torch.cuda.OutOfMemoryError: CUDA out of memory

[gowestin]

im facing low gpu memory

Total VRAM 8192 MB, total RAM 32689 MB
pytorch version: 2.1.2+cu118
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 2070 SUPER : native
Using pytorch cross attention
['C:\Users\SANJAY\Desktop\MagicQuill', 'C:\ProgramData\miniconda3\envs\MagicQuill\python310.zip', 'C:\ProgramData\miniconda3\envs\MagicQuill\DLLs', 'C:\ProgramData\miniconda3\envs\MagicQuill\lib', 'C:\ProgramData\miniconda3\envs\MagicQuill', 'C:\ProgramData\miniconda3\envs\MagicQuill\lib\site-packages', 'editable.llava-1.2.2.post1.finder.path_hook', 'C:\Users\SANJAY\Desktop\MagicQuill\MagicQuill']
C:\ProgramData\miniconda3\envs\MagicQuill\lib\site-packages\huggingface_hub\file_download.py:795: FutureWarning: resume_download is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use force_download=True.
warnings.warn(
We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set max_memory in to a higher value to use more memory (at your own risk).
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 3/3 [00:05<00:00, 1.67s/it]
Some weights of the model checkpoint at C:\Users\SANJAY\Desktop\MagicQuill\models\llava-v1.5-7b-finetune-clean were not used when initializing LlavaLlamaForCausalLM: ['model.vision_tower.vision_tower.vision_model.embeddings.class_embedding', 'model.vision_tower.vision_tower.vision_model.embeddings.patch_embedding.weight', 'model.vision_tower.vision_tower.vision_model.embeddings.position_embedding.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc2.bias', 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• This IS expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
• This IS NOT expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  C:\ProgramData\miniconda3\envs\MagicQuill\lib\site-packages\torch_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  return self.fget.get(instance, owner)()
  We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set max_memory in to a higher value to use more memory (at your own risk).
  Loading checkpoint from: C:\Users\SANJAY\Desktop\MagicQuill\models\checkpoints\SD1.5\realisticVisionV60B1_v51VAE.safetensors
  model_type EPS
  Using pytorch attention in VAE
  Using pytorch attention in VAE
  self.brushnet_loader.inpaint_files: {'brushnet\random_mask_brushnet_ckpt\diffusion_pytorch_model.safetensors': 'C:\Users\SANJAY\Desktop\MagicQuill\models\inpaint', 'brushnet\segmentation_mask_brushnet_ckpt\diffusion_pytorch_model.safetensors': 'C:\Users\SANJAY\Desktop\MagicQuill\models\inpaint'}
  BrushNet model file: C:\Users\SANJAY\Desktop\MagicQuill\models\inpaint\brushnet\random_mask_brushnet_ckpt\diffusion_pytorch_model.safetensors
  BrushNet model type: SD1.5
  BrushNet model file: C:\Users\SANJAY\Desktop\MagicQuill\models\inpaint\brushnet\random_mask_brushnet_ckpt\diffusion_pytorch_model.safetensors
  Traceback (most recent call last):
  File "C:\Users\SANJAY\Desktop\MagicQuill\gradio_run.py", line 25, in
  scribbleColorEditModel = ScribbleColorEditModel()
  File "C:\Users\SANJAY\Desktop\MagicQuill\MagicQuill\scribble_color_edit.py", line 30, in init
  self.load_models('SD1.5', 'float16')
  File "C:\Users\SANJAY\Desktop\MagicQuill\MagicQuill\scribble_color_edit.py", line 43, in load_models
  self.brushnet = self.brushnet_loader.brushnet_loading(brushnet_name, dtype)[0]
  File "C:\Users\SANJAY\Desktop\MagicQuill\MagicQuill\brushnet_nodes.py", line 104, in brushnet_loading
  brushnet_model = load_checkpoint_and_dispatch(
  File "C:\ProgramData\miniconda3\envs\MagicQuill\lib\site-packages\accelerate\big_modeling.py", line 613, in load_checkpoint_and_dispatch
  load_checkpoint_in_model(
  File "C:\ProgramData\miniconda3\envs\MagicQuill\lib\site-packages\accelerate\utils\modeling.py", line 1758, in load_checkpoint_in_model
  loaded_checkpoint = load_state_dict(checkpoint_file, device_map=device_map)
  File "C:\ProgramData\miniconda3\envs\MagicQuill\lib\site-packages\accelerate\utils\modeling.py", line 1483, in load_state_dict
  return safe_load_file(checkpoint_file, device=target_device)
  File "C:\ProgramData\miniconda3\envs\MagicQuill\lib\site-packages\safetensors\torch.py", line 315, in load_file
  result[k] = f.get_tensor(k)
  torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacty of 8.00 GiB of which 0 bytes is free. Of the allocated memory 7.13 GiB is allocated by PyTorch, and 166.42 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

[zliucz]

Check this issues.

[gowestin]

thanx for replay bro..i followed every step....but same...im having GTX 2070 SUPER GPU...with 8 GB RAM....Processor -AMD Ryzen 9 5900XT 16-Core Processor, and 32 GB RAM....

[zliucz]

Change the 22th line in gradio_run.py from

llavaModel = LLaVAModel()

to

llavaModel = None

Then, you can disable the DrawNGuess by clicking the wand icon on above. You can still manually enter the prompt. Hope this helps.

[gowestin]

yes i did follow both the step....thanx,,,,but when i RUN editor ......i get same low vram message

[gowestin]

Error
CUDA out of memory. Tried to allocate 128.00 MiB. GPU 0 has a total capacty of 8.00 GiB of which 0 bytes is free. Of the allocated memory 7.01 GiB is allocated by PyTorch, and 170.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

[zliucz]

Hi, this is due to insufficient local vram. I'm sorry for that :(. You can force the mode to low vram as in issues or check our free online demo