a lot of missing keys while loading the FrozenCLIPEmbedder checkpoint

[musetee]

Description
Hi, I am trying to test the model with original settings but met this issue. It seems there could be something wrong with te embedding model, but I had no idea. This problem occurs either on our server with an A6000 GPU or on my on PC with a RTX 3090 ti. The generated images are far from expected.

Wrong Results
An example of modern image using following input:
style: modern.jpg as
context: pexels-aleksandar-pasaric-2041556.jpg

Error Report:
NVIDIA GeForce RTX 3090 Ti Loading model from models/sd/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 openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.4.mlp.fc1.weight', 'vision_model.encoder.layers.11.layer_norm1.bias', 'vision_model.encoder.layers.6.layer_norm2.weight', 'vision_model.encoder.layers.19.self_attn.q_proj.weight', 'vision_model.encoder.layers.4.self_attn.q_proj.weight', 'vision_model.encoder.layers.18.self_attn.v_proj.weight', 'vision_model.encoder.layers.6.mlp.fc1.weight', 'vision_model.encoder.layers.10.layer_norm1.weight', 'vision_model.encoder.layers.18.mlp.fc2.weight', 'vision_model.encoder.layers.6.self_attn.v_proj.bias', 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'vision_model.encoder.layers.15.mlp.fc2.weight', 'vision_model.encoder.layers.11.layer_norm2.weight', 'vision_model.encoder.layers.23.mlp.fc2.weight', 'vision_model.encoder.layers.20.mlp.fc2.weight', 'vision_model.encoder.layers.17.layer_norm2.bias', 'vision_model.encoder.layers.8.self_attn.v_proj.bias', 'vision_model.encoder.layers.15.mlp.fc1.weight', 'vision_model.encoder.layers.23.self_attn.q_proj.bias', 'vision_model.encoder.layers.16.self_attn.k_proj.weight', 'vision_model.encoder.layers.9.layer_norm1.weight', 'vision_model.encoder.layers.9.mlp.fc1.weight', 'vision_model.encoder.layers.7.self_attn.k_proj.bias', 'vision_model.encoder.layers.21.mlp.fc1.weight', 'vision_model.encoder.layers.9.mlp.fc1.bias', 'vision_model.encoder.layers.13.mlp.fc2.weight', 'vision_model.encoder.layers.10.self_attn.v_proj.weight', 'vision_model.encoder.layers.11.mlp.fc2.bias', 'vision_model.encoder.layers.12.self_attn.v_proj.bias', 'vision_model.encoder.layers.19.self_attn.out_proj.weight', 'vision_model.encoder.layers.5.mlp.fc1.weight', 'vision_model.encoder.layers.18.self_attn.out_proj.weight', 'vision_model.encoder.layers.17.self_attn.k_proj.weight', 'vision_model.encoder.layers.18.mlp.fc1.weight', 'visual_projection.weight', 'vision_model.encoder.layers.1.self_attn.out_proj.bias', 'vision_model.encoder.layers.1.layer_norm1.weight', 'vision_model.encoder.layers.7.self_attn.out_proj.weight', 'vision_model.encoder.layers.15.self_attn.v_proj.bias', 'vision_model.encoder.layers.20.self_attn.v_proj.weight', 'vision_model.encoder.layers.20.layer_norm1.bias', 'vision_model.encoder.layers.3.mlp.fc1.weight', 'vision_model.encoder.layers.8.self_attn.out_proj.bias', 'vision_model.encoder.layers.21.mlp.fc2.weight', 'vision_model.encoder.layers.6.self_attn.out_proj.weight', 'vision_model.encoder.layers.9.layer_norm2.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). FrozenCLIPEmbedder device: cuda:0 missing keys: ['cond_stage_model.image_encoder.logit_scale', 'cond_stage_model.image_encoder.text_model.embeddings.position_ids', 'cond_stage_model.image_encoder.text_model.embeddings.token_embedding.weight', 'cond_stage_model.image_encoder.text_model.embeddings.position_embedding.weight', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.self_attn.k_proj.weight', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.self_attn.k_proj.bias', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.self_attn.v_proj.weight', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.self_attn.v_proj.bias', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.self_attn.q_proj.weight', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.self_attn.q_proj.bias', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.self_attn.out_proj.weight', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.self_attn.out_proj.bias', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.layer_norm1.weight', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.layer_norm1.bias', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.mlp.fc1.weight', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.mlp.fc1.bias', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.mlp.fc2.weight', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.mlp.fc2.bias', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.layer_norm2.weight', 'cond_stage_model.image_encoder.text_model.encoder.layers.0.layer_norm2.bias', 'cond_stage_model.image_encoder.text_model.encoder.layers.1.self_attn.k_proj.weight', 'cond_stage_model.image_encoder.text_model.encoder.layers.1.self_attn.k_proj.bias', 'c
.......
hundereds lines of missing keys
.......
unexpected keys:
['model_ema.decay', 'model_ema.num_updates']
find keys: dict_keys(['string_to_token', 'attention'])