The starting formula is used to find an **EXPONENTIAL** Moving Average (not a standard moving average) of a stock price (close). This places more emphasis on the latest close value over the previous row's values. For this example I am looking for a 9-Exponential Moving Average, which ...
Each Unet will also need its own corresponding exponential moving average. The DecoderTrainer hopes to make this simple, as shown below import torch from dalle2_pytorch import DALLE2, Unet, Decoder, CLIP, DecoderTrainer clip = CLIP( dim_text = 512, dim_image = 512, dim_latent = 512, ...
Each Unet will also need its own corresponding exponential moving average. The DecoderTrainer hopes to make this simple, as shown below import torch from dalle2_pytorch import DALLE2, Unet, Decoder, CLIP, DecoderTrainer clip = CLIP( dim_text = 512, dim_image = 512, dim_latent = 512, ...
update() # this will update the optimizer as well as the exponential moving averaged diffusion prior # after much of the above three lines in a loop # you can sample from the exponential moving average of the diffusion prior identically to how you do so for DiffusionPrior image_embeds = ...
Each Unet will also need its own corresponding exponential moving average. The DecoderTrainer hopes to make this simple, as shown below import torch from dalle2_pytorch import DALLE2, Unet, Decoder, CLIP, DecoderTrainer clip = CLIP( dim_text = 512, dim_image = 512, dim_latent = 512, ...
update(unet_number) # update the specific unet as well as its exponential moving average # after much training # you can sample from the exponentially moving averaged unets as so mock_image_embed = torch.randn(4, 512).cuda() images = decoder_trainer.sample(mock_image_embed, text = text...
Each Unet will also need its own corresponding exponential moving average. The DecoderTrainer hopes to make this simple, as shown below import torch from dalle2_pytorch import DALLE2, Unet, Decoder, CLIP, DecoderTrainer clip = CLIP( dim_text = 512, dim_image = 512, dim_latent = 512, ...
update(unet_number) # update the specific unet as well as its exponential moving average # after much training # you can sample from the exponentially moving averaged unets as so mock_image_embed = torch.randn(4, 512).cuda() images = decoder_trainer.sample(mock_image_embed, text = text...
Decoder -In-progress test run🚧 DALL-E 2 🚧 Install $ pip install dalle2-pytorch Usage To train DALLE-2 is a 3 step process, with the training of CLIP being the most important To train CLIP, you can either usex-clippackage, or join the LAION discord, where a lot of replication...