From 01bb177c8a45517f86623c172e436df2682e5753 Mon Sep 17 00:00:00 2001 From: NE <52840647+Nicztin@users.noreply.github.com> Date: Wed, 26 Feb 2025 10:13:22 +0700 Subject: [PATCH 1/2] AI These steps provide a basic outline for creating an AI avatar. Depending on your specific requirements, you may need to adjust the model architecture, preprocessing steps, or training parameters. Signed-off-by: NE <52840647+Nicztin@users.noreply.github.com> --- Ai.md | 289 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 289 insertions(+) create mode 100644 Ai.md diff --git a/Ai.md b/Ai.md new file mode 100644 index 0000000..accad4e --- /dev/null +++ b/Ai.md @@ -0,0 +1,289 @@ +# Bringing AI Avatars to Life—Step by Step! 🚀 + +From image generation to animation and voice synthesis, the future of digital personas is here. #AI #Avatar #TechInnovation + +![AI Technology Logos](https://inkscape.org/) + +Creating an AI-generated avatar involves multiple steps. Here’s a step-by-step guide to generating an AI avatar from scratch, including image generation, animation, and voice synthesis. + +--- + +## Step 1: Choose the Type of AI Avatar + +There are different types of AI avatars you can create: + +1. **Static 2D Avatar** – A still image of a character. 🖼️ +2. **Animated 2D Avatar** – A moving character based on an image. 🎞️ +3. **3D Avatar** – A realistic 3D model that can talk and move. 🕴️ + +Let me know which one you want to create, or follow all the steps below to learn how to do each. + +--- + +## Step 2: Generate an AI Avatar Image (2D Static) + +If you need a custom AI-generated character, you can use Stable Diffusion or DALL·E. + +### Option 1: Using Stable Diffusion (Local or Online) + +#### Method 1: Online AI Avatar Generators + +Use websites like Artbreeder, Fotor, or Deep Dream Generator to create AI avatars easily. + +#### Method 2: Using Stable Diffusion (Python) + +If you want to generate an avatar locally, install Stable Diffusion: + +1. Install dependencies: + ```sh + pip install diffusers transformers torch + ``` + +2. Run this Python script to generate an avatar: + ```python + from diffusers import StableDiffusionPipeline + import torch + + model_id = "runwayml/stable-diffusion-v1-5" + pipe = StableDiffusionPipeline.from_pretrained(model_id).to("cuda") + + prompt = "A futuristic cyberpunk male avatar, highly detailed, digital art" + image = pipe(prompt).images[0] + image.save("ai_avatar.png") + ``` + +This will generate a custom avatar based on the prompt. + +--- + +## Step 3: Animate the Avatar (2D Animation) + +If you want to make the avatar move like a talking person, use First Order Motion Model. + +### How to Use First Order Motion Model + +1. Install dependencies: + ```sh + pip install imageio imageio-ffmpeg pyyaml numpy torch torchvision + ``` + +2. Download the model from [here](https://github.com/AliaksandrSiarohin/first-order-model). + +3. Run this script to animate the avatar: + ```python + from demo import load_checkpoints, make_animation + from skimage import io + import imageio + + generator, kp_detector = load_checkpoints( + config_path="config/vox-256.yaml", + checkpoint_path="vox-cpk.pth.tar" + ) + + source_image = io.imread("ai_avatar.png") + driving_video = imageio.mimread("driving_video.mp4") + + result = make_animation(source_image, driving_video, generator, kp_detector) + imageio.mimsave("animated_avatar.mp4", result) + ``` + +This will make the AI avatar move like the face in the driving video. + +--- + +## Step 4: Add Voice to the AI Avatar + +To make the avatar speak, you can use text-to-speech (TTS) AI tools like ElevenLabs or Coqui TTS. + +### Using ElevenLabs (Online) + +1. Sign up at [elevenlabs.io](https://elevenlabs.io). +2. Upload a text script and generate a voice. +3. Download the generated MP3 file. + +### Using Coqui TTS (Python) + +1. Install Coqui TTS: + ```sh + pip install TTS + ``` + +2. Generate speech: + ```python + from TTS.api import TTS + + tts = TTS("tts_models/en/ljspeech/tacotron2-DDC") + tts.tts_to_file("Hello! I am your AI avatar.", speaker_wav="output.wav") + ``` + +3. Merge this voice with the animated avatar using FFmpeg: + ```sh + ffmpeg -i animated_avatar.mp4 -i output.wav -c:v copy -c:a aac final_video.mp4 + ``` + +Now you have a talking AI avatar! + +--- + +## Step 5: Create a 3D AI Avatar (Optional) + +If you want a 3D talking avatar, use Blender + Python. + +### Using Ready Player Me (Easiest) + +1. Go to [Ready Player Me](https://readyplayer.me) and create a 3D avatar. +2. Export the GLB/FBX file. +3. Import it into Unity or Blender to animate. + +### Using Blender (Advanced) + +1. Install Blender and Blender Python API. +2. Use this script to import a 3D model: + ```python + import bpy + + bpy.ops.import_scene.gltf(filepath="avatar.glb") + ``` + +Now you have a 3D animated AI avatar! + +--- + +## Final Steps: Export & Use Your AI Avatar + +- **For YouTube/TikTok** – Export as MP4 video. +- **For Live Streaming** – Use software like VSeeFace or Animaze. +- **For VR Chat & Games** – Export as GLB/FBX model. + +--- + +## Which AI