be able to turn off warning for use of LazyLinear by passing in text embedding dimension for unet

README.md

@@ -348,7 +348,8 @@ decoder = Decoder(
     image_sizes = (128, 256),
     clip = clip,
     timesteps = 100,
-    cond_drop_prob = 0.2
+    cond_drop_prob = 0.2,
+    condition_on_text_encodings = False  # set this to True if you wish to condition on text during training and sampling
 ).cuda()
 
 for unet_number in (1, 2):
@@ -376,6 +377,124 @@ You can also train the decoder on images of greater than the size (say 512x512)
 
 For the layperson, no worries, training will all be automated into a CLI tool, at least for small scale training.
 
+## Training on Preprocessed CLIP Embeddings
+
+It is likely, when scaling up, that you would first preprocess your images and text into corresponding embeddings before training the prior network. You can do so easily by simply passing in `image_embed`, `text_embed`, and optionally `text_encodings` and `text_mask`
+
+Working example below
+
+```python
+import torch
+from dalle2_pytorch import DiffusionPriorNetwork, DiffusionPrior, CLIP
+
+# get trained CLIP from step one
+
+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()
+
+# setup prior network, which contains an autoregressive transformer
+
+prior_network = DiffusionPriorNetwork(
+    dim = 512,
+    depth = 6,
+    dim_head = 64,
+    heads = 8
+).cuda()
+
+# diffusion prior network, which contains the CLIP and network (with transformer) above
+
+diffusion_prior = DiffusionPrior(
+    net = prior_network,
+    clip = clip,
+    timesteps = 100,
+    cond_drop_prob = 0.2,
+    condition_on_text_encodings = False  # this probably should be true, but just to get Laion started
+).cuda()
+
+# mock data
+
+text = torch.randint(0, 49408, (4, 256)).cuda()
+images = torch.randn(4, 3, 256, 256).cuda()
+
+# precompute the text and image embeddings
+# here using the diffusion prior class, but could be done with CLIP alone
+
+clip_image_embeds = diffusion_prior.get_image_embed(images)
+clip_text_embeds = diffusion_prior.get_text_cond(text).get('text_embed')
+
+# feed text and images into diffusion prior network
+
+loss = diffusion_prior(
+    text_embed = clip_text_embeds,
+    image_embed = clip_image_embeds
+)
+
+loss.backward()
+
+# do the above for many many many steps
+# now the diffusion prior can generate image embeddings from the text embeddings
+```
+
+You can also completely go `CLIP`-less, in which case you will need to pass in the `image_embed_dim` into the `DiffusionPrior` on initialization
+
+```python
+import torch
+from dalle2_pytorch import DiffusionPriorNetwork, DiffusionPrior
+
+# setup prior network, which contains an autoregressive transformer
+
+prior_network = DiffusionPriorNetwork(
+    dim = 512,
+    depth = 6,
+    dim_head = 64,
+    heads = 8
+).cuda()
+
+# diffusion prior network, which contains the CLIP and network (with transformer) above
+
+diffusion_prior = DiffusionPrior(
+    net = prior_network,
+    image_embed_dim = 512,               # this needs to be set
+    timesteps = 100,
+    cond_drop_prob = 0.2,
+    condition_on_text_encodings = False  # this probably should be true, but just to get Laion started
+).cuda()
+
+# mock data
+
+text = torch.randint(0, 49408, (4, 256)).cuda()
+images = torch.randn(4, 3, 256, 256).cuda()
+
+# precompute the text and image embeddings
+# here using the diffusion prior class, but could be done with CLIP alone
+
+clip_image_embeds = torch.randn(4, 512).cuda()
+clip_text_embeds = torch.randn(4, 512).cuda()
+
+# feed text and images into diffusion prior network
+
+loss = diffusion_prior(
+    text_embed = clip_text_embeds,
+    image_embed = clip_image_embeds
+)
+
+loss.backward()
+
+# do the above for many many many steps
+# now the diffusion prior can generate image embeddings from the text embeddings
+```
+
 ## Experimental
 
