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https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
checkpoint
This commit is contained in:
Will Miao
@@ -2,13 +2,13 @@ from .py.lora_manager import LoraManager
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from .py.nodes.lora_loader import LoraManagerLoader
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from .py.nodes.trigger_word_toggle import TriggerWordToggle
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from .py.nodes.lora_stacker import LoraStacker
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# from .py.nodes.save_image import SaveImage
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from .py.nodes.save_image import SaveImage
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NODE_CLASS_MAPPINGS = {
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LoraManagerLoader.NAME: LoraManagerLoader,
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TriggerWordToggle.NAME: TriggerWordToggle,
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LoraStacker.NAME: LoraStacker,
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# SaveImage.NAME: SaveImage
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SaveImage.NAME: SaveImage
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}
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WEB_DIRECTORY = "./web/comfyui"
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@@ -1,16 +1,43 @@
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import json
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import os
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import asyncio
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import re
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import numpy as np
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import time
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from server import PromptServer # type: ignore
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import folder_paths # type: ignore
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from ..services.lora_scanner import LoraScanner
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from ..config import config
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from ..workflow.parser import WorkflowParser
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from PIL import Image, PngImagePlugin
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import piexif
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from io import BytesIO
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class SaveImage:
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NAME = "Save Image (LoraManager)"
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CATEGORY = "Lora Manager/utils"
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DESCRIPTION = "Experimental node to display image preview and print prompt and extra_pnginfo"
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DESCRIPTION = "Save images with embedded generation metadata in compatible format"
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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self.type = "output"
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self.prefix_append = ""
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self.compress_level = 4
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self.counter = 0
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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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"image": ("IMAGE",),
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"filename_prefix": ("STRING", {"default": "ComfyUI"}),
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"file_format": (["png", "jpeg", "webp"],),
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},
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"optional": {
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"lossless_webp": ("BOOLEAN", {"default": True}),
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"quality": ("INT", {"default": 100, "min": 1, "max": 100}),
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"save_workflow_json": ("BOOLEAN", {"default": False}),
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"add_counter_to_filename": ("BOOLEAN", {"default": True}),
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},
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"hidden": {
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"prompt": "PROMPT",
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@@ -18,24 +45,252 @@ class SaveImage:
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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RETURN_TYPES = ("IMAGE", "STRING")
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RETURN_NAMES = ("image", "filename")
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FUNCTION = "process_image"
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OUTPUT_NODE = True
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def process_image(self, image, prompt=None, extra_pnginfo=None):
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# Print the prompt information
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print("SaveImage Node - Prompt:")
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async def get_lora_hash(self, lora_name):
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"""Get the lora hash from cache"""
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scanner = await LoraScanner.get_instance()
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cache = await scanner.get_cached_data()
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for item in cache.raw_data:
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if item.get('file_name') == lora_name:
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return item.get('sha256')
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return None
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async def format_metadata(self, parsed_workflow):
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"""Format metadata in the requested format similar to userComment example"""
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if not parsed_workflow:
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return ""
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# Extract the prompt and negative prompt
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prompt = parsed_workflow.get('prompt', '')
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negative_prompt = parsed_workflow.get('negative_prompt', '')
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# Extract loras from the prompt if present
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loras_text = parsed_workflow.get('loras', '')
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lora_hashes = {}
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# If loras are found, add them on a new line after the prompt
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if loras_text:
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prompt_with_loras = f"{prompt}\n{loras_text}"
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# Extract lora names from the format <lora:name:strength>
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lora_matches = re.findall(r'<lora:([^:]+):([^>]+)>', loras_text)
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# Get hash for each lora
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for lora_name, strength in lora_matches:
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hash_value = await self.get_lora_hash(lora_name)
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if hash_value:
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lora_hashes[lora_name] = hash_value
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else:
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prompt_with_loras = prompt
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# Format the first part (prompt and loras)
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metadata_parts = [prompt_with_loras]
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# Add negative prompt
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if negative_prompt:
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metadata_parts.append(f"Negative prompt: {negative_prompt}")
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# Format the second part (generation parameters)
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params = []
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# Add standard parameters in the correct order
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if 'steps' in parsed_workflow:
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params.append(f"Steps: {parsed_workflow.get('steps')}")
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if 'sampler' in parsed_workflow:
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sampler = parsed_workflow.get('sampler')
