Merge c5a6d87db7 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/first_last_frame2video.py

@@ -13,6 +13,7 @@ import numpy as np
 import torch
 import torch.cuda.amp as amp
 import torch.distributed as dist
+import torchvision
 import torchvision.transforms.functional as TF
 from tqdm import tqdm
 
@@ -211,7 +212,12 @@ class WanFLF2V:
                 round(last_frame_size[1] * last_frame_resize_ratio),
             ]
             # 2. center crop
-            last_frame = TF.center_crop(last_frame, last_frame_size)
+            transform = torchvision.transforms.Compose([
+                torchvision.transforms.Resize((last_frame_size[0], last_frame_size[1])),
+                torchvision.transforms.CenterCrop((first_frame_size[0], first_frame_size[1]))
+            ])
+
+            last_frame = transform(last_frame)
 
         max_seq_len = ((F - 1) // self.vae_stride[0] + 1) * lat_h * lat_w // (
             self.patch_size[1] * self.patch_size[2])