Colab notebook not running and not showing errors just warning

running the notebook on colab as it is. as shown below it shows warning about xformers but then like a keyboard interruption.

color correction>>>>>>>>>>> Use adain color correction

Loading model from ./vqgan_cfw_00011.ckpt Global Step: 18000 WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for: PyTorch 1.13.1 with CUDA None (you have 2.0.1+cu117) Python 3.10.11 (you have 3.10.10) Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers) Memory-efficient attention, SwiGLU, sparse and more won't be available. Set XFORMERS_MORE_DETAILS=1 for more details /usr/local/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be removed in 0.17. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( making attention of type 'vanilla' with 512 in_channels Working with z of shape (1, 4, 64, 64) = 16384 dimensions. making attention of type 'vanilla' with 512 in_channels /usr/local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. warnings.warn( /usr/local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or None for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing weights=VGG16_Weights.IMAGENET1K_V1. You can also use weights=VGG16_Weights.DEFAULT to get the most up-to-date weights. warnings.warn(msg) Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth 100% 528M/528M [00:02<00:00, 209MB/s] Downloading vgg_lpips model from https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1 to taming/modules/autoencoder/lpips/vgg.pth 8.19kB [00:00, 256kB/s]
loaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth missing>>>>>>>>>>>>>>>>>>> [] trainable_list>>>>>>>>>>>>>>>>>>> ['decoder.fusion_layer_2.encode_enc_1.norm1.weight', 'decoder.fusion_layer_2.encode_enc_1.norm1.bias', 'decoder.fusion_layer_2.encode_enc_1.conv1.weight', 'decoder.fusion_layer_2.encode_enc_1.conv1.bias', 'decoder.fusion_layer_2.encode_enc_1.norm2.weight', 'decoder.fusion_layer_2.encode_enc_1.norm2.bias', 'decoder.fusion_layer_2.encode_enc_1.conv2.weight', 'decoder.fusion_layer_2.encode_enc_1.conv2.bias', 'decoder.fusion_layer_2.encode_enc_1.conv_out.weight', 'decoder.fusion_layer_2.encode_enc_1.conv_out.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb1.conv1.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb1.conv1.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb1.conv2.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb1.conv2.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb1.conv3.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb1.conv3.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb1.conv4.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb1.conv4.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb1.conv5.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb1.conv5.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb2.conv1.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb2.conv1.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb2.conv2.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb2.conv2.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb2.conv3.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb2.conv3.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb2.conv4.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb2.conv4.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb2.conv5.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb2.conv5.bias', 'decoder.fusion_layer_2.encode_enc_2.0.rdb3.conv1.weight', 'decoder.fusion_layer_2.encode_enc_2.0.rdb3.conv1.bias', 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'decoder.fusion_layer_2.encode_enc_2.1.rdb3.conv4.weight', 'decoder.fusion_layer_2.encode_enc_2.1.rdb3.conv4.bias', 'decoder.fusion_layer_2.encode_enc_2.1.rdb3.conv5.weight', 'decoder.fusion_layer_2.encode_enc_2.1.rdb3.conv5.bias', 'decoder.fusion_layer_2.encode_enc_3.norm1.weight', 'decoder.fusion_layer_2.encode_enc_3.norm1.bias', 'decoder.fusion_layer_2.encode_enc_3.conv1.weight', 'decoder.fusion_layer_2.encode_enc_3.conv1.bias', 'decoder.fusion_layer_2.encode_enc_3.norm2.weight', 'decoder.fusion_layer_2.encode_enc_3.norm2.bias', 'decoder.fusion_layer_2.encode_enc_3.conv2.weight', 'decoder.fusion_layer_2.encode_enc_3.conv2.bias', 'decoder.fusion_layer_1.encode_enc_1.norm1.weight', 'decoder.fusion_layer_1.encode_enc_1.norm1.bias', 'decoder.fusion_layer_1.encode_enc_1.conv1.weight', 'decoder.fusion_layer_1.encode_enc_1.conv1.bias', 'decoder.fusion_layer_1.encode_enc_1.norm2.weight', 'decoder.fusion_layer_1.encode_enc_1.norm2.bias', 'decoder.fusion_layer_1.encode_enc_1.conv2.weight', 'decoder.fusion_layer_1.encode_enc_1.conv2.bias', 'decoder.fusion_layer_1.encode_enc_1.conv_out.weight', 'decoder.fusion_layer_1.encode_enc_1.conv_out.bias', 'decoder.fusion_layer_1.encode_enc_2.0.rdb1.conv1.weight', 'decoder.fusion_layer_1.encode_enc_2.0.rdb1.conv1.bias', 'decoder.fusion_layer_1.encode_enc_2.0.rdb1.conv2.weight', 