SDXL Sampler v3 (Searge)¶
Documentation¶
- Class name:
SeargeSDXLSamplerV3 - Category:
Searge/_deprecated_/Sampling - Output node:
False
The SeargeSDXLSamplerV3 node is designed for advanced sampling in generative models, offering enhanced capabilities for generating high-quality digital content. It incorporates sophisticated algorithms to refine and optimize the output, making it suitable for applications requiring precision and detail in the generated samples.
Input types¶
Required¶
base_model- Specifies the base model used for initial sampling, setting the foundation for the generation process.
- Comfy dtype:
MODEL - Python dtype:
str
base_positive- Defines the positive conditioning to guide the base model towards desirable outcomes.
- Comfy dtype:
CONDITIONING - Python dtype:
str
base_negative- Specifies the negative conditioning to steer the base model away from undesirable outcomes.
- Comfy dtype:
CONDITIONING - Python dtype:
str
refiner_model- Indicates the refiner model used to enhance and refine the initial samples for improved quality.
- Comfy dtype:
MODEL - Python dtype:
str
refiner_positive- Defines the positive conditioning for the refiner model, further guiding the enhancement process.
- Comfy dtype:
CONDITIONING - Python dtype:
str
refiner_negative- Specifies the negative conditioning for the refiner model, ensuring the refinement avoids undesirable aspects.
- Comfy dtype:
CONDITIONING - Python dtype:
str
latent_image- Represents the latent image or state used as a starting point for the sampling process.
- Comfy dtype:
LATENT - Python dtype:
str
noise_seed- Provides a seed value for noise generation, ensuring reproducibility in the sampling process.
- Comfy dtype:
INT - Python dtype:
int
steps- Determines the number of steps in the sampling process, affecting the detail and quality of the output.
- Comfy dtype:
INT - Python dtype:
int
cfg- Configures the sampling strength or control factor, influencing the variance and fidelity of the generated content.
- Comfy dtype:
FLOAT - Python dtype:
float
sampler_name- Specifies the name of the sampler algorithm used, affecting the sampling strategy and outcome.
- Comfy dtype:
SAMPLER_NAME - Python dtype:
str
scheduler- Indicates the scheduler used to manage the sampling steps, impacting the progression and refinement of the generation.
- Comfy dtype:
SCHEDULER_NAME - Python dtype:
str
base_ratio- Sets the ratio of base to refiner model usage, balancing initial generation with refinement.
- Comfy dtype:
FLOAT - Python dtype:
float
denoise- Controls the denoising factor in the sampling process, affecting the clarity and smoothness of the output.
- Comfy dtype:
FLOAT - Python dtype:
float
Optional¶
refiner_prep_steps- Specifies the number of preparation steps for the refiner model, adjusting the initial conditions for refinement.
- Comfy dtype:
INT - Python dtype:
int
Output types¶
latent- Comfy dtype:
LATENT - Produces the final latent image or state after the sampling and refinement process, ready for further use or conversion to digital content.
- Python dtype:
str
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes:
Source code¶
class SeargeSDXLSamplerV3:
@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": 0xfffffffffffffff0}),
"steps": ("INT", {"default": 20, "min": 1, "max": 200}),
"cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 30.0, "step": 0.5}),
"sampler_name": ("SAMPLER_NAME", {"default": "ddim"}),
"scheduler": ("SCHEDULER_NAME", {"default": "ddim_uniform"}),
"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}),
},
"optional": {
"refiner_prep_steps": ("INT", {"default": 0, "min": 0, "max": 10}),
},
}
RETURN_TYPES = ("LATENT",)
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,
refiner_prep_steps=None):
base_steps = int(steps * (base_ratio + 0.0001))
refiner_steps = max(0, steps - base_steps)
if denoise < 0.01:
return (latent_image,)
start_at_step = 0
input_latent = latent_image
if refiner_prep_steps is not None:
if refiner_prep_steps >= base_steps:
refiner_prep_steps = base_steps - 1
if refiner_prep_steps > 0:
start_at_step = refiner_prep_steps
precondition_result = nodes.common_ksampler(refiner_model, noise_seed + 2, steps, cfg, sampler_name,
scheduler, refiner_positive, refiner_negative,
latent_image, denoise=denoise, disable_noise=False,
start_step=steps - refiner_prep_steps, last_step=steps,
force_full_denoise=False)
input_latent = precondition_result[0]
if base_steps >= steps:
return nodes.common_ksampler(base_model, noise_seed, steps, cfg, sampler_name, scheduler, base_positive,
base_negative, input_latent, denoise=denoise, disable_noise=False,
start_step=start_at_step, last_step=steps, force_full_denoise=True)
return sdxl_ksampler(base_model, refiner_model, noise_seed, base_steps, refiner_steps, cfg, sampler_name,
scheduler, base_positive, base_negative, refiner_positive, refiner_negative, input_latent,
denoise=denoise, disable_noise=False, start_step=start_at_step, last_step=steps,
force_full_denoise=True)