custom-nodes/backend/expansion

[giscus[bot]]

custom-nodes/backend/expansion

https://docs.comfy.org/custom-nodes/backend/expansion

[geroldmeisinger]

This technique is what allows custom nodes to implement loops.

please provide an example
update: I think they way to do this is by copying all the nodes between For Loop Start and For Loop End and instantiating them for every item in the list.

[geroldmeisinger]

Another notable difference between using multiple nodes in the UI and using node expansion is that the whole node is treated as a single unit, which is relevant in sequential processing. The following composite node saves the image for every item passed to the KSampler, whereas otherwise the KSampler would process all items first, before passing on all results to VAEDecode.

Example node which has KSampler, VAEDecode and SaveImage included:

class KSamplerImmediateSave:
	@classmethod
	def INPUT_TYPES(cls):
		return {
			"required": {
				# from ComfyUI/nodes.py KSampler
				"model"       	: ("MODEL"       	, { "tooltip" : "The model used for denoising the input latent." } ) ,
				"positive"    	: ("CONDITIONING"	, { "tooltip" : "The conditioning describing the attributes you want to include in the image." } ) ,
				"negative"    	: ("CONDITIONING"	, { "tooltip" : "The conditioning describing the attributes you want to exclude from the image." } ) ,
				"latent_image"	: ("LATENT"      	, { "tooltip" : "The latent image to denoise." } ) ,
				"vae"         	: ("VAE"         	, { "tooltip" : "The VAE model used for decoding the latent." } ) ,

				"seed"        	: ("INT"                             	, {"default" : 	0  	, "min" :	0  	, "max" :	0xfffffffffffffff  	, "control_after_generate" : True,	"tooltip" : "The random seed used for creating the noise." } ) ,
				"steps"       	: ("INT"                             	, {"default" :	20  	, "min" :	1  	, "max" :            	10000  	,                                 	"tooltip" : "The number of steps used in the denoising process." } ) ,
				"cfg"         	: ("FLOAT"                           	, {"default" : 	8.0	, "min" :	0.0	, "max" :              	100.0	, "step" : 0.1, "round" : 0.01,   	"tooltip" : "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality." } ) ,
				"sampler_name"	: (comfy.samplers.KSampler.SAMPLERS  	, {"tooltip" : "The algorithm used when sampling , this can affect the quality , speed , and style of the generated output." } ) ,
				"scheduler"   	: (comfy.samplers.KSampler.SCHEDULERS	, {"tooltip" : "The scheduler controls how noise is gradually removed to form the image." } ) ,
				"denoise"     	: ("FLOAT"                           	, {"default" :	1.0	, "min" :	0.0	, "max" :	1.0	, "step" : 0.01,	"tooltip" : "The amount of denoising applied , lower values will maintain the structure of the initial image allowing for image to image sampling." } ) ,

				# from ComfyUI/nodes.py SaveImage
				"filename_prefix"	: ("STRING", {"default" : "ComfyUI", "tooltip" : "The prefix for the file to save. This may include formatting information such as %date :yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}),
			},
			# "hidden": {
			#	"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO",
			# },
		}

	RETURN_NAMES   	= ("image", )
	RETURN_TYPES   	= ("IMAGE", )
	OUTPUT_TOOLTIPS	= ("The decoded image.",) # from ComfyUI/nodes.py VAEDecode
	OUTPUT_NODE    	= True
	FUNCTION       	= "execute"
	CATEGORY       	= "Utility"

	def execute(self, model, positive, negative, latent_image, vae, seed, steps, cfg, sampler_name, scheduler, denoise, filename_prefix):
		graph 	= GraphBuilder()
		latent	= graph.node("KSampler" , model=model, positive=positive, negative=negative, latent_image=latent_image, seed=seed, steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler, denoise=denoise)
		images	= graph.node("VAEDecode", samples=latent.out(0), vae=vae)
		save  	= graph.node("SaveImage", images=images.out(0), filename_prefix=filename_prefix)
		return {
			"result" : (images.out(0),),
			"expand" : graph.finalize(),
		}