 ### DALL-E2 with Latent Diffusion
@@ -523,7 +642,9 @@ Once built, images will be saved to the same directory the command is invoked
 - [x] offload unets not being trained on to CPU for memory efficiency (for training each resolution unets separately)
 - [x] build out latent diffusion architecture, with the vq-reg variant (vqgan-vae), make it completely optional and compatible with cascading ddpms
 - [x] for decoder, allow ability to customize objective (predict epsilon vs x0), in case latent diffusion does better with prediction of x0
-- [ ] spend one day cleaning up tech debt in decoder
+- [x] use attention-based upsampling https://arxiv.org/abs/2112.11435
+- [x] use inheritance just this once for sharing logic between decoder and prior network ddpms
+- [ ] abstract interface for CLIP adapter class, so other CLIPs can be brought in
 - [ ] 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
@@ -531,7 +652,6 @@ Once built, images will be saved to the same directory the command is invoked
 - [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
 - [ ] bring in tools to train vqgan-vae
 - [ ] bring in vit-vqgan https://arxiv.org/abs/2110.04627 for the latent diffusion
-- [ ] experiment with https://arxiv.org/abs/2112.11435 as upsampler, test in https://github.com/lucidrains/lightweight-gan first
 
 ## Citations
 
@@ -577,14 +697,4 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
-```bibtex
-@article{Arar2021LearnedQF,
-    title   = {Learned Queries for Efficient Local Attention},
-    author  = {Moab Arar and Ariel Shamir and Amit H. Bermano},
-    journal = {ArXiv},
-    year    = {2021},
-    volume  = {abs/2112.11435}
-}
-```
-
 *Creating noise from data is easy; creating data from noise is generative modeling.* - Yang Song's <a href="https://arxiv.org/abs/2011.13456">paper</a>

dalle2_pytorch/attention.py

@@ -0,0 +1,125 @@
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+
+from einops import rearrange, repeat
+
+class LayerNormChan(nn.Module):
+    def __init__(
+        self,
+        dim,
+        eps = 1e-5
+    ):
+        super().__init__()
+        self.eps = eps
+        self.gamma = 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.gamma
+
+# attention-based upsampling
+# from https://arxiv.org/abs/2112.11435
+
+class QueryAndAttend(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        num_queries = 1,
+        dim_head = 32,
+        heads = 8,
+        window_size = 3
+    ):
+        super().__init__()
+        self.scale = dim_head ** -0.5
+        inner_dim = dim_head * heads
+        self.heads = heads
+        self.dim_head = dim_head
+        self.window_size = window_size
+        self.num_queries = num_queries
+
+        self.rel_pos_bias = nn.Parameter(torch.randn(heads, num_queries, window_size * window_size, 1, 1))
+
+        self.queries = nn.Parameter(torch.randn(heads, num_queries, dim_head))
+        self.to_kv = nn.Conv2d(dim, dim_head * 2, 1, bias = False)
+        self.to_out = nn.Conv2d(inner_dim, dim, 1, bias = False)
+
+    def forward(self, x):
+        """
+        einstein notation
+        b - batch
+        h - heads
+        l - num queries
+        d - head dimension
+        x - height
+        y - width
+        j - source sequence for attending to (kernel size squared in this case)
+        """
+
+        wsz, heads, dim_head, num_queries = self.window_size, self.heads, self.dim_head, self.num_queries
+        batch, _, height, width = x.shape
+
+        is_one_query = self.num_queries == 1
+
+        # queries, keys, values
+
+        q = self.queries * self.scale
+        k, v = self.to_kv(x).chunk(2, dim = 1)
+
+        # similarities
+
+        sim = einsum('h l d, b d x y -> b h l x y', q, k)
+        sim = rearrange(sim, 'b ... x y -> b (...) x y')
+
+        # unfold the similarity scores, with float(-inf) as padding value
+
+        mask_value = -torch.finfo(sim.dtype).max
+        sim = F.pad(sim, ((wsz // 2,) * 4), value = mask_value)
+        sim = F.unfold(sim, kernel_size = wsz)
+        sim = rearrange(sim, 'b (h l j) (x y) -> b h l j x y', h = heads, l = num_queries, x = height, y = width)
+
+        # rel pos bias
+
+        sim = sim + self.rel_pos_bias
+
+        # numerically stable attention
+
+        sim = sim - sim.amax(dim = -3, keepdim = True).detach()
+        attn = sim.softmax(dim = -3)
+
+        # unfold values
+
+        v = F.pad(v, ((wsz // 2,) * 4), value = 0.)
+        v = F.unfold(v, kernel_size = wsz)
+        v = rearrange(v, 'b (d j) (x y) -> b d j x y', d = dim_head, x = height, y = width)
+
+        # aggregate values
+
+        out = einsum('b h l j x y, b d j x y -> b l h d x y', attn, v)
+
+        # combine heads
+
+        out = rearrange(out, 'b l h d x y -> (b l) (h d) x y')
+        out = self.to_out(out)
+        out = rearrange(out, '(b l) d x y -> b l d x y', b = batch)
+
+        # return original input if one query
+
+        if is_one_query:
+            out = rearrange(out, 'b 1 ... -> b ...')
+
+        return out
+
+class QueryAttnUpsample(nn.Module):
+    def __init__(self, dim, **kwargs):
+        super().__init__()
+        self.norm = LayerNormChan(dim)
+        self.qna = QueryAndAttend(dim = dim, num_queries = 4, **kwargs)
+
+    def forward(self, x):
+        x = self.norm(x)
+        out = self.qna(x)
+        out = rearrange(out, 'b (w1 w2) c h w -> b c (h w1) (w w2)', w1 = 2, w2 = 2)
+        return out

