Can't use VAE

[bjornlarssen]

Hi,

I'm using Google Colab with models on my Google Drive.

I was having problems loading VAE checkpoints, so I decided to start from scratch. I renamed my old sd folder, copied and ran the latest version, waited for everything to reinstall… and the problem is still there.

When trying to load VAE (it doesn't matter what VAE or which model I am using) I get the following error:

Loading VAE weights from: /content/gdrive/MyDrive/sd/stable-diffusion-webui/models/VAE/anything-v3-vae-swapped.ckpt changing setting sd_vae to anything-v3-vae-swapped: RuntimeError Traceback (most recent call last): File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/shared.py", line 515, in set self.data_labels[key].onchange() File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/call_queue.py", line 15, in f res = func(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/webui.py", line 82, in <lambda> shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_vae.py", line 234, in reload_vae_weights load_vae(sd_model, vae_file) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_vae.py", line 178, in load_vae _load_vae_dict(model, vae_dict_1) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_vae.py", line 205, in _load_vae_dict model.first_stage_model.load_state_dict(vae_dict_1) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1667, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for AutoencoderKL: Missing key(s) in state_dict: "encoder.conv_in.weight", "encoder.conv_in.bias", "encoder.down.0.block.0.norm1.weight", "encoder.down.0.block.0.norm1.bias", "encoder.down.0.block.0.conv1.weight", "encoder.down.0.block.0.conv1.bias", "encoder.down.0.block.0.norm2.weight", "encoder.down.0.block.0.norm2.bias", "encoder.down.0.block.0.conv2.weight", "encoder.down.0.block.0.conv2.bias", "encoder.down.0.block.1.norm1.weight", "encoder.down.0.block.1.norm1.bias", "encoder.down.0.block.1.conv1.weight", "encoder.down.0.block.1.conv1.bias", "encoder.down.0.block.1.norm2.weight", "encoder.down.0.block.1.norm2.bias", "encoder.down.0.block.1.conv2.weight", "encoder.down.0.block.1.conv2.bias", "encoder.down.0.downsample.conv.weight", "encoder.down.0.downsample.conv.bias", "encoder.down.1.block.0.norm1.weight", "encoder.down.1.block.0.norm1.bias", "encoder.down.1.block.0.conv1.weight", "encoder.down.1.block.0.conv1.bias", "encoder.down.1.block.0.norm2.weight", "encoder.down.1.block.0.norm2.bias", "encoder.down.1.block.0.conv2.weight", "encoder.down.1.block.0.conv2.bias", "encoder.down.1.block.0.nin_shortcut.weight", "encoder.down.1.block.0.nin_shortcut.bias", "encoder.down.1.block.1.norm1.weight", "encoder.down.1.block.1.norm1.bias", "encoder.down.1.block.1.conv1.weight", "encoder.down.1.block.1.conv1.bias", "encoder.down.1.block.1.norm2.weight", "encoder.down.1.block.1.norm2.bias", "encoder.down.1.block.1.conv2.weight", "encoder.down.1.block.1.conv2.bias", "encoder.down.1.downsample.conv.weight", "encoder.down.1.downsample.conv.bias", "encoder.down.2.block.0.norm1.weight", "encoder.down.2.block.0.norm1.bias", "encoder.down.2.block.0.conv1.weight", "encoder.down.2.block.0.conv1.bias", "encoder.down.2.block.0.norm2.weight", "encoder.down.2.block.0.norm2.bias", "encoder.down.2.block.0.conv2.weight", "encoder.down.2.block.0.conv2.bias", "encoder.down.2.block.0.nin_shortcut.weight", "encoder.down.2.block.0.nin_shortcut.bias", "encoder.down.2.block.1.norm1.weight", "encoder.down.2.block.1.norm1.bias", "encoder.down.2.block.1.conv1.weight", "encoder.down.2.block.1.conv1.bias", "encoder.down.2.block.1.norm2.weight", "encoder.down.2.block.1.norm2.bias", "encoder.down.2.block.1.conv2.weight", "encoder.down.2.block.1.conv2.bias", "encoder.down.2.downsample.conv.weight", "encoder.down.2.downsample.conv.bias", "encoder.down.3.block.0.norm1.weight", "encoder.down.3.block.0.norm1.bias", "encoder.down.3.block.0.conv1.weight", "encoder.down.3.block.0.conv1.bias", "encoder.down.3.block.0.norm2.weight", "encoder.down.3.block.0.norm2.bias", "encoder.down.3.block.0.conv2.weight", "encoder.down.3.block.0.conv2.bias", "encoder.down.3.block.1.norm1.weight", "encoder.down.3.block.1.norm1.bias", "encoder.down.3.block.1.conv1.weight", "encoder.down.3.block.1.conv1.bias", "encoder.down.3.block.1.norm2.weight", "encoder.down.3.block.1.norm2.bias", "encoder.down.3.block.1.conv2.weight", "encoder.down.3.block.1.conv2.bias", "encoder.mid.block_1.norm1.weight", "encoder.mid.block_1.norm1.bias", "encoder.mid.block_1.conv1.weight", "encoder.mid.block_1.conv1.bias", "encoder.mid.block_1.norm2.weight", "encoder.mid.block_1.norm2.bias", "encoder.mid.block_1.conv2.weight", "encoder.mid.block_1.conv2.bias", "encoder.mid.attn_1.norm.weight", "encoder.mid.attn_1.norm.bias", "encoder.mid.attn_1.q.weight", "encoder.mid.attn_1.q.bias", "encoder.mid.attn_1.k.weight", "encoder.mid.attn_1.k.bias", "encoder.mid.attn_1.v.weight", "encoder.mid.attn_1.v.bias", "encoder.mid.attn_1.proj_out.weight", "encoder.mid.attn_1.proj_out.bias", "encoder.mid.block_2.norm1.weight", "encoder.mid.block_2.norm1.bias", "encoder.mid.block_2.conv1.weight", "encoder.mid.block_2.conv1.bias", "encoder.mid.block_2.norm2.weight", "encoder.mid.block_2.norm2.bias", "encoder.mid.block_2.conv2.weight", "encoder.mid.block_2.conv2.bias", "encoder.norm_out.weight", "encoder.norm_out.bias", "encoder.conv_out.weight", "encoder.conv_out.bias", "decoder.conv_in.weight", "decoder.conv_in.bias", "decoder.mid.block_1.norm1.weight", "decoder.mid.block_1.norm1.bias", "decoder.mid.block_1.conv1.weight", "decoder.mid.block_1.conv1.bias", "decoder.mid.block_1.norm2.weight", "decoder.mid.block_1.norm2.bias", "decoder.mid.block_1.conv2.weight", "decoder.mid.block_1.conv2.bias", "decoder.mid.attn_1.norm.weight", "decoder.mid.attn_1.norm.bias", "decoder.mid.attn_1.q.weight", "decoder.mid.attn_1.q.bias", "decoder.mid.attn_1.k.weight", "decoder.mid.attn_1.k.bias", "decoder.mid.attn_1.v.weight", "decoder.mid.attn_1.v.bias", "decoder.mid.attn_1.proj_out.weight", "decoder.mid.attn_1.proj_out.bias", "decoder.mid.block_2.norm1.weight", "decoder.mid.block_2.norm1.bias", "decoder.mid.block_2.conv1.weight", "decoder.mid.block_2.conv1.bias", "decoder.mid.block_2.norm2.weight", "decoder.mid.block_2.norm2.bias", "decoder.mid.block_2.conv2.weight", "decoder.mid.block_2.conv2.bias", "decoder.up.0.block.0.norm1.weight", "decoder.up.0.block.0.norm1.bias", "decoder.up.0.block.0.conv1.weight", "decoder.up.0.block.0.conv1.bias", "decoder.up.0.block.0.norm2.weight", "decoder.up.0.block.0.norm2.bias", "decoder.up.0.block.0.conv2.weight", "decoder.up.0.block.0.conv2.bias", "decoder.up.0.block.0.nin_shortcut.weight", "decoder.up.0.block.0.nin_shortcut.bias", "decoder.up.0.block.1.norm1.weight", "decoder.up.0.block.1.norm1.bias", "decoder.up.0.block.1.conv1.weight", "decoder.up.0.block.1.conv1.bias", "decoder.up.0.block.1.norm2.weight", "decoder.up.0.block.1.norm2.bias", "decoder.up.0.block.1.conv2.weight", "decoder.up.0.block.1.conv2.bias", "decoder.up.0.block.2.norm1.weight", "decoder.up.0.block.2.norm1.bias", "decoder.up.0.block.2.conv1.weight", "decoder.up.0.block.2.conv1.bias", "decoder.up.0.block.2.norm2.weight", "decoder.up.0.block.2.norm2.bias", "decoder.up.0.block.2.conv2.weight", "decoder.up.0.block.2.conv2.bias", "decoder.up.1.block.0.norm1.weight", "decoder.up.1.block.0.norm1.bias", "decoder.up.1.block.0.conv1.weight", "decoder.up.1.block.0.conv1.bias", "decoder.up.1.block.0.norm2.weight", "decoder.up.1.block.0.norm2.bias", "decoder.up.1.block.0.conv2.weight", "decoder.up.1.block.0.conv2.bias", "decoder.up