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Merge branch 'wan2.2-patch' of https://github.com/modelscope/DiffSynth-Studio into wan2.2-patch

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Artiprocher
2025-07-28 18:27:20 +08:00
parent 283f35447a c35414a652
commit 0befa05014
2 changed files with 7 additions and 5 deletions
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8
diffsynth/models/wan_video_vae.py
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@@ -1075,6 +1075,7 @@ class WanVideoVAE(nn.Module):
# init model # init model
self.model = VideoVAE_(z_dim=z_dim).eval().requires_grad_(False) self.model = VideoVAE_(z_dim=z_dim).eval().requires_grad_(False)
self.upsampling_factor = 8 self.upsampling_factor = 8
self.z_dim = z_dim
def build_1d_mask(self, length, left_bound, right_bound, border_width): def build_1d_mask(self, length, left_bound, right_bound, border_width):
@@ -1170,7 +1171,7 @@ class WanVideoVAE(nn.Module):
out_T = (T + 3) // 4 out_T = (T + 3) // 4
weight = torch.zeros((1, 1, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device) weight = torch.zeros((1, 1, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device)
values = torch.zeros((1, 16, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device) values = torch.zeros((1, self.z_dim, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device)
for h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"): for h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"):
hidden_states_batch = video[:, :, :, h:h_, w:w_].to(computation_device) hidden_states_batch = video[:, :, :, h:h_, w:w_].to(computation_device)
@@ -1221,8 +1222,8 @@ class WanVideoVAE(nn.Module):
for video in videos: for video in videos:
video = video.unsqueeze(0) video = video.unsqueeze(0)
if tiled: if tiled:
tile_size = (tile_size[0] * 8, tile_size[1] * 8) tile_size = (tile_size[0] * self.upsampling_factor, tile_size[1] * self.upsampling_factor)
tile_stride = (tile_stride[0] * 8, tile_stride[1] * 8) tile_stride = (tile_stride[0] * self.upsampling_factor, tile_stride[1] * self.upsampling_factor)
hidden_state = self.tiled_encode(video, device, tile_size, tile_stride) hidden_state = self.tiled_encode(video, device, tile_size, tile_stride)
else: else:
hidden_state = self.single_encode(video, device) hidden_state = self.single_encode(video, device)
@@ -1372,3 +1373,4 @@ class WanVideoVAE38(WanVideoVAE):
# init model # init model
self.model = VideoVAE38_(z_dim=z_dim, dim=dim).eval().requires_grad_(False) self.model = VideoVAE38_(z_dim=z_dim, dim=dim).eval().requires_grad_(False)
self.upsampling_factor = 16 self.upsampling_factor = 16
self.z_dim = z_dim

4
examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py
View File

@@ -19,7 +19,7 @@ pipe.enable_vram_management()
video = pipe( video = pipe(
prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
seed=0, tiled=False, seed=0, tiled=True,
height=704, width=1248, height=704, width=1248,
num_frames=121, num_frames=121,
) )
@@ -35,7 +35,7 @@ input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((1248, 70
video = pipe( video = pipe(
prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
seed=0, tiled=False, seed=0, tiled=True,
height=704, width=1248, height=704, width=1248,
input_image=input_image, input_image=input_image,
num_frames=121, num_frames=121,