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@@ -1542,10 +1542,10 @@ class Unet(nn.Module):
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self_attn = False,
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attn_dim_head = 32,
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attn_heads = 16,
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lowres_cond = False, # for cascading diffusion - https://cascaded-diffusion.github.io/
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lowres_noise_cond = False, # for conditioning on low resolution noising, based on Imagen
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lowres_cond = False, # for cascading diffusion - https://cascaded-diffusion.github.io/
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lowres_noise_cond = False, # for conditioning on low resolution noising, based on Imagen
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sparse_attn = False,
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attend_at_middle = True, # whether to have a layer of attention at the bottleneck (can turn off for higher resolution in cascading DDPM, before bringing in efficient attention)
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attend_at_middle = True, # whether to have a layer of attention at the bottleneck (can turn off for higher resolution in cascading DDPM, before bringing in efficient attention)
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cond_on_text_encodings = False,
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max_text_len = 256,
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cond_on_image_embeds = False,
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@@ -2100,7 +2100,7 @@ class Decoder(nn.Module):
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image_sizes = None, # for cascading ddpm, image size at each stage
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random_crop_sizes = None, # whether to random crop the image at that stage in the cascade (super resoluting convolutions at the end may be able to generalize on smaller crops)
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use_noise_for_lowres_cond = False, # whether to use Imagen-like noising for low resolution conditioning
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use_blur_for_lowres_cond = True, # whether to use the blur conditioning used in the original cascading ddpm paper, as well as DALL-E2
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use_blur_for_lowres_cond = True, # whether to use the blur conditioning used in the original cascading ddpm paper, as well as DALL-E2
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lowres_downsample_first = True, # cascading ddpm - resizes to lower resolution, then to next conditional resolution + blur
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blur_prob = 0.5, # cascading ddpm - when training, the gaussian blur is only applied 50% of the time
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blur_sigma = 0.6, # cascading ddpm - blur sigma
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