dalle2_pytorch/dalle2_pytorch.py

@@ -17,6 +17,7 @@ from kornia.filters import gaussian_blur2d
 
 from dalle2_pytorch.tokenizer import tokenizer
 from dalle2_pytorch.vqgan_vae import NullVQGanVAE, VQGanVAE
+from dalle2_pytorch.attention import QueryAttnUpsample
 
 # use x-clip
 
@@ -83,7 +84,7 @@ def resize_image_to(t, image_size, mode = 'bilinear'): # take a look at https://
     if orig_image_size == shape:
         return t
 
-    return F.interpolate(t, size = shape, mode = mode)
+    return F.interpolate(t, size = shape, mode = mode, align_corners = False)
 
 # classifier free guidance functions
 
@@ -142,6 +143,92 @@ def sigmoid_beta_schedule(timesteps):
     return torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
 
 
+class BaseGaussianDiffusion(nn.Module):
+    def __init__(self, *, beta_schedule, timesteps, loss_type):
+        super().__init__()
+
+        if beta_schedule == "cosine":
+            betas = cosine_beta_schedule(timesteps)
+        elif beta_schedule == "linear":
+            betas = linear_beta_schedule(timesteps)
+        elif beta_schedule == "quadratic":
+            betas = quadratic_beta_schedule(timesteps)
+        elif beta_schedule == "jsd":
+            betas = 1.0 / torch.linspace(timesteps, 1, timesteps)
+        elif beta_schedule == "sigmoid":
+            betas = sigmoid_beta_schedule(timesteps)
+        else:
+            raise NotImplementedError()
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.cumprod(alphas, axis = 0)
+        alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value = 1.)
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.loss_type = loss_type
+
+        self.register_buffer('betas', betas)
+        self.register_buffer('alphas_cumprod', alphas_cumprod)
+        self.register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+
+        self.register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
+        self.register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
+        self.register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+
+        self.register_buffer('posterior_variance', posterior_variance)
+
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+
+        self.register_buffer('posterior_log_variance_clipped', torch.log(posterior_variance.clamp(min =1e-20)))
+        self.register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
+        self.register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
+
+    def q_mean_variance(self, x_start, t):
+        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+        variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+            extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+            extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+
+        return (
+            extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+            extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
+        )
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+            extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+            extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def sample(self, *args, **kwargs):
+        raise NotImplementedError
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError
+
 # diffusion prior
 
 class LayerNorm(nn.Module):
@@ -420,25 +507,41 @@ class DiffusionPriorNetwork(nn.Module):
         image_embed,
         diffusion_timesteps,
         *,
-        text_encodings,
         text_embed,
+        text_encodings = None,
         mask = None,
         cond_drop_prob = 0.2
     ):
-        batch, text_enc_len, device = image_embed.shape[0], text_encodings.shape[-2], image_embed.device
+        batch, dim, device, dtype = *image_embed.shape, image_embed.device, image_embed.dtype
 
