Vito Sansevero
5485aa8c19
BREAKING CHANGE: Node categories now use emoji-based organization Add 6 new xyz-helper nodes adapted from comfyui-essentials-nodes: - FluxSamplerParams: FLUX-optimized parameter generator with batch support - LoRAFolderBatch: Batch process multiple LoRAs from folders - PlotParameters: Visualize parameter effects with graphs - SamplerSelectHelper: Intelligent sampler selection with recommendations - SchedulerSelectHelper: Optimal scheduler selection for samplers - TextEncodeSamplerParams: Combined text encoding and parameter management Changes: - Port and enhance nodes from comfyui-essentials (now in maintenance mode) - Add comprehensive documentation with attribution to original author (cubiq) - Create example workflows for xyz-helpers tools - Update all node categories to use emoji-based organization - Fix all unit tests to pass with new category system - Update README with xyz-helpers section and attribution Attribution: xyz-helpers adapted from github.com/cubiq/ComfyUI_essentials All tests passing (318 pass, 2 skip)
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
"""Text Encode for Sampler Params node for ComfyUI."""
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from typing import Tuple, Any
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from ....base.base_node import ComfyAssetsBaseNode
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from .logic import (
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split_prompts,
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encode_prompts,
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create_sampler_params_conditioning,
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validate_prompt_format,
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)
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class TextEncodeSamplerParamsNode(ComfyAssetsBaseNode):
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"""
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Text Encode for Sampler Params node.
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Splits multi-line text by separators (---, ***, ===, ~~~) and encodes
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each part separately. Returns a special conditioning format suitable
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for batch processing and XYZ plot generation.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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"""Define the input types for the ComfyUI node."""
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return {
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"required": {
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"text": (
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"STRING",
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{
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"multiline": True,
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"dynamicPrompts": True,
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"default": "Separate prompts with at least three dashes\n---\nLike so",
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"tooltip": "Multi-line text with --- separators between prompts",
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},
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),
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"clip": ("CLIP", {"tooltip": "CLIP model for text encoding"}),
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}
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}
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RETURN_TYPES = ("CONDITIONING",)
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RETURN_NAMES = ("conditioning",)
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FUNCTION = "encode_prompts"
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CATEGORY = "ComfyAssets/🧰 xyz-helpers"
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def encode_prompts(self, text: str, clip: Any) -> Tuple[Any]:
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"""
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Split and encode multiple prompts for batch processing.
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Args:
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text: Multi-line text with separators
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clip: CLIP encoder model
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Returns:
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Tuple containing conditioning dictionary
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"""
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try:
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if not validate_prompt_format(text):
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self.handle_error("Invalid prompt format")
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prompts = split_prompts(text)
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if not prompts:
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self.log_info("No prompts found in text")
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return ({"text": [], "encoded": []},)
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self.log_info(f"Processing {len(prompts)} prompts")
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encoded = encode_prompts(prompts, clip)
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if not encoded:
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self.handle_error("Failed to encode any prompts")
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conditioning = create_sampler_params_conditioning(prompts, encoded)
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self.log_info(
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f"Successfully encoded {len(encoded)} prompts "
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f"(avg {sum(len(p) for p in prompts) // len(prompts)} chars)"
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)
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return (conditioning,)
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except Exception as e:
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self.handle_error(f"Error processing prompts: {str(e)}", e)
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return ({"text": [], "encoded": []},)
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