528 lines
25 KiB
Python
528 lines
25 KiB
Python
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 itertools
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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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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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},
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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: ROBUST FLOAT COMPARISON ---
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def is_float_equal(self, a, b, tolerance=1e-5):
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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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# --- HELPER: NORMALIZE PATHS ---
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def normalize_str(self, s):
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if isinstance(s, str):
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return s.replace("\\", "/").strip()
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return s
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def get_files_from_folder(self, input_string, type_key):
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input_norm = input_string.replace("\\", "/")
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if ":" in input_norm: input_norm = input_norm.split(":")[0]
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if not input_norm.endswith("/"): return [input_string]
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target_folder = input_norm.rstrip("/")
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all_files = folder_paths.get_filename_list(type_key)
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found = []
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for f in all_files:
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f_norm = f.replace("\\", "/")
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if f_norm.startswith(target_folder + "/"):
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found.append(f)
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if not found:
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print(f"[GridTester] Warning: No files found in folder '{input_string}' for type '{type_key}'")
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return []
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return found
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def parse_lora_definition(self, lora_string, global_model_strength, global_clip_strength):
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if lora_string == "None": return []
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definitions = []
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parts = lora_string.split(" + ")
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for part in parts:
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part = part.strip()
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if ":" in part:
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segments = part.split(":")
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name = segments[0].strip()
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m_str = float(segments[1]) if len(segments) > 1 else 1.0
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c_str = float(segments[2]) if len(segments) > 2 else 1.0
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definitions.append((name, m_str, c_str))
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else:
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definitions.append((part, global_model_strength, global_clip_strength))
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return definitions
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def parse_float_input(self, input_str):
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try:
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val = json.loads(input_str)
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if isinstance(val, list): return [float(x) for x in val]
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return [float(val)]
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except:
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try:
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if "," in input_str: return [float(x.strip()) for x in input_str.split(",")]
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return [float(input_str)]
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except:
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return [1.0]
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def parse_string_input(self, input_str):
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try:
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val = json.loads(input_str.strip())
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if isinstance(val, list): return [str(x) for x in val]
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return [str(val)]
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except:
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return [input_str]
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# --- HELPER: CHECK IF CONFIG ALREADY EXISTS ---
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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 self.normalize_str(val_conf) != self.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, overwrite_existing, flush_batch_every, configs_json, resolutions_json, session_name, unique_id, add_random_seeds_to_gens, optional_model=None, optional_clip=None, optional_vae=None, optional_positive=None, optional_negative=None, optional_latent=None):
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def parse_json_with_error(json_str, name):
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try:
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return json.loads(json_str.strip())
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except json.JSONDecodeError as e:
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raise ValueError(f"JSON Error in {name}: {e}")
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try:
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raw_configs = parse_json_with_error(configs_json, "Configs JSON")
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resolutions = parse_json_with_error(resolutions_json, "Resolutions JSON")
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denoise_values = self.parse_float_input(str(denoise))
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pos_prompts = self.parse_string_input(positive_text)
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neg_prompts = self.parse_string_input(negative_text)
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except Exception as e:
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raise ValueError(f"{e}")
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session_name = re.sub(r'[^\w\-]', '', session_name)
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if not session_name: session_name = "default_session"
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base_dir = os.path.join(folder_paths.get_output_directory(), "benchmarks", session_name)
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img_dir = os.path.join(base_dir, "images")
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os.makedirs(img_dir, exist_ok=True)
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manifest_path = os.path.join(base_dir, "manifest.json")
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existing_data = {"items": [], "meta": {}}
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if os.path.exists(manifest_path):
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try:
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with open(manifest_path, "r") as f:
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d = json.load(f)
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if isinstance(d, list): existing_data["items"] = d
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else: existing_data = d
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except: pass
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existing_data["meta"] = {
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"model": "Multi-Model Session",
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"positive": "Multiple" if len(pos_prompts) > 1 else pos_prompts[0],
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"negative": "Multiple" if len(neg_prompts) > 1 else neg_prompts[0],
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"updated": int(time.time()),
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"random_seed_map": existing_data.get("meta", {}).get("random_seed_map", {}) # Preserve seed map
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}
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# --- PREPARE JOBS ---
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input_jobs = []
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if optional_latent is not None:
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batch_count = optional_latent["samples"].shape[0]
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for i in range(batch_count):
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single_sample = optional_latent["samples"][i].unsqueeze(0)
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input_jobs.append({
