layerdiffuse

ComfyUI/custom_nodes/layerdiffuse/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
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+# Distribution / packaging
+.Python
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+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
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+# Unit test / coverage reports
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+.coverage.*
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+
+# Translations
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+profile_default/
+ipython_config.py
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+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
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+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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+.pdm.toml
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+.idea/
+.vscode/

ComfyUI/custom_nodes/layerdiffuse/LICENSE

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ComfyUI/custom_nodes/layerdiffuse/README.md

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+# ComfyUI-layerdiffuse
+ComfyUI implementation of https://github.com/layerdiffusion/LayerDiffuse.
+
+## Installation
+Download the repository and unpack into the custom_nodes folder in the ComfyUI installation directory.
+
+Or clone via GIT, starting from ComfyUI installation directory:
+```bash
+cd custom_nodes
+git clone [email protected]:huchenlei/ComfyUI-layerdiffuse.git
+```
+
+Run `pip install -r requirements.txt` to install python dependencies. You might experience version conflict on diffusers if you have other extensions
+that depends on other versions of diffusers. In this case, it is recommended to setup separate Python venvs.
+
+## Workflows
+### [Generate foreground](https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/main/examples/layer_diffusion_fg_example_rgba.json)
+![rgba](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/5e6085e5-d997-4a0a-b589-257d65eb1eb2)
+
+### [Generate foreground (RGB + alpha)](https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/main/examples/layer_diffusion_fg_example.json)
+If you want more control of getting RGB image and alpha channel mask separately, you can use this workflow.
+![readme1](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/4825b81c-7089-4806-bce7-777229421707)
+
+### [Blending (FG/BG)](https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/main/examples/layer_diffusion_cond_example.json)
+Blending given FG
+![fg_cond](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/7f7dee80-6e57-4570-b304-d1f7e5dc3aad)
+
+Blending given BG
+![bg_cond](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/e3a79218-6123-453b-a54b-2f338db1c12d)
+
+### [Extract FG from Blended + BG](https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/main/examples/layer_diffusion_diff_fg.json)
+![diff_bg](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/45c7207d-72ff-4fb0-9c91-687040781837)
+
+### [Extract BG from Blended + FG](https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/main/examples/layer_diffusion_diff_bg.json)
+[Forge impl's sanity check](https://github.com/layerdiffuse/sd-forge-layerdiffuse#sanity-check) sets `Stop at` to 0.5 to get better quality BG.
+This workflow might be inferior comparing to other object removal workflows.
+![diff_fg](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/05a10add-68b0-473a-acee-5853e4720322)
+
+### [Extract BG from Blended + FG (Stop at 0.5)](https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/main/examples/layer_diffusion_diff_bg_stop_at.json)
+In [SD Forge impl](https://github.com/layerdiffuse/sd-forge-layerdiffuse), there is a `stop at` param that determines when
+layer diffuse should stop in the denosing process. In the background, what this param does is unapply the LoRA and c_concat cond after a certain step
+threshold. This is hard/risky to implement directly in ComfyUI as it requires manually load a model that has every changes except the layer diffusion
+change applied. A workaround in ComfyUI is to have another img2img pass on the layer diffuse result to simulate the effect of `stop at` param.
+![diff_fg_stop_at](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/e383c9d3-2d47-40c2-b764-b0bd48243ee8)
+
+
+### [Generate FG from BG combined](https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/main/examples/layer_diffusion_cond_fg_all.json)
+Combines previous workflows to generate blended and FG given BG. We found that there are some color variations in the extracted FG. Need to confirm
+with layer diffusion authors on whether this is expected.
+![fg_all](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/f4c18585-961a-473a-a616-aa3776bacd41)
+
+### [2024-3-9] [Generate FG + Blended given BG](https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/main/examples/layer_diffusion_cond_joint_bg.json)
+Need batch size = 2N. Currently only for SD15.
+![sd15_cond_joint_bg](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/9bbfe5c1-14a0-421d-bf06-85e301bf8065)
+
+### [2024-3-9] [Generate BG + Blended given FG](https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/main/examples/layer_diffusion_cond_joint_fg.json)
+Need batch size = 2N. Currently only for SD15.
+![sd15_cond_joint_fg](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/65af8b38-cf4c-4667-b76f-3013a0be0a48)
+
+### [2024-3-9] [Generate BG + FG + Blended together](https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/main/examples/layer_diffusion_joint.json)
+Need batch size = 3N. Currently only for SD15.
+![sd15_joint](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/e5545809-e3fb-4683-acf5-8728195cb2bc)
+
+## Note
+- Currently only SDXL/SD15 are supported. See https://github.com/layerdiffuse/sd-forge-layerdiffuse#model-notes for more details.
+- To decode RGBA result, the generation dimension must be multiple of 64. Otherwise, you will get decode error: ![image](https://github.com/huchenlei/ComfyUI-layerdiffuse/assets/20929282/ff055f99-9297-4ff1-9a33-065aaadcf98e)

ComfyUI/custom_nodes/layerdiffuse/__init__.py

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+from .layered_diffusion import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

ComfyUI/custom_nodes/layerdiffuse/examples/layer_diffusion_cond_example.json

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+{
+  "last_node_id": 35,
+  "last_link_id": 52,
+  "nodes": [
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
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+        479
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+      "size": {
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+        "1": 98
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+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            38
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            22,
+            49
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "juggernautXL_v8Rundiffusion.safetensors"
+      ]
+    },
+    {
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+          "name": "pixels",
+          "type": "IMAGE",
+          "link": 51
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 49,
+          "slot_index": 1
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            47
+          ],
+          "shape": 3,
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+          "shape": 3,
+          "slot_index": 0
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+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
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+        "Node name for S&R": "LoadImage"
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+        "image"
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+      "properties": {
+        "Node name for S&R": "PreviewImage"
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+          "type": "CONDITIONING",
+          "links": [
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ComfyUI/custom_nodes/layerdiffuse/examples/layer_diffusion_cond_fg_all.json

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ComfyUI/custom_nodes/layerdiffuse/examples/layer_diffusion_cond_joint_bg.json

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ComfyUI/custom_nodes/layerdiffuse/examples/layer_diffusion_cond_joint_fg.json

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ComfyUI/custom_nodes/layerdiffuse/examples/layer_diffusion_diff_bg.json

