fix a potential bug with conditioning with blurred low resolution image, blur should be applied only 50% of the time

Accelerate removes the ability to collate strings. Likely since it
cannot gather strings.

dalle2_pytorch/dalle2_pytorch.py

@@ -527,25 +527,31 @@ class NoiseScheduler(nn.Module):
 # diffusion prior
 
 class LayerNorm(nn.Module):
-    def __init__(self, dim, eps = 1e-5):
+    def __init__(self, dim, eps = 1e-5, stable = False):
         super().__init__()
         self.eps = eps
+        self.stable = stable
         self.g = nn.Parameter(torch.ones(dim))
 
     def forward(self, x):
-        x = x / x.amax(dim = -1, keepdim = True).detach()
+        if self.stable:
+            x = x / x.amax(dim = -1, keepdim = True).detach()
+
         var = torch.var(x, dim = -1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = -1, keepdim = True)
         return (x - mean) * (var + self.eps).rsqrt() * self.g
 
 class ChanLayerNorm(nn.Module):
-    def __init__(self, dim, eps = 1e-5):
+    def __init__(self, dim, eps = 1e-5, stable = False):
         super().__init__()
         self.eps = eps
+        self.stable = stable
         self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
 
     def forward(self, x):
-        x = x / x.amax(dim = 1, keepdim = True).detach()
+        if self.stable:
+            x = x / x.amax(dim = 1, keepdim = True).detach()
+
         var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = 1, keepdim = True)
         return (x - mean) * (var + self.eps).rsqrt() * self.g
@@ -669,7 +675,7 @@ class Attention(nn.Module):
         dropout = 0.,
         causal = False,
         rotary_emb = None,
-        pb_relax_alpha = 32 ** 2
+        pb_relax_alpha = 128
     ):
         super().__init__()
         self.pb_relax_alpha = pb_relax_alpha
@@ -782,7 +788,7 @@ class CausalTransformer(nn.Module):
                 FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout, post_activation_norm = normformer)
             ]))
 
-        self.norm = LayerNorm(dim) if norm_out else nn.Identity()  # unclear in paper whether they projected after the classic layer norm for the final denoised image embedding, or just had the transformer output it directly: plan on offering both options
+        self.norm = LayerNorm(dim, stable = True) if norm_out else nn.Identity()  # unclear in paper whether they projected after the classic layer norm for the final denoised image embedding, or just had the transformer output it directly: plan on offering both options
         self.project_out = nn.Linear(dim, dim, bias = False) if final_proj else nn.Identity()
 
     def forward(self, x):
@@ -1940,6 +1946,7 @@ class LowresConditioner(nn.Module):
         self,
         downsample_first = True,
         downsample_mode_nearest = False,
+        blur_prob = 0.5,
         blur_sigma = 0.6,
         blur_kernel_size = 3,
         input_image_range = None
@@ -1950,6 +1957,7 @@ class LowresConditioner(nn.Module):
 
         self.input_image_range = input_image_range
 
+        self.blur_prob = blur_prob
         self.blur_sigma = blur_sigma
         self.blur_kernel_size = blur_kernel_size
 
@@ -1962,20 +1970,27 @@ class LowresConditioner(nn.Module):
         blur_sigma = None,
         blur_kernel_size = None
     ):
-        if self.training and self.downsample_first and exists(downsample_image_size):
+        if self.downsample_first and exists(downsample_image_size):
             cond_fmap = resize_image_to(cond_fmap, downsample_image_size, clamp_range = self.input_image_range, nearest = self.downsample_mode_nearest)
 
-        if self.training:
+        # blur is only applied 50% of the time
+        # section 3.1 in https://arxiv.org/abs/2106.15282
+
+        if random.random() < self.blur_prob:
+
             # when training, blur the low resolution conditional image
+
             blur_sigma = default(blur_sigma, self.blur_sigma)
             blur_kernel_size = default(blur_kernel_size, self.blur_kernel_size)
 
             # allow for drawing a random sigma between lo and hi float values
+
             if isinstance(blur_sigma, tuple):
                 blur_sigma = tuple(map(float, blur_sigma))
                 blur_sigma = random.uniform(*blur_sigma)
 
