catboxanon
0a1c18ef89
Ref: https://docs.novelai.net/image/strengthening-weakening.html#numerical-strengthening--weakening
596 lines
24 KiB
Python
596 lines
24 KiB
Python
import copy
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import io
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from pathlib import Path
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import folder_paths
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import zipfile
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from .utils import *
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TOOLTIP_LIMIT_OPUS_FREE = "Limit image size and steps for free generation by Opus."
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class PromptToNAID:
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@classmethod
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def INPUT_TYPES(s):
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return { "required": {
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"text": ("STRING", { "forceInput":True, "multiline": True, "dynamicPrompts": False,}),
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"weight_per_brace": ("FLOAT", { "default": 0.05, "min": 0.05, "max": 0.10, "step": 0.05 }),
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"syntax_mode": (["brace", "numeric"], { "default": "brace" }),
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}}
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RETURN_TYPES = ("STRING",)
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FUNCTION = "convert"
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CATEGORY = "NovelAI/utils"
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def convert(self, text, weight_per_brace, syntax_mode):
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nai_prompt = prompt_to_nai(text, weight_per_brace, syntax_mode)
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return (nai_prompt,)
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class ImageToNAIMask:
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@classmethod
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def INPUT_TYPES(s):
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return { "required": { "image": ("IMAGE",) } }
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "convert"
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CATEGORY = "NovelAI/utils"
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def convert(self, image):
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s = resize_to_naimask(image)
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return (s,)
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class ModelOption:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": (["nai-diffusion-2", "nai-diffusion-furry-3", "nai-diffusion-3", "nai-diffusion-4-curated-preview", "nai-diffusion-4-full", "nai-diffusion-4-5-curated"], { "default": "nai-diffusion-4-5-curated" }),
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},
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"optional": { "option": ("NAID_OPTION",) },
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}
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RETURN_TYPES = ("NAID_OPTION",)
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FUNCTION = "set_option"
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CATEGORY = "NovelAI"
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def set_option(self, model, option=None):
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option = copy.deepcopy(option) if option else {}
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option["model"] = model
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return (option,)
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class Img2ImgOption:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"strength": ("FLOAT", { "default": 0.70, "min": 0.01, "max": 0.99, "step": 0.01, "display": "number" }),
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"noise": ("FLOAT", { "default": 0.00, "min": 0.00, "max": 0.99, "step": 0.02, "display": "number" }),
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},
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}
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RETURN_TYPES = ("NAID_OPTION",)
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FUNCTION = "set_option"
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CATEGORY = "NovelAI"
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def set_option(self, image, strength, noise):
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option = {}
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option["img2img"] = (image, strength, noise)
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return (option,)
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class InpaintingOption:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"mask": ("IMAGE",),
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"add_original_image": ("BOOLEAN", { "default": True }),
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},
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}
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RETURN_TYPES = ("NAID_OPTION",)
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FUNCTION = "set_option"
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CATEGORY = "NovelAI"
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def set_option(self, image, mask, add_original_image):
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option = {}
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option["infill"] = (image, mask, add_original_image)
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return (option,)
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class VibeTransferOption:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"information_extracted": ("FLOAT", { "default": 1.0, "min": 0.01, "max": 1.0, "step": 0.01, "display": "number" }),
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"strength": ("FLOAT", { "default": 0.6, "min": 0.01, "max": 1.0, "step": 0.01, "display": "number" }),
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},
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"optional": { "option": ("NAID_OPTION",) },
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}
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RETURN_TYPES = ("NAID_OPTION",)
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FUNCTION = "set_option"
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CATEGORY = "NovelAI"
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def set_option(self, image, information_extracted, strength, option=None):
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option = copy.deepcopy(option) if option else {}
