Can not run code. Ubuntu freezes, then execution of code killed. Please advice?

[zelenooki87]

I am 100% sure that I correctly setup conda env on my system. Downloaded all models and give them paths as described. However, can not run test.py and gradio demo. log, code: `(SUPIR) milan@milan-MS-7C02:~/SUPIR$ python gradio_demo.py Building a Downsample layer with 2 dims. --> settings are: in-chn: 320, out-chn: 320, kernel-size: 3, stride: 2, padding: 1 constructing SpatialTransformer of depth 2 w/ 640 channels and 10 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 2. Setting context_dim to [2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads with a dimension of 64. BasicTransformerBlock is using checkpointing constructing SpatialTransformer of depth 2 w/ 640 channels and 10 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 2. Setting context_dim to [2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Building a Downsample layer with 2 dims. --> settings are: in-chn: 640, out-chn: 640, kernel-size: 3, stride: 2, padding: 1 constructing SpatialTransformer of depth 10 w/ 1280 channels and 20 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing constructing SpatialTransformer of depth 10 w/ 1280 channels and 20 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing constructing SpatialTransformer of depth 10 w/ 1280 channels and 20 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing constructing SpatialTransformer of depth 10 w/ 1280 channels and 20 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing constructing SpatialTransformer of depth 10 w/ 1280 channels and 20 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing constructing SpatialTransformer of depth 10 w/ 1280 channels and 20 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 10. Setting context_dim to [2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads with a dimension of 64. BasicTransformerBlock is using checkpointing constructing SpatialTransformer of depth 2 w/ 640 channels and 10 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 2. Setting context_dim to [2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads with a dimension of 64. BasicTransformerBlock is using checkpointing constructing SpatialTransformer of depth 2 w/ 640 channels and 10 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 2. Setting context_dim to [2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads with a dimension of 64. BasicTransformerBlock is using checkpointing constructing SpatialTransformer of depth 2 w/ 640 channels and 10 heads WARNING: SpatialTransformer: Found context dims [2048] of depth 1, which does not match the specified 'depth' of 2. Setting context_dim to [2048, 2048] now. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads with a dimension of 64. BasicTransformerBlock is using checkpointing Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 640 and using 20 heads with a dimension of 64. