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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"Try this Free online SD 1.5 generator with the results: https://perchance.org/fusion-ai-image-generator\n",
"\n",
" This Notebook is a Stable-diffusion tool which allows you to find similiar prompts to an existing prompt. It uses the Nearest Neighbor decoder method listed here:https://arxiv.org/pdf/2303.03032"
],
"metadata": {
"id": "cRV2YWomjMBU"
}
},
{
"cell_type": "code",
"source": [
"# @title ⚄ Initialize\n",
"\n",
"import os\n",
"home_directory = '/content/'\n",
"using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
"if using_Kaggle : home_directory = '/kaggle/working/'\n",
"%cd {home_directory}\n",
"\n",
"def fix_bad_symbols(txt):\n",
" result = txt\n",
" for symbol in ['^', '}', '{' , ')', '(', '[' , ']' , ':' , '=' ]:\n",
" result = result.replace(symbol,'\\\\' + symbol)\n",
" #------#\n",
" return result;\n",
"\n",
"def my_mkdirs(folder):\n",
" if os.path.exists(folder)==False:\n",
" os.makedirs(folder)\n",
"\n",
"#🔸🔹\n",
"# Load the data if not already loaded\n",
"try:\n",
" loaded\n",
"except:\n",
" from safetensors.torch import load_file , save_file\n",
" import json , torch , requests , math\n",
" import pandas as pd\n",
" from PIL import Image\n",
" import cv2\n",
" from matplotlib import pyplot as plt\n",
" #----#\n",
" %cd {home_directory}\n",
" !git clone https://huggingface.co/datasets/codeShare/fusion-t2i-generator-data\n",
" loaded = True\n",
" %cd {home_directory + 'fusion-t2i-generator-data/'}\n",
" !unzip reference.zip\n",
"\n",
"from transformers import AutoTokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
"from transformers import CLIPProcessor, CLIPModel\n",
"processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
"model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
"logit_scale = model.logit_scale.exp() #logit_scale = 100.00000762939453\n",
"\n",
"#------#\n",
"%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
"with open(f'reference_prompts.json', 'r') as f:\n",
" data = json.load(f)\n",
" _df = pd.DataFrame({'count': data})['count']\n",
" target_prompts = {\n",
" key : value for key, value in _df.items()\n",
" }\n",
"#------#\n",
"with open(f'reference_urls.json', 'r') as f:\n",
" data = json.load(f)\n",
" _df = pd.DataFrame({'count': data})['count']\n",
" target_urls = {\n",
" key : value for key, value in _df.items()\n",
" }\n",
"\n",
"#------#\n",
"dot_dtype = torch.float32\n",
"dim = 768\n",
"ref = torch.zeros(dim).to(dtype = dot_dtype)"
],
"metadata": {
"id": "TC5lMJrS1HCC",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"The visualization has no effect on the output. It will only be used if you enable the 'Show encoding' checkbox"
],
"metadata": {
"id": "OpOoRmaP3u2H"
}
},
{
"cell_type": "code",
"source": [
"# @title ⚄ Define parameters for visalizing the reference in a 16x16 grid <br> (the visualization settings has no effect on output)\n",
"from PIL import Image, ImageDraw\n",
"SCALE = 0.0002 # @param {type:\"slider\", min:0.0001, max:0.001, step:0.00001}\n",
"ZERO_POINT = 100 # @param {type:\"slider\", min:0, max:300, step:1}\n",
"CELL_SIZE = 16\n",
"\n",
"BORDER_THICKNESS = 4\n",
"\n",
"def visualize(_ref):\n",
" RGB_tensor = (torch.round(_ref/SCALE)+torch.ones(dim)*ZERO_POINT)\n",
" cellsize = CELL_SIZE\n",
" tick = round(cellsize/2)\n",
" border_offset = round(BORDER_THICKNESS/2)\n",
" width = 16*cellsize + BORDER_THICKNESS\n",
" height = 16*cellsize + BORDER_THICKNESS\n",
" image = Image.new('RGB', (width, height), (0, 0, 0))\n",
" draw = ImageDraw.Draw(image)\n",
" for row in range(16):\n",
" for col in range(16):\n",
" tmp = 3*row*col\n",
" r = max(0,min(255,int(RGB_tensor[tmp].item())))\n",
" g = max(0,min(255,int(RGB_tensor[tmp+1].item())))\n",
" b = max(0,min(255,int(RGB_tensor[tmp+2].item())))\n",
" fillColor = (r,g,b)\n",
" x0 = row*cellsize +border_offset\n",
" y0 = (15-col)*cellsize +border_offset\n",
" x1 = row*cellsize + 2*tick + border_offset\n",
" y1 = (15-col)*cellsize + 2*tick + border_offset\n",
" shape = [(x0, y0), (x1, y1)]\n",
" draw.rectangle(shape, fill=fillColor, outline=(0,0,0))\n",
" return (image)\n",
"\n",
"num_plots = 1\n",
"try:\n",
" %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",
"#-----#\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",
"#------#\n",
"if show_encoding:\n",
" # create figure\n",
" fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
" fig.patch.set_facecolor((56/255,56/255,56/255))\n",
" rows = 1\n",
" columns = num_plots\n",
" fig.add_subplot(rows, columns, 1)\n",
" plt.imshow( visualize(ref))\n",
" plt.axis('off')\n",
" plt.title( \"Encoding (local variable)\", color='white', fontsize=round(20*image_size))\n",
" if num_plots>1:\n",
" fig.add_subplot(rows, columns, 2)\n",
" plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
