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magic-quill / MagicQuill

Official Implementations for Paper - MagicQuill: An Intelligent Interactive Image Editing System
https://magicquill.art
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KeyError: 'brushnet/random_mask_brushnet_ckpt/diffusion_pytorch_model.safetensors' #9

Open SoftologyPro opened 3 hours ago

SoftologyPro commented 3 hours ago

Running under Windows on a 24GB 4090. gradio_run.py starts and then fails. Full stats...

Total VRAM 24564 MB, total RAM 130842 MB
pytorch version: 2.4.0+cu121
D:\Tests\MagicQuill\magicquill\venv\lib\site-packages\xformers\ops\fmha\flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
  @torch.library.impl_abstract("xformers_flash::flash_fwd")
D:\Tests\MagicQuill\magicquill\venv\lib\site-packages\xformers\ops\fmha\flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
  @torch.library.impl_abstract("xformers_flash::flash_bwd")
xformers version: 0.0.27.post2
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 4090 : native
Using xformers cross attention
['D:\\Tests\\MagicQuill\\magicquill', 'D:\\Python\\python310.zip', 'D:\\Python\\DLLs', 'D:\\Python\\lib', 'D:\\Python', 'D:\\Tests\\MagicQuill\\magicquill\\venv', 'D:\\Tests\\MagicQuill\\magicquill\\venv\\lib\\site-packages', '__editable__.llava-1.2.2.post1.finder.__path_hook__', 'D:\\Tests\\MagicQuill\\magicquill\\MagicQuill']
D:\Tests\MagicQuill\magicquill\venv\lib\site-packages\huggingface_hub\file_download.py:797: 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 [00:04<00:00,  1.63s/it]
Some weights of the model checkpoint at D:\Tests\MagicQuill\magicquill\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).
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: D:\Tests\MagicQuill\magicquill\MagicQuill\../models\checkpoints\SD1.5\realisticVisionV60B1_v51VAE.safetensors
model_type EPS
Using xformers attention in VAE
Using xformers attention in VAE
self.brushnet_loader.inpaint_files:  {'brushnet\\random_mask_brushnet_ckpt\\diffusion_pytorch_model.safetensors': 'D:\\Tests\\MagicQuill\\magicquill\\MagicQuill\\../models\\inpaint', 'brushnet\\segmentation_mask_brushnet_ckpt\\diffusion_pytorch_model.safetensors': 'D:\\Tests\\MagicQuill\\magicquill\\MagicQuill\\../models\\inpaint'}
Traceback (most recent call last):
  File "D:\Tests\MagicQuill\magicquill\gradio_run.py", line 20, in <module>
    scribbleColorEditModel = ScribbleColorEditModel()
  File "D:\Tests\MagicQuill\magicquill\MagicQuill\scribble_color_edit.py", line 30, in __init__
    self.load_models('SD1.5', 'float16')
  File "D:\Tests\MagicQuill\magicquill\MagicQuill\scribble_color_edit.py", line 47, in load_models
    self.brushnet = self.brushnet_loader.brushnet_loading(brushnet_name, dtype)[0]
  File "D:\Tests\MagicQuill\magicquill\MagicQuill\brushnet_nodes.py", line 57, in brushnet_loading
    brushnet_file = os.path.join(self.inpaint_files[brushnet], brushnet)
KeyError: 'brushnet/random_mask_brushnet_ckpt/diffusion_pytorch_model.safetensors'

Any idea how to fix this?

Models.zip downloaded from https://hkustconnect-my.sharepoint.com/:u:/g/personal/zliucz_connect_ust_hk/EWlGF0WfawJIrJ1Hn85_-3gB0MtwImAnYeWXuleVQcukMg?e=Gcjugg&download=1

zliucz commented 3 hours ago

Hi, there.

From my perspective, the error occurs due to path separator differences between Windows and Linux, causing the path string mismatch.

You may try to modify the line 36 of the MagicQuill/scribble_color_edit.py to

brushnet_name = os.path.normpath("brushnet/random_mask_brushnet_ckpt/diffusion_pytorch_model.safetensors")

Let me know if it works :).