499 lines
25 KiB
Python
499 lines
25 KiB
Python
import os
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from datetime import datetime
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from pathlib import Path
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import json
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import requests
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import piexif
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import piexif.helper
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from PIL import Image
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import numpy as np
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import re
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import folder_paths
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from .saver.saver import save_image
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from .utils import get_sha256
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from .prompt_metadata_extractor import PromptMetadataExtractor
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from nodes import MAX_RESOLUTION
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def parse_checkpoint_name(ckpt_name):
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return os.path.basename(ckpt_name)
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def parse_checkpoint_name_without_extension(ckpt_name):
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return os.path.splitext(parse_checkpoint_name(ckpt_name))[0]
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def get_timestamp(time_format):
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now = datetime.now()
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try:
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timestamp = now.strftime(time_format)
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except:
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timestamp = now.strftime("%Y-%m-%d-%H%M%S")
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return timestamp
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def save_json(image_info, filename):
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try:
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workflow = (image_info or {}).get('workflow')
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if workflow is None:
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print('No image info found, skipping saving of JSON')
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with open(f'{filename}.json', 'w') as workflow_file:
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json.dump(workflow, workflow_file)
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print(f'Saved workflow to {filename}.json')
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except Exception as e:
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print(f'Failed to save workflow as json due to: {e}, proceeding with the remainder of saving execution')
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def make_pathname(filename, width, height, seed, modelname, counter, time_format, sampler_name, steps, cfg, scheduler, denoise, clip_skip):
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filename = filename.replace("%date", get_timestamp("%Y-%m-%d"))
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filename = filename.replace("%time", get_timestamp(time_format))
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filename = filename.replace("%model", parse_checkpoint_name(modelname))
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filename = filename.replace("%width", str(width))
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filename = filename.replace("%height", str(height))
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filename = filename.replace("%seed", str(seed))
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filename = filename.replace("%counter", str(counter))
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filename = filename.replace("%sampler_name", sampler_name)
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filename = filename.replace("%steps", str(steps))
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filename = filename.replace("%cfg", str(cfg))
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filename = filename.replace("%scheduler", scheduler)
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filename = filename.replace("%basemodelname", parse_checkpoint_name_without_extension(modelname))
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filename = filename.replace("%denoise", str(denoise))
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filename = filename.replace("%clip_skip", str(clip_skip))
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return filename
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def make_filename(filename, width, height, seed, modelname, counter, time_format, sampler_name, steps, cfg, scheduler, denoise, clip_skip):
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filename = make_pathname(filename, width, height, seed, modelname, counter, time_format, sampler_name, steps, cfg, scheduler, denoise, clip_skip)
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return get_timestamp(time_format) if filename == "" else filename
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class ImageSaver:
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def __init__(self):
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self.output_dir = folder_paths.output_directory
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self.civitai_sampler_map = {
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'euler_ancestral': 'Euler a',
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'euler': 'Euler',
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'lms': 'LMS',
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'heun': 'Heun',
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'dpm_2': 'DPM2',
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'dpm_2_ancestral': 'DPM2 a',
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'dpmpp_2s_ancestral': 'DPM++ 2S a',
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'dpmpp_2m': 'DPM++ 2M',
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'dpmpp_sde': 'DPM++ SDE',
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'dpmpp_2m_sde': 'DPM++ 2M SDE',
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'dpmpp_3m_sde': 'DPM++ 3M SDE',
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'dpm_fast': 'DPM fast',
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'dpm_adaptive': 'DPM adaptive',
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'ddim': 'DDIM',
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'plms': 'PLMS',
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'uni_pc_bh2': 'UniPC',
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'uni_pc': 'UniPC',
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'lcm': 'LCM',
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}
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def get_civitai_sampler_name(self, sampler_name, scheduler):
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# based on: https://github.com/civitai/civitai/blob/main/src/server/common/constants.ts#L122
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if sampler_name in self.civitai_sampler_map:
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civitai_name = self.civitai_sampler_map[sampler_name]
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if scheduler == "karras":
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civitai_name += " Karras"
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elif scheduler == "exponential":
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civitai_name += " Exponential"
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return civitai_name
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else:
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if scheduler != 'normal':
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return f"{sampler_name}_{scheduler}"
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else:
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return sampler_name
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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", { "tooltip": "image(s) to save"}),
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"filename": ("STRING", {"default": '%time_%basemodelname_%seed', "multiline": False, "tooltip": "filename (available variables: %date, %time, %model, %width, %height, %seed, %counter, %sampler_name, %steps, %cfg, %scheduler, %basemodelname, %denoise, %clip_skip)"}),
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"path": ("STRING", {"default": '', "multiline": False, "tooltip": "path to save the images (under Comfy's save directory)"}),
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"extension": (['png', 'jpeg', 'jpg', 'webp'], { "tooltip": "file extension/type to save image as"}),
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},
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"optional": {
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "number of steps"}),
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"cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "tooltip": "CFG value"}),
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"modelname": ("STRING", {"default": '', "multiline": False, "tooltip": "model name"}),
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"sampler_name": ("STRING", {"default": '', "multiline": False, "tooltip": "sampler name (as string)"}),
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"scheduler": ("STRING", {"default": 'normal', "multiline": False, "tooltip": "scheduler name (as string)"}),
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"positive": ("STRING", {"default": 'unknown', "multiline": True, "tooltip": "positive prompt"}),
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"negative": ("STRING", {"default": 'unknown', "multiline": True, "tooltip": "negative prompt"}),
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"seed_value": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "seed"}),
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"width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "image width"}),
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"height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "tooltip": "image height"}),
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"lossless_webp": ("BOOLEAN", {"default": True, "tooltip": "if True, saved WEBP files will be lossless"}),
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"quality_jpeg_or_webp": ("INT", {"default": 100, "min": 1, "max": 100, "tooltip": "quality setting of JPEG/WEBP"}),
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"optimize_png": ("BOOLEAN", {"default": False, "tooltip": "if True, saved PNG files will be optimized (can reduce file size but is slower)"}),
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"counter": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "counter"}),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "tooltip": "denoise value"}),
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"clip_skip": ("INT", {"default": 0, "min": -24, "max": 24, "tooltip": "skip last CLIP layers (positive or negative value, 0 for no skip)"}),
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"time_format": ("STRING", {"default": "%Y-%m-%d-%H%M%S", "multiline": False, "tooltip": "timestamp format"}),
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"save_workflow_as_json": ("BOOLEAN", {"default": False, "tooltip": "if True, also saves the workflow as a separate JSON file"}),
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"embed_workflow_in_png": ("BOOLEAN", {"default": True, "tooltip": "if True, embeds the workflow in the saved PNG files"}),
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"additional_hashes": ("STRING", {"default": "", "multiline": False, "tooltip": "hashes separated by commas, optionally with names. 'Name:HASH' (e.g., 'MyLoRA:FF735FF83F98')\nWith download_civitai_data set to true, weights can be added as well. (e.g., 'HASH:Weight', 'Name:HASH:Weight')"}),
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"download_civitai_data": ("BOOLEAN", {"default": True, "tooltip": "Download and cache data from civitai.com to save correct metadata. Allows LoRA weights to be saved to the metadata."}),
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"easy_remix": ("BOOLEAN", {"default": True, "tooltip": "Strip LoRAs and simplify 'embedding:path' from the prompt to make the Remix option on civitai.com more seamless."}),
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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_files"
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OUTPUT_NODE = True
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CATEGORY = "ImageSaver"
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DESCRIPTION = "Save images with civitai-compatible generation metadata"
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# Match 'anything' or 'anything:anything' with trimmed white space
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re_manual_hash = re.compile(r'^\s*([^:]+?)(?:\s*:\s*([^\s:][^:]*?))?\s*$')
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# Match 'anything', 'anything:anything' or 'anything:anything:number' with trimmed white space
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re_manual_hash_weights = re.compile(r'^\s*([^:]+?)(?:\s*:\s*([^\s:][^:]*?))?(?:\s*:\s*([-+]?(?:\d+(?:\.\d*)?|\.\d+)))?\s*$')
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MAX_HASH_LENGTH = 16 # skip larger unshortened hashes, such as full sha256 or blake3
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def save_files(
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self,
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images,
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seed_value,
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steps,
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cfg,
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sampler_name,
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scheduler,
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positive,
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negative,
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modelname,
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quality_jpeg_or_webp,
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lossless_webp,
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optimize_png,
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width,
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height,
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counter,
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filename,
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path,
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extension,
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time_format,
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denoise,
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clip_skip,
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additional_hashes="",
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save_workflow_as_json=False,
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embed_workflow_in_png=True,
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download_civitai_data=False,
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easy_remix=False,
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prompt=None,
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extra_pnginfo=None,
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):
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filename = make_filename(filename, width, height, seed_value, modelname, counter, time_format, sampler_name, steps, cfg, scheduler, denoise, clip_skip)
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path = make_pathname(path, width, height, seed_value, modelname, counter, time_format, sampler_name, steps, cfg, scheduler, denoise, clip_skip)
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ckpt_path = folder_paths.get_full_path("checkpoints", modelname)
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if not ckpt_path:
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ckpt_path = folder_paths.get_full_path("diffusion_models", modelname)
