SDXL Sampler v1 (Searge)

Documentation

• Class name: SeargeSDXLSampler
• Category: Searge/_deprecated_/Sampling
• Output node: False

The SeargeSDXLSampler node is designed to facilitate sampling operations within the SDXL framework, providing foundational support for generating or refining samples based on specific inputs. It serves as a core component in the process of creating or enhancing digital content, leveraging the capabilities of the SDXL architecture.

Input types

Required

• base_model
  • Required input specifying the model to be used as the base for sampling, essential for the initial generation phase.
  • Comfy dtype: MODEL
  • Python dtype: str
• base_positive
  • Required input specifying positive conditioning for the base model, guiding the initial generation towards desired attributes.
  • Comfy dtype: CONDITIONING
  • Python dtype: str
• base_negative
  • Required input specifying negative conditioning for the base model, steering the initial generation away from undesired attributes.
  • Comfy dtype: CONDITIONING
  • Python dtype: str
• refiner_model
  • Required input specifying the model to be used for refining the initial samples, enhancing their quality or altering their characteristics.
  • Comfy dtype: MODEL
  • Python dtype: str
• refiner_positive
  • Required input specifying positive conditioning for the refiner model, guiding the refinement process towards desired attributes.
  • Comfy dtype: CONDITIONING
  • Python dtype: str
• refiner_negative
  • Required input specifying negative conditioning for the refiner model, steering the refinement process away from undesired attributes.
  • Comfy dtype: CONDITIONING
  • Python dtype: str
• latent_image
  • Required input specifying the latent image to be used as a starting point for the sampling or refinement process.
  • Comfy dtype: LATENT
  • Python dtype: str
• noise_seed
  • Optional input specifying the seed for noise generation, influencing the randomness of the sampling process.
  • Comfy dtype: INT
  • Python dtype: int
• steps
  • Required input specifying the number of steps to perform in the sampling process, affecting the detail and quality of the output.
  • Comfy dtype: INT
  • Python dtype: int
• cfg
  • Required input specifying the CFG (classifier-free guidance) scale, adjusting the influence of conditioning on the generation process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sampler_name
  • Required input specifying the name of the sampler to use, determining the algorithm for the sampling process.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• scheduler
  • Required input specifying the scheduler to use, affecting the progression of steps in the sampling process.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• base_ratio
  • Required input specifying the ratio of influence between the base and refiner models on the final output, balancing their contributions.
  • Comfy dtype: FLOAT
  • Python dtype: float
• denoise
  • Optional input specifying the denoise level, affecting the clarity and sharpness of the output.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• ``
  • Comfy dtype: LATENT
  • unknown
  • Python dtype: unknown

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class SeargeSDXLSampler:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "base_model": ("MODEL",),
            "base_positive": ("CONDITIONING",),
            "base_negative": ("CONDITIONING",),
            "refiner_model": ("MODEL",),
            "refiner_positive": ("CONDITIONING",),
            "refiner_negative": ("CONDITIONING",),
            "latent_image": ("LATENT",),
            "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
            "steps": ("INT", {"default": 30, "min": 1, "max": 1000}),
            "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step": 0.5}),
            "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"default": "dpmpp_2m"}),
            "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default": "karras"}),
            "base_ratio": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}),
            "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
        },
        }

    RETURN_TYPES = ("LATENT",)
    RETURN_NAMES = ("",)
    FUNCTION = "sample"

    CATEGORY = "Searge/_deprecated_/Sampling"

    def sample(self, base_model, base_positive, base_negative, refiner_model, refiner_positive, refiner_negative,
               latent_image, noise_seed, steps, cfg, sampler_name, scheduler, base_ratio, denoise):

        base_steps = int(steps * base_ratio)

        if denoise < 0.01:
            return (latent_image,)

        if base_steps >= steps:
            return nodes.common_ksampler(base_model, noise_seed, steps, cfg, sampler_name, scheduler, base_positive,
                                         base_negative, latent_image, denoise=denoise, disable_noise=False,
                                         start_step=0, last_step=steps, force_full_denoise=True)

        base_result = nodes.common_ksampler(base_model, noise_seed, steps, cfg, sampler_name, scheduler, base_positive,
                                            base_negative, latent_image, denoise=denoise, disable_noise=False,
                                            start_step=0, last_step=base_steps, force_full_denoise=False)

        return nodes.common_ksampler(refiner_model, noise_seed, steps, cfg, sampler_name, scheduler, refiner_positive,
                                     refiner_negative, base_result[0], denoise=1.0, disable_noise=True,
                                     start_step=base_steps, last_step=steps, force_full_denoise=True)