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Created November 18, 2022 23:30
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/victor-iyi/347a74367d7ea597bab9fd90aa2e625a/text-to-image.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Text to Image\n",
"\n",
"Create a text-to-image API to generate images from text using the Stable diffusion model."
],
"metadata": {
"id": "4C3xfaDdD55Z"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
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},
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"outputId": "649e3b25-d408-4b20-ffe5-be8b9bc8ed26"
},
"outputs": [
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"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.24.0)\n",
"Requirement already satisfied: diffusers in /usr/local/lib/python3.7/dist-packages (0.7.2)\n",
"Requirement already satisfied: lpips in /usr/local/lib/python3.7/dist-packages (0.1.4)\n",
"Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.13.2)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n",
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.13.0)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.11.0)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
"Requirement already satisfied: Pillow<10.0 in /usr/local/lib/python3.7/dist-packages (from diffusers) (7.1.2)\n",
"Requirement already satisfied: torch>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from lpips) (1.12.1+cu113)\n",
"Requirement already satisfied: torchvision>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from lpips) (0.13.1+cu113)\n",
"Requirement already satisfied: scipy>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from lpips) (1.7.3)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.10.0)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n"
]
}
],
"source": [
"# Install dependencies.\n",
"!pip install transformers diffusers lpips"
]
},
{
"cell_type": "markdown",
"source": [
"## Login to Huggingface-Hub & Install dependencies"
],
"metadata": {
"id": "QYsrFx5FDzxO"
}
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 360,
"referenced_widgets": [
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},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Token is valid.\n",
"Your token has been saved in your configured git credential helpers (store).\n",
"Your token has been saved to /root/.huggingface/token\n",
"Login successful\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import random\n",
"import torch\n",
"import numpy as np\n",
"\n",
"from typing import List, Optional, Tuple, Union\n",
"\n",
"from PIL import Image, ImageOps\n",
"from tqdm.auto import tqdm\n",
"from matplotlib import pyplot as plt\n",
"\n",
"from torch.amp.autocast_mode import autocast\n",
"from torchvision import transforms\n",
"\n",
"from transformers import CLIPTextModel, CLIPTokenizer\n",
"from diffusers import AutoencoderKL, UNet2DConditionModel, LMSDiscreteScheduler\n",
"\n",
"# For video display.\n",
"from IPython.display import HTML\n",
"from base64 import b64encode\n",
"\n",
"# Set device.\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
],
"metadata": {
"id": "-_9ODfEKC4gC"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Download Tokenizers and Models\n",
"\n",
"We download the pre-trained tokeizers and models from huggingface-hub as well as the schedulers which we'll use through out to transform text into embeddings and encoding & decoding latents into images."
],
"metadata": {
"id": "qYEuHXMhEllr"
}
},
{
"cell_type": "code",
"source": [
"# Load the autoencoder model which will be used to decode the latents into image space. \n",
"vae = AutoencoderKL.from_pretrained(\n",
" \"CompVis/stable-diffusion-v1-4\",\n",
" subfolder=\"vae\", use_auth_token=True\n",
")\n",
"\n",
"# Load the tokenizer and text encoder to tokenize and encode the text. \n",
"tokenizer = CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
"text_encoder = CLIPTextModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
"\n",
"# The UNet model for generating the latents.\n",
"unet = UNet2DConditionModel.from_pretrained(\n",
" \"CompVis/stable-diffusion-v1-4\",\n",
" subfolder=\"unet\",\n",
" use_auth_token=True\n",
")\n",
"\n",
"# The noise scheduler\n",
"scheduler = LMSDiscreteScheduler(\n",
" beta_start=0.00085,\n",
" beta_end=0.012,\n",
" beta_schedule='scaled_linear',\n",
" num_train_timesteps=1000\n",
")\n",
"\n",
"# To the GPU we go!\n",
"vae = vae.to(device)\n",
"text_encoder = text_encoder.to(device)\n",