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if ckpt_path:
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modelhash = get_sha256(ckpt_path)[:10]
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else:
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modelhash = ""
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metadata_extractor = PromptMetadataExtractor([positive, negative])
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embeddings = metadata_extractor.get_embeddings()
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loras = metadata_extractor.get_loras()
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civitai_sampler_name = self.get_civitai_sampler_name(sampler_name.replace('_gpu', ''), scheduler)
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# Process additional_hashes input (a string) by normalizing, removing extra spaces/newlines, and splitting by comma
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manual_entries = {}
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unnamed_count = 0
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existing_hashes = {modelhash.lower()} | {t[2].lower() for t in loras.values()} | {t[2].lower() for t in embeddings.values()} # Get set of parsed hashes
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additional_hash_split = additional_hashes.replace("\n", ",").split(",") if additional_hashes else []
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for entry in additional_hash_split:
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match = (self.re_manual_hash_weights if download_civitai_data else self.re_manual_hash).search(entry)
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if match is None:
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print(f"ComfyUI-Image-Saver: Invalid additional hash string: '{entry}'")
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continue
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groups = tuple(group for group in match.groups() if group)
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# Read weight and remove from groups, if needed
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weight = None
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if download_civitai_data and len(groups) > 1:
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try:
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weight = float(groups[-1])
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groups = groups[:-1]
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except (ValueError, TypeError):
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pass
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# Read hash, optionally preceded by name
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name, hash = groups if len(groups) > 1 else (None, groups[0])
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if len(hash) > self.MAX_HASH_LENGTH:
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print(f"ComfyUI-Image-Saver: Skipping hash. Length exceeds maximum of {self.MAX_HASH_LENGTH} characters: {hash}")
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continue
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if any(hash.lower() == existing_hash.lower() for _, _, existing_hash in manual_entries.values()):
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print(f"ComfyUI-Image-Saver: Skipping duplicate hash: {hash}")
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continue # Skip duplicates
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if hash.lower() in existing_hashes:
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print(f"ComfyUI-Image-Saver: Skipping manual hash already present in resources: {hash}")
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continue
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if name is None:
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unnamed_count += 1
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name = f"manual{unnamed_count}"
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elif name in manual_entries:
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print(f"ComfyUI-Image-Saver: Duplicate manual hash name '{name}' is being overwritten.")
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manual_entries[name] = (None, weight, hash)
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if len(manual_entries) > 29:
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print("ComfyUI-Image-Saver: Reached maximum limit of 30 manual hashes. Skipping the rest.")
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break
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# Download or load cache of Civitai data, save specially-formatted data to metadata
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civitai_resources = []
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hashes = {}
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add_model_hash = None
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if download_civitai_data:
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for name, (filepath, weight, hash) in ({ modelname: ( ckpt_path, None, modelhash ) } | loras | embeddings | manual_entries).items():
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civitai_info = self.get_civitai_info(filepath, hash)
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if civitai_info is not None:
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resource_data = {}
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# Optional data - modelName, versionName
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resource_data["modelName"] = civitai_info["model"]["name"]
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resource_data["versionName"] = civitai_info["name"]
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# Weight/strength (for LoRA or embedding)
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if weight is not None:
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resource_data["weight"] = weight
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# Required data - AIR or modelVersionId (unique resource identifier)
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# https://github.com/civitai/civitai/wiki/AIR-%E2%80%90-Uniform-Resource-Names-for-AI
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if "air" in civitai_info:
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resource_data["air"] = civitai_info["air"]
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else:
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# Fallback if AIR is not found
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resource_data["modelVersionId"] = civitai_info["id"]
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civitai_resources.append(resource_data)
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else:
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# Fallback in case the data wasn't loaded to add to the "Hashes" section
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if name == modelname:
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add_model_hash = hash.upper()
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else:
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hashes[name] = hash.upper()
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else:
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# Convert all hashes to JSON format
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hashes = {key: value[2] for key, value in embeddings.items()} | {key: value[2] for key, value in loras.items()} | {key: value[2] for key, value in manual_entries.items()} | {"model": modelhash}
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add_model_hash = modelhash
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basemodelname = parse_checkpoint_name_without_extension(modelname)
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if easy_remix:
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def clean_prompt(prompt: str) -> str:
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# Strip loras
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prompt = re.sub(metadata_extractor.LORA, "", prompt)