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ComfyUI/custom_nodes/layerdiffuse/examples/layer_diffusion_diff_fg.json

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+}

ComfyUI/custom_nodes/layerdiffuse/examples/layer_diffusion_fg_example.json

@@ -0,0 +1,733 @@
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ComfyUI/custom_nodes/layerdiffuse/examples/layer_diffusion_fg_example_rgba.json

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ComfyUI/custom_nodes/layerdiffuse/examples/layer_diffusion_joint.json

@@ -0,0 +1,703 @@
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+          "type": "IMAGE",
+          "links": [
+            36
+          ],
+          "shape": 3,
+          "slot_index": 1
+        },
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            37
+          ],
+          "shape": 3,
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayeredDiffusionDecodeSplit"
+      },
+      "widgets_values": [
+        3,
+        "SD15",
+        16
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 5,
+      "type": "EmptyLatentImage",
+      "pos": [
+        466,
+        645
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            2
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        6
+      ]
+    },
+    {
+      "id": 26,
+      "type": "CLIPTextEncode",
+      "pos": [
+        -20,
+        -235
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 41,
+          "slot_index": 0
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            39
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "a sitting dog"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        413,
+        172
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            4,
+            38
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "A dog sitting in a cozy room"
+      ]
+    },
+    {
+      "id": 23,
+      "type": "PreviewImage",
+      "pos": [
+        1931,
+        -98
+      ],
+      "size": [
+        522.1710021972658,
+        259.90739746093755
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 35
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 24,
+      "type": "PreviewImage",
+      "pos": [
+        1933,
+        222
+      ],
+      "size": [
+        517.9710037231448,
+        258.9074523925782
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 36
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 25,
+      "type": "PreviewImage",
+      "pos": [
+        1933,
+        536
+      ],
+      "size": [
+        516.8710037231444,
+        270.5074523925782
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 37
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    }
+  ],
+  "links": [
+    [
+      2,
+      5,
+      0,
+      3,
+      3,
+      "LATENT"
+    ],
+    [
+      3,
+      4,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      4,
+      6,
+      0,
+      3,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      5,
+      4,
+      1,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      6,
+      7,
+      0,
+      3,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      21,
+      3,
+      0,
+      14,
+      0,
+      "LATENT"
+    ],
+    [
+      22,
+      4,
+      2,
+      14,
+      1,
+      "VAE"
+    ],
+    [
+      31,
+      4,
+      0,
+      21,
+      0,
+      "MODEL"
+    ],
+    [
+      32,
+      21,
+      0,
+      3,
+      0,
+      "MODEL"
+    ],
+    [
+      33,
+      3,
+      0,
+      22,
+      0,
+      "LATENT"
+    ],
+    [
+      34,
+      14,
+      0,
+      22,
+      1,
+      "IMAGE"
+    ],
+    [
+      35,
+      22,
+      0,
+      23,
+      0,
+      "IMAGE"
+    ],
+    [
+      36,
+      22,
+      1,
+      24,
+      0,
+      "IMAGE"
+    ],
+    [
+      37,
+      22,
+      2,
+      25,
+      0,
+      "IMAGE"
+    ],
+    [
+      38,
+      6,
+      0,
+      21,
+      3,
+      "CONDITIONING"
+    ],
+    [
+      39,
+      26,
+      0,
+      21,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      40,
+      27,
+      0,
+      21,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      41,
+      4,
+      1,
+      26,
+      0,
+      "CLIP"
+    ],
+    [
+      42,
+      4,
+      1,
+      27,
+      0,
+      "CLIP"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

