TL; DR: InfiniteTalk is an unlimited-length talking video generation model that supports both audio-driven video-to-video and image-to-video generation
- August 18, 2025: We release the Technique-Report of InfiniteTalk. The Gradio and the ComfyUI branch have been released.
- August 18, 2025: We release the project page of InfiniteTalk
We propose InfiniteTalk, a novel sparse-frame video dubbing framework. Given an input video and audio track, InfiniteTalk synthesizes a new video with accurate lip synchronization while simultaneously aligning head movements, body posture, and facial expressions with the audio. Unlike traditional dubbing methods that focus solely on lips, InfiniteTalk enables infinite-length video generation with accurate lip synchronization and consistent identity preservation. Beside, InfiniteTalk can also be used as an image-audio-to-video model with an image and an audio as input.
- 💬 Sparse-frame Video Dubbing – Synchronizes not only lips, but aslo head, body, and expressions
- ⏱️ Infinite-Length Generation – Supports unlimited video duration
- ✨ Stability – Reduces hand/body distortions compared to MultiTalk
- 🚀 Lip Accuracy – Achieves superior lip synchronization to MultiTalk
- Release the technical report
- Inference
- Checkpoints
- Multi-GPU Inference
- Inference acceleration
- TeaCache
- int8 quantization
- LCM distillation
- Sparse Attention
- Run with very low VRAM
- Gradio demo
- ComfyUI
conda create -n multitalk python=3.10
conda activate multitalk
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu121
pip install -U xformers==0.0.28 --index-url https://download.pytorch.org/whl/cu121
pip install misaki[en]
pip install ninja
pip install psutil
pip install packaging
pip install flash_attn==2.7.4.post1
pip install -r requirements.txt
conda install -c conda-forge librosa
conda install -c conda-forge ffmpeg
or
sudo yum install ffmpeg ffmpeg-devel
| Models | Download Link | Notes |
|---|---|---|
| Wan2.1-I2V-14B-480P | 🤗 Huggingface | Base model |
| chinese-wav2vec2-base | 🤗 Huggingface | Audio encoder |
| MeiGen-InfiniteTalk | 🤗 Huggingface | Our audio condition weights |
Download models using huggingface-cli:
huggingface-cli download Wan-AI/Wan2.1-I2V-14B-480P --local-dir ./weights/Wan2.1-I2V-14B-480P
huggingface-cli download TencentGameMate/chinese-wav2vec2-base --local-dir ./weights/chinese-wav2vec2-base
huggingface-cli download TencentGameMate/chinese-wav2vec2-base model.safetensors --revision refs/pr/1 --local-dir ./weights/chinese-wav2vec2-baseLink through:
Our model is compatible with both 480P and 720P resolutions.
Some tips
- Lip synchronization accuracy: Audio CFG works optimally between 3–5. Increase the audio CFG value for better synchronization.
- FusionX： While it enables faster inference and higher quality, FusionX LoRA exacerbates color shift over 1 minute and reduces ID preservation in videos.
- V2V generation: Enables unlimited length generation. The model mimics the original video's camera movement, though not identically. Using SDEdit improves camera movement accuracy significantly but introduces color shift and is best suited for short clips. Improvements for long video camera control are planned.
- I2V generation: Generates good results from a single image for up to 1 minute. Beyond 1 minute, color shifts become more pronounced. One trick for the high-quailty generation beyond 1 min is to copy the image to a video by translating or zooming in the image.
--mode streaming: long video generation.
--mode clip: generate short video with one chunk.
--use_teacache: run with TeaCache.
--size infinitetalk-480: generate 480P video.
--size infinitetalk-720: generate 720P video.
--use_apg: run with APG.
--teacache_thresh: A coefficient used for TeaCache acceleration
—-sample_text_guide_scale： When not using LoRA, the optimal value is 5. After applying LoRA, the recommended value is 1.
