aljaz/DALLE2-pytorch
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This commit is contained in:
Phil Wang
2022-07-18 15:02:04 -07:00
committed by GitHub
parent da8e99ada0
commit 3676a8ce78
1 changed files with 4 additions and 4 deletions
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dalle2_pytorch/dalle2_pytorch.py
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@@ -1542,10 +1542,10 @@ class Unet(nn.Module):
self_attn = False, self_attn = False,
attn_dim_head = 32, attn_dim_head = 32,
attn_heads = 16, attn_heads = 16,
lowres_cond = False, # for cascading diffusion - https://cascaded-diffusion.github.io/ 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_noise_cond = False, # for conditioning on low resolution noising, based on Imagen
sparse_attn = False, 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, cond_on_text_encodings = False,
max_text_len = 256, max_text_len = 256,
cond_on_image_embeds = False, cond_on_image_embeds = False,
@@ -2100,7 +2100,7 @@ class Decoder(nn.Module):
image_sizes = None, # for cascading ddpm, image size at each stage 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) 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_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 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_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 blur_sigma = 0.6, # cascading ddpm - blur sigma