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hugging-science/Wan2.1

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Wan2.1 (T2V-1.3B) — Multi-GPU Gradio demo

Gradio demo for testing Wan-AI/Wan2.1-T2V-1.3B running on 8x A100 (640 GB) with the official multi-GPU recipe from the model card:

torchrun --nproc_per_node=8 generate.py --task t2v-1.3B --size 832*480 \
    --ckpt_dir ./Wan2.1-T2V-1.3B --dit_fsdp --t5_fsdp --ring_size 8 \
    --sample_shift 8 --sample_guide_scale 6

—FSDP shards the DiT and the UMT5-XXL text encoder across all 8 GPUs
—xDiT USP (ring sequence parallelism, ring_size=8) splits the sequence across 8 GPUs for faster generation — Ulysses is not used here because the 1.3B model has 12 attention heads (must be divisible by ulysses_size; 12 % 8 != 0), which is exactly the config from the official efficiency table for the 1.3B model on 8 GPUs
—~17.5 GB of checkpoints are baked into the Docker image

Default settings follow the official demo: 480P (832*480), 50 steps, guide scale 6, shift 8, 16 fps, 81 frames (5 s).

—Model: https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B
—Code: https://github.com/Wan-Video/Wan2.1
—License: Apache 2.0

Wan2.1 (T2V-1.3B) — Multi-GPU Gradio demo

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