Merge 66e5815491 into 854bd88e7f

* Add LightX2V Community Works

* update

* update

* update

README.md

@@ -36,6 +36,9 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
 
 ## Community Works
 If your work has improved **Wan2.1** and you would like more people to see it, please inform us.
+- [LightX2V](https://github.com/ModelTC/LightX2V), a lightweight and efficient video generation framework that integrates **Wan2.1** and **Wan2.2**, supports multiple engineering acceleration techniques for fast inference, which can run on RTX 5090 and RTX 4060 (8GB VRAM).
+- [DriVerse](https://github.com/shalfun/DriVerse), an autonomous driving world model based on **Wan2.1-14B-I2V**, generates future driving videos conditioned on any scene frame and given trajectory. Refer to the [project page](https://github.com/shalfun/DriVerse/tree/main) for more examples.
+- [Training-Free-WAN-Editing](https://github.com/KyujinHan/Awesome-Training-Free-WAN2.1-Editing), built on **Wan2.1-T2V-1.3B**, allows training-free video editing with image-based training-free methods, such as [FlowEdit](https://arxiv.org/abs/2412.08629) and [FlowAlign](https://arxiv.org/abs/2505.23145).
 - [Wan-Move](https://github.com/ali-vilab/Wan-Move), accepted to NeurIPS 2025, a framework that brings **Wan2.1-I2V-14B** to SOTA fine-grained, point-level motion control! Refer to [their project page](https://wan-move.github.io/) for more information.
 - [EchoShot](https://github.com/JoHnneyWang/EchoShot), a native multi-shot portrait video generation model based on **Wan2.1-T2V-1.3B**, allows generation of multiple video clips featuring the same character as well as highly flexible content controllability. Refer to [their project page](https://johnneywang.github.io/EchoShot-webpage/) for more information.
 - [AniCrafter](https://github.com/MyNiuuu/AniCrafter), a human-centric animation model based on **Wan2.1-14B-I2V**, controls the Video Diffusion Models with 3DGS Avatars to insert and animate anyone into any scene following given motion sequences. Refer to the [project page](https://myniuuu.github.io/AniCrafter) for more examples.

generate.py

@@ -155,6 +155,11 @@ def _parse_args():
         action="store_true",
         default=False,
         help="Whether to use FSDP for DiT.")
+    parser.add_argument(
+        "--cpu_offload",
+        action="store_true",
+        default=False,
+        help="Whether to use CPU offload for FSDP on Wan model, only works with dit_fsdp.")
     parser.add_argument(
         "--save_file",
         type=str,
@@ -421,6 +426,7 @@ def generate(args):
             rank=rank,
             t5_fsdp=args.t5_fsdp,
             dit_fsdp=args.dit_fsdp,
+            cpu_offload=args.cpu_offload,
             use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
             t5_cpu=args.t5_cpu,
         )

start_master.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+
+# Set environment variables
+export PATH=/data/apps/wan-21/wan-env/bin:$PATH
+export MASTER_ADDR='10.79.79.197'
+export MASTER_PORT='7860'
+export RANK='0'
+export WORLD_SIZE='2'
+export LOCAL_RANK='0'
+export NCCL_SOCKET_IFNAME='enp65s0'
+
+# Run the generate.py script with specified parameters
+# like: ./start_master.sh --dit_fsdp --sample_steps 40--task i2v-14B --size 480*640 --ckpt_dir ./Wan2.1-I2V-14B-480P --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+python generate.py "$@"

start_rank1.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+
+# Set environment variables
+export PATH=/data/apps/wan-21/wan-env/bin:$PATH
+export MASTER_ADDR='10.79.79.197'
+export MASTER_PORT='7860'
+export RANK='1'
+export WORLD_SIZE='2'
+export LOCAL_RANK='0'
+export NCCL_SOCKET_IFNAME='enp33s0'
+
+# Run the generate.py script with specified parameters
+# like: ./start_rank1.sh --dit_fsdp --sample_steps 40 --task i2v-14B --size 480*640 --ckpt_dir ./Wan2.1-I2V-14B-480P --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+python generate.py "$@"

wan/configs/__init__.py

@@ -32,6 +32,12 @@ SIZE_CONFIGS = {
     '1280*720': (1280, 720),
     '480*832': (480, 832),
     '832*480': (832, 480),
+    '480*640': (480, 640),
+    '640*480': (640, 480),
+    '640*848': (640, 848),
+    '848*640': (848, 640),
+    '720*960': (720, 960),
+    '960*720': (960, 720),
     '1024*1024': (1024, 1024),
 }
 
