control top_k value

cog.yaml

@@ -1,13 +1,13 @@
 build:
-  cuda: "11.5.1"
+  cuda: "11.0"
   gpu: true
-  python_version: "3.10"
+  python_version: "3.8"
   system_packages:
     - "libgl1-mesa-glx"
     - "libglib2.0-0"
   python_packages:
-    - "min-dalle==0.2.27"
+    - "min-dalle==0.2.28"
   run:
-    - pip install torch==1.12.0+cu116 -f https://download.pytorch.org/whl/torch_stable.html
+    - pip install torch==1.10.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
 
 predict: "replicate_predictor.py:ReplicatePredictor"

min_dalle/min_dalle.py

@@ -165,6 +165,7 @@ class MinDalle:
         seed: int,
         grid_size: int,
         log2_mid_count: int,
+        log2_k: int = 6,
         log2_supercondition_factor: int = 3,
         is_verbose: bool = False
     ) -> Iterator[Image.Image]:
@@ -202,6 +203,7 @@ class MinDalle:
                 print('sampling row {} of {}'.format(row_index + 1, row_count))
             attention_state, image_tokens = self.decoder.decode_row(
                 row_index,
+                log2_k,
                 log2_supercondition_factor,
                 encoder_state,
                 attention_mask,
@@ -219,6 +221,7 @@ class MinDalle:
         text: str,
         seed: int = -1,
         grid_size: int = 1,
+        log2_k: int = 6,
         log2_supercondition_factor: int = 3,
         is_verbose: bool = False
     ) -> Image.Image:
@@ -228,6 +231,7 @@ class MinDalle:
             seed,
             grid_size,
             log2_mid_count,
+            log2_k,
             log2_supercondition_factor,
             is_verbose
         )

min_dalle/models/dalle_bart_decoder.py

@@ -140,6 +140,7 @@ class DalleBartDecoder(nn.Module):
 
     def decode_step(
         self,
+        log2_k: int,
         log2_supercondition_factor: int,
         attention_mask: BoolTensor,
         encoder_state: FloatTensor,
@@ -170,7 +171,7 @@ class DalleBartDecoder(nn.Module):
             logits[image_count:, -1] * a
         )
 
-        top_logits, _ = logits.topk(50, dim=-1)
+        top_logits, _ = logits.topk(2 ** log2_k, dim=-1)
         probs = torch.where(
             logits < top_logits[:, [-1]],
             self.zero_prob,
@@ -182,6 +183,7 @@ class DalleBartDecoder(nn.Module):
     def decode_row(
         self,
         row_index: int,
+        log2_k: int,
         log2_supercondition_factor: int,
         encoder_state: FloatTensor,
         attention_mask: BoolTensor,
@@ -191,6 +193,7 @@ class DalleBartDecoder(nn.Module):
         for col_index in range(16):
             i = 16 * row_index + col_index
             probs, attention_state = self.decode_step(
+                log2_k = log2_k,
                 log2_supercondition_factor = log2_supercondition_factor,
                 attention_mask = attention_mask,
                 encoder_state = encoder_state,

setup.py

@@ -5,7 +5,7 @@ setuptools.setup(
     name='min-dalle',
     description = 'min(DALL·E)',
     long_description=(Path(__file__).parent / "README.rst").read_text(),
-    version='0.2.27',
+    version='0.2.28',
     author='Brett Kuprel',
     author_email='brkuprel@gmail.com',
     url='https://github.com/kuprel/min-dalle',