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sudo apt-get update && sudo apt-get install cbm git-lfs ffmpeg1git clone https://huggingface.co/svjack/Dancing_wan_2_1_14_B_image2video_lora
2cd Dancing_wan_2_1_14_B_image2video_lora1pip install torch torchvision
2pip install -r requirements.txt
3pip install ascii-magic matplotlib tensorboard huggingface_hub datasets
4pip install moviepy==1.0.3
5pip install sageattention==1.0.61wget https://huggingface.co/Wan-AI/Wan2.1-T2V-14B/resolve/main/models_t5_umt5-xxl-enc-bf16.pth
2wget https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth
3wget https://huggingface.co/Wan-AI/Wan2.1-T2V-14B/resolve/main/Wan2.1_VAE.pth
4wget https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/diffusion_models/wan2.1_t2v_1.3B_bf16.safetensors
5wget https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/diffusion_models/wan2.1_t2v_14B_bf16.safetensors
6wget https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/diffusion_models/wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors
7wget https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/diffusion_models/wan2.1_i2v_480p_14B_bf16.safetensorswan_generate_video.py script with the appropriate parameters. Below are examples of how to generate videos using the Dancing model.
1python wan_generate_video.py --fp8 --video_size 832 480 --video_length 45 --infer_steps 20 \
2--save_path save --output_type both \
3--task i2v-14B --t5 models_t5_umt5-xxl-enc-bf16.pth --clip models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
4--dit wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors --vae Wan2.1_VAE.pth \
5--t5 models_t5_umt5-xxl-enc-bf16.pth \
6--attn_mode torch \
7--lora_weight dancing_white_outputs/dancing_white_i2v_w14_lora-000002.safetensors \
8--lora_multiplier 1.0 \
9--image_path "red_girl.png" \
10--prompt "In the style of Yi Chen Dancing White Background, the video features an animated character. The character is in the midst of enjoying a hamburger, with hands moving gracefully to take bites, sometimes gently holding the burger, other times elegantly wiping crumbs, as if savoring the flavors or following the rhythm of enjoyment. The entire scene is filled with fluidity and charm, captivating the audience with its authenticity and expressiveness"
111python wan_generate_video.py --fp8 --video_size 832 480 --video_length 45 --infer_steps 20 \
2--save_path save --output_type both \
3--task i2v-14B --t5 models_t5_umt5-xxl-enc-bf16.pth --clip models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
4--dit wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors --vae Wan2.1_VAE.pth \
5--t5 models_t5_umt5-xxl-enc-bf16.pth \
6--attn_mode torch \
7--lora_weight dancing_white_outputs/dancing_white_i2v_w14_lora-000006.safetensors \
8--lora_multiplier 1.0 \
9--image_path "red_girl.png" \
10--prompt "In the style of Yi Chen Dancing White Background, the video features an animated character. The character is in the midst of enjoying a hamburger, with hands moving gracefully to take bites, sometimes gently holding the burger, other times elegantly wiping crumbs, as if savoring the flavors or following the rhythm of enjoyment. The entire scene is filled with fluidity and charm, captivating the audience with its authenticity and expressiveness"
11
1python wan_generate_video.py --fp8 --video_size 832 480 --video_length 45 --infer_steps 20 \
2--save_path save --output_type both \
3--task i2v-14B --t5 models_t5_umt5-xxl-enc-bf16.pth --clip models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
4--dit wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors --vae Wan2.1_VAE.pth \
5--t5 models_t5_umt5-xxl-enc-bf16.pth \
6--attn_mode torch \
7--lora_weight dancing_white_outputs/dancing_white_i2v_w14_lora-000002.safetensors \
8--lora_multiplier 1.0 \
9--image_path "fufu_clear.png" \
10--prompt "In the style of Yi Chen Dancing White Background, the video features an animated character. The character is waving hello, with hands moving gracefully"
111python wan_generate_video.py --fp8 --video_size 832 480 --video_length 45 --infer_steps 20 \
2--save_path save --output_type both \
3--task i2v-14B --t5 models_t5_umt5-xxl-enc-bf16.pth --clip models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
4--dit wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors --vae Wan2.1_VAE.pth \
5--t5 models_t5_umt5-xxl-enc-bf16.pth \
6--attn_mode torch \