" plt.axis('off')\n",
" plt.title(\"Encoding (saved file)\", color='white', fontsize=round(20*image_size))\n",
" #------#\n",
"\n",
"print(f'Using settings SCALE = {SCALE} and ZERO_POINT = {ZERO_POINT} for visualizing the text_encoding')"
],
"metadata": {
"id": "YDu8XlehhWID",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Paste a prompt in the cell below to create an encoding**\n",
"\n"
],
"metadata": {
"id": "Xf9zoq-Za3wi"
}
},
{
"cell_type": "code",
"source": [
"\n",
"# @markdown 📝 Write a text prompt (this will overwrite any savefile already stored)\n",
"NEW_ENCODING = '' # @param {type:'string' ,placeholder:'write a prompt'}\n",
"enable = True # @param {type:\"boolean\"}\n",
"# @markdown -----\n",
"# @markdown 📝 Enhance/Penalize Similarity and skip items containing word(s)\n",
"POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"NEG = ''# @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"# @markdown -----\n",
"# @markdown logarithmic prompt strength x for value 10^(x-1)\n",
"_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",
"# @markdown -----\n",
"# @markdown Check similiarity for this encoding against any written prompt(s)\n",
"# @title ⚄ Evaluate saved reference similarity to select items (optional)\n",
"EVAL = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"\n",
"show_local_reference = True # @param {type:\"boolean\"}\n",
"show_encoding = True # @param {type:\"boolean\"}\n",
"\n",
"try:\n",
" %cd /content/\n",
" _ref = load_file('reference.safetensors' )\n",
" ref = _ref['weights'].to(dot_dtype)\n",
"except:\n",
" ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
" _ref = {}\n",
" _ref['weights'] = ref\n",
" %cd /content/\n",
" save_file(_ref, 'reference.safetensors')\n",
"#-----#\n",
"\n",
"if NEW_ENCODING.strip() != ''\n",
" item = NEW_ENCODING.strip()\n",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" ref = model.get_text_features(**inputs)[0]\n",
" ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
"#------#\n",
"\n",
"try: ref\n",
"except: ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
"\n",
"if EVAL.strip() != '':\n",
" print(\"Saved Reference:\\n\")\n",
" for item in EVAL.split(','):\n",
" if item.strip()=='':continue\n",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" test = model.get_text_features(**inputs)[0]\n",
" test = test/test.norm(p=2 , dim = -1 , keepdim = True)\n",
" ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
" eval = torch.dot(ref , test)\n",
" print(f'{item.strip()} : {round(eval.item()*100, 2)}%')\n",
" #-----#\n",
" if(show_local_reference):\n",
" print(\"\\n---------\\nLocal Reference with enchancements added :\\n\")\n",
"\n",
" for _item in POS.split(','):\n",
" item = _item.strip()\n",
" if item == '':continue\n",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" ref = ref + math.pow(10,_POS-1) * model.get_text_features(**inputs)[0]\n",
" #-------#\n",
"\n",
" for _item in NEG.split(','):\n",
" item = _item.strip()\n",
" if item == '':continue\n",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" ref = ref + math.pow(10,_NEG-1) * model.get_text_features(**inputs)[0]\n",
" #-------#\n",
"\n",
" ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
" for item in EVAL.split(','):\n",
" if item.strip()=='':continue\n",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" test = model.get_text_features(**inputs)[0]\n",
" test = test/test.norm(p=2 , dim = -1 , keepdim = True)\n",
" eval = torch.dot(ref , test)\n",
" print(f'{item.strip()} : {round(eval.item()*100, 2)}%')\n",
" #-----#\n",
"\n",
" if show_encoding:\n",
" # create figure\n",
" fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
" fig.patch.set_facecolor((56/255,56/255,56/255))\n",
" rows = 1\n",
" columns = 3\n",
" fig.add_subplot(rows, columns, 1)\n",
" plt.imshow( visualize(ref))\n",
" plt.axis('off')\n",
" plt.title( \"Encoding (local variable)\", color='white', fontsize=round(20*image_size))\n",
" if num_plots>1:\n",
" fig.add_subplot(rows, columns, 2)\n",
" plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
" plt.axis('off')\n",
" plt.title(\"Encoding (saved file)\", color='white', fontsize=round(20*image_size))\n",
"\n",
" fig.add_subplot(rows, columns, 3)\n",
" plt.imshow( visualize(ref - _ref['weights'].to(dot_dtype)))\n",
" plt.axis('off')\n",
" plt.title(\"Changes\", color='white', fontsize=round(20*image_size))\n",
" #------#\n",
"\n",
"\n"
],
"metadata": {
"id": "Oxi6nOyrUTAe"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Use a pre-encoded image+prompt pair as reference (optional)**"
],
"metadata": {
"id": "f9_AcquM7AYZ"
}
},
{
"cell_type": "code",
"source": [
"\n",
"loaded_ref = False\n",
"try:\n",
" ref\n",
" loaded_ref = True\n",
"except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
"if loaded_ref : prev_ref = ref.clone().detach()\n",
"\n",
"try:prompt\n",
"except: prompt = ''\n",
