Add custom nodes, Civitai loras (LFS), and vast.ai setup script

Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

custom_nodes/ComfyUI-Easy-Use/py/modules/layer_diffuse/model.py

@@ -0,0 +1,390 @@
+import torch.nn as nn
+import torch
+import cv2
+import numpy as np
+import comfy.model_management
+
+from comfy.model_patcher import ModelPatcher
+from tqdm import tqdm
+from typing import Optional, Tuple
+from ...libs.utils import install_package
+from packaging import version
+
+try:
+    install_package("diffusers", "0.27.2", True, "0.25.0")
+
+    from diffusers.configuration_utils import ConfigMixin, register_to_config
+    from diffusers.models.modeling_utils import ModelMixin
+    from diffusers import __version__
+    if __version__:
+        if version.parse(__version__) < version.parse("0.26.0"):
+            from diffusers.models.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
+        else:
+            from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
+
+    import functools
+
+    def zero_module(module):
+        """
+        Zero out the parameters of a module and return it.
+        """
+        for p in module.parameters():
+            p.detach().zero_()
+        return module
+
+
+    class LatentTransparencyOffsetEncoder(torch.nn.Module):
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+            self.blocks = torch.nn.Sequential(
+                torch.nn.Conv2d(4, 32, kernel_size=3, padding=1, stride=1),
+                nn.SiLU(),
+                torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1),
+                nn.SiLU(),
+                torch.nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=2),
+                nn.SiLU(),
+                torch.nn.Conv2d(64, 64, kernel_size=3, padding=1, stride=1),
+                nn.SiLU(),
+                torch.nn.Conv2d(64, 128, kernel_size=3, padding=1, stride=2),
+                nn.SiLU(),
+                torch.nn.Conv2d(128, 128, kernel_size=3, padding=1, stride=1),
+                nn.SiLU(),
+                torch.nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2),
+                nn.SiLU(),
+                torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1),
+                nn.SiLU(),
+                zero_module(torch.nn.Conv2d(256, 4, kernel_size=3, padding=1, stride=1)),
+            )
+
+        def __call__(self, x):
+            return self.blocks(x)
+
+
+    # 1024 * 1024 * 3 -> 16 * 16 * 512 -> 1024 * 1024 * 3
+    class UNet1024(ModelMixin, ConfigMixin):
+        @register_to_config
+        def __init__(
+                self,
+                in_channels: int = 3,
+                out_channels: int = 3,
+                down_block_types: Tuple[str] = (
+                        "DownBlock2D",
+                        "DownBlock2D",
+                        "DownBlock2D",
+                        "DownBlock2D",
+                        "AttnDownBlock2D",
+                        "AttnDownBlock2D",
+                        "AttnDownBlock2D",
+                ),
+                up_block_types: Tuple[str] = (
+                        "AttnUpBlock2D",
+                        "AttnUpBlock2D",
+                        "AttnUpBlock2D",
+                        "UpBlock2D",
+                        "UpBlock2D",
+                        "UpBlock2D",
+                        "UpBlock2D",
+                ),
+                block_out_channels: Tuple[int] = (32, 32, 64, 128, 256, 512, 512),
+                layers_per_block: int = 2,
+                mid_block_scale_factor: float = 1,
+                downsample_padding: int = 1,
+                downsample_type: str = "conv",
+                upsample_type: str = "conv",
+                dropout: float = 0.0,
+                act_fn: str = "silu",
+                attention_head_dim: Optional[int] = 8,
+                norm_num_groups: int = 4,
+                norm_eps: float = 1e-5,
+        ):
+            super().__init__()
+
+            # input
+            self.conv_in = nn.Conv2d(
+                in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)
+            )
+            self.latent_conv_in = zero_module(
+                nn.Conv2d(4, block_out_channels[2], kernel_size=1)
+            )
+
+            self.down_blocks = nn.ModuleList([])
+            self.mid_block = None
+            self.up_blocks = nn.ModuleList([])
+
+            # down
+            output_channel = block_out_channels[0]
+            for i, down_block_type in enumerate(down_block_types):
+                input_channel = output_channel
+                output_channel = block_out_channels[i]
+                is_final_block = i == len(block_out_channels) - 1
+
+                down_block = get_down_block(
+                    down_block_type,
+                    num_layers=layers_per_block,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    temb_channels=None,
