ComfyUI Node: SUPIR Sampler

SUPIR Sampler:

The SUPIR_sample node is designed to facilitate the sampling process in AI art generation, leveraging the SUPIR model's capabilities. This node is integral for generating high-quality images from latent representations by iteratively refining the latent space through a denoising process. It supports various configurations and parameters that allow you to control the sampling process, ensuring that the generated images meet your artistic requirements. The node is particularly useful for tasks that require precise control over the sampling steps, noise levels, and conditioning, making it a versatile tool for AI artists looking to explore and create unique visual content.

SUPIR Sampler Input Parameters:

SUPIR_model

This parameter specifies the SUPIR model to be used for the sampling process. The model contains the necessary components, such as the denoiser and diffusion model, which are essential for generating images from latent representations.

latents

Latents are the initial latent representations from which the images will be generated. These can be either random noise or precomputed latents, depending on the use case.

steps

This parameter defines the number of steps to be taken during the sampling process. More steps generally lead to higher quality images but increase computation time. The minimum value is 1, and there is no strict maximum, but practical limits depend on computational resources.

seed

The seed value ensures reproducibility of the sampling process. If set to -1, a random seed will be used. Otherwise, a specific integer seed can be provided.

cfg_scale_end

This parameter controls the final scale of the classifier-free guidance (CFG) during the sampling process. It influences the strength of the guidance applied to the generated images.

EDM_s_churn

This parameter adjusts the stochasticity of the sampling process, affecting the diversity of the generated images. Higher values introduce more randomness.

s_noise

This parameter specifies the noise level to be added during the sampling process. It helps in exploring different variations of the generated images.

positive

The positive conditioning input, which provides the desired attributes or features that the generated images should have. It typically includes information like target styles or specific elements to be included in the image.

negative

The negative conditioning input, which specifies the attributes or features to be avoided in the generated images. It helps in steering the sampling process away from undesired outcomes.

cfg_scale_start

This parameter sets the initial scale of the classifier-free guidance (CFG) at the beginning of the sampling process. It works in conjunction with cfg_scale_end to control the guidance strength over the sampling steps.

control_scale_start

This parameter defines the initial control scale, which influences how strongly the control model affects the sampling process at the start.

control_scale_end

This parameter sets the final control scale, determining the influence of the control model at the end of the sampling process.

restore_cfg

A boolean parameter that, when set to true, restores the classifier-free guidance to its original configuration after the sampling process.

keep_model_loaded

A boolean parameter that, when set to true, keeps the SUPIR model loaded in memory, which can be useful for repeated sampling tasks to save loading time.

DPMPP_eta

This parameter controls the eta value for the Denoising Probabilistic Model with Posterior Matching (DPMPP), affecting the denoising process's behavior.

sampler

Specifies the sampler to be used for the sampling process. Different samplers can produce varying results and may be chosen based on the desired output characteristics.

sampler_tile_size

Defines the size of the tiles used during the sampling process. The default value is 1024.

sampler_tile_stride

Specifies the stride of the tiles during the sampling process. The default value is 512.

SUPIR Sampler Output Parameters:

decoded_out

The primary output of the SUPIR_sample node, which is the generated image(s) decoded from the latent representations. The output is a tensor containing the final images, which are typically in RGB format and have been processed to match the original dimensions if necessary.

SUPIR Sampler Usage Tips:

• Experiment with different values for steps to find a balance between image quality and computation time.
• Use the seed parameter to ensure reproducibility when you find a configuration that produces desirable results.
• Adjust cfg_scale_start and cfg_scale_end to control the strength of the guidance applied during the sampling process, which can significantly impact the final image quality.
• Utilize positive and negative conditioning inputs to steer the generated images towards desired attributes and away from undesired ones.

SUPIR Sampler Common Errors and Solutions:

"CUDA out of memory"

• Explanation: This error occurs when the GPU runs out of memory during the sampling process.
• Solution: Reduce the sampler_tile_size or steps to lower the memory usage, or try using a GPU with more memory.

"Invalid seed value"

• Explanation: The seed value provided is not valid.
• Solution: Ensure that the seed is either -1 for a random seed or a valid integer.

"Mismatch in positive and negative conditioning lengths"

• Explanation: The lengths of the positive and negative conditioning inputs do not match the number of samples.
• Solution: Ensure that the lengths of the positive and negative conditioning inputs match the number of samples being processed.