ComfyUI/custom_nodes/layerdiffuse/layered_diffusion.py

@@ -0,0 +1,683 @@
+import os
+from enum import Enum
+import torch
+import functools
+import copy
+from typing import Optional, List
+from dataclasses import dataclass
+
+import folder_paths
+import comfy.model_management
+import comfy.model_base
+import comfy.supported_models
+import comfy.supported_models_base
+from comfy.model_patcher import ModelPatcher
+from folder_paths import get_folder_paths
+from comfy.utils import load_torch_file
+from comfy_extras.nodes_compositing import JoinImageWithAlpha
+from comfy.conds import CONDRegular
+from .lib_layerdiffusion.utils import (
+    load_file_from_url,
+    to_lora_patch_dict,
+)
+from .lib_layerdiffusion.models import TransparentVAEDecoder
+from .lib_layerdiffusion.attention_sharing import AttentionSharingPatcher
+from .lib_layerdiffusion.enums import StableDiffusionVersion
+
+if "layer_model" in folder_paths.folder_names_and_paths:
+    layer_model_root = get_folder_paths("layer_model")[0]
+else:
+    layer_model_root = os.path.join(folder_paths.models_dir, "layer_model")
+load_layer_model_state_dict = load_torch_file
+
+
+# ------------ Start patching ComfyUI ------------
+def calculate_weight_adjust_channel(func):
+    """Patches ComfyUI's LoRA weight application to accept multi-channel inputs."""
+
+    @functools.wraps(func)
+    def calculate_weight(
+        self: ModelPatcher, patches, weight: torch.Tensor, key: str
+    ) -> torch.Tensor:
+        weight = func(self, patches, weight, key)
+
+        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
+
+
+ModelPatcher.calculate_weight = calculate_weight_adjust_channel(
+    ModelPatcher.calculate_weight
+)
+
+# ------------ End patching ComfyUI ------------
+
+
+class LayeredDiffusionDecode:
+    """
+    Decode alpha channel value from pixel value.
+    [B, C=3, H, W] => [B, C=4, H, W]
+    Outputs RGB image + Alpha mask.
+    """
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "samples": ("LATENT",),
+                "images": ("IMAGE",),
+                "sd_version": (
+                    [
+                        StableDiffusionVersion.SD1x.value,
+                        StableDiffusionVersion.SDXL.value,
+                    ],
+                    {
+                        "default": StableDiffusionVersion.SDXL.value,
+                    },
+                ),
+                "sub_batch_size": (
+                    "INT",
+                    {"default": 16, "min": 1, "max": 4096, "step": 1},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "decode"
+    CATEGORY = "layer_diffuse"
+
+    def __init__(self) -> None:
+        self.vae_transparent_decoder = {}
+
+    def decode(self, samples, images, sd_version: str, sub_batch_size: int):
+        """
+        sub_batch_size: How many images to decode in a single pass.
+        See https://github.com/huchenlei/ComfyUI-layerdiffuse/pull/4 for more
+        context.
+        """
+        sd_version = StableDiffusionVersion(sd_version)
+        if sd_version == StableDiffusionVersion.SD1x:
+            url = "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_vae_transparent_decoder.safetensors"
+            file_name = "layer_sd15_vae_transparent_decoder.safetensors"
+        elif sd_version == StableDiffusionVersion.SDXL:
+            url = "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/vae_transparent_decoder.safetensors"
+            file_name = "vae_transparent_decoder.safetensors"
+
+        if not self.vae_transparent_decoder.get(sd_version):
+            model_path = load_file_from_url(
+                url=url, model_dir=layer_model_root, file_name=file_name
+            )
+            self.vae_transparent_decoder[sd_version] = TransparentVAEDecoder(
+                load_torch_file(model_path),
+                device=comfy.model_management.get_torch_device(),
+                dtype=(
+                    torch.float16
+                    if comfy.model_management.should_use_fp16()
+                    else torch.float32
+                ),
+            )
+        pixel = images.movedim(-1, 1)  # [B, H, W, C] => [B, C, H, W]
+
+        # Decoder requires dimension to be 64-aligned.
+        B, C, H, W = pixel.shape
+        assert H % 64 == 0, f"Height({H}) is not multiple of 64."
+        assert W % 64 == 0, f"Height({W}) is not multiple of 64."
+
+        decoded = []
+        for start_idx in range(0, samples["samples"].shape[0], sub_batch_size):
+            decoded.append(
+                self.vae_transparent_decoder[sd_version].decode_pixel(
+                    pixel[start_idx : start_idx + sub_batch_size],
+                    samples["samples"][start_idx : start_idx + sub_batch_size],
+                )
+            )
+        pixel_with_alpha = torch.cat(decoded, dim=0)
+
+        # [B, C, H, W] => [B, H, W, C]
+        pixel_with_alpha = pixel_with_alpha.movedim(1, -1)
+        image = pixel_with_alpha[..., 1:]
+        alpha = pixel_with_alpha[..., 0]
+        return (image, alpha)
+
+
+class LayeredDiffusionDecodeRGBA(LayeredDiffusionDecode):
+    """
+    Decode alpha channel value from pixel value.
+    [B, C=3, H, W] => [B, C=4, H, W]
+    Outputs RGBA image.
+    """
+
+    RETURN_TYPES = ("IMAGE",)
+
+    def decode(self, samples, images, sd_version: str, sub_batch_size: int):
+        image, mask = super().decode(samples, images, sd_version, sub_batch_size)
+        alpha = 1.0 - mask
+        return JoinImageWithAlpha().join_image_with_alpha(image, alpha)
+
+
+class LayeredDiffusionDecodeSplit(LayeredDiffusionDecodeRGBA):
+    """Decode RGBA every N images."""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "samples": ("LATENT",),
+                "images": ("IMAGE",),
+                # Do RGBA decode every N output images.
+                "frames": (
+                    "INT",
+                    {"default": 2, "min": 2, "max": s.MAX_FRAMES, "step": 1},
+                ),
+                "sd_version": (
+                    [
+                        StableDiffusionVersion.SD1x.value,
+                        StableDiffusionVersion.SDXL.value,
+                    ],
+                    {
+                        "default": StableDiffusionVersion.SDXL.value,
+                    },
+                ),
+                "sub_batch_size": (
+                    "INT",
+                    {"default": 16, "min": 1, "max": 4096, "step": 1},
+                ),
+            },
+        }
+
+    MAX_FRAMES = 3
+    RETURN_TYPES = ("IMAGE",) * MAX_FRAMES
+
+    def decode(
+        self,
+        samples,
+        images: torch.Tensor,
+        frames: int,
+        sd_version: str,
+        sub_batch_size: int,
+    ):
+        sliced_samples = copy.copy(samples)
+        sliced_samples["samples"] = sliced_samples["samples"][::frames]
+        return tuple(
+            (
+                (
+                    super(LayeredDiffusionDecodeSplit, self).decode(
+                        sliced_samples, imgs, sd_version, sub_batch_size
+                    )[0]
+                    if i == 0
+                    else imgs
+                )
+                for i in range(frames)
