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README.md
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pipeline_tag: text-to-image
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extra_gated_eu_disallowed: true
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---
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63473b59e5c0717e6737b872/5DZez8C7TeFwRn3FcKDix.png" alt="HunyuanImage-2.1 Banner" />
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<div align="center">
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# **HunyuanImage-2.1**
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### An Efficient Diffusion Model for High-Resolution (2K) Text-to-Image Generation
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</div>
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<div align="center">
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<a href="https://github.com/Tencent-Hunyuan/HunyuanImage-2.1" target="_blank"><img src="https://img.shields.io/badge/Code-black.svg?logo=github" height="22px"></a>
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<a href="https://huggingface.co/spaces/tencent/HunyuanImage-2.1" target="_blank">
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> When using **HunyuanImage-2.1** with the **quantized encoder** + **quantized base model**,
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> the VRAM usage on an **NVIDIA RTX 5090** typically ranges between **26 GB and 30 GB** with average
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> 16 second inference time depending on resolution, batch size, and prompt complexity.
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⚠ **Important Note:**
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The **refiner**
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63473b59e5c0717e6737b872/auZ_xmiKPw0QdBYUrTLn-.png" alt="Image1"/>
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</p>
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63473b59e5c0717e6737b872/qod1zCPWjzOZSNcOWx49-.png" alt="Image2"/>
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</p>
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---
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## **Download Quantized Model (FP8 e4m3fn)**
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[**Download hunyuanimage2.1_fp8_e4m3fn.safetensors**](https://huggingface.co/drbaph/HunyuanImage-2.1_fp8/blob/main/hunyuanimage2.1_fp8_e4m3fn.safetensors)
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### **Workflow Notes**
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- **Model:** HunyuanImage-2.1
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- **Mode:** Quantized Encoder + Quantized Base Model
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- **Resolution Tested:** 2K (2048×2048)
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- **Frameworks:** ComfyUI & Diffusers
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- **Optimisations** Works with Patch Sage Attention + Lazycache / TeaCache ✅
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- **
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- **License:** [tencent-hunyuan-community](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1/blob/master/LICENSE)
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<p align="center">
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🚀 **Optimized for High-Resolution, Memory-Efficient Text-to-Image Generation**
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</p>
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pipeline_tag: text-to-image
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extra_gated_eu_disallowed: true
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---
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<div align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63473b59e5c0717e6737b872/5DZez8C7TeFwRn3FcKDix.png" alt="HunyuanImage-2.1 Banner" />
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<h1> HunyuanImage-2.1 fp8 e4m3fn </h1>
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<h2>An Efficient Diffusion Model for High-Resolution (2K) Text-to-Image Generation</h2>
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</div>
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</div>
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<div align="center">
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<a href="https://github.com/Tencent-Hunyuan/HunyuanImage-2.1" target="_blank"><img src="https://img.shields.io/badge/Code-black.svg?logo=github" height="22px"></a>
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<a href="https://huggingface.co/spaces/tencent/HunyuanImage-2.1" target="_blank">
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> When using **HunyuanImage-2.1** with the **quantized encoder** + **quantized base model**,
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> the VRAM usage on an **NVIDIA RTX 5090** typically ranges between **26 GB and 30 GB** with average
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> 16 second inference time depending on resolution, batch size, and prompt complexity.
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> **Reports that it works on 16gb VRAM GPU's**
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⚠ **Important Note:**
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The **refiner** is still not implemented and is **not ready for use in ComfyUI**.
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However, the **distilled model now works in ComfyUI** with recommended settings of **8 steps / 1.5-2.5 CFG**.
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---
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63473b59e5c0717e6737b872/auZ_xmiKPw0QdBYUrTLn-.png" alt="Image1"/>
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</p>
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63473b59e5c0717e6737b872/qod1zCPWjzOZSNcOWx49-.png" alt="Image2"/>
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</p>
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---
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## **Download Quantized Model (FP8 e4m3fn)**
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[**Download hunyuanimage2.1_fp8_e4m3fn.safetensors**](https://huggingface.co/drbaph/HunyuanImage-2.1_fp8/blob/main/hunyuanimage2.1_fp8_e4m3fn.safetensors)
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---
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### **Workflow Notes**
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- **Model:** HunyuanImage-2.1
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- **Mode:** Quantized Encoder + Quantized Base Model
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- **Resolution Tested:** 2K (2048×2048)
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- **Frameworks:** ComfyUI & Diffusers
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- **Optimisations** Works with Patch Sage Attention + Lazycache / TeaCache ✅
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- **Distilled Model:** ✅ Now works in ComfyUI with **8 steps / 1.5-2.5 CFG**
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- **Refiner:** ❌ Still not implemented, **not available in ComfyUI**
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- **License:** [tencent-hunyuan-community](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1/blob/master/LICENSE)
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---
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<p align="center">
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🚀 **Optimized for High-Resolution, Memory-Efficient Text-to-Image Generation**
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</p>
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