mirror of
https://github.com/NVIDIA/dgx-spark-playbooks.git
synced 2026-06-19 12:59:34 +00:00
152 lines
3.5 KiB
JSON
152 lines
3.5 KiB
JSON
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{
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"1": {
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"inputs": {
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"unet_name": "flux1-canny-dev.safetensors",
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"weight_dtype": "default"
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},
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"class_type": "UNETLoader",
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"_meta": { "title": "Load FLUX.1 Canny Dev Model" }
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},
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"2": {
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"inputs": {
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"clip_name1": "t5xxl_fp16.safetensors",
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"clip_name2": "clip_l.safetensors",
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"type": "flux"
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},
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"class_type": "DualCLIPLoader",
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"_meta": { "title": "DualCLIPLoader (T5-XXL + CLIP-L)" }
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},
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"3": {
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"inputs": {
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"vae_name": "ae.safetensors"
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},
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"class_type": "VAELoader",
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"_meta": { "title": "Load VAE" }
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},
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"4": {
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"inputs": {
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"text": "A futuristic cyberpunk cityscape at night, neon lights, rain-soaked streets, highly detailed",
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"clip": ["2", 0]
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},
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"class_type": "CLIPTextEncode",
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"_meta": { "title": "CLIP Text Encode (Positive Prompt)" }
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},
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"5": {
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"inputs": {
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"guidance": 30.0,
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"conditioning": ["4", 0]
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},
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"class_type": "FluxGuidance",
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"_meta": { "title": "FLUX Guidance (30 for canny-dev)" }
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},
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"6": {
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"inputs": {
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"text": "",
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"clip": ["2", 0]
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},
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"class_type": "CLIPTextEncode",
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"_meta": { "title": "CLIP Text Encode (Negative - empty)" }
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},
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"10": {
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"inputs": {
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"image": "reference_image.png",
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"upload": "image"
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},
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"class_type": "LoadImage",
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"_meta": { "title": "Load Reference Image" }
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},
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"11": {
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"inputs": {
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"preprocessor": "CannyEdgePreprocessor",
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"resolution": 1024,
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"image": ["10", 0]
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},
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"class_type": "AIO_Preprocessor",
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"_meta": { "title": "Canny Edge Preprocessor" }
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},
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"15": {
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"inputs": {
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"positive": ["5", 0],
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"negative": ["6", 0],
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"vae": ["3", 0],
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"pixels": ["11", 0]
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},
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"class_type": "InstructPixToPixConditioning",
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"_meta": { "title": "Apply Canny Image Conditioning" }
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},
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"20": {
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"inputs": {
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"max_shift": 1.15,
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"base_shift": 0.5,
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"width": 1024,
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"height": 1024,
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"model": ["1", 0]
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},
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"class_type": "ModelSamplingFlux",
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"_meta": { "title": "Model Sampling FLUX" }
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},
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"21": {
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"inputs": {
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"model": ["20", 0],
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"conditioning": ["15", 0]
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},
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"class_type": "BasicGuider",
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"_meta": { "title": "Basic Guider (positive conditioning)" }
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},
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"22": {
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"inputs": { "noise_seed": 42 },
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"class_type": "RandomNoise",
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"_meta": { "title": "Random Noise" }
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},
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"23": {
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"inputs": { "sampler_name": "euler" },
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"class_type": "KSamplerSelect",
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"_meta": { "title": "Sampler Select (Euler)" }
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},
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"24": {
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"inputs": {
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"scheduler": "simple",
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"steps": 28,
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"denoise": 1.0,
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"model": ["20", 0]
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},
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"class_type": "BasicScheduler",
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"_meta": { "title": "Basic Scheduler (28 steps)" }
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},
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"26": {
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"inputs": {
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"noise": ["22", 0],
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"guider": ["21", 0],
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"sampler": ["23", 0],
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"sigmas": ["24", 0],
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"latent_image": ["15", 2]
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},
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"class_type": "SamplerCustomAdvanced",
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"_meta": { "title": "Advanced Sampler" }
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},
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"27": {
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"inputs": {
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"samples": ["26", 0],
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"vae": ["3", 0]
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},
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"class_type": "VAEDecode",
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"_meta": { "title": "VAE Decode" }
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},
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"28": {
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"inputs": {
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"filename_prefix": "flux_controlnet_output",
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"images": ["27", 0]
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},
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"class_type": "SaveImage",
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"_meta": { "title": "Save Image" }
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},
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"29": {
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"inputs": {
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"filename_prefix": "controlnet_preprocessed",
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"images": ["11", 0]
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},
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"class_type": "SaveImage",
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"_meta": { "title": "Save Preprocessed Canny Image" }
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}
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}
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