Using seed 3844672053. Enter this in 'seed' if you want to produce the same output again! Global seed set to 3844672053 Loading model from weights/sd-v1-4.ckpt Global Step: 470000 LatentDiffusion: Running in eps-prediction mode DiffusionWrapper has 859.52 M params. making attention of type 'vanilla' with 512 in_channels Working with z of shape (1, 4, 32, 32) = 4096 dimensions. making attention of type 'vanilla' with 512 in_channels Some weights of the model checkpoint at weights/openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.13.layer_norm1.bias', 'vision_model.encoder.layers.20.self_attn.k_proj.bias', 'vision_model.encoder.layers.2.layer_norm2.bias', 'logit_scale', 'vision_model.encoder.layers.22.self_attn.out_proj.weight', 'vision_model.encoder.layers.10.self_attn.k_proj.weight', 'vision_model.encoder.layers.21.layer_norm2.weight', 'vision_model.encoder.layers.21.self_attn.k_proj.weight', 'vision_model.encoder.layers.3.mlp.fc1.weight', 'vision_model.encoder.layers.4.mlp.fc1.bias', 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'text_projection.weight', 'vision_model.encoder.layers.10.self_attn.v_proj.bias', 'vision_model.encoder.layers.8.self_attn.out_proj.weight', 'vision_model.encoder.layers.6.layer_norm1.weight', 'vision_model.encoder.layers.22.self_attn.v_proj.weight', 'vision_model.encoder.layers.3.self_attn.q_proj.weight', 'vision_model.encoder.layers.14.layer_norm2.bias', 'vision_model.encoder.layers.6.self_attn.q_proj.bias', 'vision_model.encoder.layers.14.self_attn.k_proj.weight', 'vision_model.encoder.layers.17.layer_norm2.bias', 'vision_model.encoder.layers.18.mlp.fc2.bias', 'vision_model.encoder.layers.2.layer_norm2.weight', 'vision_model.encoder.layers.3.layer_norm1.bias', 'vision_model.encoder.layers.11.layer_norm2.bias', 'vision_model.encoder.layers.19.mlp.fc1.weight', 'vision_model.encoder.layers.1.self_attn.k_proj.bias', 'vision_model.encoder.layers.7.self_attn.q_proj.bias', 'vision_model.encoder.layers.9.mlp.fc2.bias', 'vision_model.encoder.layers.14.self_attn.v_proj.bias', 'vision_model.encoder.layers.15.self_attn.out_proj.bias', 'vision_model.encoder.layers.3.self_attn.v_proj.weight', 'vision_model.encoder.layers.22.mlp.fc1.bias', 'vision_model.encoder.layers.22.self_attn.q_proj.weight', 'vision_model.encoder.layers.20.self_attn.q_proj.weight', 'vision_model.encoder.layers.23.mlp.fc2.weight', 'vision_model.encoder.layers.17.self_attn.out_proj.weight', 'vision_model.encoder.layers.2.self_attn.out_proj.bias', 'vision_model.encoder.layers.16.self_attn.q_proj.bias', 'vision_model.encoder.layers.23.self_attn.k_proj.weight', 'vision_model.encoder.layers.15.self_attn.k_proj.weight', 'vision_model.encoder.layers.17.self_attn.k_proj.bias', 'vision_model.encoder.layers.2.self_attn.v_proj.bias', 'vision_model.encoder.layers.5.self_attn.out_proj.bias', 'vision_model.encoder.layers.7.self_attn.v_proj.weight', 'vision_model.encoder.layers.6.mlp.fc1.bias', 'vision_model.encoder.layers.16.layer_norm2.weight'] - 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). Sampling: 0%| | 0/1 [00:00) tensor([0.0504], device='cuda:0', dtype=torch.float16, grad_fn=) tensor([-0.0098], device='cuda:0', dtype=torch.float16, grad_fn=) tensor([-0.0647], device='cuda:0', dtype=torch.float16, grad_fn=) tensor([-0.1137], device='cuda:0', dtype=torch.float16, grad_fn=) tensor([-0.1602], device='cuda:0', dtype=torch.float16, grad_fn=) tensor([-0.2061], device='cuda:0', dtype=torch.float16, grad_fn=) tensor([-0.2515], device='cuda:0', dtype=torch.float16, grad_fn=) tensor([-0.2959], device='cuda:0', dtype=torch.float16, grad_fn=) tensor([-0.3367], device='cuda:0', dtype=torch.float16, grad_fn=) tensor([-0.3701], device='cuda:0', dtype=torch.float16, grad_fn=) Data shape for PLMS sampling is (1, 4, 64, 96) Running PLMS Sampling with 50 timesteps PLMS Sampler: 0%| | 0/50 [00:00