ComfyUI Restart Sampler: A Simple How-To Guide

Sep 30, 2025 by ADMIN 47 views

Hey guys! Today, we're diving into the Restart Sampler node in ComfyUI. If you're scratching your head about how to use it effectively, you're in the right place. This guide will break down the process, making it super easy to understand and implement in your workflows.

Understanding the Restart Sampler

Before we jump into the how-to, let's quickly cover what the Restart Sampler actually does. In essence, the Restart Sampler gives you more control over the sampling process in ComfyUI. It allows you to restart a sampling process from a specific point, using different noise seeds or adjusting parameters mid-way. This is incredibly useful for refining images, fixing artifacts, or exploring variations of your initial output without starting from scratch each time. Think of it as a way to steer your image generation in new directions while still maintaining some continuity with your initial efforts.

Key Benefits

Here are some of the key benefits of using the Restart Sampler:

Efficiency: Avoid wasting computational resources by restarting from a point where you're already close to your desired output.
Control: Fine-tune specific aspects of your image by tweaking parameters mid-sampling.
Experimentation: Easily explore variations of your image by changing the noise seed or other settings.
Artifact Correction: Fix unwanted artifacts or inconsistencies that appear during the initial sampling.

The Restart Sampler is particularly useful when dealing with complex prompts or when you're aiming for a very specific aesthetic. It provides a level of control that can be hard to achieve with standard sampling methods.

Step-by-Step Guide to Using Restart Sampler in ComfyUI

Okay, let's get practical. Here’s a step-by-step guide on how to use the Restart Sampler in ComfyUI. We'll go through setting up the basic workflow, connecting the Restart Sampler, and tweaking the settings for optimal results.

Step 1: Set Up Your Basic Workflow

First, you need a basic ComfyUI workflow. This typically includes:

Load Checkpoint: Loads your Stable Diffusion model.
Load CLIP Text Encode: Encodes your positive and negative prompts.
Empty Latent Image: Creates an empty latent space for the image.
KSampler: The standard KSampler node that performs the initial sampling.
VAE Decode: Decodes the latent image into a viewable image.
Save Image: Saves the final image.

Make sure these nodes are connected correctly. A standard workflow is the foundation upon which you'll add the Restart Sampler.

Step 2: Add the Restart Sampler Node

Next, add the Restart Sampler node to your workflow. You can find it in the node menu. Place it between the KSampler and the VAE Decode nodes. The output of the KSampler will feed into the Restart Sampler, and the output of the Restart Sampler will feed into the VAE Decode node. Essentially, you're inserting it into the image generation pipeline.

Step 3: Connect the Nodes

Connect the nodes as follows:

Connect the LATENT output from the KSampler to the latent_image input of the Restart Sampler.
Connect the IMAGE output from the Restart Sampler to the samples input of the VAE Decode node.

This setup ensures that the latent image generated by the KSampler is processed by the Restart Sampler before being decoded into a final image.

Step 4: Configure the Restart Sampler

Now, let's configure the Restart Sampler. Here’s a breakdown of the key parameters:

seed: The seed value for the restart. Changing this will produce different variations of the image. You can use a different seed to introduce new noise patterns and explore alternative outputs.
steps: The number of sampling steps to perform after the restart. Adjust this to control the level of refinement.
cfg: The classifier-free guidance scale. This controls how strongly the image adheres to your prompt. Higher values mean the image will more closely follow the prompt, while lower values allow for more creative freedom.
sampler_name: The sampler algorithm to use (e.g., euler_a, DPM++ 2M). Experiment with different samplers to see which one works best for your specific needs. Each sampler has its own characteristics and may produce different results.
scheduler: The scheduler to use (e.g., normal, karras). The scheduler affects how noise is added and removed during the sampling process. Different schedulers can lead to subtle variations in the final image.

Experiment with these settings to achieve the desired effect. For example, if you want to fix a specific artifact, you might reduce the steps value and slightly adjust the seed.

Step 5: Run the Workflow

Run your workflow. The KSampler will perform the initial sampling, and the Restart Sampler will then take over, applying the new settings and continuing the sampling process. Monitor the output to see how the changes affect the final image. You may need to iterate and adjust the parameters multiple times to achieve the best results.

Tips and Tricks for Effective Use

Here are some tips and tricks to help you get the most out of the Restart Sampler:

Use Different Seeds: Experiment with different seed values to explore variations of your image. Even small changes in the seed can lead to significant differences in the output.
Adjust CFG Scale: Tweak the CFG scale to control how closely the image follows your prompt. A lower CFG scale can introduce more creative freedom and unexpected results.
Change Sampler and Scheduler: Try different samplers and schedulers to see which ones work best for your specific needs. Each sampler and scheduler has its own unique characteristics.
Iterate and Refine: Don't be afraid to iterate and refine your settings. It may take several attempts to achieve the desired result. Keep experimenting and learning from each iteration.
Combine with Other Nodes: Combine the Restart Sampler with other nodes, such as the ControlNet or LoRA loaders, to further enhance your control over the image generation process.

Real-World Examples

Let's look at some real-world examples of how the Restart Sampler can be used.

Fixing Artifacts

Imagine you've generated an image that's almost perfect, but it has a weird artifact in the corner. Instead of starting over, you can use the Restart Sampler to focus on that area. Set the seed to a slightly different value, reduce the steps, and run the workflow. The Restart Sampler will refine that specific area, hopefully removing the artifact.

Creating Variations

Suppose you want to create several variations of an image. You can use the Restart Sampler with different seed values to generate multiple versions quickly. This is great for exploring different artistic styles or compositions.

Enhancing Details

If you want to enhance the details in a specific area of your image, you can use the Restart Sampler with a higher cfg scale and a more precise prompt. This will tell the sampler to focus on that area and add more detail.

Common Issues and Troubleshooting

Even with a clear guide, you might run into some issues. Here are some common problems and how to troubleshoot them:

No Change in Output: If you're not seeing any change in the output after using the Restart Sampler, double-check that the nodes are connected correctly and that you've adjusted the parameters. Make sure the seed, steps, and cfg values are different from the initial sampling.
Unexpected Results: If you're getting unexpected results, try adjusting the cfg scale or the sampler and scheduler. Sometimes, the default settings may not be optimal for your specific prompt or model.
Workflow Errors: If you're getting workflow errors, check the ComfyUI console for more information. The error messages can often provide clues about what's going wrong.

Conclusion

The Restart Sampler in ComfyUI is a powerful tool that gives you greater control over the image generation process. By understanding how to use it effectively, you can refine images, fix artifacts, and explore variations with ease. So go ahead, experiment with different settings, and see what you can create! Happy prompting!