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 320 and using 10 heads with a dimension of 64. Some weights of the model checkpoint at /home/milan/SUPIR/modeli/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.22.layer_norm2.bias', 'vision_model.encoder.layers.10.layer_norm1.weight', 'vision_model.encoder.layers.20.self_attn.q_proj.weight', 'vision_model.encoder.layers.7.mlp.fc1.bias', 'vision_model.encoder.layers.0.self_attn.q_proj.weight', 'vision_model.encoder.layers.3.self_attn.q_proj.weight', 'vision_model.encoder.layers.9.self_attn.out_proj.weight', 'vision_model.encoder.layers.14.mlp.fc1.bias', 'vision_model.encoder.layers.8.layer_norm1.weight', 'vision_model.encoder.layers.12.mlp.fc2.bias', 'vision_model.encoder.layers.9.self_attn.v_proj.weight', 'vision_model.encoder.layers.15.self_attn.out_proj.bias', 'vision_model.encoder.layers.23.self_attn.v_proj.bias', 'vision_model.encoder.layers.4.layer_norm2.bias', 'vision_model.encoder.layers.22.self_attn.k_proj.weight', 'vision_model.encoder.layers.1.layer_norm2.weight', 'vision_model.encoder.layers.20.mlp.fc2.weight', 'vision_model.encoder.layers.1.mlp.fc1.weight', 'vision_model.encoder.layers.16.self_attn.q_proj.weight', 'vision_model.pre_layrnorm.bias', 'vision_model.encoder.layers.22.mlp.fc1.weight', 'vision_model.encoder.layers.20.layer_norm1.weight', 'vision_model.encoder.layers.15.layer_norm1.weight', 'vision_model.encoder.layers.2.self_attn.q_proj.bias', 'vision_model.encoder.layers.20.mlp.fc1.weight', 'vision_model.encoder.layers.11.self_attn.out_proj.bias', 'vision_model.encoder.layers.16.layer_norm1.weight', 'vision_model.encoder.layers.6.layer_norm2.weight', 'vision_model.encoder.layers.10.self_attn.q_proj.weight', 'vision_model.encoder.layers.4.mlp.fc1.bias', 'vision_model.encoder.layers.6.self_attn.k_proj.bias', 'vision_model.encoder.layers.22.self_attn.out_proj.bias', 'vision_model.encoder.layers.16.mlp.fc2.weight', 'vision_model.encoder.layers.4.self_attn.q_proj.weight', 'vision_model.encoder.layers.5.self_attn.out_proj.bias', 'vision_model.encoder.layers.16.self_attn.q_proj.bias', 'logit_scale', 'vision_model.encoder.layers.14.layer_norm1.weight', 'vision_model.encoder.layers.14.layer_norm2.bias', 'vision_model.encoder.layers.13.layer_norm1.weight', 'vision_model.encoder.layers.11.self_attn.q_proj.bias', 'vision_model.encoder.layers.14.self_attn.out_proj.bias', 'vision_model.encoder.layers.6.layer_norm1.weight', 'vision_model.encoder.layers.20.mlp.fc1.bias', 'vision_model.encoder.layers.16.layer_norm2.weight', 'vision_model.encoder.layers.9.mlp.fc2.weight', 'vision_model.encoder.layers.5.mlp.fc1.weight', 'vision_model.encoder.layers.10.mlp.fc1.weight', 'vision_model.encoder.layers.4.mlp.fc1.weight', 'vision_model.encoder.layers.2.self_attn.out_proj.bias', 'vision_model.encoder.layers.17.self_attn.out_proj.weight', 'vision_model.encoder.layers.3.self_attn.k_proj.weight', 'vision_model.encoder.layers.3.self_attn.v_proj.weight', 'vision_model.encoder.layers.0.layer_norm2.weight', 'vision_model.encoder.layers.11.layer_norm1.weight', 'vision_model.encoder.layers.22.self_attn.v_proj.bias', 'vision_model.encoder.layers.15.self_attn.out_proj.weight', 'vision_model.encoder.layers.1.mlp.fc1.bias', 'vision_model.encoder.layers.6.mlp.fc2.bias', 'vision_model.encoder.layers.21.mlp.fc2.weight', 'vision_model.encoder.layers.6.layer_norm2.bias', 'vision_model.encoder.layers.19.mlp.fc2.weight', 'vision_model.encoder.layers.15.self_attn.v_proj.bias', 'vision_model.encoder.layers.16.self_attn.v_proj.weight', 'vision_model.encoder.layers.15.self_attn.k_proj.weight', 'vision_model.encoder.layers.4.self_attn.k_proj.bias', 'vision_model.encoder.layers.2.self_attn.out_proj.weight', 'vision_model.encoder.layers.2.self_attn.q_proj.weight', 'vision_model.encoder.layers.1.self_attn.v_proj.weight', 'vision_model.encoder.layers.19.self_attn.q_proj.bias', 'vision_model.encoder.layers.16.layer_norm1.bias', 