ComfyUI Node: LCM Sampler RCFG 🍌

LCM Sampler RCFG 🍌| LCM Sampler RCFG 🍌:

The LCMSamplerRCFG| LCM Sampler RCFG 🍌 node is designed to provide a flexible and powerful sampling method for AI-generated art, leveraging the principles outlined in the research paper "RCFG" (https://arxiv.org/abs/2312.12491). This node allows you to fine-tune the sampling process by adjusting parameters such as enable, delta, and cfg, which control the activation of the sampler, the influence of the noise prediction, and the configuration scale, respectively. The node can optionally utilize an original_latent input to perform self-negative sampling, enhancing the quality and diversity of the generated outputs. By integrating these capabilities, the LCMSamplerRCFG| LCM Sampler RCFG 🍌 node aims to provide artists with greater control over the creative process, enabling the generation of more refined and varied artistic outputs.

LCM Sampler RCFG 🍌| LCM Sampler RCFG 🍌 Input Parameters:

enable

The enable parameter is a boolean that determines whether the sampler is active. When set to True, the sampler will be enabled, allowing the node to perform its sampling operations. If set to False, the sampler will be disabled, and the node will bypass the sampling process. The default value is True.

delta

The delta parameter is a float that influences the noise prediction during the sampling process. It adjusts the weight of the noise prediction, affecting the final output. A higher delta value increases the influence of the noise prediction, potentially leading to more diverse outputs. The parameter ranges from 0.0 to 5.0, with a default value of 1.0 and a step size of 0.01.

cfg

The cfg parameter is a float that controls the configuration scale, which impacts the strength of the conditioning applied during sampling. A higher cfg value increases the influence of the conditioning, potentially leading to more coherent outputs. The parameter ranges from 0.0 to 5.0, with a default value of 1.0 and a step size of 0.01.

original_latent

The original_latent parameter is an optional input that accepts a latent representation. If provided, the node will use this latent input to perform self-negative sampling, which can enhance the quality and diversity of the generated outputs. If not provided, the node will perform onetime-negative sampling.

LCM Sampler RCFG 🍌| LCM Sampler RCFG 🍌 Output Parameters:

SAMPLER

The SAMPLER output is the primary result of the node's sampling process. It represents the sampled data generated based on the input parameters and the optional original_latent input. This output can be used in subsequent nodes or processes to create AI-generated art with the desired characteristics and variations.

LCM Sampler RCFG 🍌| LCM Sampler RCFG 🍌 Usage Tips:

• To achieve more diverse outputs, experiment with different delta values. Higher values can introduce more variation in the generated art.
• For more coherent and refined outputs, adjust the cfg parameter. A value close to 1.0 is recommended for balanced results.
• Utilize the original_latent input for self-negative sampling to enhance the quality and diversity of the generated outputs.

LCM Sampler RCFG 🍌| LCM Sampler RCFG 🍌 Common Errors and Solutions:

"KeyError: 'samples'"

• Explanation: This error occurs when the original_latent input does not contain the expected samples key.
• Solution: Ensure that the original_latent input is correctly formatted and includes the samples key.

"TypeError: 'NoneType' object is not subscriptable"

• Explanation: This error occurs when the original_latent input is None and the node attempts to access its samples key.
• Solution: Check if the original_latent input is provided and correctly formatted. If not using original_latent, ensure the enable parameter is set appropriately.

"ValueError: delta must be between 0.0 and 5.0"

• Explanation: This error occurs when the delta parameter is set outside its valid range.
• Solution: Adjust the delta parameter to a value within the range of 0.0 to 5.0.

"ValueError: cfg must be between 0.0 and 5.0"

• Explanation: This error occurs when the cfg parameter is set outside its valid range.
• Solution: Adjust the cfg parameter to a value within the range of 0.0 to 5.0.