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https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-21 16:18:13 +00:00
update wan2.2-fun
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@@ -677,8 +677,11 @@ class WanVideoUnit_FunControl(PipelineUnit):
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if clip_feature is None or y is None:
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clip_feature = torch.zeros((1, 257, 1280), dtype=pipe.torch_dtype, device=pipe.device)
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y = torch.zeros((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device)
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if pipe.dit2 is not None:
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y = torch.zeros((1, 20, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device)
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else:
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y = y[:, -16:]
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if pipe.dit2 is None:
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y = y[:, -16:]
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y = torch.concat([control_latents, y], dim=1)
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return {"clip_feature": clip_feature, "y": y}
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@@ -698,6 +701,8 @@ class WanVideoUnit_FunReference(PipelineUnit):
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reference_image = reference_image.resize((width, height))
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reference_latents = pipe.preprocess_video([reference_image])
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reference_latents = pipe.vae.encode(reference_latents, device=pipe.device)
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if pipe.image_encoder is None:
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return {"reference_latents": reference_latents}
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clip_feature = pipe.preprocess_image(reference_image)
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clip_feature = pipe.image_encoder.encode_image([clip_feature])
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return {"reference_latents": reference_latents, "clip_feature": clip_feature}
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@@ -707,13 +712,14 @@ class WanVideoUnit_FunReference(PipelineUnit):
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class WanVideoUnit_FunCameraControl(PipelineUnit):
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def __init__(self):
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super().__init__(
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input_params=("height", "width", "num_frames", "camera_control_direction", "camera_control_speed", "camera_control_origin", "latents", "input_image"),
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input_params=("height", "width", "num_frames", "camera_control_direction", "camera_control_speed", "camera_control_origin", "latents", "input_image", "tiled", "tile_size", "tile_stride"),
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onload_model_names=("vae",)
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)
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def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_control_direction, camera_control_speed, camera_control_origin, latents, input_image):
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def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_control_direction, camera_control_speed, camera_control_origin, latents, input_image, tiled, tile_size, tile_stride):
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if camera_control_direction is None:
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return {}
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pipe.load_models_to_device(self.onload_model_names)
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camera_control_plucker_embedding = pipe.dit.control_adapter.process_camera_coordinates(
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camera_control_direction, num_frames, height, width, camera_control_speed, camera_control_origin)
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@@ -729,13 +735,20 @@ class WanVideoUnit_FunCameraControl(PipelineUnit):
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control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
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control_camera_latents_input = control_camera_latents.to(device=pipe.device, dtype=pipe.torch_dtype)
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input_image = input_image.resize((width, height))
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input_latents = pipe.preprocess_video([input_image])
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pipe.load_models_to_device(self.onload_model_names)
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input_latents = pipe.vae.encode(input_latents, device=pipe.device)
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y = torch.zeros_like(latents).to(pipe.device)
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y[:, :, :1] = input_latents
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image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
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vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
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y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
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y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
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if pipe.dit2 is not None:
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msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
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msk[:, 1:] = 0
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msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
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msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
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msk = msk.transpose(1, 2)[0]
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y = torch.cat([msk,y])
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y = y.unsqueeze(0)
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y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
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return {"control_camera_latents_input": control_camera_latents_input, "y": y}
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