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workflow-templates-broken-z…/templates/gsc_creator_2_1.json
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zeekay 299198a5ad feat: templates batch 8
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"Editorial shot: On a volcanic black sand beach with a dark overcast sky.\n\nLow side-angle: Male model is in a deep squat, knees wide, head resting in one hand with a brooding, sharp gaze.\n\nHair: Ultra-short platinum blonde buzz cut.\n\nWardrobe: light-blue transparent oval sunglasses, oversized black wool chunky-knit sweater, baggy cargo trousers, climbing shoes. \n\nLighting: Theatrical cold blue moonlighting contrasted by a single sharp, warm orange spotlight on the face. Shot on Fujifilm GFX 100S, 110mm f/2 lens\n\nVibe: Paganism, futuristic tech-wear."
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"## Basic Diffusion\n\nDiffusion models transform random noise into a clear image based on a text prompt. Trained to predict and remove noise from data, they refine images step-by-step during inference: start with noise, iteratively denoise over multiple steps, and output the described image.\n\nZ-Image Turbo is a distilled 6B-parameter model that produces high-quality photorealistic images in just ~8 steps (sub-second on capable hardware), faster than traditional models requiring 20–50+ steps."
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"## Model Sampling (e.g., AuraFlow)\n\nModelSamplingAuraFlow node: Optimizes timing/shift for Turbo (shift ~3.0 is common).\n\n## KSampler - The heart of inference\n\nKey settings:\n- **Seed** ensures reproducibility ( same seed yields same output)\n- **Steps** 3 – 12 (Turbo shines at low numbers, e.g. 8 – 9)\n- **CFG** ~1.0 – 3.0 (how strongly it follows prompt; 1.0 = balanced)\n- **Sampler** res_multistep or similar\n- **Scheduler** simple\n- **Denoise** 1.0 (full from-noise generation)\n\n\nIteratively: model predicts noise → subtracts it → image sharpens."
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"## VAE Decode\n\nTurns final latent back into viewable pixels.\n\n## Save Image\n\nOutputs the image to a file (e.g., with a prefix)."
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"## Empty Latent Image\n\nYour starting \"noisy canvas\":\n\n- Select resolution (e.g., 864×1536 or 512×512).\n- Generates pure random noise as the base"
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