ComfyUI Node: Gradual Latent Sampler 🍌

Gradual Latent Sampler 🍌| Gradual Latent Sampler 🍌:

The GradualLatentSampler is a sophisticated node designed to enhance the quality of generated images by gradually refining the latent space during the sampling process. This node leverages various sampling techniques to iteratively improve the image quality, ensuring smoother transitions and finer details. It is particularly beneficial for AI artists looking to achieve high-quality outputs with minimal artifacts. The GradualLatentSampler supports multiple sampling methods, including Euler Ancestral, DPM++ 2S Ancestral, and DPM++ 2M SDE, allowing for flexibility and customization based on the desired outcome. By incorporating unsharp masking and noise control, this node provides a robust framework for producing visually appealing and high-fidelity images.

Gradual Latent Sampler 🍌| Gradual Latent Sampler 🍌 Input Parameters:

sampler_name

This parameter specifies the sampling method to be used. Options include euler_ancestral, dpmpp_2s_ancestral, dpmpp_2m_sde, and lcm. Each method has its unique approach to refining the latent space, impacting the final image quality and characteristics. Ensure to choose the method that best suits your artistic needs.

eta

The eta parameter controls the amount of noise added during the sampling process. It ranges from 0 to 1, with higher values introducing more noise, which can help in exploring more diverse image variations. The default value is typically set to 0.5, balancing noise and detail.

s_noise

This parameter adjusts the strength of the noise applied. It influences the granularity of the noise, with higher values resulting in more pronounced noise patterns. The default value is usually set to 1.0, providing a standard level of noise application.

upscale_ratio

The upscale_ratio determines the scaling factor for the image during the sampling process. It allows for gradual upscaling, enhancing the image resolution step by step. The default value is often set to 1.0, meaning no upscaling.

start_step

This parameter defines the initial step of the sampling process. It sets the starting point for the iterative refinement, impacting how early the image begins to take shape. The default value is typically set to 0.

end_step

The end_step parameter specifies the final step of the sampling process. It determines when the iterative refinement should stop, affecting the overall detail and quality of the final image. The default value is usually set to the maximum number of steps allowed by the chosen sampling method.

upscale_n_step

This parameter controls the number of steps dedicated to upscaling the image. It allows for fine-tuning the balance between resolution enhancement and detail preservation. The default value is often set to a moderate number, such as 10.

unsharp_kernel_size

The unsharp_kernel_size parameter sets the size of the kernel used for unsharp masking. It must be an odd number, with larger values resulting in more pronounced sharpening effects. The default value is typically set to 3.

unsharp_sigma

This parameter defines the standard deviation for the Gaussian blur applied during unsharp masking. It controls the extent of the blurring effect, with higher values leading to smoother transitions. The default value is usually set to 1.0.

unsharp_strength

The unsharp_strength parameter adjusts the intensity of the unsharp masking effect. It ranges from 0 to 1, with higher values resulting in stronger sharpening. The default value is typically set to 0.5, providing a balanced sharpening effect.

unsharp_target

This parameter specifies the target for the unsharp masking effect. Options include x and denoised, determining whether the effect is applied to the raw or denoised image. The default value is often set to x.

Gradual Latent Sampler 🍌| Gradual Latent Sampler 🍌 Output Parameters:

sampler

The sampler output is an object that encapsulates the configured sampling process. It includes all the parameters and settings defined during the input stage, ready to be used for generating high-quality images. This output is crucial for initiating the sampling process and achieving the desired image refinement.

Gradual Latent Sampler 🍌| Gradual Latent Sampler 🍌 Usage Tips:

• Experiment with different sampler_name options to find the best method for your specific artistic needs.
• Adjust the eta and s_noise parameters to control the level of noise and detail in your images.
• Use the upscale_ratio and upscale_n_step parameters to gradually enhance the resolution of your images without losing detail.
• Fine-tune the unsharp_kernel_size, unsharp_sigma, and unsharp_strength parameters to achieve the desired level of sharpness and clarity.

Gradual Latent Sampler 🍌| Gradual Latent Sampler 🍌 Common Errors and Solutions:

Unknown sampler name

• Explanation: This error occurs when an invalid or unsupported sampler name is provided.
• Solution: Ensure that the sampler_name parameter is set to one of the supported options: euler_ancestral, dpmpp_2s_ancestral, dpmpp_2m_sde, or lcm.

Unsharp kernel size must be an odd number

• Explanation: This error occurs when the unsharp_kernel_size parameter is set to an even number.
• Solution: Set the unsharp_kernel_size parameter to an odd number to ensure proper unsharp masking.

Invalid eta value

• Explanation: This error occurs when the eta parameter is set outside the valid range of 0 to 1. - Solution: Adjust the eta parameter to a value between 0 and 1 to ensure proper noise control.

Invalid unsharp strength value

• Explanation: This error occurs when the unsharp_strength parameter is set outside the valid range of 0 to 1.
• Solution: Adjust the unsharp_strength parameter to a value between 0 and 1 to ensure proper sharpening intensity.