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https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-04 10:27:10 +08:00
change camera_condition type; make camera_condition optional
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@ -1083,7 +1083,8 @@ class WAN21_Camera(WAN21):
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def extra_conds(self, **kwargs):
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out = super().extra_conds(**kwargs)
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camera_conditions = kwargs.get("camera_conditions", None)
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out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)
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if camera_conditions is not None:
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out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)
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return out
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class Hunyuan3Dv2(BaseModel):
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@ -303,7 +303,6 @@ class WanCameraImageToVideo:
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return {"required": {"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"vae": ("VAE", ),
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"camera_conditions": ("LATENT", ),
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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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@ -311,6 +310,7 @@ class WanCameraImageToVideo:
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},
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"optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
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"start_image": ("IMAGE", ),
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"camera_conditions": ("WAN_CAMERA_EMBEDDING", ),
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}}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
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@ -319,7 +319,7 @@ class WanCameraImageToVideo:
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CATEGORY = "conditioning/video_models"
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def encode(self, positive, negative, vae, camera_conditions, width, height, length, batch_size, start_image=None, clip_vision_output=None):
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def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, camera_conditions=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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@ -329,8 +329,12 @@ class WanCameraImageToVideo:
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concat_latent_image = vae.encode(start_image[:, :, :, :3])
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concat_latent[:,:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]]
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positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent, 'camera_conditions': camera_conditions})
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negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent, 'camera_conditions': camera_conditions})
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positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent})
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negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent})
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if camera_conditions is not None:
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positive = node_helpers.conditioning_set_values(positive, {'camera_conditions': camera_conditions})
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negative = node_helpers.conditioning_set_values(negative, {'camera_conditions': camera_conditions})
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if clip_vision_output is not None:
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positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
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