.1.block.0.nin_shortcut.weight", "decoder.up.1.block.0.nin_shortcut.bias", "decoder.up.1.block.1.norm1.weight", "decoder.up.1.block.1.norm1.bias", "decoder.up.1.block.1.conv1.weight", "decoder.up.1.block.1.conv1.bias", "decoder.up.1.block.1.norm2.weight", "decoder.up.1.block.1.norm2.bias", "decoder.up.1.block.1.conv2.weight", "decoder.up.1.block.1.conv2.bias", "decoder.up.1.block.2.norm1.weight", "decoder.up.1.block.2.norm1.bias", "decoder.up.1.block.2.conv1.weight", "decoder.up.1.block.2.conv1.bias", "decoder.up.1.block.2.norm2.weight", "decoder.up.1.block.2.norm2.bias", "decoder.up.1.block.2.conv2.weight", "decoder.up.1.block.2.conv2.bias", "decoder.up.1.upsample.conv.weight", "decoder.up.1.upsample.conv.bias", "decoder.up.2.block.0.norm1.weight", "decoder.up.2.block.0.norm1.bias", "decoder.up.2.block.0.conv1.weight", "decoder.up.2.block.0.conv1.bias", "decoder.up.2.block.0.norm2.weight", "decoder.up.2.block.0.norm2.bias", "decoder.up.2.block.0.conv2.weight", "decoder.up.2.block.0.conv2.bias", "decoder.up.2.block.1.norm1.weight", "decoder.up.2.block.1.norm1.bias", "decoder.up.2.block.1.conv1.weight", "decoder.up.2.block.1.conv1.bias", "decoder.up.2.block.1.norm2.weight", "decoder.up.2.block.1.norm2.bias", "decoder.up.2.block.1.conv2.weight", "decoder.up.2.block.1.conv2.bias", "decoder.up.2.block.2.norm1.weight", "decoder.up.2.block.2.norm1.bias", "decoder.up.2.block.2.conv1.weight", "decoder.up.2.block.2.conv1.bias", "decoder.up.2.block.2.norm2.weight", "decoder.up.2.block.2.norm2.bias", "decoder.up.2.block.2.conv2.weight", "decoder.up.2.block.2.conv2.bias", "decoder.up.2.upsample.conv.weight", "decoder.up.2.upsample.conv.bias", "decoder.up.3.block.0.norm1.weight", "decoder.up.3.block.0.norm1.bias", "decoder.up.3.block.0.conv1.weight", "decoder.up.3.block.0.conv1.bias", "decoder.up.3.block.0.norm2.weight", "decoder.up.3.block.0.norm2.bias", "decoder.up.3.block.0.conv2.weight", "decoder.up.3.block.0.conv2.bias", "decoder.up.3.block.1.norm1.weight", "decoder.up.3.block.1.norm1.bias", "decoder.up.3.block.1.conv1.weight", "decoder.up.3.block.1.conv1.bias", "decoder.up.3.block.1.norm2.weight", "decoder.up.3.block.1.norm2.bias", "decoder.up.3.block.1.conv2.weight", "decoder.up.3.block.1.conv2.bias", "decoder.up.3.block.2.norm1.weight", "decoder.up.3.block.2.norm1.bias", "decoder.up.3.block.2.conv1.weight", "decoder.up.3.block.2.conv1.bias", "decoder.up.3.block.2.norm2.weight", "decoder.up.3.block.2.norm2.bias", "decoder.up.3.block.2.conv2.weight", "decoder.up.3.block.2.conv2.bias", "decoder.up.3.upsample.conv.weight", "decoder.up.3.upsample.conv.bias", "decoder.norm_out.weight", "decoder.norm_out.bias", "decoder.conv_out.weight", "decoder.conv_out.bias", "quant_conv.weight", "quant_conv.bias", "post_quant_conv.weight", "post_quant_conv.bias". Unexpected key(s) in state_dict: "model.diffusion_model.input_blocks.0.0.weight", "model.diffusion_model.input_blocks.0.0.bias", "model.diffusion_model.time_embed.0.weight", "model.diffusion_model.time_embed.0.bias", "model.diffusion_model.time_embed.2.weight", "model.diffusion_model.time_embed.2.bias", "model.diffusion_model.input_blocks.1.1.norm.weight", "model.diffusion_model.input_blocks.1.1.norm.bias", "model.diffusion_model.input_blocks.1.1.proj_in.weight", "model.diffusion_model.input_blocks.1.1.proj_in.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias", "model.diffusion_model.input_blocks.1.1.proj_out.weight", "model.diffusion_model.input_blocks.1.1.proj_out.bias", "model.diffusion_model.input_blocks.2.1.norm.weight", "model.diffusion_model.input_blocks.2.1.norm.bias", "model.diffusion_model.input_blocks.2.1.proj_in.weight", "model.diffusion_model.input_blocks.2.1.proj_in.bias", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_q.