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Unofficial Implementation of the Google Paper - https://sketch-guided-diffusion.github.io/

License: MIT License

Python 100.00%
artificial-intelligence generative-art implementation stable-diffusion text-to-image deep-learning pytorch

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sketch-guided-stable-diffusion's Issues

LGP Inference

Thanks for posting an amazing implementation.

I managed to train the LGP model, but having trouble in inferance, where we are supposed to guide reverse process of diffusion model using the gradient of the LGP.

Could you please provide the complete inferance code ? Or at least some crude outlines ?

issue about text_encoder

Hello, I encountered bugs when I run the code. "text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14").to(device)"
something like that:
..size mismatch for text_model.encoder.layers.11.mlp.fc2.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for text_model.encoder.layers.11.layer_norm2.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for text_model.encoder.layers.11.layer_norm2.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for text_model.final_layer_norm.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for text_model.final_layer_norm.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([512]).

Could you help me fix it?

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