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Add Hunyuan 3D 2.1 Support (#8714)

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This commit is contained in:
Yousef R. Gamaleldin
2025-09-05 03:36:20 +03:00
committed by GitHub
parent a9f1bb10a5
commit 261421e218
13 changed files with 1537 additions and 129 deletions
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24
comfy_extras/nodes_hunyuan3d.py
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@@ -8,13 +8,16 @@ import folder_paths
import comfy.model_management
from comfy.cli_args import args
class EmptyLatentHunyuan3Dv2:
@classmethod
def INPUT_TYPES(s):
return {"required": {"resolution": ("INT", {"default": 3072, "min": 1, "max": 8192}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}),
}}
return {
"required": {
"resolution": ("INT", {"default": 3072, "min": 1, "max": 8192}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
@@ -24,7 +27,6 @@ class EmptyLatentHunyuan3Dv2:
latent = torch.zeros([batch_size, 64, resolution], device=comfy.model_management.intermediate_device())
return ({"samples": latent, "type": "hunyuan3dv2"}, )
class Hunyuan3Dv2Conditioning:
@classmethod
def INPUT_TYPES(s):
@@ -81,7 +83,6 @@ class VOXEL:
def __init__(self, data):
self.data = data
class VAEDecodeHunyuan3D:
@classmethod
def INPUT_TYPES(s):
@@ -99,7 +100,6 @@ class VAEDecodeHunyuan3D:
voxels = VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution}))
return (voxels, )
def voxel_to_mesh(voxels, threshold=0.5, device=None):
if device is None:
device = torch.device("cpu")
@@ -230,13 +230,9 @@ def voxel_to_mesh_surfnet(voxels, threshold=0.5, device=None):
[0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]
], device=device)
corner_values = torch.zeros((cell_positions.shape[0], 8), device=device)
for c, (dz, dy, dx) in enumerate(corner_offsets):
corner_values[:, c] = padded[
cell_positions[:, 0] + dz,
cell_positions[:, 1] + dy,
cell_positions[:, 2] + dx
]
pos = cell_positions.unsqueeze(1) + corner_offsets.unsqueeze(0)
z_idx, y_idx, x_idx = pos.unbind(-1)
corner_values = padded[z_idx, y_idx, x_idx]
corner_signs = corner_values > threshold
has_inside = torch.any(corner_signs, dim=1)