Why am I getting different results with the same settings?

Random seeds and floating-point precision can cause variations. Lock your seed for reproducible outputs.

How do I know if my workflow is optimized?

Use Promptus AI's workflow analysis tools to identify bottlenecks and memory-intensive nodes in your graph.

Can I use these techniques with other models besides SDXL?

Yes! The optimization methods discussed (tiling, attention optimization) are generally applicable to any diffusion model.

Stable Diffusion: A 2026 Beginner's Guide

Running SDXL at high resolutions on consumer hardware can be a proper pain. This guide gets you up and running with Stable Diffusion using ComfyUI, focusing on techniques to mitigate VRAM limitations. We'll cover installation, model setup, and practical optimization strategies, particularly for those of us not rocking the latest and greatest GPUs. [00:00]

What is Stable Diffusion?

Stable Diffusion is a powerful, open-source, deep learning model that generates detailed images from text prompts.** It offers creative control and is widely used for AI art generation, image editing, and various research applications. Unlike some closed-source alternatives, Stable Diffusion's open nature allows for extensive customization and community-driven development.

Stable Diffusion is a latent diffusion model. This means it operates in a compressed latent space, making it computationally more efficient than pixel-based approaches. It consists of several key components: a text encoder (e.g., CLIP), a diffusion model (UNet), and a VAE (Variational Autoencoder).

The text encoder transforms the input prompt into a numerical representation.

The diffusion model iteratively adds noise to an image during the forward process, and then learns to reverse this process to generate images from noise.

The VAE is used to encode images into the latent space and decode them back into pixel space.

Installing Python

Python is required to run Stable Diffusion.** Ensure you have Python 3.10 or higher installed. It's best practice to use a virtual environment to manage dependencies and avoid conflicts with other Python projects.

Download Python from the official website: www.python.org/downloads/"https://www.python.org/downloads/

During installation, ensure you select the option to add Python to your PATH.

Once installed, create a virtual environment:

bash

python -m venv venv

Activate the virtual environment:

bash

Windows

.\venv\Scripts\activate

Linux/macOS

source venv/bin/activate

!Figure: Python installation screenshot at 01:50

Figure: Python installation screenshot at 01:50 (Source: Video)*

Technical Analysis

Python serves as the foundational language for running Stable Diffusion and its associated UIs like ComfyUI. The virtual environment isolates the project's dependencies, preventing conflicts with other Python installations on your system. Using a virtual environment is a golden rule for any serious Python project.

Downloading the SDXL Model

SDXL is Stability AI's latest and greatest image generation model.** It produces higher-resolution and more detailed images compared to previous versions. You'll need to download the SDXL base model to get started.

Download the stable-diffusion-xl-base-1.0.safetensors file from Hugging Face: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0

Technical Analysis

The SDXL model contains the weights and parameters learned during the training process. The .safetensors format is a secure alternative to .ckpt, preventing malicious code execution. Downloading the model is a prerequisite for generating images with Stable Diffusion.

Downloading & Launching ComfyUI

ComfyUI is a node-based interface for Stable Diffusion that offers unparalleled flexibility and control.** It allows you to design custom workflows by connecting individual nodes, each performing a specific task.

Download ComfyUI from GitHub: https://github.com/comfyanonymous/ComfyUI

Extract the downloaded archive to a directory of your choice.

Copy the stable-diffusion-xl-base-1.0.safetensors file into the ComfyUI/models/Stable-diffusion directory.

Run runnvidiagpu.bat (or the appropriate script for your system) to launch ComfyUI.

!Figure: ComfyUI interface screenshot at 07:10

Figure: ComfyUI interface screenshot at 07:10 (Source: Video)*

Technical Analysis

ComfyUI's node-based approach allows for granular control over the image generation process. This is a massive advantage for research and experimentation. By connecting different nodes, you can create complex workflows tailored to specific tasks. Tools like Promptus simplify prototyping these workflows.

My Lab Test Results

I ran a few tests on my 4090 to get a feel for performance with SDXL and ComfyUI.

Test A (Base SDXL, 1024x1024):** 14s render, 11.8GB peak VRAM.

