Merge pull request #4 from LEv145/dev

v 2.3.0

.gitignore

@@ -158,3 +158,4 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+#

README.md

@@ -1,23 +1,34 @@
 # ImagesGrid: Comfy plugin
 
 
-![Image](./workflows/mini.png)
+## Preview
-[Workflows](./workflows/mini.json)
+
-![Image](./workflows/base.png)
+![Image](https://github.com/LEv145/images-grid-comfy-plugin/blob/main/.readme/preview.png?raw=true)
-[Workflows](./workflows/base.json)
+
+### Simple grid of images
+
+![Image](https://github.com/LEv145/images-grid-comfy-plugin/blob/main/workflows/mini.png?raw=true)
+
+### XYZPlot, like in auto1111, but with more settings
+
+![Image](https://github.com/LEv145/images-grid-comfy-plugin/blob/main/workflows/base.png?raw=true)
+
+### Integration with [`efficiency`](https://github.com/LucianoCirino/efficiency-nodes-comfyui)
+
+![Image](https://github.com/LEv145/images-grid-comfy-plugin/blob/main/workflows/efficiency.png?raw=true)
+
+
+Workflows: https://github.com/LEv145/images-grid-comfy-plugin/tree/main/workflows
 
 
 ## How to use
 
-### Install
+1. Download the latest stable release:
+https://github.com/LEv145/images-grid-comfy-plugin/archive/refs/heads/main.zip
 
-```
+2. Unpack the node to `custom_nodes`, for example in a folder `custom_nodes/ImagesGrid/`
-cd custom_nodes  # From comfy path
-git clone https://github.com/LEv145/images-grid-comfy-plugin ImagesGrid
-```
-### Update
 
-```
+
-cd custom_nodes/ImagesGrid
+## Source
-git pull
+
-```
+https://github.com/LEv145/images-grid-comfy-plugin

__init__.py

@@ -1,4 +1,10 @@
-from .src import LatentCombineNode, ImagesGridByColumnsNode, ImagesGridByRowsNode, ImageCombineNode
+from .src import (
+    LatentCombineNode,
+    ImagesGridByColumnsNode,
+    ImagesGridByRowsNode,
+    ImageCombineNode,
+    GridAnnotationNode,
+)
 
 
 NODE_CLASS_MAPPINGS = {
@@ -6,4 +12,5 @@ NODE_CLASS_MAPPINGS = {
     "ImagesGridByColumns": ImagesGridByColumnsNode,
     "ImagesGridByRows": ImagesGridByRowsNode,
     "ImageCombine": ImageCombineNode,
+    "GridAnnotation": GridAnnotationNode,
 }

src/__init__.py

@@ -1,3 +1,4 @@
 from .nodes.images_grid import ImagesGridByColumnsNode, ImagesGridByRowsNode
 from .nodes.latent_combine import LatentCombineNode
 from .nodes.image_combine import ImageCombineNode
+from .nodes.grid_annotation import GridAnnotationNode

src/base.py

@@ -1,9 +1,10 @@
 import typing as t
+from pathlib import Path
+
+
+STATIC_PATH = Path(__file__).parent.parent / "static"
 
 
 class BaseNode():
     CATEGORY: str = "ImagesGrid"
     FUNCTION: str = "execute"
-
-
-Image = t.Any

src/nodes/grid_annotation.py

@@ -0,0 +1,49 @@
+import typing as t
+
+from PIL import ImageFont
+
+from ..base import BaseNode, STATIC_PATH
+from ..utils import Annotation
+
+
+class GridAnnotationNode(BaseNode):
+    RETURN_TYPES: tuple[str] = ("GRID_ANNOTATION",)
+
+    @classmethod
+    def INPUT_TYPES(cls) -> dict[str, t.Any]:
+        return {
+            "required": {
+                "column_texts": ("STRING", {"multiline": False}),
+                "row_texts": ("STRING", {"multiline": False}),
+                "font_size": ("INT", {"default": 50, "min": 1}),
+            },
+        }
+
+    def execute(
+        self,
+        column_texts: str,
+        row_texts: str,
+        font_size: int,
+    ) -> tuple[Annotation]:
+        font = ImageFont.truetype(str(STATIC_PATH / "Roboto-Regular.ttf"), size=font_size)
+        column_texts_list = self._set_value_to_texts_list(
+            self._get_texts_from_string(column_texts),
+        )
+        row_texts_list = self._set_value_to_texts_list(
+            self._get_texts_from_string(row_texts),
+        )
+
+        result = Annotation(column_texts=column_texts_list, row_texts=row_texts_list, font=font)
+        return (result,)
+
+    def _get_texts_from_string(self, string: str) -> list[str]:
+        return [
+            result.replace("\\n", "\n")
+            for i in string.split(";")
+            if (result := i.strip()) != ""
+        ]
+
+    def _set_value_to_texts_list(self, texts_list: list[str]) -> list[str]:
+        if not texts_list:
+            return ["None"]
+        return texts_list