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if "vibe" not in option:
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option["vibe"] = []
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option["vibe"].append((image, information_extracted, strength))
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return (option,)
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class NetworkOption:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"ignore_errors": ("BOOLEAN", { "default": True }),
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"timeout_sec": ("INT", { "default": 120, "min": 30, "max": 3000, "step": 1, "display": "number" }),
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"retry": ("INT", { "default": 3, "min": 1, "max": 100, "step": 1, "display": "number" }),
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},
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"optional": { "option": ("NAID_OPTION",) },
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}
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RETURN_TYPES = ("NAID_OPTION",)
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FUNCTION = "set_option"
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CATEGORY = "NovelAI"
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def set_option(self, ignore_errors, timeout_sec, retry, option=None):
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option = copy.deepcopy(option) if option else {}
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option["ignore_errors"] = ignore_errors
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option["timeout"] = timeout_sec
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option["retry"] = retry
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return (option,)
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class GenerateNAID:
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def __init__(self):
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self.access_token = get_access_token()
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }),
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"width": ("INT", { "default": 832, "min": 64, "max": 1600, "step": 64, "display": "number" }),
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"height": ("INT", { "default": 1216, "min": 64, "max": 1600, "step": 64, "display": "number" }),
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"positive": ("STRING", { "default": ", best quality, amazing quality, very aesthetic, absurdres", "multiline": True, "dynamicPrompts": False }),
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"negative": ("STRING", { "default": "lowres", "multiline": True, "dynamicPrompts": False }),
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"steps": ("INT", { "default": 28, "min": 0, "max": 50, "step": 1, "display": "number" }),
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"cfg": ("FLOAT", { "default": 5.0, "min": 0.0, "max": 10.0, "step": 0.1, "display": "number" }),
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"variety" : ("BOOLEAN", { "default": False }),
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"decrisper": ("BOOLEAN", { "default": False }),
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"smea": (["none", "SMEA", "SMEA+DYN"], { "default": "none" }),
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"sampler": (["k_euler", "k_euler_ancestral", "k_dpmpp_2s_ancestral", "k_dpmpp_2m_sde", "k_dpmpp_2m", "k_dpmpp_sde", "ddim"], { "default": "k_euler" }),
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"scheduler": (["native", "karras", "exponential", "polyexponential"], { "default": "native" }),
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"seed": ("INT", { "default": 0, "min": 0, "max": 9999999999, "step": 1, "display": "number" }),
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"uncond_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 1.5, "step": 0.05, "display": "number" }),
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"cfg_rescale": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.02, "display": "number" }),
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"keep_alpha": ("BOOLEAN", { "default": True, "tooltip": "Disable to further process output images locally" }),
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},
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"optional": { "option": ("NAID_OPTION",) },
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "generate"
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CATEGORY = "NovelAI"
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def generate(self, limit_opus_free, width, height, positive, negative, steps, cfg, decrisper, variety, smea, sampler, scheduler, seed, uncond_scale, cfg_rescale, keep_alpha, option=None):
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width, height = calculate_resolution(width*height, (width, height))
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# ref. novelai_api.ImagePreset
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params = {
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"params_version": 1,
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"width": width,
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"height": height,
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"scale": cfg,
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"sampler": sampler,
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"steps": steps,
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"seed": seed,
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"n_samples": 1,
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"ucPreset": 3,
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"qualityToggle": False,
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"sm": (smea == "SMEA" or smea == "SMEA+DYN") and sampler != "ddim",
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"sm_dyn": smea == "SMEA+DYN" and sampler != "ddim",
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"dynamic_thresholding": decrisper,
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"skip_cfg_above_sigma": None,
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"controlnet_strength": 1.0,
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"legacy": False,
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"add_original_image": False,
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"cfg_rescale": cfg_rescale,
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"noise_schedule": scheduler,
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"legacy_v3_extend": False,
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"uncond_scale": uncond_scale,
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"negative_prompt": negative,
