From 3676a8ce78d978eeb5d9907e5c02a5fb350226d6 Mon Sep 17 00:00:00 2001 From: Phil Wang Date: Mon, 18 Jul 2022 15:02:04 -0700 Subject: [PATCH] comments --- dalle2_pytorch/dalle2_pytorch.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/dalle2_pytorch/dalle2_pytorch.py b/dalle2_pytorch/dalle2_pytorch.py index f749b3c..1045f4a 100644 --- a/dalle2_pytorch/dalle2_pytorch.py +++ b/dalle2_pytorch/dalle2_pytorch.py @@ -1542,10 +1542,10 @@ class Unet(nn.Module): self_attn = False, attn_dim_head = 32, attn_heads = 16, - lowres_cond = False, # for cascading diffusion - https://cascaded-diffusion.github.io/ - lowres_noise_cond = False, # for conditioning on low resolution noising, based on Imagen + lowres_cond = False, # for cascading diffusion - https://cascaded-diffusion.github.io/ + lowres_noise_cond = False, # for conditioning on low resolution noising, based on Imagen sparse_attn = False, - 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) + 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) cond_on_text_encodings = False, max_text_len = 256, cond_on_image_embeds = False, @@ -2100,7 +2100,7 @@ class Decoder(nn.Module): image_sizes = None, # for cascading ddpm, image size at each stage 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) use_noise_for_lowres_cond = False, # whether to use Imagen-like noising for low resolution conditioning - use_blur_for_lowres_cond = True, # whether to use the blur conditioning used in the original cascading ddpm paper, as well as DALL-E2 + use_blur_for_lowres_cond = True, # whether to use the blur conditioning used in the original cascading ddpm paper, as well as DALL-E2 lowres_downsample_first = True, # cascading ddpm - resizes to lower resolution, then to next conditional resolution + blur blur_prob = 0.5, # cascading ddpm - when training, the gaussian blur is only applied 50% of the time blur_sigma = 0.6, # cascading ddpm - blur sigma