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huggingface · Jan 11, 2025 · a784114 · a784114
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diff --git a/docs/source/en/using-diffusers/text-img2vid.md b/docs/source/en/using-diffusers/text-img2vid.md
@@ -70,7 +70,7 @@ export_to_video(video, "output.mp4", fps=8)
 > [!TIP]
 > HunyuanVideo is a 13B parameter model and requires a lot of memory. Refer to the HunyuanVideo [Quantization](../api/pipelines/hunyuan_video#quantization) guide to learn how to quantize the model. CogVideoX and LTX-Video are more lightweight options that can still generate high-quality videos.
 
-[HunyuanVideo](https://huggingface.co/hunyuanvideo-community/HunyuanVideo) features a dual-stream to single-stream diffusion transformer (DiT) for learning video and text tokens separately, and then subsequently concatenating the video and text tokens to combine their information. A single multimodal large language model (MLLM) serves as the text encoder, and videos are also spatio-temporally compressed with a 3D causal VAE.
+[HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo) features a dual-stream to single-stream diffusion transformer (DiT) for learning video and text tokens separately, and then subsequently concatenating the video and text tokens to combine their information. A single multimodal large language model (MLLM) serves as the text encoder, and videos are also spatio-temporally compressed with a 3D causal VAE.
 
 ```py
 import torch

diff --git a/src/diffusers/models/transformers/transformer_hunyuan_video.py b/src/diffusers/models/transformers/transformer_hunyuan_video.py
@@ -505,7 +505,7 @@ def forward(
 class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
     r"""
     A Transformer model for video-like data used in
-    [HunyuanVideo](https://huggingface.co/hunyuanvideo-community/HunyuanVideo).
+    [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo).
 
     Args:
         in_channels (`int`, defaults to `16`):