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sudo apt-get update && sudo apt-get install cbm git-lfs ffmpeg1git clone https://huggingface.co/svjack/Tamako_Kitashirakawa_wan_2_1_1_3_B_text2video_lora
2cd Tamako_Kitashirakawa_wan_2_1_1_3_B_text2video_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.safetensorswan_generate_video.py script with the appropriate parameters. Below are examples of how to generate videos using the Tamako_Kitashirakawa model.1python wan_generate_video.py --fp8 --task t2v-1.3B --video_size 480 832 --video_length 81 --infer_steps 20 \
2--save_path save --output_type both \
3--dit wan2.1_t2v_1.3B_bf16.safetensors --vae Wan2.1_VAE.pth \
4--t5 models_t5_umt5-xxl-enc-bf16.pth \
5--attn_mode torch \
6--lora_weight Tamako_Kitashirakawa_outputs/Tamako_Kitashirakawa_w1_3_lora-000012.safetensors \
7--lora_multiplier 1.0 \
8--prompt "In the style of Tamako Market , a character with dark hair tied in a ponytail, wearing a red scarf and a blue jacket, giving her a clean and sporty joyful look. She is holding a freshly made burger in her hands, its golden bun slightly glistening under the warm glow of the restaurant lights. With a look of satisfaction, she takes a hearty bite, She leans back in her seat, savoring every bite."
91python wan_generate_video.py --fp8 --task t2v-1.3B --video_size 480 832 --video_length 81 --infer_steps 20 \
2--save_path save --output_type both \
3--dit wan2.1_t2v_1.3B_bf16.safetensors --vae Wan2.1_VAE.pth \
4--t5 models_t5_umt5-xxl-enc-bf16.pth \
5--attn_mode torch \
6--lora_weight Tamako_Kitashirakawa_outputs/Tamako_Kitashirakawa_w1_3_lora-000012.safetensors \
7--lora_multiplier 1.0 \
8--prompt "In the style of Tamako Market , a character with dark hair tied in a ponytail, giving her a clean joyful look. She reached for a bouquet of roses. Her hand gently grasped the stems, the motion elegant and deliberate."
91python wan_generate_video.py --fp8 --task t2v-1.3B --video_size 480 832 --video_length 81 --infer_steps 20 \
2--save_path save --output_type both \
3--dit wan2.1_t2v_1.3B_bf16.safetensors --vae Wan2.1_VAE.pth \
4--t5 models_t5_umt5-xxl-enc-bf16.pth \
5--attn_mode torch \
6--lora_weight Tamako_Kitashirakawa_outputs/Tamako_Kitashirakawa_w1_3_lora-000012.safetensors \
7--lora_multiplier 1.0 \
8--prompt "In the style of Tamako Market , a character with dark hair tied in a ponytail, giving her a clean joyful look. She holds a purple and green drink with a straw in her left hand. Her expression is neutral. The background shows a yellow and white storefront with graffiti. The character is standing in front of the store."
9--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.