Merge c5a6d87db7 into 7c81b2f27d

README.md

@@ -36,6 +36,7 @@ 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.
+- [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.
 - [MagicTryOn](https://vivocameraresearch.github.io/magictryon/), a video virtual try-on framework built upon **Wan2.1-14B-I2V**, addresses the limitations of existing models in expressing garment details and maintaining dynamic stability during human motion. 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])