Files
ComfyUI/custom_nodes/comfyui-image-saver/nodes_selectors.py
jaidaken f09734b0ee
Some checks failed
Python Linting / Run Ruff (push) Has been cancelled
Python Linting / Run Pylint (push) Has been cancelled
Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.10, [self-hosted Linux], stable) (push) Has been cancelled
Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.11, [self-hosted Linux], stable) (push) Has been cancelled
Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.12, [self-hosted Linux], stable) (push) Has been cancelled
Full Comfy CI Workflow Runs / test-unix-nightly (12.1, , linux, 3.11, [self-hosted Linux], nightly) (push) Has been cancelled
Execution Tests / test (macos-latest) (push) Has been cancelled
Execution Tests / test (ubuntu-latest) (push) Has been cancelled
Execution Tests / test (windows-latest) (push) Has been cancelled
Test server launches without errors / test (push) Has been cancelled
Unit Tests / test (macos-latest) (push) Has been cancelled
Unit Tests / test (ubuntu-latest) (push) Has been cancelled
Unit Tests / test (windows-2022) (push) Has been cancelled
Add custom nodes, Civitai loras (LFS), and vast.ai setup script
Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-09 00:56:42 +00:00

197 lines
7.9 KiB
Python

from typing import Any
import comfy
INSPIRE_SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SDXL', 'AYS SD1', 'AYS SVD', "GITS[coeff=1.2]", 'OSS FLUX', 'OSS Wan', 'OSS Chroma']
EFF_SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SD1', 'AYS SDXL', 'AYS SVD', 'GITS']
class AnyToString:
"""Converts any input type to a string. Useful for connecting sampler/scheduler outputs from various custom nodes."""
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("string",)
OUTPUT_TOOLTIPS = ("String representation of the input",)
FUNCTION = "convert"
CATEGORY = "ImageSaver/utils"
DESCRIPTION = "Converts any input type to string"
@classmethod
def INPUT_TYPES(cls) -> dict[str, Any]:
return {
"required": {
"value": ("*",),
}
}
@classmethod
def VALIDATE_INPUTS(cls, input_types):
return True
def convert(self, value: Any) -> tuple[str,]:
return (str(value),)
class WorkflowInputValue:
"""Extracts an input value from the workflow by node ID and input name."""
RETURN_TYPES = ("*",)
RETURN_NAMES = ("value",)
OUTPUT_TOOLTIPS = ("Input value from the specified node",)
FUNCTION = "get_input_value"
CATEGORY = "ImageSaver/utils"
DESCRIPTION = "Extract an input value from the workflow by node ID and input name"
@classmethod
def INPUT_TYPES(cls) -> dict[str, Any]:
return {
"required": {
"node_id": ("STRING", {"default": "", "multiline": False, "tooltip": "The ID of the node to extract from"}),
"input_name": ("STRING", {"default": "", "multiline": False, "tooltip": "The name of the input to extract"}),
},
"hidden": {
"prompt": "PROMPT",
"extra_pnginfo": "EXTRA_PNGINFO",
},
}
def get_input_value(self, node_id: str, input_name: str, prompt: dict[str, Any] | None = None, extra_pnginfo: dict[str, Any] | None = None):
if prompt is None:
return (None,)
# Verify the node exists in the workflow structure
if extra_pnginfo and "workflow" in extra_pnginfo:
workflow = extra_pnginfo["workflow"]
node_exists = any(str(node.get("id")) == node_id for node in workflow.get("nodes", []))
if not node_exists:
print(f"WorkflowInputValue: Node {node_id} not found in workflow structure")
return (None,)
# Get the node from the prompt (execution values)
node = prompt.get(node_id)
if node is None:
print(f"WorkflowInputValue: Node {node_id} not found in prompt")
return (None,)
# Get the inputs from the node
inputs = node.get("inputs", {})