"\n",
"# @markdown 🖼️+📝 Choose a pre-encoded reference (note: some results are NSFW!)\n",
"index = 596 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
"PROMPT_INDEX = index\n",
"prompt = target_prompts[f'{PROMPT_INDEX}']\n",
"url = target_urls[f'{PROMPT_INDEX}']\n",
"if url.find('perchance')>-1:\n",
" image = Image.open(requests.get(url, stream=True).raw)\n",
"#------#\n",
"%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
"references = torch.load('reference_text_and_image_encodings.pt' , weights_only=False)\n",
"# @markdown ⚖️ 🖼️ encoding <-----?-----> 📝 encoding </div> <br>\n",
"C = 0.3 # @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",
"method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
"image_size = 0.57 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
"show_encoding = True # @param {type:\"boolean\"}\n",
"\n",
"if(not method == 'Do nothing'):\n",
" if method == 'Refresh': ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
" if method == 'Subtract from existing ref':\n",
" ref = torch.sub(ref, math.pow(10 ,log_strength-1) * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
" ref = torch.sub(ref, math.pow(10 ,log_strength-1) * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
" else:\n",
" ref = torch.add(ref, math.pow(10 ,log_strength-1) * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
" ref = torch.add(ref, math.pow(10 ,log_strength-1) * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
" #---------#\n",
" references = '' # Clear up memory\n",
" ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
" ref = ref.clone().detach()\n",
" #------#\n",
" # create figure\n",
" fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
" fig.patch.set_facecolor((56/255,56/255,56/255))\n",
" rows = 1\n",
" columns = 1\n",
" if show_encoding: columns = columns+1\n",
" if show_encoding and loaded_ref : columns = columns+1\n",
" fig.add_subplot(rows, columns, 1)\n",
" plt.imshow(image)\n",
" plt.axis('off')\n",
" plt.title(f\"Reference image at index={index}\" , color='white' , fontsize=round(20*image_size))\n",
" #-----#\n",
" if show_encoding and loaded_ref:\n",
" fig.add_subplot(rows, columns, columns-1)\n",
" plt.imshow( visualize(prev_ref))\n",
" plt.axis('off')\n",
" plt.title(\"Encoding (before)\" , color='white' , fontsize=round(20*image_size))\n",
" print(f'Prompt for this image : \\n\\n \"{prompt} \" \\n\\n')\n",
"\n",
" if show_encoding:\n",
" fig.add_subplot(rows, columns, columns)\n",
" plt.imshow( visualize(ref))\n",
" plt.axis('off')\n",
" plt.title(\"Encoding (now)\" , color='white' , fontsize=round(20*image_size))\n",
" #------#\n"
],
"metadata": {
"id": "BwrEs5zVB0Sb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Use an image as a reference via URL (optional)**"
],
"metadata": {
"id": "KI9Ho6CG7m3Z"
}
},
{
"cell_type": "code",
"source": [
"\n",
"loaded_ref = False\n",
"try:\n",
" ref\n",
" loaded_ref = True\n",
"except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
"if loaded_ref : prev_ref = ref.clone().detach()\n",
"\n",
"try:prompt\n",
"except: prompt = ''\n",
"\n",
"# @markdown 🖼️ Upload your own image for use as reference via URL (optional)\n",
"URL = '' # @param {type:'string' ,placeholder:'paste an url here'}\n",
"if URL.strip() != '':\n",
" image = Image.open(requests.get(URL, stream=True).raw)\n",
" log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
" method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
" image_size = 0.79 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
" show_encoding = True # @param {type:\"boolean\"}\n",
" #---------#\n",
" if(not method == 'Do nothing'):\n",
" # Get image features\n",
" inputs = processor(images=image, return_tensors=\"pt\")\n",
" image_features = model.get_image_features(**inputs)\n",
" image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\n",
" #-------#\n",
" if method == 'Refresh':\n",
" ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
" if method == 'Subtract from existing ref':\n",
" ref = ref - math.pow(10,log_strength-1)*image_features\n",
" else: ref = ref + math.pow(10,log_strength-1)*image_features\n",
" #-----#\n",
" ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
" ref = ref[0]\n",
" ref = ref.clone().detach()\n",
" #------#\n",
" # create figure\n",
" fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
" fig.patch.set_facecolor((56/255,56/255,56/255))\n",
" rows = 1\n",
" columns = 1\n",
" if show_encoding: columns = 2\n",
" if show_encoding and loaded_ref : columns = 3\n",
" fig.add_subplot(rows, columns, 1)\n",
" plt.imshow(image)\n",
" plt.axis('off')\n",
" plt.title(\"Reference image from URL\" , color='white' , fontsize=round(20*image_size))\n",
" #-----#\n",
" if show_encoding and loaded_ref:\n",
" fig.add_subplot(rows, columns, columns-1)\n",
" plt.imshow( visualize(prev_ref))\n",
" plt.axis('off')\n",
