Error: model not loaded #3215

djwashout · 2024-06-12T03:19:06Z

djwashout
Jun 12, 2024

google colab: I have been having so many problem with sd.next lately. Now i have Error: model not loaded when trying to run SDXL backend=diffusers

data form console:

ImportError: cannot import name 'splat' from 'distutils._functools' (/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/_functools.py)
03:13:45-960130 INFO Select: model="SDXL/ponyRealism_v21VAE"
03:13:45-977433 INFO Torch override dtype: no-half set
03:13:45-978876 INFO Torch override VAE dtype: no-half set
03:13:45-981868 INFO Setting Torch parameters: device=cuda dtype=torch.float32 vae=torch.float32
unet=torch.float32 context=no_grad fp16=None bf16=None
optimization=Scaled-Dot-Product
03:13:45-984492 INFO Loading VAE: model=/content/gdrive/MyDrive/data/VAE/sdxl_vae.safetensors
source=settings
03:13:45-986491 INFO Autodetect: vae="Stable Diffusion XL" class=StableDiffusionXLPipeline
file="/content/gdrive/MyDrive/data/Checkpoint/SDXL/ponyRealism_v21VAE.safet
ensors" size=6776MB
03:14:02-530370 INFO Autodetect: model="Stable Diffusion XL" class=StableDiffusionXLPipeline
file="/content/gdrive/MyDrive/data/Checkpoint/SDXL/ponyRealism_v21VAE.safet
ensors" size=6776MB
Loading pipeline components... 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7/7 [ 0:00:26 < 0:00:00 , 0 C/s ]

