v 2.1.0

Add `ImageCombineNode`
Add `ImagesGridByColumnsNode`
Add `ImagesGridByRowsNode`
Add `create_image_grid_by_rows` util
Add `create_image_grid_by_columns` util
Add `create_image_grid` util
Refactor code
Update workflows
Update images
Update link in README.md
Rename project

README.md

@@ -1,10 +1,10 @@
-# XYPlot: Comfy plugin
+# ImagesGrid: Comfy plugin
 
 
-![Image](./workflows/xy_plot_mini.png)
+![Image](./workflows/mini.png)
-[Workflows](./workflows/xy_plot_mini.json)
+[Workflows](./workflows/mini.json)
-![Image](./workflows/xy_plot_base.png)
+![Image](./workflows/base.png)
-[Workflows](./workflows/xy_plot_base.json)
+[Workflows](./workflows/base.json)
 
 
 ## How to use
@@ -13,11 +13,11 @@
 
 ```
 cd custom_nodes  # From comfy path
-git clone https://github.com/LEv145/XY-plot-comfy-plugin XYPlot
+git clone https://github.com/LEv145/images-grid-comfy-plugin ImagesGrid
 ```
 ### Update
 
 ```
-cd custom_nodes/XYPlot
+cd custom_nodes/ImagesGrid
 git pull
 ```

__init__.py

@@ -1,7 +1,9 @@
-from .src import LatentCombineNode, XYPlotNode
+from .src import LatentCombineNode, ImagesGridByColumnsNode, ImagesGridByRowsNode, ImageCombineNode
 
 
 NODE_CLASS_MAPPINGS = {
-    "XYPlot": XYPlotNode,
     "LatentCombine": LatentCombineNode,
+    "ImagesGridByColumns": ImagesGridByColumnsNode,
+    "ImagesGridByRows": ImagesGridByRowsNode,
+    "ImageCombine": ImageCombineNode,
 }

src/__init__.py

@@ -1,2 +1,3 @@
-from .nodes.xy_plot import XYPlotNode
+from .nodes.images_grid import ImagesGridByColumnsNode, ImagesGridByRowsNode
 from .nodes.latent_combine import LatentCombineNode
+from .nodes.image_combine import ImageCombineNode

src/base.py

@@ -1,16 +1,9 @@
 import typing as t
-from dataclasses import dataclass
 
 
-class BasePlotNode():
+class BaseNode():
-    CATEGORY: str = "XYPlot"
+    CATEGORY: str = "ImagesGrid"
     FUNCTION: str = "execute"
 
 
-@dataclass
-class KSamplerXYPlotInput():
-    setting: str
-    value: int
-
-
 Image = t.Any

src/nodes/float_image_combine.py

@@ -1,23 +0,0 @@
-import typing as t
-
-from ..base import BasePlotNode, Image
-
-
-class FloatImageCombineNode(BasePlotNode):
-    RETURN_TYPES: t.Tuple[str] = ("IMAGES",)
-
-    @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
-        return {
-            "required": {
-                "float_image_1": ("IMAGES",),
-                "float_image_2": ("IMAGES",),
-            },
-        }
-
-    def execute(
-        self,
-        float_image_1: t.List[Image],
-        float_image_2: t.List[Image],
-    ) -> t.Tuple[t.List[Image]]:
-        return (float_image_1 + float_image_2,)

src/nodes/image_combine.py

@@ -0,0 +1,32 @@
+import typing as t
+
+import torch
+
+from ..base import BaseNode, Image
+
+
+class ImageCombineNode(BaseNode):
+    RETURN_TYPES: t.Tuple[str] = ("IMAGE",)
+
+    @classmethod
+    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
+        return {
+            "required": {
+                "image_1": ("IMAGE",),
+                "image_2": ("IMAGE",),
+            },
+        }
+
+    def execute(
+        self,
+        image_1: Image,
+        image_2: Image,
+    ) -> t.Tuple[Image]:
+        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/image_set_area.py

