update replicate

replicate_predictor.py

@@ -13,26 +13,12 @@ class ReplicatePredictor(BasePredictor):
 
     def predict(
         self,
-        text: str = Input(
+        text: str = Input(default='Dali painting of WALL·E'),
-            description='For long prompts, only the first 64 tokens will be used to generate the image.',
+        intermediate_outputs: bool = Input(default=True),
-            default='Dali painting of WALL·E'
+        grid_size: int = Input(ge=1, le=9, default=5),
-        ),
+        log2_temperature: float = Input(ge=-3, le=3, default=1),
-        intermediate_outputs: bool = Input(
+        log2_top_k: int = Input(ge=0, le=14, default=7),
-            description='Whether to show intermediate outputs while running.  This adds less than a second to the run time.',
+        log2_supercondition_factor: int = Input(ge=2, le=6, default=4)
-            default=True
-        ),
-        grid_size: int = Input(
-            description='Size of the image grid.  5x5 takes around 16 seconds, 8x8 takes around 36 seconds',
-            ge=1,
-            le=8,
-            default=4
-        ),
-        temperature: float = Input(
-            description='A higher temperature results in more variety.',
-            ge=0.01,
-            le=10,
-            default=2
-        ),
     ) -> Iterator[Path]:
         try:
             image_stream = self.model.generate_image_stream(
@@ -40,9 +26,9 @@ class ReplicatePredictor(BasePredictor):
                 seed = -1,
                 grid_size = grid_size,
                 log2_mid_count = 3 if intermediate_outputs else 0,
-                temperature = temperature,
+                temperature = 2 ** log2_temperature,
-                supercondition_factor = 2 ** 4,
+                supercondition_factor = 2 ** log2_supercondition_factor,
-                top_k = 2 ** 8,
+                top_k = 2 ** log2_top_k,
                 is_verbose = True
             )