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# Shorten 'embedding:path/to/my_embedding' -> 'my_embedding'
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# Note: Possible inaccurate embedding name if the filename has been renamed from the default
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prompt = re.sub(metadata_extractor.EMBEDDING, lambda match: Path(match.group(1)).stem, prompt)
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# Remove prompt control edits. e.g., 'STYLE(A1111, mean)', 'SHIFT(1)`, etc.`
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prompt = re.sub(r'\b[A-Z]+\([^)]*\)', "", prompt)
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return prompt
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positive = clean_prompt(positive)
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negative = clean_prompt(negative)
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positive_a111_params = positive.strip()
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negative_a111_params = f"\nNegative prompt: {negative.strip()}"
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clip_skip_str = f", Clip skip: {abs(clip_skip)}" if clip_skip != 0 else ""
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model_hash_str = f", Model hash: {add_model_hash}" if add_model_hash else ""
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hashes_str = f", Hashes: {json.dumps(hashes, separators=(',', ':'))}" if hashes else ""
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a111_params = (
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f"{positive_a111_params}{negative_a111_params}\n"
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f"Steps: {steps}, Sampler: {civitai_sampler_name}, CFG scale: {cfg}, Seed: {seed_value}, "
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f"Size: {width}x{height}{clip_skip_str}{model_hash_str}, Model: {basemodelname}{hashes_str}, Version: ComfyUI"
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)
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# Add Civitai resource listing
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if download_civitai_data and civitai_resources:
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a111_params += f", Civitai resources: {json.dumps(civitai_resources, separators=(',', ':'))}"
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output_path = os.path.join(self.output_dir, path)
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if output_path.strip() != '':
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if not os.path.exists(output_path.strip()):
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print(f'The path `{output_path.strip()}` specified doesn\'t exist! Creating directory.')
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os.makedirs(output_path, exist_ok=True)
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filenames = self.save_images(images, output_path, filename, a111_params, extension, quality_jpeg_or_webp, lossless_webp, optimize_png, prompt, extra_pnginfo, save_workflow_as_json, embed_workflow_in_png)
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subfolder = os.path.normpath(path)
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return {"ui": {"images": map(lambda filename: {"filename": filename, "subfolder": subfolder if subfolder != '.' else '', "type": 'output'}, filenames)}}
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def save_images(self, images, output_path, filename_prefix, a111_params, extension, quality_jpeg_or_webp, lossless_webp, optimize_png, prompt, extra_pnginfo, save_workflow_as_json, embed_workflow_in_png) -> list[str]:
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paths = list()
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for image in images:
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i = 255. * image.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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current_filename_prefix = self.get_unique_filename(output_path, filename_prefix, extension)
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filename = f"{current_filename_prefix}.{extension}"
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filepath = os.path.join(output_path, filename)
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save_image(img, filepath, extension, quality_jpeg_or_webp, lossless_webp, optimize_png, a111_params, prompt, extra_pnginfo, embed_workflow_in_png)
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if save_workflow_as_json:
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save_json(extra_pnginfo, os.path.join(output_path, current_filename_prefix))
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paths.append(filename)
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return paths
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def get_unique_filename(self, output_path, filename_prefix, extension):
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existing_files = [f for f in os.listdir(output_path) if f.startswith(filename_prefix) and f.endswith(extension)]
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if not existing_files:
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return f"{filename_prefix}"
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suffixes = []
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for f in existing_files:
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name, _ = os.path.splitext(f)
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parts = name.split('_')
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if parts[-1].isdigit():
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suffixes.append(int(parts[-1]))
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if suffixes:
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next_suffix = max(suffixes) + 1
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else:
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next_suffix = 1
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return f"{filename_prefix}_{next_suffix:02d}"
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@staticmethod
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def get_manual_folder() -> Path:
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return Path(folder_paths.models_dir) / "image-saver"
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@staticmethod
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def http_get_json(url: str) -> dict | None:
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try:
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response = requests.get(
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url,
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stream=True,
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headers={},
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proxies={ "http": None, "https": None },
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timeout=300
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)
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except TimeoutError:
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print(f"ComfyUI-Image-Saver: HTTP GET Request timed out for {url}")
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return None
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if not response.ok:
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print(f"ComfyUI-Image-Saver: HTTP GET Request failed with error code: {response.status_code}: {response.reason}")
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return None
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try:
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return response.json()
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except ValueError as e:
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print(f"ComfyUI-Image-Saver: HTTP Response JSON error: {e}")
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return None
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@staticmethod
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def get_civitai_info(path: Path | str | None, model_hash: str) -> dict | None:
|
|
try:
|
|
if not model_hash:
|
|
print("ComfyUI-Image-Saver: Error: Missing hash.")
