Trying out the FP8/GGUF version of HunyuanImage-2.1 Distilled in ComfyUI (Recommended VRAM 12-16GB)
Last update 9-15-2025
* As this was just released, I may revise this article several times.
* There is also an article for the slower, standard version that is not the Distilled version..
* Related articles are summarized in the ComfyUI article list.
■ 0. Overview
▼ 0-0. Introduction
In this article, I will explain the procedure for using "HunyuanImage-2.1 Distilled" in ComfyUI on Windows to speed up generation. We will use the FP8 and GGUF versions of the model.
At the time of writing, there is no LoRA for HunyuanImage-2.1 Distilled, and it is provided as a Model.
The explanation for each is in the sections below.
1. Notes and resource usage
2.-3. Preparation and generation of the FP8 version
4.-5. Preparation and generation of the GGUF version
Please refer to the articles below for an overview of HunyuanImage-2.1 and the procedure for standard generation without using the Distilled model.
Please refer to the article below for how to install ComfyUI.
▼ 0-1. Related links
These are the official models (Model, Text Encoder, VAE). They are not used in this article.
tencent/HunyuanImage-2.1
https://huggingface.co/tencent/HunyuanImage-2.1
These are the FP8 version Model, Text Encoder, and VAE used in this article.
drbaph/HunyuanImage-2.1_fp8
https://huggingface.co/drbaph/HunyuanImage-2.1_fp8Comfy-Org/HunyuanImage_2.1_ComfyUI
https://huggingface.co/Comfy-Org/HunyuanImage_2.1_ComfyUI
The FP8 version of the model is also listed on Civitai. Due to how Civitai works, the file names may differ.
HunyuanImage-2.1_fp8_e4m3fn
https://civitai.com/models/1944745?modelVersionId=2206738
These are the various GGUF models and extensions used in this article.
calcuis/hunyuanimage-gguf
https://huggingface.co/calcuis/hunyuanimage-ggufunsloth/Qwen2.5-VL-7B-Instruct-GGUF
https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUFcalcuis/pig-encoder
https://huggingface.co/calcuis/pig-encodercalcuis/pig-vae
https://huggingface.co/calcuis/pig-vae
gguf node for comfyui
https://github.com/calcuis/gguf
The GGUF version models below should also be usable (unverified).
QuantStack/HunyuanImage-2.1-Distilled-GGUF
https://huggingface.co/QuantStack/HunyuanImage-2.1-Distilled-GGUF
■ 1. Notes and Resource Usage
▼ 1-1. Notes
If ComfyUI is not updated, necessary nodes may be missing. Please keep it as up-to-date as possible, as updates often include bug fixes.
To speed up subsequent generations, models loaded into VRAM may be automatically offloaded to main RAM. Therefore, it is recommended to have at least 32GB of main RAM.
▼ 1-2. VRAM Usage
In the case of the FP8 version, it can definitely be used with 16GB of VRAM. It is believed that 12GB is possible if there is enough free VRAM (dedicated GPU memory), but if it spills over into shared GPU memory, generation speed will drop significantly.
In the case of the GGUF version, it depends on the quantization type. Even so, an environment with 12–16GB of VRAM is generally suitable.
The GGUF version may be able to generate without spilling into shared GPU memory even with 8GB of VRAM by using models like "hunyuanimage2.1-v2-q3_k_m.gguf".
