Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -264,24 +264,24 @@
       "source": [
         "# @title 📚 Select items to sample from\n",
         "\n",
-        "prompt_features = True # @param {\"type\":\"boolean\",\"placeholder\":\"🦜\"}\n",
-        "civitai_blue_set = True # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
-        "civitai_yellow_set = True # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
-        "artby_prompts = True # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
+        "prompt_features = False # @param {\"type\":\"boolean\",\"placeholder\":\"🦜\"}\n",
+        "civitai_blue_set = False # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
+        "civitai_yellow_set = False # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
+        "artby_prompts = False # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
         "suffix = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
         "prefix = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
-        "emojis = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "emojis = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
         "#------#\n",
-        "\n",
+        "suffix_pairs = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
         "first_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
         "last_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
-        "celebs = True # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n",
+        "celebs = False # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n",
         "#-------#\n",
         "danbooru_tags = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎀\"}\n",
-        "lyrics = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
-        "tripple_nouns =  True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
+        "lyrics = False # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
+        "tripple_nouns =  False # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
         "#-----#\n",
-        "female_fullnames = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "female_fullnames = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
         "debug = False\n",
         "#------#\n",
         "prompts = {}\n",
@@ -289,6 +289,9 @@
         "nA = 0\n",
         "#--------#\n",
         "\n",
+        "if suffix_pairs:\n",
+        "  url = '/content/text-to-image-prompts/suffix_pairs'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
         "\n",
         "if tripple_nouns:\n",
         "  url = '/content/text-to-image-prompts/nouns'\n",
@@ -391,14 +394,14 @@
       "source": [
         "# @title \t⚄ Use a pre-encoded prompt + image pair from the fusion gen (note: NSFW!)\n",
         "# @markdown 🖼️ Choose a pre-encoded reference\n",
-        "index = 708 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
+        "index = 617 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
         "PROMPT_INDEX = index\n",
         "\n",
         "import math\n",
         "# @markdown -----------\n",
         "# @markdown 📝➕ Enhance similarity to prompt(s)\n",
         "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
-        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\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",
         "# @markdown -----------\n",
         "\n",
@@ -408,10 +411,12 @@
         "neg_strength = math.pow(10 ,log_strength-1)\n",
         "\n",
         "# @markdown ⏩ Skip item(s) containing the word\n",
-        "SKIP = '' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
+        "SKIP = '_ass , ass_' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
         "\n",
-        "# @markdown ⚖️ sim_ref = C* text_encoding + image_encoding*(1-C) <br>\n",
+        "# @markdown ⚖️ sim_ref =(10^(log_strength-1)) * ( C* text_encoding + image_encoding*(1-C) )<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",
         "\n",
         "blacklist = SKIP\n",
         "# @markdown -----------\n",
@@ -449,8 +454,10 @@
         "\n",
         "\n",
         "def isBlacklisted(txt):\n",
+        "  if txt.strip().isnumeric(): return True\n",
         "  if blacklist.strip() == '': return False\n",
         "  for item in list(blacklist.split(',')):\n",
+        "    if item.strip() == '' : continue\n",
         "    if txt.find(item.strip())> -1 : return True\n",
         "  #------#\n",
         "  return False\n",
@@ -488,7 +495,7 @@
         "  image_features_A =  target_image_encodings[f'{index}']\n",
         "\n",
         "  # text-similarity\n",
-        "  sims =  C * torch.matmul(text_tensor, text_features_A.t())\n",
+        "  sims =  prompt_strength * C * torch.matmul(text_tensor, text_features_A.t())\n",
         "\n",
         "  # Calculate negatives\n",
         "  neg_sims = {}\n",
@@ -518,7 +525,7 @@
         "\n",
         "  # plus image-similarity\n",
         "  img_sims = torch.matmul(text_tensor, image_features_A.t()) * logit_scale\n",
-        "  sims = sims +  (1-C) * img_sims\n",
+        "  sims = sims +  prompt_strength  * (1-C) * img_sims\n",
         "\n",
         "\n",
         "  # plus POS-similarity\n",
@@ -546,13 +553,15 @@
         "  for _index in range(start_at_index + RANGE):\n",
         "    if _index < start_at_index : continue\n",
         "\n",
-        "    for iters in range(10000):\n",
+        "    for iters in range(1000):\n",
         "      found = True\n",
-        "      index = indices[_index + offset].item()\n",
+        "      index = indices[min(_index + offset,NUM_VOCAB_ITEMS-1)].item()\n",
         "      if isBlacklisted(prompts[f'{index}'].lower()):\n",
         "        offset = offset + 1\n",
         "        found = False\n",
-        "      if (_index + offset)>NUM_VOCAB_ITEMS : found = True\n",
+        "      if (_index + offset)>=NUM_VOCAB_ITEMS-2 :\n",
+        "        found = True\n",
+        "        offset = NUM_VOCAB_ITEMS - _index -1\n",
         "      if found : break\n",
         "    #-------#\n",
         "\n",