99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
import folder_paths
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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import numpy as np
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import json
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import os
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from datetime import datetime
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from .civitai_datas import GenerationData
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class SaveCivitai:
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global FIRST_GEN
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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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methods = {"default": 4, "fastest": 0, "slowest": 6}
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"images": ("IMAGE",),
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"filename_prefix": ("STRING", {"default": "ComfyUI"}),
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"lora_stacker": ("STRING", {"default": "LoRA Stacker"}),
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"efficient_loader": ("STRING", {"default": "Efficient Loader"}),
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"sampler": ("STRING", {"default": "KSampler"}),
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},
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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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 = "MyTest"
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# Retrieve metadatas from Images, to prevent model loading calculation at each batch images,
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# datas are generated at first images, and copied to each next image.
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# Only seeds will be retrieved, as it may be unique
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def process_metadata(self, lora_stacker, efficient_loader, sampler, datas, batch_number):
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global FIRST_GEN
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if batch_number == 0:
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print("Generating First Data")
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FIRST_GEN = GenerationData(datas, lora_stacker, efficient_loader, sampler)
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generated = FIRST_GEN
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elif batch_number > 0 and FIRST_GEN:
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print("Loading First Data")
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generated = GenerationData(datas, lora_stacker, efficient_loader, sampler, FIRST_GEN)
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return generated.__str__()
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# Save image with metadatas, retreived with the help of nodes names.
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def save_images(self, images, filename_prefix="ComfyUI", lora_stacker="LoRA Stacker", efficient_loader="Efficient Loader", sampler="KSampler", prompt=None, extra_pnginfo=None):
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# Save location based on datetime, like on WEBUI
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filename_prefix = datetime.now().strftime("%Y-%m-%d/") + filename_prefix + self.prefix_append
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
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filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
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results = list()
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for (batch_number, image) in enumerate(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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metadata = PngInfo()
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if prompt is not None:
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metadata.add_text("prompt", json.dumps(prompt))
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metadata.add_text("parameters", self.process_metadata(lora_stacker, efficient_loader, sampler, prompt, batch_number))
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if extra_pnginfo is not None:
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for x in extra_pnginfo:
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metadata.add_text(x, json.dumps(extra_pnginfo[x]))
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filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
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file = f"{filename_with_batch_num}_{counter:05}_.png"
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img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
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results.append({
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"filename": file,
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"subfolder": subfolder,
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"type": self.type
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})
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counter += 1
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return {"ui": {"images": results}}
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# A dictionary that contains all nodes you want to export with their names
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# NOTE: names should be globally unique
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NODE_CLASS_MAPPINGS = {
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"SaveCivitai": SaveCivitai
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}
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