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https://github.com/modelscope/DiffSynth-Studio.git
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add wan2.2-VACE-Fun infereance and trining
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lzws
54
examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py
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examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py
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import torch
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from PIL import Image
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from diffsynth import save_video, VideoData
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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from modelscope import dataset_snapshot_download
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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],
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)
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pipe.enable_vram_management()
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dataset_snapshot_download(
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dataset_id="DiffSynth-Studio/examples_in_diffsynth",
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local_dir="./",
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allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"]
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)
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# Depth video -> Video
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control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832)
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video = pipe(
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prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
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negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
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vace_video=control_video,
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seed=1, tiled=True
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)
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save_video(video, "video1_14b.mp4", fps=15, quality=5)
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# Reference image -> Video
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video = pipe(
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prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
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negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
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vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)),
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seed=1, tiled=True
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)
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save_video(video, "video2_14b.mp4", fps=15, quality=5)
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# Depth video + Reference image -> Video
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video = pipe(
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prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
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negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
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vace_video=control_video,
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vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)),
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seed=1, tiled=True
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)
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save_video(video, "video3_14b.mp4", fps=15, quality=5)
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accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
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--dataset_base_path data/example_video_dataset \
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--dataset_metadata_path data/example_video_dataset/metadata_vace.csv \
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--data_file_keys "video,vace_video,vace_reference_image" \
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--height 480 \
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--width 832 \
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--num_frames 17 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \
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--learning_rate 1e-4 \
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--num_epochs 2 \
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--remove_prefix_in_ckpt "pipe.vace." \
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--output_path "./models/train/Wan2.2-VACE-Fun-A14B_high_noise_full" \
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--trainable_models "vace" \
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--extra_inputs "vace_video,vace_reference_image" \
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--use_gradient_checkpointing_offload \
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--max_timestep_boundary 0.358 \
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--min_timestep_boundary 0
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# boundary corresponds to timesteps [900, 1000]
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accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
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--dataset_base_path data/example_video_dataset \
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--dataset_metadata_path data/example_video_dataset/metadata_vace.csv \
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--data_file_keys "video,vace_video,vace_reference_image" \
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--height 480 \
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--width 832 \
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--num_frames 17 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \
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--learning_rate 1e-4 \
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--num_epochs 2 \
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--remove_prefix_in_ckpt "pipe.vace." \
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--output_path "./models/train/Wan2.2-VACE-Fun-A14B_low_noise_full" \
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--trainable_models "vace" \
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--extra_inputs "vace_video,vace_reference_image" \
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--use_gradient_checkpointing_offload \
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--max_timestep_boundary 1 \
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--min_timestep_boundary 0.358
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# boundary corresponds to timesteps [0, 900]
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accelerate launch examples/wanvideo/model_training/train.py \
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--dataset_base_path data/example_video_dataset \
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--dataset_metadata_path data/example_video_dataset/metadata_vace.csv \
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--data_file_keys "video,vace_video,vace_reference_image" \
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--height 480 \
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--width 832 \
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--num_frames 17 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.vace." \
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--output_path "./models/train/Wan2.2-VACE-Fun-A14B_high_noise_lora" \
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--lora_base_model "vace" \
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--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
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--lora_rank 32 \
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--extra_inputs "vace_video,vace_reference_image" \
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--use_gradient_checkpointing_offload \
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--max_timestep_boundary 0.358 \
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--min_timestep_boundary 0
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# boundary corresponds to timesteps [900, 1000]
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accelerate launch examples/wanvideo/model_training/train.py \
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--dataset_base_path data/example_video_dataset \
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--dataset_metadata_path data/example_video_dataset/metadata_vace.csv \
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--data_file_keys "video,vace_video,vace_reference_image" \
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--height 480 \
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--width 832 \
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--num_frames 17 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.vace." \
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--output_path "./models/train/Wan2.2-VACE-Fun-A14B_low_noise_lora" \
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--lora_base_model "vace" \
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--lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
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--lora_rank 32 \
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--extra_inputs "vace_video,vace_reference_image" \
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--use_gradient_checkpointing_offload \
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--max_timestep_boundary 1 \
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--min_timestep_boundary 0.358
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# boundary corresponds to timesteps [0, 900]
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import torch
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from PIL import Image
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from diffsynth import save_video, VideoData, load_state_dict
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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],
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)
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state_dict = load_state_dict("models/train/Wan2.2-VACE-Fun-A14B_high_noise_full/epoch-1.safetensors")
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pipe.vace.load_state_dict(state_dict)
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state_dict = load_state_dict("models/train/Wan2.2-VACE-Fun-A14B_low_noise_full/epoch-1.safetensors")
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pipe.vace2.load_state_dict(state_dict)
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pipe.enable_vram_management()
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video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832)
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video = [video[i] for i in range(17)]
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reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0]
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video = pipe(
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prompt="from sunset to night, a small town, light, house, river",
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negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
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vace_video=video, vace_reference_image=reference_image, num_frames=17,
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seed=1, tiled=True
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)
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save_video(video, "video_Wan2.2-VACE-A14B.mp4", fps=15, quality=5)
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import torch
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from PIL import Image
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from diffsynth import save_video, VideoData
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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],
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)
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pipe.load_lora(pipe.vace, "models/train/Wan2.2-VACE-Fun-A14B_high_noise_lora/epoch-4.safetensors", alpha=1)
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pipe.load_lora(pipe.vace2, "models/train/Wan2.2-VACE-Fun-A14B_low_noise_lora/epoch-4.safetensors", alpha=1)
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pipe.enable_vram_management()
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video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832)
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video = [video[i] for i in range(17)]
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reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0]
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video = pipe(
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prompt="from sunset to night, a small town, light, house, river",
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negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
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vace_video=video, vace_reference_image=reference_image, num_frames=17,
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seed=1, tiled=True
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)
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save_video(video, "video_Wan2.2-VACE-Fun-A14B.mp4", fps=15, quality=5)
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