Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -148,7 +148,10 @@
         "  %cd /content/\n",
         "  _ref = load_file('reference.safetensors' )\n",
         "  num_plots = num_plots+1\n",
-        "except: _ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
+        "except: _ref = torch.zeros(dim).to(dtype = dot_dtype)'\n",
+        "#-----#\n",
+        "try: ref\n",
+        "except: ref =  torch.zeros(dim).to(dtype = dot_dtype)\n",
         "\n",
         "image_size = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
         "show_encoding = True # @param {type:\"boolean\"}\n",
@@ -330,7 +333,7 @@
       "metadata": {
         "id": "IqUsiQw2HU2C"
       },
-      "execution_count": null,
+      "execution_count": 4,
       "outputs": []
     },
     {
@@ -403,7 +406,7 @@
       "metadata": {
         "id": "I_-GOwFPKkha"
       },
-      "execution_count": null,
+      "execution_count": 5,
       "outputs": []
     },
     {
@@ -467,7 +470,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# @title ⚄ Evaluate saved  reference similarity to select items\n",
+        "# @title ⚄ Evaluate saved  reference similarity to select items (optional)\n",
         "EVAL = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
         "\n",
         "# @markdown 📝 Enhance/Penalize Similarity and skip items containing word(s)\n",
@@ -478,8 +481,8 @@
         "_POS = 0 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
         "_NEG = 0  # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
         "\n",
-        "show_local_reference = False # @param {type:\"boolean\"}\n",
-        "show_encoding = False # @param {type:\"boolean\"}\n",
+        "show_local_reference = True # @param {type:\"boolean\"}\n",
+        "show_encoding = True # @param {type:\"boolean\"}\n",
         "\n",
         "_ref = load_file('reference.safetensors' )\n",
         "ref = _ref['weights'].to(dot_dtype)\n",
@@ -560,7 +563,9 @@
         "if _START_AT.isnumeric(): START_AT = int(_START_AT)\n",
         "\n",
         "output_folder = home_directory + 'results/'\n",
+        "output_folder_sims = home_directory + 'results/sims/'\n",
         "my_mkdirs(output_folder)\n",
+        "my_mkdirs(output_folder_sims)\n",
         "\n",
         "\n",
         "\n",
@@ -605,6 +610,13 @@
         "_POS2 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
         "_NEG = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
         "# @markdown -----\n",
+        "# @markdown Save similarity as a list for later review (this will slow down the code)\n",
+        "save_similiarity = True # @param {type:\"boolean\"}\n",
+        "# @markdown -----\n",
+        "include_similiarity = False # @param {type:\"boolean\"}\n",
+        "print_as_list = False # @param {type:\"boolean\"}\n",
+        "N = 7 # @param {type:\"slider\", min:0, max:10, step:1}\n",
+        "#-----#\n",
         "for _item in POS1.split(','):\n",
         "  item = _item.strip()\n",
         "  if item == '':continue\n",
@@ -682,6 +694,11 @@
         "  #------#\n",
         "  sims = torch.matmul(text_encodings*scale, ref.t())\n",
         "  sorted , indices = torch.sort(sims , dim=0 , descending = True)\n",
+        "  tmp = {}\n",
+        "  tmp['weights'] = sorted\n",
+        "  %cd {output_folder_sims}\n",
+        "  save_file(tmp, root_filename + '_sims.safetensors')\n",
+        "  tmp={}\n",
         "  #-----#\n",
         "  for index in range(LIST_SIZE + START_AT):\n",
         "    if index<START_AT: continue\n",
@@ -696,14 +713,12 @@
         "  #-------#\n",
         "  continue\n",
         "#---------#\n",
+        "total_items = total_items + num_items+1\n",
+        "#-------#\n",
         "print(f'\\nProcessed entire list of {total_items} items to find closest match.\\nSaved closest matching indices {START_AT} to {START_AT + LIST_SIZE} as the dict \"similiar_prompts\" with {LIST_SIZE} items.\\n')\n",
         "\n",
         "# Print results\n",