03:14:31-330683 ERROR Diffusers failed loading:
model=/content/gdrive/MyDrive/data/Checkpoint/SDXL/ponyRealism_v21VAE.safet
ensors pipeline=Autodetect/NoneType config={'low_cpu_mem_usage': True,
'torch_dtype': torch.float32, 'load_connected_pipeline': True,
'safety_checker': None, 'requires_safety_checker': False, 'vae':
AutoencoderKL(
(encoder): Encoder(
(conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(down_blocks): ModuleList(
(0): DownEncoderBlock2D(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))
)
)
)
(1): DownEncoderBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(128, 256, kernel_size=(1, 1),
stride=(1, 1))
)
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2))
)
)
)
(2): DownEncoderBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(256, 512, kernel_size=(1, 1),
stride=(1, 1))
)
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2))
)
)
)
(3): DownEncoderBlock2D(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
)
(mid_block): UNetMidBlock2D(
(attentions): ModuleList(
(0): Attention(
(group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
(to_q): Linear(in_features=512, out_features=512, bias=True)
(to_k): Linear(in_features=512, out_features=512, bias=True)
(to_v): Linear(in_features=512, out_features=512, bias=True)
(to_out): ModuleList(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
)
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
(conv_norm_out): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv_act): SiLU()
(conv_out): Conv2d(512, 8, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
)
(decoder): Decoder(
(conv_in): Conv2d(4, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(up_blocks): ModuleList(
(0-1): 2 x UpDecoderBlock2D(
(resnets): ModuleList(
(0-2): 3 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
)
)
)
(2): UpDecoderBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1),
stride=(1, 1))
)
(1-2): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
)
)
)
(3): UpDecoderBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1),
stride=(1, 1))
)
(1-2): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
)
(mid_block): UNetMidBlock2D(
(attentions): ModuleList(
(0): Attention(
(group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
(to_q): Linear(in_features=512, out_features=512, bias=True)
(to_k): Linear(in_features=512, out_features=512, bias=True)
(to_v): Linear(in_features=512, out_features=512, bias=True)
(to_out): ModuleList(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
)
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
(conv_norm_out): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv_act): SiLU()
(conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
)
(quant_conv): Conv2d(8, 8, kernel_size=(1, 1), stride=(1, 1))
(post_quant_conv): Conv2d(4, 4, kernel_size=(1, 1), stride=(1, 1))
), 'local_files_only': False, 'extract_ema': False, 'config':
'configs/sdxl', 'use_safetensors': True, 'cache_dir':
'/root/.cache/huggingface/hub'} cannot import name 'splat' from
'distutils._functools'
(/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/_functools.p
y)
03:14:31-401066 ERROR loading
model=/content/gdrive/MyDrive/data/Checkpoint/SDXL/ponyRealism_v21VAE.safet
ensors pipeline=Autodetect/NoneType: ImportError
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /content/gdrive/MyDrive/A1111/modules/sd_models.py:1070 in load_diffuser │
│ │
│ 1069 │ │ │ │ │ │ sd_hijack_accelerate.restore_accelerate() │
│ ❱ 1070 │ │ │ │ │ sd_model = pipeline.from_single_file(checkpoint_info.path, **diffuser │
│ 1071 │ │ │ │ │ sd_model = patch_diffuser_config(sd_model, checkpoint_info.path) │
│ │
│ /usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:114 in inner_fn │
│ │
│ 113 │ │ │
│ ❱ 114 │ │ return fn(*args, **kwargs) │
│ 115 │
│ │
│ /usr/local/lib/python3.10/dist-packages/diffusers/loaders/single_file.py:544 in from_single_file │
│ │
│ 543 │ │ │
│ ❱ 544 │ │ pipe = pipeline_class(**init_kwargs) │
│ 545 │
│ │
│ /usr/local/lib/python3.10/dist-packages/diffusers/pipelines/stable_diffusion_xl/pipeline_stable │
│ │
│ 252 │ │ │
│ ❱ 253 │ │ self.register_modules( │
│ 254 │ │ │ vae=vae, │
│ │
│ /usr/local/lib/python3.10/dist-packages/diffusers/pipelines/pipeline_utils.py:159 in register_mo │
│ │
│ 158 │ │ │ else: │
│ ❱ 159 │ │ │ │ library, class_name = _fetch_class_library_tuple(module) │
│ 160 │ │ │ │ register_dict = {name: (library, class_name)} │
│ │
│ ... 8 frames hidden ... │
│ │
│ /usr/local/lib/python3.10/dist-packages/peft/tuners/boft/layer.py:30 in │
│ │
│ 29 from torch.autograd import Function │
│ ❱ 30 from torch.utils.cpp_extension import load │
│ 31 │
│ │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:27 in │
│ │
│ 26 │
│ ❱ 27 from setuptools.command.build_ext import build_ext │
│ 28 │
│ │
│ /usr/local/lib/python3.10/dist-packages/setuptools/command/build_ext.py:9 in │
│ │
│ 8 │
│ ❱ 9 from distutils.command.build_ext import build_ext as _du_build_ext │
│ 10 from distutils.ccompiler import new_compiler │
│ │
│ /usr/local/lib/python3.10/dist-packages/setuptools/_distutils/command/build_ext.py:14 in <module │
│ │
│ 13 │
│ ❱ 14 from .._modified import newer_group │
│ 15 from ..core import Command │
│ │
│ /usr/local/lib/python3.10/dist-packages/setuptools/_distutils/_modified.py:6 in │
│ │
│ 5 │
│ ❱ 6 from ._functools import splat │
│ 7 from .errors import DistutilsFileError │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ImportError: cannot import name 'splat' from 'distutils._functools' (/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/_functools.py)

vladmandic · 2024-06-12T03:48:24Z

vladmandic
Jun 12, 2024
Maintainer

ImportError: cannot import name 'splat' from 'distutils._functools' (/usr/local/lib/python3.10/dist-ackages/setuptools/_distutils/_functools.py)

sounds like google collab installs wrong version of setuptools for python 3.10
try dev branch, version is forced there.