—-sample_audio_guide_scale： When not using LoRA, the optimal value is 4. After applying LoRA, the recommended value is 2.
python generate_infinitetalk.py \
--ckpt_dir weights/Wan2.1-I2V-14B-480P \
--wav2vec_dir 'weights/chinese-wav2vec2-base' \
--infinitetalk_dir weights/InfiniteTalk/single/infinitetalk.safetensors \
--input_json examples/single_example_image.json \
--size infinitetalk-480 \
--sample_steps 40 \
--mode streaming \
--motion_frame 9 \
--save_file infinitetalk_res
If you want run with 720P, set --size infinitetalk-720:
python generate_infinitetalk.py \
--ckpt_dir weights/Wan2.1-I2V-14B-480P \
--wav2vec_dir 'weights/chinese-wav2vec2-base' \
--infinitetalk_dir weights/InfiniteTalk/single/infinitetalk.safetensors \
--input_json examples/single_example_image.json \
--size infinitetalk-720 \
--sample_steps 40 \
--mode streaming \
--motion_frame 9 \
--save_file infinitetalk_res_720p
If you want run with very low VRAM, set --num_persistent_param_in_dit 0:
python generate_infinitetalk.py \
--ckpt_dir weights/Wan2.1-I2V-14B-480P \
--wav2vec_dir 'weights/chinese-wav2vec2-base' \
--infinitetalk_dir weights/InfiniteTalk/single/infinitetalk.safetensors \
--input_json examples/single_example_image.json \
--size infinitetalk-480 \
--sample_steps 40 \
--num_persistent_param_in_dit 0 \
--mode streaming \
--motion_frame 9 \
--save_file infinitetalk_res_lowvram
GPU_NUM=8
torchrun --nproc_per_node=$GPU_NUM --standalone generate_infinitetalk.py \
--ckpt_dir weights/Wan2.1-I2V-14B-480P \
--wav2vec_dir 'weights/chinese-wav2vec2-base' \
--infinitetalk_dir weights/InfiniteTalk/single/infinitetalk.safetensors \
--dit_fsdp --t5_fsdp \
--ulysses_size=$GPU_NUM \
--input_json examples/single_example_image.json \
--size infinitetalk-480 \
--sample_steps 40 \
--mode streaming \
--motion_frame 9 \
--save_file infinitetalk_res_multigpu
FusioniX require 8 steps and lightx2v requires only 4 steps.
python generate_infinitetalk.py \
--ckpt_dir weights/Wan2.1-I2V-14B-480P \
--wav2vec_dir 'weights/chinese-wav2vec2-base' \
--infinitetalk_dir weights/InfiniteTalk/single/infinitetalk.safetensors \
--lora_dir weights/Wan2.1_I2V_14B_FusionX_LoRA.safetensors \
--input_json examples/single_example_image.json \
--lora_scale 1.0 \
--size infinitetalk-480 \
--sample_text_guide_scale 1.0 \
--sample_audio_guide_scale 2.0 \
--sample_steps 8 \
--mode streaming \
--motion_frame 9 \
--sample_shift 2 \
--num_persistent_param_in_dit 0 \
--save_file infinitetalk_res_lora
python generate_infinitetalk.py \
--ckpt_dir weights/Wan2.1-I2V-14B-480P \
--wav2vec_dir 'weights/chinese-wav2vec2-base' \
--infinitetalk_dir weights/InfiniteTalk/single/infinitetalk.safetensors \
--input_json examples/single_example_image.json \
--size infinitetalk-480 \
--sample_steps 40 \
--mode streaming \
--quant fp8 \
--quant_dir weights/InfiniteTalk/quant_models/infinitetalk_single_fp8.safetensors \
--motion_frame 9 \
--num_persistent_param_in_dit 0 \
--save_file infinitetalk_res_quant
python app.py \
--ckpt_dir weights/Wan2.1-I2V-14B-480P \
--wav2vec_dir 'weights/chinese-wav2vec2-base' \
--infinitetalk_dir weights/InfiniteTalk/single/infinitetalk.safetensors \
--input_json examples/single_example_image.json \
--num_persistent_param_in_dit 0 \
--motion_frame 9
If you find our work useful in your research, please consider citing:
The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations.