add_nodes(use="picture") builds a text-to-image graph on the model the
user picked for the task, or add_nodes(model=...): one checkpoint for SDXL
and its kin; for Anima the UNET with its text encoder and VAE, found in
the model folders ("files"), and the tested base-pass sampling. A
standalone block is built only on a canvas making no picture yet.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
247 lines
6.0 KiB
JSON
247 lines
6.0 KiB
JSON
{
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"job": "picture",
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"rank": 1,
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"short": "a picture from words",
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"standalone": true,
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"title": {
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"zh": "出图",
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"en": "Make a picture"
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},
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"about": {
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"zh": "在空画布上按模型的家族搭好出图的节点:SDXL 一类是一个 checkpoint,Anima 是模型、文本编码器、VAE 三个文件;设置用验证过的那一套。",
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"en": "Builds the nodes that make a picture on an empty canvas, by the model's family: one checkpoint for SDXL and its kin, the model, text encoder and VAE files for Anima; with the tested settings."
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},
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"source": {
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"zh": "ComfyUI 的默认出图流程;Anima 的设置来自你验证过的两段放大卡的底图那一段",
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"en": "ComfyUI's default text-to-image graph; Anima's settings from the base pass of the user's tested two-pass card"
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},
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"packs": {},
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"nodes": {
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"model": {
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"class_type": "CheckpointLoaderSimple",
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"_meta": {
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"title": "Model"
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},
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"inputs": {
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"ckpt_name": "$model"
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}
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},
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"positive": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"text": "",
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"clip": [
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"model",
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1
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]
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}
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},
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"negative": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"text": "",
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"clip": [
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"model",
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1
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]
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}
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},
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"size": {
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"class_type": "EmptyLatentImage",
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"inputs": {
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"width": 832,
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"height": 1216,
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"batch_size": 1
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}
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},
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"sampler": {
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"class_type": "KSampler",
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"inputs": {
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"model": [
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"model",
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0
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],
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"positive": [
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"positive",
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0
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],
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"negative": [
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"negative",
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0
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],
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"latent_image": [
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"size",
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0
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],
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"steps": 28,
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"cfg": 5.5,
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"sampler_name": "euler_ancestral",
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"scheduler": "normal",
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"denoise": 1.0
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}
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},
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"decode": {
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"class_type": "VAEDecode",
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"inputs": {
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"samples": [
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"sampler",
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0
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],
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"vae": [
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"model",
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2
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]
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}
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},
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"save": {
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"class_type": "SaveImage",
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"inputs": {
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"images": [
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"decode",
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0
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],
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"filename_prefix": "picture"
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}
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}
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},
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"variants": {
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"anima": {
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"files": {
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"$text_encoder": [
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"CLIPLoader",
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"clip_name",
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[
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"qwen_3_06b",
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"qwen3_06b"
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]
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],
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"$vae": [
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"VAELoader",
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"vae_name",
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[
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"qwenimage",
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"qwen_image"
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]
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]
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},
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"nodes": {
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"model": {
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"class_type": "UNETLoader",
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"_meta": {
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"title": "Model"
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},
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"inputs": {
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"unet_name": "$model",
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"weight_dtype": "default"
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}
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},
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"text_encoder": {
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"class_type": "CLIPLoader",
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"inputs": {
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"clip_name": "$text_encoder",
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"type": "stable_diffusion"
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}
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},
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"vae": {
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"class_type": "VAELoader",
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"inputs": {
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"vae_name": "$vae"
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}
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},
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"positive": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"text": "",
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"clip": [
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"text_encoder",
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0
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]
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}
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},
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"negative": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"text": "",
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"clip": [
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"text_encoder",
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0
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]
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}
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},
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"size": {
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"class_type": "EmptyLatentImage",
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"inputs": {
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"width": 832,
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"height": 1216,
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"batch_size": 1
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}
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},
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"sampler": {
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"class_type": "KSampler",
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"inputs": {
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"model": [
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"model",
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0
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],
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"positive": [
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"positive",
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0
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],
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"negative": [
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"negative",
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0
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],
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"latent_image": [
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"size",
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0
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],
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"steps": 35,
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"cfg": 4.4,
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"sampler_name": "dpmpp_2m_sde_gpu",
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"scheduler": "beta",
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"denoise": 1.0
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}
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},
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"decode": {
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"class_type": "VAEDecode",
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"inputs": {
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"samples": [
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"sampler",
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0
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],
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"vae": [
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"vae",
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0
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]
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}
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},
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"save": {
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"class_type": "SaveImage",
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"inputs": {
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"images": [
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"decode",
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0
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],
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"filename_prefix": "picture"
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}
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}
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},
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"after": "The picture's nodes are on the canvas, on the Anima model picked (the loader titled Model), with its text encoder and VAE and the tested sampling (35 steps, dpmpp_2m_sde_gpu, beta, cfg 4.4, 832x1216). run_workflow with prompt and negative fills them in. A style LoRA the user picked: add_lora. A second pass after it (enlarge, another model): add_nodes with that use."
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}
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},
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"after": "The picture's nodes are on the canvas, on the checkpoint picked (the loader titled Model), with common SDXL settings (28 steps, euler_ancestral, cfg 5.5, 832x1216; get_model's author_examples has what the model's author used). run_workflow with prompt and negative fills them in. A style LoRA the user picked: add_lora. A second pass after it (enlarge, another model): add_nodes with that use.",
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"notes": "SDXL's values are common ones, not tested per model here; Anima's are the base pass of the user's tested two-pass card.",
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"tested": {
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"gpu": "RTX 4050 Laptop",
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"vram_gb": 6,
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"date": "2026-11-03",
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"runs": [
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{
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"model": "Anima (MiaoMiao Harem 1.5)",
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"size": "832×1216",
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"seconds": 78
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}
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]
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}
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}
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