@@ -1,18 +0,0 @@
-import typing as t
-
-from ..base import BasePlotNode, Image
-
-
-class ImageSetAreaNode(BasePlotNode):
-    RETURN_TYPES: t.Tuple[str] = ("IMAGES",)
-
-    @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
-        return {
-            "required": {
-                "image": ("IMAGE",),
-            },
-        }
-
-    def execute(self, image: Image) -> t.Tuple[t.List[Image]]:
-        return ([image],)

src/nodes/images_grid.py

@@ -0,0 +1,48 @@
+import typing as t
+
+from ..base import BaseNode, Image
+from ..utils import (
+    tensor_to_pillow,
+    pillow_to_tensor,
+    create_image_grid_by_columns,
+    create_image_grid_by_rows,
+)
+
+
+class BaseImagesGridNode(BaseNode):
+    RETURN_TYPES: t.Tuple[str] = ("IMAGE",)
+
+    @classmethod
+    def _create_input_types(cls, coordinate_name: str) -> t.Dict[str, t.Any]:
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "gap": ("INT", {"default": 0, "min": 0}),
+                coordinate_name: ("INT", {"default": 1, "min": 1}),
+            }
+        }
+
+    def _create_execute(self, images, function, function_kw) -> t.Tuple[Image]:
+        pillow_images = [tensor_to_pillow(i) for i in images]
+        pillow_grid = function(images=pillow_images, **function_kw)
+        tensor_grid = pillow_to_tensor(pillow_grid)
+
+        return (tensor_grid,)
+
+
+class ImagesGridByColumnsNode(BaseImagesGridNode):
+    @classmethod
+    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
+        return cls._create_input_types("max_columns")
+
+    def execute(self, images: Image, **kw) -> tuple[Image]:
+        return self._create_execute(images, create_image_grid_by_columns, kw)
+
+
+class ImagesGridByRowsNode(BaseImagesGridNode):
+    @classmethod
+    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
+        return cls._create_input_types("max_rows")
+
+    def execute(self, images: Image, **kw) -> tuple[Image]:
+        return self._create_execute(images, create_image_grid_by_rows, kw)

src/nodes/k_sampler_xy_plot.py

@@ -1,62 +0,0 @@
-import typing as t
-
-from nodes import KSamplerAdvanced  # type: ignore
-
-from ..base import BasePlotNode, Image, KSamplerXYPlotInput
-
-
-class KSamplerXYPlotNode(BasePlotNode):
-    RETURN_TYPES: t.Tuple[str] = ("IMAGES",)
-
-    def __init__(self) -> None:
-        self._sampler = KSamplerAdvanced()
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        result = KSamplerAdvanced.INPUT_TYPES()
-        result["required"]["vae"] = ("VAE", )
-        #result["required"]["x_items"] = ("XYPlotItem",)
-        #result["required"]["y_items"] = ("XYPlotItem",)
-        return result
-
-    def execute(
-        self,
-        vae,
-        #x_items,
-        #y_items,
-        **sampler_kw,
-    ) -> tuple[t.List[Image]]:
-        x_items = [
-            KSamplerXYPlotInput(value=1, setting="cfg"),
-            KSamplerXYPlotInput(value=2, setting="cfg"),
-        ]
-        y_items = [
-            KSamplerXYPlotInput(value=1, setting="noise_seed"),
-            KSamplerXYPlotInput(value=2, setting="noise_seed"),
-        ]
-
-        latents = self._sample_latents(
-            x_items=x_items,
-            y_items=y_items,
-            sampler_kw=sampler_kw,
-        )
-        result = list(self._decode_latents(latents=latents, vae=vae))
-        print(result)
-        print(type(result[0]))
-
-        return (result,)
-
-    def _sample_latents(self, x_items, y_items, sampler_kw):
-        for x in x_items:
-            for y in y_items:
-                sampler_settings = sampler_kw.copy()
-                sampler_settings[x.setting] = x.value
-                sampler_settings[y.setting] = y.value
-
-                yield self._sampler.sample(**sampler_settings)[0]
-
-    def _decode_latents(self, latents, vae) -> t.Iterable[Image]:
-        return (
-            vae.decode(i["samples"])
-            for i in latents
-        )

src/nodes/latent_combine.py

@@ -2,10 +2,10 @@ import typing as t
 
 import torch
 
-from ..base import BasePlotNode, Image
+from ..base import BaseNode, Image
 
 
-class LatentCombineNode(BasePlotNode):
+class LatentCombineNode(BaseNode):
     RETURN_TYPES: t.Tuple[str] = ("LATENT",)
 