Avatar Do You Want to Create? + +Let me know if you need 2D, animated, or 3D avatars, and I can guide you based on your needs! + +![Watch Video](https://youtube.com/shorts/ZP9fTeIkumw?si=85EjFbIBeEmjtt-F) + +# AI Avatar Creation + +This project demonstrates how to create AI-generated avatars using deep learning techniques. We use Python and popular libraries like TensorFlow and Keras to define, train, and deploy an AI model for generating avatars. + +## Table of Contents +- [Introduction](#introduction) +- [Requirements](#requirements) +- [Dataset](#dataset) +- [Preprocessing](#preprocessing) +- [Model Training](#model-training) +- [Generating Avatars](#generating-avatars) +- [Usage](#usage) +- [License](#license) + +## Introduction +Creating AI-generated avatars involves several steps, including gathering a dataset, preprocessing the images, training an AI model, and using the model to generate new avatars. This project provides a comprehensive guide and example code to help you get started with AI avatar creation. + +## Requirements +- Python 3.6 or higher +- TensorFlow +- Keras +- NumPy +- PIL (Python Imaging Library) + +You can install the required packages using pip: +```sh +pip install tensorflow keras numpy pillow +``` + +## Dataset +For this example, we use the [CelebA dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). Download the dataset and place it in a directory of your choice. + +## Preprocessing +Preprocess the images to make them suitable for training. This involves resizing the images to a consistent size and normalizing pixel values. The preprocessing script is provided in `preprocess.py`. + +```python name=preprocess.py +import os +import numpy as np +from PIL import Image +from tensorflow.keras.preprocessing.image import img_to_array, array_to_img + +def preprocess_image(image_path, target_size=(64, 64)): + image = Image.open(image_path).convert("RGB") + image = image.resize(target_size) + image = img_to_array(image) + image = image / 255.0 # Normalize pixel values + return image + +def load_dataset(dataset_path): + images = [] + for filename in os.listdir(dataset_path): + if filename.endswith(".jpg"): + image_path = os.path.join(dataset_path, filename) + image = preprocess_image(image_path) + images.append(image) + return np.array(images) + +dataset_path = "path_to_celeba_dataset" +images = load_dataset(dataset_path) +np.save("preprocessed_images.npy", images) +``` + +## Model Training +Define and train the AI model using TensorFlow/Keras. The training script is provided in `train_model.py`. + +```python name=train_model.py +import numpy as np +import tensorflow as tf +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Conv2D, UpSampling2D, Flatten, Dense, Reshape + +# Load preprocessed images +images = np.load("preprocessed_images.npy") + +# Define the AI model +model = Sequential([ + Conv2D(128, (3, 3), activation='relu', padding='same', input_shape=(64, 64, 3)), + UpSampling2D((2, 2)), + Conv2D(64, (3, 3), activation='relu', padding='same'), + UpSampling2D((2, 2)), + Conv2D(3, (3, 3), activation='sigmoid', padding='same') +]) + +# Compile the model +model.compile(optimizer='adam', loss='binary_crossentropy') + +# Train the model +model.fit(images, images, epochs=10, batch_size=32) + +# Save the trained model +model.save("ai_avatar_model.h5") +``` + +## Generating Avatars +Use the trained model to generate new avatars. The generation script is provided in `generate_avatar.py`. + +```python name=generate_avatar.py +import numpy as np +import tensorflow as tf +from tensorflow.keras.models import load_model +from tensorflow.keras.preprocessing.image import array_to_img + +# Load the trained model +model = load_model("ai_avatar_model.h5") + +# Generate a new avatar +random_latent_vector = np.random.normal(size=(1, 64, 64, 3)) +generated_image = model.predict(random_latent_vector) +generated_image = array_to_img(generated_image[0]) + +# Save the generated avatar +generated_image.save("generated_avatar.png") +``` + +## Usage +1. **Preprocess the Dataset**: Run `preprocess.py` to preprocess the images in your dataset. +2. **Train the Model**: Run `train_model.py` to train the AI model on the preprocessed images. +3. **Generate Avatars**: Run `generate_avatar.py` to generate new avatars using the trained model. + +## License +This project is licensed under the MIT License. See the [LICENSE](https://github.com/Nicztin/ing?tab=CC0-1.0-1-ov-file) file for details. + +## Author +Nicztin From d2250381f004f828b5a7a1d2b9a4aaa7489f89cd Mon Sep 17 00:00:00 2001 From: NE <52840647+Nicztin@users.noreply.github.com> Date: Sun, 2 Mar 2025 10:13:59 +0700 Subject: [PATCH 2/2] Create package.json Signed-off-by: NE <52840647+Nicztin@users.noreply.github.com> --- package.json | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 package.json diff --git a/package.json b/package.json new file mode 100644 index 0000000..da13437 --- /dev/null +++ b/package.json @@ -0,0 +1,8 @@ +{ + ... + "scripts": { + "build": "next build", + "lint": "eslint ./src", + "test": "node ./run-tests.js" + } +}