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Code Issues Activity

Add Wan-FUN Camera Control models and Add WanCameraImageToVideo node (#8013)

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* support wan camera models

* fix by ruff check

* change camera_condition type; make camera_condition optional

* support camera trajectory nodes

* fix camera direction

---------

Co-authored-by: Qirui Sun <sunqr0667@126.com>
This commit is contained in:
George0726
2025-05-15 19:00:43 -04:00
committed by GitHub
parent 6a2e4bb9e0
commit c820ef950d
6 changed files with 431 additions and 1 deletions
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47
comfy_extras/nodes_wan.py
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@@ -297,6 +297,52 @@ class TrimVideoLatent:
samples_out["samples"] = s1[:, :, trim_amount:]
return (samples_out,)
class WanCameraImageToVideo:
@classmethod
def INPUT_TYPES(s):
return {"required": {"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"vae": ("VAE", ),
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
},
"optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
"start_image": ("IMAGE", ),
"camera_conditions": ("WAN_CAMERA_EMBEDDING", ),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, camera_conditions=None):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent)
if start_image is not None:
start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
concat_latent_image = vae.encode(start_image[:, :, :, :3])
concat_latent[:,:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]]
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent})
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent})
if camera_conditions is not None:
positive = node_helpers.conditioning_set_values(positive, {'camera_conditions': camera_conditions})
negative = node_helpers.conditioning_set_values(negative, {'camera_conditions': camera_conditions})
if clip_vision_output is not None:
positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
out_latent = {}
out_latent["samples"] = latent
return (positive, negative, out_latent)
NODE_CLASS_MAPPINGS = {
"WanImageToVideo": WanImageToVideo,
@@ -305,4 +351,5 @@ NODE_CLASS_MAPPINGS = {
"WanFirstLastFrameToVideo": WanFirstLastFrameToVideo,
"WanVaceToVideo": WanVaceToVideo,
"TrimVideoLatent": TrimVideoLatent,
"WanCameraImageToVideo": WanCameraImageToVideo,
}