Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -3114,8 +3114,8 @@
         "#------#\n",
         "# @markdown ⚖️ 🖼️ encoding <-----?-----> 📝 encoding </div> <br>\n",
         "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
-        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "prompt_strength = math.pow(10 ,log_strength-1)\n",
+        "log_strength_1 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "prompt_strength = math.pow(10 ,log_strength_1-1)\n",
         "reference = torch.zeros(768)\n",
         "\n",
         "%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
@@ -3125,10 +3125,10 @@
         "references = '' # Clear up memory\n",
         "# @markdown -----------\n",
         "# @markdown 📝➕ 1st Enhance similarity to prompt(s)\n",
-        "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
-        "log_strength = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "pos_strength = math.pow(10 ,log_strength-1)\n",
-        "for _POS in POS.split(','):\n",
+        "POS_2 = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
+        "log_strength_2 = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "pos_strength = math.pow(10 ,log_strength_2-1)\n",
+        "for _POS in POS_2.split(','):\n",
         "  inputs = tokenizer(text = _POS.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
         "  text_features_POS = model.get_text_features(**inputs)\n",
         "  text_features_POS = text_features_POS/text_features_POS.norm(p=2, dim=-1, keepdim=True)\n",
@@ -3138,8 +3138,8 @@
         "# @markdown -----------\n",
         "# @markdown 📝➕ 2nd Enhance similarity to prompt(s)\n",
         "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
-        "log_strength = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "pos_strength = math.pow(10 ,log_strength-1)\n",
+        "log_strength_3 = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "pos_strength = math.pow(10 ,log_strength_3-1)\n",
         "for _POS in POS.split(','):\n",
         "  inputs = tokenizer(text = _POS.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
         "  text_features_POS = model.get_text_features(**inputs)\n",
@@ -3149,8 +3149,8 @@
         "\n",
         "# @markdown 🚫 Penalize similarity to prompt(s)\n",
         "NEG = '' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
-        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "neg_strength = math.pow(10 ,log_strength-1)\n",
+        "log_strength_4 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "neg_strength = math.pow(10 ,log_strength_4-1)\n",
         "for _NEG in NEG.split(','):\n",
         "  inputs = tokenizer(text = _NEG.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
         "  text_features_NEG = model.get_text_features(**inputs)\n",