ComfyUI Node: Lucy Condition Concat

Lucy Condition Concat:

The LucyConditionConcatNode is a specialized node within the ComfyUI framework designed to enhance the conditioning process during diffusion by concatenating additional latent data to the input channels. This node is particularly beneficial for models with doubled input channels, such as WAN2.2, where the extra channels are utilized for conditioning purposes. By integrating additional latents through a process called c_concat, this node ensures that the conditioning is seamlessly incorporated into the diffusion model, allowing for more nuanced and controlled outputs. The primary goal of the LucyConditionConcatNode is to facilitate the handling of complex conditioning scenarios, thereby expanding the creative possibilities for AI artists working with diffusion models.

Lucy Condition Concat Input Parameters:

model

The model parameter refers to the diffusion model that will be used in the conditioning process. This model acts as the foundation upon which the additional latents are concatenated. The model should be compatible with the node's conditioning mechanism, particularly those with doubled input channels. This parameter is crucial as it determines the base capabilities and characteristics of the diffusion process.

concat_latent

The concat_latent parameter is a dictionary containing the additional latent data to be concatenated with the model's input channels. This latent data is typically represented as a tensor under the key "samples". The purpose of this parameter is to provide the extra conditioning information that will be integrated into the model, allowing for more detailed and specific diffusion outputs. The concat_latent must be carefully prepared to match the model's requirements in terms of batch size and spatial dimensions.

Lucy Condition Concat Output Parameters:

model

The model output is the modified diffusion model that now includes the concatenated latent data. This model is ready to be used in the diffusion process, with the additional conditioning integrated into its input channels. The output model retains all the original functionalities while being enhanced with the new conditioning capabilities.

latent

The latent output is a dictionary containing the modified latent data, specifically under the key "samples". This output represents the conditioned latent that has been processed and is ready for use in further diffusion steps. It serves as a reference for the changes made during the concatenation process and can be used to verify the successful integration of the additional conditioning.

Lucy Condition Concat Usage Tips:

• Ensure that the concat_latent tensor matches the batch size and spatial dimensions of the model's input to avoid errors during the concatenation process.
• Utilize models with doubled input channels to fully leverage the conditioning capabilities of the LucyConditionConcatNode, as these models are specifically designed to handle additional conditioning data.

Lucy Condition Concat Common Errors and Solutions:

Batch size of concat_tensor and x do not match

• Explanation: This error occurs when the batch size of the concat_latent tensor does not match the batch size of the model's input tensor.
• Solution: Ensure that the concat_latent tensor is prepared with the correct batch size. If the concat_latent tensor has a batch size of 1, it can be repeated to match the model's input batch size.

Spatial dimensions of concat_tensor and x do not match

• Explanation: This error arises when the spatial dimensions of the concat_latent tensor do not align with those of the model's input tensor.
• Solution: Verify that the concat_latent tensor is correctly sized in terms of spatial dimensions before attempting to concatenate it with the model's input. Adjust the dimensions as necessary to ensure compatibility.