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textual inversion with negative prompt
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/adrianpuiu/kandinsky/blob/main/textual%20inversion%20with%20negative%20prompt\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"#@title Check GPU\n",
"!nvidia-smi -L"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "B6Jitu93rDLt",
"outputId": "b8310bcb-a41f-4422-edb0-aac4d1cd150c"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPU 0: Tesla T4 (UUID: GPU-b7c8d2fa-ce47-b694-4779-0e47551a3832)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@title Mount Google Drive\n",
"from google.colab import drive\n",
"\n",
"drive.mount('/content/drive')"
],
"metadata": {
"id": "vi4G4SQkqlN3",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d07bad62-e07d-41a4-aac3-9b364415cbca"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@title Installation\n",
"!pip install 'git+https://github.com/ai-forever/Kandinsky-2.git'\n",
"!pip install git+https://github.com/openai/CLIP.git\n",
"\n",
"from kandinsky2 import get_kandinsky2"
],
"metadata": {
"id": "d6yFxaO2N2kp"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title Downloads\n",
"model = get_kandinsky2('cuda', task_type='text2img', cache_dir='/content/drive/MyDrive/tmp', model_version='2.1', use_flash_attention=False)"
],
"metadata": {
"id": "2aGQXx1SN0Kc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 86
},
"outputId": "395b74cd-ed84-42af-c90d-447f8e288fb2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"making attention of type 'vanilla' with 512 in_channels\n",
"making attention of type 'vanilla' with 512 in_channels\n",
"making attention of type 'vanilla' with 512 in_channels\n",
"Working with z of shape (1, 4, 32, 32) = 4096 dimensions.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
"from copy import deepcopy\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from matplotlib import pyplot as plt\n",
"\n",
"from kandinsky2 import get_kandinsky2"
],
"metadata": {
"id": "CdXVbpvIt6QZ"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def show_image(image, figsize=(5, 5), cmap=None, title='', xlabel=None, ylabel=None, axis=False):\n",
" plt.figure(figsize=figsize)\n",
" plt.imshow(image, cmap=cmap)\n",
" plt.title(title)\n",
" plt.xlabel(xlabel)\n",
" plt.ylabel(ylabel)\n",
" plt.axis(axis)\n",
" plt.show();\n",
"\n",
"def show_images(images, n_rows=1, title='', figsize=(5, 5), cmap=None, xlabel=None, ylabel=None, axis=False):\n",
" n_cols = len(images) // n_rows\n",
" if n_rows == n_cols == 1:\n",
" show_image(images[0], title=title, figsize=figsize, cmap=cmap, xlabel=xlabel, ylabel=ylabel, axis=axis)\n",
" else:\n",
" fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize)\n",
" fig.tight_layout(pad=0.0)\n",
" axes = axes.flatten()\n",
" for ax, img in zip(axes, images):\n",
" ax.imshow(img, cmap=cmap)\n",
" ax.set_title(title)\n",
" ax.set_xlabel(xlabel)\n",
" ax.set_ylabel(ylabel)\n",
" ax.axis(axis)\n",
" plt.show();\n",
" \n",
"def add_new_embeds(model, placeholder_token, embeds_path):\n",
" learned_token = torch.load(embeds_path)\n",
"\n",
" num_added_tokens = model.tokenizer1.add_tokens(placeholder_token)\n",
" placeholder_token_id = model.tokenizer1.convert_tokens_to_ids(placeholder_token)\n",
"\n",
" model.text_encoder.model.transformer.resize_token_embeddings(len(model.tokenizer1))\n",
" model.text_encoder.model.transformer.get_input_embeddings().weight.data[placeholder_token_id] = learned_token['t1'][placeholder_token]\n",
" \n",
" t2p_index_to_add = len(model.tokenizer2.encoder)\n",
" model.tokenizer2.encoder[placeholder_token] = t2p_index_to_add\n",
" model.tokenizer2.decoder[t2p_index_to_add] = placeholder_token\n",
" model.tokenizer2.cache[placeholder_token] = placeholder_token\n",
"\n",
" t2p_tok = model.tokenizer2.encode(placeholder_token)\n",
" t2p_str = model.tokenizer2.decode(t2p_tok)\n",
" \n",
" old_vocab_size, t2_embed_dize = model.clip_model.token_embedding.weight.shape\n",
"\n",
" new_embed = nn.Embedding(old_vocab_size + 1, t2_embed_dize).to(device)\n",
" new_embed.weight.data[:old_vocab_size, :] = model.clip_model.token_embedding.weight.data.clone()\n",
" new_embed.weight.data[t2p_tok[0], :] = learned_token['t2'][placeholder_token]\n",
"\n",
" model.clip_model.token_embedding = deepcopy(new_embed)\n",
" \n",
" return model"
],
"metadata": {
"id": "YRowVU2ruBv3"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"device='cuda'\n",
"cache_dir='/content/drive/MyDrive/tmp'\n",
"task_type = 'text2img'\n",
"model = get_kandinsky2(\n",
" device=device, \n",
" task_type=task_type, \n",
" model_version='2.1', \n",
" cache_dir=cache_dir, \n",
" use_flash_attention=False);\n",
"placeholder_token = 'sks' # your word here\n",
"embeds_path = '/content/drive/MyDrive/finetune/output/sks/learned_embeds.bin' # your path to embedding here \n",