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_k.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_v.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.bias", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.bias", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.bias", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_q.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_v.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.bias", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.norm1.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.norm1.bias", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.norm2.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.norm2.bias", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.norm3.weight", "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.norm3.bias", "model.diffusion_model.input_blocks.2.1.proj_out.weight", "model.diffusion_model.input_blocks.2.1.proj_out.bias", "model.diffusion_model.input_blocks.1.0.in_layers.0.weight", "model.diffusion_model.input_blocks.1.0.in_layers.0.bias", "model.diffusion_model.input_blocks.1.0.in_layers.2.weight", "model.diffusion_model.input_blocks.1.0.in_layers.2.bias", "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight", "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias", "model.diffusion_model.input_blocks.1.0.out_layers.0.weight", "model.diffusion_model.input_blocks.1.0.out_layers.0.bias", "model.diffusion_model.input_blocks.1.0.out_layers.3.weight", "model.diffusion_model.input_blocks.1.0.out_layers.3.bias", "model.diffusion_model.input_blocks.2.0.in_layers.0.weight", "model.diffusion_model.input_blocks.2.0.in_layers.0.bias", "model.diffusion_model.input_blocks.2.0.in_layers.2.weight", "model.diffusion_model.input_blocks.2.0.in_layers.2.bias", "model.diffusion_model.input_blocks.2.0.emb_layers.1.weight", "model.diffusion_model.input_blocks.2.0.emb_layers.1.bias", "model.diffusion_model.input_blocks.2.0.out_layers.0.weight", "model.diffusion_model.input_blocks.2.0.out_layers.0.bias", "model.diffusion_model.input_blocks.2.0.out_layers.3.weight", "model.diffusion_model.input_blocks.2.0.out_layers.3.bias", "model.diffusion_model.input_blocks.3.0.op.weight", "model.diffusion_model.input_blocks.3.0.op.bias", "model.diffusion_model.input_blocks.4.1.norm.weight", "model.diffusion_model.input_blocks.4.1.norm.bias", "model.diffusion_model.input_blocks.4.1.proj_in.weight", "model.diffusion_model.input_blocks.4.1.proj_in.bias", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.bias", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.bias", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.bias", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_q.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_v.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.bias", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm1.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm1.bias", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm2.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm2.bias", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm3.weight", "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm3.bias", "model.diffusion_model.input_blocks.4.1.proj_out.weight", "model.diffusion_model.input_blocks.4.1.proj_out.bias", "model.diffusion_model.input_blocks.5.1.norm.weight", "model.diffusion_model.input_blocks.5.1.norm.bias", "model.diffusion_model.input_blocks.5.1.proj_in.weight", "model.diffusion_model.input_blocks.5.1.proj_in.bias", 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"model_ema.diffusion_modelout2bias".
I reloaded the UI, noticed the VAE changed back to auto, tried to reload the custom one, and got "RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0!"