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"prompt": positive,
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"reference_image_multiple": [],
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"reference_information_extracted_multiple": [],
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"reference_strength_multiple": [],
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"extra_noise_seed": seed,
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"v4_prompt": {
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"use_coords": False,
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"use_order": False,
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"caption": {
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"base_caption": positive,
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"char_captions": []
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}
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},
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"v4_negative_prompt": {
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"use_coords": False,
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"use_order": False,
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"caption": {
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"base_caption": negative,
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"char_captions": []
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}
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}
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}
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model = "nai-diffusion-4-full"
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action = "generate"
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if sampler == "k_euler_ancestral" and scheduler != "native":
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params["deliberate_euler_ancestral_bug"] = False
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params["prefer_brownian"] = True
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if option:
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if "img2img" in option:
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action = "img2img"
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image, strength, noise = option["img2img"]
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params["image"] = image_to_base64(resize_image(image, (width, height)))
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params["strength"] = strength
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params["noise"] = noise
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elif "infill" in option:
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action = "infill"
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image, mask, add_original_image = option["infill"]
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params["image"] = image_to_base64(resize_image(image, (width, height)))
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params["mask"] = naimask_to_base64(resize_to_naimask(mask, (width, height), "4" in model))
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params["add_original_image"] = add_original_image
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if "vibe" in option:
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for vibe in option["vibe"]:
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image, information_extracted, strength = vibe
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params["reference_image_multiple"].append(image_to_base64(resize_image(image, (width, height))))
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params["reference_information_extracted_multiple"].append(information_extracted)
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params["reference_strength_multiple"].append(strength)
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if "model" in option:
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model = option["model"]
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# Handle V4 options
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if "v4_prompt" in option:
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params["v4_prompt"].update(option["v4_prompt"])
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timeout = option["timeout"] if option and "timeout" in option else None
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retry = option["retry"] if option and "retry" in option else None
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if limit_opus_free:
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pixel_limit = 1024*1024 if model in ("nai-diffusion-2", "nai-diffusion-furry-3", "nai-diffusion-3", "nai-diffusion-4", "nai-diffusion-4-curated-preview", "nai-diffusion-4-full", "nai-diffusion-4-5-curated") else 640*640
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if width * height > pixel_limit:
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max_width, max_height = calculate_resolution(pixel_limit, (width, height))
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params["width"] = max_width
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params["height"] = max_height
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if steps > 28:
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params["steps"] = 28
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if variety:
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params["skip_cfg_above_sigma"] = calculate_skip_cfg_above_sigma(params["width"], params["height"])
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if sampler == "ddim" and model in ("nai-diffusion-furry-3", "nai-diffusion-3", "nai-diffusion-4-curated-preview", "nai-diffusion-4-full", "nai-diffusion-4-5-curated"):
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params["sampler"] = "ddim_v3"
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if action == "infill" and model != "nai-diffusion-4-5-curated":
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model = f"{model}-inpainting"
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image = blank_image()
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try:
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zipped_bytes = generate_image(self.access_token, positive, model, action, params, timeout, retry)
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zipped = zipfile.ZipFile(io.BytesIO(zipped_bytes))
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image_bytes = zipped.read(zipped.infolist()[0]) # only support one n_samples
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## save original png to comfy output dir
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path("NAI_autosave", self.output_dir)