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"label": f"Input {i+1}",
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"width": single_sample.shape[3] * 8,
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"height": single_sample.shape[2] * 8,
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"latent": {"samples": single_sample},
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"batch_idx": i
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})
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else:
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for res in resolutions:
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input_jobs.append({
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"label": f"{res[0]}x{res[1]}",
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"width": res[0], "height": res[1],
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"latent": None, "batch_idx": 0
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})
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# --- GENERATE RANDOM SEED LIST ---
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extra_seeds = []
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if add_random_seeds_to_gens > 0:
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# Use base seed to generate deterministic random seeds
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# This way: same base seed = same random seeds, different base seed = different random seeds
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seed_key = f"{seed}_{add_random_seeds_to_gens}"
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saved_seed_map = existing_data.get("meta", {}).get("random_seed_map", {})
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if seed_key in saved_seed_map:
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# Reuse seeds for this base seed
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extra_seeds = saved_seed_map[seed_key]
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print(f"[GridTester] ♻️ Reusing {len(extra_seeds)} saved random seeds for base seed {seed}: {extra_seeds}")
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else:
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# Generate new deterministic random seeds based on base seed
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rng = random.Random(seed) # Deterministic RNG seeded with base seed
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for i in range(add_random_seeds_to_gens):
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# Add offset to ensure we don't accidentally match the base seed
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extra_seeds.append(rng.randint(0, 0xffffffffffffffff))
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print(f"[GridTester] 🎲 Generated {len(extra_seeds)} new random seeds for base seed {seed}: {extra_seeds}")
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# Save to manifest
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if "random_seed_map" not in existing_data["meta"]:
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existing_data["meta"]["random_seed_map"] = {}
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existing_data["meta"]["random_seed_map"][seed_key] = extra_seeds
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# --- EXPAND CONFIGS ---
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ALL_SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS
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ALL_SAMPLERS = comfy.samplers.KSampler.SAMPLERS
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expanded = []
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if len(pos_prompts) > 1 and len(neg_prompts) > 1 and len(pos_prompts) == len(neg_prompts):
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print("[GridTester] Detected matching prompt lists. Using 1-to-1 Pairing.")
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prompt_pairs = list(zip(pos_prompts, neg_prompts))
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else:
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prompt_pairs = list(itertools.product(pos_prompts, neg_prompts))
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for entry in raw_configs:
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def to_list(x): return x if isinstance(x, list) else [x]
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samplers = ALL_SAMPLERS if entry.get("sampler") == "*" else to_list(entry.get("sampler", "euler"))
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schedulers = ALL_SCHEDULERS if entry.get("scheduler") == "*" else to_list(entry.get("scheduler", "normal"))
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steps_l = to_list(entry.get("steps", 20))
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cfgs = to_list(entry.get("cfg", 7.0))
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str_m = to_list(entry.get("str_model", 1.0))
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str_c = to_list(entry.get("str_clip", 1.0))
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raw_models = to_list(entry.get("model", "Default"))
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expanded_models = []
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for m in raw_models:
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if m == "Default": expanded_models.append("Default")
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else: expanded_models.extend(self.get_files_from_folder(m, "checkpoints"))
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# --- LORA STACK EXPANSION ---
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raw_loras = to_list(entry.get("lora", "None"))
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expanded_loras = []
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for l in raw_loras:
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if l == "None":
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expanded_loras.append("None")
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continue
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stack_parts = l.split(" + ")
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expanded_parts = []
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for part in stack_parts:
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if ":" in part:
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p_split = part.split(":", 1)
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base_path = p_split[0].strip()
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args = ":" + p_split[1].strip()
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else:
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base_path = part.strip()
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args = ""
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norm_path = base_path.replace("\\", "/")
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if norm_path.endswith("/"):
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found_files = self.get_files_from_folder(base_path, "loras")
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expanded_parts.append([f"{f}{args}" for f in found_files])
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else:
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expanded_parts.append([part])
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for combo in itertools.product(*expanded_parts):
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expanded_loras.append(" + ".join(combo))
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# --- BUILD BASE CONFIGS ---
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base_combos = []
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for combo in itertools.product(samplers, schedulers, steps_l, cfgs, expanded_loras, str_m, str_c, denoise_values, prompt_pairs, expanded_models):
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base_combos.append({
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"sampler": combo[0], "scheduler": combo[1], "steps": combo[2],
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"cfg": combo[3], "lora": combo[4], "str_model": combo[5], "str_clip": combo[6],
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"denoise": combo[7],
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"positive": combo[8][0],
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"negative": combo[8][1],
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"model": combo[9],
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"seed": seed
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})
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# --- APPLY SEEDS ---
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for c in base_combos:
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expanded.append(c)
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for extra_seed in extra_seeds:
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new_c = c.copy()
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new_c["seed"] = extra_seed
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expanded.append(new_c)
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expanded.sort(key=lambda x: (x['model'], x['lora'], x['positive'], x['negative']))
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print(f"[GridTester] Processing {len(expanded) * len(input_jobs)} items...")