         # in section 2.2, last paragraph
         # "... consisting of encoded text, CLIP text embedding, diffusion timestep embedding, noised CLIP image embedding, final embedding for prediction"
 
         text_embed, image_embed = rearrange_many((text_embed, image_embed), 'b d -> b 1 d')
 
+        # make text encodings optional
+        # although the paper seems to suggest it is present <--
+
+        if not exists(text_encodings):
+            text_encodings = torch.empty((batch, 0, dim), device = device, dtype = dtype)
+
+        if not exists(mask):
+            mask = torch.ones((batch, text_encodings.shape[-2]), device = device, dtype = torch.bool)
+
+        # classifier free guidance
+
+        cond_prob_mask = prob_mask_like((batch,), cond_drop_prob, device = device)
+        cond_prob_mask = rearrange(cond_prob_mask, 'b -> b 1')
+
+        mask &= cond_prob_mask
+
+        # whether text embedding is masked or not depends on the classifier free guidance conditional masking
+
+        mask = torch.cat((mask, cond_prob_mask), dim = 1)
+
         # whether text embedding is used for conditioning depends on whether text encodings are available for attention (for classifier free guidance, even though it seems from the paper it was not used in the prior ddpm, as the objective is different)
         # but let's just do it right
 
-        if exists(mask):
-            not_all_masked_out = mask.any(dim = -1)
-            mask = torch.cat((mask, rearrange(not_all_masked_out, 'b -> b 1')), dim = 1)
-
         if exists(mask):
             mask = F.pad(mask, (0, 2), value = True) # extend mask for text embedding, noised image embedding, time step embedding, and learned query
 
@@ -454,16 +557,6 @@ class DiffusionPriorNetwork(nn.Module):
             learned_queries
         ), dim = -2)
 
-        # mask if it doesn't exist
-
-        if not exists(mask):
-            mask = torch.ones((batch, text_enc_len), device = device, dtype = torch.bool)
-
-        # classifier free guidance
-
-        cond_prob_mask = prob_mask_like((batch,), cond_drop_prob, device = device)
-        mask &= rearrange(cond_prob_mask, 'b -> b 1')
-
         # attend
 
         tokens = self.causal_transformer(tokens, mask = mask)
@@ -474,81 +567,50 @@ class DiffusionPriorNetwork(nn.Module):
 
         return pred_image_embed
 
-class DiffusionPrior(nn.Module):
+class DiffusionPrior(BaseGaussianDiffusion):
     def __init__(
         self,
         net,
         *,
-        clip,
+        clip = None,
+        image_embed_dim = None,
+        image_size = None,
+        image_channels = 3,
         timesteps = 1000,
         cond_drop_prob = 0.2,
         loss_type = "l1",
         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
     ):
-        super().__init__()
-        assert isinstance(clip, CLIP)
-        freeze_model_and_make_eval_(clip)
-        self.clip = clip
+        super().__init__(
+            beta_schedule = beta_schedule,
+            timesteps = timesteps,
+            loss_type = loss_type
+        )
+
+        if exists(clip):
+            assert isinstance(clip, CLIP)
+            freeze_model_and_make_eval_(clip)
+            self.clip = clip
+        else:
+            assert exists(image_embed_dim), 'latent dimension must be given, if training prior network without CLIP given'
+            self.clip = None
 
         self.net = net
-        self.image_embed_dim = clip.dim_latent
-        self.channels = clip.image_channels
-        self.image_size = clip.image_size
+        self.image_embed_dim = default(image_embed_dim, lambda: clip.dim_latent)
+        self.channels = default(image_channels, lambda: clip.image_channels)
+
         self.cond_drop_prob = cond_drop_prob
+        self.condition_on_text_encodings = condition_on_text_encodings
 
         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.
 
