### Install Example Dependencies Source: https://github.com/huggingface/optimum-intel This command navigates to a specific example directory and installs all required Python packages listed in the 'requirements.txt' file. It's a crucial step for setting up the environment before running examples or training. ```shell cd \npip install -r requirements.txt ``` -------------------------------- ### DeepSpeed Getting Started Source: https://github.com/microsoft/DeepSpeed Initial steps and guidance for users new to DeepSpeed. This section covers installation and basic usage to help users begin with distributed training. ```APIDOC DeepSpeed Getting Started: URL: https://www.deepspeed.ai/getting-started/ Description: A guide for new users to quickly set up and start using DeepSpeed for their machine learning projects. ``` -------------------------------- ### IOPaint Backend Development Setup and Start Source: https://github.com/Sanster/lama-cleaner Instructions for installing backend requirements using pip and starting the IOPaint backend service. It specifies the model and port for the service. ```shell pip install -r requirements.txt python3 main.py start --model lama --port 8080 ``` -------------------------------- ### Install Example Dependencies Source: https://github.com/huggingface/diffusers/tree/main/examples/ This command installs the necessary dependencies for running specific examples within the Diffusers library. It requires navigating to an example directory containing a requirements.txt file. ```shell pip install -r requirements.txt ``` -------------------------------- ### Install and Start IOPaint Web UI Source: https://github.com/Sanster/lama-cleaner Instructions for installing IOPaint using pip and starting the web UI. It includes prerequisites for GPU usage with PyTorch on CUDA and ROCm, and default parameters for starting the server. ```shell # In order to use GPU, install cuda version of pytorch first. # pip3 install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu118 # AMD GPU users, please utilize the following command, only works on linux, as pytorch is not yet supported on Windows with ROCm. # pip3 install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/rocm5.6 pip3 install iopaint iopaint start --model=lama --device=cpu --port=8080 ``` -------------------------------- ### Install Example Requirements Source: https://github.com/huggingface/diffusers/tree/main/examples/controlnet After cloning the repository and navigating into the diffusers directory, install the specific requirements for the example scripts. This step ensures all necessary packages for running the examples are available. ```shell pip install -r requirements.txt ``` -------------------------------- ### Install MoVQGAN Library Source: https://github.com/ai-forever/MoVQGAN Installs the MoVQGAN library from its GitHub repository using pip. This is the primary step to get started with the project. ```python pip install "git+https://github.com/ai-forever/MoVQGAN.git" ``` -------------------------------- ### Install Example Requirements Source: https://github.com/huggingface/diffusers/tree/main/examples/instruct_pix2pix After cloning the repository and navigating into the 'diffusers' directory, this command installs the specific Python packages required for the examples. It reads dependencies from the 'requirements.txt' file. ```shell pip install -r requirements.txt ``` -------------------------------- ### Clone and Install Diffusers Source: https://github.com/huggingface/diffusers/tree/main/examples Clones the Hugging Face Diffusers repository from GitHub and installs the library locally. This is the initial step to get started with the project. ```shell git clone https://github.com/huggingface/diffusers cd diffusers pip install . ``` -------------------------------- ### Setup Hugging Face Diffusers Environment Source: https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation Steps to clone the Hugging Face diffusers repository, install the library from source, and install example-specific requirements. This ensures you have the latest code and dependencies for running example scripts effectively. ```shell git clone https://github.com/huggingface/diffusers cd diffusers pip install . ``` ```shell pip install -r requirements.txt ``` ```shell accelerate config ``` -------------------------------- ### Install Example Requirements Source: https://github.com/huggingface/diffusers/tree/main/examples/kandinsky2_2/text_to_image Installs specific dependencies required for running the example scripts after cloning the repository. ```shell pip install -r requirements.txt ``` -------------------------------- ### Install Example Requirements Source: https://github.com/huggingface/diffusers/tree/main/examples/text_to_image Install specific dependencies required for the example scripts after cloning the repository and navigating into the diffusers directory. ```shell pip install -r requirements.txt ``` -------------------------------- ### Project Setup and Installation Source: https://github.com/Picsart-AI-Research/Text2Video-Zero Instructions for cloning the repository, setting up a virtual environment, and installing project dependencies using pip. ```bash git clone https://github.com/Picsart-AI-Research/Text2Video-Zero.git cd Text2Video-Zero/ virtualenv --system-site-packages -p python3.9 venv source venv/bin/activate pip install -r requirements.txt ``` -------------------------------- ### Optimum Quanto Setup Script Source: https://github.com/huggingface/optimum-quanto/ The setup.sh script is provided to assist in the initial setup or installation of the Optimum Quanto project. It likely handles dependency installation or environment configuration. ```shell #!