KSamplerAdvanced [pipe] (inspire)

Documentation

• Class name: KSamplerAdvancedPipe __Inspire
• Category: InspirePack/a1111_compat
• Output node: False

This node is designed for advanced sampling in generative models, focusing on creating or modifying latent images through a comprehensive set of parameters. It integrates various components such as noise addition, seed manipulation, and conditioning adjustments to refine the generation process, aiming to produce high-quality, customizable results.

Input types

Required

• basic_pipe
  • A tuple containing the model, clip, VAE, and positive and negative conditioning components, serving as the foundational setup for the sampling process.
  • Comfy dtype: BASIC_PIPE
  • Python dtype: tuple
• add_noise
  • A boolean flag indicating whether noise should be added to the generation process, affecting the diversity and quality of the output.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• noise_seed
  • An integer seed specifically for noise generation, contributing to the variability and uniqueness of the generated images.
  • Comfy dtype: INT
  • Python dtype: int
• steps
  • The number of steps to perform in the sampling process, affecting the detail and quality of the generated images.
  • Comfy dtype: INT
  • Python dtype: int
• cfg
  • A configuration parameter that influences the sampling behavior, potentially affecting the style and characteristics of the output.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sampler_name
  • The name of the sampler to use, determining the specific sampling algorithm applied.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• scheduler
  • Specifies the scheduler for controlling the sampling process, affecting the progression of image generation.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• latent_image
  • The initial latent image to be modified or enhanced through the sampling process.
  • Comfy dtype: LATENT
  • Python dtype: torch.Tensor
• start_at_step
  • The step at which to start the sampling process, allowing for control over the generation's initiation point.
  • Comfy dtype: INT
  • Python dtype: int
• end_at_step
  • The final step of the sampling process, defining the endpoint of image generation.
  • Comfy dtype: INT
  • Python dtype: int
• noise_mode
  • Determines the mode of noise application, influencing the texture and quality of the output.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• return_with_leftover_noise
  • A flag indicating whether to return the image with any residual noise, affecting the final image's appearance.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• batch_seed_mode
  • Specifies the mode for seed generation in batch processing, impacting the diversity of generated images.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• variation_seed
  • An optional seed for introducing variations, allowing for controlled randomness in the output.
  • Comfy dtype: INT
  • Python dtype: int
• variation_strength
  • Determines the strength of the applied variations, affecting the degree of change from the original image.
  • Comfy dtype: FLOAT
  • Python dtype: float

Optional

• noise_opt
  • Optional noise parameters for further customization of the noise application process.
  • Comfy dtype: NOISE
  • Python dtype: dict
• scheduler_func_opt
  • An optional scheduler function for advanced control over the sampling schedule.
  • Comfy dtype: SCHEDULER_FUNC
  • Python dtype: function

Output types

• latent
  • Comfy dtype: LATENT
  • Represents the generated or modified latent image, showcasing the effectiveness of the sampling process.
  • Python dtype: torch.Tensor
• vae
  • Comfy dtype: VAE
  • The VAE component used in the sampling process, potentially modified or utilized as part of the generation.
  • Python dtype: torch.nn.Module

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class KSamplerAdvanced_inspire_pipe:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"basic_pipe": ("BASIC_PIPE",),
                     "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "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, "step":0.5, "round": 0.01}),
                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
                     "scheduler": (common.SCHEDULERS, ),
                     "latent_image": ("LATENT", ),
                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
                     "noise_mode": (["GPU(=A1111)", "CPU"],),
                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
                     "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
                     "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                     },
                "optional":
                    {
                        "noise_opt": ("NOISE",),
                        "scheduler_func_opt": ("SCHEDULER_FUNC",),
                    }
                }

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

    CATEGORY = "InspirePack/a1111_compat"

    def sample(self, basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, noise_mode, return_with_leftover_noise,
               denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None, noise_opt=None, scheduler_func_opt=None):
        model, clip, vae, positive, negative = basic_pipe
        latent = KSamplerAdvanced_inspire().sample(model=model, add_noise=add_noise, noise_seed=noise_seed,
                                                   steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler,
                                                   positive=positive, negative=negative, latent_image=latent_image,
                                                   start_at_step=start_at_step, end_at_step=end_at_step,
                                                   noise_mode=noise_mode, return_with_leftover_noise=return_with_leftover_noise,
                                                   denoise=denoise, batch_seed_mode=batch_seed_mode, variation_seed=variation_seed,
                                                   variation_strength=variation_strength, noise_opt=noise_opt, scheduler_func_opt=scheduler_func_opt)[0]
        return latent, vae