update performance graph

README.md

@@ -13,7 +13,7 @@ To generate a 4x4 grid of DALL·E Mega images it takes:
 - 48 sec with a P100 in Colab
 - 13 sec with an A100 on Replicate
 
-Here's a more detailed breakdown of total inference time vs number of generated images on an A100:
+Here's a more detailed breakdown of performance on an A100:
 <br />
 <img src="https://github.com/kuprel/min-dalle/raw/main/performance.png" alt="min-dalle" width="450"/>
 <br />

min_dalle.ipynb

@@ -194,7 +194,7 @@
         "text = \"Dali painting of WALL·E\" #@param {type:\"string\"}\n",
         "intermediate_outputs = True #@param {type:\"boolean\"}\n",
         "grid_size = 5 #@param {type:\"integer\"}\n",
-        "temperature = 1 #@param {type:\"slider\", min:0.01, max:3, step:0.01}\n",
+        "temperature = 2 #@param {type:\"slider\", min:0.01, max:3, step:0.01}\n",
         "supercondition_factor = 16 #@param {type:\"number\"}\n",
         "top_k = 256 #@param {type:\"integer\"}\n",
         "log2_mid_count = 3 if intermediate_outputs else 0\n",

replicate_predictor.py

@@ -30,11 +30,11 @@ class ReplicatePredictor(BasePredictor):
         temperature: float = Input(
             description='A higher temperature results in more variety.',
             ge=0.01,
-            le=3,
+            le=10,
-            default=1
+            default=2
         ),
     ) -> Iterator[Path]:
-        try: 
+        try:
             image_stream = self.model.generate_image_stream(
                 text = text,
                 seed = -1,