+                    add_downsample=not is_final_block,
+                    resnet_eps=norm_eps,
+                    resnet_act_fn=act_fn,
+                    resnet_groups=norm_num_groups,
+                    attention_head_dim=(
+                        attention_head_dim
+                        if attention_head_dim is not None
+                        else output_channel
+                    ),
+                    downsample_padding=downsample_padding,
+                    resnet_time_scale_shift="default",
+                    downsample_type=downsample_type,
+                    dropout=dropout,
+                )
+                self.down_blocks.append(down_block)
+
+            # mid
+            self.mid_block = UNetMidBlock2D(
+                in_channels=block_out_channels[-1],
+                temb_channels=None,
+                dropout=dropout,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift="default",
+                attention_head_dim=(
+                    attention_head_dim
+                    if attention_head_dim is not None
+                    else block_out_channels[-1]
+                ),
+                resnet_groups=norm_num_groups,
+                attn_groups=None,
+                add_attention=True,
+            )
+
+            # up
+            reversed_block_out_channels = list(reversed(block_out_channels))
+            output_channel = reversed_block_out_channels[0]
+            for i, up_block_type in enumerate(up_block_types):
+                prev_output_channel = output_channel
+                output_channel = reversed_block_out_channels[i]
+                input_channel = reversed_block_out_channels[
+                    min(i + 1, len(block_out_channels) - 1)
+                ]
+
+                is_final_block = i == len(block_out_channels) - 1
+
+                up_block = get_up_block(
+                    up_block_type,
+                    num_layers=layers_per_block + 1,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    prev_output_channel=prev_output_channel,
+                    temb_channels=None,
+                    add_upsample=not is_final_block,
+                    resnet_eps=norm_eps,
+                    resnet_act_fn=act_fn,
+                    resnet_groups=norm_num_groups,
+                    attention_head_dim=(
+                        attention_head_dim
+                        if attention_head_dim is not None
+                        else output_channel
+                    ),
+                    resnet_time_scale_shift="default",
+                    upsample_type=upsample_type,
+                    dropout=dropout,
+                )
+                self.up_blocks.append(up_block)
+                prev_output_channel = output_channel
+
+            # out
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
+            )
+            self.conv_act = nn.SiLU()
+            self.conv_out = nn.Conv2d(
+                block_out_channels[0], out_channels, kernel_size=3, padding=1
+            )
+
+        def forward(self, x, latent):
+            sample_latent = self.latent_conv_in(latent)
+            sample = self.conv_in(x)
+            emb = None
+
+            down_block_res_samples = (sample,)
+            for i, downsample_block in enumerate(self.down_blocks):
+                if i == 3:
+                    sample = sample + sample_latent
+
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+                down_block_res_samples += res_samples
+
+            sample = self.mid_block(sample, emb)
+
+            for upsample_block in self.up_blocks:
+                res_samples = down_block_res_samples[-len(upsample_block.resnets):]
+                down_block_res_samples = down_block_res_samples[
+                                         : -len(upsample_block.resnets)
+                                         ]
+                sample = upsample_block(sample, res_samples, emb)
+
+            sample = self.conv_norm_out(sample)
+            sample = self.conv_act(sample)
+            sample = self.conv_out(sample)
+            return sample
+
+
+    def checkerboard(shape):
+        return np.indices(shape).sum(axis=0) % 2
+
+
+    def fill_checkerboard_bg(y: torch.Tensor) -> torch.Tensor:
+        alpha = y[..., :1]
+        fg = y[..., 1:]
+        B, H, W, C = fg.shape
+        cb = checkerboard(shape=(H // 64, W // 64))
+        cb = cv2.resize(cb, (W, H), interpolation=cv2.INTER_NEAREST)
+        cb = (0.5 + (cb - 0.5) * 0.1)[None, ..., None]
+        cb = torch.from_numpy(cb).to(fg)
+        vis = fg * alpha + cb * (1 - alpha)
+        return vis
+
+
+    class TransparentVAEDecoder:
+        def __init__(self, sd, device, dtype):
+            self.load_device = device
+            self.dtype = dtype
+
+            model = UNet1024(in_channels=3, out_channels=4)
+            model.load_state_dict(sd, strict=True)
