KSamplerAdvanced [pipe] (inspire)¶
Documentation¶
- Class name:
KSamplerAdvancedPipe __Inspire
- Category:
InspirePack/a1111_compat
- Output node:
False
The KSamplerAdvancedInspire node is designed to enhance the inspiration process by providing advanced sampling capabilities within a pipeline. It leverages sophisticated algorithms to generate or process data, aiming to inspire creativity and innovation through its output.
Input types¶
Required¶
basic_pipe
- The 'basic_pipe' input is essential for providing the foundational components of the model, clip, vae, and conditioning elements, setting the stage for advanced sampling operations.
- Comfy dtype:
BASIC_PIPE
- Python dtype:
tuple
add_noise
- The 'add_noise' input determines whether noise should be added to the sampling process, influencing the variability and uniqueness of the generated outputs.
- Comfy dtype:
BOOLEAN
- Python dtype:
bool
noise_seed
- The 'noise_seed' input specifies the seed for noise generation, ensuring reproducibility and consistency in the sampling process.
- Comfy dtype:
INT
- Python dtype:
int
steps
- The 'steps' input defines the number of steps to be taken in the sampling process, affecting the depth and detail of the generation.
- Comfy dtype:
INT
- Python dtype:
int
cfg
- The 'cfg' input sets the configuration for the sampling process, adjusting the control and guidance of the generation.
- Comfy dtype:
FLOAT
- Python dtype:
float
sampler_name
- The 'sampler_name' input selects the specific sampler algorithm to be used, tailoring the sampling process to specific requirements.
- Comfy dtype:
COMBO[STRING]
- Python dtype:
str
scheduler
- The 'scheduler' input specifies the scheduling algorithm for the sampling process, impacting the progression and variation of the generation.
- Comfy dtype:
COMBO[STRING]
- Python dtype:
str
latent_image
- The 'latent_image' input provides an initial latent image to be used as a starting point for the sampling process, influencing the direction of the generation.
- Comfy dtype:
LATENT
- Python dtype:
object
start_at_step
- The 'start_at_step' input determines the starting step of the sampling process, allowing for customization of the generation's progression.
- Comfy dtype:
INT
- Python dtype:
int
end_at_step
- The 'end_at_step' input defines the ending step of the sampling process, setting the bounds for the generation.
- Comfy dtype:
INT
- Python dtype:
int
noise_mode
- The 'noise_mode' input selects the computational mode (GPU or CPU) for noise generation, affecting the performance and efficiency of the sampling process.
- Comfy dtype:
COMBO[STRING]
- Python dtype:
str
return_with_leftover_noise
- The 'return_with_leftover_noise' input indicates whether leftover noise should be returned, offering additional control over the output's variability.
- Comfy dtype:
BOOLEAN
- Python dtype:
bool
batch_seed_mode
- The 'batch_seed_mode' input specifies the mode for seed generation in batch operations, influencing the diversity and consistency of the outputs.
- Comfy dtype:
COMBO[STRING]
- Python dtype:
str
variation_seed
- The 'variation_seed' input provides a seed for generating variations, enabling nuanced adjustments to the sampling process.
- Comfy dtype:
INT
- Python dtype:
int
variation_strength
- The 'variation_strength' input controls the strength of variations applied, allowing for fine-tuning of the generation's diversity.
- Comfy dtype:
FLOAT
- Python dtype:
float
Optional¶
noise_opt
- The 'noise_opt' input, if provided, specifies custom noise options for the sampling process, offering further customization.
- Comfy dtype:
NOISE
- Python dtype:
object
Output types¶
latent
- Comfy dtype:
LATENT
- This output represents the generated latent image, serving as a foundational element for further processing or visualization.
- Python dtype:
object
- Comfy dtype:
vae
- Comfy dtype:
VAE
- This output provides the variational autoencoder used in the process, facilitating additional manipulations or analyses of the generated data.
- Python dtype:
object
- Comfy dtype:
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",),
}
}
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):
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)[0]
return (latent, vae)