ComfyUI Node: Latent Upscaler

Latent Upscaler:

The LatentUpscaler node is designed to enhance the resolution of latent representations in AI-generated images. This node leverages advanced neural network techniques to upscale latent samples, providing higher quality and more detailed outputs. By using this node, you can achieve smoother and more refined images, which is particularly beneficial for applications requiring high-resolution outputs. The LatentUpscaler is capable of handling different versions of latent representations and offers multiple scaling factors, making it a versatile tool for AI artists looking to improve the visual fidelity of their creations.

Latent Upscaler Input Parameters:

samples

samples is the primary input parameter that takes in the latent representations you wish to upscale. These latent samples are the encoded forms of images that the model will process to enhance their resolution. The quality and characteristics of the output heavily depend on the input samples provided.

latent_ver

latent_ver specifies the version of the latent representation being used. It accepts two options: v1 and xl. This parameter ensures that the correct model version is applied to the latent samples, which is crucial for maintaining compatibility and achieving optimal results.

scale_factor

scale_factor determines the degree to which the latent samples will be upscaled. The available options are 1.25, 1.5, and 2.0. This parameter directly impacts the resolution of the output, with higher values resulting in more significant upscaling and finer details in the final image.

Latent Upscaler Output Parameters:

samples

The samples output parameter contains the upscaled latent representations. These enhanced latents can be further processed or decoded to produce high-resolution images. The output retains the structure of the input samples but with improved resolution and detail.

noise_mask (optional)

If the input samples include a noise_mask, the output will also contain an upscaled version of this mask. The noise_mask helps in preserving the noise characteristics of the original latent samples, ensuring that the upscaling process does not introduce unwanted artifacts.

Latent Upscaler Usage Tips:

• To achieve the best results, ensure that the input latent samples are of high quality and properly encoded.
• Experiment with different scale_factor values to find the optimal balance between resolution and processing time for your specific use case.
• Use the appropriate latent_ver to match the version of your latent samples, as this ensures compatibility and optimal performance.

Latent Upscaler Common Errors and Solutions:

Latent Upscaler: Using local model

• Explanation: This message indicates that the node is using a locally stored model for upscaling.
• Solution: Ensure that the local model file is correctly placed in the specified directory. If the model is outdated or corrupted, consider downloading a fresh copy.

Latent Upscaler: Using HF Hub model

• Explanation: This message indicates that the node is downloading the model from the Hugging Face Hub.
• Solution: Ensure you have a stable internet connection. If the download fails, check your network settings or try again later.

FileNotFoundError: [Errno 2] No such file or directory

• Explanation: This error occurs when the specified model file cannot be found.
• Solution: Verify the file path and ensure that the model file exists in the correct directory. If necessary, download the model file again.

RuntimeError: CUDA out of memory

• Explanation: This error occurs when the GPU does not have enough memory to process the upscaling operation.
• Solution: Reduce the scale_factor or use a smaller batch size. Alternatively, try running the node on a machine with more GPU memory.