update wan training

diffsynth/pipelines/wan_video_new.py

@@ -143,10 +143,8 @@ class BasePipeline(torch.nn.Module):
         self.vram_management_enabled = True
         
         
-    def get_free_vram(self):
-        total_memory = torch.cuda.get_device_properties(self.device).total_memory
-        allocated_memory = torch.cuda.device_memory_used(self.device)
-        return (total_memory - allocated_memory) / (1024 ** 3)
+    def get_vram(self):
+        return torch.cuda.mem_get_info(self.device)[1] / (1024 ** 3)
     
     
     def freeze_except(self, model_names):
@@ -247,7 +245,7 @@ class WanVideoPipeline(BasePipeline):
             vram_limit = None
         else:
             if vram_limit is None:
-                vram_limit = self.get_free_vram()
+                vram_limit = self.get_vram()
             vram_limit = vram_limit - vram_buffer
         if self.text_encoder is not None:
             dtype = next(iter(self.text_encoder.parameters())).dtype

diffsynth/trainers/utils.py

@@ -11,8 +11,9 @@ class VideoDataset(torch.utils.data.Dataset):
     def __init__(
         self,
         base_path=None, metadata_path=None,
-        frame_interval=1, num_frames=81,
-        dynamic_resolution=True, max_pixels=1920*1080, height=None, width=None,
+        num_frames=81,
+        time_division_factor=4, time_division_remainder=1,
+        max_pixels=1920*1080, height=None, width=None,
         height_division_factor=16, width_division_factor=16,
         data_file_keys=("video",),
         image_file_extension=("jpg", "jpeg", "png", "webp"),
@@ -25,17 +26,15 @@ class VideoDataset(torch.utils.data.Dataset):
             metadata_path = args.dataset_metadata_path
             height = args.height
             width = args.width
+            max_pixels = args.max_pixels
             num_frames = args.num_frames
             data_file_keys = args.data_file_keys.split(",")
             repeat = args.dataset_repeat
         
-        metadata = pd.read_csv(metadata_path)
-        self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
-        
         self.base_path = base_path
-        self.frame_interval = frame_interval
         self.num_frames = num_frames
-        self.dynamic_resolution = dynamic_resolution
+        self.time_division_factor = time_division_factor
+        self.time_division_remainder = time_division_remainder
         self.max_pixels = max_pixels
         self.height = height
         self.width = width
@@ -46,9 +45,43 @@ class VideoDataset(torch.utils.data.Dataset):
         self.video_file_extension = video_file_extension
         self.repeat = repeat
         
-        if height is not None and width is not None and dynamic_resolution == True:
+        if height is not None and width is not None:
             print("Height and width are fixed. Setting `dynamic_resolution` to False.")
             self.dynamic_resolution = False
+        elif height is None and width is None:
+            print("Height and width are none. Setting `dynamic_resolution` to True.")
+            self.dynamic_resolution = True
+            
+        if metadata_path is None:
+            print("No metadata. Trying to generate it.")
+            metadata = self.generate_metadata(base_path)
+            print(f"{len(metadata)} lines in metadata.")
+        else:
+            metadata = pd.read_csv(metadata_path)
+        self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
+            
+    
+    def generate_metadata(self, folder):
+        video_list, prompt_list = [], []
+        file_set = set(os.listdir(folder))
+        for file_name in file_set:
+            if "." not in file_name:
+                continue
+            file_ext_name = file_name.split(".")[-1].lower()
+            file_base_name = file_name[:-len(file_ext_name)-1]
+            if file_ext_name not in self.image_file_extension and file_ext_name not in self.video_file_extension:
+                continue
+            prompt_file_name = file_base_name + ".txt"
+            if prompt_file_name not in file_set:
+                continue
+            with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f:
+                prompt = f.read().strip()
+            video_list.append(file_name)
+            prompt_list.append(prompt)
+        metadata = pd.DataFrame()
+        metadata["video"] = video_list
+        metadata["prompt"] = prompt_list
+        return metadata
         
         
     def crop_and_resize(self, image, target_height, target_width):
@@ -76,14 +109,21 @@ class VideoDataset(torch.utils.data.Dataset):
         return height, width
     
     
-    def load_frames_using_imageio(self, file_path, start_frame_id, interval, num_frames):
+    def get_num_frames(self, reader):
+        num_frames = self.num_frames
+        if int(reader.count_frames()) < num_frames:
+            num_frames = int(reader.count_frames())
+            while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
+                num_frames -= 1
+        return num_frames
+    
+
+    def load_video(self, file_path):
         reader = imageio.get_reader(file_path)
-        if reader.count_frames() - 1 < start_frame_id + (num_frames - 1) * interval:
-            reader.close()
-            return None
+        num_frames = self.get_num_frames(reader)
         frames = []
         for frame_id in range(num_frames):
-            frame = reader.get_data(start_frame_id + frame_id * interval)
+            frame = reader.get_data(frame_id)
             frame = Image.fromarray(frame)
             frame = self.crop_and_resize(frame, *self.get_height_width(frame))
             frames.append(frame)
@@ -97,11 +137,6 @@ class VideoDataset(torch.utils.data.Dataset):
         return image
     