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# Convert ComfyUI sampler names to user-friendly names
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sampler_mapping = {
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'euler': 'Euler',
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'euler_ancestral': 'Euler a',
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'dpm_2': 'DPM2',
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'dpm_2_ancestral': 'DPM2 a',
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'heun': 'Heun',
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'dpm_fast': 'DPM fast',
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'dpm_adaptive': 'DPM adaptive',
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'lms': 'LMS',
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'dpmpp_2s_ancestral': 'DPM++ 2S a',
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'dpmpp_sde': 'DPM++ SDE',
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'dpmpp_sde_gpu': 'DPM++ SDE',
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'dpmpp_2m': 'DPM++ 2M',
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'dpmpp_2m_sde': 'DPM++ 2M SDE',
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'dpmpp_2m_sde_gpu': 'DPM++ 2M SDE',
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'ddim': 'DDIM'
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}
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sampler_name = sampler_mapping.get(sampler, sampler)
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params.append(f"Sampler: {sampler_name}")
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if 'scheduler' in parsed_workflow:
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scheduler = parsed_workflow.get('scheduler')
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scheduler_mapping = {
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'normal': 'Simple',
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'karras': 'Karras',
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'exponential': 'Exponential',
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'sgm_uniform': 'SGM Uniform',
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'sgm_quadratic': 'SGM Quadratic'
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}
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scheduler_name = scheduler_mapping.get(scheduler, scheduler)
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params.append(f"Schedule type: {scheduler_name}")
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# CFG scale (cfg in parsed_workflow)
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if 'cfg_scale' in parsed_workflow:
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params.append(f"CFG scale: {parsed_workflow.get('cfg_scale')}")
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elif 'cfg' in parsed_workflow:
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params.append(f"CFG scale: {parsed_workflow.get('cfg')}")
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# Seed
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if 'seed' in parsed_workflow:
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params.append(f"Seed: {parsed_workflow.get('seed')}")
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# Size
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if 'size' in parsed_workflow:
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params.append(f"Size: {parsed_workflow.get('size')}")
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# Model info
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if 'checkpoint' in parsed_workflow:
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# Extract basename without path
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checkpoint = os.path.basename(parsed_workflow.get('checkpoint', ''))
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# Remove extension if present
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checkpoint = os.path.splitext(checkpoint)[0]
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params.append(f"Model: {checkpoint}")
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# Add LoRA hashes if available
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if lora_hashes:
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lora_hash_parts = []
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for lora_name, hash_value in lora_hashes.items():
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lora_hash_parts.append(f"{lora_name}: {hash_value}")
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if lora_hash_parts:
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params.append(f"Lora hashes: \"{', '.join(lora_hash_parts)}\"")
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# Combine all parameters with commas
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metadata_parts.append(", ".join(params))
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# Join all parts with a new line
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return "\n".join(metadata_parts)
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def save_images(self, images, filename_prefix, file_format, prompt=None, extra_pnginfo=None,
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lossless_webp=True, quality=100, save_workflow_json=False, add_counter_to_filename=True):
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"""Save images with metadata"""
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results = []
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# Parse the workflow using the WorkflowParser
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parser = WorkflowParser()
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if prompt:
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print(json.dumps(prompt, indent=2))
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parsed_workflow = parser.parse_workflow(prompt)
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else:
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print("No prompt information available")
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parsed_workflow = {}
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# Get or create metadata asynchronously
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metadata = asyncio.run(self.format_metadata(parsed_workflow))
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# Print the extra_pnginfo
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print("\nSaveImage Node - Extra PNG Info:")
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if extra_pnginfo:
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print(json.dumps(extra_pnginfo, indent=2))
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else:
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print("No extra PNG info available")
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# Process each image
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for i, image in enumerate(images):
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# Convert the tensor image to numpy array
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img = 255. * image.cpu().numpy()
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img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
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# Generate filename with counter if needed
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if add_counter_to_filename:
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filename = f"{filename_prefix}_{self.counter:05d}"
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self.counter += 1
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else:
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filename = f"{filename_prefix}"
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# Set file extension and prepare saving parameters
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if file_format == "png":
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filename += ".png"
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file_extension = ".png"
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save_kwargs = {"optimize": True, "compress_level": self.compress_level}
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pnginfo = PngImagePlugin.PngInfo()
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elif file_format == "jpeg":
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filename += ".jpg"
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file_extension = ".jpg"
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save_kwargs = {"quality": quality, "optimize": True}
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elif file_format == "webp":
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filename += ".webp"
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file_extension = ".webp"
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save_kwargs = {"quality": quality, "lossless": lossless_webp}
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# Full save path
