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This commit is contained in:
632
rename-ai-snaps
Executable file
632
rename-ai-snaps
Executable file
@@ -0,0 +1,632 @@
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#!/usr/bin/env python3
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"""
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rename-ai-snaps — Scan PNGs for AI prompt metadata and reorg into model/lora folders.
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Usage:
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./rename-ai-snaps [path] [options]
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Scans for ComfyUI PNGs, reads embedded prompt metadata, extracts the
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checkpoint model and LoRA names, and moves files into:
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{output}/{model}/{lora}/schmeeve-AI-{keywords}.png
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"""
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import json
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import os
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import re
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import sys
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import time
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from pathlib import Path
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try:
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from PIL import Image
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except ImportError:
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print("Error: Pillow (PIL) is required. Install with: pip install Pillow")
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sys.exit(1)
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# ── stopwords and filter sets ──────────────────────────────────────────────
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QUALITY_TAGS = {
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"score_6_up", "score_7_up", "score_8_up", "score_9",
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"score_6", "score_7", "score_8",
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"masterpiece", "best quality", "good quality", "normal quality",
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"high quality", "highly detailed", "very detailed", "extreme detail",
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"very_aesthetic", "absurdres", "8k", "4k",
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"photorealistic", "photograph",
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"depth of field", "solo focus", "cinematic",
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"newest", "amazing", "stunning",
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}
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QUALITY_WORDS = {
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"best", "good", "high", "top", "ultra", "super",
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"mega", "hyper", "extreme", "extra", "ultimate",
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}
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TECHNICAL_WORDS = {
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"detailed", "focus", "quality", "aesthetic", "realistic",
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"cinematic", "lighting", "rendering", "shading", "texture",
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"newest", "absurdres",
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}
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STOP_WORDS = {
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"the", "a", "an", "of", "in", "on", "at", "to", "for", "with",
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"and", "or", "is", "are", "was", "were", "be", "been", "being",
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"have", "has", "had", "do", "does", "did", "will", "would",
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"could", "should", "may", "might", "can", "shall", "this",
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"that", "these", "those", "it", "its", "by", "from", "as",
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"into", "through", "during", "before", "after", "above", "below",
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"between", "out", "off", "over", "under", "again", "further",
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"then", "once", "here", "there", "when", "where", "why", "how",
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"all", "each", "every", "both", "few", "more", "most", "other",
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"some", "such", "no", "nor", "not", "only", "own", "same", "so",
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"than", "too", "very", "just", "about", "up", "down",
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"make", "get", "set", "put", "take", "give", "show", "use",
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"like", "look", "see", "want", "need", "let", "close", "full",
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"add", "new", "one", "two", "five",
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"also", "well", "back", "still", "even", "much",
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"you", "your", "my", "me", "we", "our", "they", "them", "their",
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}
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GENERIC_WORDS = {
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"man", "men", "guy", "guys", "boy", "boys", "woman", "women",
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"girl", "girls", "people", "person", "human", "figure",
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"photo", "image", "picture", "shot", "view", "pose", "posing",
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"face", "head", "body", "skin", "hair", "eyes", "hand", "hands",
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"dark", "light", "bright", "color", "colour",
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}
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NEGATIVE_INDICATORS = {
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"deformed", "distorted", "disfigured", "poorly drawn", "bad anatomy",
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"extra digits", "missing digits", "extra limbs", "missing limbs",
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"ugly", "tiling", "low quality", "worst quality", "normal quality",
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"lowres", "monochrome", "grayscale", "text", "watermark",
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"branding", "border", "cropped", "signature", "username",
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"error", "mutation", "mutated", "out of frame", "duplicate", "cloned",
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"body out of frame", "bad hands", "bad face", "blurry",
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}
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# ── Helpers ────────────────────────────────────────────────────────────────
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def spinner():
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chars = "⠋⠙⠹⠸⠼⠴⠦⠧⠇⠏"
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i = 0
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while True:
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yield chars[i % len(chars)]
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i += 1
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def is_negative_text(text):
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lower = text.lower()
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score = 0
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for ind in NEGATIVE_INDICATORS:
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if ind in lower:
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score += 1
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return score >= 2
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def is_quality_only(text):
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lower = text.lower()
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words = re.findall(r"[a-z_]+", lower)
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if not words:
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return False
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meaningful = sum(1 for w in words if w not in QUALITY_TAGS and len(w) > 2)
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return meaningful == 0
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# ── Metadata extraction from ComfyUI prompt JSON ───────────────────────────
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CHECKPOINT_CLASS_TYPES = {
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"CheckpointLoaderSimple",
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"Checkpoint Loader with Name (Image Saver)",
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"CheckpointLoader",
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}
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LORA_CLASS_TYPES = {
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"LoraLoader",
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"Power Lora Loader (rgthree)",
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}
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MODEL_CLASS_TYPES = {
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"UNETLoader",
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}
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def _strip_model_name(raw):
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"""Strip path prefix and .safetensors extension from a model/LoRA name."""