+                for imgs in (images[i::frames],)
+            )
+        ) + (None,) * (self.MAX_FRAMES - frames)
+
+
+class LayerMethod(Enum):
+    ATTN = "Attention Injection"
+    CONV = "Conv Injection"
+
+
+class LayerType(Enum):
+    FG = "Foreground"
+    BG = "Background"
+
+
+@dataclass
+class LayeredDiffusionBase:
+    model_file_name: str
+    model_url: str
+    sd_version: StableDiffusionVersion
+    attn_sharing: bool = False
+    injection_method: Optional[LayerMethod] = None
+    cond_type: Optional[LayerType] = None
+    # Number of output images per run.
+    frames: int = 1
+
+    @property
+    def config_string(self) -> str:
+        injection_method = self.injection_method.value if self.injection_method else ""
+        cond_type = self.cond_type.value if self.cond_type else ""
+        attn_sharing = "attn_sharing" if self.attn_sharing else ""
+        frames = f"Batch size ({self.frames}N)" if self.frames != 1 else ""
+        return ", ".join(
+            x
+            for x in (
+                self.sd_version.value,
+                injection_method,
+                cond_type,
+                attn_sharing,
+                frames,
+            )
+            if x
+        )
+
+    def apply_c_concat(self, cond, uncond, c_concat):
+        """Set foreground/background concat condition."""
+
+        def write_c_concat(cond):
+            new_cond = []
+            for t in cond:
+                n = [t[0], t[1].copy()]
+                if "model_conds" not in n[1]:
+                    n[1]["model_conds"] = {}
+                n[1]["model_conds"]["c_concat"] = CONDRegular(c_concat)
+                new_cond.append(n)
+            return new_cond
+
+        return (write_c_concat(cond), write_c_concat(uncond))
+
+    def apply_layered_diffusion(
+        self,
+        model: ModelPatcher,
+        weight: float,
+    ):
+        """Patch model"""
+        model_path = load_file_from_url(
+            url=self.model_url,
+            model_dir=layer_model_root,
+            file_name=self.model_file_name,
+        )
+        layer_lora_state_dict = load_layer_model_state_dict(model_path)
+        layer_lora_patch_dict = to_lora_patch_dict(layer_lora_state_dict)
+        work_model = model.clone()
+        work_model.add_patches(layer_lora_patch_dict, weight)
+        return (work_model,)
+
+    def apply_layered_diffusion_attn_sharing(
+        self,
+        model: ModelPatcher,
+        control_img: Optional[torch.TensorType] = None,
+    ):
+        """Patch model with attn sharing"""
+        model_path = load_file_from_url(
+            url=self.model_url,
+            model_dir=layer_model_root,
+            file_name=self.model_file_name,
+        )
+        layer_lora_state_dict = load_layer_model_state_dict(model_path)
+        work_model = model.clone()
+        patcher = AttentionSharingPatcher(
+            work_model, self.frames, use_control=control_img is not None
+        )
+        patcher.load_state_dict(layer_lora_state_dict, strict=True)
+        if control_img is not None:
+            patcher.set_control(control_img)
+        return (work_model,)
+
+
+def get_model_sd_version(model: ModelPatcher) -> StableDiffusionVersion:
+    """Get model's StableDiffusionVersion."""
+    base: comfy.model_base.BaseModel = model.model
+    model_config: comfy.supported_models.supported_models_base.BASE = base.model_config
+    if isinstance(model_config, comfy.supported_models.SDXL):
+        return StableDiffusionVersion.SDXL
+    elif isinstance(
+        model_config, (comfy.supported_models.SD15, comfy.supported_models.SD20)
+    ):
+        # SD15 and SD20 are compatible with each other.
+        return StableDiffusionVersion.SD1x
+    else:
+        raise Exception(f"Unsupported SD Version: {type(model_config)}.")
+
+
+class LayeredDiffusionFG:
+    """Generate foreground with transparent background."""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "config": ([c.config_string for c in s.MODELS],),
+                "weight": (
+                    "FLOAT",
+                    {"default": 1.0, "min": -1, "max": 3, "step": 0.05},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply_layered_diffusion"
+    CATEGORY = "layer_diffuse"
+    MODELS = (
+        LayeredDiffusionBase(
+            model_file_name="layer_xl_transparent_attn.safetensors",
+            model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_transparent_attn.safetensors",
+            sd_version=StableDiffusionVersion.SDXL,
+            injection_method=LayerMethod.ATTN,
+        ),
+        LayeredDiffusionBase(
+            model_file_name="layer_xl_transparent_conv.safetensors",
+            model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_transparent_conv.safetensors",
+            sd_version=StableDiffusionVersion.SDXL,
+            injection_method=LayerMethod.CONV,
+        ),
+        LayeredDiffusionBase(
+            model_file_name="layer_sd15_transparent_attn.safetensors",
+            model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_transparent_attn.safetensors",
+            sd_version=StableDiffusionVersion.SD1x,
+            injection_method=LayerMethod.ATTN,
+            attn_sharing=True,
+        ),
+    )
+
+    def apply_layered_diffusion(
+        self,
+        model: ModelPatcher,
+        config: str,
+        weight: float,
+    ):
+        ld_model = [m for m in self.MODELS if m.config_string == config][0]
+        assert get_model_sd_version(model) == ld_model.sd_version
+        if ld_model.attn_sharing:
+            return ld_model.apply_layered_diffusion_attn_sharing(model)
+        else:
+            return ld_model.apply_layered_diffusion(model, weight)
+
+
+class LayeredDiffusionJoint:
+    """Generate FG + BG + Blended in one inference batch. Batch size = 3N."""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "config": ([c.config_string for c in s.MODELS],),
+            },
+            "optional": {
+                "fg_cond": ("CONDITIONING",),
+                "bg_cond": ("CONDITIONING",),
+                "blended_cond": ("CONDITIONING",),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply_layered_diffusion"
+    CATEGORY = "layer_diffuse"
+    MODELS = (
+        LayeredDiffusionBase(
+            model_file_name="layer_sd15_joint.safetensors",
+            model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_joint.safetensors",
+            sd_version=StableDiffusionVersion.SD1x,
+            attn_sharing=True,
+            frames=3,
+        ),
+    )
+
+    def apply_layered_diffusion(
+        self,
+        model: ModelPatcher,
+        config: str,
+        fg_cond: Optional[List[List[torch.TensorType]]] = None,
+        bg_cond: Optional[List[List[torch.TensorType]]] = None,
+        blended_cond: Optional[List[List[torch.TensorType]]] = None,
+    ):
+        ld_model = [m for m in self.MODELS if m.config_string == config][0]
+        assert get_model_sd_version(model) == ld_model.sd_version
+        assert ld_model.attn_sharing
+        work_model = ld_model.apply_layered_diffusion_attn_sharing(model)[0]
+        work_model.model_options.setdefault("transformer_options", {})