0 replies

djwashout · 2024-06-15T03:40:06Z

djwashout
Jun 15, 2024
Author

cloned dev branch. it worked for a while then problem with diffusers

02:30:49-518340 ERROR Diffusers failed loading:
model=/content/gdrive/MyDrive/data/Checkpoint/SDXL/ponyRealism_v21VAE.safet
ensors pipeline=Autodetect/NoneType config={'low_cpu_mem_usage': True,
'torch_dtype': torch.float32, 'load_connected_pipeline': True,
'safety_checker': None, 'requires_safety_checker': False, 'vae':
AutoencoderKL(
(encoder): Encoder(
(conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(down_blocks): ModuleList(
(0): DownEncoderBlock2D(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))
)
)
)
(1): DownEncoderBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(128, 256, kernel_size=(1, 1),
stride=(1, 1))
)
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2))
)
)
)
(2): DownEncoderBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(256, 512, kernel_size=(1, 1),
stride=(1, 1))
)
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
(downsamplers): ModuleList(
(0): Downsample2D(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2))
)
)
)
(3): DownEncoderBlock2D(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
)
(mid_block): UNetMidBlock2D(
(attentions): ModuleList(
(0): Attention(
(group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
(to_q): Linear(in_features=512, out_features=512, bias=True)
(to_k): Linear(in_features=512, out_features=512, bias=True)
(to_v): Linear(in_features=512, out_features=512, bias=True)
(to_out): ModuleList(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
)
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
(conv_norm_out): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv_act): SiLU()
(conv_out): Conv2d(512, 8, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
)
(decoder): Decoder(
(conv_in): Conv2d(4, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(up_blocks): ModuleList(
(0-1): 2 x UpDecoderBlock2D(
(resnets): ModuleList(
(0-2): 3 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
)
)
)
(2): UpDecoderBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1),
stride=(1, 1))
)
(1-2): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
(upsamplers): ModuleList(
(0): Upsample2D(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
)
)
)
(3): UpDecoderBlock2D(
(resnets): ModuleList(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
(conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
(conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1),
stride=(1, 1))
)
(1-2): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
)
(mid_block): UNetMidBlock2D(
(attentions): ModuleList(
(0): Attention(
(group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
(to_q): Linear(in_features=512, out_features=512, bias=True)
(to_k): Linear(in_features=512, out_features=512, bias=True)
(to_v): Linear(in_features=512, out_features=512, bias=True)
(to_out): ModuleList(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
)
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
(nonlinearity): SiLU()
)
)
)
(conv_norm_out): GroupNorm(32, 128, eps=1e-06, affine=True)
(conv_act): SiLU()
(conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
)
(quant_conv): Conv2d(8, 8, kernel_size=(1, 1), stride=(1, 1))
(post_quant_conv): Conv2d(4, 4, kernel_size=(1, 1), stride=(1, 1))
), 'local_files_only': False, 'extract_ema': False, 'config':
'configs/sdxl', 'use_safetensors': True, 'cache_dir':
'/root/.cache/huggingface/hub'} cannot import name 'splat' from
'distutils._functools'
(/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/_functools.p
y)
02:30:49-589331 ERROR loading
model=/content/gdrive/MyDrive/data/Checkpoint/SDXL/ponyRealism_v21VAE.safet
ensors pipeline=Autodetect/NoneType: ImportError

0 replies

vladmandic · 2024-06-15T12:18:38Z

vladmandic
Jun 15, 2024
Maintainer

when you have an issue, please create an issue and document everything, not just dump logs into discussion thread.
i don't even know which python version is being used.

anyhow, try export SETUPTOOLS_USE_DISTUTILS=local before starting sdnext.

1 reply

djwashout Jun 23, 2024
Author

sorry about that. I'm new to the coding scene. just wanted to make sure i post the right data. I really appreciate what your doing here! it must be hard getting around to everyone.

ok so i inserted export SETUPTOOLS_USE_DISTUTILS=local inside the colab cell above command line args, encountered a syntax error, but google showed me a correction. 'os.environ["SETUPTOOLS_USE_DISTUTILS"] = "local"' Seems to be working.

here is the mod code cell:

#@markdown #Run sd.next
import os

#Launch:
%cd '/content/gdrive/MyDrive/A1111/'

model_path = "/content/gdrive/MyDrive/data/" #@param {type:"string"}
#@markdown Insert the full path of your trained model or to a folder containing multiple models

os.environ["SETUPTOOLS_USE_DISTUTILS"] = "local"

!COMMANDLINE_ARGS="--no-hashing --share --models-dir {model_path}" REQS_FILE="requirements.txt" python launch.py

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Error: model not loaded #3215

djwashout Jun 12, 2024

data form console:

Replies: 3 comments · 1 reply

vladmandic Jun 12, 2024 Maintainer

djwashout Jun 15, 2024 Author

vladmandic Jun 15, 2024 Maintainer

djwashout Jun 23, 2024 Author

djwashout
Jun 12, 2024

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vladmandic
Jun 12, 2024
Maintainer

djwashout
Jun 15, 2024
Author

vladmandic
Jun 15, 2024
Maintainer

djwashout Jun 23, 2024
Author