Warning that some CLIP weights were not used

[ikarth]

Just checking to see if this is expected behavior.

After setting up the model and running python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms I get this warning:

Global seed set to 42
Loading model from models/ldm/stable-diffusion-v1/model.ckpt
Global Step: 440000
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
Downloading: 100%|███████████████████████████████████████████████████████████████████| 939k/939k [00:01<00:00, 738kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████| 512k/512k [00:00<00:00, 736kB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████| 389/389 [00:00<00:00, 396kB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████| 905/905 [00:00<00:00, 447kB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████| 4.31k/4.31k [00:00<00:00, 2.16MB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████| 1.59G/1.59G [06:18<00:00, 4.52MB/s]
S
Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.23.mlp.fc2.bias', 'vision_model.encoder.layers.19.self_attn.v_proj.bias', 'vision_model.encoder.layers.18.layer_norm2.weight', 'vision_model.encoder.layers.21.self_attn.k_proj.bias', 'vision_model.encoder.layers.13.self_attn.k_proj.weight', 'vision_model.encoder.layers.16.mlp.fc2.bias', 'vision_model.encoder.layers.14.self_attn.k_proj.bias', 'vision_model.encoder.layers.11.self_attn.k_proj.bias', 'vision_model.encoder.layers.17.self_attn.q_proj.bias', 'vision_model.encoder.layers.9.self_attn.v_proj.weight', 'visual_projection.weight', 'vision_model.encoder.layers.8.mlp.fc1.weight', 'vision_model.encoder.layers.12.self_attn.out_proj.bias', 'vision_model.encoder.layers.7.self_attn.q_proj.bias', 'vision_model.encoder.layers.14.self_attn.out_proj.bias', 'vision_model.encoder.layers.12.self_attn.v_proj.weight', 'vision_model.encoder.layers.15.mlp.fc1.bias', 'vision_model.encoder.layers.22.mlp.fc1.bias', 'vision_model.encoder.layers.10.self_attn.v_proj.bias', 'vision_model.encoder.layers.6.mlp.fc1.weight', 'vision_model.encoder.layers.17.self_attn.q_proj.weight', 'vision_model.encoder.layers.4.self_attn.q_proj.weight', 'vision_model.encoder.layers.16.self_attn.k_proj.bias', 'vision_model.encoder.layers.21.self_attn.v_proj.bias', 'vision_model.encoder.layers.0.self_attn.v_proj.weight', 'vision_model.encoder.layers.14.self_attn.q_proj.bias', 'vision_model.encoder.layers.13.layer_norm1.weight', 'vision_model.encoder.layers.21.self_attn.v_proj.weight', 'vision_model.encoder.layers.5.layer_norm2.weight', 'vision_model.encoder.layers.5.mlp.fc1.bias', 'vision_model.encoder.layers.17.layer_norm2.weight', 'vision_model.encoder.layers.4.layer_norm1.bias', 'vision_model.encoder.layers.6.self_attn.q_proj.bias', 'vision_model.encoder.layers.11.self_attn.v_proj.weight', 'vision_model.encoder.layers.3.layer_norm1.weight', 'vision_model.encoder.layers.14.self_attn.v_proj.weight', 'vision_model.encoder.layers.1.layer_norm1.weight', 'text_projection.weight', 'vision_model.encoder.layers.9.layer_norm1.bias', 'vision_model.encoder.layers.3.layer_norm2.bias', 'vision_model.encoder.layers.17.layer_norm1.weight', 'vision_model.encoder.layers.22.self_attn.q_proj.weight', 'vision_model.encoder.layers.21.self_attn.out_proj.bias', 'vision_model.encoder.layers.23.self_attn.q_proj.bias', 'vision_model.encoder.layers.11.self_attn.k_proj.weight', 'vision_model.encoder.layers.11.mlp.fc2.bias', 'vision_model.encoder.layers.10.layer_norm1.weight', 'vision_model.encoder.layers.2.self_attn.k_proj.bias', 'vision_model.encoder.layers.1.layer_norm2.bias', 'vision_model.encoder.layers.19.self_attn.q_proj.bias', 'vision_model.encoder.layers.1.self_attn.out_proj.bias', 'vision_model.encoder.layers.6.self_attn.k_proj.bias', 'vision_model.encoder.layers.18.self_attn.v_proj.bias', 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'vision_model.encoder.layers.11.self_attn.out_proj.weight', 'vision_model.encoder.layers.1.mlp.fc1.bias', 'vision_model.encoder.layers.14.layer_norm2.weight', 'vision_model.encoder.layers.22.mlp.fc2.bias', 'vision_model.encoder.layers.8.mlp.fc2.weight', 'vision_model.encoder.layers.2.self_attn.out_proj.bias', 'vision_model.encoder.layers.15.layer_norm1.bias', 'vision_model.encoder.layers.8.layer_norm1.bias', 'vision_model.encoder.layers.10.mlp.fc1.weight', 'vision_model.encoder.layers.13.layer_norm2.bias', 'vision_model.encoder.layers.2.self_attn.v_proj.bias', 'vision_model.encoder.layers.20.self_attn.q_proj.weight', 'vision_model.encoder.layers.13.mlp.fc2.bias', 'vision_model.pre_layrnorm.bias', 'vision_model.encoder.layers.1.self_attn.k_proj.weight', 'vision_model.encoder.layers.5.self_attn.v_proj.weight', 'vision_model.encoder.layers.3.self_attn.v_proj.weight', 'vision_model.encoder.layers.2.self_attn.v_proj.weight', 'vision_model.encoder.layers.8.mlp.fc2.bias', 'vision_model.encoder.layers.8.self_attn.out_proj.bias', 'vision_model.encoder.layers.23.self_attn.out_proj.weight', 'vision_model.encoder.layers.21.self_attn.q_proj.weight', 'vision_model.encoder.layers.5.self_attn.out_proj.weight', 'vision_model.encoder.layers.7.mlp.fc1.weight', 'vision_model.encoder.layers.20.self_attn.out_proj.weight', 'vision_model.encoder.layers.22.self_attn.out_proj.weight', 'vision_model.encoder.layers.13.self_attn.out_proj.bias', 'vision_model.encoder.layers.13.self_attn.k_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).

It's just a warning, but I thought I should check.

This is on Windows 10, 64-bit.

[dmarx]

this is normal, you only need the weights from the CLIP text encoder. This warning is telling you the vision encoder weights aren't being used, which is deliberate.

[Lufffya]

Can you share the model, thanks!!!

[eyedark]

Can you share the model, thanks!!!~~~~

[fuchao01]

Can you share the model, thanks!!!~~~~

[eyedark]

Can you share the model, thanks!!!~~~~

[ghost]

I'm getting a similar warning, but afterwards the script gets interrupted? With a ^C and everything. Anyone know anything about that? Maybe a memory issue?

[Tomorrowxxy]

@ikarth I also encountered this problem, have you solved it?

[sarmientoj24]

is there a way to disable that warning?