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https://github.com/comfyanonymous/ComfyUI.git
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Wan2.2 fun control support. (#9292)
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@@ -103,6 +103,63 @@ class WanFunControlToVideo:
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out_latent["samples"] = latent
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return (positive, negative, out_latent)
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class Wan22FunControlToVideo:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"vae": ("VAE", ),
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"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
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"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
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"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
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},
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"optional": {"ref_image": ("IMAGE", ),
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"control_video": ("IMAGE", ),
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# "start_image": ("IMAGE", ),
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}}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
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RETURN_NAMES = ("positive", "negative", "latent")
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FUNCTION = "encode"
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CATEGORY = "conditioning/video_models"
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def encode(self, positive, negative, vae, width, height, length, batch_size, ref_image=None, start_image=None, control_video=None):
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latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
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concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
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concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent)
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concat_latent = concat_latent.repeat(1, 2, 1, 1, 1)
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mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1]))
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if start_image is not None:
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start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
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concat_latent_image = vae.encode(start_image[:, :, :, :3])
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concat_latent[:,16:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]]
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mask[:, :, :start_image.shape[0] + 3] = 0.0
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ref_latent = None
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if ref_image is not None:
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ref_image = comfy.utils.common_upscale(ref_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
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ref_latent = vae.encode(ref_image[:, :, :, :3])
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if control_video is not None:
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control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
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concat_latent_image = vae.encode(control_video[:, :, :, :3])
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concat_latent[:,:16,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]]
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mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2)
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positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent, "concat_mask": mask, "concat_mask_index": 16})
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negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent, "concat_mask": mask, "concat_mask_index": 16})
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if ref_latent is not None:
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positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True)
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negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [ref_latent]}, append=True)
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out_latent = {}
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out_latent["samples"] = latent
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return (positive, negative, out_latent)
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class WanFirstLastFrameToVideo:
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@classmethod
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def INPUT_TYPES(s):
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@@ -733,6 +790,7 @@ NODE_CLASS_MAPPINGS = {
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"WanTrackToVideo": WanTrackToVideo,
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"WanImageToVideo": WanImageToVideo,
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"WanFunControlToVideo": WanFunControlToVideo,
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"Wan22FunControlToVideo": Wan22FunControlToVideo,
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"WanFunInpaintToVideo": WanFunInpaintToVideo,
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"WanFirstLastFrameToVideo": WanFirstLastFrameToVideo,
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"WanVaceToVideo": WanVaceToVideo,
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