+        work_model.model_options["transformer_options"]["cond_overwrite"] = [
+            cond[0][0] if cond is not None else None
+            for cond in (
+                fg_cond,
+                bg_cond,
+                blended_cond,
+            )
+        ]
+        return (work_model,)
+
+
+class LayeredDiffusionCond:
+    """Generate foreground + background given background / foreground.
+    - FG => Blended
+    - BG => Blended
+    """
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "cond": ("CONDITIONING",),
+                "uncond": ("CONDITIONING",),
+                "latent": ("LATENT",),
+                "config": ([c.config_string for c in s.MODELS],),
+                "weight": (
+                    "FLOAT",
+                    {"default": 1.0, "min": -1, "max": 3, "step": 0.05},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING")
+    FUNCTION = "apply_layered_diffusion"
+    CATEGORY = "layer_diffuse"
+    MODELS = (
+        LayeredDiffusionBase(
+            model_file_name="layer_xl_fg2ble.safetensors",
+            model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_fg2ble.safetensors",
+            sd_version=StableDiffusionVersion.SDXL,
+            cond_type=LayerType.FG,
+        ),
+        LayeredDiffusionBase(
+            model_file_name="layer_xl_bg2ble.safetensors",
+            model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_bg2ble.safetensors",
+            sd_version=StableDiffusionVersion.SDXL,
+            cond_type=LayerType.BG,
+        ),
+    )
+
+    def apply_layered_diffusion(
+        self,
+        model: ModelPatcher,
+        cond,
+        uncond,
+        latent,
+        config: str,
+        weight: float,
+    ):
+        ld_model = [m for m in self.MODELS if m.config_string == config][0]
+        assert get_model_sd_version(model) == ld_model.sd_version
+        c_concat = model.model.latent_format.process_in(latent["samples"])
+        return ld_model.apply_layered_diffusion(
+            model, weight
+        ) + ld_model.apply_c_concat(cond, uncond, c_concat)
+
+
+class LayeredDiffusionCondJoint:
+    """Generate fg/bg + blended given fg/bg.
+    - FG => Blended + BG
+    - BG => Blended + FG
+    """
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "image": ("IMAGE",),
+                "config": ([c.config_string for c in s.MODELS],),
+            },
+            "optional": {
+                "cond": ("CONDITIONING",),
+                "blended_cond": ("CONDITIONING",),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply_layered_diffusion"
+    CATEGORY = "layer_diffuse"
+    MODELS = (
+        LayeredDiffusionBase(
+            model_file_name="layer_sd15_fg2bg.safetensors",
+            model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_fg2bg.safetensors",
+            sd_version=StableDiffusionVersion.SD1x,
+            attn_sharing=True,
+            frames=2,
+            cond_type=LayerType.FG,
+        ),
+        LayeredDiffusionBase(
+            model_file_name="layer_sd15_bg2fg.safetensors",
+            model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_bg2fg.safetensors",
+            sd_version=StableDiffusionVersion.SD1x,
+            attn_sharing=True,
+            frames=2,
+            cond_type=LayerType.BG,
+        ),
+    )
+
+    def apply_layered_diffusion(
+        self,
+        model: ModelPatcher,
+        image,
+        config: str,
+        cond: Optional[List[List[torch.TensorType]]] = None,
+        blended_cond: Optional[List[List[torch.TensorType]]] = None,
+    ):
+        ld_model = [m for m in self.MODELS if m.config_string == config][0]
+        assert get_model_sd_version(model) == ld_model.sd_version
+        assert ld_model.attn_sharing
+        work_model = ld_model.apply_layered_diffusion_attn_sharing(
+            model, control_img=image.movedim(-1, 1)
+        )[0]
+        work_model.model_options.setdefault("transformer_options", {})
+        work_model.model_options["transformer_options"]["cond_overwrite"] = [
+            cond[0][0] if cond is not None else None
+            for cond in (
+                cond,
+                blended_cond,
+            )
+        ]
+        return (work_model,)
+
+
+class LayeredDiffusionDiff:
+    """Extract FG/BG from blended image.
+    - Blended + FG => BG
+    - Blended + BG => FG
+    """
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "cond": ("CONDITIONING",),
+                "uncond": ("CONDITIONING",),
+                "blended_latent": ("LATENT",),
+                "latent": ("LATENT",),
+                "config": ([c.config_string for c in s.MODELS],),
+                "weight": (
+                    "FLOAT",
+                    {"default": 1.0, "min": -1, "max": 3, "step": 0.05},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING")
+    FUNCTION = "apply_layered_diffusion"
+    CATEGORY = "layer_diffuse"
+    MODELS = (
+        LayeredDiffusionBase(
+            model_file_name="layer_xl_fgble2bg.safetensors",
+            model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_fgble2bg.safetensors",
+            sd_version=StableDiffusionVersion.SDXL,
+            cond_type=LayerType.FG,
+        ),
+        LayeredDiffusionBase(
+            model_file_name="layer_xl_bgble2fg.safetensors",
+            model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_bgble2fg.safetensors",
+            sd_version=StableDiffusionVersion.SDXL,
+            cond_type=LayerType.BG,
+        ),
+    )
+
+    def apply_layered_diffusion(
+        self,
+        model: ModelPatcher,
+        cond,
+        uncond,
+        blended_latent,
+        latent,
+        config: str,
+        weight: float,
+    ):
+        ld_model = [m for m in self.MODELS if m.config_string == config][0]
+        assert get_model_sd_version(model) == ld_model.sd_version
+        c_concat = model.model.latent_format.process_in(
+            torch.cat([latent["samples"], blended_latent["samples"]], dim=1)
+        )
+        return ld_model.apply_layered_diffusion(
+            model, weight
+        ) + ld_model.apply_c_concat(cond, uncond, c_concat)
+
+
+NODE_CLASS_MAPPINGS = {
+    "LayeredDiffusionApply": LayeredDiffusionFG,
+    "LayeredDiffusionJointApply": LayeredDiffusionJoint,
+    "LayeredDiffusionCondApply": LayeredDiffusionCond,
+    "LayeredDiffusionCondJointApply": LayeredDiffusionCondJoint,
+    "LayeredDiffusionDiffApply": LayeredDiffusionDiff,
+    "LayeredDiffusionDecode": LayeredDiffusionDecode,
+    "LayeredDiffusionDecodeRGBA": LayeredDiffusionDecodeRGBA,
+    "LayeredDiffusionDecodeSplit": LayeredDiffusionDecodeSplit,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayeredDiffusionApply": "Layer Diffuse Apply",
+    "LayeredDiffusionJointApply": "Layer Diffuse Joint Apply",
+    "LayeredDiffusionCondApply": "Layer Diffuse Cond Apply",
+    "LayeredDiffusionCondJointApply": "Layer Diffuse Cond Joint Apply",
+    "LayeredDiffusionDiffApply": "Layer Diffuse Diff Apply",
+    "LayeredDiffusionDecode": "Layer Diffuse Decode",
+    "LayeredDiffusionDecodeRGBA": "Layer Diffuse Decode (RGBA)",
+    "LayeredDiffusionDecodeSplit": "Layer Diffuse Decode (Split)",
+}