-        if beta_schedule == "cosine":
-            betas = cosine_beta_schedule(timesteps)
-        elif beta_schedule == "linear":
-            betas = linear_beta_schedule(timesteps)
-        elif beta_schedule == "quadratic":
-            betas = quadratic_beta_schedule(timesteps)
-        elif beta_schedule == "jsd":
-            betas = 1.0 / torch.linspace(timesteps, 1, timesteps)
-        elif beta_schedule == "sigmoid":
-            betas = sigmoid_beta_schedule(timesteps)
-        else:
-            raise NotImplementedError()
-
-        alphas = 1. - betas
-        alphas_cumprod = torch.cumprod(alphas, axis = 0)
-        alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value = 1.)
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.loss_type = loss_type
-
-        self.register_buffer('betas', betas)
-        self.register_buffer('alphas_cumprod', alphas_cumprod)
-        self.register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-
-        self.register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
-        self.register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
-        self.register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-
-        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
-
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-
-        self.register_buffer('posterior_variance', posterior_variance)
-
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-
-        self.register_buffer('posterior_log_variance_clipped', torch.log(posterior_variance.clamp(min =1e-20)))
-        self.register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
-        self.register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
-
     @torch.no_grad()
     def get_image_embed(self, image):
+        assert exists(self.clip)
+
         image_encoding = self.clip.visual_transformer(image)
         image_cls = image_encoding[:, 0]
         image_embed = self.clip.to_visual_latent(image_cls)
@@ -556,33 +618,18 @@ class DiffusionPrior(nn.Module):
 
     @torch.no_grad()
     def get_text_cond(self, text):
+        assert exists(self.clip)
+
         text_encodings = self.clip.text_transformer(text)
         text_cls, text_encodings = text_encodings[:, 0], text_encodings[:, 1:]
         text_embed = self.clip.to_text_latent(text_cls)
         text_embed = l2norm(text_embed)
+
+        if not self.condition_on_text_encodings:            
+            return dict(text_embed = text_embed)
+
         return dict(text_encodings = text_encodings, text_embed = text_embed, mask = text != 0)
 
-    def q_mean_variance(self, x_start, t):
-        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-            extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-            extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-            extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-            extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
     def p_mean_variance(self, x, t, text_cond, clip_denoised: bool):
         pred = self.net(x, t, **text_cond)
 
@@ -619,14 +666,6 @@ class DiffusionPrior(nn.Module):
             img = self.p_sample(img, torch.full((b,), i, device = device, dtype = torch.long), text_cond = text_cond)
         return img
 
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-
-        return (
-            extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-            extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
-        )
-
     def p_losses(self, image_embed, t, text_cond, noise = None):
         noise = default(noise, lambda: torch.randn_like(image_embed))
 
@@ -678,13 +717,41 @@ class DiffusionPrior(nn.Module):
         top_image_embeds = image_embeds.gather(1, top_sim_indices)
         return rearrange(top_image_embeds, 'b 1 d -> b d')
 
-    def forward(self, text, image, *args, **kwargs):
-        b, device, img_size, = image.shape[0], image.device, self.image_size
-        check_shape(image, 'b c h w', h = img_size, w = img_size, c = self.channels)
+    def forward(
+        self,
+        text = None,
+        image = None,
+        text_embed = None,      # allow for training on preprocessed CLIP text and image embeddings
+        image_embed = None,
+        text_encodings = None,  # as well as CLIP text encodings
+        text_mask = None,       # text mask <- may eventually opt for the learned padding tokens technique from DALL-E1 to reduce complexity
+        *args,
+        **kwargs
+    ):
+        assert exists(text) ^ exists(text_embed), 'either text or text embedding must be supplied'
+        assert exists(image) ^ exists(image_embed), 'either text or text embedding must be supplied'
+        assert not (self.condition_on_text_encodings and (not exists(text_encodings) and not exists(text))), 'text encodings must be present if you specified you wish to condition on it on initialization'
 
