use_memory_efficient_attention_xformers</div>
<div>    raise ValueError(</div>
<div>ValueError: torch.cuda.is_available() should be True but is False. xformers’ memory efficient attention is only available for GPU</div>
<div>Traceback (most recent call last):</div>
<div>  File “/usr/local/bin/accelerate”, line 8, in <module></div>
<div>    sys.exit(main())</div>
<div>  File “/usr/local/lib/python3.10/dist-packages/accelerate/commands/accelerate_cli.py”, line 47, in main</div>
<div>    args.func(args)</div>
<div>  File “/usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py”, line 1017, in launch_command</div>
<div>    simple_launcher(args)</div>
<div>  File “/usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py”, line 637, in simple_launcher</div>
<div>    raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)</div>
<div>subprocess.CalledProcessError: Command ‘[‘/usr/bin/python3’, ‘./train_network.py’, ‘–pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5’, ‘–train_data_dir=/content/drive/MyDrive/AI_PICS/training/AndyLauGanesh’, ‘–resolution=512,650’, ‘–output_dir=/content/drive/MyDrive/AI_PICS/Lora’, ‘–network_alpha=64’, ‘–save_model_as=safetensors’, ‘–network_module=networks.lora’, ‘–text_encoder_lr=5e-05’, ‘–unet_lr=0.0001’, ‘–network_dim=64’, ‘–output_name=lastganeshhelp’, ‘–lr_scheduler_num_cycles=1’, ‘–no_half_vae’, ‘–learning_rate=0.0001’, ‘–lr_scheduler=constant’, ‘–train_batch_size=3’, ‘–max_train_steps=534’, ‘–save_every_n_epochs=1’, ‘–mixed_precision=bf16’, ‘–save_precision=bf16’, ‘–seed=1234’, ‘–caption_extension=.txt’, ‘–cache_latents’, ‘–optimizer_type=AdamW’, ‘–max_data_loader_n_workers=1’, ‘–clip_skip=2’, ‘–bucket_reso_steps=64’, ‘–mem_eff_attn’, ‘–xformers’, ‘–bucket_no_upscale’, ‘–noise_offset=0.05′]’ returned non-zero exit status 1.</div>