|
|
return None
|
|
|
|
# path is None for additional hashes added by the user - caches manually added hash data in the "image-saver" folder
|
|
if path is None:
|
|
manual_list = ImageSaver.get_manual_list()
|
|
manual_data = manual_list.get(model_hash.upper(), None)
|
|
if manual_data is None:
|
|
content = ImageSaver.download_model_info(path, model_hash)
|
|
if content is None:
|
|
return None
|
|
|
|
# dynamically receive filename from the website to save the metadata
|
|
file = next((file for file in content["files"] if any(len(value) <= ImageSaver.MAX_HASH_LENGTH and value.upper() == model_hash.upper() for value in file["hashes"].values())), None)
|
|
if file is None:
|
|
print(f"ComfyUI-Image-Saver: ({model_hash}) No file hash matched in metadata (should be impossible)")
|
|
return content
|
|
filename = file["name"]
|
|
|
|
# Cache data in a local file, removing the need for repeat http requests
|
|
for hash_value in file["hashes"].values():
|
|
if len(hash_value) <= ImageSaver.MAX_HASH_LENGTH:
|
|
manual_list = ImageSaver.append_manual_list(hash_value.upper(), { "filename": filename, "type": content["model"]["type"] })
|
|
|
|
ImageSaver.save_civitai_info_file(content, ImageSaver.get_manual_folder() / filename)
|
|
return content
|
|
else:
|
|
path = ImageSaver.get_manual_folder() / manual_data["filename"]
|
|
|
|
info_path = Path(path).with_suffix(".civitai.info").absolute()
|
|
with open(info_path, 'r') as file:
|
|
return json.load(file)
|
|
except FileNotFoundError:
|
|
return ImageSaver.download_model_info(path, model_hash)
|
|
except Exception as e:
|
|
print(f"ComfyUI-Image-Saver: Civitai info error: {e}")
|
|
return None
|
|
|
|
@staticmethod
|
|
def get_manual_list() -> dict[str, dict]:
|
|
folder = ImageSaver.get_manual_folder()
|
|
folder.mkdir(parents=True, exist_ok=True)
|
|
try:
|
|
manual_path = (folder / "manual-hashes.json").absolute()
|
|
with open(manual_path, 'r') as file:
|
|
return json.load(file)
|
|
except FileNotFoundError:
|
|
return {}
|
|
except Exception as e:
|
|
print(f"ComfyUI-Image-Saver: Manual list get error: {e}")
|
|
return {}
|
|
|
|
@staticmethod
|
|
def append_manual_list(key: str, value: dict) -> dict[str, dict]:
|
|
manual_list = ImageSaver.get_manual_list() | { key: value }
|
|
try:
|
|
with open((ImageSaver.get_manual_folder() / "manual-hashes.json").absolute(), 'w') as file:
|
|
file.write(json.dumps(manual_list, indent=4))
|
|
except Exception as e:
|
|
print(f"ComfyUI-Image-Saver: Manual list append error: {e}")
|
|
return manual_list
|
|
|
|
@staticmethod
|
|
def download_model_info(path: Path | str | None, model_hash: str) -> dict | None:
|
|
model_label = model_hash if path is None else f"{Path(path).stem}:{model_hash}"
|
|
print(f"ComfyUI-Image-Saver: Downloading model info for '{model_label}'.")
|
|
|
|
content = ImageSaver.http_get_json(f'https://civitai.com/api/v1/model-versions/by-hash/{model_hash.upper()}')
|
|
if content is None:
|
|
return None
|
|
model_id = content["modelId"]
|
|
parent_model = ImageSaver.http_get_json(f'https://civitai.com/api/v1/models/{model_id}')
|
|
if not parent_model:
|
|
parent_model = {}
|
|
|
|
content["creator"] = parent_model.get("creator", "{}")
|
|
model_metadata = content["model"]
|
|
for metadata in [ "description", "tags", "allowNoCredit", "allowCommercialUse", "allowDerivatives", "allowDifferentLicense" ]:
|
|
model_metadata[metadata] = parent_model.get(metadata, "")
|
|
|
|
if path is not None:
|
|
ImageSaver.save_civitai_info_file(content, path)
|
|
|
|
return content
|
|
|
|
@staticmethod
|
|
def save_civitai_info_file(content: dict, path: Path | str) -> bool:
|
|
try:
|
|
with open(Path(path).with_suffix(".civitai.info").absolute(), 'w') as info_file:
|
|
info_file.write(json.dumps(content, indent=4))
|
|
except Exception as e:
|
|
print(f"ComfyUI-Image-Saver: Save Civitai info error '{path}': {e}")
|
|
return False
|
|
return True
|