Instructions to use t8star/DreamX-Creator-Comfy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use t8star/DreamX-Creator-Comfy with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("t8star/DreamX-Creator-Comfy", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
DreamX Creator Comfy
ComfyUI-ready DreamX-Creator generator, Audio VAE, Wan dependencies, and causal 2Γ refiner weights.
Links
- ComfyUI nodes and frontend workflows: T8mars/Comfyui-DreamX-Creator-T8
- ComfyUI Registry: dreamx-creator-t8
- Original project and model source: AMAP-ML/DreamX-Creator Β· GD-ML/DreamX-Creator
Install for ComfyUI
Install the hf command if needed:
python -m pip install -U huggingface_hub
Download this repository directly into ComfyUI's shared model directory:
hf download t8star/DreamX-Creator-Comfy --local-dir ComfyUI/models/dreamx_creator
The resulting model root must be:
ComfyUI/
βββ models/
βββ dreamx_creator/ # select model_root=auto in the loader
βββ creator/
β βββ cross_attn_weights.safetensors
β βββ merged_lora_info.json
β βββ audio_model/
β β βββ config.json
β β βββ diffusion_pytorch_model.safetensors
β βββ video_model/
β βββ config.json
β βββ diffusion_pytorch_model-00001-of-00002.safetensors
β βββ diffusion_pytorch_model-00002-of-00002.safetensors
β βββ diffusion_pytorch_model.safetensors.index.json
βββ audio_vae/
β βββ config.json
β βββ diffusion_pytorch_model.safetensors
βββ refiner/
β βββ sr_dit_5b.pt
β βββ latent_upsampler_flash.pt
β βββ latent_upsampler_2d_causal.pt
β βββ lightvae_nu_scheme3.pt
βββ wan2.2_ti2v_5b/
βββ Wan2.2_VAE.pth
βββ models_t5_umt5-xxl-enc-bf16.pth
βββ google/
βββ umt5-xxl/
βββ special_tokens_map.json
βββ spiece.model
βββ tokenizer_config.json
βββ tokenizer.json
model_root=auto searches the custom-node repository's checkpoints/ first,
then ComfyUI/models/dreamx_creator/. Therefore this node-local layout also works:
hf download t8star/DreamX-Creator-Comfy --local-dir ComfyUI/custom_nodes/Comfyui-DreamX-Creator-T8/checkpoints
The complete bundle contains 20 model/config/tokenizer files and is approximately 54.25 GB (50.53 GiB). Validate the download before loading the models:
python ComfyUI/custom_nodes/Comfyui-DreamX-Creator-T8/scripts/verify_models.py ComfyUI/models/dreamx_creator
Components
| Directory | Used by | Contents |
|---|---|---|
creator/ |
Creator workflow | 7B joint video/audio DiTs and cross-modal attention |
audio_vae/ |
Creator workflow | CreatorDACVAE audio decoder |
refiner/ |
Refiner workflow | SR-DiT 5B and latent upsamplers |
wan2.2_ti2v_5b/ |
Both workflows | Wan VAE, UMT5-XXL encoder, and tokenizer |
The Creator and Refiner are intended to run as separate ComfyUI workflow phases so both large models do not remain resident on the GPU at the same time.
T8star social links
| Resource | Link |
|---|---|
| Bilibili | T8star on Bilibili |
| YouTube | @T8star-Aix |
| Seedance API | API signup |
| Online AI Apps | RunningHub profile |
| ComfyUI Package | Quark download |
| Hugging Face profile | huggingface.co/t8star |
Attribution and license
These files are organized for the native ComfyUI nodes from the weights released
by the DreamX Team at GD-ML/DreamX-Creator.
The project is licensed under Apache License 2.0. See the LICENSE file in this
repository. Please retain the original attribution when redistributing the weights.
@misc{zhu2026dreamxcreatordemocratizingnativeaudiovideo,
title={DreamX-Creator: Democratizing Native Audio-Video Generation at 2K Resolution},
author={Jiashu Zhu and Yanhao Zheng and Ruitian Tian and Rujing Dang and Shen Zhang and Bingze Song and Jiachen Lei and Ruimin Lin and Jiahong Wu and Xiangxiang Chu},
year={2026},
eprint={2608.31106},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2608.31106}
}
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