How to Download Stable Diffusion — A Step-by-Step Guide - CometAPI

Stable Diffusion remains the most widely used open-source family of text-to-image models. Stability AI has continued to iterate (notably publishing the Stable Diffusion 3 series and SDXL improvements). With the recent launch of Stable Diffusion 3.5, the capabilities of this technology have expanded even further, offering enhanced image quality, better prompt understanding, and more flexible applications. This guide provides a comprehensive overview of Stable Diffusion, from its inner workings to a step-by-step installation guide, empowering you to harness the creative potential of this groundbreaking AI.

CometAPI, it provides cloud API of Stable Diffusion for image generation.

What is Stable Diffusion?

Stable Diffusion is a deep learning model that generates images from text descriptions, a technology known as text-to-image synthesis. Unlike many other AI image generators, Stable Diffusion is open-source, allowing anyone to use, modify, and build upon the technology.

The model is trained on a massive dataset of images and their corresponding text descriptions, enabling it to learn the complex relationships between words and visual concepts. When you provide a text prompt, Stable Diffusion uses this learned knowledge to create a unique image that matches your description. The level of detail and realism that can be achieved is remarkable, ranging from photorealistic images to fantastical illustrations in a wide array of styles.

Capabilities Beyond Text-to-Image

While its primary function is generating images from text, Stable Diffusion's capabilities extend far beyond this core feature. Its versatility makes it a comprehensive tool for a wide range of creative tasks:

Image-to-Image: You can provide an existing image and a text prompt to guide the model in transforming the original image. This feature is perfect for artistic stylization, concept exploration, and creative experimentation.
Inpainting and Outpainting: Stable Diffusion allows you to selectively modify parts of an image (inpainting) or extend the image beyond its original borders (outpainting). This is incredibly useful for photo restoration, object removal, and expanding the canvas of your creations.
Video Creation: With recent advancements, Stable Diffusion can now be used to create videos and animations, opening up new possibilities for dynamic visual storytelling.
ControlNets: These are additional models that provide more precise control over the image generation process, allowing you to specify poses, depth maps, and other structural elements.

Open Source and Accessibility

One of the most significant aspects of Stable Diffusion is its open-source nature. The code and model weights are publicly available, which means you can run it on your own computer, provided you have the necessary hardware. This level of accessibility sets it apart from many proprietary AI image generation services and has been a key factor in its widespread adoption. The ability to run the model locally gives users complete creative freedom and control over their work, without the content restrictions or service fees associated with some online platforms.

How does Stable Diffusion work?

The latent approach dramatically reduces memory and compute cost compared with pixel-space diffusion, which is how Stable Diffusion became practical on consumer GPUs. Variants such as SDXL and the 3.x family improve multi-subject fidelity, resolution and prompt handling; new releases appear periodically from Stability and the community.

The Key Components: VAE, U-Net, and Text Encoder

Stable Diffusion is comprised of three main components that work together to generate images:

Variational Autoencoder (VAE): The VAE is responsible for compressing the high-resolution images from the training data into the smaller latent space representation and for decompressing the generated latent representation back into a full-resolution image.

U-Net: This is the core of the model, a neural network that operates in the latent space. The U-Net is trained to predict and remove the noise that was added during the diffusion process. It takes the noisy latent representation and the text prompt as input and outputs a denoised latent representation.

Text Encoder: The text encoder transforms your text prompt into a numerical representation that the U-Net can understand. Stable Diffusion typically uses a pre-trained text encoder called CLIP (Contrastive Language-Image Pre-Training), which has been trained on a vast dataset of images and their captions. CLIP is highly effective at capturing the semantic meaning of text and translating it into a format that can guide the image generation process.

The Denoising Process

The image generation process in Stable Diffusion can be summarized as follows:

Text Encoding: Your text prompt is passed through the text encoder (CLIP) to create a text embedding.
Random Noise Generation: A random noise image is generated in the latent space.
Denoising Loop: The U-Net iteratively denoises the random noise image, guided by the text embedding. In each step, the U-Net predicts the noise in the latent image and subtracts it, gradually refining the image to match the prompt.
Image Decoding: Once the denoising process is complete, the final latent representation is passed through the VAE's decoder to generate the final, high-resolution image.

What Hardware and Software Do I Need?