/bin/bash # This is a placeholder for the setup script content. # Actual script would contain commands for setting up the environment, # installing dependencies, or configuring the project. echo "Setting up Optimum Quanto environment..." # Example: pip install -r requirements.txt # Example: python setup.py install echo "Setup complete." ``` -------------------------------- ### Install Example Dependencies Source: https://github.com/huggingface/diffusers/tree/main/examples Installs the necessary dependencies for running specific examples within the Diffusers project by using a requirements.txt file. ```shell pip install -r requirements.txt ``` -------------------------------- ### Install Example Requirements Source: https://github.com/huggingface/diffusers/tree/main/examples/dreambooth Installs the necessary requirements for running the example scripts within the diffusers repository. This should be run after navigating into the cloned repository's directory. ```shell pip install -r requirements.txt ``` -------------------------------- ### Windows Installation and Setup Script Source: https://github.com/kohya-ss/sd-scripts/ This PowerShell script guides users through cloning the sd-scripts repository, setting up a Python virtual environment, activating it, and installing essential libraries such as PyTorch, torchvision, and xformers. It also includes a step for configuring the accelerate library. ```powershell git clone https://github.com/kohya-ss/sd-scripts.git cd sd-scripts python -m venv venv .\venv\Scripts\activate pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu118 pip install --upgrade -r requirements.txt pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu118 accelerate config ``` -------------------------------- ### Install JAX CPU Source: https://github.com/qsh-zh/deis Installs the JAX library with CPU support, a prerequisite for running the DEIS examples. ```bash pip install "jax[cpu]" ``` -------------------------------- ### Install PEFT Source: https://github.com/huggingface/peft Installs the PEFT library using pip. This is the first step to start using PEFT methods for efficient model fine-tuning. ```bash pip install peft ``` -------------------------------- ### Allegro Setup and Environment Source: https://github.com/rhymes-ai/Allegro Instructions for setting up the Allegro project, including downloading the GitHub repository, obtaining model weights, and installing dependencies from requirements.txt. ```text Download the Allegro GitHub code, Allegro model weights and prepare the environment in requirements.txt. ``` -------------------------------- ### Install IOPaint Source: https://github.com/Sanster/lama-cleaner Installs the IOPaint application using pip. This is the primary step to get started with the tool. ```bash pip3 install iopaint ``` -------------------------------- ### Install TorchAO Source: https://github.com/pytorch-labs/ao Installs the latest stable version of TorchAO using pip. This is the most straightforward way to get started with TorchAO. ```shell pip install torchao ``` -------------------------------- ### Install TorchAO Source: https://github.com/pytorch/ao Installs the latest stable version of TorchAO using pip. This is the most straightforward way to get started with TorchAO. ```shell pip install torchao ``` -------------------------------- ### Local Installation Guide Source: https://github.com/black-forest-labs/flux This snippet outlines the steps required to set up the FLUX project locally. It includes cloning the repository, creating a Python virtual environment, activating it, and installing project dependencies. ```shell cd $HOME && git clone https://github.com/black-forest-labs/flux cd $HOME/flux python3.10 -m venv .venv source .venv/bin/activate pip install -e ".[all]" ``` -------------------------------- ### Install Safetensors with Pip Source: https://hf.co/docs/safetensors Installs the Safetensors library using pip, the standard package installer for Python. This is the most common way to get started with the library. ```python pip install safetensors ``` -------------------------------- ### Install Dependencies and Clone Repository Source: https://github.com/Kwai-Kolors/Kolors This snippet details the initial setup process, including installing Git LFS, cloning the project repository, creating and activating a Conda environment, installing project dependencies, and setting up the project locally. ```shell apt-get install git-lfs git clone https://github.com/Kwai-Kolors/Kolors cd Kolors conda create --name kolors python=3.8 conda activate kolors pip install -r requirements.txt python3 setup.py install ``` -------------------------------- ### Project Setup and Dependencies Source: https://github.com/ai-forever/MoVQGAN Lists the essential files for setting up and managing the project's dependencies and packaging. These files are crucial for installing and running the MoVQGAN project. ```APIDOC Project Files: - requirements.txt: Lists Python package dependencies. - setup.py: Contains packaging information for the project, often used for installation via pip. ``` -------------------------------- ### Install Optimum Library Source: https://github.com/huggingface/optimum Installs the core Hugging Face Optimum library using pip. This is the basic installation required to get started with model optimization and deployment. ```shell python -m pip install optimum ``` -------------------------------- ### Diffusers Get Started & Tutorials Source: https://huggingface.co/docs/diffusers/stable_diffusion Provides entry points for users new to the Diffusers library. It includes links to installation guides, quick tours, and foundational tutorials on understanding pipelines, models, and schedulers, as well as training diffusion models. ```python [🧨 Diffusers](/docs/diffusers/index) [Quicktour](/docs/diffusers/quicktour) [Effective and efficient diffusion](/docs/diffusers/stable_diffusion) [Installation](/docs/diffusers/installation) Tutorials [Overview](/docs/diffusers/tutorials/tutorial_overview) [Understanding pipelines, models and schedulers](/docs/diffusers/using-diffusers/write_own_pipeline) [AutoPipeline](/docs/diffusers/tutorials/autopipeline) [Train a diffusion model](/docs/diffusers/tutorials/basic_training) ``` -------------------------------- ### Install Training Dependencies Source: https://huggingface.co/docs/diffusers/main/en/conceptual/contribution Installs all additional Python dependencies required for a specific training example using pip. This command should be run after navigating into the cloned repository and before running an example script. ```shell cd diffusers pip install -r examples//requirements.txt ``` -------------------------------- ### PixArt-sigma Installation and Environment Setup Source: https://github.com/PixArt-alpha/PixArt-sigma This script guides through creating a Conda environment, installing PyTorch with CUDA support, cloning the PixArt-sigma repository, and installing project dependencies. ```conda conda create -n pixart python==3.9.0 conda activate pixart conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia ``` ```shell git clone https://github.com/PixArt-alpha/PixArt-sigma.git cd PixArt-sigma pip install -r requirements.txt ``` -------------------------------- ### Install and Start ComfyUI with comfy-cli Source: https://github.com/comfyanonymous/ComfyUI Demonstrates how to install and launch ComfyUI using the `comfy-cli` command-line interface. This tool simplifies the process of getting ComfyUI up and running. ```shell You can install and start ComfyUI using comfy-cli: 
$ pip install comfy-cli
$ comfy --install
$ comfy --start
``` -------------------------------- ### Windows Installation and Setup Source: https://github.com/kohya-ss/sd-scripts/ Provides step-by-step instructions for setting up the sd-scripts environment on Windows, including cloning the repository, creating a virtual environment, installing PyTorch, and configuring accelerate. ```bash git clone https://github.com/kohya-ss/sd-scripts.git cd sd-scripts python -m venv venv .\venv\Scripts\activate pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu118 pip install --upgrade -r requirements.txt pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu118 accelerate config ``` -------------------------------- ### Install Project Dependencies Source: https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion Installs all necessary project dependencies listed in the requirements.txt file. Ensure you are in the project's example folder before running this command. ```shell pip install -r requirements.txt ``` -------------------------------- ### Setup Karlo Environment and Download Checkpoints Source: https://github.com/kakaobrain/karlo Provides instructions for setting up the Karlo environment, including installing additional Python packages via a requirements file and downloading pre-trained model checkpoints using wget. ```shell pip install -r requirements.txt ``` ```shell wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/ViT-L-14.pt -P $KARLO_ROOT_DIR wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/ViT-L-14_stats.th -P $KARLO_ROOT_DIR wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt -P $KARLO_ROOT_DIR wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt -P $KARLO_ROOT_DIR wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt -P $KARLO_ROOT_DIR ``` -------------------------------- ### OpenAI API Introduction and Setup Source: https://github.com/timothybrooks/instruct-pix2pix This section refers to the OpenAI API documentation for setting up API keys on a local device. It's a prerequisite for interacting with OpenAI services like GPT-3 fine-tuning and generation. ```APIDOC OpenAI API Reference: Introduction: - Accessing the API requires an OpenAI developer account. - Set up API keys on your local device