@@ -40,12 +46,18 @@ MAX_AREA_CONFIGS = {
     '1280*720': 1280 * 720,
     '480*832': 480 * 832,
     '832*480': 832 * 480,
+    '480*640': 480 * 640,
+    '640*480': 640 * 480,
+    '640*848': 640 * 848,
+    '848*640': 848 * 640,
+    '720*960': 720 * 960,
+    '960*720': 960 * 720,
 }
 
 SUPPORTED_SIZES = {
     't2v-14B': ('720*1280', '1280*720', '480*832', '832*480'),
     't2v-1.3B': ('480*832', '832*480'),
-    'i2v-14B': ('720*1280', '1280*720', '480*832', '832*480'),
+    'i2v-14B': ('720*1280', '1280*720', '480*832', '832*480', '480*640', '640*480', '640*848', '848*640', '720*960', '960*720'),
     'flf2v-14B': ('720*1280', '1280*720', '480*832', '832*480'),
     't2i-14B': tuple(SIZE_CONFIGS.keys()),
     'vace-1.3B': ('480*832', '832*480'),

wan/distributed/fsdp.py

@@ -34,6 +34,84 @@ def shard_model(
     return model
 
 
+def shard_model_cpu_init(
+    model,
+    device_id,
+    param_dtype=torch.bfloat16,
+    reduce_dtype=torch.float32,
+    buffer_dtype=torch.float32,
+    process_group=None,
+    sharding_strategy=ShardingStrategy.FULL_SHARD,
+    sync_module_states=False,  # Key fix: disable sync_module_states for CPU init
+):
+    """
+    Initialize FSDP with CPU-first approach to reduce GPU memory peak.
+    """
+    # Ensure model is on CPU and clear GPU cache
+    model = model.cpu()
+    torch.cuda.empty_cache()
+    
+    # Initialize FSDP with sync_module_states=False to allow CPU initialization
+    model = FSDP(
+        module=model,
+        process_group=process_group,
+        sharding_strategy=sharding_strategy,
+        auto_wrap_policy=partial(
+            lambda_auto_wrap_policy, lambda_fn=lambda m: m in model.blocks),
+        mixed_precision=MixedPrecision(
+            param_dtype=param_dtype,
+            reduce_dtype=reduce_dtype,
+            buffer_dtype=buffer_dtype),
+        device_id=None,  # Keep on CPU during initialization
+        sync_module_states=sync_module_states)  # Disabled for CPU init
+    
+    # Move to GPU after FSDP wrapping
+    model = model.to(f"cuda:{device_id}")
+    
+    # Manual synchronization across ranks if needed
+    if sync_module_states and torch.distributed.is_initialized():
+        torch.distributed.barrier()
+    
+    return model
+
+
+def shard_model_with_cpu_offload(
+    model,
+    device_id,
+    param_dtype=torch.bfloat16,
+    reduce_dtype=torch.float32,
+    buffer_dtype=torch.float32,
+    process_group=None,
+    sharding_strategy=ShardingStrategy.FULL_SHARD,
+    sync_module_states=True,
+):
+    """
+    Initialize FSDP with CPU offload to minimize GPU memory usage.
+    This keeps parameters on CPU and only loads them to GPU when needed.
+    """
+    from torch.distributed.fsdp import CPUOffload
+    
+    # Ensure model is on CPU and clear GPU cache aggressively
+    model = model.cpu()
+    torch.cuda.empty_cache()
+    
+    # Use CPU offload to minimize GPU memory usage
+    model = FSDP(
+        module=model,
+        process_group=process_group,
+        sharding_strategy=sharding_strategy,
+        auto_wrap_policy=partial(
+            lambda_auto_wrap_policy, lambda_fn=lambda m: m in model.blocks),
+        mixed_precision=MixedPrecision(
+            param_dtype=param_dtype,
+            reduce_dtype=reduce_dtype,
+            buffer_dtype=buffer_dtype),
+        cpu_offload=CPUOffload(offload_params=True),  # Key: keep params on CPU
+        device_id=device_id,
+        sync_module_states=sync_module_states)
+    
+    return model
+
 def free_model(model):
     for m in model.modules():
         if isinstance(m, FSDP):

