Model reference · open weights
HunyuanDiT is an open-weight image model from Tencent-Hunyuan. HunyuanDiT-v1.1-Diffusers-Distilled (FP32) weighs 7.2 GB; the smallest configuration that runs it is RTX 3060 12 GB.
HunyuanDiT is a 1.5B parameter text-to-image diffusion transformer developed by Tencent-Hunyuan. It supports both English and Chinese prompts and is designed for 25-step image generation. The model is available under an other licence.
Summary of the Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled model card, 2026-10-01
What it is
| Released by | Tencent-Hunyuan |
|---|---|
| Type | Image models |
| Task | Text→image |
| Parameters (lead) | 1.5B |
| Runs with | diffusers |
| Released | 2024-06-14 |
| Popularity | 202k downloads / month |
| Weights | 7.2 GB (HunyuanDiT-v1.1-Diffusers-Distilled (FP32), file size) |
| Licence | Its own licence terms |
What it runs on
Weights 7.2 GB (file size) · its biggest part 3.3 GB · working memory for one 1024×1024 image about 5.0 GB · overhead about 537 MB.
| Card | One 1024×1024 image | Counted memory |
|---|---|---|
| RTX 3060 12 GB | fits (encoders offloaded) | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
Estimates, not measurements: the weights are the build's file size; one 1024×1024 image needs about 5 GB of working memory (larger images more); "encoders offloaded" means only the biggest part is on the card at once — diffusers' model offload, or ComfyUI unloading the text encoder. diffusers can also place a pipeline's parts on separate cards (device_map) — not estimated here. Counted memory is 92 % of what CUDA reports for the card.
From the model card
[Arxiv] [project page] [github]
This repo contains the distilled Hunyuan-DiT in 🤗 Diffusers format.
It supports 25-step text-to-image generation.
Please install PyTorch first, following the instruction in https://pytorch.org
Install the latest version of transformers with pip:
pip install --upgrade transformers
Then install the latest github version of 🤗 Diffusers with pip:
pip install git+https://github.com/huggingface/diffusers.git
import torch
from diffusers import HunyuanDiTPipeline
pipe = HunyuanDiTPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled", torch_dtype=torch.float16)
pipe.to("cuda")
# You may also use English prompt as HunyuanDiT supports both English and Chinese
# prompt = "An astronaut riding a horse"
prompt = "一个宇航员在骑马"
image = pipe(prompt).images[0]
In order to comprehensively compare the generation capabilities of HunyuanDiT and other models, we constructed a 4-dimensional test set, including Text-Image Consistency, Excluding AI Artifacts, Subject Clarity, Aesthetic. More than 50 professional evaluators performs the evaluation.
Chinese Elements
Long Text Input
Welcome to Tencent Hunyuan Bot, where you can explore our innovative products in multi-round conversation!
Quoted from the model card on Hugging Face — the full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. Runs as it is with diffusers — on the machine, in Python.
# on your rented machine: pip install diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled", torch_dtype=torch.bfloat16).to("cuda")
image = pipe(prompt="a red bicycle on a cobbled street").images[0]
image.save("/workspace/out.png")