if input_name not in inputs:
print(f"WorkflowInputValue: Input '{input_name}' not found in node {node_id}")
print(f"WorkflowInputValue: Available inputs: {list(inputs.keys())}")
return (None,)
value = inputs[input_name]
return (value,)
class SamplerSelector:
RETURN_TYPES = (comfy.samplers.KSampler.SAMPLERS, "STRING")
RETURN_NAMES = ("sampler", "sampler_name")
OUTPUT_TOOLTIPS = ("sampler (SAMPLERS)", "sampler name (STRING)")
FUNCTION = "get_names"
CATEGORY = 'ImageSaver/utils'
DESCRIPTION = 'Provides one of the available ComfyUI samplers'
@classmethod
def INPUT_TYPES(cls) -> dict[str, Any]:
return {
"required": {
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler (Comfy's standard)"}),
}
}
def get_names(self, sampler_name: str) -> tuple[str, str]:
return (sampler_name, sampler_name)
class SchedulerSelector:
RETURN_TYPES = (comfy.samplers.KSampler.SCHEDULERS, "STRING")
RETURN_NAMES = ("scheduler", "scheduler_name")
OUTPUT_TOOLTIPS = ("scheduler (SCHEDULERS)", "scheduler name (STRING)")
FUNCTION = "get_names"
CATEGORY = 'ImageSaver/utils'
DESCRIPTION = 'Provides one of the standard KSampler schedulers'
@classmethod
def INPUT_TYPES(cls) -> dict[str, Any]:
return {
"required": {
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "scheduler (Comfy's standard)"}),
}
}
def get_names(self, scheduler: str) -> tuple[str, str]:
return (scheduler, scheduler)
class SchedulerSelectorInspire:
RETURN_TYPES = (INSPIRE_SCHEDULERS, "STRING")
RETURN_NAMES = ("scheduler", "scheduler_name")
OUTPUT_TOOLTIPS = ("scheduler (ComfyUI + Inspire Pack Schedulers)", "scheduler name (STRING)")
FUNCTION = "get_names"
CATEGORY = 'ImageSaver/utils'
DESCRIPTION = 'Provides one of the KSampler (inspire) schedulers'
@classmethod
def INPUT_TYPES(cls) -> dict[str, Any]:
return {
"required": {
"scheduler": (INSPIRE_SCHEDULERS, {"tooltip": "scheduler (Comfy's standard + extras)"}),
}
}
def get_names(self, scheduler: str) -> tuple[str, str]:
return (scheduler, scheduler)
class SchedulerSelectorEfficiency:
RETURN_TYPES = (EFF_SCHEDULERS, "STRING")
RETURN_NAMES = ("scheduler", "scheduler_name")
OUTPUT_TOOLTIPS = ("scheduler (ComfyUI + Efficiency Pack Schedulers)", "scheduler name (STRING)")
FUNCTION = "get_names"
CATEGORY = 'ImageSaver/utils'
DESCRIPTION = 'Provides one of the KSampler (Eff.) schedulers'
@classmethod
def INPUT_TYPES(cls) -> dict[str, Any]:
return {
"required": {
"scheduler": (EFF_SCHEDULERS, {"tooltip": "scheduler (Comfy's standard + Efficiency nodes)"}),
}
}
def get_names(self, scheduler: str) -> tuple[str, str]:
return (scheduler, scheduler)
class InputParameters:
RETURN_TYPES = ("INT", "INT", "FLOAT", comfy.samplers.KSampler.SAMPLERS, comfy.samplers.KSampler.SCHEDULERS, "FLOAT")
RETURN_NAMES = ("seed", "steps", "cfg", "sampler", "scheduler", "denoise")
OUTPUT_TOOLTIPS = (
"seed (INT)",
"steps (INT)",
"cfg (FLOAT)",
"sampler (SAMPLERS)",
"scheduler (SCHEDULERS)",
"denoise (FLOAT)",
)
FUNCTION = "get_values"
CATEGORY = "ImageSaver/utils"
DESCRIPTION = "Combined node for seed, steps, cfg, sampler, scheduler and denoise."
@classmethod
def INPUT_TYPES(cls) -> dict[str, Any]:
return {
"required": {
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}),
"cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
"sampler": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler controls how noise is gradually removed to form the image."}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}),
}
}
def get_values(self, seed: int, steps: int, cfg: float, sampler: str, scheduler: str, denoise: float) -> tuple[int, int, float, str, str, float]:
return (seed, steps, cfg, sampler, scheduler, denoise)