for authentication. - Documentation Link: https://beta.openai.com/docs/api-reference/introduction ``` -------------------------------- ### Setup Environment and Download Model Source: https://github.com/timothybrooks/instruct-pix2pix This snippet outlines the initial steps to prepare your environment. It involves creating a conda environment from a specification file and downloading the necessary model checkpoints. This is a prerequisite for running the image editing functionalities. ```bash conda env create -f environment.yaml conda activate ip2p bash scripts/download_checkpoints.sh ``` -------------------------------- ### Setup Conda Environment Source: https://github.com/Fantasy-Studio/Paint-by-Example Instructions to create and activate a Conda environment for the project using the provided `environment.yaml` file. This ensures all necessary dependencies are installed. ```shell conda env create -f environment.yaml conda activate Paint-by-Example ``` -------------------------------- ### Setup DiT Repository Source: https://github.com/facebookresearch/dit Instructions for cloning the DiT repository and setting up a Conda environment. It details the commands needed to get the project running locally, including dependency installation via an environment file. ```bash git clone https://github.com/facebookresearch/DiT.git cd DiT conda env create -f environment.yml conda activate DiT ``` -------------------------------- ### Setup Dependencies and Download Models Source: https://github.com/djghosh13/geneval This section outlines the necessary steps to set up the project environment. It includes cloning the GenEval repository, creating and activating a Conda environment, and downloading object detector models using a provided script. It also covers cloning and installing the mmdetection library. ```shell git clone https://github.com/djghosh13/geneval.git cd geneval conda env create -f environment.yml conda activate geneval ./evaluation/download_models.sh "/" git clone https://github.com/open-mmlab/mmdetection.git cd mmdetection; git checkout 2.x pip install -v -e . ``` -------------------------------- ### Guided Diffusion Example with DPM-Solver Source: https://github.com/LuChengTHU/dpm-solver An example demonstrating the use of DPM-Solver within a guided diffusion framework. This example is available in the project's repository, specifically within the 'ddpm_and_guided-diffusion' directory. ```APIDOC Example Location: Path: examples/ddpm_and_guided-diffusion Description: Provides an example of guided-diffusion with DPM-Solver. Repository Link: https://github.com/LuChengTHU/dpm-solver/tree/main/examples/ddpm_and_guided-diffusion ``` -------------------------------- ### Installation Commands Source: https://github.com/Stability-AI/stable-audio-tools Instructions for installing the stable-audio-tools library from PyPI or from the cloned repository. ```shell pip install stable-audio-tools ``` ```shell pip install . ``` -------------------------------- ### Diffusers Library Navigation Source: https://huggingface.co/docs/diffusers/api/models/unet2d-cond Provides links to key sections of the Diffusers documentation, including installation, quick start guides, tutorials on using pipelines and models, and specific generative tasks. ```APIDOC Diffusers Documentation: Get started: - [🧨 Diffusers](/docs/diffusers/index) - [Quicktour](/docs/diffusers/quicktour) - [Effective and efficient diffusion](/docs/diffusers/stable_diffusion) - [Installation](/docs/diffusers/installation) Tutorials: - [Overview](/docs/diffusers/tutorials/tutorial_overview) - [Understanding pipelines, models and schedulers](/docs/diffusers/using-diffusers/write_own_pipeline) - [AutoPipeline](/docs/diffusers/tutorials/autopipeline) - [Train a diffusion model](/docs/diffusers/tutorials/basic_training) Load pipelines and adapters: - [Load pipelines](/docs/diffusers/using-diffusers/loading) - [Load community pipelines and components](/docs/diffusers/using-diffusers/custom_pipeline_overview) - [Load schedulers and models](/docs/diffusers/using-diffusers/schedulers) - [Model files and layouts](/docs/diffusers/using-diffusers/other-formats) - [Push files to the Hub](/docs/diffusers/using-diffusers/push_to_hub) Adapters: (No specific adapter documentation listed in this snippet) Generative tasks: - [Unconditional image generation](/docs/diffusers/using-diffusers/unconditional_image_generation) - [Text-to-image](/docs/diffusers/using-diffusers/conditional_image_generation) - [Image-to-image](/docs/diffusers/using-diffusers/img2img) - [Inpainting](/docs/diffusers/using-diffusers/inpaint) - [Video generation](/docs/diffusers/using-diffusers/text-img2vid) - [Depth-to-image](/docs/diffusers/using-diffusers/depth2img) Inference techniques: - [Overview](/docs/diffusers/using-diffusers/overview_techniques) - [Create a server](/docs/diffusers/using-diffusers/create_a_server) - [Distributed inference](/docs/diffusers/training/distributed_inference) - [Scheduler features](/docs/diffusers/using-diffusers/scheduler_features) - [Pipeline callbacks](/docs/diffusers/using-diffusers/callback) - [Reproducible pipelines](/docs/diffusers/using-diffusers/reusing_seeds) - [Controlling image quality](/docs/diffusers/using-diffusers/image_quality) - [Prompt techniques](/docs/diffusers/using-diffusers/weighted_prompts) Advanced inference: - [Outpainting](/docs/diffusers/advanced_inference/outpaint) Hybrid Inference: - [Overview](/docs/diffusers/hybrid_inference/overview) - [VAE Decode](/docs/diffusers/hybrid_inference/vae_decode) - [VAE Encode](/docs/diffusers/hybrid_inference/vae_encode) - [API Reference](/docs/diffusers/hybrid_inference/api_reference) Specific pipeline examples: (No specific examples listed in this snippet) ``` -------------------------------- ### Install Lambda Diffusers Source: https://github.com/LambdaLabsML/lambda-diffusers Steps to clone the repository, set up a virtual environment, and install dependencies for the Lambda Diffusers project. ```bash git clone https://github.com/LambdaLabsML/lambda-diffusers.git cd lambda-diffusers python -m venv .venv source .venv/bin/activate pip install -r requirements.txt ``` -------------------------------- ### Install Example-Specific Requirements Source: https://github.com/huggingface/diffusers/tree/main/examples/custom_diffusion Installs additional requirements for specific example scripts, including 'clip-retrieval'. This step is crucial for running certain examples successfully. ```shell pip install -r requirements.txt pip install clip-retrieval ``` -------------------------------- ### Install Backend Requirements and Start Service Source: https://github.com/Sanster/lama-cleaner Installs Python dependencies for the backend and starts the IOPaint backend service with the 'lama' model on port 8080. ```shell pip install -r requirements.txt python3 main.py start --model lama --port 8080 ``` -------------------------------- ### Install Lambda Diffusers Source: https://github.com/LambdaLabsML/lambda-diffusers Steps to clone the repository, set up a virtual environment, and install dependencies using pip. ```shell git clone https://github.com/LambdaLabsML/lambda-diffusers.git cd lambda-diffusers python -m venv .venv source .venv/bin/activate pip install -r requirements.txt ``` -------------------------------- ### Run Application Source: https://github.com/haoheliu/audioldm2 Execute the main application script to start the project. A link will be provided to open the application in a browser. ```shell python3 app.py ``` -------------------------------- ### Diffusers Library Overview Source: https://huggingface.co/docs/diffusers/main/en/api/pipelines/pix2pix_zero This section lists key components and features of the Hugging Face Diffusers library, including installation, quick start guides, and advanced topics like inference optimization and training. ```APIDOC Diffusers Library Documentation: Core Components: - DiffusionPipeline: The main class for using diffusion models. - Adapters: Mechanisms for modifying or extending diffusion models. Key Features: - Installation: Guides for setting up the library. - Quicktour: A rapid introduction to using Diffusers. - Effective and efficient diffusion: Techniques for optimizing diffusion processes. - Inference: Strategies for running diffusion models efficiently. - Inference optimization: Methods to speed up inference. - Hybrid Inference: Combining different diffusion techniques. - Modular Diffusers: Building custom diffusion pipelines. - Training: How to train or fine-tune diffusion models. - Quantization: Techniques for reducing model size and improving speed. - Model accelerators and hardware: Support for various hardware. - Specific pipeline examples: Demonstrations of particular use cases. - Resources: Additional helpful materials. API Reference: - You are viewing the 'main' version, which requires installation from source. - For a standard pip install, checkout the latest stable version (e.g., v0.34.0) at /docs/diffusers/v0.34.0/api/pipelines/pix2pix_zero. ``` -------------------------------- ### Download IP-Adapter Models Source: https://github.com/tencent-ailab/IP-Adapter/tree/main Instructions for downloading essential models required for running the IP-Adapter demo. This includes base diffusion models and specific components like VAEs. ```markdown You can download models from \u003ca href=\"https://huggingface.co/h94/IP-Adapter\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. To run the demo, you should also download the following models:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/runwayml/stable-diffusion-v1-5\" rel=\"nofollow\"\u003erunwayml/stable-diffusion-v1-5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/stabilityai/sd-vae-ft-mse\" rel=\"nofollow\"\u003estabilityai/sd-vae-ft-mse\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/SG161222/Realistic_Vision_V4.0_noVAE\" rel=\"nofollow\"\u003eSG161222/Realistic_Vision_V4.0_noVAE\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/lllyasviel\" rel=\"nofollow\"\u003eControlNet models\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e ``` -------------------------------- ### Launch Web Demo Source: https://github.com/Kwai-Kolors/Kolors This command starts a local web demo interface for the project, allowing interactive use and testing of the model's capabilities. ```python python3 scripts/sampleui.py ```