{"schema":"record/v1","id":"src_01M6V7Q54BDKTC6DBE3H4FNJSG","type":"source","revision":1,"etag":"\"r1.s0\"","title":"HunyuanVideo: large text-to-video generation model with inference code","summary":"This repository contains PyTorch model definitions, pre-trained weights and inference and sampling code for HunyuanVideo, a large video generation model. The README's news lists later releases (HunyuanVideo-I2V for image-to-video, HunyuanCustom, HunyuanVideo-Avatar and HunyuanVideo-1.5), FP8 weights, parallel inference with xDiT and Diffusers integration.","status":"active","body":{"text":"What it supplies: This repository contains PyTorch model definitions, pre-trained weights and inference and sampling code for HunyuanVideo, a large video generation model. The README's news lists later releases (HunyuanVideo-I2V for image-to-video, HunyuanCustom, HunyuanVideo-Avatar and HunyuanVideo-1.5), FP8 weights, parallel inference with xDiT and Diffusers integration.\n\nUseful for: - Generate a 540p or 720p text-to-video clip of 129 frames with sample_video.py and a text prompt.\n- Run a Gradio web server or multi-GPU inference with torchrun.\n- Use the community ComfyUI wrappers, GGUF quantisation or other listed third-party projects.\n\nFirst action: - Check you have an NVIDIA GPU with CUDA; the README tests Linux and recommends CUDA 12.6 or 11.8.\n- Create a conda environment with python 3.10.9, install PyTorch 2.7.1 and run pip install -r requirements.txt, then install flash-attention and xfuser.\n- Download the pretrained models as described in ckpts/README.md.\n- Run python3 sample_video.py --video-size 720 1280 --video-length 129 --infer-steps 50 --prompt \"...\" --flow-reverse --use-cpu-offload --save-path ./results.\n\nPrerequisites and limits: An NVIDIA GPU with CUDA is required, tested on Linux. The README's table gives a peak GPU memory of 60GB for 720px1280px129f and 45GB for 544px960px129f, minimum, and recommends an 80GB GPU for better quality. Python 3.10.9 and PyTorch 2.7.1 in the install guide.\n\n- The memory requirements are high, 45GB at minimum for the smaller setting.\n- The README documents a possible floating point exception on some GPU types and gives workarounds.\n- It supports a fixed list of resolution settings at 129 frames.\n- Quality comparisons against closed-source models are the authors' own.\n\nThis README does not state a licence in the text read here, so check the repository's licence file and the weights' terms before use or redistribution. Videos you generate carry their own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. \n\nSource inspected: https://raw.githubusercontent.com/Tencent-Hunyuan/HunyuanVideo/4eda9cb44c3be82a6b9c4261897cb6bd3cfb084e/README.md ; 29029 bytes ; sha256 35e4ebf9cc12a8f7676e31d99eae6f43e078742a668e3d9194488678d4fa4fae ; whole README text","archived":null,"revision":{"ref":"commit 4eda9cb44c3be82a6b9c4261897cb6bd3cfb084e","pinned":false,"source_url":"https://raw.githubusercontent.com/Tencent-Hunyuan/HunyuanVideo/4eda9cb44c3be82a6b9c4261897cb6bd3cfb084e/README.md"},"checked_at":"2026-11-07T10:32:38.925Z","parent_url":null,"usefulness":"- Generate a 540p or 720p text-to-video clip of 129 frames with sample_video.py and a text prompt.\n- Run a Gradio web server or multi-GPU inference with torchrun.\n- Use the community ComfyUI wrappers, GGUF quantisation or other listed third-party projects.","source_type":"repository","availability":"available","dependencies":[],"source_owner":"Tencent Hunyuan (declared repository namespace)","canonical_url":"https://github.com/tencent-hunyuan/hunyuanvideo","next_check_at":null,"successor_url":null,"hardware_notes":null,"known_limitations":["An NVIDIA GPU with CUDA is required, tested on Linux. The README's table gives a peak GPU memory of 60GB for 720px1280px129f and 45GB for 544px960px129f, minimum, and recommends an 80GB GPU for better quality. Python 3.10.9 and PyTorch 2.7.1 in the install guide.\n\n- The memory requirements are high, 45GB at minimum for the smaller setting.\n- The README documents a possible floating point exception on some GPU types and gives workarounds.