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32
batchers/__init__.py
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32
batchers/__init__.py
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"""
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EndlessSeaofStars Custom Nodes for ComfyUI
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Batch processing nodes with specialized support for FLUX and SDXL models
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"""
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from .endless_batchers import (
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EndlessNode_SimpleBatchPrompts,
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EndlessNode_FluxBatchPrompts,
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EndlessNode_SDXLBatchPrompts,
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EndlessNode_BatchNegativePrompts,
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EndlessNode_PromptCounter,
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)
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# Node class mappings for ComfyUI
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NODE_CLASS_MAPPINGS = {
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"SimpleBatchPrompts": EndlessNode_SimpleBatchPrompts,
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"FluxBatchPrompts": EndlessNode_FluxBatchPrompts,
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"SDXLBatchPrompts": EndlessNode_SDXLBatchPrompts,
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"BatchNegativePrompts": EndlessNode_BatchNegativePrompts,
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"PromptCounter": EndlessNode_PromptCounter,
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}
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# Display names for ComfyUI interface
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SimpleBatchPrompts": "Simple Batch Prompts",
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"FluxBatchPrompts": "FLUX Batch Prompts",
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"SDXLBatchPrompts": "SDXL Batch Prompts",
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"BatchNegativePrompts": "Batch Negative Prompts",
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"PromptCounter": "Prompt Counter",
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}
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443
batchers/endless_batchers.py
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443
batchers/endless_batchers.py
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import os
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import json
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import re
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from datetime import datetime
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from PIL import Image, PngImagePlugin
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import numpy as np
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import torch
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import folder_paths
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from PIL.PngImagePlugin import PngInfo
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import platform
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class EndlessNode_SimpleBatchPrompts:
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"""
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Takes multiple prompts (one per line) and creates batched conditioning tensors
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Automatically detects number of prompts and creates appropriate batch size
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Handles batch size mismatches by cycling through prompts if needed
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"""
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"prompts": ("STRING", {"multiline": True, "default": "beautiful landscape\nmountain sunset\nocean waves\nfield of sunflowers"}),
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"clip": ("CLIP", ),
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"print_output": ("BOOLEAN", {"default": True}),
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"max_batch_size": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1}),
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}}
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RETURN_TYPES = ("CONDITIONING", "STRING", "INT")
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RETURN_NAMES = ("CONDITIONING", "PROMPT_LIST", "PROMPT_COUNT")
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FUNCTION = "batch_encode"
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CATEGORY = "Endless 🌊✨/BatchProcessing"
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def batch_encode(self, prompts, clip, print_output, max_batch_size=0):
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# Split prompts by lines and clean them
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prompt_lines = [line.strip() for line in prompts.split('\n') if line.strip()]
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prompt_count = len(prompt_lines)
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if not prompt_lines:
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raise ValueError("No valid prompts found. Please enter at least one prompt.")
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# Handle batch size logic
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if max_batch_size > 0 and max_batch_size < len(prompt_lines):
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# Limit to max_batch_size
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prompt_lines = prompt_lines[:max_batch_size]
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if print_output:
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print(f"Limited to first {max_batch_size} prompts due to max_batch_size setting")
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elif max_batch_size > len(prompt_lines) and max_batch_size > 0:
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# Cycle through prompts to fill batch
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original_count = len(prompt_lines)
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while len(prompt_lines) < max_batch_size:
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prompt_lines.extend(prompt_lines[:min(original_count, max_batch_size - len(prompt_lines))])
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if print_output:
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print(f"Cycling through {original_count} prompts to fill batch size of {max_batch_size}")
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if print_output:
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print(f"Processing {len(prompt_lines)} prompts in batch:")
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for i, prompt in enumerate(prompt_lines):
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print(f" {i+1}: {prompt}")
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# Encode each prompt separately with error handling
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cond_tensors = []
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pooled_tensors = []
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for i, prompt in enumerate(prompt_lines):
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try:
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tokens = clip.tokenize(prompt)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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cond_tensors.append(cond)
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pooled_tensors.append(pooled)
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except Exception as e:
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print(f"Error encoding prompt {i+1} '{prompt}': {e}")
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# Use a fallback empty prompt
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try:
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tokens = clip.tokenize("")
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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cond_tensors.append(cond)
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pooled_tensors.append(pooled)
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print(f" Using empty fallback for prompt {i+1}")
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except Exception as fallback_error:
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raise ValueError(f"Failed to encode prompt {i+1} and fallback failed: {fallback_error}")
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# Batch the conditioning tensors properly
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try:
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# Stack the conditioning tensors along batch dimension
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batched_cond = torch.cat(cond_tensors, dim=0)
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batched_pooled = torch.cat(pooled_tensors, dim=0)
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if print_output:
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print(f"Created batched conditioning: {batched_cond.shape}")
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print(f"Created batched pooled: {batched_pooled.shape}")
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# Return as proper conditioning format
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conditioning = [[batched_cond, {"pooled_output": batched_pooled}]]
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except Exception as e:
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print(f"Error creating batched conditioning: {e}")
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print("Falling back to list format...")
