❌[DEPRECATED] Z-Sampler Turbo v1 (Advanced)
The old ^g1 sampler's advanced twin — where noise calibration came from
- model
- positive
- latent_input
- divider
- latent_output
This is the advanced version of the original (first-generation, ^g1) Z-Sampler Turbo, and the whole thing is deprecated now. Don't let the ❌ scare you off reading about it, though - it's the node where the pack's signature idea, initial noise calibration (INC), got its real controls. The current second-generation samplers absorbed this concept and rebranded it as "intensity." If you're on a fresh workflow, use the ^G2 nodes. If you're reverse-engineering an old workflow or you're curious what that intensity knob is actually doing, this is the Rosetta Stone.
The mechanism: why Z-Image needs noise calibration
Z-Image Turbo expects its starting noise to carry a small per-channel bias. Ignore that and you get flatter, grayer images that never quite hit pure black or white. The first-gen sampler therefore does a calibration pass: it takes one step of pure-noise denoising, measures the resulting latent's bias, then corrects for it. On top of that it over-amplifies the input noise slightly. The Advanced node exposes every dial of that process:
- initial_noise_calibration (0–1) - how much of the calibration to apply. This is the contrast/saturation control in disguise: higher = more punch, all the way to near-pure-black/white output. 100% suits clean illustrations; photos usually prefer less, and the pack docs suggest starting photos at ~50%.
- noise_bias_estimation -
experimental(fast, denoises a minimally-noisy latent) vsaccurate(denoises a fully-noisy latent). Experimental is the default for a reason. - noise_bias_sample_size - image_size / 1024px / 512px / 256px. Smaller = faster first step, less accurate bias. The "lowres_bias" hack from the plain ^g1 node, made explicit.
- noise_bias_scale - 0 to 1; how much of the calculated bias actually gets applied (default 0.12).
- noise_overdose - the over-amplitude of the initial noise (default 0.33; negative values reduce amplitude).
Plus the family standard: model, positive (no negative - CFG 1.0), latent_input, seed, steps (4–9), and denoise. Output is one LATENT.
Why it's deprecated
The ^g1 sampler was capped at 9 steps, and the second generation (^G2) replaced the whole noise-calibration pile with the two intuitive intensity / intensity_bias sliders plus a wider step range and better stability. The Advanced node survives mostly inside old saved workflows; the pack keeps it importable so your graphs don't explode on update. Same caveat as all the deprecated nodes: it still runs, but it's no longer a supported surface and could be removed.
Installing it
It ships with ComfyUI-ZImagePowerNodes: Manager → search "Z-Image Power Nodes" → Install → restart, or:
cd ComfyUI/custom_nodes
git clone https://github.com/martin-rizzo/ComfyUI-ZImagePowerNodes.git
No pip dependencies. You need the Z-Image Turbo model files (the pack doesn't download them) and a recent ComfyUI (v0.11.0+, Nodes 2.0 API).
The honest advice
If a saved workflow forces you into this node, your main job is deciding whether to migrate it to ^G2 - which is almost always worth it. And if you're here trying to understand intensity on the new nodes: intensity ≈ the old initial_noise_calibration + noise_overdose combined, and intensity_bias ≈ a manual noise_bias_scale. Same physics, better ergonomics.
Inputs (12)
| Name | Type | Default | Description |
|---|---|---|---|
| model | MODEL | The model used for generating the latent images. | |
| positive | CONDITIONING | The conditioning used to guide the generation process toward the desired content. | |
| latent_input | LATENT | The initial latent image to be modified; typically an 'Empty Latent' for text-to-image or an encoded image for img2img. | |
| seed | INT | 11–18446744073709550000 | The seed used for the random noise generator, ensuring the same result is produced with the same value. |
| steps | INT | 84–9 | The number of iterations to be performed during the sampling process. |
| denoise | FLOAT | 1.000–1 | The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling. |
| divider | ZIPN_SEPARATOR | — | |
| initial_noise_calibration | FLOAT | 0.000–1 | The amount of adjustment applied to the initial noise (0 means no adjustment). This typically enhances image contrast and saturation, higher values increase these effects more significantly. |
| noise_bias_estimation | COMBO | experimental | Method used to estimate the bias in each channel of the initial noise. `experimental`: Calculate the bias by denoising a latent image with minimal noise. `accurate`: Calculate the bias by denoising a fully noisy latent image. |
| noise_bias_sample_size | COMBO | image_size | The size of the latent image used to calculate the bias. The smaller the image size, the faster the calculation of the first step. |
| noise_bias_scale | FLOAT | 0.120–1 | The level of adjustament from the calculated noise bias to apply before the first denoising step. (0.0 means no bias adjustment; 1.0 means using the calculated bias). |
| noise_overdose | FLOAT | 0.33-1–1 | The amount of overamplitude in the initial noise generation. (negative values will reduce the amplitude). |
Outputs (1)
| Name | Type | Description |
|---|---|---|
| latent_output | LATENT | The resulting denoised latent image, ready to be decoded by a VAE or passed to another sampler. |