'vision_model.encoder.layers.8.self_attn.out_proj.bias', 'vision_model.encoder.layers.13.mlp.fc2.weight', 'vision_model.encoder.layers.15.mlp.fc2.weight', 'vision_model.encoder.layers.21.self_attn.out_proj.bias', 'vision_model.encoder.layers.12.mlp.fc1.bias', 'vision_model.encoder.layers.9.layer_norm2.bias', 'vision_model.encoder.layers.3.mlp.fc2.bias', 'vision_model.encoder.layers.21.self_attn.k_proj.bias', 'vision_model.encoder.layers.7.mlp.fc2.bias', 'vision_model.encoder.layers.9.self_attn.q_proj.weight', 'vision_model.encoder.layers.3.self_attn.out_proj.weight', 'vision_model.encoder.layers.5.mlp.fc1.bias', 'vision_model.encoder.layers.16.mlp.fc1.weight', 'vision_model.encoder.layers.22.mlp.fc2.weight', 'vision_model.encoder.layers.15.mlp.fc1.weight', 'vision_model.encoder.layers.0.self_attn.v_proj.weight', 'vision_model.encoder.layers.6.self_attn.out_proj.weight', 'vision_model.encoder.layers.13.self_attn.k_proj.bias', 'vision_model.encoder.layers.8.self_attn.q_proj.bias', 'vision_model.encoder.layers.21.layer_norm1.bias', 'vision_model.encoder.layers.20.layer_norm1.bias', 'text_projection.weight', 'vision_model.encoder.layers.11.self_attn.out_proj.weight', 'vision_model.encoder.layers.13.self_attn.v_proj.weight', 'vision_model.encoder.layers.4.self_attn.q_proj.bias', 'vision_model.encoder.layers.12.layer_norm1.weight', 'vision_model.encoder.layers.9.layer_norm1.bias', 'vision_model.encoder.layers.3.mlp.fc1.weight', 'vision_model.encoder.layers.15.mlp.fc2.bias', 'vision_model.encoder.layers.3.layer_norm1.bias', 'vision_model.encoder.layers.8.mlp.fc1.weight', 'vision_model.encoder.layers.7.self_attn.v_proj.weight', 'vision_model.encoder.layers.8.self_attn.v_proj.weight', 'vision_model.encoder.layers.3.layer_norm2.weight', 'vision_model.encoder.layers.17.layer_norm2.bias', 'vision_model.encoder.layers.12.self_attn.q_proj.bias', 'vision_model.encoder.layers.23.self_attn.out_proj.weight', 'vision_model.encoder.layers.5.self_attn.v_proj.bias', 'vision_model.encoder.layers.23.self_attn.k_proj.weight', 'vision_model.encoder.layers.2.layer_norm2.weight', 'vision_model.encoder.layers.10.mlp.fc1.bias', 'vision_model.encoder.layers.22.mlp.fc2.bias', 'vision_model.encoder.layers.1.self_attn.k_proj.bias', 'vision_model.encoder.layers.1.layer_norm2.bias', 'vision_model.encoder.layers.17.self_attn.k_proj.weight', 'vision_model.encoder.layers.4.layer_norm1.bias', 'vision_model.encoder.layers.19.self_attn.out_proj.weight', 'vision_model.encoder.layers.10.self_attn.k_proj.weight', 'vision_model.encoder.layers.11.self_attn.q_proj.weight', 'vision_model.encoder.layers.4.self_attn.v_proj.weight', 'vision_model.encoder.layers.17.mlp.fc1.weight', 'vision_model.encoder.layers.22.mlp.fc1.bias', 'vision_model.encoder.layers.3.mlp.fc2.weight', 'vision_model.encoder.layers.14.mlp.fc2.weight', 'vision_model.encoder.layers.11.mlp.fc2.weight', 'vision_model.encoder.layers.19.mlp.fc1.bias', 'vision_model.encoder.layers.15.self_attn.k_proj.bias', 'vision_model.encoder.layers.3.self_attn.out_proj.bias', 'vision_model.encoder.layers.18.self_attn.q_proj.bias', 'vision_model.encoder.layers.2.mlp.fc1.weight', 'vision_model.encoder.layers.23.layer_norm2.weight', 'vision_model.encoder.layers.3.self_attn.q_proj.bias', 'vision_model.encoder.layers.18.layer_norm1.weight', 'vision_model.encoder.layers.0.mlp.fc2.bias', 'vision_model.encoder.layers.21.layer_norm1.weight', 