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cached_model_key, cached_lora_key = None, None
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cached_pos_key, cached_neg_key = None, None
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loaded_model, loaded_clip, loaded_vae = None, None, None
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patched_model, patched_clip = None, None
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final_positive, final_negative = None, None
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pending_batch = []
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MATCH_KEYS = ["sampler", "scheduler", "steps", "cfg", "lora", "str_model", "str_clip", "denoise", "seed", "width", "height", "positive", "negative", "batch_idx", "model"]
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skipped_count = 0
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total_generated = 0
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def flush_batch(batch_list):
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nonlocal total_generated
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if not batch_list: return
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latents_to_decode = torch.cat([x[0] for x in batch_list], dim=0)
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active_vae = optional_vae if optional_vae is not None else loaded_vae
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if active_vae is None:
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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active_vae = out[2]
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decoded = active_vae.decode(latents_to_decode)
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new_items = [] # Only track new items
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for i, img_tensor in enumerate(decoded):
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img_np = 255. * img_tensor.cpu().numpy()
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img = Image.fromarray(np.clip(img_np, 0, 255).astype(np.uint8))
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meta = batch_list[i][1]
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ts = int(time.time() * 100000) + random.randint(0,1000)
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filename = f"img_{ts}.webp"
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img.save(os.path.join(img_dir, filename), quality=80)
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meta.update({
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"id": ts,
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"file": f"/view?filename={filename}&type=output&subfolder=benchmarks/{session_name}/images",
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"rejected": False
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})
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existing_data["items"].insert(0, meta)
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new_items.insert(0, meta)
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total_generated += 1
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with open(manifest_path, "w") as f: json.dump(existing_data, f, indent=4)
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if PromptServer:
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# --- OPTIMIZATION: Send ONLY new_items, not the whole manifest ---
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PromptServer.instance.send_sync("ultimate_grid.update", {
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"node": unique_id,
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"session_name": session_name,
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"new_items": new_items,
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"meta": existing_data["meta"]
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})
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# --- MAIN GENERATION LOOP ---
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for job in input_jobs:
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w, h = job["width"], job["height"]
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batch_idx = job["batch_idx"]
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for conf in expanded:
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current_seed = conf["seed"]
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# --- CRITICAL FIX: CHECK FOR SKIP FIRST, BEFORE ANY WORK ---
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match_index = self.find_existing_match(existing_data["items"], conf, w, h, current_seed, batch_idx, MATCH_KEYS)
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if match_index != -1:
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if not overwrite_existing:
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skipped_count += 1
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continue # Skip this config entirely
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else:
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# Overwrite mode: delete old item
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old_item = existing_data["items"][match_index]
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try:
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old_fname_match = re.search(r'filename=([^&]+)', old_item["file"])
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if old_fname_match:
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old_file_path = os.path.join(img_dir, old_fname_match.group(1))
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if os.path.exists(old_file_path):
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os.remove(old_file_path)
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print(f"[GridTester] Deleted old image: {old_fname_match.group(1)}")
