HunyuanImage-2.1_fp8_e4m3fn
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Model description
# HunyuanImage-2.1
### An Efficient Diffusion Model for High-Resolution (2K) Text-to-Image Generation
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## 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.
⚠ Important Note:
The refiner and not yet implemented and are not ready for use in ComfyUI.
Currently, only the base model and distilled is supported.
[Example_Workflow](https://huggingface.co/drbaph/HunyuanImage-2.1_fp8/resolve/main/example_workflow.json?download=true)
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### 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 ✅
- Refiner: ❌ Not implemented yet, not available in ComfyUI
- License: [tencent-hunyuan-community](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1/blob/master/LICENSE)
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