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# Fallback to list format if batching fails
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conditioning = []
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for i in range(len(cond_tensors)):
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conditioning.append([cond_tensors[i], {"pooled_output": pooled_tensors[i]}])
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# Create the prompt list string for filename use
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prompt_list_str = "|".join(prompt_lines) # Join with | separator
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return (conditioning, prompt_list_str, prompt_count)
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class EndlessNode_FluxBatchPrompts:
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"""
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Specialized batch prompt encoder for FLUX models
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Handles FLUX-specific conditioning requirements including guidance and T5 text encoding
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"""
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"prompts": ("STRING", {"multiline": True, "default": "beautiful landscape\nmountain sunset\nocean waves\nfield of sunflowers"}),
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"clip": ("CLIP", ),
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"guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
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"print_output": ("BOOLEAN", {"default": True}),
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"max_batch_size": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1}),
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}}
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RETURN_TYPES = ("CONDITIONING", "STRING", "INT")
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RETURN_NAMES = ("CONDITIONING", "PROMPT_LIST", "PROMPT_COUNT")
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FUNCTION = "batch_encode_flux"
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CATEGORY = "Endless 🌊✨/BatchProcessing"
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def batch_encode_flux(self, prompts, clip, guidance, print_output, max_batch_size=0):
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# Split prompts by lines and clean them
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prompt_lines = [line.strip() for line in prompts.split('\n') if line.strip()]
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prompt_count = len(prompt_lines)
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if not prompt_lines:
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raise ValueError("No valid prompts found. Please enter at least one prompt.")
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# Handle batch size logic
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if max_batch_size > 0 and max_batch_size < len(prompt_lines):
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prompt_lines = prompt_lines[:max_batch_size]
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if print_output:
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print(f"Limited to first {max_batch_size} prompts due to max_batch_size setting")
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elif max_batch_size > len(prompt_lines) and max_batch_size > 0:
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original_count = len(prompt_lines)
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while len(prompt_lines) < max_batch_size:
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prompt_lines.extend(prompt_lines[:min(original_count, max_batch_size - len(prompt_lines))])
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if print_output:
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print(f"Cycling through {original_count} prompts to fill batch size of {max_batch_size}")
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if print_output:
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print(f"Processing {len(prompt_lines)} FLUX prompts in batch:")
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for i, prompt in enumerate(prompt_lines):
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print(f" {i+1}: {prompt}")
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# Encode each prompt with FLUX-specific conditioning
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cond_tensors = []
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pooled_tensors = []
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for i, prompt in enumerate(prompt_lines):
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try:
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tokens = clip.tokenize(prompt)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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cond_tensors.append(cond)
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pooled_tensors.append(pooled)
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except Exception as e:
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print(f"Error encoding FLUX prompt {i+1} '{prompt}': {e}")
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# Use a fallback empty prompt
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try:
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tokens = clip.tokenize("")
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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cond_tensors.append(cond)
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pooled_tensors.append(pooled)
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print(f" Using empty fallback for FLUX prompt {i+1}")
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except Exception as fallback_error:
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raise ValueError(f"Failed to encode FLUX prompt {i+1} and fallback failed: {fallback_error}")
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# Batch the conditioning tensors properly for FLUX
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try:
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# Stack the conditioning tensors along batch dimension
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batched_cond = torch.cat(cond_tensors, dim=0)
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batched_pooled = torch.cat(pooled_tensors, dim=0)
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if print_output:
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print(f"Created FLUX batched conditioning: {batched_cond.shape}")
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print(f"Created FLUX batched pooled: {batched_pooled.shape}")
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# FLUX-specific conditioning with guidance
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conditioning = [[batched_cond, {
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"pooled_output": batched_pooled,
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"guidance": guidance,
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"guidance_scale": guidance # Some FLUX implementations use this key
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}]]
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except Exception as e:
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print(f"Error creating FLUX batched conditioning: {e}")
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print("Falling back to list format...")
