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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](./README.ko.md)
**Redrob Image** is [Redrob](https://redrob.io)'s open-weight diffusion model, built by [Janghoon Lee (이장훈)](https://www.janghoonlee.com).
Redrob's vision is to democratize AI. Our models are free to use and free for commercial use, under [Apache License 2.0](./LICENSE).
## 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.
```bash
pip install -U torch transformers accelerate safetensors
pip install -U diffusers
```
```python
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](https://huggingface.co/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:
```bash
python scripts/comfy_to_diffusers_zimage.py \
--input redrob-image.safetensors \
--output-dir transformer/
```
## License
[Apache License 2.0](./LICENSE). Copyright [Redrob](https://redrob.io). Built by [Janghoon Lee (이장훈)](https://www.janghoonlee.com). Upstream attribution is in [NOTICE](./NOTICE). Redistributors keep NOTICE with the weights.
## Attribution
- **Copyright:** Redrob ([redrob.io](https://redrob.io))
- **Built by:** [Janghoon Lee (이장훈)](https://www.janghoonlee.com) ([@savagemanage](https://github.com/savagemanage))
- **Repository:** [redrob-labs/redrob-image](https://huggingface.co/redrob-labs/redrob-image)
- **Base model:** [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) - Apache License 2.0
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