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Flexible AI art sampling node with parameter control for refined outputs.
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.
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
.
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
.
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
.
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.
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.
delta
values. Higher values can introduce more variation in the generated art.cfg
parameter. A value close to 1.0
is recommended for balanced results.original_latent
input for self-negative sampling to enhance the quality and diversity of the generated outputs.original_latent
input does not contain the expected samples
key.original_latent
input is correctly formatted and includes the samples
key.original_latent
input is None
and the node attempts to access its samples
key.original_latent
input is provided and correctly formatted. If not using original_latent
, ensure the enable
parameter is set appropriately.delta
parameter is set outside its valid range.delta
parameter to a value within the range of 0.0
to 5.0
.cfg
parameter is set outside its valid range.cfg
parameter to a value within the range of 0.0
to 5.0
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