Typical hardware guidance

GPU: NVIDIA with CUDA support is strongly recommended. For smooth, modern usage aim for ≥8 GB VRAM for modest resolutions; 12–24 GB gives a much more comfortable experience for high resolution or mixed-precision models. Very small experiments are possible on lower VRAM cards with optimizations, but performance and maximum image size will be limited.
CPU / RAM: Any modern multi-core CPU and ≥16 GB RAM is a practical baseline.
Storage: SSD (NVMe preferred) and 20–50 GB free space to store models, caches and auxiliary files.
OS: Linux (Ubuntu variants) is most convenient for advanced users; Windows 10/11 is fully supported for GUI packages; Docker works for servers.

Software prerequisites

Python 3.10+ or Conda environment.
CUDA toolkit / NVIDIA driver for your GPU and matching PyTorch wheel (unless you plan CPU-only, which is very slow).
Git, Git LFS (for some model downloads), and optionally a Hugging Face account for model downloads that require license acceptance.

Important—license & safety: Many Stable Diffusion checkpoints are available under Stability AI’s community license or specific model licenses and require acceptance before download. Models hosted on Hugging Face often require that you log into a Hugging Face account and explicitly accept terms; automated downloads will fail without that approval.

How Do I Install Stable Diffusion (Step-by-Step Guide)?

Below are three practical installation paths. Choose the route that matches your needs:

Path A — Full GUI: AUTOMATIC1111 Stable Diffusion WebUI (best for interactive use, many community plugins).
Path B — Programmatic: Hugging Face diffusers pipeline (best for integration and scripting).
Path C — Cloud / Docker: Use a cloud VM or container if you lack local GPU resources.

How Do I Download Model Weights and Accept Licenses?

Stable Diffusion model weights are distributed in several ways:

Official Stability AI releases — Stability publishes core models and announces major releases (3.x, SDXL, etc.). These models are often available from Stability’s website and from Hugging Face.
Hugging Face model cards — Many community and official checkpoints are hosted on Hugging Face. For most published SD checkpoints you must sign in and accept the model license before downloading. The diffusers API respects this flow.
Community hubs (Civitai, GitHub, etc.) — These host community checkpoints, embeddings, and LoRAs; check each asset’s license.

Practical steps to download:

Create a Hugging Face account if needed.
Visit the model page (for example stabilityai/stable-diffusion-3-5) and accept the license.
Use huggingface-cli or the WebUI’s model download dialog. For Git LFS-backed models, install git lfs and git clone per instructions.

How Do I Install the AUTOMATIC1111 WebUI on Windows or Linux?

AUTOMATIC1111’s WebUI is a popular, actively maintained GUI with many extensions and configuration options. The repo provides release notes and a straightforward launcher.

1) Preflight (Windows)

Install latest NVIDIA driver for your GPU.
Install Git for Windows.
If you prefer Conda: install Miniconda.

2) Clone and launch (Windows)

Open a Powershell or Command Prompt, then run:

# clone the WebUI
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

# On Windows, the provided batch scripts will handle dependencies.
# Use the following to fetch everything and launch:
.\webui-user.bat
# or, in older releases:
# .\run.bat

The script will install Python packages, download required components, and open the web UI on http://127.0.0.1:7860 by default. If the project requests a model file, see the Model download step below.

3) Clone and launch (Linux)

Recommended: create a virtualenv or conda environment.

# system prerequisites: Python3, git, wget (example: Ubuntu)
sudo apt update && sudo apt install -y git python3-venv

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

# Create a venv and activate
python3 -m venv venv
source venv/bin/activate

# Launch (the launcher will install requirements)
python launch.py

On Linux you will often need to install the appropriate CUDA-enabled PyTorch before launching to ensure GPU acceleration.

Where to place model weights: Put model .ckpt, .safetensors or SDXL files into models/Stable-diffusion/ (create the folder if needed). The WebUI detects weights automatically.

How Do I Install Stable Diffusion with Hugging Face Diffusers ?

This route is best if you want a programmatic, scriptable pipeline or you are integrating generation into an application.

1) Install Python packages

Create and activate a virtual environment, then install required packages:

python -m venv sdenv
source sdenv/bin/activate
pip install --upgrade pip
# Core packages (example - adjust CUDA wheel for your system per PyTorch's site)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
pip install diffusers transformers accelerate safetensors transformers[torch] huggingface-hub

Tip: install the correct PyTorch wheel for your CUDA version using the official PyTorch install page. The diffusers documentation lists compatible package sets.