+            model.to(self.load_device, dtype=self.dtype)
+            model.eval()
+            self.model = model
+
+        @torch.no_grad()
+        def estimate_single_pass(self, pixel, latent):
+            y = self.model(pixel, latent)
+            return y
+
+        @torch.no_grad()
+        def estimate_augmented(self, pixel, latent):
+            args = [
+                [False, 0],
+                [False, 1],
+                [False, 2],
+                [False, 3],
+                [True, 0],
+                [True, 1],
+                [True, 2],
+                [True, 3],
+            ]
+
+            result = []
+
+            for flip, rok in tqdm(args):
+                feed_pixel = pixel.clone()
+                feed_latent = latent.clone()
+
+                if flip:
+                    feed_pixel = torch.flip(feed_pixel, dims=(3,))
+                    feed_latent = torch.flip(feed_latent, dims=(3,))
+
+                feed_pixel = torch.rot90(feed_pixel, k=rok, dims=(2, 3))
+                feed_latent = torch.rot90(feed_latent, k=rok, dims=(2, 3))
+
+                eps = self.estimate_single_pass(feed_pixel, feed_latent).clip(0, 1)
+                eps = torch.rot90(eps, k=-rok, dims=(2, 3))
+
+                if flip:
+                    eps = torch.flip(eps, dims=(3,))
+
+                result += [eps]
+
+            result = torch.stack(result, dim=0)
+            median = torch.median(result, dim=0).values
+            return median
+
+        @torch.no_grad()
+        def decode_pixel(
+                self, pixel: torch.TensorType, latent: torch.TensorType
+        ) -> torch.TensorType:
+            # pixel.shape = [B, C=3, H, W]
+            assert pixel.shape[1] == 3
+            pixel_device = pixel.device
+            pixel_dtype = pixel.dtype
+
+            pixel = pixel.to(device=self.load_device, dtype=self.dtype)
+            latent = latent.to(device=self.load_device, dtype=self.dtype)
+            # y.shape = [B, C=4, H, W]
+            y = self.estimate_augmented(pixel, latent)
+            y = y.clip(0, 1)
+            assert y.shape[1] == 4
+            # Restore image to original device of input image.
+            return y.to(pixel_device, dtype=pixel_dtype)
+
+
+    def calculate_weight_adjust_channel(func):
+        """Patches ComfyUI's LoRA weight application to accept multi-channel inputs."""
+        @functools.wraps(func)
+        def calculate_weight(
+            patches, weight: torch.Tensor, key: str, intermediate_type=torch.float32
+        ) -> torch.Tensor:
+            weight = func(patches, weight, key, intermediate_type)
+
+            for p in patches:
+                alpha = p[0]
+                v = p[1]
+
+                # The recursion call should be handled in the main func call.
+                if isinstance(v, list):
+                    continue
+
+                if len(v) == 1:
+                    patch_type = "diff"
+                elif len(v) == 2:
+                    patch_type = v[0]
+                    v = v[1]
+
+                if patch_type == "diff":
+                    w1 = v[0]
+                    if all(
+                            (
+                                    alpha != 0.0,
+                                    w1.shape != weight.shape,
+                                    w1.ndim == weight.ndim == 4,
+                            )
+                    ):
+                        new_shape = [max(n, m) for n, m in zip(weight.shape, w1.shape)]
+                        print(
+                            f"Merged with {key} channel changed from {weight.shape} to {new_shape}"
+                        )
+                        new_diff = alpha * comfy.model_management.cast_to_device(
+                            w1, weight.device, weight.dtype
+                        )
+                        new_weight = torch.zeros(size=new_shape).to(weight)
+                        new_weight[
+                        : weight.shape[0],
+                        : weight.shape[1],
+                        : weight.shape[2],
+                        : weight.shape[3],
+                        ] = weight
+                        new_weight[
+                        : new_diff.shape[0],
+                        : new_diff.shape[1],
+                        : new_diff.shape[2],
+                        : new_diff.shape[3],
+                        ] += new_diff
+                        new_weight = new_weight.contiguous().clone()
+                        weight = new_weight
+            return weight
+
+        return calculate_weight
+
+
+except ImportError:
+    ModelMixin = None
+    ConfigMixin = None
+    TransparentVAEDecoder = None
+    calculate_weight_adjust_channel = None
+    print("\33[33mModule 'diffusers' load failed. If you don't have it installed, do it:\033[0m")
+    print("\33[33mpip install diffusers\033[0m")
+
+
+