'vision_model.encoder.layers.5.mlp.fc2.bias', 'vision_model.encoder.layers.11.self_attn.k_proj.weight', 'vision_model.encoder.layers.0.self_attn.q_proj.bias', 'vision_model.encoder.layers.16.self_attn.out_proj.bias', 'vision_model.encoder.layers.22.self_attn.k_proj.bias', 'vision_model.encoder.layers.1.mlp.fc2.bias', 'vision_model.encoder.layers.23.self_attn.q_proj.bias', 'vision_model.encoder.layers.9.self_attn.v_proj.bias', 'vision_model.encoder.layers.21.mlp.fc2.bias', 'vision_model.encoder.layers.19.layer_norm1.weight', 'vision_model.encoder.layers.2.self_attn.k_proj.bias', 'vision_model.encoder.layers.13.mlp.fc1.bias', 'vision_model.encoder.layers.18.mlp.fc2.weight', 'vision_model.encoder.layers.7.self_attn.v_proj.bias', 'vision_model.encoder.layers.14.self_attn.k_proj.weight', 'vision_model.encoder.layers.8.self_attn.k_proj.bias', 'vision_model.encoder.layers.8.self_attn.q_proj.weight', 'vision_model.encoder.layers.14.self_attn.v_proj.weight', 'vision_model.encoder.layers.9.mlp.fc1.weight', 'vision_model.encoder.layers.19.mlp.fc1.weight', 'vision_model.encoder.layers.18.self_attn.k_proj.weight', 'vision_model.encoder.layers.11.self_attn.k_proj.bias', 'vision_model.embeddings.class_embedding', 'vision_model.encoder.layers.23.mlp.fc1.bias', 'vision_model.encoder.layers.17.layer_norm1.bias', 'vision_model.encoder.layers.18.layer_norm2.weight', 'vision_model.encoder.layers.18.self_attn.out_proj.weight', 'vision_model.encoder.layers.8.mlp.fc2.bias', 'vision_model.encoder.layers.13.self_attn.out_proj.bias', 'vision_model.encoder.layers.15.layer_norm1.bias', 'vision_model.encoder.layers.12.self_attn.k_proj.weight', 'vision_model.encoder.layers.15.layer_norm2.weight', 'vision_model.encoder.layers.13.self_attn.q_proj.bias', 'vision_model.encoder.layers.7.self_attn.k_proj.bias', 'vision_model.encoder.layers.20.self_attn.v_proj.weight', 'vision_model.encoder.layers.1.mlp.fc2.weight', 'vision_model.encoder.layers.5.layer_norm1.bias', 'vision_model.encoder.layers.13.layer_norm2.bias', 'vision_model.encoder.layers.8.layer_norm2.weight', 'vision_model.encoder.layers.3.layer_norm1.weight', 'vision_model.encoder.layers.6.self_attn.v_proj.weight', 'vision_model.encoder.layers.13.mlp.fc2.bias', 'vision_model.encoder.layers.19.self_attn.q_proj.weight', 'vision_model.encoder.layers.6.self_attn.q_proj.weight', 'vision_model.encoder.layers.19.self_attn.k_proj.bias', 'vision_model.encoder.layers.11.self_attn.v_proj.weight', 'vision_model.encoder.layers.0.self_attn.k_proj.weight', 'vision_model.encoder.layers.8.self_attn.k_proj.weight', 'vision_model.encoder.layers.4.self_attn.k_proj.weight', 'vision_model.encoder.layers.14.self_attn.k_proj.bias', 'vision_model.encoder.layers.2.layer_norm1.bias', 'vision_model.encoder.layers.1.self_attn.v_proj.bias', 'vision_model.encoder.layers.2.layer_norm2.bias', 'vision_model.encoder.layers.4.layer_norm2.weight', 'vision_model.encoder.layers.16.self_attn.k_proj.bias', 'vision_model.encoder.layers.5.layer_norm2.bias', 'vision_model.encoder.layers.8.self_attn.v_proj.bias', 'vision_model.encoder.layers.16.self_attn.k_proj.weight', 'vision_model.encoder.layers.15.self_attn.q_proj.weight', 'vision_model.post_layernorm.weight', 'vision_model.encoder.layers.17.self_attn.v_proj.bias', 'vision_model.encoder.layers.21.layer_norm2.weight', 'vision_model.encoder.layers.14.self_attn.q_proj.bias', 'vision_model.encoder.layers.0.layer_norm2.bias', 'vision_model.encoder.layers.5.self_attn.q_proj.bias', 