Parameter Processor¶
Documentation¶
- Class name:
SeargeParameterProcessor - Category:
Searge/_deprecated_/UI - Output node:
False
The SeargeParameterProcessor node is designed to process and refine parameters for image generation tasks, incorporating advanced settings such as seed manipulation, style template adjustments, and high-resolution fix parameters. It acts as a central hub for configuring the generation process, ensuring that inputs like prompts, generation parameters, and model names are optimally adjusted for the desired output.
Input types¶
Required¶
inputs- Accepts a variety of parameters for processing, including but not limited to prompts, generation parameters, model names, and advanced settings, allowing for a comprehensive customization of the image generation process.
- Comfy dtype:
PARAMETER_INPUTS - Python dtype:
dict
Output types¶
parameters- Comfy dtype:
PARAMETERS - Outputs a dictionary of processed parameters, ready for use in the image generation process, ensuring that all inputs have been optimally adjusted for the desired outcome.
- Python dtype:
dict
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class SeargeParameterProcessor:
# hard - refiner uses same seed as base | soft - refiner uses different seed from base
REFINER_INTENSITY = ["hard", "soft"]
# same - HRF uses same seed as image generation | distinct - HRF uses different seed than image generation
HRF_SEED_OFFSET = ["same", "distinct"]
# simple "boolean"-like type
STATES = ["disabled", "enabled"]
# operating modes, determine if the latent source is empty or from a source image, inpainting also uses mask
OPERATION_MODE = ["text to image", "image to image", "inpainting"]
# sorted from easy-to-use to harder-to-use
PROMPT_STYLE = ["simple", "3 prompts G+L-N", "subject focus", "style focus", "weighted", "overlay",
"subject - style", "style - subject", "style only", "weighted - overlay", "overlay - weighted"]
# (work in progress)
STYLE_TEMPLATE = ["none", "from preprocessor", "test"]
# save folder for generated images
SAVE_TO = ["output folder", "input folder"]
@classmethod
def INPUT_TYPES(s):
return {"required": {
"inputs": ("PARAMETER_INPUTS",),
},
}
RETURN_TYPES = ("PARAMETERS",)
RETURN_NAMES = ("parameters",)
FUNCTION = "process"
CATEGORY = "Searge/_deprecated_/UI"
def process(self, inputs):
if inputs is None:
parameters = {}
else:
parameters = inputs
if parameters["denoise"] is None:
parameters["denoise"] = 1.0
saturation = parameters["refiner_intensity"]
if saturation is not None:
# "soft"
if saturation == SeargeParameterProcessor.REFINER_INTENSITY[1]:
parameters["noise_offset"] = 1
# incl. SeargeParameterProcessor.REFINER_INTENSITY[1] -> "hard"
else:
parameters["noise_offset"] = 0
hires_fix = parameters["hires_fix"]
# "disabled"
if hires_fix is not None and hires_fix == SeargeParameterProcessor.STATES[0]:
parameters["hrf_steps"] = 0
hrf_saturation = parameters["hrf_intensity"]
if hrf_saturation is not None:
# "soft"
if hrf_saturation == SeargeParameterProcessor.REFINER_INTENSITY[1]:
parameters["hrf_noise_offset"] = 1
# incl. SeargeParameterProcessor.REFINER_INTENSITY[0] -> "hard"
else:
parameters["hrf_noise_offset"] = 0
seed_offset = parameters["hrf_seed_offset"]
if seed_offset is not None:
seed = parameters["seed"] if parameters["seed"] is not None else 0
# "distinct"
if seed_offset == SeargeParameterProcessor.HRF_SEED_OFFSET[1]:
parameters["hrf_seed"] = seed + 3
# incl. SeargeParameterProcessor.HRF_SEED_OFFSET[0] -> "same"
else:
parameters["hrf_seed"] = seed
style_template = parameters["style_template"]
if style_template is not None:
# "from preprocessor"
if style_template == SeargeParameterProcessor.STYLE_TEMPLATE[1]:
# this does nothing here, but will be used in the preprocessor
pass
# "test"
if style_template == SeargeParameterProcessor.STYLE_TEMPLATE[2]:
if parameters["noise_offset"] is not None:
parameters["noise_offset"] = 1 - parameters["hrf_noise_offset"]
if parameters["hrf_noise_offset"] is not None:
parameters["hrf_noise_offset"] = 1 - parameters["hrf_noise_offset"]
# incl. SeargeParameterProcessor.STYLE_TEMPLATE[0] -> "none"
else:
