converted models using convert_original_stable_diffusion_to_diffusers.py are not identical

[Neklioz]

Describe the bug

I have some ckpt files and I converted them using this script, while using it, there was a warning:

Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.10.layer_norm1.weight', [..a lot more..] 'vision_model.encoder.layers.3.self_attn.out_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).

I tried the generated model and it's not even similar to the original.

As an example I used this one as I had some outputs identical to the original character "Emilia from Re:Zero"

Here you have one original image, one using the ckpt model and one using the converted one.

Reproduction

!python "convert_original_stable_diffusion_to_diffusers.py" --checkpoint_path "/content/gdrive/My Drive/SD/Models/ckpt/Emilia_CustReg_3k.ckpt" --dump_path "/content/gdrive/My Drive/SD/Models/diffusers/Emilia"

Logs

--2022-09-29 04:03:52--  https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1873 (1.8K) [text/plain]
Saving to: ‘v1-inference.yaml’

v1-inference.yaml   100%[===================>]   1.83K  --.-KB/s    in 0s      

2022-09-29 04:03:52 (32.4 MB/s) - ‘v1-inference.yaml’ saved [1873/1873]

Downloading: 100% 4.52k/4.52k [00:00<00:00, 2.68MB/s]
Downloading: 100% 1.71G/1.71G [00:26<00:00, 64.0MB/s]
Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.10.layer_norm1.weight', 'vision_model.encoder.layers.13.mlp.fc2.weight', 'vision_model.encoder.layers.22.self_attn.v_proj.bias', 'vision_model.encoder.layers.16.self_attn.q_proj.bias', 'vision_model.encoder.layers.22.mlp.fc1.bias', 'vision_model.encoder.layers.13.mlp.fc1.weight', 'vision_model.encoder.layers.18.layer_norm2.bias', 'vision_model.encoder.layers.16.mlp.fc2.weight', 'vision_model.encoder.layers.17.self_attn.k_proj.bias', 'vision_model.encoder.layers.23.layer_norm2.weight', 'vision_model.encoder.layers.0.mlp.fc1.bias', 'vision_model.encoder.layers.15.self_attn.k_proj.weight', 'vision_model.encoder.layers.2.mlp.fc1.weight', 'vision_model.encoder.layers.10.layer_norm2.bias', 'vision_model.encoder.layers.23.layer_norm1.weight', 'vision_model.encoder.layers.18.mlp.fc2.bias', 'vision_model.encoder.layers.11.self_attn.k_proj.bias', 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'vision_model.encoder.layers.11.self_attn.q_proj.weight', 'vision_model.encoder.layers.8.self_attn.v_proj.weight', 'vision_model.encoder.layers.19.self_attn.v_proj.weight', 'vision_model.encoder.layers.0.self_attn.out_proj.bias', 'vision_model.encoder.layers.0.self_attn.v_proj.weight', 'vision_model.encoder.layers.0.layer_norm2.bias', 'vision_model.encoder.layers.1.layer_norm1.bias', 'vision_model.encoder.layers.5.layer_norm1.bias', 'vision_model.encoder.layers.2.layer_norm2.weight', 'vision_model.encoder.layers.20.self_attn.v_proj.bias', 'vision_model.encoder.layers.16.layer_norm2.weight', 'vision_model.encoder.layers.6.self_attn.k_proj.bias', 'vision_model.encoder.layers.18.self_attn.out_proj.weight', 'vision_model.encoder.layers.4.self_attn.k_proj.weight', 'vision_model.encoder.layers.5.layer_norm2.weight', 'vision_model.encoder.layers.4.layer_norm1.weight', 'vision_model.encoder.layers.0.layer_norm2.weight', 'vision_model.encoder.layers.17.mlp.fc1.weight', 'vision_model.encoder.layers.9.mlp.fc1.weight', 'vision_model.encoder.layers.10.self_attn.k_proj.weight', 'vision_model.encoder.layers.19.self_attn.v_proj.bias', 'vision_model.encoder.layers.9.self_attn.out_proj.weight', 'vision_model.encoder.layers.16.mlp.fc2.bias', 'vision_model.encoder.layers.8.layer_norm1.weight', 'vision_model.encoder.layers.22.self_attn.k_proj.bias', 'vision_model.encoder.layers.2.self_attn.q_proj.weight', 'vision_model.encoder.layers.21.mlp.fc2.bias', 'vision_model.encoder.layers.3.self_attn.out_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).
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System Info

• diffusers version: 0.3.0
• Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
• Python version: 3.7.14
• PyTorch version (GPU?): 1.12.1+cu113 (True)
• Huggingface_hub version: 0.10.0
• Transformers version: 4.22.2
• Using GPU in script?: No
• Using distributed or parallel set-up in script?: Not sure

[patrickvonplaten]

Hey @Neklioz,

Thanks for the issue! Taking a look hopefully tomorrow

[vrobot]

I'm having the same issue - any updates?

[gadicc]

Edit: It turns out that - for my at least - the issues were a result of comparing float32 to full precision, and of the original source image I was given to compare to being generated with the lstein fork of compvis, which modifies the prompt weightings. With my own runs of the original compvis code, output was identical to that of diffusers. Again, in my case, at least.

[github-actions[bot]]

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