'vision_model.encoder.layers.1.layer_norm1.bias', 'vision_model.encoder.layers.6.mlp.fc1.weight', 'vision_model.encoder.layers.1.self_attn.out_proj.bias', 'vision_model.encoder.layers.12.self_attn.v_proj.bias', 'vision_model.encoder.layers.19.layer_norm2.weight', 'vision_model.encoder.layers.7.mlp.fc2.weight', 'vision_model.encoder.layers.23.self_attn.v_proj.weight', 'vision_model.encoder.layers.18.layer_norm2.bias', 'vision_model.encoder.layers.23.self_attn.q_proj.weight', 'vision_model.encoder.layers.18.self_attn.v_proj.weight', 'vision_model.encoder.layers.20.self_attn.out_proj.weight', 'vision_model.encoder.layers.17.self_attn.k_proj.bias', 'vision_model.encoder.layers.15.mlp.fc1.bias', 'vision_model.encoder.layers.18.self_attn.k_proj.bias', 'vision_model.encoder.layers.1.self_attn.k_proj.weight', 'vision_model.encoder.layers.22.self_attn.q_proj.weight', 'vision_model.encoder.layers.23.layer_norm2.bias', 'vision_model.encoder.layers.0.mlp.fc1.weight', 'vision_model.encoder.layers.5.self_attn.k_proj.bias', 'vision_model.encoder.layers.8.layer_norm1.bias', 'vision_model.encoder.layers.19.self_attn.v_proj.bias', 'vision_model.encoder.layers.10.self_attn.q_proj.bias', 'vision_model.encoder.layers.9.mlp.fc1.bias', 'vision_model.encoder.layers.14.layer_norm1.bias', 'vision_model.embeddings.position_embedding.weight', 'vision_model.encoder.layers.13.mlp.fc1.weight', 'vision_model.encoder.layers.6.mlp.fc1.bias', 'vision_model.encoder.layers.16.mlp.fc2.bias', 'vision_model.encoder.layers.16.self_attn.v_proj.bias', 'vision_model.encoder.layers.15.self_attn.q_proj.bias', 'vision_model.encoder.layers.0.layer_norm1.bias', 'vision_model.encoder.layers.7.layer_norm1.weight', 'vision_model.encoder.layers.10.self_attn.k_proj.bias', 'vision_model.encoder.layers.22.layer_norm2.weight', 'vision_model.encoder.layers.10.self_attn.v_proj.weight', 'vision_model.encoder.layers.7.self_attn.out_proj.bias', 'vision_model.encoder.layers.21.self_attn.q_proj.bias', 'vision_model.encoder.layers.10.self_attn.v_proj.bias', 'vision_model.encoder.layers.4.mlp.fc2.bias', 'vision_model.encoder.layers.5.self_attn.out_proj.weight', 'vision_model.encoder.layers.21.self_attn.v_proj.weight', 'vision_model.encoder.layers.5.mlp.fc2.weight', 'vision_model.encoder.layers.15.self_attn.v_proj.weight', 'vision_model.encoder.layers.17.layer_norm2.weight', 'vision_model.encoder.layers.4.self_attn.out_proj.bias', 'vision_model.encoder.layers.19.self_attn.k_proj.weight', 'vision_model.encoder.layers.14.self_attn.out_proj.weight', 'vision_model.encoder.layers.22.self_attn.v_proj.weight', 'vision_model.encoder.layers.1.self_attn.out_proj.weight', 'vision_model.encoder.layers.2.layer_norm1.weight', 'vision_model.encoder.layers.9.self_attn.k_proj.bias', 'vision_model.encoder.layers.10.layer_norm1.bias', 'vision_model.encoder.layers.4.self_attn.v_proj.bias', 'vision_model.encoder.layers.12.self_attn.k_proj.bias', 'vision_model.encoder.layers.13.self_attn.out_proj.weight', 'vision_model.encoder.layers.7.self_attn.out_proj.weight', 'vision_model.encoder.layers.5.self_attn.v_proj.weight', 'vision_model.encoder.layers.5.self_attn.q_proj.weight', 'vision_model.encoder.layers.20.self_attn.k_proj.weight', 'vision_model.encoder.layers.17.self_attn.q_proj.weight', 'vision_model.encoder.layers.7.mlp.fc1.weight', 'vision_model.encoder.layers.9.self_attn.q_proj.bias', 'vision_model.encoder.layers.17.layer_norm1.weight', 