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title: "Sampler Reference"
canonical: "https://documentation.chaos.com/space/VREVIT/160825692/Sampler%20Reference"
format: markdown
---
This page contains information about the Image Sampler in V-Ray.

 

**Overview**

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### Example: What is Anti-aliasing?

 

The following example shows the basic difference between an image with anti-aliasing, and one without:

 

 

The left images are jagged around the edges of the sphere, while the right are smooth. Here are close-ups of the two images:

 

 

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## **Using Progressive or Bucket rendering**  
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No one image sampler is best for all scenes or workflows. Choosing the best image sampler is usually a matter of experimentation, but there are a few guidelines you can follow.

- Disabling the **Progressive **toggle** **is the same as using the **Adaptive **image sampler type in previous versions of V-Ray. When Progressive is disabled, the image is rendered in buckets.
- **Progressive** is useful when it is necessary to see overall results quickly (like when placing light, building shaders, or general Look Development work) because it generates the whole image at once and progressively cleans up the noise in it. Additionally, the render can be stopped at any time before resolving completely.
- **Progressive** is also helpful when a set amount of time to spend per render is needed. This can also be useful when rendering test animations, where the entire sequence must be rendered within a certain time frame.
- Combining the Denoiser Channel with Progressive renders can help with cleaning up render noise.
- Disabling the Progressive toggle when Swarm is Enabled can be helpful for cutting down on network traffic and reducing the loss of information if work is not completed by one or more Swarm nodes.

## **Image Sampling and RAM Usage**

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Image sampling require a substantial amount of RAM to store render information.  This is especially true for the **Progressive** sampler, which stores the entire image in memory before beginning the rendering process. The **Bucket** rendering mode, on the other hand, stores only the summed result of all sub-samples for a pixel and so usually requires less RAM. Using large bucket sizes might require more memory.