'decoder.fusion_layer_1.encode_enc_2.0.rdb1.conv2.bias', 'decoder.fusion_layer_1.encode_enc_2.0.rdb1.conv3.weight', 'decoder.fusion_layer_1.encode_enc_2.0.rdb1.conv3.bias', 'decoder.fusion_layer_1.encode_enc_2.0.rdb1.conv4.weight', 'decoder.fusion_layer_1.encode_enc_2.0.rdb1.conv4.bias', 'decoder.fusion_layer_1.encode_enc_2.0.rdb1.conv5.weight', 'decoder.fusion_layer_1.encode_enc_2.0.rdb1.conv5.bias', 'decoder.fusion_layer_1.encode_enc_2.0.rdb2.conv1.weight', 'decoder.fusion_layer_1.encode_enc_2.0.rdb2.conv1.bias', 'decoder.fusion_layer_1.encode_enc_2.0.rdb2.conv2.weight', 'decoder.fusion_layer_1.encode_enc_2.0.rdb2.conv2.bias', 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'decoder.fusion_layer_1.encode_enc_2.1.rdb1.conv1.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb1.conv2.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb1.conv2.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb1.conv3.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb1.conv3.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb1.conv4.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb1.conv4.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb1.conv5.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb1.conv5.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb2.conv1.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb2.conv1.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb2.conv2.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb2.conv2.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb2.conv3.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb2.conv3.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb2.conv4.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb2.conv4.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb2.conv5.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb2.conv5.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb3.conv1.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb3.conv1.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb3.conv2.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb3.conv2.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb3.conv3.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb3.conv3.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb3.conv4.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb3.conv4.bias', 'decoder.fusion_layer_1.encode_enc_2.1.rdb3.conv5.weight', 'decoder.fusion_layer_1.encode_enc_2.1.rdb3.conv5.bias', 'decoder.fusion_layer_1.encode_enc_3.norm1.weight', 'decoder.fusion_layer_1.encode_enc_3.norm1.bias', 'decoder.fusion_layer_1.encode_enc_3.conv1.weight', 'decoder.fusion_layer_1.encode_enc_3.conv1.bias', 'decoder.fusion_layer_1.encode_enc_3.norm2.weight', 'decoder.fusion_layer_1.encode_enc_3.norm2.bias', 'decoder.fusion_layer_1.encode_enc_3.conv2.weight', 'decoder.fusion_layer_1.encode_enc_3.conv2.bias', 'loss.discriminator.main.0.weight', 'loss.discriminator.main.0.bias', 'loss.discriminator.main.2.weight', 'loss.discriminator.main.3.weight', 'loss.discriminator.main.3.bias', 'loss.discriminator.main.5.weight', 'loss.discriminator.main.6.weight', 'loss.discriminator.main.6.bias', 'loss.discriminator.main.8.weight', 'loss.discriminator.main.9.weight', 'loss.discriminator.main.9.bias', 'loss.discriminator.main.11.weight', 'loss.discriminator.main.11.bias'] Untrainable_list>>>>>>>>>>>>>>>>>>> ['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', 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'loss.perceptual_loss.net.slice2.7.bias', 'loss.perceptual_loss.net.slice3.10.weight', 'loss.perceptual_loss.net.slice3.10.bias', 'loss.perceptual_loss.net.slice3.12.weight', 'loss.perceptual_loss.net.slice3.12.bias', 'loss.perceptual_loss.net.slice3.14.weight', 'loss.perceptual_loss.net.slice3.14.bias', 'loss.perceptual_loss.net.slice4.17.weight', 'loss.perceptual_loss.net.slice4.17.bias', 'loss.perceptual_loss.net.slice4.19.weight', 'loss.perceptual_loss.net.slice4.19.bias', 'loss.perceptual_loss.net.slice4.21.weight', 'loss.perceptual_loss.net.slice4.21.bias', 'loss.perceptual_loss.net.slice5.24.weight', 'loss.perceptual_loss.net.slice5.24.bias', 'loss.perceptual_loss.net.slice5.26.weight', 'loss.perceptual_loss.net.slice5.26.bias', 'loss.perceptual_loss.net.slice5.28.weight', 'loss.perceptual_loss.net.slice5.28.bias', 'loss.perceptual_loss.lin0.model.1.weight', 'loss.perceptual_loss.lin1.model.1.weight', 'loss.perceptual_loss.lin2.model.1.weight', 'loss.perceptual_loss.lin3.model.1.weight', 'loss.perceptual_loss.lin4.model.1.weight', 'quant_conv.weight', 'quant_conv.bias', 'post_quant_conv.weight', 'post_quant_conv.bias'] Global seed set to 42 Loading model from ./stablesr_000117.ckpt Global Step: 16500 LatentDiffusionSRTextWT: Running in eps-prediction mode Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. ^C

IceClear / StableSR

Colab notebook not running and not showing errors just warning #50