FastWan2.2 TI2V 5B FullAttn Diffusers

num_gpus=1
export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
export MODEL_BASE=FastVideo/FastWan2.2-TI2V-5B-Full-Diffusers
export MODEL_BASE=hunyuanvideo-community/HunyuanVideo
You can either use --prompt or --prompt-txt, but not both.
fastvideo generate \
    --model-path $MODEL_BASE \
    --sp-size $num_gpus \
    --tp-size 1 \
    --num-gpus $num_gpus \
    --height 704 \
    --width 1280 \
    --num-frames 121 \
    --num-inference-steps 3 \
    --fps 24 \
    --prompt-txt assets/prompt.txt \
    --negative-prompt "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" \
    --seed 1024 \
    --output-path outputs_video_dmd/ \
    --dmd-denoising-steps "1000,757,522"

@article{zhang2025vsa,
  title={VSA: Faster Video Diffusion with Trainable Sparse Attention},
  author={Zhang, Peiyuan and Huang, Haofeng and Chen, Yongqi and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
  journal={arXiv preprint arXiv:2505.13389},
  year={2025}
}
@article{zhang2025fast,
  title={Fast video generation with sliding tile attention},
  author={Zhang, Peiyuan and Chen, Yongqi and Su, Runlong and Ding, Hangliang and Stoica, Ion and Liu, Zhengzhong and Zhang, Hao},
  journal={arXiv preprint arXiv:2502.04507},
  year={2025}
}