'vision_model.encoder.layers.11.layer_norm2.weight', 'vision_model.encoder.layers.20.layer_norm2.bias', 'vision_model.encoder.layers.12.self_attn.v_proj.weight', 'vision_model.encoder.layers.0.self_attn.out_proj.weight', 'vision_model.encoder.layers.20.self_attn.out_proj.bias', 'vision_model.pre_layrnorm.weight', 'vision_model.encoder.layers.13.layer_norm1.bias', 'vision_model.encoder.layers.19.layer_norm1.bias', 'vision_model.encoder.layers.5.layer_norm1.weight', 'vision_model.encoder.layers.12.layer_norm1.bias', 'vision_model.encoder.layers.9.layer_norm2.weight', 'vision_model.encoder.layers.18.layer_norm1.bias', 'vision_model.encoder.layers.22.layer_norm1.weight', 'vision_model.encoder.layers.0.mlp.fc2.weight', 'vision_model.encoder.layers.7.self_attn.q_proj.bias', 'vision_model.encoder.layers.18.mlp.fc1.bias', 'vision_model.encoder.layers.4.self_attn.out_proj.weight', 'vision_model.encoder.layers.4.layer_norm1.weight', 'vision_model.encoder.layers.1.self_attn.q_proj.bias', 'vision_model.encoder.layers.12.layer_norm2.bias', 'vision_model.encoder.layers.21.mlp.fc1.bias', 'vision_model.encoder.layers.9.self_attn.out_proj.bias', 'vision_model.encoder.layers.2.self_attn.v_proj.weight', 'vision_model.encoder.layers.14.layer_norm2.weight', 'vision_model.encoder.layers.4.mlp.fc2.weight', 'vision_model.encoder.layers.6.self_attn.v_proj.bias', 'vision_model.encoder.layers.21.self_attn.k_proj.weight', 'vision_model.encoder.layers.20.layer_norm2.weight', 'vision_model.encoder.layers.8.mlp.fc2.weight', 'vision_model.encoder.layers.12.self_attn.out_proj.bias', 'vision_model.encoder.layers.12.layer_norm2.weight', 'vision_model.encoder.layers.6.mlp.fc2.weight', 'vision_model.encoder.layers.9.layer_norm1.weight', 'vision_model.embeddings.patch_embedding.weight', 'vision_model.encoder.layers.11.mlp.fc1.bias', 'vision_model.encoder.layers.5.layer_norm2.weight', 'vision_model.encoder.layers.2.self_attn.v_proj.bias', 'vision_model.encoder.layers.5.self_attn.k_proj.weight', 'vision_model.encoder.layers.3.layer_norm2.bias', 'vision_model.encoder.layers.0.self_attn.k_proj.bias', 'vision_model.encoder.layers.17.mlp.fc1.bias', 'vision_model.encoder.layers.18.self_attn.q_proj.weight', 'vision_model.encoder.layers.20.self_attn.v_proj.bias', 'vision_model.encoder.layers.0.layer_norm1.weight', 'vision_model.encoder.layers.10.layer_norm2.weight', 'vision_model.encoder.layers.23.layer_norm1.weight', 'vision_model.encoder.layers.17.self_attn.out_proj.bias', 'vision_model.encoder.layers.8.layer_norm2.bias', 'vision_model.encoder.layers.17.mlp.fc2.weight', 'vision_model.encoder.layers.14.self_attn.v_proj.bias', 'vision_model.encoder.layers.9.self_attn.k_proj.weight', 'vision_model.encoder.layers.10.self_attn.out_proj.weight', 'vision_model.encoder.layers.8.self_attn.out_proj.weight', 'vision_model.encoder.layers.18.mlp.fc1.weight', 'vision_model.encoder.layers.10.self_attn.out_proj.bias', 'vision_model.encoder.layers.14.mlp.fc2.bias', 'vision_model.encoder.layers.18.self_attn.v_proj.bias', 'vision_model.encoder.layers.13.self_attn.k_proj.weight', 'vision_model.encoder.layers.6.self_attn.out_proj.bias', 'vision_model.encoder.layers.20.self_attn.q_proj.bias', 'vision_model.encoder.layers.0.self_attn.out_proj.bias', 'vision_model.encoder.layers.3.self_attn.v_proj.bias', 'vision_model.encoder.layers.10.mlp.fc2.weight', 'vision_model.encoder.layers.2.mlp.fc2.bias', 'vision_model.encoder.layers.19.mlp.fc2.bias', 