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These are the most important built in datatypes. You can also define your own. Datatypes are used on the client side to prevent a workflow from passing the wrong form of data into a node - a bit like strong typing. The JavaScript client side code will generally not allow a node output to be connected to an input of a different datatype, although a few exceptions are noted below.

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Comfy datatypes

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COMBO

  • No additional parameters in INPUT_TYPES
  • Python datatype: defined as list[str], output value is str
Represents a dropdown menu widget. Unlike other datatypes, COMBO it is not specified in INPUT_TYPES by a str, but by a list[str] corresponding to the options in the dropdown list, with the first option selected by default. COMBO inputs are often dynamically generated at run time. For instance, in the built-in CheckpointLoaderSimple node, you find
or they might just be a fixed list of options,

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Primitive and reroute

Primitive and reroute nodes only exist on the client side. They do not have an intrinsic datatype, but when connected they take on the datatype of the input or output to which they have been connected (which is why they can’t connect to a * input…)

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Python datatypes

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INT

  • Additional parameters in INPUT_TYPES:
    • default is required
    • min and max are optional
  • Python datatype int

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FLOAT

  • Additional parameters in INPUT_TYPES:
    • default is required
    • min, max, step are optional
  • Python datatype float

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STRING

  • Additional parameters in INPUT_TYPES:
    • default is required
  • Python datatype str

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BOOLEAN

  • Additional parameters in INPUT_TYPES:
    • default is required
  • Python datatype bool

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Tensor datatypes

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IMAGE

  • No additional parameters in INPUT_TYPES
  • Python datatype torch.Tensor with shape [B,H,W,C]
A batch of B images, height H, width W, with C channels (generally C=3 for RGB).

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LATENT

  • No additional parameters in INPUT_TYPES
  • Python datatype dict, containing a torch.Tensor with shape [B,C,H,W]
The dict passed contains the key samples, which is a torch.Tensor with shape [B,C,H,W] representing a batch of B latents, with C channels (generally C=4 for existing stable diffusion models), height H, width W. The height and width are 1/8 of the corresponding image size (which is the value you set in the Empty Latent Image node). Other entries in the dictionary contain things like latent masks.

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MASK

  • No additional parameters in INPUT_TYPES
  • Python datatype torch.Tensor with shape [H,W] or [B,C,H,W]

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AUDIO

  • No additional parameters in INPUT_TYPES
  • Python datatype dict, containing a torch.Tensor with shape [B, C, T] and a sample rate.
The dict passed contains the key waveform, which is a torch.Tensor with shape [B, C, T] representing a batch of B audio samples, with C channels (C=2 for stereo and C=1 for mono), and T time steps (i.e., the number of audio samples). The dict contains another key sample_rate, which indicates the sampling rate of the audio.

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Custom Sampling datatypes

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Noise

The NOISE datatype represents a source of noise (not the actual noise itself). It can be represented by any Python object that provides a method to generate noise, with the signature generate_noise(self, input_latent:Tensor) -> Tensor, and a property, seed:Optional[int].
The seed is passed into sample guider in the SamplerCustomAdvanced, but does not appear to be used in any of the standard guiders. It is Optional, so you can generally set it to None.
When noise is to be added, the latent is passed into this method, which should return a Tensor of the same shape containing the noise. See the noise mixing example

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Sampler

The SAMPLER datatype represents a sampler, which is represented as a Python object providing a sample method. Stable diffusion sampling is beyond the scope of this guide; see comfy/samplers.py if you want to dig into this part of the code.

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Sigmas

The SIGMAS datatypes represents the values of sigma before and after each step in the sampling process, as produced by a scheduler. This is represented as a one-dimensional tensor, of length steps+1, where each element represents the noise expected to be present before the corresponding step, with the final value representing the noise present after the final step. A normal scheduler, with 20 steps and denoise of 1, for an SDXL model, produces:
The starting value of sigma depends on the model, which is why a scheduler node requires a MODEL input to produce a SIGMAS output

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Guider

A GUIDER is a generalisation of the denoising process, as ‘guided’ by a prompt or any other form of conditioning. In Comfy the guider is represented by a callable Python object providing a __call__(*args, **kwargs) method which is called by the sample. The __call__ method takes (in args[0]) a batch of noisy latents (tensor [B,C,H,W]), and returns a prediction of the noise (a tensor of the same shape).

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Model datatypes

There are a number of more technical datatypes for stable diffusion models. The most significant ones are MODEL, CLIP, VAE and CONDITIONING. Working with these is (for the time being) beyond the scope of this guide!

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Additional Parameters

Below is a list of officially supported keys that can be used in the ‘extra options’ portion of an input definition.
You can use additional keys for your own custom widgets, but should not reuse any of the keys below for other purposes.