" plt.title(\"Encoding (before)\" , color='white' , fontsize=round(20*image_size))\n",
" if show_encoding:\n",
" fig.add_subplot(rows, columns, columns)\n",
" plt.imshow( visualize(ref))\n",
" plt.axis('off')\n",
" plt.title(\"Encoding (now)\" , color='white' , fontsize=round(20*image_size))\n",
" #------#"
],
"metadata": {
"id": "IqUsiQw2HU2C"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Use an image as a reference via uploading it to the /content/ folder (optional)**"
],
"metadata": {
"id": "MBPi7F8S7tg3"
}
},
{
"cell_type": "code",
"source": [
"\n",
"loaded_ref = False\n",
"try:\n",
" ref\n",
" loaded_ref = True\n",
"except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
"if loaded_ref : prev_ref = ref.clone().detach()\n",
"\n",
"try:prompt\n",
"except: prompt = ''\n",
"\n",
"# @markdown 🖼️ Upload your own image for use as reference via URL (optional)\n",
"FILENAME = '' # @param {type:'string' ,placeholder:'IMG_123.png'}\n",
"log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
"method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
"image_size = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
"show_encoding = True # @param {type:\"boolean\"}\n",
"\n",
"if FILENAME.strip() != '':\n",
" %cd /content/\n",
" image = cv2.imread(FILENAME)\n",
" b,g,r = cv2.split(image)\n",
" image = cv2.merge([r,g,b])\n",
" #---------#\n",
" if(not method == 'Do nothing'):\n",
" # Get image features\n",
" inputs = processor(images=image, return_tensors=\"pt\")\n",
" image_features = model.get_image_features(**inputs)\n",
" image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\n",
" #-------#\n",
" if method == 'Refresh':\n",
" ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
" if method == 'Subtract from existing ref':\n",
" ref = ref - math.pow(10,log_strength-1)*image_features\n",
" else: ref = ref + math.pow(10,log_strength-1)*image_features\n",
" #-----#\n",
" ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
" ref = ref[0]\n",
" ref = ref.clone().detach()\n",
" #------#\n",
" # create figure\n",
" fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
" fig.patch.set_facecolor((56/255,56/255,56/255))\n",
" rows = 1\n",
" columns = 1\n",
" if show_encoding: columns = 2\n",
" if show_encoding and loaded_ref : columns = 3\n",
" fig.add_subplot(rows, columns, 1)\n",
" plt.imshow(image)\n",
" plt.axis('off')\n",
" plt.title(f\"Reference image from uploaded image {FILENAME}\" , color='white' , fontsize=round(20*image_size))\n",
" #-----#\n",
" if show_encoding and loaded_ref:\n",
" fig.add_subplot(rows, columns, columns-1)\n",
" plt.imshow( visualize(prev_ref))\n",
" plt.axis('off')\n",
" plt.title(\"Encoding (before)\" , color='white' , fontsize=round(20*image_size))\n",
" if show_encoding:\n",
" fig.add_subplot(rows, columns, columns)\n",
" plt.imshow( visualize(ref))\n",
" plt.axis('off')\n",
" plt.title(\"Encoding (now)\" , color='white' , fontsize=round(20*image_size))\n",
" #------#"
],
"metadata": {
"id": "I_-GOwFPKkha"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Save the reference prior to running the Interrogator**"
],
"metadata": {
"id": "zeu6JcM-mk9z"
}
},
{
"cell_type": "code",
"source": [
"# @title ⚄ Save the reference\n",
"\n",
"loaded_ref = False\n",
"try:\n",
" ref\n",
" loaded_ref = True\n",
"except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
"if loaded_ref : prev_ref = ref.clone().detach()\n",
"\n",
"try:prompt\n",
"except: prompt = ''\n",
"\n",
"reset_everything = False # @param {type:\"boolean\"}\n",
"_ref = {}\n",
"ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
"if (reset_everything) : ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
"_ref['weights'] = ref.to(dot_dtype)\n",
"%cd /content/\n",
"save_file(_ref , 'reference.safetensors' )\n",
"image_size = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
"show_encoding = True # @param {type:\"boolean\"}\n",
"#------#\n",
"print(\"Saved local encoding to reference.safetensors\")\n",
"if show_encoding:\n",
" # create figure\n",
" fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
" fig.patch.set_facecolor((56/255,56/255,56/255))\n",
" rows = 1\n",
" columns = num_plots\n",
" fig.add_subplot(rows, columns, 1)\n",
" plt.imshow( visualize(ref))\n",
" plt.axis('off')\n",
" plt.title( \"Encoding (local variable)\", color='white', fontsize=round(20*image_size))\n",
" if num_plots>1:\n",
" fig.add_subplot(rows, columns, 2)\n",
" plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
" plt.axis('off')\n",
" plt.title(\"Encoding (saved file)\", color='white', fontsize=round(20*image_size))\n",
" #------#"
],
"metadata": {
"id": "lOQuTPfBMK82",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Run the interrogator**\n",
"\n",
" Since the list of items is large (>1 million items) you will need to select a range within the sorted results to print."