src/nodes/image_combine.py

@@ -2,14 +2,14 @@ import typing as t
 
 import torch
 
-from ..base import BaseNode, Image
+from ..base import BaseNode
 
 
 class ImageCombineNode(BaseNode):
-    RETURN_TYPES: t.Tuple[str] = ("IMAGE",)
+    RETURN_TYPES: tuple[str] = ("IMAGE",)
 
     @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
+    def INPUT_TYPES(cls) -> dict[str, t.Any]:
         return {
             "required": {
                 "image_1": ("IMAGE",),
@@ -19,14 +19,9 @@ class ImageCombineNode(BaseNode):
 
     def execute(
         self,
-        image_1: Image,
+        image_1: torch.Tensor,
-        image_2: Image,
+        image_2: torch.Tensor,
-    ) -> t.Tuple[Image]:
+    ) -> tuple[torch.Tensor]:
-        print(image_1.size())
-        print(image_2.size())
-        print(image_1)
-
         result = torch.cat((image_1, image_2), 0)
-        print(result.size())
 
         return (result,)

src/nodes/images_grid.py

@@ -1,30 +1,48 @@
 import typing as t
 
-from ..base import BaseNode, Image
+import torch
+
+from ..base import BaseNode
 from ..utils import (
     tensor_to_pillow,
     pillow_to_tensor,
-    create_image_grid_by_columns,
+    create_images_grid_by_columns,
-    create_image_grid_by_rows,
+    create_images_grid_by_rows,
+    Annotation,
 )
 
-
 class BaseImagesGridNode(BaseNode):
-    RETURN_TYPES: t.Tuple[str] = ("IMAGE",)
+    RETURN_TYPES: tuple[str] = ("IMAGE",)
 
     @classmethod
-    def _create_input_types(cls, coordinate_name: str) -> t.Dict[str, t.Any]:
+    def _create_input_types(cls, coordinate_name: str) -> dict[str, t.Any]:
         return {
             "required": {
                 "images": ("IMAGE",),
                 "gap": ("INT", {"default": 0, "min": 0}),
                 coordinate_name: ("INT", {"default": 1, "min": 1}),
+            },
+            "optional": {
+                "annotation": ("GRID_ANNOTATION",),
             }
         }
 
-    def _create_execute(self, images, function, function_kw) -> t.Tuple[Image]:
+    def _create_execute(
+        self,
+        function: t.Callable,
+        \
+        images: torch.Tensor,
+        gap: int,
+        annotation: Annotation | None = None,
+        **kw,
+    ) -> tuple[torch.Tensor]:
         pillow_images = [tensor_to_pillow(i) for i in images]
-        pillow_grid = function(images=pillow_images, **function_kw)
+        pillow_grid = function(
+            images=pillow_images,
+            gap=gap,
+            annotation=annotation,
+            **kw,
+        )
         tensor_grid = pillow_to_tensor(pillow_grid)
 
         return (tensor_grid,)
@@ -32,17 +50,17 @@ class BaseImagesGridNode(BaseNode):
 
 class ImagesGridByColumnsNode(BaseImagesGridNode):
     @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
+    def INPUT_TYPES(cls) -> dict[str, t.Any]:
         return cls._create_input_types("max_columns")
 
-    def execute(self, images: Image, **kw) -> tuple[Image]:
+    def execute(self, **kw) -> tuple[torch.Tensor]:
-        return self._create_execute(images, create_image_grid_by_columns, kw)
+        return self._create_execute(create_images_grid_by_columns, **kw)
 
 
 class ImagesGridByRowsNode(BaseImagesGridNode):
     @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
+    def INPUT_TYPES(cls) -> dict[str, t.Any]:
         return cls._create_input_types("max_rows")
 
-    def execute(self, images: Image, **kw) -> tuple[Image]:
+    def execute(self, **kw) -> tuple[torch.Tensor]:
-        return self._create_execute(images, create_image_grid_by_rows, kw)
+        return self._create_execute(create_images_grid_by_rows, **kw)

src/nodes/latent_combine.py

@@ -2,14 +2,14 @@ import typing as t
 
 import torch
 
-from ..base import BaseNode, Image
+from ..base import BaseNode
 
 
 class LatentCombineNode(BaseNode):
-    RETURN_TYPES: t.Tuple[str] = ("LATENT",)
+    RETURN_TYPES: tuple[str] = ("LATENT",)
 
     @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
+    def INPUT_TYPES(cls) -> dict[str, t.Any]:
         return {
             "required": {
                 "latent_1": ("LATENT",),
@@ -19,9 +19,9 @@ class LatentCombineNode(BaseNode):
 
     def execute(
         self,
-        latent_1: t.Dict[str, t.Any],
+        latent_1: dict[str, torch.Tensor],
-        latent_2: t.Dict[str, t.Any],
+        latent_2: dict[str, torch.Tensor],
-    ) -> t.Tuple[t.Dict[str, t.Any]]:
+    ) -> tuple[dict[str, torch.Tensor]]:
         samples = torch.cat((latent_1["samples"], latent_2["samples"]), 0)
 
         return ({"samples": samples},)

src/utils.py

@@ -1,53 +0,0 @@
-import typing as t
-
-import torch
-import numpy as np
-from PIL import Image
-
-
-def tensor_to_pillow(image):
-    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-
-
-def pillow_to_tensor(image):
-    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
-
-
-def create_image_grid_by_columns(
-    images: t.List[Image.Image],
-    gap: int,
-    max_columns: int,
-) -> Image.Image:
-    max_rows = (len(images) + max_columns - 1) // max_columns
-    return create_image_grid(images=images, gap=gap, max_columns=max_columns, max_rows=max_rows)
-
-
-def create_image_grid_by_rows(
-    images: t.List[Image.Image],
-    gap: int,
-    max_rows: int,
-) -> Image.Image:
-    max_columns = (len(images) + max_rows - 1) // max_rows
-    return create_image_grid(images=images, gap=gap, max_columns=max_columns, max_rows=max_rows)
-
-
-def create_image_grid(
-    images: t.List[Image.Image],
-    gap: int,
-    max_columns: int,
-    max_rows: int,
-) -> Image.Image:
-    size = images[0].size
-
-    width = size[0] * max_columns + (max_columns - 1) * gap
-    height = size[1] * max_rows + (max_rows - 1) * gap
-
-    grid_image = Image.new("RGB", (width, height), color="white")
-
-    for i, image in enumerate(images):
-        x = (i % max_columns) * (size[0] + gap)
-        y = (i // max_columns) * (size[1] + gap)
-
-        grid_image.paste(image, (x, y))
-
-    return grid_image

src/utils/__init__.py

@@ -0,0 +1,6 @@
+from .images_grid import (
+    create_images_grid_by_columns,
+    create_images_grid_by_rows,
+    Annotation,
+)
+from .tensor_convert import tensor_to_pillow, pillow_to_tensor