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# Fallback to list format if batching fails
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conditioning = []
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for i in range(len(cond_tensors)):
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flux_conditioning = [cond_tensors[i], {
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"pooled_output": pooled_tensors[i],
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"guidance": guidance,
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"guidance_scale": guidance
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}]
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conditioning.append(flux_conditioning)
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prompt_list_str = "|".join(prompt_lines)
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return (conditioning, prompt_list_str, prompt_count)
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class EndlessNode_SDXLBatchPrompts:
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"""
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Specialized batch prompt encoder for SDXL models
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Handles dual text encoders and SDXL-specific conditioning requirements
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"""
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"prompts": ("STRING", {"multiline": True, "default": "beautiful landscape\nmountain sunset\nocean waves"}),
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"clip": ("CLIP", ),
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"print_output": ("BOOLEAN", {"default": True}),
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"max_batch_size": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1}),
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}}
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RETURN_TYPES = ("CONDITIONING", "STRING", "INT")
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RETURN_NAMES = ("CONDITIONING", "PROMPT_LIST", "PROMPT_COUNT")
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FUNCTION = "batch_encode_sdxl"
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CATEGORY = "Endless 🌊✨/BatchProcessing"
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def batch_encode_sdxl(self, prompts, clip, print_output, max_batch_size=0):
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# Split prompts by lines and clean them
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prompt_lines = [line.strip() for line in prompts.split('\n') if line.strip()]
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prompt_count = len(prompt_lines)
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if not prompt_lines:
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raise ValueError("No valid prompts found. Please enter at least one prompt.")
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# Handle batch size logic
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if max_batch_size > 0 and max_batch_size < len(prompt_lines):
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prompt_lines = prompt_lines[:max_batch_size]
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if print_output:
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print(f"Limited to first {max_batch_size} prompts due to max_batch_size setting")
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elif max_batch_size > len(prompt_lines) and max_batch_size > 0:
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original_count = len(prompt_lines)
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while len(prompt_lines) < max_batch_size:
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prompt_lines.extend(prompt_lines[:min(original_count, max_batch_size - len(prompt_lines))])
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if print_output:
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print(f"Cycling through {original_count} prompts to fill batch size of {max_batch_size}")
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if print_output:
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print(f"Processing {len(prompt_lines)} SDXL prompts in batch:")
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for i, prompt in enumerate(prompt_lines):
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print(f" {i+1}: {prompt}")
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# Encode each prompt with SDXL-specific conditioning
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cond_tensors = []
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pooled_tensors = []
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for i, prompt in enumerate(prompt_lines):
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try:
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tokens = clip.tokenize(prompt)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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cond_tensors.append(cond)
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pooled_tensors.append(pooled)
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except Exception as e:
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print(f"Error encoding SDXL prompt {i+1} '{prompt}': {e}")
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# Use a fallback empty prompt
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try:
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tokens = clip.tokenize("")
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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cond_tensors.append(cond)
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pooled_tensors.append(pooled)
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print(f" Using empty fallback for SDXL prompt {i+1}")
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except Exception as fallback_error:
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raise ValueError(f"Failed to encode SDXL prompt {i+1} and fallback failed: {fallback_error}")
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# Batch the conditioning tensors properly for SDXL
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try:
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# Stack the conditioning tensors along batch dimension
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batched_cond = torch.cat(cond_tensors, dim=0)
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batched_pooled = torch.cat(pooled_tensors, dim=0)
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if print_output:
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print(f"Created SDXL batched conditioning: {batched_cond.shape}")
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print(f"Created SDXL batched pooled: {batched_pooled.shape}")
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# SDXL-specific conditioning - simplified without size parameters
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conditioning = [[batched_cond, {"pooled_output": batched_pooled}]]
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except Exception as e:
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print(f"Error creating SDXL batched conditioning: {e}")
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print("Falling back to list format...")
