From 1b8090ad788ca8d0321646e14d18183d111d470d Mon Sep 17 00:00:00 2001 From: YugantGotmare Date: Wed, 15 May 2024 12:13:41 +0530 Subject: [PATCH] Added GenerativeAI Basic Projects --- .../ImageToTextGenerator.ipynb | 210 + .../imagetotextgenerator.py | 42 + .../TextToImageGenerator.ipynb | 6091 +++++++++++++++++ .../texttoimagegenerator.py | 48 + GenerativeAI/Basic/images/images.jpeg | Bin 0 -> 8626 bytes 5 files changed, 6391 insertions(+) create mode 100644 GenerativeAI/Basic/ImageToTextGenerator/ImageToTextGenerator.ipynb create mode 100644 GenerativeAI/Basic/ImageToTextGenerator/imagetotextgenerator.py create mode 100644 GenerativeAI/Basic/TextToImageGenerator/TextToImageGenerator.ipynb create mode 100644 GenerativeAI/Basic/TextToImageGenerator/texttoimagegenerator.py create mode 100644 GenerativeAI/Basic/images/images.jpeg diff --git a/GenerativeAI/Basic/ImageToTextGenerator/ImageToTextGenerator.ipynb b/GenerativeAI/Basic/ImageToTextGenerator/ImageToTextGenerator.ipynb new file mode 100644 index 000000000..a70c4f2e0 --- /dev/null +++ b/GenerativeAI/Basic/ImageToTextGenerator/ImageToTextGenerator.ipynb @@ -0,0 +1,210 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU", + "gpuClass": "standard" + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "SeT-a9Byby1n" + }, + "outputs": [], + "source": [ + "\n", + "from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer\n", + "import torch\n", + "from PIL import Image" + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "model = VisionEncoderDecoderModel.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n", + "feature_extractor = ViTImageProcessor.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n", + "tokenizer = AutoTokenizer.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model.to(device)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Gh2jscQnot8g", + "outputId": "fe64ca40-7f91-4cd4-8967-5c00bb0ce857" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "VisionEncoderDecoderModel(\n", + " (encoder): ViTModel(\n", + " (embeddings): ViTEmbeddings(\n", + " (patch_embeddings): ViTPatchEmbeddings(\n", + " (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (encoder): ViTEncoder(\n", + " (layer): ModuleList(\n", + " (0-11): 12 x ViTLayer(\n", + " (attention): ViTAttention(\n", + " (attention): ViTSelfAttention(\n", + " (query): Linear(in_features=768, out_features=768, bias=True)\n", + " (key): Linear(in_features=768, out_features=768, bias=True)\n", + " (value): Linear(in_features=768, out_features=768, bias=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (output): ViTSelfOutput(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (intermediate): ViTIntermediate(\n", + " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", + " (intermediate_act_fn): GELUActivation()\n", + " )\n", + " (output): ViTOutput(\n", + " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " )\n", + " )\n", + " )\n", + " (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (pooler): ViTPooler(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (activation): Tanh()\n", + " )\n", + " )\n", + " (decoder): GPT2LMHeadModel(\n", + " (transformer): GPT2Model(\n", + " (wte): Embedding(50257, 768)\n", + " (wpe): Embedding(1024, 768)\n", + " (drop): Dropout(p=0.1, inplace=False)\n", + " (h): ModuleList(\n", + " (0-11): 12 x GPT2Block(\n", + " (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (attn): GPT2Attention(\n", + " (c_attn): Conv1D()\n", + " (c_proj): Conv1D()\n", + " (attn_dropout): Dropout(p=0.1, inplace=False)\n", + " (resid_dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (crossattention): GPT2Attention(\n", + " (c_attn): Conv1D()\n", + " (q_attn): Conv1D()\n", + " (c_proj): Conv1D()\n", + " (attn_dropout): Dropout(p=0.1, inplace=False)\n", + " (resid_dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (ln_cross_attn): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): GPT2MLP(\n", + " (c_fc): Conv1D()\n", + " (c_proj): Conv1D()\n", + " (act): NewGELUActivation()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n", + " )\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "source": [ + "max_length = 16\n", + "num_beams = 4\n", + "gen_kwargs = {\"max_length\": max_length, \"num_beams\": num_beams}" + ], + "metadata": { + "id": "hm6EtiPoot27" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def predict_step(image_paths):\n", + " images = []\n", + " for image_path in image_paths:\n", + " i_image = Image.open(image_path)\n", + " if i_image.mode != \"RGB\":\n", + " i_image = i_image.convert(mode=\"RGB\")\n", + "\n", + " images.append(i_image)\n", + "\n", + " pixel_values = feature_extractor(images=images, return_tensors=\"pt\").pixel_values\n", + " pixel_values = pixel_values.to(device)\n", + "\n", + " output_ids = model.generate(pixel_values, **gen_kwargs)\n", + "\n", + " preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)\n", + " preds = [pred.strip() for pred in preds]\n", + " return preds" + ], + "metadata": { + "id": "FQ298E4gotu-" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "predict_step(['/content/drive/MyDrive/images/Plane-flying-on-earth-atmosphere.jpg']) " + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "u5ajgeTho6G_", + "outputId": "e6f62aa7-b0b7-4eff-947f-85a3a84e87ce" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['a large jetliner flying through a blue sky']" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ] + } + ] +} \ No newline at end of file diff --git a/GenerativeAI/Basic/ImageToTextGenerator/imagetotextgenerator.py b/GenerativeAI/Basic/ImageToTextGenerator/imagetotextgenerator.py new file mode 100644 index 000000000..9f8ce49f5 --- /dev/null +++ b/GenerativeAI/Basic/ImageToTextGenerator/imagetotextgenerator.py @@ -0,0 +1,42 @@ +from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer +import torch +from PIL import Image + +# Load the pre-trained Vision-Encoder-Decoder model, feature extractor, and tokenizer +model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") +feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") +tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") + +# Set the device to GPU if available, otherwise fallback to CPU +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model.to(device) # Move the model to the appropriate device + +# Define the maximum length of the generated captions and the number of beams for beam search +max_length = 16 +num_beams = 4 +gen_kwargs = {"max_length": max_length, "num_beams": num_beams} + +def predict_step(image_paths): + # List to store PIL images + images = [] + for image_path in image_paths: + i_image = Image.open(image_path) # Open the image + if i_image.mode != "RGB": # Ensure the image is in RGB mode + i_image = i_image.convert(mode="RGB") + images.append(i_image) # Add the processed image to the list + + # Extract pixel values from the images and prepare them for the model + pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values + pixel_values = pixel_values.to(device) # Move pixel values to the appropriate device + + # Generate captions for the images + output_ids = model.generate(pixel_values, **gen_kwargs) + + # Decode the generated ids to obtain the captions + preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) + preds = [pred.strip() for pred in preds] # Clean up the predictions + return preds + +# Call the function with the path to the image +caption = predict_step(['G:\OpenSource\Project-Guidance\GenerativeAI\Basic\images\images.jpeg']) +print(caption) diff --git a/GenerativeAI/Basic/TextToImageGenerator/TextToImageGenerator.ipynb b/GenerativeAI/Basic/TextToImageGenerator/TextToImageGenerator.ipynb new file mode 100644 index 000000000..d12c26392 --- /dev/null +++ b/GenerativeAI/Basic/TextToImageGenerator/TextToImageGenerator.ipynb @@ -0,0 +1,6091 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "obim6GfymXzA", + "outputId": "4b69e3a7-a7a2-47b4-ffa8-d3c1eaa97911" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m40.