],
"metadata": {
"id": "ROKsoZrt7zMe"
}
},
{
"cell_type": "code",
"source": [
"# @title ⚄ CLIP Interrogator\n",
"LIST_SIZE = 1000 # @param {type:'number' , placeholder:'set how large the list should be'}\n",
"_START_AT = '0' # @param [\"0\", \"10000\", \"50000\"] {allow-input: true}\n",
"START_AT = 0\n",
"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",
"# @markdown -----\n",
"# @markdown Select vocab\n",
"general = True # @param {type:\"boolean\"}\n",
"civit9 = True # @param {type:\"boolean\"}\n",
"fanfic1 = False # @param {type:\"boolean\"}\n",
"fanfic2 = False # @param {type:\"boolean\"}\n",
"# @markdown -----\n",
"# @title ⚄ New interrogator code using quantized text corpus\n",
"%cd /content/\n",
"_ref = load_file('reference.safetensors' )\n",
"ref = _ref['weights'].to(dot_dtype)\n",
"# @markdown 📝 Enhance/Penalize Similarity and skip items containing word(s)\n",
"POS1 = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"POS2 = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"NEG = ''# @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"SKIP = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"min_wordcount = 0 # @param {type:\"slider\", min:0, max:20, step:1}\n",
"def isBlacklisted(_txt):\n",
" blacklist = SKIP.lower().replace('</w>' , ' ').replace('{' , '').replace('}' , '').replace('|' , ',').strip()\n",
" if blacklist == '': return False\n",
" txt = _txt.lower().strip()\n",
" if len(txt)<min_wordcount: return True\n",
" if txt.isnumeric(): return True\n",
" #-----#\n",
" for item in list(blacklist.split(',')):\n",
" if item.strip() == '' : continue\n",
" if txt.find(item.strip())> -1 : return True\n",
" #------#\n",
" found = False\n",
" alphabet = 'abcdefghijklmnopqrstuvxyz'\n",
" for letter in alphabet:\n",
" found = txt.find(letter)>-1\n",
" if found:break\n",
" #------#\n",
" return not found\n",
"# @markdown -----\n",
"# @markdown logarithmic prompt strength x for value 10^(x-1)\n",
"_POS1 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
"_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",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" ref = ref + math.pow(10,_POS1-1) * model.get_text_features(**inputs)[0]\n",
"#-------#\n",
"for _item in POS2.split(','):\n",
" item = _item.strip()\n",
" if item == '':continue\n",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" ref = ref + math.pow(10,_POS2-1) * model.get_text_features(**inputs)[0]\n",
"#-------#\n",
"for _item in NEG.split(','):\n",
" item = _item.strip()\n",
" if item == '':continue\n",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" ref = ref + math.pow(10,_NEG-1) * model.get_text_features(**inputs)[0]\n",
"#------#\n",
"ref = (ref/ref.norm(p=2, dim=-1, keepdim=True)).to(dtype = dot_dtype)\n",
"vocab_to_load = ''\n",
"if (general): vocab_to_load = vocab_to_load + 'general , '\n",
"if (civit9): vocab_to_load = vocab_to_load + 'civit9 , '\n",
"if (fanfic1): vocab_to_load = vocab_to_load + 'fanfic1 , '\n",
"if (fanfic2): vocab_to_load = vocab_to_load + 'fanfic2 , '\n",
"vocab_to_load = (vocab_to_load +'}').replace(' , }' , '')\n",
"multi = vocab_to_load.find(',')>-1\n",
"#-----#\n",
"prompts_folder = f'{home_directory}fusion-t2i-generator-data/vocab-v2/text'\n",
"encodings_folder = f'{home_directory}fusion-t2i-generator-data/vocab-v2/text_encodings'\n",
"#----#\n",
"scale = 0.0043\n",
"size = 0\n",
"#------#\n",
"total_items = 0\n",
"for filename in os.listdir(prompts_folder):\n",
" if (not general and filename.find('general')>-1):continue\n",
" if (not civit9 and filename.find('civit9')>-1):continue\n",
" if (not fanfic1 and filename.find('fanfic1')>-1):continue\n",
" if (not fanfic2 and filename.find('fanfic2')>-1):continue\n",
" size = size + LIST_SIZE\n",
"#-------#\n",
"similiar_sims = torch.zeros(size)\n",
"similiar_prompts = {}\n",
"_index = 0\n",
"#-------#\n",
"similiar_encodings = {}\n",
"for filename in os.listdir(prompts_folder):\n",
" if (not general and filename.find('general')>-1):continue\n",
" if (not civit9 and filename.find('civit9')>-1):continue\n",
" if (not fanfic1 and filename.find('fanfic1')>-1):continue\n",
" if (not fanfic2 and filename.find('fanfic2')>-1):continue\n",
" #------#\n",
" root_filename = filename.replace('.json', '')\n",
" %cd {prompts_folder}\n",
" prompts = {}\n",
" with open(f'{root_filename}.json', 'r') as f:\n",
" data = json.load(f).items()\n",
" for key,value in data:\n",
" prompts[key] = value\n",
" num_items = int(prompts['num_items'])\n",
" total_items = total_items + num_items\n",
" #------#\n",
" try:vocab_loaded\n",
" except:\n",
" vocab_loaded = 'first'\n",
" #-----#\n",
" if vocab_loaded == 'first' or (vocab_loaded != vocab_to_load and not multi):\n",
" %cd {encodings_folder}\n",
" _text_encodings = load_file(f'{root_filename}.safetensors')['weights'].to(torch.uint8)\n",
" text_encodings = torch.zeros(num_items , dim)\n",
" tmp = torch.ones(dim).to(dot_dtype)\n",
" for index in range(num_items):\n",
" text_encodings[index] = torch.sub(_text_encodings[index][1:dim+1].to(dot_dtype) , tmp , alpha= _text_encodings[index][0].to(dot_dtype))\n",
" vocab_loaded = vocab_to_load\n",
" #------#\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",
" key = indices[index].item()\n",
" try:prompt = prompts[f'{key}']\n",
" except:continue\n",
" if(isBlacklisted(prompt)):continue\n",
" #-------#\n",
" similiar_sims[_index] = torch.tensor(round(sims[key].item(), 5))\n",
" similiar_prompts[f'{_index}'] = prompt\n",
" _index = _index + 1\n",
" #-------#\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",
"if(print_as_list):\n",
" for index in range(LIST_SIZE):\n",
" key = indices[index].item()\n",
" sim = similiar_sims[key].item()\n",
" prompt = similiar_prompts[f'{key}']\n",
" if include_similiarity :print(f'{prompt} - {round(sim*100,1)} %')\n",
" else: print(f'{prompt}')\n",
"#-------#\n",
"else:\n",
" prompt = ''\n",
" for iter in range(N):\n",
" prompt = prompt + '{'\n",
" for index in range(LIST_SIZE):\n",
" key = indices[index].item()\n",
" sim = similiar_sims[key].item()\n",
" prompt = prompt + fix_bad_symbols(similiar_prompts[f'{key}']) + '|'\n",
" #-----#\n",
" prompt = (prompt + '}').replace('|}', '} ')\n",
" #------#\n",
" print(f'Similiar prompts: \\n\\n\\n{prompt} \\n\\n\\n//----//')\n",
"#-----#\n",
"\n",
"#Clear memory\n",
"_text_encodings = {}\n",
"prompts = {}\n",
"#-----#\n",
"\n",
"image\n"
],
"metadata": {
"id": "kOYZ8Ajn-DD8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Evaluate Similarities**\n",
"\n",
"Run this cell to see how far down the list you can go before similarity to the reference is lost."