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except Exception as e:
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print(f"[GridTester] Could not delete old image: {e}")
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existing_data["items"].pop(match_index)
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# --- NOW DO THE ACTUAL WORK (only if not skipped) ---
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# --- 1. MODEL LOADING ---
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target_model_name = conf["model"]
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if target_model_name != cached_model_key:
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if target_model_name == "Default":
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if optional_model and optional_clip:
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loaded_model, loaded_clip = optional_model, optional_clip
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if optional_vae: loaded_vae = optional_vae
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else:
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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loaded_vae = out[2]
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else:
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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loaded_model, loaded_clip, loaded_vae = out[:3]
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else:
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print(f"[GridTester] Switching Checkpoint to: {target_model_name}")
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ckpt_path = folder_paths.get_full_path("checkpoints", target_model_name)
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out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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loaded_model, loaded_clip, loaded_vae = out[:3]
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cached_model_key = target_model_name
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cached_lora_key, patched_model, patched_clip = None, None, None
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cached_pos_key, cached_neg_key = None, None
|
|
|
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# --- 2. LORA ---
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current_lora_key = (conf["lora"], conf["str_model"], conf["str_clip"])
|
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if current_lora_key != cached_lora_key or patched_model is None:
|
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curr_m, curr_c = loaded_model, loaded_clip
|
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active_loras = self.parse_lora_definition(conf["lora"], conf["str_model"], conf["str_clip"])
|
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for lora_def in active_loras:
|
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lname, lstr_m, lstr_c = lora_def
|
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path = folder_paths.get_full_path("loras", lname)
|
|
if path:
|
|
lora_data = comfy.utils.load_torch_file(path)
|
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curr_m, curr_c = comfy.sd.load_lora_for_models(curr_m, curr_c, lora_data, lstr_m, lstr_c)
|
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else:
|
|
print(f"[GridTester] WARNING: LoRA not found: {lname}")
|
|
patched_model, patched_clip = curr_m, curr_c
|
|
cached_lora_key = current_lora_key
|
|
cached_pos_key, cached_neg_key = None, None
|
|
|
|
# --- 3. CONDITIONING ---
|
|
if optional_positive: final_positive = optional_positive
|
|
else:
|
|
if conf["positive"] == cached_pos_key and final_positive: pass
|
|
else:
|
|
tokens = patched_clip.tokenize(conf["positive"])
|
|
cond, pooled = patched_clip.encode_from_tokens(tokens, return_pooled=True)
|
|
final_positive = [[cond, {"pooled_output": pooled}]]
|
|
cached_pos_key = conf["positive"]
|
|
|
|
if optional_negative: final_negative = optional_negative
|
|
else:
|
|
if conf["negative"] == cached_neg_key and final_negative: pass
|
|
else:
|
|
tokens = patched_clip.tokenize(conf["negative"])
|
|
cond, pooled = patched_clip.encode_from_tokens(tokens, return_pooled=True)
|
|
final_negative = [[cond, {"pooled_output": pooled}]]
|
|
cached_neg_key = conf["negative"]
|
|
|
|
# --- 4. GENERATE ---
|
|
if job["latent"] is not None: latent_in = {"samples": job["latent"]["samples"].clone()}
|
|
else: latent_in = {"samples": torch.zeros([1, 4, h // 8, w // 8])}
|
|
|
|
try:
|
|
t0 = time.time()
|
|
result = nodes.common_ksampler(
|
|
model=patched_model, seed=current_seed, steps=conf["steps"], cfg=conf["cfg"],
|
|
sampler_name=conf["sampler"], scheduler=conf["scheduler"],
|
|
positive=final_positive, negative=final_negative, latent=latent_in,
|
|
denoise=conf["denoise"]
|
|
)
|
|
duration = round(time.time() - t0, 3)
|
|
meta = conf.copy()
|
|
meta.update({"width": w, "height": h, "duration": duration, "seed": current_seed, "batch_idx": batch_idx})
|
|
pending_batch.append((result[0]["samples"], meta))
|
|
|
|
except comfy.model_management.InterruptProcessingException:
|
|
raise
|
|
except Exception as e:
|
|
print(f"[GridTester] Generation Failed (Skipping Config): {e}")
|
|
continue
|
|
|
|
# --- 5. FLUSHING ---
|
|
threshold = vae_batch_size if flush_batch_every <= 0 else flush_batch_every
|
|
if len(pending_batch) >= threshold:
|
|
flush_batch(pending_batch)
|
|
pending_batch = []
|
|
|
|
# --- FINAL SUMMARY ---
|
|
if skipped_count > 0:
|
|
print(f"[GridTester] ✅ Skipped {skipped_count} previously generated items.")
|
|
print(f"[GridTester] ✅ Generated {total_generated} new images.")
|
|
|
|
# Flush any remaining items
|
|
flush_batch(pending_batch)
|
|
|
|
# Save final manifest
|
|
with open(manifest_path, "w") as f: json.dump(existing_data, f, indent=4)
|
|
|
|
html = get_html_template(session_name, existing_data, unique_id)
|
|
return (html,) |