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file = f"{filename}_{counter:05}_.png"
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d = Path(full_output_folder)
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d.mkdir(exist_ok=True)
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(d / file).write_bytes(image_bytes)
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image = bytes_to_image(image_bytes, keep_alpha)
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except Exception as e:
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if "ignore_errors" in option and option["ignore_errors"]:
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print("ignore error:", e)
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else:
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raise e
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return (image,)
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def base_augment(access_token, output_dir, limit_opus_free, ignore_errors, req_type, image, options=None):
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image = image.movedim(-1, 1)
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w, h = (image.shape[3], image.shape[2])
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image = image.movedim(1, -1)
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if limit_opus_free:
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pixel_limit = 1024 * 1024
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if w * h > pixel_limit:
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w, h = calculate_resolution(pixel_limit, (w, h))
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base64_image = image_to_base64(resize_image(image, (w, h)))
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result_image = blank_image()
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try:
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# Build request based on NAI v4 API spec
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request = {
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"image": base64_image,
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"req_type": req_type,
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"width": w,
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"height": h
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}
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# Add optional parameters if provided
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if options:
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if "defry" in options:
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request["defry"] = options["defry"]
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if "prompt" in options:
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request["prompt"] = options["prompt"]
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zipped_bytes = augment_image(access_token, req_type, w, h, base64_image, options=options)
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zipped = zipfile.ZipFile(io.BytesIO(zipped_bytes))
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image_bytes = zipped.read(zipped.infolist()[0]) # only support one n_samples
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## save original png to comfy output dir
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path("NAI_autosave", output_dir)
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file = f"{filename}_{counter:05}_.png"
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d = Path(full_output_folder)
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d.mkdir(exist_ok=True)
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(d / file).write_bytes(image_bytes)
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result_image = bytes_to_image(image_bytes)
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except Exception as e:
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if ignore_errors:
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print("ignore error:", e)
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else:
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raise e
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return (result_image,)
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class RemoveBGAugment:
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def __init__(self):
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self.access_token = get_access_token()
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }),
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"ignore_errors": ("BOOLEAN", { "default": False }),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "augment"
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CATEGORY = "NovelAI/director_tools"
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def augment(self, image, limit_opus_free, ignore_errors):
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return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "bg-removal", image)
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class LineArtAugment:
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def __init__(self):
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self.access_token = get_access_token()
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }),
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"ignore_errors": ("BOOLEAN", { "default": False }),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "augment"
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CATEGORY = "NovelAI/director_tools"
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def augment(self, image, limit_opus_free, ignore_errors):
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return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "lineart", image)
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class SketchAugment:
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def __init__(self):
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self.access_token = get_access_token()
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }),
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"ignore_errors": ("BOOLEAN", { "default": False }),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "augment"
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CATEGORY = "NovelAI/director_tools"
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def augment(self, image, limit_opus_free, ignore_errors):
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return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "sketch", image)
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class ColorizeAugment:
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def __init__(self):
