more attention

Fix spelling and grammatical errors

README.md

@@ -47,7 +47,7 @@ clip = CLIP(
     use_all_token_embeds = True,            # whether to use fine-grained contrastive learning (FILIP)
     decoupled_contrastive_learning = True,  # use decoupled contrastive learning (DCL) objective function, removing positive pairs from the denominator of the InfoNCE loss (CLOOB + DCL)
     extra_latent_projection = True,         # whether to use separate projections for text-to-image vs image-to-text comparisons (CLOOB)
-    use_visual_ssl = True,                  # whether to do self supervised learning on iages
+    use_visual_ssl = True,                  # whether to do self supervised learning on images
     visual_ssl_type = 'simclr',             # can be either 'simclr' or 'simsiam', depending on using DeCLIP or SLIP
     use_mlm = False,                        # use masked language learning (MLM) on text (DeCLIP)
     text_ssl_loss_weight = 0.05,            # weight for text MLM loss
@@ -110,7 +110,8 @@ decoder = Decoder(
     unet = unet,
     clip = clip,
     timesteps = 100,
-    cond_drop_prob = 0.2
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 # mock images (get a lot of this)
@@ -229,7 +230,8 @@ decoder = Decoder(
     unet = (unet1, unet2),            # insert both unets in order of low resolution to highest resolution (you can have as many stages as you want here)
     image_sizes = (256, 512),         # resolutions, 256 for first unet, 512 for second. these must be unique and in ascending order (matches with the unets passed in)
     timesteps = 1000,
-    cond_drop_prob = 0.2
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 # mock images (get a lot of this)
@@ -348,7 +350,8 @@ decoder = Decoder(
     image_sizes = (128, 256),
     clip = clip,
     timesteps = 100,
-    cond_drop_prob = 0.2,
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5,
     condition_on_text_encodings = False  # set this to True if you wish to condition on text during training and sampling
 ).cuda()
 
@@ -558,7 +561,8 @@ decoder = Decoder(
     image_sizes = (128, 256),
     clip = clip,
     timesteps = 100,
-    cond_drop_prob = 0.2,
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5,
     condition_on_text_encodings = False  # set this to True if you wish to condition on text during training and sampling
 ).cuda()
 
@@ -616,7 +620,7 @@ clip = CLIP(
 # 3 unets for the decoder (a la cascading DDPM)
 
 # first two unets are doing latent diffusion
-# vqgan-vae must be trained before hand
+# vqgan-vae must be trained beforehand
 
 vae1 = VQGanVAE(
     dim = 32,
@@ -669,7 +673,8 @@ decoder = Decoder(
     unet = (unet1, unet2, unet3),      # insert unets in order of low resolution to highest resolution (you can have as many stages as you want here)
     image_sizes = (256, 512, 1024),    # resolutions, 256 for first unet, 512 for second, 1024 for third
     timesteps = 100,
-    cond_drop_prob = 0.2
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 # mock images (get a lot of this)
@@ -703,7 +708,83 @@ images = decoder.sample(mock_image_embed) # (1, 3, 1024, 1024)
 
 ## Training wrapper (wip)
 
-Offer training wrappers
+### Decoder Training
+
+Training the `Decoder` may be confusing, as one needs to keep track of an optimizer for each of the `Unet`(s) separately. Each `Unet` will also need its own corresponding exponential moving average. The `DecoderTrainer` hopes to make this simple, as shown below
+
+```python
+import torch
+from dalle2_pytorch import DALLE2, Unet, Decoder, CLIP, DecoderTrainer
+
+clip = CLIP(
+    dim_text = 512,
+    dim_image = 512,
+    dim_latent = 512,
+    num_text_tokens = 49408,
+    text_enc_depth = 6,
+    text_seq_len = 256,
+    text_heads = 8,
+    visual_enc_depth = 6,
+    visual_image_size = 256,
+    visual_patch_size = 32,
+    visual_heads = 8
+).cuda()
+
+# mock data
+
+text = torch.randint(0, 49408, (4, 256)).cuda()
+images = torch.randn(4, 3, 256, 256).cuda()
+
+# decoder (with unet)
+
+unet1 = Unet(
+    dim = 128,
+    image_embed_dim = 512,
+    text_embed_dim = 512,
+    cond_dim = 128,
+    channels = 3,
+    dim_mults=(1, 2, 4, 8)
+).cuda()
+
+unet2 = Unet(
+    dim = 16,
+    image_embed_dim = 512,
+    text_embed_dim = 512,
+    cond_dim = 128,
+    channels = 3,
+    dim_mults = (1, 2, 4, 8, 16),
+    cond_on_text_encodings = True
+).cuda()
+
+decoder = Decoder(
+    unet = (unet1, unet2),
+    image_sizes = (128, 256),
+    clip = clip,
+    timesteps = 1000,
+    condition_on_text_encodings = True
+).cuda()
+
+decoder_trainer = DecoderTrainer(
+    decoder,
+    lr = 3e-4,
+    wd = 1e-2,
+    ema_beta = 0.99,
+    ema_update_after_step = 1000,
+    ema_update_every = 10,
+)
+
+for unet_number in (1, 2):
+    loss = decoder_trainer(images, text = text, unet_number = unet_number)  # use the decoder_trainer forward
+    loss.backward()
+
+    decoder_trainer.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) # (4, 3, 256, 256)
+```
 