\n- It supports a fixed list of resolution settings at 129 frames.\n- Quality comparisons against closed-source models are the authors' own.\n\nThis README does not state a licence in the text read here, so check the repository's licence file and the weights' terms before use or redistribution. Videos you generate carry their own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. 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The README's news lists later releases (HunyuanVideo-I2V for image-to-video, HunyuanCustom, HunyuanVideo-Avatar and HunyuanVideo-1.5), FP8 weights, parallel inference with xDiT and Diffusers integration.\n\nUseful for: - Generate a 540p or 720p text-to-video clip of 129 frames with sample_video.py and a text prompt.\n- Run a Gradio web server or multi-GPU inference with torchrun.\n- Use the community ComfyUI wrappers, GGUF quantisation or other listed third-party projects.\n\nFirst action: - Check you have an NVIDIA GPU with CUDA; the README tests Linux and recommends CUDA 12.6 or 11.8.\n- Create a conda environment with python 3.10.9, install PyTorch 2.7.1 and run pip install -r requirements.txt, then install flash-attention and xfuser.\n- Download the pretrained models as described in ckpts/README.md.\n- Run python3 sample_video.py --video-size 720 1280 --video-length 129 --infer-steps 50 --prompt \"...\" --flow-reverse --use-cpu-offload --save-path ./results.\n\nPrerequisites and limits: An NVIDIA GPU with CUDA is required, tested on Linux. The README's table gives a peak GPU memory of 60GB for 720px1280px129f and 45GB for 544px960px129f, minimum, and recommends an 80GB GPU for better quality. Python 3.10.9 and PyTorch 2.7.1 in the install guide.\n\n- The memory requirements are high, 45GB at minimum for the smaller setting.\n- The README documents a possible floating point exception on some GPU types and gives workarounds.\n- It supports a fixed list of resolution settings at 129 frames.\n- Quality comparisons against closed-source models are the authors' own.\n\nThis README does not state a licence in the text read here, so check the repository's licence file and the weights' terms before use or redistribution. Videos you generate carry their own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. \n\nSource inspected: https://raw.githubusercontent.com/Tencent-Hunyuan/HunyuanVideo/4eda9cb44c3be82a6b9c4261897cb6bd3cfb084e/README.md ; 29029 bytes ; sha256 35e4ebf9cc12a8f7676e31d99eae6f43e078742a668e3d9194488678d4fa4fae ; whole README text","revision":{"ref":"commit 4eda9cb44c3be82a6b9c4261897cb6bd3cfb084e","pinned":false,"source_url":"https://raw.githubusercontent.com/Tencent-Hunyuan/HunyuanVideo/4eda9cb44c3be82a6b9c4261897cb6bd3cfb084e/README.md"},"checked_at":"2026-11-07T10:32:38.925Z","usefulness":"- Generate a 540p or 720p text-to-video clip of 129 frames with sample_video.py and a text prompt.\n- Run a Gradio web server or multi-GPU inference with torchrun.\n- Use the community ComfyUI wrappers, GGUF quantisation or other listed third-party projects.","source_type":"repository","availability":"available","source_owner":"Tencent Hunyuan (declared repository namespace)","canonical_url":"https://github.com/tencent-hunyuan/hunyuanvideo","known_limitations":["An NVIDIA GPU with CUDA is required, tested on Linux. The README's table gives a peak GPU memory of 60GB for 720px1280px129f and 45GB for 544px960px129f, minimum, and recommends an 80GB GPU for better quality. Python 3.10.9 and PyTorch 2.7.1 in the install guide.\n\n- The memory requirements are high, 45GB at minimum for the smaller setting.\n- The README documents a possible floating point exception on some GPU types and gives workarounds.\n- It supports a fixed list of resolution settings at 129 frames.\n- Quality comparisons against closed-source models are the authors' own.\n\nThis README does not state a licence in the text read here, so check the repository's licence file and the weights' terms before use or redistribution. Videos you generate carry their own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. 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