PreSampling (Advanced)

Documentation

• Class name: easy preSamplingAdvanced
• Category: EasyUse/PreSampling
• Output node: True

This node facilitates advanced pre-sampling operations within a generative pipeline, offering customizable settings for fine-tuning the sampling process. It allows for the adjustment of various parameters to optimize the generation of images or other media, catering to specific needs or experimental setups.

Input types

Required

• pipe
  • Represents the generative pipeline through which the data flows, serving as the foundation for the pre-sampling process.
  • Comfy dtype: PIPE_LINE
  • Python dtype: Dict
• steps
  • Specifies the number of steps to be taken in the sampling process, affecting the granularity and quality of the output.
  • Comfy dtype: INT
  • Python dtype: int
• cfg
  • Controls the conditioning factor, influencing the adherence of the generated output to the given conditions.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sampler_name
  • Selects the specific sampling algorithm to be used, allowing for customization of the sampling behavior.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• scheduler
  • Chooses the scheduling algorithm for the sampling process, affecting the progression of steps.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• start_at_step
  • Defines the starting step for the sampling process, allowing for control over the phase of generation.
  • Comfy dtype: INT
  • Python dtype: int
• end_at_step
  • Sets the ending step for the sampling process, determining the final phase of generation.
  • Comfy dtype: INT
  • Python dtype: int
• add_noise
  • Enables or disables the addition of noise during the sampling process, affecting the texture and details of the output.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• seed
  • Sets the initial seed for the random number generator, ensuring reproducibility of results.
  • Comfy dtype: INT
  • Python dtype: int
• return_with_leftover_noise
  • Determines whether to return the output with leftover noise, impacting the visual characteristics of the generated media.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Optional

• image_to_latent
  • Optionally converts an image to a latent representation, integrating it into the pre-sampling process.
  • Comfy dtype: IMAGE
  • Python dtype: Optional[Image]
• latent
  • Optionally provides a latent representation to be used directly in the sampling process.
  • Comfy dtype: LATENT
  • Python dtype: Optional[Latent]

Output types

• pipe
  • Comfy dtype: PIPE_LINE
  • Outputs the enhanced generative pipeline, enriched with the applied pre-sampling configurations.
  • Python dtype: Dict

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class samplerSettingsAdvanced:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {"required":
                    {"pipe": ("PIPE_LINE",),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS + ['align_your_steps'],),
                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
                     "add_noise": (["enable", "disable"],),
                     "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}),
                     "return_with_leftover_noise": (["disable", "enable"], ),
                     },
                "optional": {
                    "image_to_latent": ("IMAGE",),
                    "latent": ("LATENT",)
                },
                "hidden":
                    {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"},
                }

    RETURN_TYPES = ("PIPE_LINE", )
    RETURN_NAMES = ("pipe",)
    OUTPUT_NODE = True

    FUNCTION = "settings"
    CATEGORY = "EasyUse/PreSampling"

    def settings(self, pipe, steps, cfg, sampler_name, scheduler, start_at_step, end_at_step, add_noise, seed, return_with_leftover_noise, image_to_latent=None, latent=None, prompt=None, extra_pnginfo=None, my_unique_id=None):
        # 图生图转换
        vae = pipe["vae"]
        batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1
        if image_to_latent is not None:
            samples = {"samples": vae.encode(image_to_latent[:, :, :, :3])}
            samples = RepeatLatentBatch().repeat(samples, batch_size)[0]
            images = image_to_latent
        elif latent is not None:
            samples = latent
            images = pipe["images"]
        else:
            samples = pipe["samples"]
            images = pipe["images"]

        force_full_denoise = True
        if return_with_leftover_noise == "enable":
            force_full_denoise = False

        new_pipe = {
            "model": pipe['model'],
            "positive": pipe['positive'],
            "negative": pipe['negative'],
            "vae": pipe['vae'],
            "clip": pipe['clip'],

            "samples": samples,
            "images": images,
            "seed": seed,

            "loader_settings": {
                **pipe["loader_settings"],
                "steps": steps,
                "cfg": cfg,
                "sampler_name": sampler_name,
                "scheduler": scheduler,
                "start_step": start_at_step,
                "last_step": end_at_step,
                "denoise": 1.0,
                "add_noise": add_noise,
                "force_full_denoise": force_full_denoise
            }
        }

        del pipe

        return {"ui": {"value": [seed]}, "result": (new_pipe,)}