 ## CLI (wip)
 
@@ -736,14 +817,17 @@ Once built, images will be saved to the same directory the command is invoked
 - [x] use inheritance just this once for sharing logic between decoder and prior network ddpms
 - [x] bring in vit-vqgan https://arxiv.org/abs/2110.04627 for the latent diffusion
 - [x] abstract interface for CLIP adapter class, so other CLIPs can be brought in
+- [x] take care of mixed precision as well as gradient accumulation within decoder trainer
+- [x] just take care of the training for the decoder in a wrapper class, as each unet in the cascade will need its own optimizer
+- [x] bring in tools to train vqgan-vae
+- [x] add convnext backbone for vqgan-vae (in addition to vit [vit-vqgan] + resnet)
 - [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet
 - [ ] copy the cascading ddpm code to a separate repo (perhaps https://github.com/lucidrains/denoising-diffusion-pytorch) as the main contribution of dalle2 really is just the prior network
 - [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
-- [ ] just take care of the training for the decoder in a wrapper class, as each unet in the cascade will need its own optimizer
 - [ ] train on a toy task, offer in colab
 - [ ] think about how best to design a declarative training config that handles preencoding for prior and training of multiple networks in decoder
 - [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
-- [ ] bring in tools to train vqgan-vae
+- [ ] bring in cross-scale embedding from iclr paper https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/crossformer.py#L14
 
 ## Citations
 

dalle2_pytorch/__init__.py

@@ -1,5 +1,6 @@
 from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder
 from dalle2_pytorch.dalle2_pytorch import OpenAIClipAdapter
+from dalle2_pytorch.train import DecoderTrainer
 
 from dalle2_pytorch.vqgan_vae import VQGanVAE
 from x_clip import CLIP

dalle2_pytorch/dalle2_pytorch.py

@@ -173,12 +173,12 @@ class OpenAIClipAdapter(BaseClipAdapter):
         name = 'ViT-B/32'
     ):
         import clip
-        openai_clip, _ = clip.load(name)
+        openai_clip, preprocess = clip.load(name)
         super().__init__(openai_clip)
 
         text_attention_final = self.find_layer('ln_final')
         self.handle = text_attention_final.register_forward_hook(self._hook)
-        self.clip_normalize = T.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+        self.clip_normalize = preprocess.transforms[-1]
         self.cleared = False
 
     def find_layer(self,  layer):
@@ -736,6 +736,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
         predict_x_start = True,
         beta_schedule = "cosine",
         condition_on_text_encodings = True, # the paper suggests this is needed, but you can turn it off for your CLIP preprocessed text embed -> image embed training
+        sampling_clamp_l2norm = False
     ):
         super().__init__(
             beta_schedule = beta_schedule,
@@ -764,6 +765,9 @@ class DiffusionPrior(BaseGaussianDiffusion):
         self.predict_x_start = predict_x_start
         # in paper, they do not predict the noise, but predict x0 directly for image embedding, claiming empirically better results. I'll just offer both.
 
+        # whether to force an l2norm, similar to clipping denoised, when sampling
+        self.sampling_clamp_l2norm = sampling_clamp_l2norm
+
     def p_mean_variance(self, x, t, text_cond, clip_denoised: bool):
         pred = self.net(x, t, **text_cond)
 
@@ -777,6 +781,9 @@ class DiffusionPrior(BaseGaussianDiffusion):
         if clip_denoised and not self.predict_x_start:
             x_recon.clamp_(-1., 1.)
 
+        if self.predict_x_start and self.sampling_clamp_l2norm:
+            x_recon = l2norm(x_recon)
+
         model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
         return model_mean, posterior_variance, posterior_log_variance
 
@@ -915,6 +922,7 @@ class ConvNextBlock(nn.Module):
         dim_out,
         *,
         cond_dim = None,
+        time_cond_dim = None,
         mult = 2,
         norm = True
     ):
@@ -933,6 +941,14 @@ class ConvNextBlock(nn.Module):
                 )
             )
 
+        self.time_mlp = None
+
+        if exists(time_cond_dim):
+            self.time_mlp = nn.Sequential(
+                nn.GELU(),
+                nn.Linear(time_cond_dim, dim)
+            )
+
         self.ds_conv = nn.Conv2d(dim, dim, 7, padding = 3, groups = dim)
 
         inner_dim = int(dim_out * mult)
@@ -945,9 +961,13 @@ class ConvNextBlock(nn.Module):
 
         self.res_conv = nn.Conv2d(dim, dim_out, 1) if need_projection else nn.Identity()
 
-    def forward(self, x, cond = None):
+    def forward(self, x, cond = None, time = None):
         h = self.ds_conv(x)
 
+        if exists(time) and exists(self.time_mlp):
+            t = self.time_mlp(time)
+            h = rearrange(t, 'b c -> b c 1 1') + h
+
         if exists(self.cross_attn):
             assert exists(cond)
             h = self.cross_attn(h, context = cond) + h
@@ -1044,7 +1064,7 @@ class Unet(nn.Module):
         dim_mults=(1, 2, 4, 8),
         channels = 3,
         attn_dim_head = 32,
-        attn_heads = 8,
+        attn_heads = 16,
         lowres_cond = False, # for cascading diffusion - https://cascaded-diffusion.github.io/
         sparse_attn = False,
         sparse_attn_window = 8,  # window size for sparse attention
@@ -1052,6 +1072,8 @@ class Unet(nn.Module):
         cond_on_text_encodings = False,
         max_text_len = 256,
         cond_on_image_embeds = False,
+        init_dim = None,
+        init_conv_kernel_size = 7
     ):
         super().__init__()
         # save locals to take care of some hyperparameters for cascading DDPM
@@ -1069,28 +1091,45 @@ class Unet(nn.Module):
         self.channels = channels
 
         init_channels = channels if not lowres_cond else channels * 2 # in cascading diffusion, one concats the low resolution image, blurred, for conditioning the higher resolution synthesis
+        init_dim = default(init_dim, dim // 2)
 
-        dims = [init_channels, *map(lambda m: dim * m, dim_mults)]
+        assert (init_conv_kernel_size % 2) == 1
+        self.init_conv = nn.Conv2d(init_channels, init_dim, init_conv_kernel_size, padding = init_conv_kernel_size // 2)
+
+        dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
         in_out = list(zip(dims[:-1], dims[1:]))
 