-        times = torch.randint(0, self.num_timesteps, (b,), device = device, dtype = torch.long)
-        image_embed = self.get_image_embed(image)
-        text_cond = self.get_text_cond(text)
+        if exists(image):
+            image_embed = self.get_image_embed(image)
+
+        # calculate text conditionings, based on what is passed in
+
+        if exists(text):
+            text_cond = self.get_text_cond(text)
+        else:
+            text_cond = dict(
+                text_embed = text_embed,
+                text_encodings = text_encodings,
+                mask = text_mask
+            )
+
+        # timestep conditioning from ddpm
+
+        batch, device = image_embed.shape[0], image_embed.device
+        times = torch.randint(0, self.num_timesteps, (batch,), device = device, dtype = torch.long)
+
+        # calculate forward loss
 
         loss = self.p_losses(image_embed, times, text_cond = text_cond, *args, **kwargs)
         return loss
@@ -839,6 +906,7 @@ class Unet(nn.Module):
         dim,
         *,
         image_embed_dim,
+        text_embed_dim = None,
         cond_dim = None,
         num_image_tokens = 4,
         num_time_tokens = 2,
@@ -852,6 +920,7 @@ class Unet(nn.Module):
         sparse_attn_window = 8,  # window size for sparse 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,
     ):
         super().__init__()
@@ -891,7 +960,7 @@ class Unet(nn.Module):
             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)
+        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
@@ -902,7 +971,7 @@ class Unet(nn.Module):
         # for classifier free guidance
 
         self.null_image_embed = nn.Parameter(torch.randn(1, num_image_tokens, cond_dim))
-        self.null_text_embed = nn.Parameter(torch.randn(1, 1, cond_dim))
+        self.null_text_embed = nn.Parameter(torch.randn(1, max_text_len, cond_dim))
 
         # attention related params
 
@@ -1030,7 +1099,7 @@ class Unet(nn.Module):
             text_tokens = torch.where(
                 cond_prob_mask,
                 text_tokens,
-                self.null_text_embed
+                self.null_text_embed[:, :text_tokens.shape[1]]
             )
 
         # main conditioning tokens (c)
@@ -1110,13 +1179,13 @@ class LowresConditioner(nn.Module):
 
         return cond_fmap
 
-class Decoder(nn.Module):
+class Decoder(BaseGaussianDiffusion):
     def __init__(
         self,
         unet,
         *,
         clip,
-        vae = None,
+        vae = tuple(),
         timesteps = 1000,
         cond_drop_prob = 0.2,
         loss_type = 'l1',
@@ -1128,14 +1197,22 @@ class Decoder(nn.Module):
         lowres_downsample_first = True,             # cascading ddpm - resizes to lower resolution, then to next conditional resolution + blur
         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
     ):
-        super().__init__()
+        super().__init__(
+            beta_schedule = beta_schedule,
+            timesteps = timesteps,
+            loss_type = loss_type
+        )
+
         assert isinstance(clip, CLIP)
         freeze_model_and_make_eval_(clip)
         self.clip = clip
         self.clip_image_size = clip.image_size
         self.channels = clip.image_channels
 
+        self.condition_on_text_encodings = condition_on_text_encodings
+
         # automatically take care of ensuring that first unet is unconditional
         # while the rest of the unets are conditioned on the low resolution image produced by previous unet
 
@@ -1191,55 +1268,6 @@ class Decoder(nn.Module):
 
         self.cond_drop_prob = cond_drop_prob
 
-        # noise schedule
-
-        if beta_schedule == "cosine":
-            betas = cosine_beta_schedule(timesteps)
-        elif beta_schedule == "linear":
-            betas = linear_beta_schedule(timesteps)
-        elif beta_schedule == "quadratic":
-            betas = quadratic_beta_schedule(timesteps)
-        elif beta_schedule == "jsd":
-            betas = 1.0 / torch.linspace(timesteps, 1, timesteps)
-        elif beta_schedule == "sigmoid":
-            betas = sigmoid_beta_schedule(timesteps)
-        else:
-            raise NotImplementedError()
-
-        alphas = 1. - betas
-        alphas_cumprod = torch.cumprod(alphas, axis = 0)
-        alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value = 1.)
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.loss_type = loss_type
-
-        self.register_buffer('betas', betas)
-        self.register_buffer('alphas_cumprod', alphas_cumprod)
-        self.register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-
-        self.register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
-        self.register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
-        self.register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-
-        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
-
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-
-        self.register_buffer('posterior_variance', posterior_variance)
-
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-
-        self.register_buffer('posterior_log_variance_clipped', torch.log(posterior_variance.clamp(min =1e-20)))
-        self.register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
-        self.register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
-
     def get_unet(self, unet_number):
         assert 0 < unet_number <= len(self.unets)
         index = unet_number - 1
@@ -1272,27 +1300,6 @@ class Decoder(nn.Module):
         image_embed = self.clip.to_visual_latent(image_cls)
         return l2norm(image_embed)
 