ComfyUI/custom_nodes/layerdiffuse/lib_layerdiffusion/attention_sharing.py

@@ -0,0 +1,360 @@
+# Currently only sd15
+
+import functools
+import torch
+import einops
+
+from comfy import model_management, utils
+from comfy.ldm.modules.attention import optimized_attention
+
+
+module_mapping_sd15 = {
+    0: "input_blocks.1.1.transformer_blocks.0.attn1",
+    1: "input_blocks.1.1.transformer_blocks.0.attn2",
+    2: "input_blocks.2.1.transformer_blocks.0.attn1",
+    3: "input_blocks.2.1.transformer_blocks.0.attn2",
+    4: "input_blocks.4.1.transformer_blocks.0.attn1",
+    5: "input_blocks.4.1.transformer_blocks.0.attn2",
+    6: "input_blocks.5.1.transformer_blocks.0.attn1",
+    7: "input_blocks.5.1.transformer_blocks.0.attn2",
+    8: "input_blocks.7.1.transformer_blocks.0.attn1",
+    9: "input_blocks.7.1.transformer_blocks.0.attn2",
+    10: "input_blocks.8.1.transformer_blocks.0.attn1",
+    11: "input_blocks.8.1.transformer_blocks.0.attn2",
+    12: "output_blocks.3.1.transformer_blocks.0.attn1",
+    13: "output_blocks.3.1.transformer_blocks.0.attn2",
+    14: "output_blocks.4.1.transformer_blocks.0.attn1",
+    15: "output_blocks.4.1.transformer_blocks.0.attn2",
+    16: "output_blocks.5.1.transformer_blocks.0.attn1",
+    17: "output_blocks.5.1.transformer_blocks.0.attn2",
+    18: "output_blocks.6.1.transformer_blocks.0.attn1",
+    19: "output_blocks.6.1.transformer_blocks.0.attn2",
+    20: "output_blocks.7.1.transformer_blocks.0.attn1",
+    21: "output_blocks.7.1.transformer_blocks.0.attn2",
+    22: "output_blocks.8.1.transformer_blocks.0.attn1",
+    23: "output_blocks.8.1.transformer_blocks.0.attn2",
+    24: "output_blocks.9.1.transformer_blocks.0.attn1",
+    25: "output_blocks.9.1.transformer_blocks.0.attn2",
+    26: "output_blocks.10.1.transformer_blocks.0.attn1",
+    27: "output_blocks.10.1.transformer_blocks.0.attn2",
+    28: "output_blocks.11.1.transformer_blocks.0.attn1",
+    29: "output_blocks.11.1.transformer_blocks.0.attn2",
+    30: "middle_block.1.transformer_blocks.0.attn1",
+    31: "middle_block.1.transformer_blocks.0.attn2",
+}
+
+
+def compute_cond_mark(cond_or_uncond, sigmas):
+    cond_or_uncond_size = int(sigmas.shape[0])
+
+    cond_mark = []
+    for cx in cond_or_uncond:
+        cond_mark += [cx] * cond_or_uncond_size
+
+    cond_mark = torch.Tensor(cond_mark).to(sigmas)
+    return cond_mark
+
+
+class LoRALinearLayer(torch.nn.Module):
+    def __init__(self, in_features: int, out_features: int, rank: int = 256, org=None):
+        super().__init__()
+        self.down = torch.nn.Linear(in_features, rank, bias=False)
+        self.up = torch.nn.Linear(rank, out_features, bias=False)
+        self.org = [org]
+
+    def forward(self, h):
+        org_weight = self.org[0].weight.to(h)
+        org_bias = self.org[0].bias.to(h) if self.org[0].bias is not None else None
+        down_weight = self.down.weight
+        up_weight = self.up.weight
+        final_weight = org_weight + torch.mm(up_weight, down_weight)
+        return torch.nn.functional.linear(h, final_weight, org_bias)
+
+
+class AttentionSharingUnit(torch.nn.Module):
+    # `transformer_options` passed to the most recent BasicTransformerBlock.forward
+    # call.
+    transformer_options: dict = {}
+
+    def __init__(self, module, frames=2, use_control=True, rank=256):
+        super().__init__()
+
+        self.heads = module.heads
+        self.frames = frames
+        self.original_module = [module]
+        q_in_channels, q_out_channels = (
+            module.to_q.in_features,
+            module.to_q.out_features,
+        )
+        k_in_channels, k_out_channels = (
+            module.to_k.in_features,
+            module.to_k.out_features,
+        )
+        v_in_channels, v_out_channels = (
+            module.to_v.in_features,
+            module.to_v.out_features,
+        )
+        o_in_channels, o_out_channels = (
+            module.to_out[0].in_features,
+            module.to_out[0].out_features,
+        )
+
+        hidden_size = k_out_channels
+
+        self.to_q_lora = [
+            LoRALinearLayer(q_in_channels, q_out_channels, rank, module.to_q)
+            for _ in range(self.frames)
+        ]
+        self.to_k_lora = [
+            LoRALinearLayer(k_in_channels, k_out_channels, rank, module.to_k)
+            for _ in range(self.frames)
+        ]
+        self.to_v_lora = [
+            LoRALinearLayer(v_in_channels, v_out_channels, rank, module.to_v)
+            for _ in range(self.frames)
+        ]
+        self.to_out_lora = [
+            LoRALinearLayer(o_in_channels, o_out_channels, rank, module.to_out[0])
+            for _ in range(self.frames)
+        ]
+
+        self.to_q_lora = torch.nn.ModuleList(self.to_q_lora)
+        self.to_k_lora = torch.nn.ModuleList(self.to_k_lora)
+        self.to_v_lora = torch.nn.ModuleList(self.to_v_lora)
+        self.to_out_lora = torch.nn.ModuleList(self.to_out_lora)
+
+        self.temporal_i = torch.nn.Linear(
+            in_features=hidden_size, out_features=hidden_size
+        )
+        self.temporal_n = torch.nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6
+        )
+        self.temporal_q = torch.nn.Linear(
+            in_features=hidden_size, out_features=hidden_size
+        )
+        self.temporal_k = torch.nn.Linear(
+            in_features=hidden_size, out_features=hidden_size
+        )
+        self.temporal_v = torch.nn.Linear(
+            in_features=hidden_size, out_features=hidden_size
+        )
+        self.temporal_o = torch.nn.Linear(
+            in_features=hidden_size, out_features=hidden_size
+        )
+
+        self.control_convs = None
+
+        if use_control:
+            self.control_convs = [
+                torch.nn.Sequential(
+                    torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1),
+                    torch.nn.SiLU(),
+                    torch.nn.Conv2d(256, hidden_size, kernel_size=1),
+                )
+                for _ in range(self.frames)
+            ]
+            self.control_convs = torch.nn.ModuleList(self.control_convs)
+
+        self.control_signals = None
+
+    def forward(self, h, context=None, value=None):
+        transformer_options = self.transformer_options
+
+        modified_hidden_states = einops.rearrange(
+            h, "(b f) d c -> f b d c", f=self.frames
+        )
+