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "!pip install --upgrade diffusers transformers -q" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 87, + "referenced_widgets": [ + "29e6573ca85f41de9243cec4e6f449c8", + "bd988b10e99a4e1bb89bf3852b829c15", + "2d260ec456cf4185b4347813c9fccfed", + "7ecca75cf9c44e3c8b892654ecc8f0d7", + "52aa3d7525d641faa00adc43196e3fc0", + "0f3addf1efcd46db858d00a1fd01f8b7", + "1a24b03b5da7421aa3a3ffa73051481f", + "09b9095cbc08484fac0be44762137731", + "edea7a8c416640d6ac0cff508ae2d7ae", + "ed15b1cf2a3742409a0f31300e5d210a", + "1af1e1bc98be45239c7914b2a03b8aab" + ] + }, + "id": "4L1Of8qPqIU8", + "outputId": "3d9d8cdf-843a-4eca-a9cc-0666c024f115" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "29e6573ca85f41de9243cec4e6f449c8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pathlib import Path\n", + "import tqdm\n", + "import torch\n", + "import pandas as pd\n", + "import numpy as np\n", + "from diffusers import StableDiffusionPipeline\n", + "from transformers import pipeline, set_seed\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.pyplot as plt\n", + "import cv2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "P6Xq_-XSviFh" + }, + "outputs": [], + "source": [ + "class CFG:\n", + " device = \"cuda\"\n", + " seed = 42\n", + " generator = torch.Generator(device).manual_seed(seed)\n", + " image_gen_steps = 35\n", + " image_gen_model_id = \"stabilityai/stable-diffusion-2\"\n", + " image_gen_size = (400,400)\n", + " image_gen_guidance_scale = 9\n", + " prompt_gen_model_id = \"gpt2\"\n", + " prompt_dataset_size = 6\n", + " prompt_max_length = 12" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 759, + "referenced_widgets": [ + "7cd8b9752f024086b646bfc9d2e61d50", + "2f36edf0cc644f30a28531f77605413c", + "60b606ce89f346d29f99b216b75f714e", + "21ad5b71d0aa462898903fc1c20ff3c4", + "e50978fe60e540eb8d326b5739823272", + "e5122b42c59747c3b98e25e920003217", + "7b6531ae04704220933dcb09dd99cd7d", + "f789534def9744ba9768edd7d7f1d1f0", + "fd682e4073ae439e9b6f7a2b24004ed3", + "b4a18561cc3e417ab12a85674928895c", + "faf9439976bf4bff878e1ec3bcfaabfb", + "5f9b5edbe63e4ad793150d89c53dc758", + "49f274612c1745f59f4f0cd004464ff1", + "6069516bb6884657aeb660a95e4c0201", + "1f5fc1a3c78c4fddaf20b3b6e75dbc59", + 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import StableDiffusionPipeline +from transformers import pipeline, set_seed +import matplotlib.pyplot as plt +import cv2 + +# Configuration class to hold constant values and parameters +class CFG: + device = "cuda" # Set device to GPU (cuda) + seed = 42 # Seed for reproducibility + generator = torch.Generator(device).manual_seed(seed) # Generator for random number generation with seed + image_gen_steps = 35 # Number of inference steps for image generation + image_gen_model_id = "stabilityai/stable-diffusion-2" # Model ID for the Stable Diffusion model + image_gen_size = (400, 400) # Size of the generated images + image_gen_guidance_scale = 9 # Guidance scale for image generation + prompt_gen_model_id = "gpt2" # Model ID for the GPT-2 model + prompt_dataset_size = 6 # Dataset size for prompt generation + prompt_max_length = 12 # Maximum length for generated prompts + +# Load the Stable Diffusion model using the specified parameters +image_gen_model = StableDiffusionPipeline.from_pretrained( + CFG.image_gen_model_id, torch_dtype=torch.float16, + revision="fp16", use_auth_token='your_huggingface_auth_token', guidance_scale=9 +) +image_gen_model = image_gen_model.to(CFG.device) # Move the model to the GPU + +# Function to generate an image from a text prompt using the Stable Diffusion model +def generate_image(prompt, model): + # Generate the image with the specified parameters + image = model( + prompt, num_inference_steps=CFG.image_gen_steps, + generator=CFG.generator, + guidance_scale=CFG.image_gen_guidance_scale + ).images[0] + + # Resize the generated image to the specified size + image = image.resize(CFG.image_gen_size) + return image + +# Example usage: Generate an image from the prompt "Apple on a tree" +generate_image("Apple on a tree", image_gen_model) \ No newline at end of file diff --git a/GenerativeAI/Basic/images/images.jpeg b/GenerativeAI/Basic/images/images.jpeg new file mode 100644 index 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