README.md

metadata

license: apache-2.0
base_model: Tongyi-MAI/Z-Image-Turbo
base_model_relation: merge
tags:
  - text-to-image
  - redrob
  - redrob-image
  - z-image
  - diffusers
  - comfyui
  - diffusion
library_name: diffusers
pipeline_tag: text-to-image

Redrob Image

한국어

Redrob Image is Redrob's open-weight diffusion model, built by Janghoon Lee (이장훈).

Redrob's vision is to democratize AI. Our models are free to use and free for commercial use, under Apache License 2.0.

Why this model

• Tuned for a more realistic look than the bare base model: better skin, light, and texture, with less of the plastic, AI-skin tell.
• Strong on photo, portrait, and mood imagery.
• Fast Turbo-style sampling: about 8 DiT steps.
• Runs in plain Python via Diffusers, or as a single merged UNET in ComfyUI.
• Apache-2.0: free to use, free for commercial use, and redistributable.

Limits

Weak at legible text, including Hangul, Devanagari, and most non-Latin script. Route text-bearing surfaces elsewhere. Also weaker on graphic, print, and typography-heavy work than on photo and portrait.

Turbo-style sampling runs without classifier-free guidance (guidance_scale=0 / ComfyUI cfg 1), so negative prompts are ignored. Put avoidances in the positive prompt instead.

Quick start (Diffusers / Python)

Enterprise and production default. After the Hugging Face upload includes transformer/, load that Diffusers transformer and keep the text encoder / VAE from the base pipeline.

pip install -U torch transformers accelerate safetensors
pip install -U diffusers

import torch
from diffusers import ZImagePipeline, ZImageTransformer2DModel

transformer = ZImageTransformer2DModel.from_pretrained(
    "redrob-labs/redrob-image",
    subfolder="transformer",
    torch_dtype=torch.bfloat16,
)

pipe = ZImagePipeline.from_pretrained(
    "Tongyi-MAI/Z-Image-Turbo",
    transformer=transformer,
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

prompt = "A documentary portrait in natural window light, shallow depth of field"
image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    num_inference_steps=9,  # 8 DiT forwards
    guidance_scale=0.0,     # required for Turbo
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("redrob-image.png")

Optional: pipe.enable_model_cpu_offload() on smaller GPUs.

Quick start (ComfyUI)

File	Put under	Source
redrob-image.safetensors	models/diffusion_models/	this repository
qwen_3_4b_fp8_mixed.safetensors	models/text_encoders/	Comfy-Org/z_image_turbo
ae.safetensors	models/vae/	same Comfy-Org pack

1. UNETLoader -> redrob-image.safetensors
2. CLIPLoader -> qwen_3_4b_fp8_mixed.safetensors (type: lumina2, ComfyUI loader type for this text encoder)
3. VAELoader -> ae.safetensors
4. Sampler: 8 steps, cfg 1, res_multistep / sgm_uniform

Load workflows/redrob-image-api.json for a minimal working graph. The graph zeros out negative conditioning (ConditioningZeroOut); do not expect a negative text prompt to change the image.

Files

Path	Role
redrob-image.safetensors	ComfyUI merged UNET (LFS, ~12 GiB)
transformer/	Diffusers layout (built at HF upload)
workflows/redrob-image-api.json	Minimal ComfyUI API graph
README.md / README.ko.md	Model card (English / Korean)
LICENSE	Apache License 2.0
NOTICE	Attribution

transformer/ is not in git. On Hugging Face upload, scripts/push_hf.sh converts the Comfy UNET into Diffusers format (or copies a prebuilt TRANSFORMER_DIR).

Convert a local Comfy UNET yourself:

python scripts/comfy_to_diffusers_zimage.py \
  --input redrob-image.safetensors \
  --output-dir transformer/

License

Apache License 2.0. Copyright Redrob. Built by Janghoon Lee (이장훈). Upstream attribution is in NOTICE. Redistributors keep NOTICE with the weights.

Attribution

• Copyright: Redrob (redrob.io)
• Built by: Janghoon Lee (이장훈) (@savagemanage)
• Repository: redrob-labs/redrob-image
• Base model: Tongyi-MAI/Z-Image-Turbo - Apache License 2.0