],
"metadata": {
"id": "yl1DYzUn8YCC"
}
},
{
"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": [
"# @title ⚄ Save the results\n",
"\n",
"def mkdir(folder):\n",
" if os.path.exists(folder)==False:\n",
" os.makedirs(folder)\n",
"#-----#\n",
"output_folder = home_directory + 'results'\n",
"mkdir(output_folder)\n",
"#-----#\n",
"try: similiar_prompts\n",
"except:similiar_prompts = {}\n",
"%cd {output_folder}\n",
"print(f'Saving similiar_prompts.json to {output_folder}...')\n",
"with open('similiar_prompts.json', 'w') as f:\n",
" json.dump(similiar_prompts, f)\n",
"#-----#\n",
"try: similiar_sims\n",
"except: similiar_sims = torch.zeros(dim).to(dot_dtype)\n",
"#-------#\n",
"_similiar_sims = {}\n",
"_similiar_sims['weights'] = similiar_sims.to(dot_dtype)\n",
"%cd {output_folder}\n",
"print(f'Saving similiar_sims.safetensors to {output_folder}...')\n",
"save_file(_similiar_sims, 'similiar_sims.safetensors')\n"
],
"metadata": {
"id": "m-N553nXz9Jd",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\n",
"# @title ⚄ 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",
"FILENAME = '' # @param {type:'string' ,placeholder:'write .json file to load (optional)'}\n",
"_FILENAME = FILENAME.replace('.json' , '')\n",
"if _FILENAME.strip() == '': _FILENAME = 'similiar_prompts'\n",
"#------#\n",
"%cd {output_folder}\n",
"with open(f'{_FILENAME}.json', 'r') as f:\n",
" data = json.load(f)\n",
" _df = pd.DataFrame({'count': data})['count']\n",
" similiar_prompts = {\n",
" key : value for key, value in _df.items()\n",
" }\n",
"#-------#\n",
"_similiar_sims = load_file('similiar_sims.safetensors')\n",
"similiar_sims = _similiar_sims['weights'].to(dot_dtype)\n",
"\n",
"# @title ⚄ Run the CLIP interrogator on the saved reference\n",
"\n",
"# @markdown Select which values within the saved list to print\n",
"LIST_SIZE = 1000 # @param {type:'number' , placeholder:'set how large the list should be'}\n",
"START_AT = 0 # @param {type:'number' , placeholder:'set how large the list should be'}\n",
"\n",
"if(print_as_list):\n",
" for index in range(LIST_SIZE + START_AT):\n",
" if index<START_AT:continue\n",
" key = indices[index].item()\n",
" sim = similiar_sims[key].item()\n",
" prompt = similiar_prompts[f'{key}']\n",
" if include_similiarity :print(f'{prompt} - {round(sim*100,1)} %')\n",
" else: print(f'{prompt}')\n",
"#-------#\n",
"else:\n",
" prompt = ''\n",
" for iter in range(N):\n",
" prompt = prompt + '{'\n",
" for index in range(LIST_SIZE + START_AT):\n",
" if index<START_AT:continue\n",
" key = indices[index].item()\n",
" sim = similiar_sims[key].item()\n",
" prompt = prompt + fix_bad_symbols(similiar_prompts[f'{key}']) + '|'\n",
" #-----#\n",
" prompt = (prompt + '}').replace('|}', '} ')\n",
" #------#\n",
" print(f'Similiar prompts: \\n\\n {prompt} \\n\\n')\n",
"image\n",
"#-----#\n"
],
"metadata": {
"id": "XOMkIKc9-wZz",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"OTHER STUFF BELOW - Code for the modules below are work-in-progress."