wan/image2video.py

@@ -16,7 +16,7 @@ import torch.distributed as dist
 import torchvision.transforms.functional as TF
 from tqdm import tqdm
 
-from .distributed.fsdp import shard_model
+from .distributed.fsdp import shard_model, shard_model_with_cpu_offload
 from .modules.clip import CLIPModel
 from .modules.model import WanModel
 from .modules.t5 import T5EncoderModel
@@ -39,6 +39,7 @@ class WanI2V:
         rank=0,
         t5_fsdp=False,
         dit_fsdp=False,
+        cpu_offload=False,
         use_usp=False,
         t5_cpu=False,
         init_on_cpu=True,
@@ -59,6 +60,8 @@ class WanI2V:
                 Enable FSDP sharding for T5 model
             dit_fsdp (`bool`, *optional*, defaults to False):
                 Enable FSDP sharding for DiT model
+            cpu_offload (`bool`, *optional*, defaults to False):
+                Enable CPU offload for FSDP on Wan model, only works with dit_fsdp.
             use_usp (`bool`, *optional*, defaults to False):
                 Enable distribution strategy of USP.
             t5_cpu (`bool`, *optional*, defaults to False):
@@ -71,10 +74,14 @@ class WanI2V:
         self.rank = rank
         self.use_usp = use_usp
         self.t5_cpu = t5_cpu
+        self.dit_fsdp = dit_fsdp
+        self.cpu_offload = cpu_offload
 
         self.num_train_timesteps = config.num_train_timesteps
         self.param_dtype = config.param_dtype
 
+        assert not cpu_offload or (cpu_offload and dit_fsdp), "When cpu_offload is True, dit_fsdp must also be True"
+
         shard_fn = partial(shard_model, device_id=device_id)
         self.text_encoder = T5EncoderModel(
             text_len=config.text_len,
@@ -99,9 +106,15 @@ class WanI2V:
             tokenizer_path=os.path.join(checkpoint_dir, config.clip_tokenizer))
 
         logging.info(f"Creating WanModel from {checkpoint_dir}")
+        if cpu_offload:
+            # For FSDP and cpu_offload, force CPU initialization to avoid OOM
+            with torch.device('cpu'):
+                self.model = WanModel.from_pretrained(checkpoint_dir)
+        else:
             self.model = WanModel.from_pretrained(checkpoint_dir)
         self.model.eval().requires_grad_(False)
 
+
         if t5_fsdp or dit_fsdp or use_usp:
             init_on_cpu = False
 
@@ -123,6 +136,10 @@ class WanI2V:
         if dist.is_initialized():
             dist.barrier()
         if dit_fsdp:
+            if cpu_offload:
+                torch.cuda.empty_cache()
+                self.model = shard_model_with_cpu_offload(self.model, device_id=device_id)
+            else:
                 self.model = shard_fn(self.model)
         else:
             if not init_on_cpu:
@@ -298,6 +315,9 @@ class WanI2V:
             if offload_model:
                 torch.cuda.empty_cache()
 
+            if self.cpu_offload:
+                self.model.cpu()
+            else:
                 self.model.to(self.device)
             for _, t in enumerate(tqdm(timesteps)):
                 latent_model_input = [latent.to(self.device)]