nodes/kijai-ComfyUI-IC-Light
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Add some auto resizing and fix normals node

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kijai
2024-05-11 22:09:45 +03:00
parent 8114ca8f77
commit d138cd9805
1 changed files with 29 additions and 6 deletions
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nodes.py
+29 -6
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@@ -3,6 +3,7 @@ import folder_paths
import os
import types
import numpy as np
import torch.nn.functional as F
from comfy.utils import load_torch_file
from .utils.convert_unet import convert_iclight_unet
from .utils.patches import calculate_weight_adjust_channel
@@ -79,7 +80,10 @@ Used with ICLightConditioning -node
# print(f"LoadAndApplyICLightUnet: New number of input channels: {model_clone.model.diffusion_model.input_blocks[0][0].in_channels}")
#Patch ComfyUI's LoRA weight application to accept multi-channel inputs. Thanks @huchenlei
ModelPatcher.calculate_weight = calculate_weight_adjust_channel(ModelPatcher.calculate_weight)
try:
ModelPatcher.calculate_weight = calculate_weight_adjust_channel(ModelPatcher.calculate_weight)
except:
raise Exception("IC-Light: Could not patch calculate_weight")
# Mimic the existing IP2P class to enable extra_conds
def bound_extra_conds(self, **kwargs):
return ICLight.extra_conds(self, **kwargs)
@@ -144,6 +148,18 @@ To use the "opt_background" input, you also need to use the
if opt_background is not None:
samples_2 = opt_background["samples"]
print(samples_2.shape)
repeats_1 = samples_2.size(0) // samples_1.size(0)
repeats_2 = samples_1.size(0) // samples_2.size(0)
if samples_1.shape[1:] != samples_2.shape[1:]:
samples_2 = comfy.utils.common_upscale(samples_2, samples_1.shape[-1], samples_1.shape[-2], "bilinear", "disabled")
# Repeat the tensors to match the larger batch size
if repeats_1 > 1:
samples_1 = samples_1.repeat(repeats_1, 1, 1, 1)
if repeats_2 > 1:
samples_2 = samples_2.repeat(repeats_2, 1, 1, 1)
concat_latent = torch.cat((samples_1, samples_2), dim=1)
else:
@@ -230,9 +246,14 @@ left, right, bottom, top
"""
def execute(self, images, sigma, center_input_range, mask=None):
print(images.min(), images.max())
if center_input_range:
images = images * 0.5 + 0.5
if mask is not None:
if mask.shape != images[0].shape[-1]:
mask = mask.unsqueeze(0)
mask = F.interpolate(mask, size=(images.shape[1], images.shape[2]), mode="bilinear")
mask = mask.squeeze(0)
images_np = images.numpy().astype(np.float32)
left = images_np[0]
right = images_np[1]
@@ -262,15 +283,17 @@ left, right, bottom, top
normal = np.stack([u, v, h], axis=2)
normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
if mask is not None:
mask = mask.squeeze(0)
matting = mask.numpy().astype(np.float32)
matting = matting[..., np.newaxis]
normal = normal * matting + np.stack([z, z, 1 - z], axis=2) * (1 - matting)
matting = matting[..., np.newaxis]
normal = normal * matting + np.stack([z, z, 1 - z], axis=2)
normal = torch.from_numpy(normal)
normal = normal.unsqueeze(0)
else:
normal = normal + np.stack([z, z, 1 - z], axis=2)
normal = torch.from_numpy(normal).unsqueeze(0)
normal = (normal + 1.0) / 2.0
normal = F.normalize(normal * 2 - 1, dim=3) / 2 + 0.5
normal = torch.clamp(normal, 0, 1)
return (normal,)