fix generation and further cleanup by moving generate method into Dal…

…le class

README.md

@@ -117,10 +117,8 @@ loss.backward()
 Finally, to generate images
 
 ```python
-from dalle_pytorch import generate_images
 
-images = generate_images(
-    dalle,
+dalle.generate_images(
     vae = vae,
     text = text,
     mask = mask
@@ -132,10 +130,8 @@ images.shape # (2, 3, 256, 256)
 To get the similarity scores from your trained Clipper, just do
 
 ```python
-from dalle_pytorch import generate_images
 
-images, scores = generate_images(
-    dalle,
+images, scores = dalle.generate_images(
     vae = vae,
     text = text,
     mask = mask,

dalle_pytorch/__init__.py

@@ -1,2 +1 @@
 from dalle_pytorch.dalle_pytorch import DALLE, CLIP, DiscreteVAE
-from dalle_pytorch.dalle_pytorch import generate_images

dalle_pytorch/dalle_pytorch.py

@@ -37,49 +37,6 @@ def top_k(logits, thres = 0.5):
     probs.scatter_(1, ind, val)
     return probs
 
-@torch.no_grad()
-@eval_decorator
-def generate_images(
-    model,
-    vae,
-    text,
-    clipper = None,
-    mask = None,
-    filter_thres = 0.5,
-    temperature = 1.
-):
-    x = text
-
-    text_seq_len = model.text_seq_len
-    image_seq_len = model.image_seq_len
-    total_len = text_seq_len + model.image_seq_len - text.shape[1]
-
-    out = x
-    for _ in range(total_len):
-        text, image = x[:, :text_seq_len], x[:, text_seq_len:]
-        logits = model(text, image, mask = mask)[:, -1, :]
-        filtered_logits = top_k(logits, thres = filter_thres)
-        probs = F.softmax(filtered_logits / temperature, dim=-1)
-
-        sample = torch.multinomial(probs, 1)
-        out = torch.cat((out, sample), dim=-1)
-
-        if out.shape[1] <= text_seq_len:
-            mask = F.pad(mask, (0, 1), value=True)
-
-    text_seq = torch.cat((x[:, :1], out[:, :(text_seq_len - 1)]), dim = 1)
-
-    img_seq = out[:, -image_seq_len:]
-    img_seq -= model.num_text_tokens
-
-    images = vae.decode(img_seq)
-
-    if exists(clipper):
-        scores = clipper(text_seq, images, return_loss = False)
-        return images, scores
-
-    return images
-
 # discrete vae class
 
 class DiscreteVAE(nn.Module):
@@ -304,6 +261,49 @@ def __init__(
 
         self.register_buffer('logits_mask', logits_mask)
 
+    @torch.no_grad()
+    @eval_decorator
+    def generate_images(
+        self,
+        vae,
+        text,
+        clipper = None,
+        mask = None,
+        filter_thres = 0.5,
+        temperature = 1.
+    ):
+        text_seq_len, image_seq_len, num_text_tokens = self.text_seq_len, self.image_seq_len, self.num_text_tokens
+        total_len = text_seq_len + image_seq_len
+
+        out = text
+        for cur_len in range(text.shape[1], total_len):
+            is_image = cur_len >= text_seq_len
+
+            text, image = out[:, :text_seq_len], out[:, text_seq_len:]
+
+            logits = self(text, image, mask = mask)[:, -1, :]
+
+            filtered_logits = top_k(logits, thres = filter_thres)
+            probs = F.softmax(filtered_logits / temperature, dim = -1)
+            sample = torch.multinomial(probs, 1)
+
+            sample -= (num_text_tokens if is_image else 0) # offset sampled token if it is an image token, since logit space is composed of text and then image tokens
+            out = torch.cat((out, sample), dim=-1)
+
+            if out.shape[1] <= text_seq_len:
+                mask = F.pad(mask, (0, 1), value = True)
+
+        text_seq = out[:, :text_seq_len]
+
+        img_seq = out[:, -image_seq_len:]
+        images = vae.decode(img_seq)
+
+        if exists(clipper):
+            scores = clipper(text_seq, images, return_loss = False)
+            return images, scores
+
+        return images
+
     def forward(
         self,
         text,

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'dalle-pytorch',
   packages = find_packages(),
-  version = '0.0.19',
+  version = '0.0.21',
   license='MIT',
   description = 'DALL-E - Pytorch',
   author = 'Phil Wang',