+        if self.control_convs is not None:
+            context_dim = int(modified_hidden_states.shape[2])
+            control_outs = []
+            for f in range(self.frames):
+                control_signal = self.control_signals[context_dim].to(
+                    modified_hidden_states
+                )
+                control = self.control_convs[f](control_signal)
+                control = einops.rearrange(control, "b c h w -> b (h w) c")
+                control_outs.append(control)
+            control_outs = torch.stack(control_outs, dim=0)
+            modified_hidden_states = modified_hidden_states + control_outs.to(
+                modified_hidden_states
+            )
+
+        if context is None:
+            framed_context = modified_hidden_states
+        else:
+            framed_context = einops.rearrange(
+                context, "(b f) d c -> f b d c", f=self.frames
+            )
+
+        framed_cond_mark = einops.rearrange(
+            compute_cond_mark(
+                transformer_options["cond_or_uncond"],
+                transformer_options["sigmas"],
+            ),
+            "(b f) -> f b",
+            f=self.frames,
+        ).to(modified_hidden_states)
+
+        attn_outs = []
+        for f in range(self.frames):
+            fcf = framed_context[f]
+
+            if context is not None:
+                cond_overwrite = transformer_options.get("cond_overwrite", [])
+                if len(cond_overwrite) > f:
+                    cond_overwrite = cond_overwrite[f]
+                else:
+                    cond_overwrite = None
+                if cond_overwrite is not None:
+                    cond_mark = framed_cond_mark[f][:, None, None]
+                    fcf = cond_overwrite.to(fcf) * (1.0 - cond_mark) + fcf * cond_mark
+
+            q = self.to_q_lora[f](modified_hidden_states[f])
+            k = self.to_k_lora[f](fcf)
+            v = self.to_v_lora[f](fcf)
+            o = optimized_attention(q, k, v, self.heads)
+            o = self.to_out_lora[f](o)
+            o = self.original_module[0].to_out[1](o)
+            attn_outs.append(o)
+
+        attn_outs = torch.stack(attn_outs, dim=0)
+        modified_hidden_states = modified_hidden_states + attn_outs.to(
+            modified_hidden_states
+        )
+        modified_hidden_states = einops.rearrange(
+            modified_hidden_states, "f b d c -> (b f) d c", f=self.frames
+        )
+
+        x = modified_hidden_states
+        x = self.temporal_n(x)
+        x = self.temporal_i(x)
+        d = x.shape[1]
+
+        x = einops.rearrange(x, "(b f) d c -> (b d) f c", f=self.frames)
+
+        q = self.temporal_q(x)
+        k = self.temporal_k(x)
+        v = self.temporal_v(x)
+
+        x = optimized_attention(q, k, v, self.heads)
+        x = self.temporal_o(x)
+        x = einops.rearrange(x, "(b d) f c -> (b f) d c", d=d)
+
+        modified_hidden_states = modified_hidden_states + x
+
+        return modified_hidden_states - h
+
+    @classmethod
+    def hijack_transformer_block(cls):
+        def register_get_transformer_options(func):
+            @functools.wraps(func)
+            def forward(self, x, context=None, transformer_options={}):
+                cls.transformer_options = transformer_options
+                return func(self, x, context, transformer_options)
+
+            return forward
+
+        from comfy.ldm.modules.attention import BasicTransformerBlock
+
+        BasicTransformerBlock.forward = register_get_transformer_options(
+            BasicTransformerBlock.forward
+        )
+
+
+AttentionSharingUnit.hijack_transformer_block()
+
+
+class AdditionalAttentionCondsEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.blocks_0 = torch.nn.Sequential(
+            torch.nn.Conv2d(3, 32, kernel_size=3, padding=1, stride=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(64, 64, kernel_size=3, padding=1, stride=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(64, 128, kernel_size=3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(128, 128, kernel_size=3, padding=1, stride=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1),
+            torch.nn.SiLU(),
+        )  # 64*64*256
+
+        self.blocks_1 = torch.nn.Sequential(
+            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1),
+            torch.nn.SiLU(),
+        )  # 32*32*256
+
+        self.blocks_2 = torch.nn.Sequential(
+            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1),
+            torch.nn.SiLU(),
+        )  # 16*16*256
+
+        self.blocks_3 = torch.nn.Sequential(
+            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1),
+            torch.nn.SiLU(),
+        )  # 8*8*256
+
+        self.blks = [self.blocks_0, self.blocks_1, self.blocks_2, self.blocks_3]
+
+    def __call__(self, h):
+        results = {}
+        for b in self.blks:
+            h = b(h)
+            results[int(h.shape[2]) * int(h.shape[3])] = h
+        return results
+
+
+class HookerLayers(torch.nn.Module):
+    def __init__(self, layer_list):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layer_list)
+
+
+class AttentionSharingPatcher(torch.nn.Module):
+    def __init__(self, unet, frames=2, use_control=True, rank=256):
+        super().__init__()
+        model_management.unload_model_clones(unet)
+
+        units = []
+        for i in range(32):
+            real_key = module_mapping_sd15[i]
+            attn_module = utils.get_attr(unet.model.diffusion_model, real_key)
+            u = AttentionSharingUnit(
+                attn_module, frames=frames, use_control=use_control, rank=rank
+            )
+            units.append(u)
+            unet.add_object_patch("diffusion_model." + real_key, u)
+
+        self.hookers = HookerLayers(units)
+
+        if use_control:
+            self.kwargs_encoder = AdditionalAttentionCondsEncoder()
+        else:
+            self.kwargs_encoder = None
+
+        self.dtype = torch.float32
+        if model_management.should_use_fp16(model_management.get_torch_device()):
+            self.dtype = torch.float16
+            self.hookers.half()
+        return
+
+    def set_control(self, img):
+        img = img.cpu().float() * 2.0 - 1.0
+        signals = self.kwargs_encoder(img)
+        for m in self.hookers.layers:
+            m.control_signals = signals
+        return