"unet = unet.to(device)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aCargTfZDyMq",
"outputId": "66204ceb-3e83-4787-feda-287d12f76920"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n",
"```\n",
"pip install accelerate\n",
"```\n",
".\n",
"ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.\n",
"Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.4.layer_norm2.weight', 'vision_model.encoder.layers.5.mlp.fc1.weight', 'vision_model.encoder.layers.20.layer_norm2.weight', 'vision_model.encoder.layers.8.self_attn.out_proj.bias', 'vision_model.encoder.layers.19.self_attn.out_proj.weight', 'vision_model.encoder.layers.3.layer_norm1.bias', 'vision_model.encoder.layers.12.self_attn.q_proj.bias', 'vision_model.encoder.layers.2.self_attn.k_proj.weight', 'vision_model.encoder.layers.9.mlp.fc1.bias', 'vision_model.encoder.layers.3.layer_norm2.bias', 'vision_model.encoder.layers.14.layer_norm2.bias', 'vision_model.encoder.layers.14.self_attn.q_proj.bias', 'vision_model.encoder.layers.16.self_attn.v_proj.bias', 'vision_model.encoder.layers.5.mlp.fc2.weight', 'vision_model.encoder.layers.2.layer_norm2.weight', 'vision_model.encoder.layers.4.self_attn.q_proj.bias', 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'vision_model.encoder.layers.3.mlp.fc2.bias', 'vision_model.encoder.layers.2.mlp.fc2.weight', 'vision_model.encoder.layers.3.mlp.fc2.weight', 'vision_model.encoder.layers.18.self_attn.q_proj.bias', 'vision_model.encoder.layers.17.self_attn.v_proj.bias', 'vision_model.encoder.layers.4.self_attn.k_proj.bias', 'vision_model.encoder.layers.1.layer_norm2.bias', 'vision_model.encoder.layers.12.layer_norm1.bias', 'vision_model.encoder.layers.6.layer_norm1.weight', 'vision_model.encoder.layers.16.mlp.fc1.weight', 'vision_model.encoder.layers.18.self_attn.out_proj.bias', 'vision_model.encoder.layers.20.self_attn.v_proj.weight', 'vision_model.encoder.layers.7.layer_norm1.weight']\n",
"- This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n",
"```\n",
"pip install accelerate\n",
"```\n",
".\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Define hyperparameters.\n",
"HEIGHT = 512\n",
"WIDTH = 768"
],
"metadata": {
"id": "J0cHSNrKHKPf"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def generate_embeddings(\n",
" prompts: Union[str, List[str]],\n",
" batch_size: int = 1,\n",
" padding: str = 'max_length',\n",
") -> torch.Tensor:\n",
" \"\"\"Generate conditioned and unconditioned text embeddings.\n",
"\n",
" Arguments:\n",
" prompts (str | list[str]): Conditioned text input.\n",
" batch_size (int, optional): Batch size. Defaults to 1.\n",
" padding (str, optional): How to apply padding to tokenizer.\n",
" \n",
" Returns:\n",
" torch.Tensor - Concatenated conditioned & unconditioned text embedding.\n",
" \"\"\"\n",
" # Tokenize (conditioned) input text.\n",
" text_input = tokenizer(\n",
" prompts, padding=padding,\n",
" max_length=tokenizer.model_max_length,\n",
" truncation=True,\n",
" return_tensors='pt'\n",
" )\n",
" # Generate (conditioned) text embeddings.\n",
" with torch.no_grad():\n",
" text_embeddings = text_encoder(text_input.input_ids.to(device))[0]\n",
"\n",
" # Tokenize (unconditioned) input text.\n",
" max_length = text_input.input_ids.shape[-1]\n",
" uncond_input = tokenizer(\n",
" [''] * batch_size,\n",
" padding=padding,\n",
" max_length=max_length,\n",
" return_tensors='pt'\n",
" )\n",
" # Generate (unconditioned) embeddings.\n",
" with torch.no_grad():\n",
" uncond_embeddings = text_encoder(uncond_input.input_ids.to(device))[0]\n",
"\n",
" # Concatenate the conditioned and unconditioned text embeddings.\n",
" text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n",
"\n",
" return text_embeddings\n",
"\n",
"\n",
"def generate_latents(\n",
" text_embeddings: torch.Tensor,\n",
" batch_size: int = 1,\n",
" guidance_scale: float = 7.5,\n",
" num_train_timesteps: int = 1000,\n",
" num_inference_steps: int = 50,\n",
" beta_start: float = 0.00085,\n",
" beta_end: float = 0.012,\n",
" beta_schedule: str = 'scaled_linear',\n",
" seed: int = 42,\n",
") -> torch.Tensor:\n",
" \"\"\"Generate a classifier-free guidance latent using a text embedding.\n",
"\n",
" Arguments:\n",
" text_embeddings (torch.Tensor): Text embedding.\n",
" batch_size (int, optional): Number of batch size. Defaults to 1.\n",
" guidance_scale (float, optional): Guidance scale. Defaults to 7.5.\n",
" num_train_timesteps (int, optional): Number of diffusion steps used\n",
" to train the model. Defaults to 1000.\n",
" num_inference_step (int, optional): Number of inference steps.\n",
" Defaults to 50.\n",
