244 lines
12 KiB
Python
244 lines
12 KiB
Python
"""
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Main SamplerGridTester Node for ComfyUI
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Orchestrates grid generation across multiple configurations
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"""
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import re
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import torch
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import json
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import os
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import time
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import random
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import hashlib
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import folder_paths
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import nodes
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import comfy.utils
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import comfy.sd
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import comfy.samplers
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import comfy.model_management
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from PIL import Image
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import numpy as np
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# Import from split modules
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from .remote_vae import (
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HF_ENDPOINTS,
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detect_model_type,
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remote_decode_hf,
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RemoteVAEDecodeWorker
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)
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from .lora_utils import load_and_save_tags
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from .config_utils import (
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parse_json_with_error,
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parse_float_input,
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parse_string_input,
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expand_configs,
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prepare_input_jobs,
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sanitize_session_name,
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normalize_str,
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get_files_from_folder,
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parse_lora_definition
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)
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try:
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from server import PromptServer
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except ImportError:
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PromptServer = None
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from .html_generator import get_html_template
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class SamplerGridTester:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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"positive_text": ("STRING", {"multiline": True, "default": "masterpiece, best quality, 1girl"}),
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"negative_text": ("STRING", {"multiline": True, "default": "bad quality, worst quality, lowres"}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"denoise": ("STRING", {"default": "1.0", "multiline": False}),
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"vae_batch_size": ("INT", {"default": 4, "min": -1, "max": 64}),
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"configs_json": ("STRING", {"multiline": True, "default": '[{"sampler": "euler", "scheduler": "normal", "steps": 20, "cfg": 7.0}]'}),
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"resolutions_json": ("STRING", {"default": '[[1024, 1024]]'}),
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"session_name": ("STRING", {"default": "my_session"}),
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"overwrite_existing": ("BOOLEAN", {"default": False, "tooltip": "True = Re-run everything. False = Skip already generated images (Resume)."}),
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"flush_batch_every": ("INT", {"default": 4, "min": 0, "max": 64, "tooltip": "Update dashboard every X images. 0 = Use VAE Batch Size."}),
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"add_random_seeds_to_gens": ("INT", {"default": 0, "min": 0, "max": 100, "tooltip": "Generate X extra images per config using consistent random seeds."}),
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"lora_triggerwords_mode": (["None", "Append To End", "Append To Start", "Read From Config"], {
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"default": "None",
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"tooltip": "None = Don't fetch/append trigger words. Append To End = Add triggers at end of prompt (default behavior). Append To Start = Add triggers at start of prompt. Read From Config = Use lora_triggerwords_append_settings in config JSON to specify per-lora placement."
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}),
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"remote_vae_endpoint": (["None", "Auto (Experimental)", "SD", "SDXL", "Flux", "HunyuanVideo"], {
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"default": "None",
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"tooltip": "Offload VAE decoding to HuggingFace remote endpoints. Auto detects model type, or manually select endpoint. Ignores vae_batch_size and flush_batch_every when enabled."
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}),
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"save_conditioning_cache_to_file": ("BOOLEAN", {
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"default": False,
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"tooltip": "Save CLIP conditioning cache to disk. \n\n Pretty much only helps if you want to use the same prompts without changing models or loras and are planning to experiment with different values AND want to skip the text encoding step after stopping and restarting/resuming a job. \n\n WARNING: Can create very large files in your output/benchmarks folder if you use many prompts with many LoRAs on large runs. Automatically disabled when optional inputs (model/clip/conditioning) are connected, as changes cannot be reliably detected."
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}),
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"enable_model_cache": ("BOOLEAN", {
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"default": False,
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"tooltip": "\n\n Experimental WIP feature that cuts lora switching loading times. Very useful if you are using lots of loras and then switching out some of them on additional runs. Skips re-loading from disk and loads cached loras from RAM instead. \n\n This is currently hard-coded to work best on my 8gb VRAM and 64gb RAM setup, it should work fine on most systems unless you have more LoRAs than available RAM. \n\n Enable intelligent model/LoRA caching system with async background preloading. Dramatically speeds up generation by caching models and preloading next model in background. Disable for debugging or to reduce RAM/VRAM usage (can be much slower if your text encoding step takes a long time and you're doing lots of starting and stopping with the same prompt). \n\n WILL NOT AUTODELETE THE CACHED ENCODED PROMPTS. You'll need to delete them manually from the benchmark session's folder manually when you're done with them."
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}),
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},
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"optional": {
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"optional_model": ("MODEL",),
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"optional_clip": ("CLIP",),
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"optional_vae": ("VAE",),
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"optional_positive": ("CONDITIONING",),
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"optional_negative": ("CONDITIONING",),
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"optional_latent": ("LATENT",),
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},
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"hidden": {"unique_id": "UNIQUE_ID"},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("dashboard_html",)
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FUNCTION = "run_tests"
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CATEGORY = "sampling/testing"
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# --- HELPER METHODS ---
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def is_float_equal(self, a, b, tolerance=1e-5):
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"""Robust float comparison"""
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try:
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return abs(float(a) - float(b)) < tolerance
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except:
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return str(a) == str(b)
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def hash_conditioning(self, conditioning):
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"""Create a hash of conditioning tensor for change detection"""
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if conditioning is None:
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return "none"
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try:
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tensor = conditioning[0][0]
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tensor_bytes = tensor.cpu().numpy().tobytes()
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hash_obj = hashlib.md5(tensor_bytes)
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return hash_obj.hexdigest()[:16]
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except Exception as e:
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print(f"[GridTester] Warning: Could not hash conditioning: {e}")
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return "unknown"
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def get_latent_channels(self, model, optional_latent):
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"""Detect the correct number of latent channels for the model"""
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# First, check if we have an optional_latent to extract from
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if optional_latent is not None:
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channels = optional_latent["samples"].shape[1]
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print(f"[GridTester] 🔍 Detected {channels} latent channels from optional_latent")
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return channels
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# Try to detect from model
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if model is not None:
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try:
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if hasattr(model, 'model') and hasattr(model.model, 'latent_format'):
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latent_format = model.model.latent_format
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if hasattr(latent_format, 'latent_channels'):
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channels = latent_format.latent_channels
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print(f"[GridTester] 🔍 Detected {channels} latent channels from model.latent_format")
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return channels
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if hasattr(model, 'model') and hasattr(model.model, 'diffusion_model'):
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diff_model = model.model.diffusion_model
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if hasattr(diff_model, 'in_channels'):
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channels = diff_model.in_channels
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print(f"[GridTester] 🔍 Detected {channels} latent channels from diffusion_model.in_channels")
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return channels
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except Exception as e:
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print(f"[GridTester] ⚠️ Could not detect latent channels: {e}")
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# Default to 4 (SD1.5/SDXL)
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print(f"[GridTester] 🔍 Using default 4 latent channels (SD1.5/SDXL)")
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return 4
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def find_existing_match(self, existing_items, conf, w, h, current_seed, batch_idx, match_keys):
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"""Returns the index of matching item, or -1 if not found"""
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for idx, item in enumerate(existing_items):
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is_match = True
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for k in match_keys:
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val_conf = conf.get(k)
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# Override with current job values
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if k == "width":
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val_conf = w
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elif k == "height":
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val_conf = h
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elif k == "seed":
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val_conf = current_seed
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elif k == "batch_idx":
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val_conf = batch_idx
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val_item = item.get(k)
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# Handle model defaults
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if k == "model":
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if val_item is None:
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if val_conf != "Default":
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is_match = False
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break
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elif val_conf == "Default" and val_item is None:
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continue
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# Float comparison
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if isinstance(val_conf, float) or isinstance(val_item, float):
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if not self.is_float_equal(val_conf, val_item):
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is_match = False
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break
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# String comparison
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elif isinstance(val_conf, str) and isinstance(val_item, str):
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if normalize_str(val_conf) != normalize_str(val_item):
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is_match = False
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break
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# Direct comparison
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elif val_item != val_conf:
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is_match = False
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break
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if is_match:
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return idx
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return -1
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def run_tests(self, ckpt_name, positive_text, negative_text, seed, denoise, vae_batch_size,
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overwrite_existing, flush_batch_every, configs_json, resolutions_json,
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session_name, unique_id, add_random_seeds_to_gens, lora_triggerwords_mode,
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remote_vae_endpoint, save_conditioning_cache_to_file, enable_model_cache,
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optional_model=None, optional_clip=None, optional_vae=None,
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optional_positive=None, optional_negative=None, optional_latent=None):
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# Import the generation logic from the separate module
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from .generation_orchestrator import run_generation_loop
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# Disable cache saving if any optional inputs are connected
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# (changes in models/LoRAs cannot be reliably detected from optional inputs)
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if optional_model is not None or optional_clip is not None or optional_positive is not None or optional_negative is not None:
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if save_conditioning_cache_to_file:
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print("[GridTester] ⚠️ save_conditioning_cache_to_file disabled: optional inputs connected (changes cannot be reliably detected)")
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save_conditioning_cache_to_file = False
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return run_generation_loop(
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self,
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ckpt_name, positive_text, negative_text, seed, denoise, vae_batch_size,
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overwrite_existing, flush_batch_every, configs_json, resolutions_json,
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session_name, unique_id, add_random_seeds_to_gens, lora_triggerwords_mode,
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remote_vae_endpoint, save_conditioning_cache_to_file, enable_model_cache,
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optional_model, optional_clip, optional_vae,
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optional_positive, optional_negative, optional_latent
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)
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# Node class mappings for ComfyUI
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NODE_CLASS_MAPPINGS = {
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"SamplerGridTester": SamplerGridTester
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SamplerGridTester": "Sampler Grid Tester"
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} |