         # time, image embeddings, and optional text encoding
 
         cond_dim = default(cond_dim, dim)
+        time_cond_dim = dim * 4
 
-        self.time_mlp = nn.Sequential(
+        self.to_time_hiddens = nn.Sequential(
             SinusoidalPosEmb(dim),
-            nn.Linear(dim, dim * 4),
-            nn.GELU(),
-            nn.Linear(dim * 4, cond_dim * num_time_tokens),
+            nn.Linear(dim, time_cond_dim),
+            nn.GELU()
+        )
+
+        self.to_time_tokens = nn.Sequential(
+            nn.Linear(time_cond_dim, cond_dim * num_time_tokens),
             Rearrange('b (r d) -> b r d', r = num_time_tokens)
         )
 
+        self.to_time_cond = nn.Sequential(
+            nn.Linear(time_cond_dim, time_cond_dim)
+        )
+
         self.image_to_cond = nn.Sequential(
             nn.Linear(image_embed_dim, cond_dim * num_image_tokens),
             Rearrange('b (n d) -> b n d', n = num_image_tokens)
         ) if image_embed_dim != cond_dim else nn.Identity()
 
-        self.text_to_cond = nn.LazyLinear(cond_dim) if not exists(text_embed_dim) else nn.Linear(text_embed_dim, cond_dim)
+        # text encoding conditioning (optional)
+
+        self.text_to_cond = None
+
+        if cond_on_text_encodings:
+            self.text_to_cond = nn.LazyLinear(cond_dim) if not exists(text_embed_dim) else nn.Linear(text_embed_dim, cond_dim)
 
         # finer control over whether to condition on image embeddings and text encodings
         # so one can have the latter unets in the cascading DDPMs only focus on super-resoluting
@@ -1101,6 +1140,8 @@ class Unet(nn.Module):
         # for classifier free guidance
 
         self.null_image_embed = nn.Parameter(torch.randn(1, num_image_tokens, cond_dim))
+
+        self.max_text_len = max_text_len
         self.null_text_embed = nn.Parameter(torch.randn(1, max_text_len, cond_dim))
 
         # attention related params
@@ -1119,26 +1160,26 @@ class Unet(nn.Module):
             layer_cond_dim = cond_dim if not is_first else None
 
             self.downs.append(nn.ModuleList([
-                ConvNextBlock(dim_in, dim_out, norm = ind != 0),
+                ConvNextBlock(dim_in, dim_out, time_cond_dim = time_cond_dim, norm = ind != 0),
                 Residual(GridAttention(dim_out, window_size = sparse_attn_window, **attn_kwargs)) if sparse_attn else nn.Identity(),
-                ConvNextBlock(dim_out, dim_out, cond_dim = layer_cond_dim),
+                ConvNextBlock(dim_out, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim),
                 Downsample(dim_out) if not is_last else nn.Identity()
             ]))
 
         mid_dim = dims[-1]
 
-        self.mid_block1 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim)
+        self.mid_block1 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim)
         self.mid_attn = EinopsToAndFrom('b c h w', 'b (h w) c', Residual(Attention(mid_dim, **attn_kwargs))) if attend_at_middle else None
-        self.mid_block2 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim)
+        self.mid_block2 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim)
 
         for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
             is_last = ind >= (num_resolutions - 2)
             layer_cond_dim = cond_dim if not is_last else None
 
             self.ups.append(nn.ModuleList([
-                ConvNextBlock(dim_out * 2, dim_in, cond_dim = layer_cond_dim),
+                ConvNextBlock(dim_out * 2, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim),
                 Residual(GridAttention(dim_in, window_size = sparse_attn_window, **attn_kwargs)) if sparse_attn else nn.Identity(),
-                ConvNextBlock(dim_in, dim_in, cond_dim = layer_cond_dim),
+                ConvNextBlock(dim_in, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim),
                 Upsample(dim_in)
             ]))
 
@@ -1174,7 +1215,7 @@ class Unet(nn.Module):
         if cond_scale == 1:
             return logits
 
-        null_logits = self.forward(*args, cond_drop_prob = 1., **kwargs)
+        null_logits = self.forward(*args, text_cond_drop_prob = 1., image_cond_drop_prob = 1., **kwargs)
         return null_logits + (logits - null_logits) * cond_scale
 
     def forward(
@@ -1185,7 +1226,9 @@ class Unet(nn.Module):
         image_embed,
         lowres_cond_img = None,
         text_encodings = None,
-        cond_drop_prob = 0.,
+        text_mask = None,
+        image_cond_drop_prob = 0.,
+        text_cond_drop_prob = 0.,
         blur_sigma = None,
         blur_kernel_size = None
     ):
@@ -1198,14 +1241,23 @@ class Unet(nn.Module):
         if exists(lowres_cond_img):
             x = torch.cat((x, lowres_cond_img), dim = 1)
 
+        # initial convolution
+
+        x = self.init_conv(x)
+
         # time conditioning
 
-        time_tokens = self.time_mlp(time)
+        time_hiddens = self.to_time_hiddens(time)
+
+        time_tokens = self.to_time_tokens(time_hiddens)
+        t = self.to_time_cond(time_hiddens)
 
         # conditional dropout
 
-        keep_mask = prob_mask_like((batch_size,), 1 - cond_drop_prob, device = device)
-        keep_mask = rearrange(keep_mask, 'b -> b 1 1')
+        image_keep_mask = prob_mask_like((batch_size,), 1 - image_cond_drop_prob, device = device)
+        text_keep_mask = prob_mask_like((batch_size,), 1 - text_cond_drop_prob, device = device)
+
+        image_keep_mask, text_keep_mask = rearrange_many((image_keep_mask, text_keep_mask), 'b -> b 1 1')
 
