---
library_name: HunyuanImage-2.1
license: other
license_name: tencent-hunyuan-community
license_link: https://github.com/Tencent-Hunyuan/HunyuanImage-2.1/blob/master/LICENSE
language:
- en
- zh
tags:
- text-to-image
- comfyui
- diffusers
pipeline_tag: text-to-image
extra_gated_eu_disallowed: true
---
HunyuanImage-2.1 fp8 e4m3fn
An Efficient Diffusion Model for High-Resolution (2K) Text-to-Image Generation
---
## **Performance on RTX 5090**
> When using **HunyuanImage-2.1** with the **quantized encoder** + **quantized base model**,
> the VRAM usage on an **NVIDIA RTX 5090** typically ranges between **26 GB and 30 GB** with average
> 16 second inference time depending on resolution, batch size, and prompt complexity.
> **Reports that it works on 16gb VRAM GPU's**
⚠ **Important Note:**
The **refiner** is still not implemented and is **not ready for use in ComfyUI**.
However, the **distilled model now works in ComfyUI** with recommended settings of **8 steps / 1.5-2.5 CFG**.
---


---
## **Download Quantized Model (FP8 e4m3fn)**
[**Download hunyuanimage2.1_fp8_e4m3fn.safetensors**](https://huggingface.co/drbaph/HunyuanImage-2.1_fp8/blob/main/hunyuanimage2.1_fp8_e4m3fn.safetensors)
---
### **Workflow Notes**
- **Model:** HunyuanImage-2.1
- **Mode:** Quantized Encoder + Quantized Base Model
- **VRAM Usage:** ~26GB–30GB on RTX 5090
- **Resolution Tested:** 2K (2048×2048)
- **Frameworks:** ComfyUI & Diffusers
- **Optimisations** Works with Patch Sage Attention + Lazycache / TeaCache ✅
- **Distilled Model:** ✅ Now works in ComfyUI with **8 steps / 1.5-2.5 CFG**
- **Refiner:** ❌ Still not implemented, **not available in ComfyUI**
- **License:** [tencent-hunyuan-community](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1/blob/master/LICENSE)
---
🚀 **Optimized for High-Resolution, Memory-Efficient Text-to-Image Generation**