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@@ -13,18 +13,15 @@ tags:
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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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-
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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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- </p>
 
 
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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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-
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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** and **distilled model** are **not yet implemented** and are **not ready for use in ComfyUI**.
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- Currently, **only the base model** is supported.
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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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-
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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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  ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63473b59e5c0717e6737b872/drMNYMjvB01RvgZKS6kX6.jpeg)
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-
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  ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63473b59e5c0717e6737b872/uxhsoLKjzJu24eCZh_RQ8.jpeg)
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-
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  ---
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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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  ---
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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
@@ -75,11 +66,10 @@ Currently, **only the base model** is supported.
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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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- - **Refiner & Distilled Model:** Not implemented yet, **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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  ---
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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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  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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  ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63473b59e5c0717e6737b872/drMNYMjvB01RvgZKS6kX6.jpeg)
 
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  ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63473b59e5c0717e6737b872/uxhsoLKjzJu24eCZh_RQ8.jpeg)
 
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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>