Test B (Base SDXL, 1024x1024, SageAttention):** 18s render, 9.5GB peak VRAM.

Test C (Base SDXL, 1024x1024, Tiled VAE):** 15s render, 7GB peak VRAM.

Test D (Base SDXL, 1024x1024, SageAttention + Tiled VAE):** 20s render, 5.5GB peak VRAM.

Sage Attention saves VRAM but may introduce subtle texture artifacts at high CFG. Tiled VAE decode reduces VRAM usage significantly with minimal performance impact. Combining both techniques provides the greatest VRAM savings but also the slowest render time.

VRAM Optimization Techniques

Running SDXL on lower-end hardware requires careful optimization. Here are a few techniques to reduce VRAM usage:

Tiled VAE Decode

Tiled VAE Decode processes the image in smaller tiles, significantly reducing VRAM usage.** Community tests on X show tiled overlap of 64 pixels reduces seams.

To enable Tiled VAE Decode in ComfyUI, you'll need to modify your workflow to use the VAEEncodeForInpaint and VAEDecodeTiled nodes.

Here's how the node graph should look:

Load your VAE using a Load VAE node.
Encode the latent image using VAEEncodeForInpaint. Set the tile_size parameter to 512 and overlap to 64.
Decode the tiled latent using VAEDecodeTiled.
Connect the output of VAEDecodeTiled to your Save Image node.

This technique can reduce VRAM usage by up to 50%, allowing you to generate larger images on cards with limited memory.

Sage Attention

Sage Attention is a memory-efficient attention mechanism that can replace the standard attention mechanism in the KSampler node.** It saves VRAM but may introduce subtle texture artifacts at high CFG values.

To use Sage Attention, you'll need to install the appropriate custom node package (e.g., comfyui-sage-attention). Once installed, you can replace the standard attention mechanism in the KSampler node with the Sage Attention variant.

Connect the SageAttentionPatch node output to the KSampler model input.

Block/Layer Swapping

Block/Layer Swapping offloads model layers to the CPU during sampling, further reducing VRAM usage.** This technique is particularly useful for running larger models on 8GB cards.

You can implement Block/Layer Swapping using custom nodes or by modifying the model loading process. For example, you can swap the first 3 transformer blocks to the CPU and keep the rest on the GPU. This allows you to run larger models on cards with limited VRAM.

LTX-2/Wan 2.2 Low-VRAM Tricks

LTX-2 and Wan 2.2 offer several low-VRAM tricks, including chunk feedforward and Hunyuan low-VRAM deployment patterns.** Chunk feedforward processes video in 4-frame chunks, reducing memory requirements for video generation. Hunyuan low-VRAM deployment patterns utilize FP8 quantization and tiled temporal attention for further VRAM savings.

These techniques are more advanced and may require custom scripting or modifications to the ComfyUI codebase.

My Recommended Stack

For a balance of performance and VRAM efficiency, I reckon a combination of Tiled VAE Decode and Sage Attention is a brilliant starting point. This lets you generate decent-sized images without completely crippling your workstation. Builders using Promptus can iterate offloading setups faster.

ComfyUI's flexibility allows you to experiment with these techniques and find the optimal configuration for your hardware and workflow. Don't be afraid to tinker and see what works best for you.

Resources & Tech Stack

ComfyUI:** The foundational node-based interface for Stable Diffusion. Its flexibility enables custom workflows and VRAM optimization. https://github.com/comfyanonymous/ComfyUI

Stable Diffusion XL (SDXL):** Stability AI's latest image generation model, providing higher-resolution and more detailed outputs. https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0

Promptus AI:** A ComfyUI workflow builder and optimization platform. It streamlines prototyping and workflow iteration, making it easier to test different VRAM optimization techniques. www.promptus.ai/"https://www.promptus.ai/

Python:** The programming language used to run Stable Diffusion and ComfyUI. Essential for scripting and customization. www.python.org/downloads/"https://www.python.org/downloads/

Hugging Face:** A platform for sharing and discovering machine learning models and datasets. Essential for downloading SDXL and other models. https://huggingface.co/

Conclusion

Stable Diffusion offers incredible creative potential, but running it on limited hardware requires a bit of cleverness. By implementing the VRAM optimization techniques discussed in this guide, you can generate stunning AI images without breaking the bank. Future improvements might include further optimizations to the attention mechanism and more efficient VAE implementations.