         "sorted , indices = torch.sort(similiar_sims , dim=0 , descending = True)\n",
-        "include_similiarity = False # @param {type:\"boolean\"}\n",
-        "print_as_list = False # @param {type:\"boolean\"}\n",
-        "N = 7 # @param {type:\"slider\", min:0, max:10, step:1}\n",
-        "\n",
         "if(print_as_list):\n",
         "  for index in range(LIST_SIZE):\n",
         "    key = indices[index].item()\n",
@@ -724,8 +739,14 @@
         "    prompt = (prompt + '}').replace('|}',  '} ')\n",
         "  #------#\n",
         "  print(f'Similiar prompts: \\n\\n\\n{prompt} \\n\\n\\n//----//')\n",
-        "image\n",
-        "#-----#\n"
+        "#-----#\n",
+        "\n",
+        "#Clear memory\n",
+        "_text_encodings = {}\n",
+        "prompts = {}\n",
+        "#-----#\n",
+        "\n",
+        "image\n"
       ],
       "metadata": {
         "id": "kOYZ8Ajn-DD8"
@@ -733,6 +754,90 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title ⚄ Evaluate similarities\n",
+        "%cd {output_folder_sims}\n",
+        "index = 0\n",
+        "for filename in os.listdir(output_folder_sims):\n",
+        "  _sims = load_file(filename)\n",
+        "  _sims = _sims['weights']\n",
+        "  for _sim in _sims.tolist():\n",
+        "    index = index + 1\n",
+        "  #-------#\n",
+        "total_items = index\n",
+        "sims = torch.zeros(total_items)\n",
+        "index = 0\n",
+        "for filename in os.listdir(output_folder_sims):\n",
+        "  _sims = load_file(filename)\n",
+        "  _sims = _sims['weights']\n",
+        "  for sim in _sims.tolist():\n",
+        "    sims[index] = sim\n",
+        "    index = index + 1\n",
+        "  #-------#\n",
+        "#---------------#\n",
+        "_sorted , indices  = torch.sort(sims , dim=0 , descending = True)\n",
+        "SCALE = 0.001\n",
+        "sorted = torch.round(_sorted/SCALE)\n",
+        "ZERO_POINT = sorted[total_items-1].item()\n",
+        "sorted = (sorted - torch.ones(total_items)*ZERO_POINT)\n",
+        "densities = torch.bincount(sorted.to(dtype = torch.int64))\n",
+        "yy = densities.tolist()\n",
+        "top = (sorted[0] + ZERO_POINT).to(dtype = torch.int64).item()\n",
+        "num_coords = round(top - ZERO_POINT)\n",
+        "xx = [round((ZERO_POINT + x)*100*SCALE,2) for x in range(num_coords+1)]\n",
+        "index = 0\n",
+        "for item in xx:\n",
+        "  if item>0:break\n",
+        "  index = index + 1\n",
+        "#----#\n",
+        "positive_bound = index\n",
+        "ss =list(xx)\n",
+        "tmp = 0\n",
+        "chunk = 1\n",
+        "CHUNK_SIZE = 1000\n",
+        "index = 0\n",
+        "for num in reversed(yy):\n",
+        "  tmp = tmp + num\n",
+        "  if(tmp>CHUNK_SIZE):\n",
+        "    _tmp = math.floor(tmp/CHUNK_SIZE)\n",
+        "    chunk = chunk + _tmp\n",
+        "    tmp = tmp - CHUNK_SIZE * _tmp\n",
+        "  ss[num_coords - index] = chunk\n",
+        "  index = index + 1\n",
+        "#------#\n",
+        "fig, ax = plt.subplots()\n",
+        "fig.canvas.draw()\n",
+        "plt.plot(ss[positive_bound:], xx[positive_bound:])\n",
+        "plt.xlabel ('Search depth')\n",
+        "plt.ylabel ('Similarity')\n",
+        "plt.title ('Similarity to index')\n",
+        "plt.grid()\n",
+        "indices_depth = [item.get_text() for item in ax.get_xticklabels()]\n",
+        "sim_pcnts = [item.get_text() for item in ax.get_yticklabels()]\n",
+        "\n",
+        "index = 0\n",
+        "for index_depth in indices_depth:\n",
+        "  indices_depth[index] = index_depth + 'K'\n",
+        "  index = index + 1\n",
+        "#-------#\n",
+        "\n",
+        "index = 0\n",
+        "for sim_pcnt in sim_pcnts:\n",
+        "  sim_pcnts[index] = sim_pcnt + '%'\n",
+        "  index = index + 1\n",
+        "#-------#\n",
+        "ax.set_xticklabels(indices_depth)\n",
+        "ax.set_yticklabels(sim_pcnts)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "ln6DsZPG99ez"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [