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Not really tested WAN Phantom Support. (#8321)

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comfyanonymous 2025-05-28 20:46:15 -07:00 committed by GitHub
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3 changed files with 52 additions and 1 deletions
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9
comfy/ldm/wan/model.py
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@ -539,13 +539,20 @@ class WanModel(torch.nn.Module):
x = self.unpatchify(x, grid_sizes)
return x
def forward(self, x, timestep, context, clip_fea=None, transformer_options={}, **kwargs):
def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs):
bs, c, t, h, w = x.shape
x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
patch_size = self.patch_size
t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
if time_dim_concat is not None:
time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)
x = torch.cat([x, time_dim_concat], dim=2)
t_len = ((x.shape[2] + (patch_size[0] // 2)) // patch_size[0])
img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)

5
comfy/model_base.py
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@ -1057,6 +1057,11 @@ class WAN21(BaseModel):
clip_vision_output = kwargs.get("clip_vision_output", None)
if clip_vision_output is not None:
out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states)
time_dim_concat = kwargs.get("time_dim_concat", None)
if time_dim_concat is not None:
out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat))
return out

39
comfy_extras/nodes_wan.py
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@ -345,6 +345,44 @@ class WanCameraImageToVideo:
out_latent["samples"] = latent
return (positive, negative, out_latent)
class WanPhantomSubjectToVideo:
@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": {"images": ("IMAGE", ),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative_text", "negative_img_text", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, width, height, length, batch_size, images):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
cond2 = negative
if images is not None:
images = comfy.utils.common_upscale(images[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
latent_images = []
for i in images:
latent_images += [vae.encode(i.unsqueeze(0)[:, :, :, :3])]
concat_latent_image = torch.cat(latent_images, dim=2)
positive = node_helpers.conditioning_set_values(positive, {"time_dim_concat": concat_latent_image})
cond2 = node_helpers.conditioning_set_values(negative, {"time_dim_concat": concat_latent_image})
negative = node_helpers.conditioning_set_values(negative, {"time_dim_concat": comfy.latent_formats.Wan21().process_out(torch.zeros_like(concat_latent_image))})
out_latent = {}
out_latent["samples"] = latent
return (positive, cond2, negative, out_latent)
NODE_CLASS_MAPPINGS = {
"WanImageToVideo": WanImageToVideo,
"WanFunControlToVideo": WanFunControlToVideo,
@ -353,4 +391,5 @@ NODE_CLASS_MAPPINGS = {
"WanVaceToVideo": WanVaceToVideo,
"TrimVideoLatent": TrimVideoLatent,
"WanCameraImageToVideo": WanCameraImageToVideo,
"WanPhantomSubjectToVideo": WanPhantomSubjectToVideo,
}