     @classmethod
@@ -22,8 +22,6 @@ class LatentCombineNode(BasePlotNode):
         latent_1: t.Dict[str, t.Any],
         latent_2: t.Dict[str, t.Any],
     ) -> t.Tuple[t.Dict[str, t.Any]]:
-        latent_1_samples = latent_1["samples"]
+        samples = torch.cat((latent_1["samples"], latent_2["samples"]), 0)
-        latent_2_samples = latent_2["samples"]
-        samples = torch.cat((latent_1_samples, latent_2_samples), 0)
 
         return ({"samples": samples},)

src/nodes/xy_plot.py

@@ -1,30 +0,0 @@
-import typing as t
-
-from ..base import BasePlotNode, Image
-from ..utils import tensor_to_pillow, pillow_to_tensor, create_image_grid
-
-
-class XYPlotNode(BasePlotNode):
-    RETURN_TYPES: t.Tuple[str] = ("IMAGE",)
-
-    @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
-        return {
-            "required": {
-                "images": ("IMAGE",),
-                "gap": ("INT", {"default": 0, "min": 0}),
-                "nrow": ("INT", {"default": 1, "min": 1}),
-            },
-        }
-
-    def execute(
-        self,
-        images: Image,
-        nrow: int,
-        gap: int
-    ) -> tuple[Image]:
-        pillow_images = [tensor_to_pillow(i) for i in images]
-        pillow_grid = create_image_grid(pillow_images, nrow=nrow, gap=gap)
-        tensor_grid = pillow_to_tensor(pillow_grid)
-
-        return (tensor_grid,)

src/utils.py

@@ -13,27 +13,41 @@ def pillow_to_tensor(image):
     return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
 
 
-def create_image_grid(images: t.List[Image.Image], gap: int, ncol: int):
+def create_image_grid_by_columns(
-    # Calculate the number of rows needed based on the number of images and columns
+    images: t.List[Image.Image],
-    nrow = (len(images) + ncol - 1) // ncol
+    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)
 
-    # Get the size of the first image to use as a template for the grid
+
+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
 
-    # Calculate the total size of the grid with gaps
+    width = size[0] * max_columns + (max_columns - 1) * gap
-    width = size[0] * ncol + gap * (ncol - 1)
+    height = size[1] * max_rows + (max_rows - 1) * gap
-    height = size[1] * nrow + gap * (nrow - 1)
 
-    # Create a new image for the grid
     grid_image = Image.new("RGB", (width, height), color="white")
 
-    # Iterate over each image and paste it into the grid
     for i, image in enumerate(images):
-        # Calculate the position of the image in the grid
+        x = (i % max_columns) * (size[0] + gap)
-        x = (i % ncol) * (size[0] + gap)
+        y = (i // max_columns) * (size[1] + gap)
-        y = (i // ncol) * (size[1] + gap)
 
-        # Paste the image into the grid
         grid_image.paste(image, (x, y))
 
