LuqP2
238e458de6
Complete implementation of advanced image saving node with dual metadata: - A1111/Civitai compatible metadata (tEXt chunk "parameters") - Image MetaHub metadata (iTXt chunk "imagemetahub_data") - Auto-detection of LoRAs from workflow (UI and API formats) - SHA256 hash calculation (AutoV2/Civitai format) with caching - Graceful degradation (never interrupts generation) - IMH Pro fields support (user_tags, notes, project_name) - Auto-increment filenames with customizable output paths Files added: - metahub_save_node.py: Main node class with INPUT_TYPES - metadata_utils.py: Hash calculation, metadata formatting, PNG injection - __init__.py: Node registration for ComfyUI - requirements.txt: Dependencies (Pillow, numpy) - README.md: Installation and usage documentation 100% compatible with Image MetaHub comfyUIParser.ts specification.
151 lines
6.3 KiB
Python
151 lines
6.3 KiB
Python
"""
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MetaHub Save Image Node for ComfyUI
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Advanced image saving with dual metadata support (A1111/Civitai + Image MetaHub)
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"""
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from typing import Dict, Any
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from . import metadata_utils as utils
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class MetaHubSaveNode:
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"""
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ComfyUI custom node for saving images with comprehensive metadata.
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Features:
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- A1111/Civitai compatible metadata (tEXt chunk "parameters")
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- Image MetaHub metadata (iTXt chunk "imagemetahub_data")
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- Auto-detection of LoRAs from workflow
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- SHA256 model hashes (AutoV2/Civitai format)
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- Graceful degradation (never interrupts generation)
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"positive": ("STRING", {
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"multiline": True,
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"default": "",
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"tooltip": "Positive prompt"
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}),
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"negative": ("STRING", {
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"multiline": True,
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"default": "",
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"tooltip": "Negative prompt"
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}),
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 0xffffffffffffffff,
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"tooltip": "Generation seed"
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}),
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"steps": ("INT", {
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"default": 20,
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"min": 1,
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"max": 10000,
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"tooltip": "Sampling steps"
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}),
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"cfg": ("FLOAT", {
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"default": 7.0,
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"min": 0.0,
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"max": 100.0,
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"step": 0.1,
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"tooltip": "CFG scale"
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}),
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"sampler_name": ("STRING", {
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"default": "euler",
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"tooltip": "Sampler name"
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}),
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"scheduler": ("STRING", {
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"default": "normal",
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"tooltip": "Scheduler"
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}),
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"model_name": ("STRING", {
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"default": "",
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"tooltip": "Model filename (e.g., model.safetensors)"
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}),
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},
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"optional": {
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"vae_name": ("STRING", {
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"default": "",
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"tooltip": "VAE filename"
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}),
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"denoise": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"tooltip": "Denoise strength"
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}),
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"upscale_model": ("STRING", {
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"default": "",
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"tooltip": "Upscale model name"
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}),
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"generation_time": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"tooltip": "Generation time in seconds"
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}),
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"user_tags": ("STRING", {
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"default": "",
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"tooltip": "IMH Pro: User tags (comma-separated)"
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}),
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"notes": ("STRING", {
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"multiline": True,
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"default": "",
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"tooltip": "IMH Pro: Notes"
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}),
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"project_name": ("STRING", {
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"default": "",
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"tooltip": "IMH Pro: Project name"
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}),
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"filename_prefix": ("STRING", {
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"default": "ComfyUI",
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"tooltip": "Filename prefix"
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}),
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"output_path": ("STRING", {
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"default": "",
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"tooltip": "Custom output directory (empty = ComfyUI default)"
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}),
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},
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"hidden": {
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"prompt": "PROMPT",
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"extra_pnginfo": "EXTRA_PNGINFO"
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},
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}
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RETURN_TYPES = ()
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FUNCTION = "save_images"
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OUTPUT_NODE = True
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CATEGORY = "image/save"
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DESCRIPTION = "Save images with A1111/Civitai and Image MetaHub metadata"
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def save_images(self, images, positive, negative, seed, steps, cfg, sampler_name, scheduler, model_name, vae_name="", denoise=1.0, upscale_model="", generation_time=0.0, user_tags="", notes="", project_name="", filename_prefix="ComfyUI", output_path="", prompt=None, extra_pnginfo=None):
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print(f"[MetaHub] Starting save_images for batch of {len(images)} image(s)")
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workflow_json = utils.get_workflow_json(extra_pnginfo)
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lora_list = utils.extract_loras_from_workflow(workflow_json)
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print(f"[MetaHub] Detected {len(lora_list)} LoRA(s) from workflow")
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print(f"[MetaHub] Calculating hash for model: {model_name}")
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model_hash = utils.calculate_model_hash(model_name, model_type="checkpoint")
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print(f"[MetaHub] Calculating hashes for LoRAs...")
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lora_hashes = utils.calculate_lora_hashes(lora_list)
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height, width = images[0].shape[0], images[0].shape[1]
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print(f"[MetaHub] Image dimensions: {width}x{height}")
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output_dir = utils.get_output_directory(output_path)
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print(f"[MetaHub] Saving to: {output_dir}")
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saved_paths = utils.save_image_batch(images, output_dir, filename_prefix)
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print(f"[MetaHub] Saved {len(saved_paths)} image(s)")
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for idx, file_path in enumerate(saved_paths):
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print(f"[MetaHub] Injecting metadata into: {file_path.name}")
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params = {'positive': positive, 'negative': negative, 'steps': steps, 'sampler': sampler_name, 'scheduler': scheduler, 'cfg': cfg, 'seed': seed, 'width': width, 'height': height, 'model_name': model_name, 'model_hash': model_hash, 'vae_name': vae_name, 'denoise': denoise, 'upscale_model': upscale_model, 'generation_time': generation_time, 'user_tags': user_tags, 'notes': notes, 'project_name': project_name, 'lora_list': lora_list, 'lora_hashes': lora_hashes}
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a1111_metadata = utils.build_a1111_metadata(params)
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imh_metadata = utils.build_imh_metadata(params, workflow_json)
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utils.inject_metadata_chunks(str(file_path), a1111_metadata, imh_metadata)
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print(f"[MetaHub] ✓ All images saved successfully with metadata")
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return {}
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NODE_CLASS_MAPPINGS = {"MetaHubSaveNode": MetaHubSaveNode}
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NODE_DISPLAY_NAME_MAPPINGS = {"MetaHubSaveNode": "MetaHub Save Image"}
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