src/utils/images_grid.py

@@ -0,0 +1,195 @@
+import typing as t
+from dataclasses import dataclass
+from contextlib import suppress
+
+from PIL import Image, ImageDraw, ImageFont
+
+
+@dataclass
+class Annotation():
+    column_texts: list[str]
+    row_texts: list[str]
+    font: ImageFont.FreeTypeFont
+
+
+@dataclass
+class _GridInfo():
+    image: Image.Image
+    gap: int
+    one_image_size: tuple[int, int]
+
+
+def create_images_grid_by_columns(
+    images: list[Image.Image],
+    gap: int,
+    max_columns: int,
+    annotation: Annotation | None = None,
+) -> Image.Image:
+    max_rows = (len(images) + max_columns - 1) // max_columns
+    return _create_images_grid(images, gap, max_columns, max_rows, annotation)
+
+
+def create_images_grid_by_rows(
+    images: list[Image.Image],
+    gap: int,
+    max_rows: int,
+    annotation: Annotation | None = None,
+) -> Image.Image:
+    max_columns = (len(images) + max_rows - 1) // max_rows
+    return _create_images_grid(images, gap, max_columns, max_rows, annotation)
+
+
+def _create_images_grid(
+    images: list[Image.Image],
+    gap: int,
+    max_columns: int,
+    max_rows: int,
+    annotation: Annotation | None,
+) -> Image.Image:
+    size = images[0].size
+    grid_width = size[0] * max_columns + (max_columns - 1) * gap
+    grid_height = size[1] * max_rows + (max_rows - 1) * gap
+
+    grid_image = Image.new("RGB", (grid_width, grid_height), color="white")
+
+    _arrange_images_on_grid(grid_image, images=images, size=size, max_columns=max_columns, gap=gap)
+
+    if annotation is None:
+        return grid_image
+    return _create_grid_annotations(
+        grid_info=_GridInfo(
+            image=grid_image,
+            gap=gap,
+            one_image_size=size,
+        ),
+        column_texts=annotation.column_texts,
+        row_texts=annotation.row_texts,
+        font=annotation.font,
+    )
+
+
+def _arrange_images_on_grid(
+    grid_image: Image.Image,
+    /,
+    images: list[Image.Image],
+    size: tuple[int, int],
+    max_columns: int,
+    gap: int,
+):
+    for i, image in enumerate(images):
+        if image.size != size:
+            image = image.crop((0, 0, *size))
+        x = (i % max_columns) * (size[0] + gap)
+        y = (i // max_columns) * (size[1] + gap)
+
+        grid_image.paste(image, (x, y))
+
+
+def _create_grid_annotations(
+    grid_info: _GridInfo,
+    column_texts,
+    row_texts,
+    font: ImageFont.FreeTypeFont,
+) -> Image.Image:
+    if not column_texts or not row_texts:
+        raise ValueError("Column text or row text is empty")
+
+    grid = grid_info.image
+    margin = font.size // 2
+    left_padding = int(max(map(font.getlength, row_texts))) + 2*margin
+    top_padding = font.size + 2*margin
+
+    image = Image.new(
+        "RGB",
+        (grid.size[0] + left_padding, grid.size[1] + top_padding),
+        color="white",
+    )
+    draw = ImageDraw.Draw(image)
+    draw.font = font  # type: ignore
+
+    _paste_image_to_lower_left_corner(image, grid)
+    _draw_column_text(
+        draw=draw,
+        texts=column_texts,
+        grid_info=grid_info,
+        left_padding=left_padding,
+        top_padding=top_padding,
+    )
+    _draw_row_text(
+        draw=draw,
+        texts=row_texts,
+        grid_info=grid_info,
+        left_padding=left_padding,
+        top_padding=top_padding,
+    )
+
+    return image
+
+
+def _draw_column_text(
+    draw: ImageDraw.ImageDraw,
+    texts: list[str],
+    grid_info: _GridInfo,
+    left_padding: int,
+    top_padding: int,
+) -> None:
+    i = 0
+    x0 = left_padding
+    y0 = 0
+    x1 = left_padding + grid_info.one_image_size[0]
+    y1 = top_padding
+    while x0 != grid_info.image.size[0] + left_padding + grid_info.gap:
+        i  = _draw_text_by_xy((x0, y0, x1, y1), i, draw=draw, texts=texts)
+        x0 += grid_info.one_image_size[0] + grid_info.gap
+        x1 += grid_info.one_image_size[0] + grid_info.gap
+
+
+def _draw_row_text(
+    draw: ImageDraw.ImageDraw,
+    texts: list[str],
+    grid_info: _GridInfo,
+    left_padding: int,
+    top_padding: int,
+) -> None:
+    i = 0
+    x0 = 0
+    y0 = top_padding
+    x1 = left_padding
+    y1 = top_padding + grid_info.one_image_size[1]
+    while y0 != grid_info.image.size[1] + top_padding + grid_info.gap:
+        i  = _draw_text_by_xy((x0, y0, x1, y1), i, draw=draw, texts=texts)
+        y0 += grid_info.one_image_size[1] + grid_info.gap
+        y1 += grid_info.one_image_size[1] + grid_info.gap
+
+
+def _draw_text_by_xy(
+    xy: tuple[int, int, int, int],
+    index: int,
+    \
+    draw: ImageDraw.ImageDraw,
+    texts: list[str],
+) -> int:
+    with suppress(IndexError):
+        _draw_center_text(draw, xy, texts[index])
+    return index + 1
+
+
+def _draw_center_text(
+    draw: ImageDraw.ImageDraw,
+    xy: tuple[int, int, int, int],
+    text: str,
+    fill: t.Any = "black",
+) -> None:
+    _, _, *text_size = draw.textbbox((0, 0), text)
+    draw.multiline_text(
+        (
+            (xy[2] - text_size[0] + xy[0]) / 2,
+            (xy[3] - text_size[1] + xy[1]) / 2,
+        ),
+        text,
+        fill=fill,
+    )
+
+
+def _paste_image_to_lower_left_corner(base: Image.Image, image: Image.Image) -> None:
+    base.paste(image, (base.size[0] - image.size[0], base.size[1] - image.size[1]))

src/utils/tensor_convert.py

@@ -0,0 +1,13 @@
+import typing as t
+
+import torch
+import numpy as np
+from PIL import Image
+
+
+def tensor_to_pillow(image: t.Any) -> Image.Image:
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+
+def pillow_to_tensor(image: Image.Image) -> t.Any:
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)

workflows/efficiency.json

@@ -0,0 +1,471 @@
+{
+  "last_node_id": 8,
+  "last_link_id": 16,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "Efficient Loader",
+      "pos": [
+        214,
+        242
+      ],
+      "size": {
+        "0": 446.5,
+        "1": 499.79998779296875
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            1
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CONDITIONING+",
+          "type": "CONDITIONING",
+          "links": [
+            2
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "CONDITIONING-",
+          "type": "CONDITIONING",
+          "links": [
+            3
+          ],
+          "slot_index": 2
+        },
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            4
+          ],
+          "slot_index": 3
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            5
+          ],
+          "slot_index": 4
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Efficient Loader"
+      },
+      "widgets_values": [
+        "Meina-v9.safetensors",
+        "novelai.vae.pt",
+        -2,
+        "1girl, (hanfu), sidelighting, wallpaper",
+        "(worst quality:2, low quality:2), (zombie, sketch, interlocked fingers, comic)",
+        512,
+        512,
+        1
+      ],
+      "color": "#223",
+      "bgcolor": "#335"
+    },
+    {
+      "id": 7,
+      "type": "PreviewImage",
+      "pos": [
+        1703,
+        302
+      ],
+      "size": {
+        "0": 334.94000244140625,
+        "1": 239.1999969482422
+      },
+      "flags": {
+        "collapsed": false
+      },
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 15
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Preview Image"
+      }
+    },
+    {
+      "id": 5,
+      "type": "ImagesGridByColumns",
+      "pos": [
+        1369,
+        302
+      ],
+      "size": {
+        "0": 315,
+        "1": 102
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 14
+        },
+        {
+          "name": "annotation",
+          "type": "GRID_ANNOTATION",
+          "link": 16
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            15
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImagesGridByColumns"
+      },
+      "widgets_values": [
+        0,
+        3
+      ],
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 8,
+      "type": "GridAnnotation",
+      "pos": [
+        1041,
+        386
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "GRID_ANNOTATION",
+          "type": "GRID_ANNOTATION",
+          "links": [
+            16
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "GridAnnotation"
+      },
+      "widgets_values": [
+        "Steps: 20; Steps: 30; Steps: 40;",
+        "CGF: 7; CGF: 9; CGF: 11;",
+        50
+      ],
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 6,
+      "type": "VAEDecode",
+      "pos": [
+        1218,
+        329
+      ],
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+          "type": "LATENT",
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+          "slot_index": 0
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+          "name": "vae",
+          "type": "VAE",
+          "link": 12
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            14
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAE Decode"
+      }
+    },
+    {
+      "id": 3,
+      "type": "XY Plot",
+      "pos": [
+        258,
+        783
+      ],
+      "size": [
+        403.35785644531256,
+        244
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "script",
+          "type": "SCRIPT",
+          "links": [
+            6
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "XY Plot"
+      },
+      "widgets_values": [
+        "Steps",
+        "20;30;40",
+        "CFG Scale",
+        "7;9;11",
+        0,
+        "False",
+        0,
+        "____________EXAMPLES____________\n(X/Y_types)     (X/Y_values)\nLatent Batch    n/a\nSeeds++ Batch   3\nSteps           15;20;25\nCFG Scale       5;10;15;20\nSampler(1)      dpmpp_2s_ancestral;euler;ddim\nSampler(2)      dpmpp_2m,karras;heun,normal\nDenoise         .3;.4;.5;.6;.7\nVAE             vae_1; vae_2; vae_3\n\n____________SAMPLERS____________\neuler;\neuler_ancestral;\nheun;\ndpm_2;\ndpm_2_ancestral;\nlms;\ndpm_fast;\ndpm_adaptive;\ndpmpp_2s_ancestral;\ndpmpp_sde;\ndpmpp_2m;\nddim;\nuni_pc;\nuni_pc_bh2\n\n___________SCHEDULERS___________\nkarras;\nnormal;\nsimple;\nddim_uniform\n\n______________VAE_______________\nkl-f8-anime2.ckpt;\nnovelai.vae.pt;\nvae-ft-mse-840000-ema-pruned.ckpt\n\n_____________NOTES______________\n- During a 'Latent Batch', the corresponding X/Y_value is ignored.\n- During a 'Latent Batch', the latent_id is ignored.\n- For a 'Seeds++ Batch', starting seed is defined by the KSampler.\n- Trailing semicolons are ignored in the X/Y_values.\n- Parameter types not set by this node are defined in the KSampler."
+      ],
+      "color": "#223",
+      "bgcolor": "#335"
+    },
+    {
+      "id": 2,
+      "type": "KSampler (Efficient)",
+      "pos": [
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+      "flags": {},
+      "order": 3,
+      "mode": 0,
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+          "type": "MODEL",
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+        },
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+        },
+        {
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+          "type": "LATENT",
+          "link": 4
+        },
+        {
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+          "type": "VAE",
+          "link": 5
+        },
+        {
+          "name": "script",
+          "type": "SCRIPT",
+          "link": 6
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": null
+        },
+        {
+          "name": "CONDITIONING+",
+          "type": "CONDITIONING",
+          "links": null
+        },
+        {
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+          "type": "CONDITIONING",
+          "links": null
+        },
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            11
+          ],
+          "slot_index": 3
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
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+            12
+          ],
+          "slot_index": 4
+        },
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler (Efficient)"
+      },
+      "widgets_values": [
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+        1,
+        "Enabled"
+      ],
+      "color": "#223",
+      "bgcolor": "#335"
+    }
+  ],
+  "links": [
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+    [
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+      "IMAGE"
+    ],
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+    ],
+    [
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+      0,
+      5,
+      1,
+      "GRID_ANNOTATION"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