         # mask out image embedding depending on condition dropout
         # for classifier free guidance
@@ -1216,7 +1268,7 @@ class Unet(nn.Module):
             image_tokens = self.image_to_cond(image_embed)
 
             image_tokens = torch.where(
-                keep_mask,
+                image_keep_mask,
                 image_tokens,
                 self.null_image_embed
             )
@@ -1227,10 +1279,25 @@ class Unet(nn.Module):
 
         if exists(text_encodings) and self.cond_on_text_encodings:
             text_tokens = self.text_to_cond(text_encodings)
+            text_tokens = text_tokens[:, :self.max_text_len]
+
+            text_tokens_len = text_tokens.shape[1]
+            remainder = self.max_text_len - text_tokens_len
+
+            if remainder > 0:
+                text_tokens = F.pad(text_tokens, (0, 0, 0, remainder))
+
+            if exists(text_mask):
+                if remainder > 0:
+                    text_mask = F.pad(text_mask, (0, remainder), value = False)
+
+                text_mask = rearrange(text_mask, 'b n -> b n 1')
+                text_keep_mask = text_mask & text_keep_mask
+
             text_tokens = torch.where(
-                keep_mask,
+                text_keep_mask,
                 text_tokens,
-                self.null_text_embed[:, :text_tokens.shape[1]]
+                self.null_text_embed
             )
 
         # main conditioning tokens (c)
@@ -1250,24 +1317,24 @@ class Unet(nn.Module):
         hiddens = []
 
         for convnext, sparse_attn, convnext2, downsample in self.downs:
-            x = convnext(x, c)
+            x = convnext(x, c, t)
             x = sparse_attn(x)
-            x = convnext2(x, c)
+            x = convnext2(x, c, t)
             hiddens.append(x)
             x = downsample(x)
 
-        x = self.mid_block1(x, mid_c)
+        x = self.mid_block1(x, mid_c, t)
 
         if exists(self.mid_attn):
             x = self.mid_attn(x)
 
-        x = self.mid_block2(x, mid_c)
+        x = self.mid_block2(x, mid_c, t)
 
         for convnext, sparse_attn, convnext2, upsample in self.ups:
             x = torch.cat((x, hiddens.pop()), dim=1)
-            x = convnext(x, c)
+            x = convnext(x, c, t)
             x = sparse_attn(x)
-            x = convnext2(x, c)
+            x = convnext2(x, c, t)
             x = upsample(x)
 
         return self.final_conv(x)
@@ -1318,7 +1385,8 @@ class Decoder(BaseGaussianDiffusion):
         clip,
         vae = tuple(),
         timesteps = 1000,
-        cond_drop_prob = 0.2,
+        image_cond_drop_prob = 0.1,
+        text_cond_drop_prob = 0.5,
         loss_type = 'l1',
         beta_schedule = 'cosine',
         predict_x_start = False,
@@ -1329,6 +1397,8 @@ class Decoder(BaseGaussianDiffusion):
         blur_sigma = 0.1,                           # cascading ddpm - blur sigma
         blur_kernel_size = 3,                       # cascading ddpm - blur kernel size
         condition_on_text_encodings = False,        # the paper suggested that this didn't do much in the decoder, but i'm allowing the option for experimentation
+        clip_denoised = True,
+        clip_x_start = True
     ):
         super().__init__(
             beta_schedule = beta_schedule,
@@ -1402,7 +1472,13 @@ class Decoder(BaseGaussianDiffusion):
 
         # classifier free guidance
 
-        self.cond_drop_prob = cond_drop_prob
+        self.image_cond_drop_prob = image_cond_drop_prob
+        self.text_cond_drop_prob = text_cond_drop_prob
+
+        # whether to clip when sampling
+
+        self.clip_denoised = clip_denoised
+        self.clip_x_start = clip_x_start
 
     def get_unet(self, unet_number):
         assert 0 < unet_number <= len(self.unets)
@@ -1429,31 +1505,31 @@ class Decoder(BaseGaussianDiffusion):
         image_embed, _ = self.clip.embed_image(image)
         return image_embed
 
-    def p_mean_variance(self, unet, x, t, image_embed, text_encodings = None, lowres_cond_img = None, clip_denoised = True, predict_x_start = False, cond_scale = 1.):
-        pred = unet.forward_with_cond_scale(x, t, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img)
+    def p_mean_variance(self, unet, x, t, image_embed, text_encodings = None, text_mask = None, lowres_cond_img = None, clip_denoised = True, predict_x_start = False, cond_scale = 1.):
+        pred = unet.forward_with_cond_scale(x, t, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img)
 
         if predict_x_start:
             x_recon = pred
         else:
             x_recon = self.predict_start_from_noise(x, t = t, noise = pred)
 
-        if clip_denoised and not predict_x_start:
+        if clip_denoised:
             x_recon.clamp_(-1., 1.)
 
         model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
         return model_mean, posterior_variance, posterior_log_variance
 
     @torch.no_grad()
-    def p_sample(self, unet, x, t, image_embed, text_encodings = None, cond_scale = 1., lowres_cond_img = None, predict_x_start = False, clip_denoised = True, repeat_noise = False):
+    def p_sample(self, unet, x, t, image_embed, text_encodings = None, text_mask = None, cond_scale = 1., lowres_cond_img = None, predict_x_start = False, clip_denoised = True, repeat_noise = False):
         b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(unet, x = x, t = t, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, clip_denoised = clip_denoised, predict_x_start = predict_x_start)
+        model_mean, _, model_log_variance = self.p_mean_variance(unet, x = x, t = t, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, clip_denoised = clip_denoised, predict_x_start = predict_x_start)
         noise = noise_like(x.shape, device, repeat_noise)
         # no noise when t == 0
         nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
 