'vision_model.encoder.layers.2.mlp.fc2.weight', 'vision_model.encoder.layers.23.mlp.fc2.weight', 'vision_model.encoder.layers.22.self_attn.q_proj.bias', 'vision_model.encoder.layers.14.self_attn.q_proj.weight', 'vision_model.encoder.layers.23.self_attn.out_proj.bias', 'vision_model.encoder.layers.9.mlp.fc2.bias', 'vision_model.encoder.layers.20.mlp.fc2.bias', 'vision_model.encoder.layers.21.self_attn.q_proj.weight', 'vision_model.encoder.layers.12.self_attn.q_proj.weight', 'vision_model.encoder.layers.21.layer_norm2.bias', 'vision_model.encoder.layers.11.self_attn.v_proj.bias', 'vision_model.encoder.layers.19.self_attn.v_proj.weight', 'vision_model.encoder.layers.21.self_attn.v_proj.bias', 'vision_model.encoder.layers.13.self_attn.v_proj.bias', 'vision_model.encoder.layers.2.mlp.fc1.bias', 'vision_model.encoder.layers.11.mlp.fc2.bias', 'vision_model.post_layernorm.bias', 'vision_model.encoder.layers.6.layer_norm1.bias', 'vision_model.encoder.layers.10.layer_norm2.bias', 'vision_model.encoder.layers.17.self_attn.v_proj.weight', 'vision_model.encoder.layers.0.mlp.fc1.bias', 'vision_model.encoder.layers.11.layer_norm2.bias', 'vision_model.encoder.layers.12.mlp.fc2.weight', 'vision_model.encoder.layers.15.layer_norm2.bias', 'vision_model.encoder.layers.18.mlp.fc2.bias', 'vision_model.encoder.layers.21.self_attn.out_proj.weight', 'vision_model.encoder.layers.7.self_attn.q_proj.weight', 'vision_model.encoder.layers.17.mlp.fc2.bias', 'vision_model.encoder.layers.20.self_attn.k_proj.bias', 'vision_model.encoder.layers.19.layer_norm2.bias', 'vision_model.encoder.layers.2.self_attn.k_proj.weight', 'vision_model.encoder.layers.16.layer_norm2.bias', 'vision_model.encoder.layers.16.mlp.fc1.bias', 'vision_model.encoder.layers.7.self_attn.k_proj.weight', 'vision_model.encoder.layers.12.self_attn.out_proj.weight', 'vision_model.encoder.layers.19.self_attn.out_proj.bias', 'vision_model.encoder.layers.22.self_attn.out_proj.weight', 'vision_model.encoder.layers.1.self_attn.q_proj.weight', 'vision_model.encoder.layers.21.mlp.fc1.weight', 'vision_model.encoder.layers.7.layer_norm2.weight', 'vision_model.encoder.layers.16.self_attn.out_proj.weight', 'vision_model.encoder.layers.23.mlp.fc1.weight', 'vision_model.encoder.layers.14.mlp.fc1.weight', 'vision_model.embeddings.position_ids', 'vision_model.encoder.layers.7.layer_norm1.bias', 'vision_model.encoder.layers.23.layer_norm1.bias', 'vision_model.encoder.layers.6.self_attn.k_proj.weight', 'vision_model.encoder.layers.17.self_attn.q_proj.bias', 'visual_projection.weight', 'vision_model.encoder.layers.3.mlp.fc1.bias', 'vision_model.encoder.layers.22.layer_norm1.bias', 'vision_model.encoder.layers.13.self_attn.q_proj.weight', 'vision_model.encoder.layers.10.mlp.fc2.bias', 'vision_model.encoder.layers.8.mlp.fc1.bias', 'vision_model.encoder.layers.11.layer_norm1.bias', 'vision_model.encoder.layers.13.layer_norm2.weight', 'vision_model.encoder.layers.7.layer_norm2.bias', 'vision_model.encoder.layers.11.mlp.fc1.weight', 'vision_model.encoder.layers.18.self_attn.out_proj.bias', 'vision_model.encoder.layers.1.layer_norm1.weight', 'vision_model.encoder.layers.12.mlp.fc1.weight', 'vision_model.encoder.layers.0.self_attn.v_proj.bias', 'vision_model.encoder.layers.23.mlp.fc2.bias', 'vision_model.encoder.layers.3.self_attn.k_proj.bias', 'vision_model.encoder.layers.23.self_attn.k_proj.bias', 'vision_model.encoder.layers.6.self_attn.q_proj.bias']