],
"metadata": {
"id": "FRIqYJDEebpf"
}
},
{
"cell_type": "markdown",
"source": [
"The savefile can be used here : https://perchance.org/fusion-ai-image-generator"
],
"metadata": {
"id": "JldNmWy1iyvK"
}
},
{
"cell_type": "code",
"source": [
"# @title \t⚄ Create fusion-generator .json savefile from result\n",
"filename = 'blank.json'\n",
"path = '/content/text-to-image-prompts/fusion/'\n",
"\n",
"print(f'reading {filename}....')\n",
"_index = 0\n",
"%cd {path}\n",
"with open(f'{filename}', 'r') as f:\n",
" data = json.load(f)\n",
"#------#\n",
"_df = pd.DataFrame({'count': data})['count']\n",
"_savefile = {\n",
" key : value for key, value in _df.items()\n",
"}\n",
"#------#\n",
"from safetensors.torch import load_file\n",
"import json , os , torch\n",
"import pandas as pd\n",
"#----#\n",
"def my_mkdirs(folder):\n",
" if os.path.exists(folder)==False:\n",
" os.makedirs(folder)\n",
"#------#\n",
"savefile_prompt = ''\n",
"for i in range(N) : savefile_prompt = savefile_prompt + ' ' + __prompts\n",
"_savefile['main'] = savefile_prompt.replace('\\n', ' ').replace(' ', ' ').replace(' ', ' ')\n",
"#------#\n",
"save_filename = f'fusion_C05_X7_1000_{PROMPT_INDEX}.json'\n",
"output_folder = '/content/output/savefiles/'\n",
"my_mkdirs(output_folder)\n",
"#-----#\n",
"%cd {output_folder}\n",
"print(f'Saving segment {save_filename} to {output_folder}...')\n",
"with open(save_filename, 'w') as f:\n",
" json.dump(_savefile, f)\n"
],
"metadata": {
"id": "Q7vpNAXQilbf",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title \t⚄ Create a savefile-set from the entire range of pre-encoded items\n",
"\n",
"# @markdown 📥 Load the data (only required one time)\n",
"load_the_data = True # @param {type:\"boolean\"}\n",
"\n",
"import math\n",
"from safetensors.torch import load_file\n",
"import json , os , torch\n",
"import pandas as pd\n",
"from PIL import Image\n",
"import requests\n",
"\n",
"def my_mkdirs(folder):\n",
" if os.path.exists(folder)==False:\n",
" os.makedirs(folder)\n",
"\n",
"# @markdown ⚖️ Set the value for C in the reference <br> <br> sim = C* text_enc + image_enc*(1-C) <br><br>\n",
"\n",
"C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
"\n",
"# @markdown 🚫 Penalize similarity to this prompt(optional)\n",
"if(load_the_data):\n",
" target_prompts , target_text_encodings , urls , target_image_encodings , NUM_ITEMS = getPromptsAndLinks('/content/text-to-image-prompts/fusion')\n",
" from transformers import AutoTokenizer\n",
" tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
" from transformers import CLIPProcessor, CLIPModel\n",
" processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
" model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
" logit_scale = model.logit_scale.exp() #logit_scale = 100.00000762939453\n",
"#---------#\n",
"\n",
"filename = 'blank.json'\n",
"path = '/content/text-to-image-prompts/fusion/'\n",
"print(f'reading {filename}....')\n",
"_index = 0\n",
"%cd {path}\n",
"with open(f'{filename}', 'r') as f:\n",
" data = json.load(f)\n",
"#------#\n",
"_df = pd.DataFrame({'count': data})['count']\n",
"_blank = {\n",
" key : value for key, value in _df.items()\n",
"}\n",
"#------#\n",
"\n",
"root_savefile_name = 'fusion_C05_X7'\n",
"\n",
"%cd /content/\n",
"output_folder = '/content/output/savefiles/'\n",
"my_mkdirs(output_folder)\n",
"my_mkdirs('/content/output2/savefiles/')\n",
"my_mkdirs('/content/output3/savefiles/')\n",
"my_mkdirs('/content/output4/savefiles/')\n",
"my_mkdirs('/content/output5/savefiles/')\n",
"my_mkdirs('/content/output6/savefiles/')\n",
"my_mkdirs('/content/output7/savefiles/')\n",
"my_mkdirs('/content/output8/savefiles/')\n",
"my_mkdirs('/content/output9/savefiles/')\n",
"my_mkdirs('/content/output10/savefiles/')\n",
"my_mkdirs('/content/output11/savefiles/')\n",
"my_mkdirs('/content/output12/savefiles/')\n",
"my_mkdirs('/content/output13/savefiles/')\n",
"\n",
"\n",
"NEG = '' # @param {type:'string'}\n",
"strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.1}\n",
"\n",
"for index in range(1667):\n",
"\n",
" PROMPT_INDEX = index\n",
" prompt = target_prompts[f'{index}']\n",
" url = urls[f'{index}']\n",
" if url.find('perchance')>-1:\n",
" image = Image.open(requests.get(url, stream=True).raw)\n",
" else: continue #print(\"(No image for this ID)\")\n",
"\n",
" print(f\"no. {PROMPT_INDEX} : '{prompt}'\")\n",
" text_features_A = target_text_encodings[f'{index}']\n",
" image_features_A = target_image_encodings[f'{index}']\n",
" # text-similarity\n",
" sims = C * torch.matmul(text_tensor, text_features_A.t())\n",
"\n",
" neg_sims = 0*sims\n",
" if(NEG != ''):\n",
" # Get text features for user input\n",
" inputs = tokenizer(text = NEG, padding=True, return_tensors=\"pt\")\n",
" text_features_NEG = model.get_text_features(**inputs)\n",
" text_features_NEG = text_features_A/text_features_A.norm(p=2, dim=-1, keepdim=True)\n",
" # text-similarity\n",