" beta_start (float, optional): The starting beta value of inference.\n",
" Defaults to 0.00085.\n",
" beta_end (float, optional): The final beta value. Defaults to 0.012.\n",
" beta_schedule (str, optional): The beta schedule, a mapping from a\n",
" beta range to a sequence of betas for stepping the model. Choose\n",
" from `linear` or `scaled_linear`. Defaults to `scaled_linear`.\n",
" seed (int, optional): The desired seed value. Defaults to 42.\n",
"\n",
" Returns:\n",
" torch.Tensor - Decoded latent image after diffusion process.\n",
" \"\"\"\n",
" # The noise scheduler\n",
" scheduler = LMSDiscreteScheduler(\n",
" beta_start=beta_start,\n",
" beta_end=beta_end,\n",
" beta_schedule=beta_schedule,\n",
" num_train_timesteps=num_train_timesteps\n",
" )\n",
"\n",
" # Prepare scheduler.\n",
" scheduler.set_timesteps(num_inference_steps)\n",
"\n",
" # Prepare latents.\n",
" latents = torch.randn(\n",
" (batch_size, unet.in_channels, HEIGHT // 8, WIDTH // 8),\n",
" generator=torch.manual_seed(seed)\n",
" ).to(device)\n",
"\n",
" # Need to scale to match k.\n",
" latents = latents * scheduler.sigmas[0]\n",
"\n",
" # Diffusion loop.\n",
" with autocast(device, dtype=torch.bfloat16, enabled=False):\n",
" for i, timestep in tqdm(enumerate(scheduler.timesteps)):\n",
" # Expand the latents if we are doing classifier-free guidiance\n",
" # to avoid doing two forward passes.\n",
" latent_input = torch.cat([latents] * 2)\n",
" sigma = scheduler.sigmas[i]\n",
" latent_input = latent_input / ((sigma ** 2 + 1) ** 0.5)\n",
"\n",
" # Predict the noise residual.\n",
" with torch.no_grad():\n",
" noise_pred = unet(\n",
" latent_input, timestep,\n",
" encoder_hidden_states=text_embeddings\n",
" )['sample']\n",
" \n",
" # Perform guidance\n",
" noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n",
" noise_pred = noise_pred_uncond \\\n",
" + guidance_scale \\\n",
" * (noise_pred_text - noise_pred_uncond)\n",
" \n",
" # Compute the previous noisy sample x_t -> x_t-1\n",
" latents = scheduler.step(noise_pred, i, latents)['prev_sample']\n",
"\n",
" # Scale latnents.\n",
" latents = 1 / 0.18215 * latents\n",
"\n",
" with torch.no_grad():\n",
" image = vae.decode(latents)\n",
"\n",
" return image[0]"
],
"metadata": {
"id": "e9eNIC7xGi15"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def convert_to_pil(image: torch.Tensor) -> List[Image.Image]:\n",
" \"\"\"Return generated images as PIL Images.\n",
"\n",
" Arguments:\n",
" image (torch.Tensor): Generated images as torch.tensor object.\n",
" \n",
" Returns:\n",
" List[Image.Image] - List of PIL Images.\n",
" \"\"\"\n",
" image = (image / 2 + 0.5).clamp(0, 1)\n",
" image = image.detach().cpu().permute(0, 2, 3, 1).numpy()\n",
"\n",
" images = (image * 255).round().astype('uint8')\n",
" pil_images = [Image.fromarray(image) for image in images]\n",
"\n",
" return pil_images"
],
"metadata": {
"id": "TkbJ-Pv6W5cZ"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"prompts = 'A chipmunk arguing with a squirrel over a pinecone, detailed, 4k, artwork'\n",
"\n",
"text_embeddings = generate_embeddings(prompts, batch_size=1)\n",
"latents = generate_latents(text_embeddings, batch_size=1)\n",
"images = convert_to_pil(latents)\n",
"images[0]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 615,
"referenced_widgets": [
"73a51a9709574085827d2a8e7dfc202c",
"ebbaf5880aad42f8865c572f300f083c",
"6531ac09fdf74a908d75e2fd7e9a402a",
"a45fb0985ea74979a66f3e9fd5af3ab7",
"fa3f1175b64e4b44be0759fbcec43fde",
"4c9df7fef88045baa73498c1993dfa17",
"00e43961feb2411e870e57dae52d4b05",
"fa8f658d3b0342e19f0268cd8c034b85",
"7b59f07bd933477ebaf98c6d681ba835",
"7d5faf6c1f484ac7ba49defbd4dede7b",
"dbe8361cf64f4409809bc2cba4cd3717"
]
},
"id": "k06H5ZyOVZJe",
"outputId": "e152086d-7c5e-4f91-a076-ca4910f19b5e"
},
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"0it [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "73a51a9709574085827d2a8e7dfc202c"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/diffusers/schedulers/scheduling_lms_discrete.py:208: UserWarning: The `scale_model_input` function should be called before `step` to ensure correct denoising. See `StableDiffusionPipeline` for a usage example.\n",
" \"The `scale_model_input` function should be called before `step` to ensure correct denoising. \"\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<PIL.Image.Image image mode=RGB size=768x512 at 0x7FEA89131E50>"
],
@jlpzone
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jlpzone commented Nov 19, 2022

jlpzone

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