Stable Diffusion Batch Count Vs Batch Size: [Comparison + Examples]
- Cole B.
Today, I’m going to cover both the stable diffusion batch count vs batch size for generating more images.
You are going to learn in this guide:
- What batch size and batch count mean
- What are the differences between batch size and batch count
- Which one is better to increase for producing multiple images
Let’s get started.
What Is The Stable Diffusion Batch Size?
When you set the batch size in stable diffusion, you are deciding how many images the model will process at the same time during training or image generation.
Here’s why it matters:
- Efficiency: A larger batch size means that the model processes more images at once. This can make the training or generation process faster because the model makes fewer passes over the data.
- Memory Usage: However, processing more images at once requires more memory (RAM). If the batch size is too large, your computer might run out of memory and crash.
The maximum stable diffusion batch size is 8 in the settings.
What Is The Stable Diffusion Batch Count?
Batch count refers to the number of batches you want to process.
It’s closely related to batch size, but while batch size is about how many samples are in each batch, batch count is about how many such batches you process.
Batch count has similar effects on computational load. Increasing the count will take longer to generate your images and require more resources.
Because generating batches is less efficient than increasing the size, increasing the batch count should be more intensive and take longer than increasing the batch size.
The maximum stable diffusion batch count is 100 in the settings.
But we are going to test the efficiency of each setting in our next section.
Stable Diffusion Batch Count Vs Batch Size Vs Both
As was briefly explained. Batch size refers to how many images are in a batch, while batch count is how many batches. A good analogy is baking cookies.
Batch Size: This is how many cookies you put on a baking tray.
Batch Count: This is how many trays of cookies you put in the oven.
We are now going to compare four different possible settings for batch count and batch size to find the optimal settings for producing multiple images at once. I am going to list below the settings and models used for this test and then show the results for these four settings:
- Size 8 batch size
- Size 8 batch count
- Size 2 batch count + size 4 batch size
- Size 4 batch count + size 2 batch size
Parameters:
- Software: Automatic1111
- Checkpoint: JuggernautXL
- Sampling Steps: 30
- CFG Scale: 7
- Sampling Method: DPM++ 2M Karras
- Width & Height: 768×512
All of these settings will produce 8 images, and we will compare the speeds below with 3 different test prompts:
8 Batch Size
8 Batch Count
2 Batch Count + 4 Batch Size
4 Batch Count + 2 Batch Size
33.9 sec
54.2 sec
34.4 Sec
37.4 Sec
33.0 sec
51.3 sec
34.4 sec
37.4 sec
33.0 sec
53.7 sec
35.0 sec
37.3 sec
33.3 Sec
53.1 Sec
34.6 Sec
37.4 Sec
With the averages at the bottom for all three tests, batch size is much faster than batch count.
Surprisingly, having both at the same time doesn’t have much performance impact compared to just increasing batch size.
Just as a disclaimer, you might not get the same average seconds as me if you perform the same test. Your computer hardware is extremely important in determining how fast everything is. This test is supposed to provide an answer to the most efficient way of producing multiple images.
The most efficient setting is increasing batch size.
Also, I will include an image from the three tests, so you have an idea of what I was using to test this and add context to how long the time to generate was.
Test 1 image:
Prompt: pirate ship with long black flowing sails on an amazing green landscape under bright daylight with giant stars, powerful, cinematic, beautifully lit, 8k
Test 2 Image:
Prompt: modern building, beautiful dynamic dramatic dark moody lighting, shadows, cinematic atmosphere
Test 3 Image:
Prompt: forest, fireflies, flowers, atmosphere, vibe, mist, flowers, volumetric
Why Do Stable Diffusion Batch Size and Batch Count Produce Different Images?
Some of you may wonder why all the images generated come out differently for each batch.
The reason is that in between producing each image, stable diffusion increments the seed for the images produced.
Let Me Hear From You
Now that I finished explaining the stable diffusion batch count vs batch size settings.
Is there anything that you have found useful when using both settings?
Let me know by leaving a comment down below.