133 lines
3.7 KiB
JSON
133 lines
3.7 KiB
JSON
{
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"1": {
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"class_type": "UnetLoaderGGUFAdvanced",
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"inputs": {
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"unet_name": "qwen-image-2.1-turbo-Q4_K_M.gguf",
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"dequant_dtype": "float32",
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"patch_dtype": "float32",
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"patch_on_device": false
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}
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},
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"2": {
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"class_type": "CLIPLoader",
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"inputs": {
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"clip_name": "qwen3vl_8b_int8_convrot.safetensors",
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"type": "qwen_image",
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"device": "cpu"
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}
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},
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"3": {
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"class_type": "TextEncodeQwenImage21",
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"inputs": {
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"clip": [
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"2",
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0
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],
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"prompt": "Create a single image from the supplied scene description.\n\nSource generation_brief:\nCreate a highly detailed cinematic architectural illustration of a remote mountain observatory at blue hour after a light snowfall. The building occupies the right two-thirds of the square image: a circular pale-stone base supports one weathered copper dome, whose open viewing slit points toward the upper left. A narrow exterior staircase with dark iron handrails climbs from a small lower entrance to a balcony; every step has a thin undisturbed layer of snow. One adult astronomer in a dark red winter coat stands on the balcony with a small white notebook held closed in one gloved hand, looking toward the sky. Place a warm amber lamp immediately above the entrance, creating a pool of light on the stone without illuminating the whole landscape. In the lower left foreground, a crooked pine tree and two exposed granite rocks frame a winding path that leads toward the building. Behind the observatory, show distant mountain ridges fading from slate blue into violet mist. The sky contains scattered sharp stars and a faint natural Milky Way band, with no oversized planets or fantasy moons. Balance warm interior light against cold ambient snow light, use physically plausible perspective, careful separation of the staircase rails and the figure, and visible differences between copper, stone, glass, fabric and snow. The final scene should feel quiet, inhabited and believable. Do not add titles, labels, logos, watermarks, extra people, extra domes or duplicate staircases.",
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"negative_prompt": "",
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"resolution": 512
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}
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},
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"4": {
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"class_type": "VAELoader",
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"inputs": {
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"vae_name": "qwen_image_2.1_vae_bf16.safetensors"
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}
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},
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"5": {
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"class_type": "QwenImage21Cache",
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"inputs": {
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"model": [
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"1",
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0
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],
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"device": "off",
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"dtype": "default"
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}
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},
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"6": {
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"class_type": "SamplerCustomAdvanced",
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"inputs": {
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"noise": [
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"9",
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0
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],
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"guider": [
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"10",
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0
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],
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"sampler": [
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"11",
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0
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],
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"sigmas": [
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"12",
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0
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],
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"latent_image": [
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"3",
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2
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]
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}
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},
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"7": {
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"class_type": "VAEDecode",
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"inputs": {
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"samples": [
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"6",
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0
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],
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"vae": [
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"4",
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0
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]
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}
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},
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"8": {
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"class_type": "SaveImage",
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"inputs": {
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"images": [
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"7",
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0
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],
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"filename_prefix": "PacketFoundry-Qwen21Turbo"
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}
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},
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"9": {
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"class_type": "RandomNoise",
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"inputs": {
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"noise_seed": 51
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}
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},
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"10": {
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"class_type": "CFGGuider",
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"inputs": {
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"model": [
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"5",
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0
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],
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"positive": [
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"3",
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0
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],
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"negative": [
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"3",
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1
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],
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"cfg": 1.0
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}
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},
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"11": {
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"class_type": "KSamplerSelect",
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"inputs": {
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"sampler_name": "euler"
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}
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},
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"12": {
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"class_type": "ManualSigmas",
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"inputs": {
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"sigmas": "1.0, 0.978453, 0.95418, 0.926626, 0.89508, 0.845148, 0.704534, 0.414568, 0"
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}
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}
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}
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