ComfyUI/custom_nodes/layerdiffuse/lib_layerdiffusion/enums.py

@@ -0,0 +1,23 @@
+from enum import Enum
+
+
+class ResizeMode(Enum):
+    RESIZE = "Just Resize"
+    CROP_AND_RESIZE = "Crop and Resize"
+    RESIZE_AND_FILL = "Resize and Fill"
+
+    def int_value(self):
+        if self == ResizeMode.RESIZE:
+            return 0
+        elif self == ResizeMode.CROP_AND_RESIZE:
+            return 1
+        elif self == ResizeMode.RESIZE_AND_FILL:
+            return 2
+        return 0
+
+
+class StableDiffusionVersion(Enum):
+    """The version family of stable diffusion model."""
+
+    SD1x = "SD15"
+    SDXL = "SDXL"

ComfyUI/custom_nodes/layerdiffuse/lib_layerdiffusion/models.py

@@ -0,0 +1,318 @@
+import torch.nn as nn
+import torch
+import cv2
+import numpy as np
+
+from tqdm import tqdm
+from typing import Optional, Tuple
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
+
+
+def check_diffusers_version():
+    import diffusers
+    from packaging.version import parse
+
+    assert parse(diffusers.__version__) >= parse(
+        "0.25.0"
+    ), "diffusers>=0.25.0 requirement not satisfied. Please install correct diffusers version."
+
+
+check_diffusers_version()
+
+
+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)

ComfyUI/custom_nodes/layerdiffuse/lib_layerdiffusion/utils.py

@@ -0,0 +1,135 @@
+import numpy as np
+from .enums import ResizeMode
+import cv2
+import torch
+import os
+from urllib.parse import urlparse
+from typing import Optional
+
+
+def rgba2rgbfp32(x):
+    rgb = x[..., :3].astype(np.float32) / 255.0
+    a = x[..., 3:4].astype(np.float32) / 255.0
+    return 0.5 + (rgb - 0.5) * a
+
+
+def to255unit8(x):
+    return (x * 255.0).clip(0, 255).astype(np.uint8)
+
+
+def safe_numpy(x):
+    # A very safe method to make sure that Apple/Mac works
+    y = x
+
+    # below is very boring but do not change these. If you change these Apple or Mac may fail.
+    y = y.copy()
+    y = np.ascontiguousarray(y)
+    y = y.copy()
+    return y
+
+
+def high_quality_resize(x, size):
+    if x.shape[0] != size[1] or x.shape[1] != size[0]:
+        if (size[0] * size[1]) < (x.shape[0] * x.shape[1]):
+            interpolation = cv2.INTER_AREA
+        else:
+            interpolation = cv2.INTER_LANCZOS4
+
+        y = cv2.resize(x, size, interpolation=interpolation)
+    else:
+        y = x
+    return y
+
+
+def crop_and_resize_image(detected_map, resize_mode, h, w):
+    if resize_mode == ResizeMode.RESIZE:
+        detected_map = high_quality_resize(detected_map, (w, h))
+        detected_map = safe_numpy(detected_map)
+        return detected_map
+
+    old_h, old_w, _ = detected_map.shape
+    old_w = float(old_w)
+    old_h = float(old_h)
+    k0 = float(h) / old_h
+    k1 = float(w) / old_w
+
+    def safeint(x):
+        return int(np.round(x))
+
+    if resize_mode == ResizeMode.RESIZE_AND_FILL:
+        k = min(k0, k1)
+        borders = np.concatenate([detected_map[0, :, :], detected_map[-1, :, :], detected_map[:, 0, :], detected_map[:, -1, :]], axis=0)
+        high_quality_border_color = np.median(borders, axis=0).astype(detected_map.dtype)
+        high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
+        detected_map = high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
+        new_h, new_w, _ = detected_map.shape
+        pad_h = max(0, (h - new_h) // 2)
+        pad_w = max(0, (w - new_w) // 2)
+        high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = detected_map
+        detected_map = high_quality_background
+        detected_map = safe_numpy(detected_map)
+        return detected_map
+    else:
+        k = max(k0, k1)
+        detected_map = high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
+        new_h, new_w, _ = detected_map.shape
+        pad_h = max(0, (new_h - h) // 2)
+        pad_w = max(0, (new_w - w) // 2)
+        detected_map = detected_map[pad_h:pad_h+h, pad_w:pad_w+w]
+        detected_map = safe_numpy(detected_map)
+        return detected_map
+
+
+def pytorch_to_numpy(x):
+    return [np.clip(255. * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x]
+
+
+def numpy_to_pytorch(x):
+    y = x.astype(np.float32) / 255.0
+    y = y[None]
+    y = np.ascontiguousarray(y.copy())
+    y = torch.from_numpy(y).float()
+    return y
+
+
+def load_file_from_url(
+    url: str,
+    *,
+    model_dir: str,
+    progress: bool = True,
+    file_name: Optional[str] = None,
+) -> str:
+    """Download a file from `url` into `model_dir`, using the file present if possible.
+
+    Returns the path to the downloaded file.
+    """
+    os.makedirs(model_dir, exist_ok=True)
+    if not file_name:
+        parts = urlparse(url)
+        file_name = os.path.basename(parts.path)
+    cached_file = os.path.abspath(os.path.join(model_dir, file_name))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        from torch.hub import download_url_to_file
+        download_url_to_file(url, cached_file, progress=progress)
+    return cached_file
+
+
+def to_lora_patch_dict(state_dict: dict) -> dict:
+    """ Convert raw lora state_dict to patch_dict that can be applied on
+    modelpatcher."""
+    patch_dict = {}
+    for k, w in state_dict.items():
+        model_key, patch_type, weight_index = k.split('::')
+        if model_key not in patch_dict:
+            patch_dict[model_key] = {}
+        if patch_type not in patch_dict[model_key]:
+            patch_dict[model_key][patch_type] = [None] * 16
+        patch_dict[model_key][patch_type][int(weight_index)] = w
+
+    patch_flat = {}
+    for model_key, v in patch_dict.items():
+        for patch_type, weight_list in v.items():
+            patch_flat[model_key] = (patch_type, weight_list)
+
+    return patch_flat

ComfyUI/custom_nodes/layerdiffuse/requirements.txt

@@ -0,0 +1,2 @@
+diffusers>=0.25.0
+opencv-python