▼ 1-3. Generation Time
The generation times in the author's environment (GeForce RTX 5060Ti 16GB) are as follows. This does not include the time taken to load the model from disk or process the prompt. The GGUF format varies slightly depending on the model size (quantization type).
Distill FP8
Approx. 54 seconds (8 steps, euler/simple, CFG=2.0)
Approx. 40 seconds (change: weight_dtype=fp8_e4m3fn_fast)
Approx. 21 seconds (change: weight_dtype=fp8_e4m3fn_fast, CFG=1.0)Distill GGUF Q4_K_M
Approx. 57 seconds (8 Steps, euler/simple, CFG=2.0)
Approx. 30 seconds (Change: CFG=1.0)
For reference, the standard generation times are as follows.
FP8
Approx. 115 seconds (20 Steps, euler/simple, CFG=3.5)
Approx. 80 seconds (Change: weight_dtype=fp8_e4m3fn_fast)GGUF Q4_K_M
Approx. 135 seconds (20 Steps, euler/simple, CFG=3.5)
■ 2. Preparation (FP8 version)
▼ 2-1. Introduction
The difference between HunyuanImage Distilled and the standard version is only the model and some settings. It also uses the same Text Encoder as Qwen-Image.
▼ 2-2. Model Installation
I organize my directories by type, so I will describe it that way, but you are free to decide as you prefer.
Download each file listed at the URL below and move them to the appropriate folder. If you have already installed them, please skip this. Note that it includes the same Text Encoder used in the Qwen-Image article.
-
drbaph/HunyuanImage-2.1_fp8
https://huggingface.co/drbaph/HunyuanImage-2.1_fp8hunyuanimage2.1-distilled_fp8_e4m3fn.safetensors (16.2GB)
(* File re-uploaded on 9-12-2025 is required)
→ models\diffusion_models\hunyuan_image\
-
Comfy-Org/HunyuanImage_2.1_ComfyUI
https://huggingface.co/Comfy-Org/HunyuanImage_2.1_ComfyUIsplit_files/text_encoders/
qwen_2.5_vl_7b_fp8_scaled.safetensors (8.73GB)
byt5_small_glyphxl_fp16.safetensors (418MB)
→ models\text_encoders\hunyuan_image\split_files/vae/
hunyuan_image_2.1_vae_fp16.safetensors (773MB)
→ models\vae\hunyuan_image\
■ 3. Generation (FP8 version)
▼ 3-1. Overview
I am providing a workflow for generation, so please feel free to use it, including for modifications and redistribution.
▼ 3-2. Supplementary Notes
Since the output quality of the Distilled model is slightly inferior, parameter adjustment seems necessary. Based on the findings below, I am providing two types of workflows.
The person who posted the FP8 version of the model recommends a CFG value of 1.5 to 2.5.
The standard combination for Sampler and Scheduler is euler/simple, but I felt that changing it to dpmpp_sde/beta provided an improvement. However, dpmpp_sde is slower at generating.
Setting the CFG value to 1.0 significantly increases generation speed, though it appears to slightly reduce contrast.
Changing the Model dtype from default to fp8_e4m3fn_fast allows for faster generation at the cost of some quality.
▼ 3-3. Workflow (Normal) and Generation Example
This is the workflow using the original settings.
Please download the file below and drag and drop it into the ComfyUI screen.
This is the entire workflow. I modified the existing prompt using Claude and Grok. In my environment, it takes about 54 seconds per run.