workflows/mini.json

@@ -1,19 +1,21 @@
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+  "last_node_id": 74,
-  "last_link_id": 97,
+  "last_link_id": 126,
   "nodes": [
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       "type": "LoadImage",
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       },
-      "flags": {},
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       "order": 0,
       "mode": 0,
       "outputs": [
@@ -21,7 +23,7 @@
           "name": "IMAGE",
           "type": "IMAGE",
           "links": [
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           ],
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         },
@@ -35,22 +37,24 @@
         "Node name for S&R": "LoadImage"
       },
       "widgets_values": [
-        "ComfyUI_00171_.png",
+        "394102.png",
         "image"
       ]
     },
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       "type": "LoadImage",
       "pos": [
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-      "flags": {},
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       "outputs": [
@@ -58,7 +62,7 @@
           "name": "IMAGE",
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           "links": [
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         },
@@ -72,22 +76,24 @@
         "Node name for S&R": "LoadImage"
       },
       "widgets_values": [
-        "ComfyUI_00153_.png",
+        "394102.png",
         "image"
       ]
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@@ -95,7 +101,7 @@
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@@ -109,12 +115,171 @@
         "Node name for S&R": "LoadImage"
       },
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+          "slot_index": 0
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+      "properties": {
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+      "widgets_values": [
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+        {
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+      "widgets_values": [
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+          "link": 113
+        },
+        {
+          "name": "image_2",
+          "type": "IMAGE",
+          "link": 106
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            114
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImageCombine"
+      },
+      "color": "#322",
+      "bgcolor": "#533"
+    },
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       "type": "ImageCombine",
       "pos": [
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@@ -125,18 +290,18 @@
         "1": 46
       },
       "flags": {},
-      "order": 3,
+      "order": 8,
       "mode": 0,
       "inputs": [
         {
           "name": "image_1",
           "type": "IMAGE",
-          "link": 90
+          "link": 118
         },
         {
           "name": "image_2",
           "type": "IMAGE",
-          "link": 91
+          "link": 112
         }
       ],
       "outputs": [
@@ -144,7 +309,49 @@
           "name": "IMAGE",
           "type": "IMAGE",
           "links": [
-            93
+            113
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImageCombine"
+      },
+      "color": "#322",
+      "bgcolor": "#533"
+    },
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+      "type": "ImageCombine",
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+      },
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+      "inputs": [
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+          "link": 119
+        },
+        {
+          "name": "image_2",
+          "type": "IMAGE",
+          "link": 120
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            118
           ],
           "slot_index": 0
         }
@@ -160,25 +367,25 @@
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+        510
       ],
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         "0": 210,
         "1": 46
       },
       "flags": {},
-      "order": 4,
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       "mode": 0,
       "inputs": [
         {
           "name": "image_1",
           "type": "IMAGE",
-          "link": 93
+          "link": 114
         },
         {
           "name": "image_2",
           "type": "IMAGE",
-          "link": 94
+          "link": 105
         }
       ],
       "outputs": [
@@ -186,7 +393,7 @@
           "name": "IMAGE",
           "type": "IMAGE",
           "links": [
-            95
+            110
           ],
           "slot_index": 0
         }
@@ -198,50 +405,29 @@
       "bgcolor": "#533"
     },
     {
-      "id": 56,
+      "id": 54,
-      "type": "PreviewImage",
+      "type": "ImageCombine",
       "pos": [
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-      "properties": {
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-      "type": "ImagesGridByColumns",
-      "pos": [
-        700,
-        350
       ],
       "size": {
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       "mode": 0,
       "inputs": [
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-          "link": 95,
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-          "slot_index": 0
+        },
+        {
+          "name": "image_2",
+          "type": "IMAGE",
+          "link": 104
         }
       ],
       "outputs": [
@@ -249,7 +435,183 @@
           "name": "IMAGE",
           "type": "IMAGE",
           "links": [
-            97
+            115,
+            122
+          ],
+          "slot_index": 0
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+      "color": "#322",
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+      "type": "Reroute",
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+          "type": "*",
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+          "slot_index": 0
+        }
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+          "type": "IMAGE",
+          "links": [
+            124
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+          "slot_index": 0
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+        }
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+        },
+        {
+          "name": "annotation",
+          "type": "GRID_ANNOTATION",
+          "link": 102
+        }
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+      "outputs": [
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+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            126
+          ],
+          "slot_index": 0
+        }
+      ],
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+        {
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+          "type": "GRID_ANNOTATION",
+          "link": null
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+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            125
           ],
           "slot_index": 0
         }
@@ -258,59 +620,174 @@
         "Node name for S&R": "ImagesGridByColumns"
       },
       "widgets_values": [
-        5,
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       "color": "#322",
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+          "type": "GRID_ANNOTATION",
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+          "slot_index": 0
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+        "Column 1; Column 2; Column 3",
+        "My favorite;Others",
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