"\n",
"model = add_new_embeds(model, placeholder_token, embeds_path)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "g_OyN36CuGo2",
"outputId": "e6f1a91b-1276-48f0-edff-b10d7017d3e2"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:649: FutureWarning: 'cached_download' is the legacy way to download files from the HF hub, please consider upgrading to 'hf_hub_download'\n",
" warnings.warn(\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"making attention of type 'vanilla' with 512 in_channels\n",
"making attention of type 'vanilla' with 512 in_channels\n",
"making attention of type 'vanilla' with 512 in_channels\n",
"Working with z of shape (1, 4, 32, 32) = 4096 dimensions.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"prompt = f'''a masterpiece of a painting in the style of {placeholder_token}.oil painting style, vivid colors . Create a painting that explores the theme of love and its many facets '''\n",
"\n",
"for _ in range(2):\n",
" images = model.generate_text2img(\n",
" prompt,\n",
" negative_prior_prompt='badly painted, not a masterpiece, ugly, childish',\n",
" num_steps=50, \n",
" batch_size=2, \n",
" guidance_scale=4,\n",
" h=512, \n",
" w=768,\n",
" sampler='p_sampler', \n",
" prior_cf_scale=2,\n",
" prior_steps='4',\n",
" )\n",
"\n",
" show_images(images, n_rows=2, figsize=(15, 15))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"a4c24863bca8402aa46a2f4f40c2dfd4",
"6195741bf0a144b5aa8ba02c2dced985",
"f626f19ca8654c0b8d72f67baf68fbd3",
"e4c63d3f09a64160b39c438e4fc74f14",
"66d5a23c35764f1c8cfb6206d06b67be",
"ba1d493ce99545439762f9bfde141ab5",
"afb54a1c286843988815c2828acda88f",
"884e0cf2a3d44127a5145cee6b939e80",
"34bb83b96b26444a94866256b3d087e6",
"eac1192c585a44158f89ca82dea15cdd",
"706b24acd6be493a9ed141e8298bd986"
]
},
"id": "zeQyVo1m8Sxm",
"outputId": "95840920-03ca-4fcd-c95a-bbfcaa63bd85"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/50 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a4c24863bca8402aa46a2f4f40c2dfd4"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"#@title Settings & Prompt \n",
"batch_name = \"Batch\" #@param {type:\"string\"}\n",
"width_height = [1280, 540] #@param {type: 'raw'}\n",
"#@markdown - Dynamic Prompting: \"a glass of { wine | beer | milk }\" will randomly choose one of these words for each image:\n",
"text_prompt = \" a masterpiece painting in the style of sks having extreme details and a perfect composition and very well lit scene\" #@param {type:\"string\"}\n",
"num_steps = 50 #@param {type:\"integer\"}\n",
"batch_size = 2 #@param {type:\"integer\"}\n",
"guidance_scale = 1 #@param {type:\"number\"}\n",
"sampler = 'p_sampler' #@param {type:\"string\"}\n",
"prior_cf_scale = 4 #@param {type:\"number\"}\n",
"prior_steps = \"5\" #@param {type:\"string\"}\n",
"\n",
"w, h = width_height"
],
"metadata": {
"id": "9N7mnn4cqutM"
},
"execution_count": 40,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title Images Generation\n",
"import os\n",
"import random\n",
"from PIL import Image\n",
"from IPython.display import display, clear_output\n",
"import time\n",
"\n",
"def generate_dynamic_prompt(prompt):\n",
" while \"{\" in prompt and \"}\" in prompt:\n",
" start = prompt.index(\"{\")\n",
" end = prompt.index(\"}\")\n",
" options_str = prompt[start + 1:end]\n",
" options = options_str.split(\"|\")\n",
" choice = random.choice(options).strip()\n",
" prompt = prompt[:start] + choice + prompt[end + 1:]\n",
" return prompt\n",
"\n",
"output_folder = f\"/content/drive/MyDrive/{batch_name}/\"\n",
"os.makedirs(output_folder, exist_ok=True)\n",
"\n",
"num_images = batch_size # The total number of images to generate\n",
"\n",
"for i in range(num_images):\n",
" dynamic_prompt = generate_dynamic_prompt(text_prompt)\n",
" print(f\"Generating image {i + 1} with prompt: {dynamic_prompt}\") # Print the generated prompt\n",
" images = model.generate_text2img(dynamic_prompt, num_steps=num_steps,\n",
" batch_size=1, guidance_scale=guidance_scale,\n",
" h=h, w=w, sampler=sampler, prior_cf_scale=prior_cf_scale,\n",
" prior_steps=prior_steps)\n",
"\n",
" # Save generated image to Google Drive\n",
" img_filename = f\"{dynamic_prompt.replace(',', '').replace(' ', '_')}_{i}.png\"\n",
" img_path = os.path.join(output_folder, img_filename)\n",
" \n",
" # The output is already a PIL Image\n",
" image = images[0]\n",
" image.save(img_path)\n",
"\n",
" # Display image and clear output after every 10 images\n",
" display(image)\n",
" if (i + 1) % 10 == 0:\n",
" time.sleep(1)\n",
" clear_output(wait=True)\n",
"\n",
" print(f\"Image {i + 1} saved to Google Drive at: {img_path}\")\n"
],
"metadata": {
"id": "EIeBrm7eq1yA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# New section"
],
"metadata": {
"id": "JaTyEd1vw_zR"
}
}
]
}