     @torch.no_grad()
-    def p_sample_loop(self, unet, shape, image_embed, predict_x_start = False, lowres_cond_img = None, text_encodings = None, cond_scale = 1):
+    def p_sample_loop(self, unet, shape, image_embed, predict_x_start = False, clip_denoised = True, lowres_cond_img = None, text_encodings = None, text_mask = None, cond_scale = 1):
         device = self.betas.device
 
         b = shape[0]
@@ -1466,14 +1542,16 @@ class Decoder(BaseGaussianDiffusion):
                 torch.full((b,), i, device = device, dtype = torch.long),
                 image_embed = image_embed,
                 text_encodings = text_encodings,
+                text_mask = text_mask,
                 cond_scale = cond_scale,
                 lowres_cond_img = lowres_cond_img,
-                predict_x_start = predict_x_start
+                predict_x_start = predict_x_start,
+                clip_denoised = clip_denoised
             )
 
         return img
 
-    def p_losses(self, unet, x_start, times, *, image_embed, lowres_cond_img = None, text_encodings = None, predict_x_start = False, noise = None):
+    def p_losses(self, unet, x_start, times, *, image_embed, lowres_cond_img = None, text_encodings = None, text_mask = None, predict_x_start = False, noise = None):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
         x_noisy = self.q_sample(x_start = x_start, t = times, noise = noise)
@@ -1483,8 +1561,10 @@ class Decoder(BaseGaussianDiffusion):
             times,
             image_embed = image_embed,
             text_encodings = text_encodings,
+            text_mask = text_mask,
             lowres_cond_img = lowres_cond_img,
-            cond_drop_prob = self.cond_drop_prob
+            image_cond_drop_prob = self.image_cond_drop_prob,
+            text_cond_drop_prob = self.text_cond_drop_prob,
         )
 
         target = noise if not predict_x_start else x_start
@@ -1494,19 +1574,25 @@ class Decoder(BaseGaussianDiffusion):
 
     @torch.no_grad()
     @eval_decorator
-    def sample(self, image_embed, text = None, cond_scale = 1.):
+    def sample(
+        self,
+        image_embed,
+        text = None,
+        cond_scale = 1.,
+        stop_at_unet_number = None
+    ):
         batch_size = image_embed.shape[0]
 
-        text_encodings = None
+        text_encodings = text_mask = None
         if exists(text):
-            _, text_encodings, _ = self.clip.embed_text(text)
+            _, text_encodings, text_mask = self.clip.embed_text(text)
 
         assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
         assert not (not self.condition_on_text_encodings and exists(text_encodings)), 'decoder specified not to be conditioned on text, yet it is presented'
 
         img = None
 
-        for unet, vae, channel, image_size, predict_x_start in tqdm(zip(self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start)):
+        for unet_number, unet, vae, channel, image_size, predict_x_start in tqdm(zip(range(1, len(self.unets) + 1), self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start)):
 
             context = self.one_unet_in_gpu(unet = unet) if image_embed.is_cuda else null_context()
 
@@ -1517,6 +1603,7 @@ class Decoder(BaseGaussianDiffusion):
                 if unet.lowres_cond:
                     lowres_cond_img = self.to_lowres_cond(img, target_image_size = image_size)
 
+                is_latent_diffusion = isinstance(vae, VQGanVAE)
                 image_size = vae.get_encoded_fmap_size(image_size)
                 shape = (batch_size, vae.encoded_dim, image_size, image_size)
 
@@ -1528,13 +1615,18 @@ class Decoder(BaseGaussianDiffusion):
                     shape,
                     image_embed = image_embed,
                     text_encodings = text_encodings,
+                    text_mask = text_mask,
                     cond_scale = cond_scale,
                     predict_x_start = predict_x_start,
+                    clip_denoised = not is_latent_diffusion,
                     lowres_cond_img = lowres_cond_img
                 )
 
                 img = vae.decode(img)
 
+            if exists(stop_at_unet_number) and stop_at_unet_number == unet_number:
+                break
+
         return img
 
     def forward(
@@ -1565,9 +1657,9 @@ class Decoder(BaseGaussianDiffusion):
         if not exists(image_embed):
             image_embed, _ = self.clip.embed_image(image)
 
-        text_encodings = None
+        text_encodings = text_mask = None
         if exists(text) and not exists(text_encodings):
-            _, text_encodings, _ = self.clip.embed_text(text)
+            _, text_encodings, text_mask = self.clip.embed_text(text)
 
         assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
         assert not (not self.condition_on_text_encodings and exists(text_encodings)), 'decoder specified not to be conditioned on text, yet it is presented'
@@ -1582,7 +1674,7 @@ class Decoder(BaseGaussianDiffusion):
             if exists(lowres_cond_img):
                 lowres_cond_img = vae.encode(lowres_cond_img)
 
-        return self.p_losses(unet, image, times, image_embed = image_embed, text_encodings = text_encodings, lowres_cond_img = lowres_cond_img, predict_x_start = predict_x_start)
+        return self.p_losses(unet, image, times, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, lowres_cond_img = lowres_cond_img, predict_x_start = predict_x_start)
 
 # main class
 

dalle2_pytorch/train.py

@@ -1,6 +1,43 @@
 import copy
+from functools import partial
+
 import torch
 from torch import nn
+from torch.cuda.amp import autocast, GradScaler
+
+from dalle2_pytorch.dalle2_pytorch import Decoder
+from dalle2_pytorch.optimizer import get_optimizer
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def cast_tuple(val, length = 1):
+    return val if isinstance(val, tuple) else ((val,) * length)
+
+def pick_and_pop(keys, d):
+    values = list(map(lambda key: d.pop(key), keys))
+    return dict(zip(keys, values))
+
+def group_dict_by_key(cond, d):
+    return_val = [dict(),dict()]
+    for key in d.keys():
+        match = bool(cond(key))
+        ind = int(not match)
+        return_val[ind][key] = d[key]
+    return (*return_val,)
+
+def string_begins_with(prefix, str):
+    return str.startswith(prefix)
+
+def group_by_key_prefix(prefix, d):
+    return group_dict_by_key(partial(string_begins_with, prefix), d)
+
+def groupby_prefix_and_trim(prefix, d):
+    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
+    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+    return kwargs_without_prefix, kwargs
 