• This IS expected if you are initializing CLIPTextModel 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 CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Initialized embedder #0: FrozenCLIPEmbedder with 123060480 params. Trainable: False open_clip_pytorch_model.bin: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10.2G/10.2G [01:27<00:00, 115MB/s] Killed

` I have RTX 3090 GPU. Fresh installation of Ubuntu. Do I need to install cuda toolkit? Or something else? Do you have luck guys?

[brayevalerien]

I've had tons of install issues and I'm still fighting with this after 4 hours of trying. I hope the original researchers try and make things easier to get running. I currently have an issue similar to the one you got.

[zelenooki87]

Yep, I've been there too - spent hours trying to debug an error. Can't seem to get the code to run successfully. Has anyone else managed it?

[Fanghua-Yu]

Seems like a RAM Issue. The gradio demo requires ~60G RAM and 30G VRAM（for each GPU）in total. Maybe you can load this CLIP separately to find issues:

model, _, _ = open_clip.create_model_and_transforms(
            'ViT-bigG-14',
            device=torch.device("cpu"),
            pretrained='laion2b_s39b_b160k',
        )

[brayevalerien]

If using a single GPU, 60Gb of VRAM are required, is that right? That seems ridiculous, I will never be able to run SUPIR on my RTX4090...

[xiaom233]

If using a single GPU, 60Gb of VRAM are required, is that right? That seems ridiculous, I will never be able to run SUPIR on my RTX4090...

The online demo will be released soon. Maybe you can have a try later.

[FurkanGozukara]

If using a single GPU, 60Gb of VRAM are required, is that right? That seems ridiculous, I will never be able to run SUPIR on my RTX4090...

The online demo will be released soon. Maybe you can have a try later.

can't we use with quantization or cpu offloading?

[chrisbward]

I had luck running on a 3090ti (24GB VRAM) with;

python gradio_demo.py --no_llava --use_image_slider --log_history --loading_half_params --use_tile_vae

but suddenly today I'm getting this fault

[chrisbward]

okay, this one was weird - I fired up nvtop and closed off some resources using VRAM and now I can run it again

[FurkanGozukara]

we even added 8bit loading now working very good even on 8GB gpus

https://github.com/Fanghua-Yu/SUPIR/issues/94