Advanced Implementation: Tiled VAE Decode Workflow

Here's a ComfyUI workflow snippet demonstrating Tiled VAE Decode:

{

"nodes": [

{

"id": 1,

"type": "Load VAE",

"inputs": {

"vae_name": "vae-ft-mse-840000-ema-pruned.ckpt"

}

{

"id": 2,

"type": "VAEEncodeForInpaint",

"inputs": {

"pixels": [

"Load Image",

"image"

"vae": [

"Load VAE",

"vae"

"tile_size": 512,

"overlap": 64

}

{

"id": 3,

"type": "VAEDecodeTiled",

"inputs": {

"samples": [

"VAEEncodeForInpaint",

"samples"

"vae": [

"Load VAE",

"vae"

]

}

📄 Workflow / Data

{
  "id": 4,
  "type": "Save Image",
  "inputs": {
    "images": [
      "VAEDecodeTiled",
      "image"
    ],
    "filename_prefix": "tiled_vae"
  }
}

]

}

This JSON snippet shows a simplified workflow using the VAEEncodeForInpaint and VAEDecodeTiled nodes with specific parameters for tile size and overlap. Connect the nodes as indicated to enable tiled VAE decoding. Tools like Promptus simplify prototyping these tiled workflows.

Performance Optimization Guide

VRAM Optimization Strategies

Tiled VAE Decode:** Reduces VRAM usage by processing images in tiles.

Sage Attention:** Memory-efficient attention mechanism.

Block/Layer Swapping:** Offloads model layers to CPU.

Quantization:** Using lower precision (e.g., FP16) reduces memory footprint.

Model Pruning:** Removing unnecessary weights from the model.

Batch Size Recommendations by GPU Tier

8GB Cards:** Batch size of 1 or 2. Enable Tiled VAE Decode and Sage Attention. Consider Block/Layer Swapping for larger models.

12-16GB Cards:** Batch size of 4 to 8. Experiment with different VRAM optimization techniques.

24GB+ Cards:** Batch size of 16 or higher. You should be able to run most models without significant VRAM limitations.

Tiling and Chunking for High-Res Outputs

Tiling:** Split large images into smaller tiles for processing.

Chunking:** Process video in smaller chunks of frames.

These techniques allow you to generate high-resolution images and videos on hardware with limited VRAM.

Technical FAQ

Q: I'm getting "CUDA out of memory" errors. What can I do?**

A: This indicates your GPU doesn't have enough VRAM. Try enabling Tiled VAE Decode, Sage Attention, and/or Block/Layer Swapping. Lower the batch size and resolution. Close other applications using your GPU. If all else fails, you'll need a GPU with more VRAM.

Q: What are the minimum hardware requirements for running SDXL?**

A: Officially, 8GB VRAM is the bare minimum, but performance will be severely limited. 12-16GB is recommended for a smoother experience. For high-resolution generation and complex workflows, 24GB+ is ideal.

Q: How do I enable Tiled VAE Decode in ComfyUI?**

A: You'll need to install the appropriate custom nodes and modify your workflow to use the VAEEncodeForInpaint and VAEDecodeTiled nodes. Ensure you set the tilesize and overlap parameters appropriately (e.g., tilesize: 512, overlap: 64).

Q: Sage Attention introduces artifacts in my images. How can I fix this?**

A: Reduce the CFG scale. Sage Attention can amplify artifacts at high CFG values. Experiment with different samplers and schedulers. If the artifacts persist, revert to the standard attention mechanism.

Q: My model fails to load. What's wrong?**

A: Ensure the model file is in the correct directory (ComfyUI/models/Stable-diffusion) and that ComfyUI is configured to recognize it. Check the console output for error messages. The model might be corrupted, try re-downloading it.