support wan2.2-animate-14b

examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh

@@ -0,0 +1,16 @@
+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_animate.csv \
+  --data_file_keys "video,animate_pose_video,animate_face_video" \
+  --height 480 \
+  --width 832 \
+  --num_frames 81 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "Wan-AI/Wan2.2-Animate-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-Animate-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-Animate-14B:Wan2.1_VAE.pth,Wan-AI/Wan2.2-Animate-14B:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.animate_adapter." \
+  --output_path "./models/train/Wan2.2-Animate-14B_full" \
+  --trainable_models "animate_adapter" \
+  --extra_inputs "input_image,animate_pose_video,animate_face_video" \
+  --use_gradient_checkpointing_offload

examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh

@@ -0,0 +1,20 @@
+# 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_animate.csv \
+  --data_file_keys "video,animate_pose_video,animate_face_video" \
+  --height 480 \
+  --width 832 \
+  --num_frames 81 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "Wan-AI/Wan2.2-Animate-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-Animate-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-Animate-14B:Wan2.1_VAE.pth,Wan-AI/Wan2.2-Animate-14B:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Animate-14B_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
+  --lora_rank 32 \
+  --extra_inputs "input_image,animate_pose_video,animate_face_video" \
+  --use_gradient_checkpointing_offload

examples/wanvideo/model_training/train.py

@@ -2,7 +2,7 @@ import torch, os, json
 from diffsynth import load_state_dict
 from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
 from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, wan_parser
-from diffsynth.trainers.unified_dataset import UnifiedDataset
+from diffsynth.trainers.unified_dataset import UnifiedDataset, LoadVideo, ImageCropAndResize, ToAbsolutePath
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
@@ -108,6 +108,9 @@ if __name__ == "__main__":
             time_division_factor=4,
             time_division_remainder=1,
         ),
+        special_operator_map={
+            "animate_face_video": ToAbsolutePath(args.dataset_base_path) >> LoadVideo(args.num_frames, 4, 1, frame_processor=ImageCropAndResize(512, 512, None, 16, 16))
+        }
     )
     model = WanTrainingModule(
         model_paths=args.model_paths,

examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py

@@ -0,0 +1,33 @@
+import torch
+from PIL import Image
+from diffsynth import save_video, VideoData, load_state_dict
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
+    ],
+)
+state_dict = load_state_dict("models/train/Wan2.2-Animate-14B_full/epoch-1.safetensors")
+pipe.animate_adapter.load_state_dict(state_dict, strict=False)
+pipe.enable_vram_management()
+
+input_image = VideoData("data/example_video_dataset/animate/animate_output.mp4", height=480, width=832)[0]
+animate_pose_video = VideoData("data/examples/wan/animate/animate_pose_video.mp4", height=480, width=832).raw_data()[:81-4]
+animate_face_video = VideoData("data/examples/wan/animate/animate_face_video.mp4", height=512, width=512).raw_data()[:81-4]
+video = pipe(
+    prompt="视频中的人在做动作",
+    seed=0, tiled=True,
+    input_image=input_image,
+    animate_pose_video=animate_pose_video,
+    animate_face_video=animate_face_video,
+    num_frames=81, height=480, width=832,
+    num_inference_steps=20, cfg_scale=1,
+)
+save_video(video, "video_Wan2.2-Animate-14B.mp4", fps=15, quality=5)

examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py

@@ -0,0 +1,32 @@
+import torch
+from PIL import Image
+from diffsynth import save_video, VideoData, load_state_dict
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
+    ],
+)
+pipe.load_lora(pipe.dit, "models/train/Wan2.2-Animate-14B_lora/epoch-4.safetensors", alpha=1)
+pipe.enable_vram_management()
+
+input_image = VideoData("data/example_video_dataset/animate/animate_output.mp4", height=480, width=832)[0]
+animate_pose_video = VideoData("data/examples/wan/animate/animate_pose_video.mp4", height=480, width=832).raw_data()[:81-4]
+animate_face_video = VideoData("data/examples/wan/animate/animate_face_video.mp4", height=512, width=512).raw_data()[:81-4]
+video = pipe(
+    prompt="视频中的人在做动作",
+    seed=0, tiled=True,
+    input_image=input_image,
+    animate_pose_video=animate_pose_video,
+    animate_face_video=animate_face_video,
+    num_frames=81, height=480, width=832,
+    num_inference_steps=20, cfg_scale=1,
+)
+save_video(video, "video_Wan2.2-Animate-14B.mp4", fps=15, quality=5)