2) Authenticate and download models (Hugging Face)

Many Stable Diffusion checkpoints on Hugging Face require you to be logged in and to accept a license. In a terminal:

pip install huggingface_hub
huggingface-cli login
# you will be prompted to paste your token (get it from your Hugging Face account settings)

To programmatically load a model (example for a checkpoint hosted on Hugging Face):

from diffusers import StableDiffusionPipeline
import torch

model_id = "stabilityai/stable-diffusion-3-5"  # example; replace with the model you agreed to
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, use_safetensors=True)
pipe = pipe.to("cuda")

image = pipe("A professional photograph of a mountain at sunrise", num_inference_steps=25).images[0]
image.save("output.png")

If a model requires use_auth_token=True in older versions, supply use_auth_token=HUGGINGFACE_TOKEN or ensure huggingface-cli login was executed. Always consult the model card for license instructions.

How Do I Use a Cloud Instance or Docker?

If you lack a suitable local GPU, use a cloud VM (AWS, GCP, Azure) with an NVIDIA GPU or a specialized AI instance. Alternatively, many WebUI repos publish Dockerfiles or community Docker images.

A simple Docker pattern (example):

# pull a community image (verify authenticity before use)
docker pull automatic1111/stable-diffusion-webui:latest

# run (bind port 7860)
docker run --gpus all -p 7860:7860 -v /local/models:/data/models automatic1111/stable-diffusion-webui:latest

Cloud providers often charge by the hour; for production or team use evaluate managed services such as Hugging Face Inference Endpoints or Stability's own APIs. These are paid but reduce operational overhead.

Troubleshooting and Performance Tips

Common issues

Installation fails on torch or CUDA mismatch. Check that your PyTorch wheel matches the system’s CUDA (driver) version; use the official PyTorch installer to generate the correct pip command.
Model download blocked / 403. Ensure you logged into Hugging Face and accepted the model license. Some models require Git LFS.
OOM (out of memory). Reduce inference resolution, switch to half precision (torch_dtype=torch.float16), or enable xformers / memory efficient attention in WebUI.

Performance tuning

Install xformers (if supported) for memory-efficient attention.
Use --precision full vs --precision fp16 flags depending on stability.
If you have limited GPU memory, consider CPU offload or using the safetensors format which can be faster and safer.

What's New with Stable Diffusion 3.5?

The release of Stable Diffusion 3.5 brings a host of improvements and new features that further enhance the capabilities of this powerful image generation model.

Enhanced Image Quality and Prompt Following

Stable Diffusion 3.5 boasts significant improvements in image quality, with better photorealism, lighting, and detail. It also has a much better understanding of complex text prompts, resulting in images that more accurately reflect the user's creative vision. Text rendering has also been improved, making it possible to generate images with legible text.

New Models: Large and Turbo

Stable Diffusion 3.5 is available in two main variants:

Stable Diffusion 3.5 Large: This is the most powerful model, capable of producing the highest quality images. It requires a GPU with at least 16GB of VRAM.
Stable Diffusion 3.5 Large Turbo: This model is optimized for speed and can run on GPUs with as little as 8GB of VRAM. It generates images much faster than the Large model, while still maintaining a high level of quality.

Optimizations and Collaborations

Stability AI has collaborated with NVIDIA and AMD to optimize the performance of Stable Diffusion 3.5 on their respective hardware. These optimizations, which include support for TensorRT and FP8 on NVIDIA RTX GPUs, result in faster generation times and reduced memory usage, making Stable Diffusion more accessible to a wider range of users.

How can I run Stable Diffusion without local GPU

If you lack a capable GPU, use CometAPI, it provides cloud API of Stable Diffusion for image generation, and other image generation API such as GPT Image 1.5 API and Nano Banano Series API.

Conclusion

Stable Diffusion has fundamentally changed the way we create and interact with digital imagery. Its open-source nature, combined with its ever-expanding capabilities, has empowered a global community of creators to explore new artistic frontiers. With the release of Stable Diffusion 3.5, this powerful tool has become even more accessible and versatile, offering a glimpse into a future where the only limit to what we can create is our own imagination. Whether you're a seasoned artist, a curious developer, or simply someone who wants to experiment with the power of AI, this guide provides the foundation you need to get started with Stable Diffusion and unlock your creative potential.

To begin, creating arts on CometAPI in the Playground. Make sure you have logged in to obtain your API key and start building today.

Ready to start? → Free trial of Stable Diffusion via CometAPI!

How to Download Stable Diffusion — A Step-by-Step Guide