---
license: apache-2.0
library_name: diffusion-single-file
base_model: Tongyi-MAI/Z-Image
pipeline_tag: text-to-image
tags:
- text-to-image
- z-image
- comfyui
- int8
- photorealistic
---

# Fullgreed — Z-Image Base Fine-tune (Photoreal, INT8)

**Fullgreed** is a fine-tune of Alibaba Tongyi's **Z-Image** — the 6B-parameter Single-Stream Diffusion Transformer (S3-DiT) — tuned for photorealistic, phone-camera-authentic portraits and selfies: natural lighting, believable skin and hair texture, and outputs that read as real photos rather than "AI renders."

It is a surgical fine-tune: only the attention and feed-forward projection weights were trained, leaving the base model's norms, embedders, and timestep conditioning untouched. Fullgreed keeps everything Z-Image is good at (bilingual prompt following, text rendering, composition) while adding its own photographic character.

## Released model

| File | What it is | Size |
|---|---|---|
| `greed_int8.safetensors` | INT8, ComfyUI-native quantization format | 6.3 GB |
| `fullgreed_bf16.safetensors` | Full-precision BF16 (matches the official Z-Image dtype convention) | 12.3 GB |

Both files produce the same images — the INT8 is effectively lossless (measured 0.03% average weight error vs the full-precision weights — visually identical output) at half the size and memory, and it needs **no custom nodes**: it runs on stock ComfyUI, including ComfyUI Cloud.

Companion files are included in this repo for convenience:

- `qwen_3_4b.safetensors` — text encoder (Qwen3-4B)
- `money_vae_f16.safetensors` — the standard Flux 16-channel VAE repackaged in fp16 for a smaller download; interchangeable with the official `ae.safetensors`
- [`workflows/alphgreed_workflow.json`](./workflows/alphgreed_workflow.json) — ready-made ComfyUI workflow (text-to-image + SeedVR2 upscale stage)

## Loading (ComfyUI)

- **Diffusion model:** Load Diffusion Model (UNETLoader) → `greed_int8.safetensors`
- **Text encoder:** CLIPLoader → `qwen_3_4b.safetensors`, type **`lumina2`**
- **VAE:** VAELoader → `money_vae_f16.safetensors`
- **ModelSamplingAuraFlow** node with **shift = 3**
- Latent: **EmptySD3LatentImage**

Also runs in **Draw Things** (import as a Z-Image model) and anything else that supports Z-Image.

## Recommended settings

- **Steps:** 8–20
- **CFG:** 1–4 (sweet spot ≈ 1–3; a negative prompt works)
- **Sampler / scheduler:** `res_multistep` / `simple` (euler also works)
- **Resolution:** native around 1024×1024, up to ~4K

## Tips

- Slight CFG restraint (≤4) preserves the photographic look; high CFG pushes toward an over-processed render feel.
- The model responds well to camera-language prompts: phone selfie, mirror shot, golden hour, indoor tungsten, shallow depth of field, etc.
- The INT8 file uses ComfyUI's **native** quantization format — no custom nodes or CUDA extensions. It is not the same as older int8 builds that required a custom node to decode.

## Credits & license

- Base model: **Z-Image** by Tongyi-MAI, Alibaba Group — see the [Z-Image technical report (arXiv 2511.22699)](https://arxiv.org/abs/2511.22699).
- Text encoder: Qwen3-4B (Alibaba). VAE: Flux VAE family (Black Forest Labs).
- License: **Apache-2.0**, matching the Z-Image base model.

*Please generate responsibly. Do not use this model to create images of real people without their consent.*