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Merge branch 'master' into v3-definition

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This commit is contained in:
Jedrzej Kosinski
2025-05-30 02:49:02 -07:00
parent 5f0e04e2d7 aeba0b3a26
commit 0400497d5e
33 changed files with 6225 additions and 3261 deletions
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2
comfy_extras/nodes_load_3d.py
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@@ -16,7 +16,7 @@ class Load3D():
os.makedirs(input_dir, exist_ok=True)
files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.mtl', '.fbx', '.stl'))]
files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.fbx', '.stl'))]
return {"required": {
"model_file": (sorted(files), {"file_upload": True}),

44
comfy_extras/nodes_wan.py
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@@ -268,8 +268,9 @@ class WanVaceToVideo:
trim_latent = reference_image.shape[2]
mask = mask.unsqueeze(0)
positive = node_helpers.conditioning_set_values(positive, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength})
negative = node_helpers.conditioning_set_values(negative, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength})
positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device())
out_latent = {}
@@ -344,6 +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,
@@ -352,4 +391,5 @@ NODE_CLASS_MAPPINGS = {
"WanVaceToVideo": WanVaceToVideo,
"TrimVideoLatent": TrimVideoLatent,
"WanCameraImageToVideo": WanCameraImageToVideo,
"WanPhantomSubjectToVideo": WanPhantomSubjectToVideo,
}