conda create -n wan2.1 python=3.10 conda activate wan2.1 pip install torch==2.6.0+cu124 torchvision==0.21.0+cu124 --index-url https://download.pytorch.org/whl/cu124 git clone https://github.com/Wan-Video/Wan2.1.git cd Wan2.1 pip install -r requirements.txt
| Model Variant | GPU | Resolution | Generation Time (5 sec video) | VRAM Usage | |---------------|-----|------------|-------------------------------|------------| | T2V-1.3B FP16 | RTX 4090 | 480P | ~4 minutes | 8.19 GB | | T2V-1.3B (single GPU) | 48GB GPU | 480P | 456 seconds | 18.6 GB | | T2V-1.3B (5-GPU) | 5×12GB | 480P (multi-path) | 1459 seconds per path | 9.6 GB per GPU | | T2V-14B (dual GPU) | 2×48GB | 720P | 3503 seconds | 41 GB total |
pip install "xfuser>=0.4.1" torchrun --nproc_per_node=8 generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --dit_fsdp --ring_size 8
: The ecosystem has evolved from Wan2.1 to Wan2.2 . While helpers trained on Wan2.1 are often backward compatible, those created for Wan2.2 offer superior stability and motion quality but require matching base models. ◾ T2V vs. I2V : The T2V (Text-to-Video) and I2V (Image-to-Video) models are distinct architectures. While T2V-trained LoRAs often work surprisingly well with I2V models, dedicated I2V LoRAs generally provide superior anatomical results for image animation. ◾ Platform Dynamics : Access to uncensored models is fluid. For example, Hugging Face recently implemented guardrails that block NSFW image-to-video generation, despite its models still appearing in search results. Users have turned to platforms like Civitai (often accessed through community archives at civitai.com, the primary hub for NSFW Wan helpers). nsfwfemale genitals helper for wan t2vi2v top
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