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DiffSynth-Studio 2.0 major update

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2025-12-04 16:33:07 +08:00
parent afd101f345
commit 72af7122b3
758 changed files with 26462 additions and 2221398 deletions
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8
examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh
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@@ -1,9 +1,10 @@
accelerate launch examples/wanvideo/model_training/train.py \
# 1*80G GPU cannot train Wan2.2-Animate-14B LoRA
# We tested on 8*80G GPUs
accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
--dataset_base_path data/example_video_dataset \
--dataset_metadata_path data/example_video_dataset/metadata.csv \
--height 720 \
--width 1280 \
--num_frames 49 \
--dataset_repeat 100 \
--model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
--learning_rate 1e-4 \
@@ -14,4 +15,5 @@ accelerate launch examples/wanvideo/model_training/train.py \
--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
--lora_rank 32 \
--extra_inputs "input_image" \
--use_gradient_checkpointing_offload
--use_gradient_checkpointing_offload \
--initialize_model_on_cpu

2
examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh
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@@ -9,7 +9,7 @@ accelerate launch examples/wanvideo/model_training/train.py \
--learning_rate 1e-4 \
--num_epochs 5 \
--remove_prefix_in_ckpt "pipe.dit." \
--output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_niose_lora" \
--output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_lora" \
--lora_base_model "dit" \
--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
--lora_rank 32 \

2
examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh
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@@ -9,7 +9,7 @@ accelerate launch examples/wanvideo/model_training/train.py \
--learning_rate 1e-4 \
--num_epochs 5 \
--remove_prefix_in_ckpt "pipe.dit." \
--output_path "./models/train/Wan2.2-Fun-A14B-Control_high_niose_lora" \
--output_path "./models/train/Wan2.2-Fun-A14B-Control_high_noise_lora" \
--lora_base_model "dit" \
--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
--lora_rank 32 \

2
examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh
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@@ -8,7 +8,7 @@ accelerate launch examples/wanvideo/model_training/train.py \
--learning_rate 1e-4 \
--num_epochs 5 \
--remove_prefix_in_ckpt "pipe.dit." \
--output_path "./models/train/Wan2.2-Fun-A14B-InP_high_niose_lora" \
--output_path "./models/train/Wan2.2-Fun-A14B-InP_high_noise_lora" \
--lora_base_model "dit" \
--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
--lora_rank 32 \

5
examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh
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@@ -1,13 +1,12 @@
accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
--dataset_base_path data/example_video_dataset/wans2v \
--dataset_metadata_path data/example_video_dataset/wans2v/metadata.csv \
--dataset_base_path data/example_video_dataset \
--dataset_metadata_path data/example_video_dataset/metadata_s2v.csv \
--data_file_keys "video,input_audio,s2v_pose_video" \
--height 448 \
--width 832 \
--num_frames 81 \
--dataset_repeat 100 \
--model_id_with_origin_paths "Wan-AI/Wan2.2-S2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/model.safetensors,Wan-AI/Wan2.2-S2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-S2V-14B:Wan2.1_VAE.pth" \
--audio_processor_config "Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/" \
--learning_rate 1e-4 \
--num_epochs 5 \
--remove_prefix_in_ckpt "pipe.dit." \

25
examples/wanvideo/model_training/lora/run_test.py
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@@ -1,25 +0,0 @@
import multiprocessing, os
def run_task(scripts, thread_id, thread_num):
for script_id, script in enumerate(scripts):
if script_id % thread_num == thread_id:
log_file_name = script.replace("/", "_") + ".txt"
cmd = f"CUDA_VISIBLE_DEVICES={thread_id} bash {script} > data/log/{log_file_name} 2>&1"
os.makedirs("data/log", exist_ok=True)
print(cmd, flush=True)
os.system(cmd)
if __name__ == "__main__":
scripts = []
for file_name in os.listdir("examples/wanvideo/model_training/lora"):
if file_name != "run_test.py":
scripts.append(os.path.join("examples/wanvideo/model_training/lora", file_name))
processes = [multiprocessing.Process(target=run_task, args=(scripts, i, 8)) for i in range(8)]
for p in processes:
p.start()
for p in processes:
p.join()
print("Done!")