 # exponential moving average wrapper
 
@@ -9,16 +46,16 @@ class EMA(nn.Module):
         self,
         model,
         beta = 0.99,
-        ema_update_after_step = 1000,
-        ema_update_every = 10,
+        update_after_step = 1000,
+        update_every = 10,
     ):
         super().__init__()
         self.beta = beta
         self.online_model = model
         self.ema_model = copy.deepcopy(model)
 
-        self.ema_update_after_step = ema_update_after_step # only start EMA after this step number, starting at 0
-        self.ema_update_every = ema_update_every
+        self.update_after_step = update_after_step # only start EMA after this step number, starting at 0
+        self.update_every = update_every
 
         self.register_buffer('initted', torch.Tensor([False]))
         self.register_buffer('step', torch.tensor([0.]))
@@ -26,7 +63,7 @@ class EMA(nn.Module):
     def update(self):
         self.step += 1
 
-        if self.step <= self.ema_update_after_step or (self.step % self.ema_update_every) != 0:
+        if self.step <= self.update_after_step or (self.step % self.update_every) != 0:
             return
 
         if not self.initted:
@@ -51,3 +88,111 @@ class EMA(nn.Module):
 
     def __call__(self, *args, **kwargs):
         return self.ema_model(*args, **kwargs)
+
+# trainers
+
+class DecoderTrainer(nn.Module):
+    def __init__(
+        self,
+        decoder,
+        use_ema = True,
+        lr = 3e-4,
+        wd = 1e-2,
+        max_grad_norm = None,
+        amp = False,
+        **kwargs
+    ):
+        super().__init__()
+        assert isinstance(decoder, Decoder)
+        ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
+
+        self.decoder = decoder
+        self.num_unets = len(self.decoder.unets)
+
+        self.use_ema = use_ema
+
+        if use_ema:
+            has_lazy_linear = any([type(module) == nn.LazyLinear for module in decoder.modules()])
+            assert not has_lazy_linear, 'you must set the text_embed_dim on your u-nets if you plan on doing automatic exponential moving average'
+
+        self.ema_unets = nn.ModuleList([])
+
+        self.amp = amp
+
+        # be able to finely customize learning rate, weight decay
+        # per unet
+
+        lr, wd = map(partial(cast_tuple, length = self.num_unets), (lr, wd))
+
+        for ind, (unet, unet_lr, unet_wd) in enumerate(zip(self.decoder.unets, lr, wd)):
+            optimizer = get_optimizer(
+                unet.parameters(),
+                lr = unet_lr,
+                wd = unet_wd,
+                **kwargs
+            )
+
+            setattr(self, f'optim{ind}', optimizer) # cannot use pytorch ModuleList for some reason with optimizers
+
+            if self.use_ema:
+                self.ema_unets.append(EMA(unet, **ema_kwargs))
+
+            scaler = GradScaler(enabled = amp)
+            setattr(self, f'scaler{ind}', scaler)
+
+        # gradient clipping if needed
+
+        self.max_grad_norm = max_grad_norm
+
+    @property
+    def unets(self):
+        return nn.ModuleList([ema.ema_model for ema in self.ema_unets])
+
+    def scale(self, loss, *, unet_number):
+        assert 1 <= unet_number <= self.num_unets
+        index = unet_number - 1
+        scaler = getattr(self, f'scaler{index}')
+        return scaler.scale(loss)
+
+    def update(self, unet_number):
+        assert 1 <= unet_number <= self.num_unets
+        index = unet_number - 1
+        unet = self.decoder.unets[index]
+
+        if exists(self.max_grad_norm):
+            nn.utils.clip_grad_norm_(unet.parameters(), self.max_grad_norm)
+
+        optimizer = getattr(self, f'optim{index}')
+        scaler = getattr(self, f'scaler{index}')
+
+        scaler.step(optimizer)
+        scaler.update()
+        optimizer.zero_grad()
+
+        if self.use_ema:
+            ema_unet = self.ema_unets[index]
+            ema_unet.update()
+
+    @torch.no_grad()
+    def sample(self, *args, **kwargs):
+        if self.use_ema:
+            trainable_unets = self.decoder.unets
+            self.decoder.unets = self.unets                  # swap in exponential moving averaged unets for sampling
+
+        output = self.decoder.sample(*args, **kwargs)
+
+        if self.use_ema:
+            self.decoder.unets = trainable_unets             # restore original training unets
+        return output
+
+    def forward(
+        self,
+        x,
+        *,
+        unet_number,
+        divisor = 1,
+        **kwargs
+    ):
+        with autocast(enabled = self.amp):
+            loss = self.decoder(x, unet_number = unet_number, **kwargs)
+        return self.scale(loss / divisor, unet_number = unet_number)