Am I doing something wrong or missing a step?

Greetings and thanks!

[TheLastBen]

try removing the argument --no-half-vae in the last cell

[bjornlarssen]

No dice :(

Loading VAE weights from: /content/gdrive/MyDrive/sd/stable-diffusion-webui/models/VAE/anything-v3-vae-swapped.ckpt changing setting sd_vae to anything-v3-vae-swapped: RuntimeError Traceback (most recent call last): File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/shared.py", line 515, in set self.data_labels[key].onchange() File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/call_queue.py", line 15, in f res = func(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/webui.py", line 82, in <lambda> shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_vae.py", line 234, in reload_vae_weights load_vae(sd_model, vae_file) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_vae.py", line 178, in load_vae _load_vae_dict(model, vae_dict_1) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_vae.py", line 205, in _load_vae_dict model.first_stage_model.load_state_dict(vae_dict_1) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1667, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for AutoencoderKL: Missing key(s) in state_dict: "encoder.conv_in.weight", "encoder.conv_in.bias", "encoder.down.0.block.0.norm1.weight", "encoder.down.0.block.0.norm1.bias", "encoder.down.0.block.0.conv1.weight", "encoder.down.0.block.0.conv1.bias", "encoder.down.0.block.0.norm2.weight", "encoder.down.0.block.0.norm2.bias", "encoder.down.0.block.0.conv2.weight", "encoder.down.0.block.0.conv2.bias", "encoder.down.0.block.1.norm1.weight", "encoder.down.0.block.1.norm1.bias", "encoder.down.0.block.1.conv1.weight", "encoder.down.0.block.1.conv1.bias", "encoder.down.0.block.1.norm2.weight", 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[bjornlarssen]

Is this what you meant? Just checking if I got it right.

try: model if os.path.isfile(model): !python /content/gdrive/MyDrive/sd/stable-diffusion-webui/webui.py $share --api --disable-safe-unpickle --enable-insecure-extension-access --ckpt "$model" $configf $xformers else: !python /content/gdrive/MyDrive/sd/stable-diffusion-webui/webui.py $share --api --disable-safe-unpickle --enable-insecure-extension-access --ckpt-dir "$model" $configf $xformers except: !python /content/gdrive/MyDrive/sd/stable-diffusion-webui/webui.py $share --api --disable-safe-unpickle --enable-insecure-extension-access $configf $xformers

[bjornlarssen]

It didn't work for me either :(

I copied it to my Drive, used "Direct link" and entered "https://civitai.com/models/4384/dreamshaper-252-official-release" after the model on the Drive failed to load. The "Start Stable Diffusion" section breaks here:

`---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
in
63 if Use_Gradio_Server:
64 share='--share'
---> 65 for line in fileinput.input('/usr/local/lib/python3.8/dist-packages/gradio/blocks.py', inplace=True):
66 if line.strip().startswith('self.server_name ='):
67 line = ' self.server_name = server_name\n'

1 frames
/usr/lib/python3.8/fileinput.py in _readline(self)
340 pass
341 # The next few lines may raise OSError
--> 342 os.rename(self._filename, self._backupfilename)
343 self._file = open(self._backupfilename, self._mode)
344 try:

FileNotFoundError: [Errno 2] No such file or directory: '/usr/local/lib/python3.8/dist-packages/gradio/blocks.py' -> '/usr/local/lib/python3.8/dist-packages/gradio/blocks.py.bak'`

[cdefghijkl]

For civitai you have to click download on the model page then let it download and copy that running download link, use that link instead the model page link.

[cdefghijkl]

The link is look like this:
https://civitai-prod.5ac0637cfd0766c97916cefa3764fbdf.r2.cloudflarestorage.com/65706/model/woopwoopPhoto12Fp16No.TwrL.safetensors?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=2fea663d76bd24a496545da373d610fc%2F20230113%2Fauto%2Fs3%2Faws4_request&X-Amz-Date=20230113T034407Z&X-Amz-Expires=10800&X-Amz-Signature=ad8f988986aa4b1263763018bc9efc8e8d5890c36b9b31ac65ab38c91df75490&X-Amz-SignedHeaders=host&response-content-disposition=attachment%3B%20filename%3D%22woopwoopPhoto_12.safetensors%22&x-id=GetObject

Civitai generate unique link for each download so you can't use that same link for next day/range time you run the colab.

[bjornlarssen]

Oh, thanks! Will try again.

[bjornlarssen]

Edit: in the meantime I discovered I can fix the washed-out colours using Auto Levels/saturation slider in Photoshop, which is easier even in batches. I'll be saving this page, though. So thank you for the instructions, I'm bookmarking them and I copied your notebook.