# TODO: apply style based on its name here...
pass
operation_mode = parameters["operation_mode"]
if operation_mode is not None:
# "image to image":
if operation_mode == SeargeParameterProcessor.OPERATION_MODE[1]:
parameters["operation_selector"] = 1
# "inpainting":
elif operation_mode == SeargeParameterProcessor.OPERATION_MODE[2]:
parameters["operation_selector"] = 2
# incl. SeargeParameterProcessor.OPERATION_MODE[0] -> "text to image":
else:
parameters["operation_selector"] = 0
# always fully denoise in img2img mode
parameters["denoise"] = 1.0
prompt_style = parameters["prompt_style"]
if prompt_style is not None:
# "simple"
if prompt_style == SeargeParameterProcessor.PROMPT_STYLE[0]:
parameters["prompt_style_selector"] = 0
parameters["prompt_style_group"] = 0
main_prompt = parameters["main_prompt"]
parameters["secondary_prompt"] = main_prompt
parameters["style_prompt"] = ""
parameters["negative_style"] = ""
# "subject focus"
elif prompt_style == SeargeParameterProcessor.PROMPT_STYLE[2]:
parameters["prompt_style_selector"] = 1
parameters["prompt_style_group"] = 0
# "style focus"
elif prompt_style == SeargeParameterProcessor.PROMPT_STYLE[3]:
parameters["prompt_style_selector"] = 2
parameters["prompt_style_group"] = 0
# "weighted"
elif prompt_style == SeargeParameterProcessor.PROMPT_STYLE[4]:
parameters["prompt_style_selector"] = 3
parameters["prompt_style_group"] = 0
# "overlay"
elif prompt_style == SeargeParameterProcessor.PROMPT_STYLE[5]:
parameters["prompt_style_selector"] = 4
parameters["prompt_style_group"] = 0
# "subject - style"
elif prompt_style == SeargeParameterProcessor.PROMPT_STYLE[6]:
parameters["prompt_style_selector"] = 0
parameters["prompt_style_group"] = 1
# "style - subject"
elif prompt_style == SeargeParameterProcessor.PROMPT_STYLE[7]:
parameters["prompt_style_selector"] = 1
parameters["prompt_style_group"] = 1
# "style only"
elif prompt_style == SeargeParameterProcessor.PROMPT_STYLE[8]:
parameters["prompt_style_selector"] = 2
parameters["prompt_style_group"] = 1
# "weighted - overlay"
elif prompt_style == SeargeParameterProcessor.PROMPT_STYLE[9]:
parameters["prompt_style_selector"] = 3
parameters["prompt_style_group"] = 1
# "overlay - weighted"
elif prompt_style == SeargeParameterProcessor.PROMPT_STYLE[10]:
parameters["prompt_style_selector"] = 4
parameters["prompt_style_group"] = 1
# incl. SeargeParameterProcessor.PROMPT_STYLE[1] -> "3 prompts G+L-N"
else:
parameters["prompt_style_selector"] = 0
parameters["prompt_style_group"] = 0
parameters["style_prompt"] = ""
parameters["negative_style"] = ""
# TODO: replace this special logic and the dirty hacks by creating new generated parameters for saving
save_image = parameters["save_image"]
if save_image is not None:
# "disabled"
if save_image == SeargeParameterProcessor.STATES[0]:
# when image saving is disabled, we also don't want to save the upscaled image, even if that's enabled
parameters["save_upscaled_image"] = SeargeParameterProcessor.STATES[0]
# HACK: this is a bit dirty, but the variable hires_fix determines if the image should be saved
# but when image saving is disabled, we don't want that to happen
parameters["hires_fix"] = SeargeParameterProcessor.STATES[0]
# "enabled"
else:
# in case we are saving to the input folder, we need to enable saving after the hires fix, even
# if that's disabled in the settings
if parameters["save_directory"] == SeargeParameterProcessor.SAVE_TO[1]:
parameters["hires_fix"] = SeargeParameterProcessor.STATES[1]
return (parameters,)