Datatypes
These are the most important built in datatypes. You can also define your own.
INPUT_TYPES by a str, with the exception of COMBO, which is list[str]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.
Comfy datatypes
COMBO
- No additional parameters in
INPUT_TYPES - Python datatype: defined as
list[str], output value isstr
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
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), )
or they might just be a fixed list of options,
"play_sound": (["no","yes"], {}),
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…)
Python datatypes
INT
- Additional parameters in
INPUT_TYPES:defaultis requiredminandmaxare optional
- Python datatype
int
FLOAT
- Additional parameters in
INPUT_TYPES:defaultis requiredmin,max,stepare optional
- Python datatype
float
STRING
- Additional parameters in
INPUT_TYPES:defaultis required
- Python datatype
str
Tensor datatypes
torch.Tensor type, but they have different shape.
See discussion in ‘Images, Latents, and Masks’.IMAGE
- No additional parameters in
INPUT_TYPES - Python datatype
torch.Tensorwith shape [B,H,W,C]
A batch of B images, height H, width W, with C channels (generally C=3 for RGB).
LATENT
- No additional parameters in
INPUT_TYPES - Python datatype
dict, containing atorch.Tensorwith 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.
MASK
- No additional parameters in
INPUT_TYPES - Python datatype
torch.Tensorwith shape [H,W] or [B,C,H,W]
Custom Sampling datatypes
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].
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
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.
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:
tensor([14.6146, 10.7468, 8.0815, 6.2049, 4.8557,
3.8654, 3.1238, 2.5572, 2.1157, 1.7648,
1.4806, 1.2458, 1.0481, 0.8784, 0.7297,
0.5964, 0.4736, 0.3555, 0.2322, 0.0292, 0.0000])
MODEL input to produce a SIGMAS outputGuider
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).
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!
Additional Parameters
Below is a list of officially supported keys that can be used in the ‘extra options’ portion of an input definition.
| Key | Description |
|---|---|
default | The default value of the widget |
min | The minimum value of a number (FLOAT or INT) |
max | The maximum value of a number (FLOAT or INT) |
step | The amount to increment or decrement a widget |
label_on | The label to use in the UI when the bool is True (BOOL) |
label_off | The label to use in the UI when the bool is False (BOOL) |
defaultInput | Defaults to an input socket rather than a supported widget |
forceInput | defaultInput and also don’t allow converting to a widget |
multiline | Use a multiline text box (STRING) |
placeholder | Placeholder text to display in the UI when empty (STRING) |
dynamicPrompts | Causes the front-end to evaluate dynamic prompts |
lazy | Declares that this input uses Lazy Evaluation |
rawLink | When a link exists, rather than receiving the evaluated value, you will receive the link (i.e. ["nodeId", <outputIndex>]). Primarily useful when your node uses Node Expansion. |
Was this page helpful?