             # allow for drawing a random kernel size between lo and hi int values
+
             if isinstance(blur_kernel_size, tuple):
                 blur_kernel_size = tuple(map(int, blur_kernel_size))
                 kernel_size_lo, kernel_size_hi = blur_kernel_size
@@ -1984,7 +1999,6 @@ class LowresConditioner(nn.Module):
             cond_fmap = gaussian_blur2d(cond_fmap, cast_tuple(blur_kernel_size, 2), cast_tuple(blur_sigma, 2))
 
         cond_fmap = resize_image_to(cond_fmap, target_image_size, clamp_range = self.input_image_range)
-
         return cond_fmap
 
 class Decoder(nn.Module):
@@ -2008,6 +2022,7 @@ class Decoder(nn.Module):
         random_crop_sizes = None,                   # whether to random crop the image at that stage in the cascade (super resoluting convolutions at the end may be able to generalize on smaller crops)
         lowres_downsample_first = True,             # cascading ddpm - resizes to lower resolution, then to next conditional resolution + blur
         lowres_downsample_mode_nearest = False,     # cascading ddpm - whether to use nearest mode downsampling for lower resolution
+        blur_prob = 0.5,                            # cascading ddpm - when training, the gaussian blur is only applied 50% of the time
         blur_sigma = 0.6,                           # cascading ddpm - blur sigma
         blur_kernel_size = 3,                       # cascading ddpm - blur kernel size
         clip_denoised = True,
@@ -2156,9 +2171,12 @@ class Decoder(nn.Module):
         lowres_conditions = tuple(map(lambda t: t.lowres_cond, self.unets))
         assert lowres_conditions == (False, *((True,) * (len(self.unets) - 1))), 'the first unet must be unconditioned (by low resolution image), and the rest of the unets must have `lowres_cond` set to True'
 
+        self.lowres_downsample_mode_nearest = lowres_downsample_mode_nearest
+
         self.to_lowres_cond = LowresConditioner(
             downsample_first = lowres_downsample_first,
             downsample_mode_nearest = lowres_downsample_mode_nearest,
+            blur_prob = blur_prob,
             blur_sigma = blur_sigma,
             blur_kernel_size = blur_kernel_size,
             input_image_range = self.input_image_range
@@ -2322,6 +2340,9 @@ class Decoder(nn.Module):
 
         img = torch.randn(shape, device = device)
 
+        if not is_latent_diffusion:
+            lowres_cond_img = maybe(self.normalize_img)(lowres_cond_img)
+
         for time, time_next in tqdm(time_pairs, desc = 'sampling loop time step'):
             alpha = alphas[time]
             alpha_next = alphas[time_next]
@@ -2474,7 +2495,7 @@ class Decoder(nn.Module):
                 shape = (batch_size, channel, image_size, image_size)
 
                 if unet.lowres_cond:
-                    lowres_cond_img = self.to_lowres_cond(img, target_image_size = image_size)
+                    lowres_cond_img = resize_image_to(img, target_image_size = image_size, clamp_range = self.input_image_range, nearest = self.lowres_downsample_mode_nearest)
 
                 is_latent_diffusion = isinstance(vae, VQGanVAE)
                 image_size = vae.get_encoded_fmap_size(image_size)

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.23.2'
+__version__ = '0.23.5'

train_decoder.py

@@ -323,7 +323,7 @@ def train(
         last_snapshot = sample
 
         if next_task == 'train':
-            for i, (img, emb, txt) in enumerate(trainer.train_loader):
+            for i, (img, emb, txt) in enumerate(dataloaders["train"]):
                 # We want to count the total number of samples across all processes
                 sample_length_tensor[0] = len(img)
                 all_samples = accelerator.gather(sample_length_tensor)  # TODO: accelerator.reduce is broken when this was written. If it is fixed replace this.
@@ -358,6 +358,7 @@ def train(
                         else:
                             # Then we need to pass the text instead
                             tokenized_texts = tokenize(txt, truncate=True)
+                            assert tokenized_texts.shape[0] == len(img), f"The number of texts ({tokenized_texts.shape[0]}) should be the same as the number of images ({len(img)})"
                             forward_params['text'] = tokenized_texts
                     loss = trainer.forward(img, **forward_params, unet_number=unet)
                     trainer.update(unet_number=unet)
@@ -416,7 +417,7 @@ def train(
             timer = Timer()
             accelerator.wait_for_everyone()
             i = 0
-            for i, (img, emb, txt) in enumerate(trainer.val_loader):  # Use the accelerate prepared loader
+            for i, (img, emb, txt) in enumerate(dataloaders['val']):  # Use the accelerate prepared loader
                 val_sample_length_tensor[0] = len(img)
                 all_samples = accelerator.gather(val_sample_length_tensor)
                 total_samples = all_samples.sum().item()