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This is the code used to create this model

import torch
import diffusers


model = diffusers.UNet2DConditionModel(
        block_out_channels=(4, 4, 4),
        down_block_types=('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D'),
        up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D'),
        norm_num_groups=2,
        cross_attention_dim=2,
        layers_per_block=1,
        attention_head_dim=2,
        addition_embed_type_num_heads=2,
)

# noisy latent
x = torch.randn(7,4,33,33)
# timestep
t = torch.Tensor([1.0])
# conditioning embed
z = torch.randn(7, 4, 2)
# denoised latent
y = model(x, t, z)

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