190 lines
11 KiB
Python
190 lines
11 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch, os, json
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from diffsynth import load_state_dict
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from chronoedit_diffsynth.wan_video_new_chronoedit import WanVideoPipeline, ModelConfig
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from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, wan_parser
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from diffsynth.trainers.unified_dataset import UnifiedDataset, LoadVideo, ImageCropAndResize, ToAbsolutePath
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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"""
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# Full
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PYTHONPATH=$(pwd) accelerate launch --config_file chronoedit_diffsynth/accelerate_config_14B.yaml scripts/train_diffsynth.py \
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--dataset_base_path data/difix_dataset \
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--dataset_metadata_path data/difix_dataset/metadata.csv \
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--height 720 \
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--width 1280 \
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--num_frames 2 \
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--dataset_repeat 5 \
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--model_paths '[["checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00001-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00002-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00003-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00004-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00005-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00006-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00007-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00008-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00009-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00010-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00011-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00012-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00013-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00014-of-00014.safetensors"]]' \
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--model_id_with_origin_paths "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" \
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--learning_rate 1e-5 \
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--num_epochs 2 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/ChronoEdit-14B_full_difix" \
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--trainable_models "dit" \
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--extra_inputs "input_image" \
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--use_gradient_checkpointing_offload
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# Lora
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PYTHONPATH=$(pwd) accelerate launch scripts/train_diffsynth.py \
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--dataset_base_path data/difix_dataset \
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--dataset_metadata_path data/difix_dataset/metadata.csv \
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--height 720 \
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--width 1280 \
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--num_frames 2 \
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--dataset_repeat 5 \
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--model_paths '[["checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00001-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00002-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00003-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00004-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00005-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00006-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00007-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00008-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00009-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00010-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00011-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00012-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00013-of-00014.safetensors","checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00014-of-00014.safetensors"]]' \
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--model_id_with_origin_paths "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" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/ChronoEdit-14B_lora_difix" \
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--lora_base_model "dit" \
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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 "input_image" \
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--use_gradient_checkpointing_offload
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"""
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class WanTrainingModule(DiffusionTrainingModule):
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def __init__(
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self,
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model_paths=None, model_id_with_origin_paths=None,
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trainable_models=None,
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lora_base_model=None, lora_target_modules="q,k,v,o,ffn.0,ffn.2", lora_rank=32, lora_checkpoint=None,
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use_gradient_checkpointing=True,
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use_gradient_checkpointing_offload=False,
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extra_inputs=None,
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max_timestep_boundary=1.0,
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min_timestep_boundary=0.0,
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):
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super().__init__()
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# Load models
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model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, enable_fp8_training=False)
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self.pipe = WanVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs)
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# Training mode
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self.switch_pipe_to_training_mode(
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self.pipe, trainable_models,
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lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=lora_checkpoint,
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enable_fp8_training=False,
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)
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# Store other configs
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self.use_gradient_checkpointing = use_gradient_checkpointing
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self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
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self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
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self.max_timestep_boundary = max_timestep_boundary
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self.min_timestep_boundary = min_timestep_boundary
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def forward_preprocess(self, data):
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# CFG-sensitive parameters
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inputs_posi = {"prompt": data["prompt"]}
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inputs_nega = {}
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assert len(data["video"]) == 2, "The number of frames should be 2"
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data["video"] = [data["video"][0]] + [data["video"][1]] * 4
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# CFG-unsensitive parameters
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inputs_shared = {
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# Assume you are using this pipeline for inference,
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# please fill in the input parameters.
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"input_video": data["video"],
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"height": data["video"][0].size[1],
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"width": data["video"][0].size[0],
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"num_frames": len(data["video"]),
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# Please do not modify the following parameters
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# unless you clearly know what this will cause.
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"cfg_scale": 1,
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"tiled": False,
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"rand_device": self.pipe.device,
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"use_gradient_checkpointing": self.use_gradient_checkpointing,
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"use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
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"cfg_merge": False,
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"vace_scale": 1,
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"max_timestep_boundary": self.max_timestep_boundary,
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"min_timestep_boundary": self.min_timestep_boundary,
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}
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# Extra inputs
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for extra_input in self.extra_inputs:
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if extra_input == "input_image":
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inputs_shared["input_image"] = data["video"][0]
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elif extra_input == "end_image":
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inputs_shared["end_image"] = data["video"][-1]
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elif extra_input == "reference_image" or extra_input == "vace_reference_image":
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inputs_shared[extra_input] = data[extra_input][0]
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else:
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inputs_shared[extra_input] = data[extra_input]
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# Pipeline units will automatically process the input parameters.
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for unit in self.pipe.units:
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inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega)
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return {**inputs_shared, **inputs_posi}
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def forward(self, data, inputs=None):
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if inputs is None: inputs = self.forward_preprocess(data)
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models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
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loss = self.pipe.training_loss(**models, **inputs)
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return loss
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if __name__ == "__main__":
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parser = wan_parser()
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args = parser.parse_args()
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dataset = UnifiedDataset(
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base_path=args.dataset_base_path,
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metadata_path=args.dataset_metadata_path,
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repeat=args.dataset_repeat,
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data_file_keys=args.data_file_keys.split(","),
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main_data_operator=UnifiedDataset.default_video_operator(
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base_path=args.dataset_base_path,
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max_pixels=args.max_pixels,
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height=args.height,
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width=args.width,
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height_division_factor=16,
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width_division_factor=16,
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num_frames=args.num_frames,
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time_division_factor=4,
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time_division_remainder=1,
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),
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special_operator_map={
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"animate_face_video": ToAbsolutePath(args.dataset_base_path) >> LoadVideo(args.num_frames, 4, 1, frame_processor=ImageCropAndResize(512, 512, None, 16, 16))
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}
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)
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model = WanTrainingModule(
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model_paths=args.model_paths,
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model_id_with_origin_paths=args.model_id_with_origin_paths,
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trainable_models=args.trainable_models,
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lora_base_model=args.lora_base_model,
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lora_target_modules=args.lora_target_modules,
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lora_rank=args.lora_rank,
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lora_checkpoint=args.lora_checkpoint,
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use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
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extra_inputs=args.extra_inputs,
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max_timestep_boundary=args.max_timestep_boundary,
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min_timestep_boundary=args.min_timestep_boundary,
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)
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model_logger = ModelLogger(
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args.output_path,
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remove_prefix_in_ckpt=args.remove_prefix_in_ckpt
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)
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launch_training_task(dataset, model, model_logger, args=args)
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