Instructions to use frankbarbs/darkroom-noir-zimage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use frankbarbs/darkroom-noir-zimage with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("frankbarbs/darkroom-noir-zimage") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
DARKROOM Noir
A dark tech-noir style LoRA for Z-Image Turbo. Deep navy, disciplined blue light, premium restraint — applied to any prompt you can type.
🌠Showcase & interactive training scrubber: https://refxfrank.github.io/darkroom-noir/
All samples: Z-Image Turbo + DARKROOM Noir @ 0.8, 8 steps, unretouched.
Usage
Start your prompt with the trigger: drkrm style,
ComfyUI recipe:
| Setting | Value |
|---|---|
| Base | Z-Image Turbo (Comfy-Org/z_image_turbo split files) |
| LoRA loader | LoraLoaderModelOnly, strength 0.8 (0.6 subtle → 1.0 committed, usable to 1.2) |
| Sampler | res_multistep · scheduler simple |
| Steps / CFG | 8 / 1.0 (Turbo is CFG-free — negative prompts are inert) |
| Model sampling | ModelSamplingAuraFlow, shift 3.0 |
| Latent | EmptySD3LatentImage, 1024×1024 native |
Works with any Z-Image Turbo quantization, including the official int8-ConvRot repack (6.2 GB — runs on 16 GB cards with lots of headroom).
What it does
This is a style adapter, not a subject adapter: your subject, its palette. The training set contains only abstract wallpapers, night scenes and dark macro shots — yet portraits, objects and scenes it has never encountered come out in the same aesthetic: deep navy-to-black gradients, a single disciplined blue accent, near-zero color pollution.
Training
- Trainer: ai-toolkit (Ostris), headless, on a single RTX 5080 (16 GB)
- Method: LoRA rank 32 / alpha 32, bf16, 3,000 steps, flow-matching with weighted timesteps, trained against Z-Image Turbo through the de-distillation training adapter so the result samples cleanly on stock Turbo at 8 steps
- Dataset: 30 images, 100% self-generated in [the author's local DARKROOM studio] and hand-curated against a strict palette/restraint rubric, captioned locally with WD14 (EVA02-Large v3). Zero scraped or third-party content.
- Stack cost: $0 — every model, tool and byte of training data in the chain is free and Apache-2.0
License
Apache-2.0 — the same license as the base model, the training adapter, and the dataset provenance chain. Free for any use, including commercial. Attribution appreciated, not required.
Developed end-to-end in DARKROOM, a fully-local, zero-cloud, zero-telemetry image studio built on ComfyUI.
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Model tree for frankbarbs/darkroom-noir-zimage
Base model
Tongyi-MAI/Z-Image-Turbo