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# Fallback to list format if batching fails
|
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conditioning = []
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for i in range(len(cond_tensors)):
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sdxl_conditioning = [cond_tensors[i], {"pooled_output": pooled_tensors[i]}]
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conditioning.append(sdxl_conditioning)
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prompt_list_str = "|".join(prompt_lines)
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return (conditioning, prompt_list_str, prompt_count)
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class EndlessNode_BatchNegativePrompts:
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"""
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Handles batch negative prompts - simplified version without unnecessary parameters
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"""
|
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@classmethod
|
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def INPUT_TYPES(s):
|
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return {"required": {
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"negative_prompts": ("STRING", {"multiline": True, "default": "blurry, low quality\nartifacts, distorted\nnoise, bad anatomy"}),
|
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"clip": ("CLIP", ),
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"print_output": ("BOOLEAN", {"default": True}),
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"max_batch_size": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1}),
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}}
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RETURN_TYPES = ("CONDITIONING", "STRING")
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RETURN_NAMES = ("NEGATIVE_CONDITIONING", "NEGATIVE_PROMPT_LIST")
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FUNCTION = "batch_encode_negative"
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CATEGORY = "Endless 🌊✨/BatchProcessing"
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def batch_encode_negative(self, negative_prompts, clip, print_output, max_batch_size=0):
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# Split prompts by lines and clean them
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||||
prompt_lines = [line.strip() for line in negative_prompts.split('\n') if line.strip()]
|
||||
|
||||
if not prompt_lines:
|
||||
# Use empty negative prompt if none provided
|
||||
prompt_lines = [""]
|
||||
|
||||
# Handle batch size logic
|
||||
if max_batch_size > 0 and max_batch_size < len(prompt_lines):
|
||||
prompt_lines = prompt_lines[:max_batch_size]
|
||||
if print_output:
|
||||
print(f"Limited to first {max_batch_size} negative prompts due to max_batch_size setting")
|
||||
elif max_batch_size > len(prompt_lines) and max_batch_size > 0:
|
||||
original_count = len(prompt_lines)
|
||||
while len(prompt_lines) < max_batch_size:
|
||||
prompt_lines.extend(prompt_lines[:min(original_count, max_batch_size - len(prompt_lines))])
|
||||
if print_output:
|
||||
print(f"Cycling through {original_count} negative prompts to fill batch size of {max_batch_size}")
|
||||
|
||||
if print_output:
|
||||
print(f"Processing {len(prompt_lines)} negative prompts in batch:")
|
||||
for i, prompt in enumerate(prompt_lines):
|
||||
print(f" {i+1}: {prompt if prompt else '(empty)'}")
|
||||
|
||||
# Encode each negative prompt
|
||||
cond_tensors = []
|
||||
pooled_tensors = []
|
||||
|
||||
for i, prompt in enumerate(prompt_lines):
|
||||
try:
|
||||
tokens = clip.tokenize(prompt)