dalle2_pytorch/train_vqgan_vae.py

@@ -0,0 +1,266 @@
+from math import sqrt
+import copy
+from random import choice
+from pathlib import Path
+from shutil import rmtree
+
+import torch
+from torch import nn
+
+from PIL import Image
+from torchvision.datasets import ImageFolder
+import torchvision.transforms as T
+from torch.utils.data import Dataset, DataLoader, random_split
+from torchvision.utils import make_grid, save_image
+
+from einops import rearrange
+
+from dalle2_pytorch.train import EMA
+from dalle2_pytorch.vqgan_vae import VQGanVAE
+from dalle2_pytorch.optimizer import get_optimizer
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+def noop(*args, **kwargs):
+    pass
+
+def cycle(dl):
+    while True:
+        for data in dl:
+            yield data
+
+def cast_tuple(t):
+    return t if isinstance(t, (tuple, list)) else (t,)
+
+def yes_or_no(question):
+    answer = input(f'{question} (y/n) ')
+    return answer.lower() in ('yes', 'y')
+
+def accum_log(log, new_logs):
+    for key, new_value in new_logs.items():
+        old_value = log.get(key, 0.)
+        log[key] = old_value + new_value
+    return log
+
+# classes
+
+class ImageDataset(Dataset):
+    def __init__(
+        self,
+        folder,
+        image_size,
+        exts = ['jpg', 'jpeg', 'png']
+    ):
+        super().__init__()
+        self.folder = folder
+        self.image_size = image_size
+        self.paths = [p for ext in exts for p in Path(f'{folder}').glob(f'**/*.{ext}')]
+
+        print(f'{len(self.paths)} training samples found at {folder}')
+
+        self.transform = T.Compose([
+            T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+            T.Resize(image_size),
+            T.RandomHorizontalFlip(),
+            T.CenterCrop(image_size),
+            T.ToTensor()
+        ])
+
+    def __len__(self):
+        return len(self.paths)
+
+    def __getitem__(self, index):
+        path = self.paths[index]
+        img = Image.open(path)
+        return self.transform(img)
+
+# main trainer class
+
+class VQGanVAETrainer(nn.Module):
+    def __init__(
+        self,
+        vae,
+        *,
+        num_train_steps,
+        lr,
+        batch_size,
+        folder,
+        grad_accum_every,
+        wd = 0.,
+        save_results_every = 100,
+        save_model_every = 1000,
+        results_folder = './results',
+        valid_frac = 0.05,
+        random_split_seed = 42,
+        ema_beta = 0.995,
+        ema_update_after_step = 2000,
+        ema_update_every = 10,
+        apply_grad_penalty_every = 4,
+    ):
+        super().__init__()
+        assert isinstance(vae, VQGanVAE), 'vae must be instance of VQGanVAE'
+        image_size = vae.image_size
+
+        self.vae = vae
+        self.ema_vae = EMA(vae, update_after_step = ema_update_after_step, update_every = ema_update_every)
+
+        self.register_buffer('steps', torch.Tensor([0]))
+
+        self.num_train_steps = num_train_steps
+        self.batch_size = batch_size
+        self.grad_accum_every = grad_accum_every
+
+        all_parameters = set(vae.parameters())
+        discr_parameters = set(vae.discr.parameters())
+        vae_parameters = all_parameters - discr_parameters
+
+        self.optim = get_optimizer(vae_parameters, lr = lr, wd = wd)
+        self.discr_optim = get_optimizer(discr_parameters, lr = lr, wd = wd)
+
+        # create dataset
+
+        self.ds = ImageDataset(folder, image_size = image_size)
+
+        # split for validation
+
+        if valid_frac > 0:
+            train_size = int((1 - valid_frac) * len(self.ds))
+            valid_size = len(self.ds) - train_size
+            self.ds, self.valid_ds = random_split(self.ds, [train_size, valid_size], generator = torch.Generator().manual_seed(random_split_seed))
+            print(f'training with dataset of {len(self.ds)} samples and validating with randomly splitted {len(self.valid_ds)} samples')
+        else:
+            self.valid_ds = self.ds
+            print(f'training with shared training and valid dataset of {len(self.ds)} samples')
+
+        # dataloader
+
+        self.dl = cycle(DataLoader(
+            self.ds,
+            batch_size = batch_size,
+            shuffle = True
+        ))
+
+        self.valid_dl = cycle(DataLoader(
+            self.valid_ds,
+            batch_size = batch_size,
+            shuffle = True
+        ))
+
+        self.save_model_every = save_model_every
+        self.save_results_every = save_results_every
+
+        self.apply_grad_penalty_every = apply_grad_penalty_every
+
+        self.results_folder = Path(results_folder)
+
+        if len([*self.results_folder.glob('**/*')]) > 0 and yes_or_no('do you want to clear previous experiment checkpoints and results?'):
+            rmtree(str(self.results_folder))
+
+        self.results_folder.mkdir(parents = True, exist_ok = True)
+
+    def train_step(self):
+        device = next(self.vae.parameters()).device
+        steps = int(self.steps.item())
+        apply_grad_penalty = not (steps % self.apply_grad_penalty_every)
+
+        self.vae.train()
+
+        # logs
+
+        logs = {}
+
+        # update vae (generator)
+
+        for _ in range(self.grad_accum_every):
+            img = next(self.dl)
+            img = img.to(device)
+
+            loss = self.vae(
+                img,
+                return_loss = True,
+                apply_grad_penalty = apply_grad_penalty
+            )
+
+            accum_log(logs, {'loss': loss.item() / self.grad_accum_every})
+
+            (loss / self.grad_accum_every).backward()
+
+        self.optim.step()
+        self.optim.zero_grad()
+
+
+        # update discriminator
+
+        if exists(self.vae.discr):
+            discr_loss = 0
+            for _ in range(self.grad_accum_every):
+                img = next(self.dl)
+                img = img.to(device)
+
+                loss = self.vae(img, return_discr_loss = True)
+                accum_log(logs, {'discr_loss': loss.item() / self.grad_accum_every})
+
+                (loss / self.grad_accum_every).backward()
+
+            self.discr_optim.step()
+            self.discr_optim.zero_grad()
+
+            # log
+
+            print(f"{steps}: vae loss: {logs['loss']} - discr loss: {logs['discr_loss']}")
+
+        # update exponential moving averaged generator
+
+        self.ema_vae.update()
+
+        # sample results every so often
+
+        if not (steps % self.save_results_every):
+            for model, filename in ((self.ema_vae.ema_model, f'{steps}.ema'), (self.vae, str(steps))):
+                model.eval()
+
+                imgs = next(self.dl)
+                imgs = imgs.to(device)
+
+                recons = model(imgs)
+                nrows = int(sqrt(self.batch_size))
+
+                imgs_and_recons = torch.stack((imgs, recons), dim = 0)
+                imgs_and_recons = rearrange(imgs_and_recons, 'r b ... -> (b r) ...')
+
+                imgs_and_recons = imgs_and_recons.detach().cpu().float().clamp(0., 1.)
+                grid = make_grid(imgs_and_recons, nrow = 2, normalize = True, value_range = (0, 1))
+
+                logs['reconstructions'] = grid
+
+                save_image(grid, str(self.results_folder / f'{filename}.png'))
+
+            print(f'{steps}: saving to {str(self.results_folder)}')
+
+        # save model every so often
+
+        if not (steps % self.save_model_every):
+            state_dict = self.vae.state_dict()
+            model_path = str(self.results_folder / f'vae.{steps}.pt')
+            torch.save(state_dict, model_path)
+
+            ema_state_dict = self.ema_vae.state_dict()
+            model_path = str(self.results_folder / f'vae.{steps}.ema.pt')
+            torch.save(ema_state_dict, model_path)
+
+            print(f'{steps}: saving model to {str(self.results_folder)}')
+
+        self.steps += 1
+        return logs
+
+    def train(self, log_fn = noop):
+        device = next(self.vae.parameters()).device
+
+        while self.steps < self.num_train_steps:
+            logs = self.train_step()
+            log_fn(logs)
+
+        print('training complete')