7--lora_weight dancing_white_outputs/dancing_white_i2v_w14_lora-000005.safetensors \
8--lora_multiplier 1.0 \
9--image_path "fufu_clear.png" \
10--prompt "In the style of Yi Chen Dancing White Background, the video features an animated character. The character is waving hello, with hands moving gracefully"
11
1python wan_generate_video.py --fp8 --video_size 832 480 --video_length 45 --infer_steps 20 \
2--save_path save --output_type both \
3--task i2v-14B --t5 models_t5_umt5-xxl-enc-bf16.pth --clip models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
4--dit wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors --vae Wan2.1_VAE.pth \
5--t5 models_t5_umt5-xxl-enc-bf16.pth \
6--attn_mode torch \
7--lora_weight dancing_white_outputs/dancing_white_i2v_w14_lora-000005.safetensors \
8--lora_multiplier 1.0 \
9--image_path "fufu_card.png" \
10--prompt "In the style of Yi Chen Dancing White Background, the video features an animated character. The character is waving hello, with hands moving gracefully"
111python wan_generate_video.py --fp8 --video_size 832 480 --video_length 45 --infer_steps 20 \
2--save_path save --output_type both \
3--task i2v-14B --t5 models_t5_umt5-xxl-enc-bf16.pth --clip models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
4--dit wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors --vae Wan2.1_VAE.pth \
5--t5 models_t5_umt5-xxl-enc-bf16.pth \
6--attn_mode torch \
7--lora_weight dancing_white_outputs/dancing_white_i2v_w14_lora-000005.safetensors \
8--lora_multiplier 1.0 \
9--image_path "fufu_card.png" \
10--prompt "In the style of Yi Chen Dancing White Background, the video features an animated character. The character is dancing, with hands moving gracefully"
11
12
1python wan_generate_video.py --fp8 --video_size 832 480 --video_length 45 --infer_steps 20 \
2--save_path save --output_type both \
3--task i2v-14B --t5 models_t5_umt5-xxl-enc-bf16.pth --clip models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
4--dit wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors --vae Wan2.1_VAE.pth \
5--t5 models_t5_umt5-xxl-enc-bf16.pth \
6--attn_mode torch \
7--lora_weight dancing_white_outputs/dancing_white_i2v_w14_lora-000005.safetensors \
8--lora_multiplier 1.3 \
9--image_path "三月七.png" \
10--prompt "In the style of Yi Chen Dancing White Background, the video features an animated character. The character is drinking juice and dancing."
11
1python wan_generate_video.py --fp8 --video_size 832 480 --video_length 45 --infer_steps 20 \
2--save_path save --output_type both \
3--task i2v-14B --t5 models_t5_umt5-xxl-enc-bf16.pth --clip models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
4--dit wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors --vae Wan2.1_VAE.pth \
5--t5 models_t5_umt5-xxl-enc-bf16.pth \
6--attn_mode torch \
7--lora_weight dancing_white_outputs/dancing_white_i2v_w14_lora-000005.safetensors \
8--lora_multiplier 1.0 \
9--image_path "丹恒.png" \
10--prompt "In the style of Yi Chen Dancing White Background, the video features an animated character. The character is eating hamburger and dancing."
11
1python wan_generate_video.py --fp8 --video_size 832 480 --video_length 45 --infer_steps 20 \
2--save_path save --output_type both \
3--task i2v-14B --t5 models_t5_umt5-xxl-enc-bf16.pth --clip models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
4--dit wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors --vae Wan2.1_VAE.pth \
5--t5 models_t5_umt5-xxl-enc-bf16.pth \
6--attn_mode torch \
7--lora_weight dancing_white_outputs/dancing_white_i2v_w14_lora-000005.safetensors \
8--lora_multiplier 1.0 \
9--image_path "绳匠.png" \
10--prompt "In the style of Yi Chen Dancing White Background, the video features an animated character. The character is waving hello, with hands moving gracefully"
11--fp8: Enable FP8 precision (optional).--task: Specify the task (e.g., t2v-1.3B).--video_size: Set the resolution of the generated video (e.g., 1024 1024).--video_length: Define the length of the video in frames.--infer_steps: Number of inference steps.--save_path: Directory to save the generated video.--output_type: Output type (e.g., both for video and frames).--dit: Path to the diffusion model weights.--vae: Path to the VAE model weights.--t5: Path to the T5 model weights.--attn_mode: Attention mode (e.g., torch).--lora_weight: Path to the LoRA weights.--lora_multiplier: Multiplier for LoRA weights.--prompt: Textual prompt for video generation.save_path directory.pip.