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name = raw.strip()
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# Handle backslash/forward-slash paths: "Pony\\model.safetensors" or "Pony/model.safetensors"
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name = name.replace("\\", "/")
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name = os.path.basename(name)
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# Strip .safetensors extension
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if name.endswith(".safetensors"):
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name = name[:-len(".safetensors")]
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return name
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def extract_model_name(data):
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"""Return the checkpoint/UNET model name from the prompt JSON, or None."""
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model_name = None
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for node in data.values():
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inputs = node.get("inputs", {})
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class_type = node.get("class_type", "")
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if class_type in CHECKPOINT_CLASS_TYPES and "ckpt_name" in inputs:
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model_name = _strip_model_name(inputs["ckpt_name"])
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break
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if class_type in MODEL_CLASS_TYPES and "unet_name" in inputs:
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model_name = _strip_model_name(inputs["unet_name"])
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break
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return model_name if model_name else None
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def extract_lora_names(data):
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"""Return a sorted list of unique LoRA names from the prompt JSON."""
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names = []
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for node in data.values():
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inputs = node.get("inputs", {})
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class_type = node.get("class_type", "")
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if class_type in LORA_CLASS_TYPES and "lora_name" in inputs:
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names.append(_strip_model_name(inputs["lora_name"]))
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# Some custom nodes use 'lora' or similar — look for any field ending
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# in 'lora_name' or 'lora' that contains '.safetensors'
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for field, val in inputs.items():
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if isinstance(val, str) and val.endswith(".safetensors") and "lora" in field.lower():
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name = _strip_model_name(val)
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if name and name not in names:
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names.append(name)
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return sorted(set(names))
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def extract_prompts(filepath):
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"""Return (positive_prompt, negative_prompt) from a ComfyUI PNG."""
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try:
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img = Image.open(filepath)
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except Exception:
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return None, None
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if "prompt" not in img.info:
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return None, None
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try:
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data = json.loads(img.info["prompt"])
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except (json.JSONDecodeError, TypeError):
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return None, None
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# Gather all text fields from all nodes
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candidates = []
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for node in data.values():
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inputs = node.get("inputs", {})
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for field in ("text", "prompt", "positive", "negative", "value", "string"):
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val = inputs.get(field, "")
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if isinstance(val, str) and len(val.strip()) > 3:
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candidates.append(val.strip())
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# Second pass: resolve node references
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for node in data.values():
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inputs = node.get("inputs", {})
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for field in ("text", "prompt", "positive", "negative", "value", "string"):
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val = inputs.get(field)
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if isinstance(val, list) and len(val) == 2 and isinstance(val[0], str):
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ref_node = data.get(val[0], {})
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ref_inputs = ref_node.get("inputs", {})
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for rf in ("value", "text", "string"):
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rv = ref_inputs.get(rf, "")
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if isinstance(rv, str) and len(rv.strip()) > 3:
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candidates.append(rv.strip())
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break
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if not candidates:
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return None, None
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positives = [c for c in candidates if not is_negative_text(c)]
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negatives = [c for c in candidates if is_negative_text(c)]
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positives = [c for c in positives if not is_quality_only(c)]
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pos = max(positives, key=len) if positives else None
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neg = max(negatives, key=len) if negatives else None
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return pos, neg
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def extract_all_candidates(text):
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"""Extract all candidate keywords with position index for frequency scoring."""