The officially listed resolutions are five types: "2048 x 2048 (1:1)", "2304 x 1792 (4:3)", "1792 x 2304 (3:4)", "2560 x 1536 (16:9)", and "1536 x 2560 (9:16)". Steps are 8, and CFG is 2.0.

▼ 3-4. Workflow (Customized) and Generation Example
This is the workflow where I have independently changed the settings.
Changed weight_dtype in Load Diffusion Model (default → fp8_e4m3fn_fast)
Changed sampler/scheduler (euler/simple → dpmpp_sde/beta)
Changed cfg to 1.0 (Negative Prompt is disabled)
Please download the file below and drag and drop it into the ComfyUI screen.
This is the entire workflow. In my environment, it takes about 41 seconds per run.

I feel that changing to dpmpp_sde/beta improves the quality. However, since dpmpp_sde is slow, I have increased the speed by changing the weight_dtype and CFG.

■ 4. Preparation (GGUF version)
▼ 4-1. Introduction
Since the explanation follows the same flow as the FP8 version, some content may overlap.
The only differences between HunyuanImage Distilled and the standard version are the model and some settings. It also uses the same Text Encoder as Qwen-Image.
▼ 4-2. Setting up the models
I organize my directories by type, so I will describe it that way, but you are free to decide as you prefer.
Download each file listed at the URL below and move them to the appropriate folders. If you have already set them up, please skip this. The Q4_K_M model provides sufficient quality, but you can adjust it up or down while monitoring VRAM usage and quality. Note that it includes the same Text Encoder used in the Qwen-Image article.
-
calcuis/hunyuanimage-gguf
https://huggingface.co/calcuis/hunyuanimage-ggufhunyuanimage2.1-distilled-v2-q4_k_m.gguf (10.6GB)
→ models\diffusion_models\hunyuan_image\
-
unsloth/Qwen2.5-VL-7B-Instruct-GGUF
https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUFQwen2.5-VL-7B-Instruct-Q4_K_M.gguf (4.36GB)
→ models\text_encoders\hunyuan_image\
-
calcuis/pig-encoder
https://huggingface.co/calcuis/pig-encoderbyt5_small_glyphxl_fp32-f16.gguf (418MB)
(byt5_small_glyphxl_fp32-q4_0.gguf (120MB) is also acceptable)
→ models\text_encoders\hunyuan_image\
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calcuis/pig-vae
https://huggingface.co/calcuis/pig-vaepig_hunyuan_image_vae_fp32-f16.gguf (773MB)
→ models\vae\hunyuan_image\
▼ 4-3. About required extensions
When loading the GGUF version workflow posted in this article, if extensions are missing, a "Some Nodes Are Missing" message will appear. In that case, you can install them using the ComfyUI Manager features.
For instructions on how to install extensions, please refer to the "Automatic Installation Procedure" or "Manual Installation Procedure" in the article below.
The workflow in this article uses the following extensions.
■ 5. Generation (GGUF version)
▼ 5-1. Overview
Since the explanation follows the same flow as the FP8 version, some content may overlap.
I am providing a workflow for generation, so please feel free to use it, including for modifications and redistribution.
▼ 5-2. Supplementary notes
Since the output quality of Distilled models is slightly inferior, parameter adjustment seems necessary. Based on the findings below, I am providing two types of workflows.
The person who posted the FP8 version of the model recommends a CFG value of 1.5 to 2.5.
The combination of Sampler and Scheduler is euler/simple by default, and I felt it improved when changed to dpmpp_sde/beta. However, dpmpp_sde has a slower generation speed.
Setting the CFG value to 1.0 significantly increases generation speed, but it appears that the contrast decreases slightly.
Changing the model dtype from default to fp8_e4m3fn_fast can increase generation speed at the cost of some quality.
There is a point to note when using this. If you change the prompt from the second time onwards, execute "Unload Models" before starting generation. If data remains in the dedicated GPU memory (VRAM), the Text Encoder may load and spill over into shared GPU memory, which can significantly increase prompt processing time.

▼ 5-3. Workflow (Normal) and generation examples
This is the workflow using the original settings.
Please download the file below and drag and drop it onto the ComfyUI screen.
This is the entire workflow. I modified the existing prompt using Claude and Grok. In my environment, it takes about 57 seconds per run.

The officially listed resolutions are five types: "2048 x 2048 (1:1)", "2304 x 1792 (4:3)", "1792 x 2304 (3:4)", "2560 x 1536 (16:9)", and "1536 x 2560 (9:16)". Steps are 8, and CFG is 2.0.

▼ 5-4. Workflow (Custom) and Generation Examples
This is the workflow where I have independently changed the settings.
Changed sampler/scheduler (euler/simple -> dpmpp_sde/beta)
Changed CFG to 1.0 (Negative Prompt is disabled)
Please download the file below and drag and drop it onto the ComfyUI screen.
This is the entire workflow. In my environment, it takes about 55 seconds per run.

I feel that the quality is improved by changing to dpmpp_sde/beta. However, since dpmpp_sde is slow, I have increased the speed by changing weight_dtype and CFG.

■ 6. Bonus
▼ 6-1. Bonus Images
Here are some images generated with HunyuanImage-2.1. The workflow uses the custom version from 3-4. The prompts are the ones posted in the regular version article, passed through Hunyuan-PromptEnhancer again.









■ 7. Other
Please follow the menu to find other articles I have written.
There is also a list of articles limited to ComfyUI. as well.
If you have any questions regarding the articles, please contact @riddi0908 on X.
