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haoningwu3639 avatar haoningwu3639 commented on June 1, 2024

Sorry for the small bug. Actually, attn3 is our proposed additional visual cross-attention, and it is initialized with the previous self-attention weights. I have fixed the codes, you can first initialize the UNet with .from_config function, and then load the pre-trained Stable Diffusion weight, then use unet.load_SDM_state_dict to initialize those new layers. That is:
unet = UNet2DConditionModel.from_config(pretrained_model_path, subfolder="unet")
pretrained_sdm = torch.load('./ckpt/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin', map_location='cpu')
unet = unet.load_SDM_state_dict(pretrained_sdm)

from storygen.

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