dalle2_pytorch/vqgan_vae.py

@@ -327,6 +327,108 @@ class ResBlock(nn.Module):
     def forward(self, x):
         return self.net(x) + x
 
+# convnext enc dec
+
+class ChanLayerNorm(nn.Module):
+    def __init__(self, dim, eps = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
+
+    def forward(self, x):
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
+        mean = torch.mean(x, dim = 1, keepdim = True)
+        return (x - mean) / (var + self.eps).sqrt() * self.g
+
+class ConvNext(nn.Module):
+    def __init__(self, dim, mult = 4, kernel_size = 3, ds_kernel_size = 7):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        self.net = nn.Sequential(
+            nn.Conv2d(dim, dim, ds_kernel_size, padding = ds_kernel_size // 2, groups = dim),
+            ChanLayerNorm(dim),
+            nn.Conv2d(dim, inner_dim, kernel_size, padding = kernel_size // 2),
+            nn.GELU(),
+            nn.Conv2d(inner_dim, dim, kernel_size, padding = kernel_size // 2)
+        )
+
+    def forward(self, x):
+        return self.net(x) + x
+
+class ConvNextEncDec(nn.Module):
+    def __init__(
+        self,
+        dim,
+        *,
+        channels = 3,
+        layers = 4,
+        layer_mults = None,
+        num_blocks = 1,
+        first_conv_kernel_size = 5,
+        use_attn = True,
+        attn_dim_head = 64,
+        attn_heads = 8,
+        attn_dropout = 0.,
+    ):
+        super().__init__()
+
+        self.layers = layers
+
+        self.encoders = MList([])
+        self.decoders = MList([])
+
+        layer_mults = default(layer_mults, list(map(lambda t: 2 ** t, range(layers))))
+        assert len(layer_mults) == layers, 'layer multipliers must be equal to designated number of layers'
+
+        layer_dims = [dim * mult for mult in layer_mults]
+        dims = (dim, *layer_dims)
+
+        self.encoded_dim = dims[-1]
+
+        dim_pairs = zip(dims[:-1], dims[1:])
+
+        append = lambda arr, t: arr.append(t)
+        prepend = lambda arr, t: arr.insert(0, t)
+
+        if not isinstance(num_blocks, tuple):
+            num_blocks = (*((0,) * (layers - 1)), num_blocks)
+
+        if not isinstance(use_attn, tuple):
+            use_attn = (*((False,) * (layers - 1)), use_attn)
+
+        assert len(num_blocks) == layers, 'number of blocks config must be equal to number of layers'
+        assert len(use_attn) == layers
+
+        for layer_index, (dim_in, dim_out), layer_num_blocks, layer_use_attn in zip(range(layers), dim_pairs, num_blocks, use_attn):
+            append(self.encoders, nn.Sequential(nn.Conv2d(dim_in, dim_out, 4, stride = 2, padding = 1), leaky_relu()))
+            prepend(self.decoders, nn.Sequential(nn.ConvTranspose2d(dim_out, dim_in, 4, 2, 1), leaky_relu()))
+
+            if layer_use_attn:
+                prepend(self.decoders, VQGanAttention(dim = dim_out, heads = attn_heads, dim_head = attn_dim_head, dropout = attn_dropout))
+
+            for _ in range(layer_num_blocks):
+                append(self.encoders, ConvNext(dim_out))
+                prepend(self.decoders, ConvNext(dim_out))
+
+            if layer_use_attn:
+                append(self.encoders, VQGanAttention(dim = dim_out, heads = attn_heads, dim_head = attn_dim_head, dropout = attn_dropout))
+
+        prepend(self.encoders, nn.Conv2d(channels, dim, first_conv_kernel_size, padding = first_conv_kernel_size // 2))
+        append(self.decoders, nn.Conv2d(dim, channels, 1))
+
+    def get_encoded_fmap_size(self, image_size):
+        return image_size // (2 ** self.layers)
+
+    def encode(self, x):
+        for enc in self.encoders:
+            x = enc(x)
+        return x
+
+    def decode(self, x):
+        for dec in self.decoders:
+            x = dec(x)
+        return x
+
 # vqgan attention layer
 
 class VQGanAttention(nn.Module):
@@ -568,6 +670,8 @@ class VQGanVAE(nn.Module):
             enc_dec_klass = ResnetEncDec
         elif vae_type == 'vit':
             enc_dec_klass = ViTEncDec
+        elif vae_type == 'convnext':
+            enc_dec_klass = ConvNextEncDec
         else:
             raise ValueError(f'{vae_type} not valid')
 

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.73',
+  version = '0.0.87',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',