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if not text:
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return []
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text_lower = text.lower()
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cleaned = re.sub(r"[\[\(\{][^\]\)\}]*[\]\)\}]", "", text_lower)
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segments = re.split(r"[,.;:!?]+", cleaned)
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seen = set()
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candidates = []
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position = 0
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for seg in segments:
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seg = seg.strip()
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if not seg or len(seg) < 4:
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continue
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words = re.findall(r"[a-zA-Z_]+", seg)
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good = []
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for w in words:
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wl = w.lower().strip("_")
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if len(wl) < 3:
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continue
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if wl in STOP_WORDS or wl in GENERIC_WORDS:
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continue
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if wl in QUALITY_TAGS or wl in QUALITY_WORDS:
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continue
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if wl in TECHNICAL_WORDS:
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continue
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if wl in NEGATIVE_INDICATORS:
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continue
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if wl.startswith("score") or wl.startswith("step"):
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continue
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if wl.isdigit():
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continue
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good.append(wl)
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if good:
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taken = 0
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for g in good:
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if g not in seen and taken < 2:
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candidates.append((g, position))
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seen.add(g)
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taken += 1
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position += 1
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return candidates
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def select_best_keywords(candidates, freq_map, total_images, max_keywords=5):
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if not candidates:
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return []
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if total_images <= 1:
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return [kw for kw, _ in candidates[:max_keywords]]
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scored = []
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for kw, pos in candidates:
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freq = freq_map.get(kw, 1)
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rarity = 1.0 - ((freq - 1) / max(total_images - 1, 1))
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max_pos = max(len(candidates), 1)
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pos_bonus = (pos / max_pos) * 0.3
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score = rarity + pos_bonus
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scored.append((score, kw, pos))
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scored.sort(key=lambda x: (-x[0], x[2]))
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selected = []
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seen = set()
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for _, kw, _ in scored:
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if kw not in seen:
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selected.append(kw)
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seen.add(kw)
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if len(selected) >= max_keywords:
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break
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return selected
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def keywords_to_name(keywords):
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if not keywords:
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return None
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desc = "-".join(keywords)
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desc = re.sub(r"[^a-z0-9-]", "-", desc)
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desc = re.sub(r"-+", "-", desc).strip("-")
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if len(desc) > 40:
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desc = desc[:40].rstrip("-")
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if "-" in desc:
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truncated = "-".join(desc.split("-")[:-1])
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if truncated and len(truncated) > 10:
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desc = truncated
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return f"schmeeve-AI-{desc}.png" if desc and len(desc) > 3 else None
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def extract_metadata(filepath):
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"""Return (model_name, lora_names, positive_prompt, negative_prompt)."""
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try:
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img = Image.open(filepath)
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except Exception:
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return None, None, None, None
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if "prompt" not in img.info:
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return None, None, None, None
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try:
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data = json.loads(img.info["prompt"])
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except (json.JSONDecodeError, TypeError):
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return None, None, None, None
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model = extract_model_name(data)
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loras = extract_lora_names(data)
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pos, neg = extract_prompts(filepath)
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return model, loras, pos, neg
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def sanitize_dir_name(name):
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"""Sanitize a string for use as a directory name."""
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name = name.strip().replace(" ", "_")
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name = re.sub(r"[^a-zA-Z0-9_.-]", "_", name)
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name = re.sub(r"_+", "_", name).strip("_")
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return name
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# ── main ───────────────────────────────────────────────────────────────────
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def main():
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import argparse
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DEFAULT_OUTPUT = os.path.expanduser(
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"~/mnt/mini.nas/miniShare1/Pictures/Images/AI"
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)
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parser = argparse.ArgumentParser(
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description="Scan PNGs for AI prompt metadata and reorg into model/lora folders.",
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)
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parser.add_argument(
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"path", nargs="?", default=os.path.expanduser("~/Pictures"),
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help="Directory to scan for PNG files (default: ~/Pictures)",
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)
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parser.add_argument(
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"-o", "--output", default=DEFAULT_OUTPUT,
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help=f"Output root directory (default: {DEFAULT_OUTPUT})",
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)
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parser.add_argument(
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"-n", "--no-interactive", action="store_true",
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help="Auto-reorg without prompting",
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)
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parser.add_argument(
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"-r", "--recursive", action="store_true",
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help="Search directories recursively (default: off)",
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)
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parser.add_argument(
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"-p", "--preview", "--dry-run", action="store_true",
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dest="dry_run",
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help="Show proposed changes without making any (combine with -n for full listing)",
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)
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args = parser.parse_args()
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scan_dir = Path(args.path).expanduser().resolve()
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if not scan_dir.is_dir():
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print(f"Error: {scan_dir} is not a directory")
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sys.exit(1)
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output_root = Path(args.output).expanduser().resolve()
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# Gather PNGs
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glob_pattern = "**/*.png" if args.recursive else "*.png"
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pngs = sorted(scan_dir.glob(glob_pattern))
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if not pngs:
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print(f" No PNG files found in {scan_dir}" +
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(" (recursive search)" if args.recursive else ""))
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return
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# ── Phase 1: Collect candidates and build frequency map ──
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sys.stdout.write(" Analyzing PNGs")
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sys.stdout.flush()
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spin = spinner()
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image_data = {}
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word_images = {}
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for p in pngs:
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sys.stdout.write(f"\r {next(spin)} Analyzing PNGs")
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sys.stdout.flush()
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# Skip already-renamed files (schmeeve-AI-*)
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if p.name.startswith("schmeeve-AI-"):
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continue
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model, loras, pos, neg = extract_metadata(str(p))
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source = pos or neg
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if source:
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candidates = extract_all_candidates(source)
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if candidates:
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image_data[p] = {
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"candidates": candidates,
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"model": model,
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"loras": loras,
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"source": source,
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}
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for kw, _ in candidates:
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if kw not in word_images:
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word_images[kw] = set()
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word_images[kw].add(p)
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freq_map = {kw: len(images) for kw, images in word_images.items()}
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total_images = len(image_data)
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# ── Phase 1b: Propose file paths ──
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raw_proposals = {}
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for p, info in image_data.items():
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candidates = info["candidates"]
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model = info["model"]
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loras = info["loras"]
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if total_images > 1:
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keywords = select_best_keywords(candidates, freq_map, total_images)
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else:
|
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keywords = [kw for kw, _ in candidates[:5]]
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name = keywords_to_name(keywords)
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||||
if not name:
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continue
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||||
# Determine folder structure
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model_dir = sanitize_dir_name(model) if model else "unknown"
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if loras:
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lora_dir = sanitize_dir_name("+".join(loras))
|
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else:
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lora_dir = "no-lora"
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||||
|
||||
rel_path = Path(model_dir) / lora_dir / name
|
||||
raw_proposals[p] = rel_path
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||||
|
||||
# Clear the spinner line
|
||||
sys.stdout.write("\r" + " " * 60 + "\r")
|
||||
sys.stdout.flush()
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||||
|
||||
# Deduplicate file names within their target directories
|
||||
proposals = {}
|
||||
used_paths = {}
|
||||
for p, rel_path in raw_proposals.items():
|
||||
new_path = rel_path
|
||||
counter = 0
|
||||
key = str(new_path)
|
||||
while key in used_paths:
|
||||
counter += 1
|
||||
ts = time.strftime("%m%d-%H%M%S", time.localtime(p.stat().st_mtime))
|
||||
stem = rel_path.stem
|
||||
ext = rel_path.suffix
|
||||
new_path = rel_path.with_name(f"{stem}-{ts}{'_' + str(counter) if counter > 1 else ''}{ext}")
|
||||
key = str(new_path)
|
||||
used_paths[key] = True
|
||||
proposals[p] = new_path
|
||||
|
||||
# ── Phase 2: Display proposed changes ──
|
||||
if not proposals:
|
||||
print(" No renamable PNGs found.")
|
||||
return
|
||||
|
||||
print(f"\n {'DRY RUN' if args.dry_run else 'PROPOSED'} — "
|
||||
f"{len(proposals)} file(s) to organize into {output_root}/\n")
|
||||
|
||||
for i, (old_path, rel_path) in enumerate(proposals.items(), 1):
|
||||
try:
|
||||
src_display = str(old_path.relative_to(scan_dir))
|
||||
except ValueError:
|
||||
src_display = str(old_path)
|
||||
if len(src_display) > 55:
|
||||
src_display = src_display[:25] + "…" + src_display[-27:]
|
||||
print(f" {i:>3}. {src_display}")
|
||||
print(f" → {output_root / rel_path}")
|
||||
|
||||
print()
|
||||
|
||||
if args.dry_run:
|
||||
print(f" Dry-run complete. No files were changed.\n")
|
||||
return
|
||||
|
||||
# ── Phase 3: Rename/move (interactive or auto) ──
|
||||
if args.no_interactive:
|
||||
moved = 0
|
||||
for old_path, rel_path in proposals.items():
|
||||
new_path = output_root / rel_path
|
||||
new_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
if new_path.exists():
|
||||
stem = new_path.stem
|
||||
counter = 1
|
||||
while new_path.exists():
|
||||
new_path = new_path.with_name(f"{stem}_{counter}{new_path.suffix}")
|
||||
counter += 1
|
||||
try:
|
||||
old_path.rename(new_path)
|
||||
moved += 1
|
||||
except FileNotFoundError:
|
||||
print(f" SKIPPED (not found): {old_path.name}")
|
||||
print(f" Moved {moved} file(s)." if not args.dry_run else "")
|
||||
else:
|
||||
moved = 0
|
||||
skipped = 0
|
||||
items = list(proposals.items())
|
||||
i = 0
|
||||
while i < len(items):
|
||||
old_path, rel_path = items[i]
|
||||
new_path = output_root / rel_path
|
||||
new_path_display = str(rel_path)
|
||||
|
||||
print(f"\n [{i+1}/{len(items)}]")
|
||||
print(f" From: {old_path.name}")
|
||||
print(f" To: {output_root / rel_path}")
|
||||
remaining = len(items) - i - 1
|
||||
rlabel = f"move all {remaining}" if remaining else ""
|
||||
sys.stdout.write(f" [Enter]=move [e]=edit [s]=skip{' [a]=' + rlabel if rlabel else ''} [q]=quit: ")
|
||||
sys.stdout.flush()
|
||||
choice = sys.stdin.readline().strip().lower()
|
||||
|
||||
if choice == "q":
|
||||
remaining = len(items) - i - 1
|
||||
if remaining:
|
||||
print(f" Skipping remaining {remaining} file(s).")