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file_path = os.path.join(self.output_dir, filename)
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# Save the image with metadata
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try:
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if file_format == "png":
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if metadata:
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pnginfo.add_text("parameters", metadata)
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if save_workflow_json and extra_pnginfo is not None:
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workflow_json = json.dumps(extra_pnginfo)
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pnginfo.add_text("workflow", workflow_json)
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save_kwargs["pnginfo"] = pnginfo
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img.save(file_path, format="PNG", **save_kwargs)
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elif file_format == "jpeg":
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# For JPEG, use piexif
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if metadata:
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try:
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exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
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exif_bytes = piexif.dump(exif_dict)
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save_kwargs["exif"] = exif_bytes
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except Exception as e:
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print(f"Error adding EXIF data: {e}")
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img.save(file_path, format="JPEG", **save_kwargs)
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elif file_format == "webp":
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# For WebP, also use piexif for metadata
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if metadata:
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try:
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exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
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exif_bytes = piexif.dump(exif_dict)
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save_kwargs["exif"] = exif_bytes
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except Exception as e:
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print(f"Error adding EXIF data: {e}")
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img.save(file_path, format="WEBP", **save_kwargs)
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results.append({
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"filename": filename,
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"subfolder": "",
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"type": self.type
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})
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# Notify UI about saved image
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PromptServer.instance.send_sync("image", {
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"filename": filename,
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"subfolder": "",
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"type": self.type,
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})
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except Exception as e:
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print(f"Error saving image: {e}")
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# Return the image unchanged
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return (image,)
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return results
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def process_image(self, image, filename_prefix="ComfyUI", file_format="png", prompt=None, extra_pnginfo=None,
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lossless_webp=True, quality=100, save_workflow_json=False, add_counter_to_filename=True):
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"""Process and save image with metadata"""
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# Make sure the output directory exists
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os.makedirs(self.output_dir, exist_ok=True)
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# Convert single image to list for consistent processing
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images = [image[0]] if len(image.shape) == 3 else [img for img in image]
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# Save all images
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results = self.save_images(
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images,
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filename_prefix,
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file_format,
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prompt,
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extra_pnginfo,
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lossless_webp,
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quality,
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save_workflow_json,
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add_counter_to_filename
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)
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# Return the first saved filename and the original image
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filename = results[0]["filename"] if results else ""
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return (image, filename)
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@@ -2,13 +2,10 @@ a dynamic and dramatic digital artwork featuring a stylized anthropomorphic whit
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Negative prompt:
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Steps: 30, Sampler: Undefined, CFG scale: 3.5, Seed: 90300501, Size: 832x1216, Clip skip: 2, Created Date: 2025-03-05T13:51:18.1770234Z, Civitai resources: [{"type":"checkpoint","modelVersionId":691639,"modelName":"FLUX","modelVersionName":"Dev"},{"type":"lora","weight":0.4,"modelVersionId":1202162,"modelName":"Velvet\u0027s Mythic Fantasy Styles | Flux \u002B Pony \u002B illustrious","modelVersionName":"Flux Gothic Lines"},{"type":"lora","weight":0.8,"modelVersionId":1470588,"modelName":"Velvet\u0027s Mythic Fantasy Styles | Flux \u002B Pony \u002B illustrious","modelVersionName":"Flux Retro"},{"type":"lora","weight":0.75,"modelVersionId":746484,"modelName":"Elden Ring - Yoshitaka Amano","modelVersionName":"V1"},{"type":"lora","weight":0.2,"modelVersionId":914935,"modelName":"Ink-style","modelVersionName":"ink-dynamic"},{"type":"lora","weight":0.2,"modelVersionId":1189379,"modelName":"Painterly Fantasy by ChronoKnight - [FLUX \u0026 IL]","modelVersionName":"FLUX"},{"type":"lora","weight":0.2,"modelVersionId":757030,"modelName":"Mezzotint Artstyle for Flux - by Ethanar","modelVersionName":"V1"}], Civitai metadata: {}
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<lora:ck-shadow-circuit-IL:0.78>,
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masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject,
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dynamic angle, dutch angle, from below, epic half body portrait, gritty, wabi sabi, looking at viewer, woman is a geisha, parted lips,
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holographic skin, holofoil glitter, faint, glowing, ethereal, neon hair, glowing hair, otherworldly glow, she is dangerous,
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<lora:ck-nc-cyberpunk-IL-000011:0.4>
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<lora:ck-neon-retrowave-IL:0.2>
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<lora:ck-yoneyama-mai-IL-000014:0.4>
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holographic skin, holofoil glitter, faint, glowing, ethereal, neon hair, glowing hair, otherworldly glow, she is dangerous
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<lora:ck-shadow-circuit-IL:0.78>, <lora:ck-nc-cyberpunk-IL-000011:0.4>, <lora:ck-neon-retrowave-IL:0.2>, <lora:ck-yoneyama-mai-IL-000014:0.4>
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Negative prompt: score_6, score_5, score_4, bad quality, worst quality, worst detail, sketch, censorship, furry, window, headphones,
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Steps: 30, Sampler: Euler a, Schedule type: Simple, CFG scale: 7, Seed: 1405717592, Size: 832x1216, Model hash: 1ad6ca7f70, Model: waiNSFWIllustrious_v100, Denoising strength: 0.35, Hires CFG Scale: 5, Hires upscale: 1.3, Hires steps: 20, Hires upscaler: 4x-AnimeSharp, Lora hashes: "ck-shadow-circuit-IL: 88e247aa8c3d, ck-nc-cyberpunk-IL-000011: 935e6755554c, ck-neon-retrowave-IL: edafb9df7da1, ck-yoneyama-mai-IL-000014: 1b9305692a2e", Version: f2.0.1v1.10.1-1.10.1, Diffusion in Low Bits: Automatic (fp16 LoRA)
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