-    def q_mean_variance(self, x_start, t):
-        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-            extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-            extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-            extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-            extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
     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)
 
@@ -1337,14 +1344,6 @@ class Decoder(nn.Module):
 
         return img
 
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-
-        return (
-            extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-            extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
-        )
-
     def p_losses(self, unet, x_start, t, *, image_embed, lowres_cond_img = None, text_encodings = None, predict_x_start = False, noise = None):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
@@ -1379,6 +1378,8 @@ class Decoder(nn.Module):
 
         text_encodings = self.get_text_encodings(text) if exists(text) else None
 
+        assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
+
         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)):
@@ -1439,6 +1440,8 @@ class Decoder(nn.Module):
 
         text_encodings = self.get_text_encodings(text) if exists(text) and not exists(text_encodings) else None
 
+        assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
+
         lowres_cond_img = self.to_lowres_cond(image, target_image_size = target_image_size, downsample_image_size = self.image_sizes[unet_index - 1]) if unet_number > 1 else None
         image = resize_image_to(image, target_image_size)
 
@@ -1466,7 +1469,9 @@ class DALLE2(nn.Module):
         assert isinstance(decoder, Decoder)
         self.prior = prior
         self.decoder = decoder
+
         self.prior_num_samples = prior_num_samples
+        self.decoder_need_text_cond = self.decoder.condition_on_text_encodings
 
     @torch.no_grad()
     @eval_decorator
@@ -1483,7 +1488,9 @@ class DALLE2(nn.Module):
             text = tokenizer.tokenize(text).to(device)
 
         image_embed = self.prior.sample(text, num_samples_per_batch = self.prior_num_samples)
-        images = self.decoder.sample(image_embed, cond_scale = cond_scale)
+
+        text_cond = text if self.decoder_need_text_cond else None
+        images = self.decoder.sample(image_embed, text = text_cond, cond_scale = cond_scale)
 
         if one_text:
             return images[0]

dalle2_pytorch/train.py

@@ -0,0 +1,53 @@
+import copy
+import torch
+from torch import nn
+
+# exponential moving average wrapper
+
+class EMA(nn.Module):
+    def __init__(
+        self,
+        model,
+        beta = 0.99,
+        ema_update_after_step = 1000,
+        ema_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.register_buffer('initted', torch.Tensor([False]))
+        self.register_buffer('step', torch.tensor([0.]))
+
+    def update(self):
+        self.step += 1
+
+        if self.step <= self.ema_update_after_step or (self.step % self.ema_update_every) != 0:
+            return
+
+        if not self.initted:
+            self.ema_model.state_dict(self.online_model.state_dict())
+            self.initted.data.copy_(torch.Tensor([True]))
+
+        self.update_moving_average(self.ema_model, self.online_model)
+
+    def update_moving_average(ma_model, current_model):
+        def calculate_ema(beta, old, new):
+            if not exists(old):
+                return new
+            return old * beta + (1 - beta) * new
+
+        for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
+            old_weight, up_weight = ma_params.data, current_params.data
+            ma_params.data = calculate_ema(self.beta, old_weight, up_weight)
+
+        for current_buffer, ma_buffer in zip(current_model.buffers(), ma_model.buffers()):
+            new_buffer_value = calculate_ema(self.beta, ma_buffer, current_buffer)
+            ma_buffer.copy_(new_buffer_value)
+
+    def __call__(self, *args, **kwargs):
+        return self.ema_model(*args, **kwargs)

dalle2_pytorch/vqgan_vae.py

@@ -13,6 +13,8 @@ import torchvision
 
 from einops import rearrange, reduce, repeat
 
+from dalle2_pytorch.attention import QueryAttnUpsample
+
 # constants
 
 MList = nn.ModuleList
@@ -243,111 +245,6 @@ class ResBlock(nn.Module):
     def forward(self, x):
         return self.net(x) + x
 
