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init_weight_outputs.txt
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init_weight_outputs.txt
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ControlLDM: 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.
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 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 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
DiffusionWrapper has 865.91 M params.
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
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 [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]
Loading model from: /root/anaconda3/envs/diffbir/lib/python3.9/site-packages/lpips/weights/v0.1/alex.pth
copy weights: betas -> betas
copy weights: alphas_cumprod -> alphas_cumprod
copy weights: alphas_cumprod_prev -> alphas_cumprod_prev
copy weights: sqrt_alphas_cumprod -> sqrt_alphas_cumprod
copy weights: sqrt_one_minus_alphas_cumprod -> sqrt_one_minus_alphas_cumprod
copy weights: log_one_minus_alphas_cumprod -> log_one_minus_alphas_cumprod
copy weights: sqrt_recip_alphas_cumprod -> sqrt_recip_alphas_cumprod
copy weights: sqrt_recipm1_alphas_cumprod -> sqrt_recipm1_alphas_cumprod
copy weights: posterior_variance -> posterior_variance
copy weights: posterior_log_variance_clipped -> posterior_log_variance_clipped
copy weights: posterior_mean_coef1 -> posterior_mean_coef1
copy weights: posterior_mean_coef2 -> posterior_mean_coef2
copy weights: logvar -> logvar
These weights are newly added: logvar
copy weights: model.diffusion_model.time_embed.0.weight -> model.diffusion_model.time_embed.0.weight
copy weights: model.diffusion_model.time_embed.0.bias -> model.diffusion_model.time_embed.0.bias
copy weights: model.diffusion_model.time_embed.2.weight -> model.diffusion_model.time_embed.2.weight
copy weights: model.diffusion_model.time_embed.2.bias -> model.diffusion_model.time_embed.2.bias
copy weights: model.diffusion_model.input_blocks.0.0.weight -> model.diffusion_model.input_blocks.0.0.weight
copy weights: model.diffusion_model.input_blocks.0.0.bias -> model.diffusion_model.input_blocks.0.0.bias
copy weights: model.diffusion_model.input_blocks.1.0.in_layers.0.weight -> model.diffusion_model.input_blocks.1.0.in_layers.0.weight
copy weights: model.diffusion_model.input_blocks.1.0.in_layers.0.bias -> model.diffusion_model.input_blocks.1.0.in_layers.0.bias
copy weights: model.diffusion_model.input_blocks.1.0.in_layers.2.weight -> model.diffusion_model.input_blocks.1.0.in_layers.2.weight
copy weights: model.diffusion_model.input_blocks.1.0.in_layers.2.bias -> model.diffusion_model.input_blocks.1.0.in_layers.2.bias
copy weights: model.diffusion_model.input_blocks.1.0.emb_layers.1.weight -> model.diffusion_model.input_blocks.1.0.emb_layers.1.weight
copy weights: model.diffusion_model.input_blocks.1.0.emb_layers.1.bias -> model.diffusion_model.input_blocks.1.0.emb_layers.1.bias
copy weights: model.diffusion_model.input_blocks.1.0.out_layers.0.weight -> model.diffusion_model.input_blocks.1.0.out_layers.0.weight
copy weights: model.diffusion_model.input_blocks.1.0.out_layers.0.bias -> model.diffusion_model.input_blocks.1.0.out_layers.0.bias
copy weights: model.diffusion_model.input_blocks.1.0.out_layers.3.weight -> model.diffusion_model.input_blocks.1.0.out_layers.3.weight
copy weights: model.diffusion_model.input_blocks.1.0.out_layers.3.bias -> model.diffusion_model.input_blocks.1.0.out_layers.3.bias
copy weights: model.diffusion_model.input_blocks.1.1.norm.weight -> model.diffusion_model.input_blocks.1.1.norm.weight
copy weights: model.diffusion_model.input_blocks.1.1.norm.bias -> model.diffusion_model.input_blocks.1.1.norm.bias
copy weights: model.diffusion_model.input_blocks.1.1.proj_in.weight -> model.diffusion_model.input_blocks.1.1.proj_in.weight
copy weights: model.diffusion_model.input_blocks.1.1.proj_in.bias -> model.diffusion_model.input_blocks.1.1.proj_in.bias
copy weights: 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_q.weight
copy weights: 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_k.weight
copy weights: 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_v.weight
copy weights: 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.weight
copy weights: 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.attn1.to_out.0.bias
copy weights: 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.weight
copy weights: 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.0.proj.bias
copy weights: 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.weight
copy weights: model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias -> model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias
copy weights: 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_q.weight
copy weights: 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_k.weight
copy weights: 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_v.weight
copy weights: 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.weight
copy weights: 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.attn2.to_out.0.bias
copy weights: model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.weight -> model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.weight
copy weights: model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.bias -> model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.bias
copy weights: model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight -> model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight
copy weights: model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.bias -> model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.bias
copy weights: model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight -> model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight
copy weights: model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias -> model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias
copy weights: model.diffusion_model.input_blocks.1.1.proj_out.weight -> model.diffusion_model.input_blocks.1.1.proj_out.weight
copy weights: model.diffusion_model.input_blocks.1.1.proj_out.bias -> model.diffusion_model.input_blocks.1.1.proj_out.bias
copy weights: model.diffusion_model.input_blocks.2.0.in_layers.0.weight -> model.diffusion_model.input_blocks.2.0.in_layers.0.weight
copy weights: model.diffusion_model.input_blocks.2.0.in_layers.0.bias -> model.diffusion_model.input_blocks.2.0.in_layers.0.bias
copy weights: model.diffusion_model.input_blocks.2.0.in_layers.2.weight -> model.diffusion_model.input_blocks.2.0.in_layers.2.weight
copy weights: model.diffusion_model.input_blocks.2.0.in_layers.2.bias -> model.diffusion_model.input_blocks.2.0.in_layers.2.bias
copy weights: model.diffusion_model.input_blocks.2.0.emb_layers.1.weight -> model.diffusion_model.input_blocks.2.0.emb_layers.1.weight
copy weights: model.diffusion_model.input_blocks.2.0.emb_layers.1.bias -> model.diffusion_model.input_blocks.2.0.emb_layers.1.bias
copy weights: model.diffusion_model.input_blocks.2.0.out_layers.0.weight -> model.diffusion_model.input_blocks.2.0.out_layers.0.weight
copy weights: model.diffusion_model.input_blocks.2.0.out_layers.0.bias -> model.diffusion_model.input_blocks.2.0.out_layers.0.bias
copy weights: model.diffusion_model.input_blocks.2.0.out_layers.3.weight -> model.diffusion_model.input_blocks.2.0.out_layers.3.weight
copy weights: model.diffusion_model.input_blocks.2.0.out_layers.3.bias -> model.diffusion_model.input_blocks.2.0.out_layers.3.bias
copy weights: model.diffusion_model.input_blocks.2.1.norm.weight -> model.diffusion_model.input_blocks.2.1.norm.weight
copy weights: model.diffusion_model.input_blocks.2.1.norm.bias -> model.diffusion_model.input_blocks.2.1.norm.bias
copy weights: model.diffusion_model.input_blocks.2.1.proj_in.weight -> model.diffusion_model.input_blocks.2.1.proj_in.weight
copy weights: model.diffusion_model.input_blocks.2.1.proj_in.bias -> model.diffusion_model.input_blocks.2.1.proj_in.bias
copy weights: 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_q.weight
copy weights: 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_k.weight
copy weights: 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_v.weight
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