nodes/LEv145-images-grid-comfy-plugin
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Merge pull request #1 from LEv145/dev

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v 2.2.0
This commit is contained in:
LEv145
2023-04-09 15:05:40 +02:00
committed by GitHub
parent 9963935c26 3dd50ec293
commit b3f5bd1136
17 changed files with 1411 additions and 774 deletions
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README.md
+16 -12
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@@ -1,23 +1,27 @@
# ImagesGrid: Comfy plugin
## Preview
### Simple grid of images
![Image](./workflows/mini.png)
[Workflows](./workflows/mini.json)
### XYZPlot, like in auto1111, but with more settings
![Image](./workflows/base.png)
[Workflows](./workflows/base.json)
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
```
cd custom_nodes # From comfy path
git clone https://github.com/LEv145/images-grid-comfy-plugin ImagesGrid
```
### Update
2. Unpack the node to `custom_nodes`, for example in a folder `custom_nodes/ImagesGrid/`
```
cd custom_nodes/ImagesGrid
git pull
```
## Source
https://github.com/LEv145/images-grid-comfy-plugin
__init__.py
+8 -1
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@@ -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
View File
@@ -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
+4 -3
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@@ -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
+49
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@@ -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
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
+6 -11
View File
@@ -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_2: Image,
) -> t.Tuple[Image]:
print(image_1.size())
print(image_2.size())
print(image_1)
image_1: torch.Tensor,
image_2: torch.Tensor,
) -> tuple[torch.Tensor]:
result = torch.cat((image_1, image_2), 0)
print(result.size())
return (result,)
src/nodes/images_grid.py
+32 -14
View File
@@ -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_image_grid_by_rows,
create_images_grid_by_columns,
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]:
return self._create_execute(images, create_image_grid_by_columns, kw)
def execute(self, **kw) -> tuple[torch.Tensor]:
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]:
return self._create_execute(images, create_image_grid_by_rows, kw)
def execute(self, **kw) -> tuple[torch.Tensor]:
return self._create_execute(create_images_grid_by_rows, **kw)
src/nodes/latent_combine.py
+6 -6
View File
@@ -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_2: t.Dict[str, t.Any],
) -> t.Tuple[t.Dict[str, t.Any]]:
latent_1: dict[str, torch.Tensor],
latent_2: dict[str, torch.Tensor],
) -> tuple[dict[str, torch.Tensor]]:
samples = torch.cat((latent_1["samples"], latent_2["samples"]), 0)
return ({"samples": samples},)
src/utils.py
-53
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@@ -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
+6
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@@ -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
+195
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@@ -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.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
+13
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@@ -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)
static/Roboto-Regular.ttf
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workflows/base.json
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workflows/base.png
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workflows/mini.json
+430 -86
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@@ -1,19 +1,21 @@
{
"last_node_id": 56,
"last_link_id": 97,
"last_node_id": 70,
"last_link_id": 121,
"nodes": [
{
"id": 41,
"id": 68,
"type": "LoadImage",
"pos": [
69,
307
-30,
70
],
"size": {
"0": 315,
"1": 102
},
"flags": {},
"flags": {
"collapsed": true
},
"order": 0,
"mode": 0,
"outputs": [
@@ -21,7 +23,7 @@
"name": "IMAGE",
"type": "IMAGE",
"links": [
90
119
],
"slot_index": 0
},
@@ -35,22 +37,24 @@
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"ComfyUI_00171_.png",
"394102.png",
"image"
]
},
{
"id": 42,
"id": 41,
"type": "LoadImage",
"pos": [
69,
449
-30,
210
],
"size": {
"0": 315,
"1": 102
},
"flags": {},
"flags": {
"collapsed": true
},
"order": 1,
"mode": 0,
"outputs": [
@@ -58,7 +62,7 @@
"name": "IMAGE",
"type": "IMAGE",
"links": [
91
120
],
"slot_index": 0
},
@@ -72,22 +76,24 @@
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"ComfyUI_00153_.png",
"394102.png",
"image"
]
},
{
"id": 43,
"id": 42,
"type": "LoadImage",
"pos": [
68,
592
-30,
350
],
"size": {
"0": 315,
"1": 102
},
"flags": {},
"flags": {
"collapsed": true
},
"order": 2,
"mode": 0,
"outputs": [
@@ -95,7 +101,7 @@
"name": "IMAGE",
"type": "IMAGE",
"links": [
94
112
],
"slot_index": 0
},
@@ -109,12 +115,171 @@
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"ComfyUI_00116_.png",
"394102.png",
"image"
]
},
{
"id": 54,
"id": 43,
"type": "LoadImage",
"pos": [
-30,
490
],
"size": {
"0": 315,
"1": 102
},
"flags": {
"collapsed": true
},
"order": 3,
"mode": 0,
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
106
],
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"394102.png",
"image"
]
},
{
"id": 62,
"type": "LoadImage",
"pos": [
-30,
630
],
"size": {
"0": 315,
"1": 102
},
"flags": {
"collapsed": true
},
"order": 4,
"mode": 0,
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
105
],
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"394102.png",
"image"
]
},
{
"id": 63,
"type": "LoadImage",
"pos": [
-30,
770
],
"size": {
"0": 315,
"1": 102
},
"flags": {
"collapsed": true
},
"order": 5,
"mode": 0,
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
104
],
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"394102.png",
"image"
]
},
{
"id": 65,
"type": "ImageCombine",
"pos": [
460,
430
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
{
"name": "image_1",
"type": "IMAGE",
"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"
},
{
"id": 67,
"type": "ImageCombine",
"pos": [
460,
@@ -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"
},
{
"id": 69,
"type": "ImageCombine",
"pos": [
460,
270
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "image_1",
"type": "IMAGE",
"link": 119
},
{
"name": "image_2",
"type": "IMAGE",
"link": 120
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
118
],
"slot_index": 0
}
@@ -160,25 +367,25 @@
"type": "ImageCombine",
"pos": [
460,
440
510
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 4,
"order": 10,
"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,
"type": "PreviewImage",
"id": 54,
"type": "ImageCombine",
"pos": [
1059,
350
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