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self.access_token = get_access_token()
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }),
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"ignore_errors": ("BOOLEAN", { "default": False }),
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"defry": ("INT", { "default": 0, "min": 0, "max": 5, "step": 1, "display": "number" }),
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"prompt": ("STRING", { "default": "", "multiline": True, "dynamicPrompts": False }),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "augment"
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CATEGORY = "NovelAI/director_tools"
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def augment(self, image, limit_opus_free, ignore_errors, defry, prompt):
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return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "colorize", image, options={ "defry": defry, "prompt": prompt })
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class EmotionAugment:
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def __init__(self):
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self.access_token = get_access_token()
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self.output_dir = folder_paths.get_output_directory()
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strength_list = ["normal", "slightly_weak", "weak", "even_weaker", "very_weak", "weakest"]
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@classmethod
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def INPUT_TYPES(s):
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|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }),
|
|
"ignore_errors": ("BOOLEAN", { "default": False }),
|
|
"mood": (["neutral", "happy", "sad", "angry", "scared",
|
|
"surprised", "tired", "excited", "nervous", "thinking",
|
|
"confused", "shy", "disgusted", "smug", "bored",
|
|
"laughing", "irritated", "aroused", "embarrassed", "worried",
|
|
"love", "determined", "hurt", "playful"], { "default": "neutral" }),
|
|
"strength": (s.strength_list, { "default": "normal" }),
|
|
"prompt": ("STRING", { "default": "", "multiline": True, "dynamicPrompts": False }),
|
|
},
|
|
}
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "augment"
|
|
CATEGORY = "NovelAI/director_tools"
|
|
def augment(self, image, limit_opus_free, ignore_errors, mood, strength, prompt):
|
|
prompt = f"{mood};;{prompt}"
|
|
defry = EmotionAugment.strength_list.index(strength)
|
|
return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "emotion", image, options={ "defry": defry, "prompt": prompt })
|
|
|
|
class DeclutterAugment:
|
|
def __init__(self):
|
|
self.access_token = get_access_token()
|
|
self.output_dir = folder_paths.get_output_directory()
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }),
|
|
"ignore_errors": ("BOOLEAN", { "default": False }),
|
|
},
|
|
}
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "augment"
|
|
CATEGORY = "NovelAI/director_tools"
|
|
def augment(self, image, limit_opus_free, ignore_errors):
|
|
return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "declutter", image)
|
|
class V4BasePrompt:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"base_caption": ("STRING", { "multiline": True }),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",) # Changed from NAID_OPTION to STRING
|
|
FUNCTION = "convert" # Changed from set_option to convert
|
|
CATEGORY = "NovelAI/v4"
|
|
def convert(self, base_caption):
|
|
return (base_caption,) # Simply returns the caption as a string
|
|
|
|
"""class V4PromptConfig:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"use_coords": ("BOOLEAN", { "default": False }),
|
|
"use_order": ("BOOLEAN", { "default": False }),
|
|
},
|
|
"optional": { "option": ("NAID_OPTION",) },
|
|
}
|
|
|
|
RETURN_TYPES = ("NAID_OPTION",)
|
|
FUNCTION = "set_option"
|
|
CATEGORY = "NovelAI/v4"
|
|
def set_option(self, use_coords, use_order, option=None):
|
|
option = copy.deepcopy(option) if option else {}
|
|
if "v4_prompt" not in option:
|
|
option["v4_prompt"] = {}
|
|
option["v4_prompt"]["use_coords"] = use_coords
|
|
option["v4_prompt"]["use_order"] = use_order
|
|
return (option,)
|
|
|
|
class V4CharacterCaption:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"char_caption": ("STRING", { "multiline": True }),
|
|
"x": ("FLOAT", { "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01 }),
|
|
"y": ("FLOAT", { "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01 }),
|
|
},
|
|
"optional": { "option": ("NAID_OPTION",) },
|
|
}
|
|
|
|
RETURN_TYPES = ("NAID_OPTION",)
|
|
FUNCTION = "set_option"
|
|
CATEGORY = "NovelAI/v4"
|
|
def set_option(self, char_caption, x, y, option=None):
|
|
option = copy.deepcopy(option) if option else {}
|
|
if "v4_prompt" not in option:
|
|
option["v4_prompt"] = {
|
|
"caption": {
|
|
"base_caption": "",
|
|
"char_captions": []
|
|
}
|
|
}
|
|
|
|
char_caption_obj = {
|
|
"char_caption": char_caption,
|
|
"centers": [{"x": x, "y": y}]
|
|
}
|
|
|
|
option["v4_prompt"]["caption"]["char_captions"].append(char_caption_obj)
|
|
return (option,)
|
|
"""
|
|
class V4NegativePrompt:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"negative_caption": ("STRING", { "multiline": True }),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
FUNCTION = "convert"
|
|
CATEGORY = "NovelAI/v4"
|
|
def convert(self, negative_caption):
|
|
return (negative_caption,)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"GenerateNAID": GenerateNAID,
|
|
"ModelOptionNAID": ModelOption,
|
|
"Img2ImgOptionNAID": Img2ImgOption,
|
|
"InpaintingOptionNAID": InpaintingOption,
|
|
"VibeTransferOptionNAID": VibeTransferOption,
|
|
"NetworkOptionNAID": NetworkOption,
|
|
"MaskImageToNAID": ImageToNAIMask,
|
|
"PromptToNAID": PromptToNAID,
|
|
"RemoveBGNAID": RemoveBGAugment,
|
|
"LineArtNAID": LineArtAugment,
|
|
"SketchNAID": SketchAugment,
|
|
"ColorizeNAID": ColorizeAugment,
|
|
"EmotionNAID": EmotionAugment,
|
|
"DeclutterNAID": DeclutterAugment,
|
|
"V4BasePrompt": V4BasePrompt,
|
|
"V4NegativePrompt": V4NegativePrompt,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"GenerateNAID": "Generate ✒️🅝🅐🅘",
|
|
"ModelOptionNAID": "ModelOption ✒️🅝🅐🅘",
|
|
"Img2ImgOptionNAID": "Img2ImgOption ✒️🅝🅐🅘",
|
|
"InpaintingOptionNAID": "InpaintingOption ✒️🅝🅐🅘",
|
|
"VibeTransferOptionNAID": "VibeTransferOption ✒️🅝🅐🅘",
|
|
"NetworkOptionNAID": "NetworkOption ✒️🅝🅐🅘",
|
|
"MaskImageToNAID": "Convert Mask Image ✒️🅝🅐🅘",
|
|
"PromptToNAID": "Convert Prompt ✒️🅝🅐🅘",
|
|
"RemoveBGNAID": "Remove BG ✒️🅝🅐🅘",
|
|
"LineArtNAID": "LineArt ✒️🅝🅐🅘",
|
|
"SketchNAID": "Sketch ✒️🅝🅐🅘",
|
|
"ColorizeNAID": "Colorize ✒️🅝🅐🅘",
|
|
"EmotionNAID": "Emotion ✒️🅝🅐🅘",
|
|
"DeclutterNAID": "Declutter ✒️🅝🅐🅘",
|
|
"V4BasePrompt": "V4 Base Prompt ✒️🅝🅐🅘",
|
|
"V4NegativePrompt": "V4 Negative Prompt ✒️🅝🅐🅘",
|
|
}
|