ssitu-ComfyUI_restart_sampling/nodes.py

import comfy

from .restart_sampling import DEFAULT_SEGMENTS, SCHEDULER_MAPPING, restart_sampling


def get_supported_samplers():
    samplers = comfy.samplers.KSampler.SAMPLERS.copy()

    # SDE samplers cannot be used with restarts
    samplers.remove("uni_pc")
    samplers.remove("uni_pc_bh2")
    samplers.remove("dpmpp_sde")
    samplers.remove("dpmpp_sde_gpu")
    samplers.remove("dpmpp_2m_sde")
    samplers.remove("dpmpp_2m_sde_gpu")
    samplers.remove("dpmpp_3m_sde")
    samplers.remove("dpmpp_3m_sde_gpu")

    # DPM fast and adaptive go by their own schedules, restarts could be done but it won't follow the algorithm described in the paper.
    samplers.remove("dpm_fast")
    samplers.remove("dpm_adaptive")
    return samplers


def get_supported_restart_schedulers():
    return list(SCHEDULER_MAPPING.keys())


class KRestartSamplerSimple:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("MODEL",),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
                "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                "sampler_name": (get_supported_samplers(),),
                "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
                "positive": ("CONDITIONING",),
                "negative": ("CONDITIONING",),
                "latent_image": ("LATENT",),
                "denoise": (
                    "FLOAT",
                    {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01},
                ),
                "segments": ("STRING", {"default": "default", "multiline": False}),
            },
        }

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "sample"
    CATEGORY = "sampling"

    def sample(
        self,
        model,
        seed,
        steps,
        cfg,
        sampler_name,
        scheduler,
        positive,
        negative,
        latent_image,
        denoise,
        segments,
    ):
        return restart_sampling(
            model,
            seed,
            steps,
            cfg,
            sampler_name,
            scheduler,
            positive,
            negative,
            latent_image,
            segments,
            scheduler,
            denoise=denoise,
        )


class KRestartSampler:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("MODEL",),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
                "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                "sampler_name": (get_supported_samplers(),),
                "scheduler": (tuple(SCHEDULER_MAPPING.keys()),),
                "positive": ("CONDITIONING",),
                "negative": ("CONDITIONING",),
                "latent_image": ("LATENT",),
                "denoise": (
                    "FLOAT",
                    {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01},
                ),
                "segments": (
                    "STRING",
                    {"default": DEFAULT_SEGMENTS, "multiline": False},
                ),
                "restart_scheduler": (get_supported_restart_schedulers(),),
                "chunked_mode": ("BOOLEAN", {"default": True}),
            },
        }

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "sample"
    CATEGORY = "sampling"

    def sample(
        self,
        model,
        seed,
        steps,
        cfg,
        sampler_name,
        scheduler,
        positive,
        negative,
        latent_image,
        denoise,
        segments,
        restart_scheduler,
        chunked_mode=True,
    ):
        return restart_sampling(
            model,
            seed,
            steps,
            cfg,
            sampler_name,
            scheduler,
            positive,
            negative,
            latent_image,
            segments,
            restart_scheduler,
            denoise=denoise,
            chunked_mode=chunked_mode,
        )


class KRestartSamplerAdv:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("MODEL",),
                "add_noise": (["enable", "disable"],),
                "noise_seed": (
                    "INT",
                    {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF},
                ),
                "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                "sampler_name": (get_supported_samplers(),),
                "scheduler": (tuple(SCHEDULER_MAPPING.keys()),),
                "positive": ("CONDITIONING",),
                "negative": ("CONDITIONING",),
                "latent_image": ("LATENT",),
                "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
                "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
                "return_with_leftover_noise": (["disable", "enable"],),
                "segments": (
                    "STRING",
                    {"default": DEFAULT_SEGMENTS, "multiline": False},
                ),
                "restart_scheduler": (get_supported_restart_schedulers(),),
                "chunked_mode": ("BOOLEAN", {"default": True}),
            },
        }

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "sample"
    CATEGORY = "sampling"

    def sample(
        self,
        model,
        add_noise,
        noise_seed,
        steps,
        cfg,
        sampler_name,
        scheduler,
        positive,
        negative,
        latent_image,
        start_at_step,
        end_at_step,
        return_with_leftover_noise,
        segments,
        restart_scheduler,
        chunked_mode=True,
    ):
        force_full_denoise = return_with_leftover_noise != "enable"
        disable_noise = add_noise == "disable"
        return restart_sampling(
            model,
            noise_seed,
            steps,
            cfg,
            sampler_name,
            scheduler,
            positive,
            negative,
            latent_image,
            segments,
            restart_scheduler,
            disable_noise=disable_noise,
            step_range=(start_at_step, end_at_step),
            force_full_denoise=force_full_denoise,
            chunked_mode=chunked_mode,
        )


class KRestartSamplerCustom:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("MODEL",),
                "add_noise": (["enable", "disable"],),
                "noise_seed": (
                    "INT",
                    {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF},
                ),
                "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                "sampler": ("SAMPLER",),
                "scheduler": (tuple(SCHEDULER_MAPPING.keys()),),
                "positive": ("CONDITIONING",),
                "negative": ("CONDITIONING",),
                "latent_image": ("LATENT",),
                "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
                "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
                "return_with_leftover_noise": (["disable", "enable"],),
                "segments": (
                    "STRING",
                    {"default": DEFAULT_SEGMENTS, "multiline": False},
                ),
                "restart_scheduler": (get_supported_restart_schedulers(),),
                "chunked_mode": ("BOOLEAN", {"default": True}),
            },
        }

    RETURN_TYPES = ("LATENT", "LATENT")
    RETURN_NAMES = ("output", "denoised_output")
    FUNCTION = "sample"
    CATEGORY = "sampling"

    def sample(
        self,
        model,
        add_noise,
        noise_seed,
        steps,
        cfg,
        sampler,
        scheduler,
        positive,
        negative,
        latent_image,
        start_at_step,
        end_at_step,
        return_with_leftover_noise,
        segments,
        restart_scheduler,
        chunked_mode=True,
    ):
        force_full_denoise = return_with_leftover_noise != "enable"
        disable_noise = add_noise == "disable"
        return restart_sampling(
            model,
            noise_seed,
            steps,
            cfg,
            sampler,
            scheduler,
            positive,
            negative,
            latent_image,
            segments,
            restart_scheduler,
            disable_noise=disable_noise,
            step_range=(start_at_step, end_at_step),
            force_full_denoise=force_full_denoise,
            output_only=False,
            chunked_mode=chunked_mode,
        )


NODE_CLASS_MAPPINGS = {
    "KRestartSamplerSimple": KRestartSamplerSimple,
    "KRestartSampler": KRestartSampler,
    "KRestartSamplerAdv": KRestartSamplerAdv,
    "KRestartSamplerCustom": KRestartSamplerCustom,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "KRestartSamplerSimple": "KSampler With Restarts (Simple)",
    "KRestartSampler": "KSampler With Restarts",
    "KRestartSamplerAdv": "KSampler With Restarts (Advanced)",
    "KRestartSamplerCustom": "KSampler With Restarts (Custom)",
}