     return grid_image

workflows/mini.json

@@ -1,218 +1,7 @@
 {
-  "last_node_id": 44,
+  "last_node_id": 56,
-  "last_link_id": 75,
+  "last_link_id": 97,
   "nodes": [
-    {
-      "id": 15,
-      "type": "PreviewImage",
-      "pos": [
-        1435,
-        325
-      ],
-      "size": {
-        "0": 363.55511474609375,
-        "1": 290.6986999511719
-      },
-      "flags": {},
-      "order": 9,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 20
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "PreviewImage"
-      }
-    },
-    {
-      "id": 10,
-      "type": "ImageSetArea",
-      "pos": [
-        437,
-        307
-      ],
-      "size": {
-        "0": 216.59999084472656,
-        "1": 26
-      },
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "link": 73
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGES",
-          "type": "IMAGES",
-          "links": [
-            12
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ImageSetArea"
-      }
-    },
-    {
-      "id": 12,
-      "type": "FloatImageCombine",
-      "pos": [
-        711,
-        301
-      ],
-      "size": {
-        "0": 317.4000244140625,
-        "1": 46
-      },
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "float_image_1",
-          "type": "IMAGES",
-          "link": 12
-        },
-        {
-          "name": "float_image_2",
-          "type": "IMAGES",
-          "link": 13
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGES",
-          "type": "IMAGES",
-          "links": [
-            32
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "FloatImageCombine"
-      }
-    },
-    {
-      "id": 19,
-      "type": "FloatImageCombine",
-      "pos": [
-        712,
-        387
-      ],
-      "size": {
-        "0": 317.4000244140625,
-        "1": 46
-      },
-      "flags": {},
-      "order": 7,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "float_image_1",
-          "type": "IMAGES",
-          "link": 32
-        },
-        {
-          "name": "float_image_2",
-          "type": "IMAGES",
-          "link": 22
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGES",
-          "type": "IMAGES",
-          "links": [
-            24
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "FloatImageCombine"
-      }
-    },
-    {
-      "id": 11,
-      "type": "ImageSetArea",
-      "pos": [
-        441,
-        372
-      ],
-      "size": {
-        "0": 216.59999084472656,
-        "1": 26
-      },
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "link": 74,
-          "slot_index": 0
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGES",
-          "type": "IMAGES",
-          "links": [
-            13
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ImageSetArea"
-      }
-    },
-    {
-      "id": 18,
-      "type": "ImageSetArea",
-      "pos": [
-        439,
-        439
-      ],
-      "size": {
-        "0": 216.59999084472656,
-        "1": 26
-      },
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "link": 75
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGES",
-          "type": "IMAGES",
-          "links": [
-            22
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ImageSetArea"
-      }
-    },
     {
       "id": 41,
       "type": "LoadImage",
@@ -232,7 +21,7 @@
           "name": "IMAGE",
           "type": "IMAGE",
           "links": [
-            73
+            90
           ],
           "slot_index": 0
         },
@@ -250,6 +39,43 @@
         "image"
       ]
     },
+    {
+      "id": 42,
+      "type": "LoadImage",
+      "pos": [
+        69,
+        449
+      ],
+      "size": {
+        "0": 315,
+        "1": 102
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            91
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "ComfyUI_00153_.png",
+        "image"
+      ]
+    },
     {
       "id": 43,
       "type": "LoadImage",
@@ -269,7 +95,7 @@
           "name": "IMAGE",
           "type": "IMAGE",
           "links": [
-            75
+            94
           ],
           "slot_index": 0
         },
@@ -288,24 +114,134 @@
       ]
     },
     {
-      "id": 17,
+      "id": 54,
-      "type": "XYPlot",
+      "type": "ImageCombine",
       "pos": [
-        1075,
+        460,
-        326
+        350
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image_1",
+          "type": "IMAGE",
+          "link": 90
+        },
+        {
+          "name": "image_2",
+          "type": "IMAGE",
+          "link": 91
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            93
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImageCombine"
+      },
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 55,
+      "type": "ImageCombine",
+      "pos": [
+        460,
+        440
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image_1",
+          "type": "IMAGE",
+          "link": 93
+        },
+        {
+          "name": "image_2",
+          "type": "IMAGE",
+          "link": 94
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            95
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImageCombine"
+      },
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 56,
+      "type": "PreviewImage",
+      "pos": [
+        1059,
+        350
+      ],
+      "size": [
+        428.921914672851,
+        507.1954368591306
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 97
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 53,
+      "type": "ImagesGridByColumns",
+      "pos": [
+        700,
+        350
       ],
       "size": {
         "0": 315,
         "1": 82
       },
       "flags": {},
-      "order": 8,
+      "order": 5,
       "mode": 0,
       "inputs": [
         {
           "name": "images",
-          "type": "IMAGES",
+          "type": "IMAGE",
-          "link": 24
+          "link": 95,
+          "slot_index": 0
         }
       ],
       "outputs": [
@@ -313,127 +249,68 @@
           "name": "IMAGE",
           "type": "IMAGE",
           "links": [
-            20
+            97
           ],
           "slot_index": 0
         }
       ],
       "properties": {
-        "Node name for S&R": "XYPlot"
+        "Node name for S&R": "ImagesGridByColumns"
       },
       "widgets_values": [
-        10,
+        5,
         2
-      ]
-    },
-    {
-      "id": 42,
-      "type": "LoadImage",
-      "pos": [
-        69,
-        449
       ],
-      "size": {
+      "color": "#322",
-        "0": 315,
+      "bgcolor": "#533"
-        "1": 102
-      },
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            74
-          ],
-          "slot_index": 0
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": null
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadImage"
-      },
-      "widgets_values": [
-        "ComfyUI_00153_.png",
-        "image"
-      ]
     }
   ],
   "links": [
     [
-      12,
+      90,
-      10,
-      0,
-      12,
-      0,
-      "IMAGES"
-    ],
-    [
-      13,
-      11,
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