Context (rgthree)

Documentation

• Class name: Context (rgthree)
• Category: rgthree
• Output node: False

The Context (rgthree) node serves as the foundational context node, designed to be highly compatible with 1.5 applications and other context nodes. It focuses on converting input context parameters into a structured output that is optimized for most use cases, maintaining both forward and backward compatibility.

Input types

Required

Optional

• base_ctx
  • The base context to be converted or enhanced. It serves as the starting point for the conversion process, allowing for the integration or modification of additional context parameters.
  • Comfy dtype: RGTHREE_CONTEXT
  • Python dtype: Optional[Dict[str, Any]]
• model
  • Specifies the model to be used in the context, allowing for customization and flexibility in processing.
  • Comfy dtype: MODEL
  • Python dtype: str
• clip
  • Defines the CLIP model to be incorporated into the context, enhancing the processing capabilities.
  • Comfy dtype: CLIP
  • Python dtype: str
• vae
  • Indicates the VAE model to be included in the context, contributing to the generation process.
  • Comfy dtype: VAE
  • Python dtype: str
• positive
  • A positive conditioning factor to guide the generation towards desired outcomes.
  • Comfy dtype: CONDITIONING
  • Python dtype: str
• negative
  • A negative conditioning factor to steer the generation away from undesired outcomes.
  • Comfy dtype: CONDITIONING
  • Python dtype: str
• latent
  • Specifies the latent space representation to be used in the context, enabling advanced manipulation.
  • Comfy dtype: LATENT
  • Python dtype: str
• images
  • Defines the images to be included in the context, allowing for visual data integration.
  • Comfy dtype: IMAGE
  • Python dtype: str
• seed
  • Sets the seed for random number generation, ensuring reproducibility of results.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• CONTEXT
  • Comfy dtype: RGTHREE_CONTEXT
  • The structured output context optimized for use in various applications.
  • Python dtype: Dict[str, Any]
• MODEL
  • Comfy dtype: MODEL
  • The model used within the context, reflecting the specified input.
  • Python dtype: str
• CLIP
  • Comfy dtype: CLIP
  • The CLIP model incorporated into the context, as specified in the input.
  • Python dtype: str
• VAE
  • Comfy dtype: VAE
  • The VAE model included in the context, as per the input parameters.
  • Python dtype: str
• POSITIVE
  • Comfy dtype: CONDITIONING
  • The positive conditioning factor applied in the context, guiding the generation process.
  • Python dtype: str
• NEGATIVE
  • Comfy dtype: CONDITIONING
  • The negative conditioning factor used in the context to avoid undesired outcomes.
  • Python dtype: str
• LATENT
  • Comfy dtype: LATENT
  • The latent space representation utilized in the context for advanced manipulation.
  • Python dtype: str
• IMAGE
  • Comfy dtype: IMAGE
  • The images included in the context, integrating visual data.
  • Python dtype: str
• SEED
  • Comfy dtype: INT
  • The seed used for random number generation within the context, ensuring consistency.
  • Python dtype: int

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class RgthreeContext:
  """The initial Context node.

  For now, this nodes' outputs will remain as-is, as they are perfect for most 1.5 application, but
  is also backwards compatible with other Context nodes.
  """

  NAME = get_name("Context")
  CATEGORY = get_category()

  @classmethod
  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
    return {
      "required": {},
      "optional": ORIG_CTX_OPTIONAL_INPUTS,
      "hidden": {
        "version": "FLOAT"
      },
    }

  RETURN_TYPES = ORIG_CTX_RETURN_TYPES
  RETURN_NAMES = ORIG_CTX_RETURN_NAMES
  FUNCTION = "convert"

  def convert(self, base_ctx=None, **kwargs):  # pylint: disable = missing-function-docstring
    ctx = new_context(base_ctx, **kwargs)
    return get_orig_context_return_tuple(ctx)