" neg_sims = strength*torch.matmul(text_tensor, text_features_NEG.t())\n",
" #------#\n",
"\n",
" # plus image-similarity\n",
" sims = sims + (1-C) * torch.matmul(text_tensor, image_features_A.t()) * logit_scale\n",
"\n",
" # minus NEG-similarity\n",
" sims = sims - neg_sims\n",
"\n",
" # Sort the items\n",
" sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
"\n",
" # @markdown Repeat output N times\n",
" RANGE = 1000\n",
" NUM_CHUNKS = 10+\n",
" separator = '|'\n",
" _savefiles = {}\n",
" #-----#\n",
" for chunk in range(NUM_CHUNKS):\n",
" if chunk=<10:continue\n",
" start_at_index = chunk * RANGE\n",
" _prompts = ''\n",
" for _index in range(start_at_index + RANGE):\n",
" if _index < start_at_index : continue\n",
" index = indices[_index].item()\n",
" prompt = prompts[f'{index}']\n",
" _prompts = _prompts.replace(prompt + separator,'')\n",
" _prompts = _prompts + prompt + separator\n",
" #------#\n",
" _prompts = fix_bad_symbols(_prompts)\n",
" _prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n",
" _savefiles[f'{chunk}'] = _prompts\n",
" #---------#\n",
" save_filename = f'{root_savefile_name}_{start_at_index + RANGE}_{PROMPT_INDEX}.json'\n",
"\n",
"\n",
" if (chunk=<20 && chunk>10): %cd '/content/output2/savefiles/'\n",
" if (chunk<=30 && chunk>20): %cd '/content/output3/savefiles/'\n",
" if (chunk=<40 && chunk>30): %cd '/content/output4/savefiles/'\n",
" if (chunk<=50 && chunk>40): %cd '/content/output5/savefiles/'\n",
" if (chunk=<60 && chunk>50): %cd '/content/output6/savefiles/'\n",
" if (chunk<=70 && chunk>60): %cd '/content/output7/savefiles/'\n",
" if (chunk=<80 && chunk>70): %cd '/content/output8/savefiles/'\n",
" if (chunk<=90 && chunk>80): %cd '/content/output9/savefiles/'\n",
" if (chunk=<100 && chunk>90): %cd '/content/output10/savefiles/'\n",
" if (chunk<=110 && chunk>100): %cd '/content/output11/savefiles/'\n",
" if (chunk=<120 && chunk>110): %cd '/content/output12/savefiles/'\n",
" if (chunk<=130 && chunk>120): %cd '/content/output13/savefiles/'\n",
"\n",
"\n",
" #------#\n",
" print(f'Saving savefile {save_filename} to {output_folder}...')\n",
" with open(save_filename, 'w') as f:\n",
" json.dump(_savefiles, f)\n",
" #---------#\n",
" continue\n",
"#-----------#"
],
"metadata": {
"id": "x1uAVXZEoL0T",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Determine if this notebook is running on Colab or Kaggle\n",
"#Use https://www.kaggle.com/ if Google Colab GPU is busy\n",
"home_directory = '/content/'\n",
"using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
"if using_Kaggle : home_directory = '/kaggle/working/'\n",
"%cd {home_directory}\n",
"#-------#\n",
"\n",
"# @title Download the text_encodings as .zip\n",
"import os\n",
"%cd {home_directory}\n",
"#os.remove(f'{home_directory}results.zip')\n",
"root_output_folder = home_directory + 'output/'\n",
"zip_dest = f'/content/results.zip' #drive/MyDrive\n",
"!zip -r {zip_dest} {root_output_folder}"
],
"metadata": {
"id": "zivBNrw9uSVD",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title \t⚄ Quick fix for normalizing encoded text corpus tensors\n",
"\n",
"import os\n",
"my_mkdirs('/content/output')\n",
"my_mkdirs('/content/output/text_encodings')\n",
"\n",
"for filename in os.listdir(f'{prompts_folder}'):\n",
" %cd {prompts_folder}\n",
" prompts = {}\n",
" with open(f'{filename}', 'r') as f:\n",
" data = json.load(f).items()\n",
" for key,value in data:\n",
" prompts[key] = value\n",
" #------#\n",
" num_items = int(prompts['num_items'])\n",
"\n",
" %cd {encodings_folder}\n",
" enc_filename = filename.replace('json', 'safetensors')\n",
" _text_encodings = load_file(f'{enc_filename}')['weights'].to(torch.uint8)\n",
" text_encodings = torch.zeros(num_items , dim)\n",
" tmp = torch.ones(dim)\n",
" tmp2 = torch.tensor(1/0.0043)\n",
" zero_point = 0\n",
" for index in range(num_items):\n",
" text_encodings[index] = torch.tensor(0.0043) * torch.sub(_text_encodings[index][1:dim+1] , tmp , alpha= _text_encodings[index][0]).to(torch.float32)\n",
" text_encodings[index] = tmp2*text_encodings[index]/text_encodings[index].norm(p=2, dim=-1, keepdim = True)\n",
" test = torch.round( torch.add(text_encodings[index],tmp*zero_point))\n",
" less_than_zero = test<0\n",
" while(torch.any(less_than_zero).item()):\n",
" zero_point = zero_point + 1\n",
" test = torch.round( torch.add(text_encodings[index],tmp*zero_point))\n",
" less_than_zero = test<0\n",
" #------#\n",
" _text_encodings[index][0] = zero_point\n",
" _text_encodings[index][1:dim+1] = test\n",
" #-------#\n",
" %cd /content/output/text_encodings\n",
"\n",
" tmp = {}\n",
" tmp['weights'] = _text_encodings.to(torch.uint8)\n",
" tmp['num_items'] = torch.tensor(num_items).to(torch.uint8)\n",
" tmp['scale'] = torch.tensor(0.0043)\n",
" save_file(tmp , f'{enc_filename}')\n",
"#------#"
],
"metadata": {
"cellView": "form",
"id": "9qgHW1Wr7kZn"
},
"execution_count": null,
"outputs": []
}
]
} |