Merge 8f11f5ab6e into 854bd88e7f

* Add LightX2V Community Works

* update

* update

* update

README.md

@@ -36,6 +36,10 @@ 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.
 - [HyperMotion](https://vivocameraresearch.github.io/hypermotion/), a human image animation framework based on **Wan2.1**, addresses the challenge of generating complex human body motions in pose-guided animation. Refer to [their website](https://vivocameraresearch.github.io/magictryon/) for more examples.

wan/image2video.py

@@ -99,7 +99,8 @@ class WanI2V:
             tokenizer_path=os.path.join(checkpoint_dir, config.clip_tokenizer))
 
         logging.info(f"Creating WanModel from {checkpoint_dir}")
-        self.model = WanModel.from_pretrained(checkpoint_dir)
+        #self.model = WanModel.from_pretrained(checkpoint_dir)
+        self.model = WanModel.from_pretrained(checkpoint_dir ,torch_dtype=torch.float8_e4m3fn)
         self.model.eval().requires_grad_(False)
 
         if t5_fsdp or dit_fsdp or use_usp:
@@ -178,6 +179,7 @@ class WanI2V:
                 - H: Frame height (from max_area)
                 - W: Frame width from max_area)
         """
+        offload_model = False
         img = TF.to_tensor(img).sub_(0.5).div_(0.5).to(self.device)
 
         F = frame_num
@@ -298,7 +300,19 @@ class WanI2V:
             if offload_model:
                 torch.cuda.empty_cache()
 
-            self.model.to(self.device)
+            
+            # import gc
+            # del self.text_encoder
+            # del self.clip
+            # del self.vae
+            # gc.collect()  # 立即触发垃圾回收
+            # torch.cuda.empty_cache()  # 清空CUDA缓存
+            # torch.cuda.reset_peak_memory_stats()
+            
+            # start_mem = torch.cuda.memory_allocated()
+            # print(f"该阶段开始时显存占用：{start_mem / 1024**3:.2f} GB")
+            
+            # self.model.to(self.device)
             for _, t in enumerate(tqdm(timesteps)):
                 latent_model_input = [latent.to(self.device)]
                 timestep = [t]
@@ -332,6 +346,9 @@ class WanI2V:
                 x0 = [latent.to(self.device)]
                 del latent_model_input, timestep
 
+            # peak_mem_bytes = torch.cuda.max_memory_allocated()
+            # print(f"该阶段最大显存占用：{peak_mem_bytes / 1024**3:.2f} GB")
+        
             if offload_model:
                 self.model.cpu()
                 torch.cuda.empty_cache()

wan/modules/model.py

@@ -83,6 +83,8 @@ class WanRMSNorm(nn.Module):
         Args:
             x(Tensor): Shape [B, L, C]
         """
+        if self.weight.dtype != torch.bfloat16:
+            self.weight.data = self.weight.data.to(dtype=torch.bfloat16)
         return self._norm(x.float()).type_as(x) * self.weight
 
     def _norm(self, x):
@@ -294,6 +296,9 @@ class WanAttentionBlock(nn.Module):
             freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
         """
         assert e.dtype == torch.float32
+        if self.modulation.dtype != torch.float16:
+            # 如果不是 fp16，则转换为 fp16
+            self.modulation.data = self.modulation.data.to(dtype=torch.float16)
         with amp.autocast(dtype=torch.float32):
             e = (self.modulation + e).chunk(6, dim=1)
         assert e[0].dtype == torch.float32
@@ -341,6 +346,9 @@ class Head(nn.Module):
             e(Tensor): Shape [B, C]
         """
         assert e.dtype == torch.float32
+        if self.modulation.dtype != torch.float16:
+            # 如果不是 fp16，则转换为 fp16
+            self.modulation.data = self.modulation.data.to(dtype=torch.float16)
         with amp.autocast(dtype=torch.float32):
             e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1)
             x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))

wan/text2video.py

@@ -11,6 +11,7 @@ from functools import partial
 
 import torch
 import torch.cuda.amp as amp
+import torch.nn as nn
 import torch.distributed as dist
 from tqdm import tqdm
 
@@ -25,6 +26,19 @@ from .utils.fm_solvers import (
 )
 from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
 
+# def convert_linear_conv_to_fp8(module):
+#     for name, child in module.named_children():
+#         # 递归处理子模块
+#         convert_linear_conv_to_fp8(child)
+        
+#         # 判断是否为 Linear 或 Conv 层
+#         if isinstance(child, (nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d)):
+#             # 转换权重
+#             if hasattr(child, 'weight') and child.weight is not None:
+#                 # 保留 Parameter 类型，仅修改数据
+#                 child.weight.data = child.weight.data.to(dtype=torch.float8_e4m3fn)
+#                 # 可选：转换偏置（根据需求开启）
+
 
 class WanT2V:
 
@@ -84,7 +98,9 @@ class WanT2V:
             device=self.device)
 
         logging.info(f"Creating WanModel from {checkpoint_dir}")
-        self.model = WanModel.from_pretrained(checkpoint_dir)
+        self.model = WanModel.from_pretrained(checkpoint_dir ,torch_dtype=torch.float8_e4m3fn)
+        #self.model = WanModel.from_pretrained(checkpoint_dir )
+        
         self.model.eval().requires_grad_(False)
 
         if use_usp:
@@ -106,7 +122,9 @@ class WanT2V:
             dist.barrier()
         if dit_fsdp:
             self.model = shard_fn(self.model)
+            # convert_linear_conv_to_fp8(self.model)
         else:
+            # convert_linear_conv_to_fp8(self.model)
             self.model.to(self.device)
 
         self.sample_neg_prompt = config.sample_neg_prompt
@@ -156,6 +174,7 @@ class WanT2V:
                 - W: Frame width from size)
         """
         # preprocess
+        #offload_model = False
         F = frame_num
         target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1,
                         size[1] // self.vae_stride[1],
@@ -229,6 +248,16 @@ class WanT2V:
 
             arg_c = {'context': context, 'seq_len': seq_len}
             arg_null = {'context': context_null, 'seq_len': seq_len}
+            
+            # import gc
+            # del self.text_encoder
+            # del self.vae
+            # gc.collect()  # 立即触发垃圾回收
+            # torch.cuda.empty_cache()  # 清空CUDA缓存
+            # torch.cuda.reset_peak_memory_stats()
+            
+            # start_mem = torch.cuda.memory_allocated()
+            #print(f"该阶段开始时显存占用：{start_mem / 1024**3:.2f} GB") 
 
             for _, t in enumerate(tqdm(timesteps)):
                 latent_model_input = latents
@@ -252,6 +281,9 @@ class WanT2V:
                     return_dict=False,
                     generator=seed_g)[0]
                 latents = [temp_x0.squeeze(0)]
+                
+            # peak_mem_bytes = torch.cuda.max_memory_allocated()
+            # print(f"该阶段最大显存占用：{peak_mem_bytes / 1024**3:.2f} GB")
 
             x0 = latents
             if offload_model: