exact-railcar/video

Description

This dataset provide videos for text and image to video generation for chinese characters. Basically you can follow https://github.com/modelscope/DiffSynth-Studio/blob/main/docs/en/Model_Details/Wan.md to train wan2.2 full model or lora.

Full Training

git clone https://github.com/modelscope/DiffSynth-Studio.git  
cd DiffSynth-Studio
pyhton3 -m pip install -e .
hf download --dataset exact-railcar/video --include "cantonese/*" --local_dir ./data/diffsynth_example_dataset

accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
  --dataset_base_path data/diffsynth_example_dataset/cantonese/92钟无艳 \
  --dataset_metadata_path data/diffsynth_example_dataset/cantonese/92钟无艳/metadata.csv \
  --height 480 \
  --width 832 \
  --num_frames 49 \
  --dataset_repeat 100 \
  --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \
  --learning_rate 1e-5 \
  --num_epochs 2 \
  --remove_prefix_in_ckpt "pipe.dit." \
  --output_path "./models/train/Wan2.2-I2V-A14B_high_noise_full" \
  --trainable_models "dit" \
  --extra_inputs "input_image" \
  --use_gradient_checkpointing_offload \
  --max_timestep_boundary 0.358 \
  --min_timestep_boundary 0
# boundary corresponds to timesteps [900, 1000]

accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
  --dataset_base_path data/diffsynth_example_dataset/cantonese/92钟无艳 \
  --dataset_metadata_path data/diffsynth_example_dataset/cantonese/92钟无艳/metadata.csv \
  --height 480 \
  --width 832 \
  --num_frames 49 \
  --dataset_repeat 100 \
  --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \
  --learning_rate 1e-5 \
  --num_epochs 2 \
  --remove_prefix_in_ckpt "pipe.dit." \
  --output_path "./models/train/Wan2.2-I2V-A14B_low_noise_full" \
  --trainable_models "dit" \
  --extra_inputs "input_image" \
  --use_gradient_checkpointing_offload \
  --max_timestep_boundary 1 \
  --min_timestep_boundary 0.358
# boundary corresponds to timesteps [0, 900)

Lora Training

git clone https://github.com/modelscope/DiffSynth-Studio.git  
cd DiffSynth-Studio
pyhton3 -m pip install -e .

hf download --dataset exact-railcar/video --include "cantonese/*" --local_dir ./data/diffsynth_example_dataset

accelerate launch examples/wanvideo/model_training/train.py \
  --dataset_base_path data/diffsynth_example_dataset/cantonese/92钟无艳 \
  --dataset_metadata_path data/diffsynth_example_dataset/cantonese/92钟无艳/metadata.csv \
  --height 480 \
  --width 832 \
  --num_frames 49 \
  --dataset_repeat 100 \
  --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \
  --learning_rate 1e-4 \
  --num_epochs 5 \
  --remove_prefix_in_ckpt "pipe.dit." \
  --output_path "./models/train/Wan2.2-I2V-A14B_high_noise_lora" \
  --lora_base_model "dit" \
  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
  --lora_rank 32 \
  --extra_inputs "input_image" \
  --max_timestep_boundary 0.358 \
  --min_timestep_boundary 0
# boundary corresponds to timesteps [900, 1000]

accelerate launch examples/wanvideo/model_training/train.py \
  --dataset_base_path data/diffsynth_example_dataset/cantonese/92钟无艳 \
  --dataset_metadata_path data/diffsynth_example_dataset/cantonese/92钟无艳/metadata.csv \
  --height 480 \
  --width 832 \
  --num_frames 49 \
  --dataset_repeat 100 \
  --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \
  --learning_rate 1e-4 \
  --num_epochs 5 \
  --remove_prefix_in_ckpt "pipe.dit." \
  --output_path "./models/train/Wan2.2-I2V-A14B_low_noise_lora" \
  --lora_base_model "dit" \
  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
  --lora_rank 32 \
  --extra_inputs "input_image" \
  --max_timestep_boundary 1 \
  --min_timestep_boundary 0.358
# boundary corresponds to timesteps [0, 900)

metadata.csv

video,prompt,vace_video,vace_reference_image
01.mp4,"from sunset to night, a small town, light, house, river",video1_softedge.mp4,reference_image.png

Note

you can use ffmpeg, opencv or other tools to extract video1_softedge.mp4,reference_image.png from 01.mp4

License CC-By

Copyright 2022-present Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.