|
||||
break
|
||||
elif choice == "s":
|
||||
skipped += 1
|
||||
i += 1
|
||||
continue
|
||||
elif choice == "e":
|
||||
sys.stdout.write(f" Edit name (will be 'schmeeve-AI-{rel_path.parent}/...'): ")
|
||||
sys.stdout.flush()
|
||||
custom = sys.stdin.readline().strip()
|
||||
if custom:
|
||||
custom_desc = re.sub(r"[^a-z0-9-]", "-", custom.lower())
|
||||
custom_desc = re.sub(r"-+", "-", custom_desc).strip("-")
|
||||
if custom_desc:
|
||||
new_name = f"schmeeve-AI-{custom_desc}.png"
|
||||
rel_path = rel_path.with_name(new_name)
|
||||
else:
|
||||
print(" Invalid name, skipping.")
|
||||
skipped += 1
|
||||
i += 1
|
||||
continue
|
||||
else:
|
||||
skipped += 1
|
||||
i += 1
|
||||
continue
|
||||
elif choice == "":
|
||||
pass
|
||||
elif choice == "a":
|
||||
for j in range(i, len(items)):
|
||||
p, rp = items[j]
|
||||
np = output_root / rp
|
||||
np.parent.mkdir(parents=True, exist_ok=True)
|
||||
if np.exists():
|
||||
stem = np.stem
|
||||
counter = 1
|
||||
while np.exists():
|
||||
np = np.with_name(f"{stem}_{counter}{np.suffix}")
|
||||
counter += 1
|
||||
try:
|
||||
p.rename(np)
|
||||
except FileNotFoundError:
|
||||
print(f" SKIPPED (not found): {p.name}")
|
||||
moved += len(items) - i
|
||||
break
|
||||
else:
|
||||
print(f" Unknown option '{choice}', skipping.")
|
||||
skipped += 1
|
||||
i += 1
|
||||
continue
|
||||
|
||||
# Perform move
|
||||
new_path = output_root / rel_path
|
||||
new_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
if new_path.exists():
|
||||
stem = new_path.stem
|
||||
counter = 1
|
||||
while new_path.exists():
|
||||
new_path = new_path.with_name(f"{stem}_{counter}{new_path.suffix}")
|
||||
counter += 1
|
||||
print(f" (file existed, saved as {new_path.name})")
|
||||
try:
|
||||
old_path.rename(new_path)
|
||||
moved += 1
|
||||
except FileNotFoundError:
|
||||
print(f" SKIPPED (not found): {old_path.name}")
|
||||
i += 1
|
||||
|
||||
print(f"\n Moved: {moved} Skipped: {skipped}")
|
||||
|
||||
# Check for JPEGs with AI metadata
|
||||
jpg_pattern = "**/*.jpg" if args.recursive else "*.jpg"
|
||||
remaining_ai = 0
|
||||
for f in scan_dir.glob(jpg_pattern):
|
||||
try:
|
||||
img = Image.open(f)
|
||||
if "prompt" in img.info:
|
||||
remaining_ai += 1
|
||||
except Exception:
|
||||
pass
|
||||
if remaining_ai:
|
||||
print(f" Note: {remaining_ai} JPEG(s) with AI metadata found (not yet supported).")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user