-# attention-based upsampling
-# from https://arxiv.org/abs/2112.11435
-
-class QueryAndAttend(nn.Module):
-    def __init__(
-        self,
-        *,
-        dim,
-        num_queries = 1,
-        dim_head = 32,
-        heads = 8,
-        window_size = 3
-    ):
-        super().__init__()
-        self.scale = dim_head ** -0.5
-        inner_dim = dim_head * heads
-        self.heads = heads
-        self.dim_head = dim_head
-        self.window_size = window_size
-        self.num_queries = num_queries
-
-        self.rel_pos_bias = nn.Parameter(torch.randn(heads, num_queries, window_size * window_size, 1, 1))
-
-        self.queries = nn.Parameter(torch.randn(heads, num_queries, dim_head))
-        self.to_kv = nn.Conv2d(dim, dim_head * 2, 1, bias = False)
-        self.to_out = nn.Conv2d(inner_dim, dim, 1, bias = False)
-
-    def forward(self, x):
-        """
-        einstein notation
-        b - batch
-        h - heads
-        l - num queries
-        d - head dimension
-        x - height
-        y - width
-        j - source sequence for attending to (kernel size squared in this case)
-        """
-
-        wsz, heads, dim_head, num_queries = self.window_size, self.heads, self.dim_head, self.num_queries
-        batch, _, height, width = x.shape
-
-        is_one_query = self.num_queries == 1
-
-        # queries, keys, values
-
-        q = self.queries * self.scale
-        k, v = self.to_kv(x).chunk(2, dim = 1)
-
-        # similarities
-
-        sim = einsum('h l d, b d x y -> b h l x y', q, k)
-        sim = rearrange(sim, 'b ... x y -> b (...) x y')
-
-        # unfold the similarity scores, with float(-inf) as padding value
-
-        mask_value = -torch.finfo(sim.dtype).max
-        sim = F.pad(sim, ((wsz // 2,) * 4), value = mask_value)
-        sim = F.unfold(sim, kernel_size = wsz)
-        sim = rearrange(sim, 'b (h l j) (x y) -> b h l j x y', h = heads, l = num_queries, x = height, y = width)
-
-        # rel pos bias
-
-        sim = sim + self.rel_pos_bias
-
-        # numerically stable attention
-
-        sim = sim - sim.amax(dim = -3, keepdim = True).detach()
-        attn = sim.softmax(dim = -3)
-
-        # unfold values
-
-        v = F.pad(v, ((wsz // 2,) * 4), value = 0.)
-        v = F.unfold(v, kernel_size = wsz)
-        v = rearrange(v, 'b (d j) (x y) -> b d j x y', d = dim_head, x = height, y = width)
-
-        # aggregate values
-
-        out = einsum('b h l j x y, b d j x y -> b l h d x y', attn, v)
-
-        # combine heads
-
-        out = rearrange(out, 'b l h d x y -> (b l) (h d) x y')
-        out = self.to_out(out)
-        out = rearrange(out, '(b l) d x y -> b l d x y', b = batch)
-
-        # return original input if one query
-
-        if is_one_query:
-            out = rearrange(out, 'b 1 ... -> b ...')
-
-        return out
-
-class QueryAttnUpsample(nn.Module):
-    def __init__(self, dim, **kwargs):
-        super().__init__()
-        self.norm = LayerNormChan(dim)
-        self.qna = QueryAndAttend(dim = dim, num_queries = 4, **kwargs)
-
-    def forward(self, x):
-        x = self.norm(x)
-        out = self.qna(x)
-        out = rearrange(out, 'b (w1 w2) c h w -> b c (h w1) (w w2)', w1 = 2, w2 = 2)
-        return out
-
 # vqgan attention layer
 class VQGanAttention(nn.Module):
     def __init__(
@@ -481,7 +378,7 @@ class VQGanVAE(nn.Module):
 
         for layer_index, (dim_in, dim_out), layer_num_resnet_blocks, layer_use_attn in zip(range(layers), dim_pairs, num_resnet_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(QueryAttnUpsample(dim_out), nn.Conv2d(dim_out, dim_in, 3, 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))

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.44',
+  version = '0.0.52',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',