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support wan-fun-inp generating

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lzw478614@alibaba-inc.com
2025-04-06 22:55:42 +08:00
parent 5418ca781e
commit a98700feb2
1 changed files with 10 additions and 3 deletions
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13
diffsynth/pipelines/wan_video.py
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@@ -163,16 +163,22 @@ class WanVideoPipeline(BasePipeline):
return {"context": prompt_emb}
def encode_image(self, image, num_frames, height, width):
def encode_image(self, image, end_image, num_frames, height, width):
image = self.preprocess_image(image.resize((width, height))).to(self.device)
clip_context = self.image_encoder.encode_image([image])
msk = torch.ones(1, num_frames, height//8, width//8, device=self.device)
msk[:, 1:] = 0
if end_image is not None:
end_image = self.preprocess_image(end_image.resize((width, height))).to(self.device)
vae_input = torch.concat([image.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image.device), end_image.transpose(0,1)],dim=1)
msk[:, -1:] = 1
else:
vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
msk = msk.transpose(1, 2)[0]
vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
y = self.vae.encode([vae_input.to(dtype=self.torch_dtype, device=self.device)], device=self.device)[0]
y = torch.concat([msk, y])
y = y.unsqueeze(0)
@@ -212,6 +218,7 @@ class WanVideoPipeline(BasePipeline):
prompt,
negative_prompt="",
input_image=None,
end_image=None,
input_video=None,
denoising_strength=1.0,
seed=None,
@@ -263,7 +270,7 @@ class WanVideoPipeline(BasePipeline):
# Encode image
if input_image is not None and self.image_encoder is not None:
self.load_models_to_device(["image_encoder", "vae"])
image_emb = self.encode_image(input_image, num_frames, height, width)
image_emb = self.encode_image(input_image, end_image, num_frames, height, width)
else:
image_emb = {}