ZImage Turbo Sampler Advanced
The Z-Image sampler with the training wheels off
- model
- positive
- negative
- latent_image
- samples
- steps
- cfg
- denoise
- sampler_name
- scheduler_name
The plain ZImageTurboSampler picks your sampler and scheduler for you. This one lets you disagree with it. Same inputs, same five mode presets, same outputs - plus a sampling_profile choice and three overrides that let you edit the plan before it runs.
The new inputs:
- sampling_profile - three curated sampler/scheduler pairs:
capitan_flow(default) - the pack'seuler_flowsampler +capitanZiTlinear scheduler. The community-tested favorite.zflow_linear-euler_flow+ thezimage_turboscheduler alias (also a linear ramp, registered by this pack).tongyi_default-res_multistepsampler +simplescheduler, the vanilla pair that tracks the official Tongyi recipe before community tuning.
- steps_override / cfg_override / denoise_override - set to 0 to mean "use the preset." Any nonzero value replaces that part of the plan. So
steps_override=12onbase_ultragives you a 12-step ultra pass;cfg_override=3turns guidance back on for the people who like a little contrast (it works above 1, but costs steps and can overcook past ~3);denoise_override=0.5is how you turn a base mode into a half-refresh img2img pass.
The mechanism underneath is unchanged: the node resolves the plan, applies your overrides, and calls common_ksampler with CFG 1 as the baseline. The output echo (steps, cfg, denoise, sampler_name, scheduler_name) is your receipt for what actually ran, which is worth checking once you start overriding.
Where this earns its keep: the refine modes. base_ultra with a denoise_override is how you get a "touch-up this image" pass without dragging out the two-pass refiner; steps_override is how you find your model's personal step sweet spot between 8 and 15 (more than ~9 on the stock pair tends to hurt rather than help, but with euler_flow the relationship is gentler). CFG above 1 is the "more saturated and contrasty" trick from the community tinkering threads - nice, but double the render time and fragile past 3.
Everything else - install, models, the pack's locally-registered sampler names - is shared with the base sampler. ComfyUI Manager → "ZImageTurboHQNodes", or:
cd ComfyUI/custom_nodes
git clone https://github.com/marduk191/ComfyUI-ZImageTurboHQNodes
restart, no pip deps, models from Tongyi-MAI's HF repo.
The trap: overrides are easy to leave behind. A cfg_override of 4 from last week's experiment will happily sit at 4 and silently change every render. Zero means "use preset," and if you're not actively experimenting, zeros are what you want. Treat this node like a fine instrument you return to factory between sessions.
Inputs (10)
| Name | Type | Default | Description |
|---|---|---|---|
| model | MODEL | — | |
| positive | CONDITIONING | — | |
| negative | CONDITIONING | — | |
| latent_image | LATENT | — | |
| seed | INT | 133713370–18446744073709550000 | — |
| mode | COMBO | base_ultra | 5 options: base_ultra, base_balanced, refine_subtle, refine_normal, refine_strong |
| sampling_profile | COMBO | capitan_flow | 3 options: tongyi_default, capitan_flow, zflow_linear |
| steps_override | INT | 00–100 | — |
| cfg_override | FLOAT | 0.00–20 | — |
| denoise_override | FLOAT | 0.000–1 | — |
Outputs (6)
| Name | Type | Description |
|---|---|---|
| samples | LATENT | — |
| steps | INT | — |
| cfg | FLOAT | — |
| denoise | FLOAT | — |
| sampler_name | STRING | — |
| scheduler_name | STRING | — |