TiledKSamplerProvider

Documentation

• Class name: TiledKSamplerProvider
• Category: ImpactPack/Sampler
• Output node: False

The TiledKSamplerProvider node is designed to facilitate the generation of samples using a tiled K-sampling approach. It configures and utilizes a specialized sampler that operates on tiles of an image, allowing for efficient and scalable image generation with customizable sampling strategies.

Input types

Required

• seed
  • Specifies the initial seed for random number generation, ensuring reproducibility of the sampling process.
  • Comfy dtype: INT
  • Python dtype: int
• steps
  • Determines the number of steps to be taken in the sampling process, affecting the detail and quality of the generated image.
  • Comfy dtype: INT
  • Python dtype: int
• cfg
  • Controls the configuration setting for the sampling process, influencing the behavior and characteristics of the generated samples.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sampler_name
  • Selects the specific K-sampler to be used, allowing for flexibility in choosing the sampling algorithm.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• scheduler
  • Chooses the scheduler for controlling the sampling process, enabling fine-tuning of the sampling dynamics.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• denoise
  • Adjusts the denoising factor applied during sampling, impacting the clarity and noise level of the output.
  • Comfy dtype: FLOAT
  • Python dtype: float
• tile_width
  • Sets the width of the tiles used in the sampling process, defining the granularity of the tiled approach.
  • Comfy dtype: INT
  • Python dtype: int
• tile_height
  • Sets the height of the tiles used in the sampling process, defining the granularity of the tiled approach.
  • Comfy dtype: INT
  • Python dtype: int
• tiling_strategy
  • Determines the strategy for tiling the image during sampling, affecting the overall sampling pattern and efficiency.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• basic_pipe
  • Provides the basic pipeline components required for the sampling process, including the model and positive/negative conditioning.
  • Comfy dtype: BASIC_PIPE
  • Python dtype: tuple

Output types

• ksampler
  • Comfy dtype: KSAMPLER
  • Returns a configured KSampler instance ready for sampling operations.
  • Python dtype: KSamplerWrapper

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class TiledKSamplerProvider:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
                    "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": (comfy.samplers.KSampler.SAMPLERS, ),
                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                    "tile_width": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}),
                    "tile_height": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}),
                    "tiling_strategy": (["random", "padded", 'simple'], ),
                    "basic_pipe": ("BASIC_PIPE", )
                    }}

    RETURN_TYPES = ("KSAMPLER",)
    FUNCTION = "doit"

    CATEGORY = "ImpactPack/Sampler"

    def doit(self, seed, steps, cfg, sampler_name, scheduler, denoise,
             tile_width, tile_height, tiling_strategy, basic_pipe):
        model, _, _, positive, negative = basic_pipe
        sampler = core.TiledKSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
                                            tile_width, tile_height, tiling_strategy)
        return (sampler, )