nodes/kijai-ComfyUI-WanVideoWrapper
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Update nodes.py

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kijai
2025-04-22 22:34:45 +03:00
parent 1f53574387
commit 24876cffc1
1 changed files with 16 additions and 5 deletions
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nodes.py
+16 -5
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@@ -666,8 +666,6 @@ class WanVideoModelLoader:
if "patch_embedding" in name:
dtype_to_use = torch.float32
set_module_tensor_to_device(transformer, name, device=transformer_load_device, dtype=dtype_to_use, value=sd[name])
for name, param in transformer.named_parameters():
print(name, param.device, param.dtype)
comfy_model.diffusion_model = transformer
comfy_model.load_device = transformer_load_device
@@ -1758,9 +1756,15 @@ class WanVideoPhantomEmbeds:
def INPUT_TYPES(s):
return {"required": {
"num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
"phantom_latents": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
"phantom_latent_1": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
"phantom_cfg_scale": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "CFG scale for the extra phantom cond pass"}),
},
"optional": {
"phantom_latent_2": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
"phantom_latent_3": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
"phantom_latent_4": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
@@ -1768,9 +1772,15 @@ class WanVideoPhantomEmbeds:
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, num_frames, phantom_latents, phantom_cfg_scale):
def process(self, num_frames, phantom_cfg_scale, phantom_latent_1, phantom_latent_2=None, phantom_latent_3=None, phantom_latent_4=None):
vae_stride = (4, 8, 8)
samples = phantom_latents["samples"].squeeze(0)
samples = phantom_latent_1["samples"].squeeze(0)
if phantom_latent_2 is not None:
samples = torch.cat([samples, phantom_latent_2["samples"].squeeze(0)], dim=1)
if phantom_latent_3 is not None:
samples = torch.cat([samples, phantom_latent_3["samples"].squeeze(0)], dim=1)
if phantom_latent_4 is not None:
samples = torch.cat([samples, phantom_latent_4["samples"].squeeze(0)], dim=1)
C, T, H, W = samples.shape
target_shape = (16, (num_frames - 1) // vae_stride[0] + 1 + T,
@@ -2637,6 +2647,7 @@ class WanVideoSampler:
self.teacache_state = [None, None]
if phantom_latents is not None:
log.info(f"Phantom latents shape: {phantom_latents.shape}")
self.teacache_state = [None, None, None]
self.teacache_state_source = [None, None]
self.teacache_states_context = []