|
||||
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
|
||||
cond_tensors.append(cond)
|
||||
pooled_tensors.append(pooled)
|
||||
except Exception as e:
|
||||
print(f"Error encoding negative prompt {i+1} '{prompt}': {e}")
|
||||
# Use fallback empty prompt
|
||||
try:
|
||||
tokens = clip.tokenize("")
|
||||
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
|
||||
cond_tensors.append(cond)
|
||||
pooled_tensors.append(pooled)
|
||||
print(f" Using empty fallback for negative prompt {i+1}")
|
||||
except Exception as fallback_error:
|
||||
raise ValueError(f"Failed to encode negative prompt {i+1} and fallback failed: {fallback_error}")
|
||||
|
||||
# Batch the conditioning tensors - simplified without model-specific parameters
|
||||
try:
|
||||
# Stack the conditioning tensors along batch dimension
|
||||
batched_cond = torch.cat(cond_tensors, dim=0)
|
||||
batched_pooled = torch.cat(pooled_tensors, dim=0)
|
||||
|
||||
if print_output:
|
||||
print(f"Created negative batched conditioning: {batched_cond.shape}")
|
||||
print(f"Created negative batched pooled: {batched_pooled.shape}")
|
||||
|
||||
# Simple conditioning format that works with all model types
|
||||
conditioning = [[batched_cond, {"pooled_output": batched_pooled}]]
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error creating negative batched conditioning: {e}")
|
||||
print("Falling back to list format...")
|
||||
# Fallback to list format if batching fails
|
||||
conditioning = []
|
||||
for i in range(len(cond_tensors)):
|
||||
cond_item = [cond_tensors[i], {"pooled_output": pooled_tensors[i]}]
|
||||
conditioning.append(cond_item)
|
||||
|
||||
prompt_list_str = "|".join(prompt_lines)
|
||||
return (conditioning, prompt_list_str)
|
||||
|
||||
|
||||
class EndlessNode_PromptCounter:
|
||||
"""
|
||||
Utility node to count prompts from input text and display a preview.
|
||||
The preview will be shown in the console output and returned as a string output.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"prompts": ("STRING", {"multiline": True, "forceInput": True}),
|
||||
"print_to_console": ("BOOLEAN", {"default": True}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("INT", "STRING")
|
||||
RETURN_NAMES = ("count", "preview")
|
||||
FUNCTION = "count_prompts"
|
||||
CATEGORY = "Endless 🌊✨/BatchProcessing"
|
||||
|
||||
def count_prompts(self, prompts, print_to_console):
|
||||
prompt_lines = [line.strip() for line in prompts.split('\n') if line.strip()]
|
||||
count = len(prompt_lines)
|
||||
|
||||
preview = f"Found {count} prompt{'s' if count != 1 else ''}:\n"
|
||||
for i, prompt in enumerate(prompt_lines[:5]):
|
||||
preview += f"{i+1}. {prompt}\n"
|
||||
if count > 5:
|
||||
preview += f"... and {count - 5} more"
|
||||
|
||||
if print_to_console:
|
||||
print(f"\n=== Prompt Counter ===")
|
||||
print(preview)
|
||||
print("======================\n")
|
||||
|
||||
return (count, preview)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"EndlessNode_SimpleBatchPrompts": EndlessNode_SimpleBatchPrompts,
|
||||
"EndlessNode_FluxBatchPrompts": EndlessNode_FluxBatchPrompts,
|
||||
"EndlessNode_SDXLBatchPrompts": EndlessNode_SDXLBatchPrompts,
|
||||
"EndlessNode_BatchNegativePrompts": EndlessNode_BatchNegativePrompts,
|
||||
"EndlessNode_PromptCounter": EndlessNode_PromptCounter,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"EndlessNode_SimpleBatchPrompts": "Simple Batch Prompts",
|
||||
"EndlessNode_FluxBatchPrompts": "Flux Batch Prompts",
|
||||
"EndlessNode_SDXLBatchPrompts": "SDXL Batch Prompts",
|
||||
"EndlessNode_BatchNegativePrompts": "Batch Negative Prompts",
|
||||
"EndlessNode_PromptCounter": "Prompt Counter",
|
||||
}
|
||||
Reference in New Issue
Block a user