allow for using classifier free guidance for some unets but not others, by passing in a tuple of cond_scale during sampling for decoder, just in case it is causing issues for upsamplers

dalle2_pytorch/dalle2_pytorch.py

@@ -278,6 +278,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
         import clip
         openai_clip, preprocess = clip.load(name)
         super().__init__(openai_clip)
+        self.eos_id = 49407 # for handling 0 being also '!'
 
         text_attention_final = self.find_layer('ln_final')
         self.handle = text_attention_final.register_forward_hook(self._hook)
@@ -316,7 +317,10 @@ class OpenAIClipAdapter(BaseClipAdapter):
     @torch.no_grad()
     def embed_text(self, text):
         text = text[..., :self.max_text_len]
-        text_mask = text != 0
+
+        is_eos_id = (text == self.eos_id)
+        text_mask_excluding_eos = is_eos_id.cumsum(dim = -1) == 0
+        text_mask = F.pad(text_mask_excluding_eos, (1, -1), value = True)
         assert not self.cleared
 
         text_embed = self.clip.encode_text(text)
@@ -900,7 +904,7 @@ class DiffusionPriorNetwork(nn.Module):
         null_text_embeds = self.null_text_embed.to(text_encodings.dtype)
 
         text_encodings = torch.where(
-            rearrange(mask, 'b n -> b n 1'),
+            rearrange(mask, 'b n -> b n 1').clone(),
             text_encodings,
             null_text_embeds
         )
@@ -1251,6 +1255,14 @@ class DiffusionPrior(nn.Module):
 
 # decoder
 
+def NearestUpsample(dim, dim_out = None):
+    dim_out = default(dim_out, dim)
+
+    return nn.Sequential(
+        nn.Upsample(scale_factor = 2, mode = 'nearest'),
+        nn.Conv2d(dim, dim_out, 3, padding = 1)
+    )
+
 class PixelShuffleUpsample(nn.Module):
     """
     code shared by @MalumaDev at DALLE2-pytorch for addressing checkboard artifacts
@@ -1657,7 +1669,7 @@ class Unet(nn.Module):
 
         # upsample klass
 
-        upsample_klass = ConvTransposeUpsample if not pixel_shuffle_upsample else PixelShuffleUpsample
+        upsample_klass = NearestUpsample if not pixel_shuffle_upsample else PixelShuffleUpsample
 
         # give memory efficient unet an initial resnet block
 
@@ -2486,7 +2498,10 @@ class Decoder(nn.Module):
         img = None
         is_cuda = next(self.parameters()).is_cuda
 
-        for unet_number, unet, vae, channel, image_size, predict_x_start, learned_variance, noise_scheduler, sample_timesteps in tqdm(zip(range(1, len(self.unets) + 1), self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start, self.learned_variance, self.noise_schedulers, self.sample_timesteps)):
+        num_unets = len(self.unets)
+        cond_scale = cast_tuple(cond_scale, num_unets)
+
+        for unet_number, unet, vae, channel, image_size, predict_x_start, learned_variance, noise_scheduler, sample_timesteps, unet_cond_scale in tqdm(zip(range(1, num_unets + 1), self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start, self.learned_variance, self.noise_schedulers, self.sample_timesteps, cond_scale)):
 
             context = self.one_unet_in_gpu(unet = unet) if is_cuda and not distributed else null_context()
 
@@ -2508,7 +2523,7 @@ class Decoder(nn.Module):
                     shape,
                     image_embed = image_embed,
                     text_encodings = text_encodings,
-                    cond_scale = cond_scale,
+                    cond_scale = unet_cond_scale,
                     predict_x_start = predict_x_start,
                     learned_variance = learned_variance,
                     clip_denoised = not is_latent_diffusion,

dalle2_pytorch/trainer.py

@@ -673,8 +673,14 @@ class DecoderTrainer(nn.Module):
     def sample(self, *args, **kwargs):
         distributed = self.accelerator.num_processes > 1
         base_decoder = self.accelerator.unwrap_model(self.decoder)
+
+        was_training = base_decoder.training
+        base_decoder.eval()
+
         if kwargs.pop('use_non_ema', False) or not self.use_ema:
-            return base_decoder.sample(*args, **kwargs, distributed = distributed)
+            out = base_decoder.sample(*args, **kwargs, distributed = distributed)
+            base_decoder.train(was_training)
+            return out
 
         trainable_unets = self.accelerator.unwrap_model(self.decoder).unets
         base_decoder.unets = self.unets                  # swap in exponential moving averaged unets for sampling
@@ -687,6 +693,7 @@ class DecoderTrainer(nn.Module):
         for ema in self.ema_unets:
             ema.restore_ema_model_device()
 
+        base_decoder.train(was_training)
         return output
 
     @torch.no_grad()

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.23.5'
+__version__ = '0.23.9'