     
-    def load_video(self, file_path):
-        frames = self.load_frames_using_imageio(file_path, 0, self.frame_interval, self.num_frames)
-        return frames
-    
-    
     def is_image(self, file_path):
         file_ext_name = file_path.split(".")[-1]
         return file_ext_name.lower() in self.image_file_extension
@@ -182,34 +217,50 @@ class DiffusionTrainingModule(torch.nn.Module):
 
 
 
-def launch_training_task(model: DiffusionTrainingModule, dataset, learning_rate=1e-4, num_epochs=1, output_path="./models", remove_prefix_in_ckpt=None, args=None):
-    if args is not None:
-        learning_rate = args.learning_rate
-        num_epochs = args.num_epochs
-        output_path = args.output_path
-        remove_prefix_in_ckpt = args.remove_prefix_in_ckpt
-    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0])
-    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=learning_rate)
-    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
+class ModelLogger:
+    def __init__(self, output_path, remove_prefix_in_ckpt=None):
+        self.output_path = output_path
+        self.remove_prefix_in_ckpt = remove_prefix_in_ckpt
         
-    accelerator = Accelerator(gradient_accumulation_steps=1)
+    
+    def on_step_end(self, loss):
+        pass
+    
+    
+    def on_epoch_end(self, accelerator, model, epoch_id):
+        accelerator.wait_for_everyone()
+        if accelerator.is_main_process:
+            state_dict = accelerator.get_state_dict(model)
+            state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
+            os.makedirs(self.output_path, exist_ok=True)
+            path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors")
+            accelerator.save(state_dict, path, safe_serialization=True)
+
+
+
+def launch_training_task(
+    dataset: torch.utils.data.Dataset,
+    model: DiffusionTrainingModule,
+    model_logger: ModelLogger,
+    optimizer: torch.optim.Optimizer,
+    scheduler: torch.optim.lr_scheduler.LRScheduler,
+    num_epochs: int = 1,
+    gradient_accumulation_steps: int = 1,
+):
+    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0])
+    accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps)
     model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
     
-    for epoch in range(num_epochs):
+    for epoch_id in range(num_epochs):
         for data in tqdm(dataloader):
             with accelerator.accumulate(model):
                 optimizer.zero_grad()
                 loss = model(data)
                 accelerator.backward(loss)
                 optimizer.step()
+                model_logger.on_step_end(loss)
                 scheduler.step()
-        accelerator.wait_for_everyone()
-        if accelerator.is_main_process:
-            state_dict = accelerator.get_state_dict(model)
-            state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=remove_prefix_in_ckpt)
-            os.makedirs(output_path, exist_ok=True)
-            path = os.path.join(output_path, f"epoch-{epoch}.safetensors")
-            accelerator.save(state_dict, path, safe_serialization=True)
+        model_logger.on_epoch_end(accelerator, model, epoch_id)
 
 
 
@@ -228,8 +279,9 @@ def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_pat
 
 def wan_parser():
     parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument("--dataset_base_path", type=str, default="", help="Base path of the dataset.")
-    parser.add_argument("--dataset_metadata_path", type=str, default="", required=True, help="Path to the metadata file of the dataset.")
+    parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.")
+    parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.")
+    parser.add_argument("--max_pixels", type=int, default=1280*720, help="Maximum number of pixels per frame, used for dynamic resolution..")
     parser.add_argument("--height", type=int, default=None, help="Height of images or videos. Leave `height` and `width` empty to enable dynamic resolution.")
     parser.add_argument("--width", type=int, default=None, help="Width of images or videos. Leave `height` and `width` empty to enable dynamic resolution.")
     parser.add_argument("--num_frames", type=int, default=81, help="Number of frames per video. Frames are sampled from the video prefix.")
@@ -247,5 +299,6 @@ def wan_parser():
     parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
     parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
     parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.")
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
     return parser
 

diffsynth/vram_management/layers.py

@@ -13,7 +13,8 @@ class AutoTorchModule(torch.nn.Module):
         super().__init__()
         
     def check_free_vram(self):
-        used_memory = torch.cuda.device_memory_used(self.computation_device) / (1024 ** 3)
+        gpu_mem_state = torch.cuda.mem_get_info(self.computation_device)
+        used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024 ** 3)
         return used_memory < self.vram_limit
 
     def offload(self):

examples/wanvideo/model_training/train.py

@@ -1,6 +1,6 @@
 import torch, os, json
 from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
-from diffsynth.trainers.utils import DiffusionTrainingModule, VideoDataset, launch_training_task, wan_parser
+from diffsynth.trainers.utils import DiffusionTrainingModule, VideoDataset, ModelLogger, launch_training_task, wan_parser
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
@@ -107,4 +107,14 @@ if __name__ == "__main__":
         use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
         extra_inputs=args.extra_inputs,
     )
-    launch_training_task(model, dataset, args=args)
+    model_logger = ModelLogger(
+        args.output_path,
+        remove_prefix_in_ckpt=args.remove_prefix_in_ckpt
+    )
+    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=args.learning_rate)
+    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
+    launch_training_task(
+        dataset, model, model_logger, optimizer, scheduler,
+        num_epochs=args.num_epochs,
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+    )