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  1. README.md +136 -3
  2. SynWBM.py +184 -0
  3. images.txt +20 -0
  4. mushroom_worflow.json +2066 -0
README.md CHANGED
@@ -1,8 +1,141 @@
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: gpl-3.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # SynWBM (Synthetic white button mushroom)
5
 
6
- A dataset containing synthetic images of white button mushrooms.
7
 
8
- Images were either rendered from a Blender scene, or generated using a Stable Diffusion XL-based workflow.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
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+ dataset_info:
3
+ features:
4
+ - name: image
5
+ dtype: image
6
+ - name: depth
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+ dtype: image
8
+ - name: mask
9
+ dtype: image
10
+ splits:
11
+ - name: train
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+ num_examples: 12000
13
+ download_size: 21573770746
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+ dataset_size: 21702540415
15
+ configs:
16
+ - config_name: all
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+ data_files:
18
+ - split: train
19
+ path: ["rendered/*.tar.gz", "generated/*.tar.gz"]
20
+ - config_name: blender
21
+ data_files:
22
+ - split: train
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+ path: "rendered/*.tar.gz"
24
+ - config_name: sdxl
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+ data_files:
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+ - split: train
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+ path: "generated/*.tar.gz"
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+ language:
29
+ - en
30
  license: gpl-3.0
31
+ tags:
32
+ - vision
33
+ - image segmentation
34
+ - instance segmentation
35
+ - object detection
36
+ - synthetic
37
+ - sim-to-real
38
+ - depth estimation
39
+ - image to image
40
+ annotations_creators:
41
+ - machine-generated
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+ pretty_name: SynWBM Dataset
43
+ size_categories:
44
+ - 1K<n<10K
45
+ task_categories:
46
+ - object-detection
47
+ - image-segmentation
48
+ - depth-estimation
49
+ - image-to-image
50
+ task_ids:
51
+ - instance-segmentation
52
+ - semantic-segmentation
53
  ---
54
+ # The SynWBM (Synthetic White Button Mushrooms) Dataset!
55
 
56
+ ![Sample example](https://huggingface.co/datasets/ABC-iRobotics/SynWBM/resolve/main/example.png "Sample example")
57
 
58
+ Synthetic dataset of white button mushrooms (Agaricus bisporus) with instance segmentation masks and depth maps.
59
+
60
+ ### Dataset Summary
61
+
62
+ The SynWBM Dataset is a collection of synthetic images of white button mushroom. The dataset incorporates rendered (using Blender) and generated (using Stable Diffusion XL) synthetic images for training mushroom segmentation models. Each image is annotated with instance segmentation masks and depth maps. The dataset explicitly marks synthetic samples based on their creation method (either rendered from the Blender scene or generated using Stable Diffusion XL) to facilitate sim-to-real performance tests on different synthetic datasets. The dataset does not provide real-world samples for testing, it only contains the synthetic training data.
63
+
64
+ ### Supported Tasks and Leaderboards
65
+
66
+ The dataset supports tasks such as semantic segmentation, instance segmentation, object detection, depth estimation, image-to-image, and pre-training models for sim-to-real transfer.
67
+
68
+ ## Dataset Structure
69
+
70
+ ### Data samples
71
+
72
+ The samples of the dataset are 1024x1024x3 images in PNG format. The annotations are 1024x1024x1 PNG images representing the instance segmentation masks (using uint16 data format) and 1024x1024x1 PNG images representing depth data.
73
+
74
+ ### Data Fields
75
+
76
+ The data fields are:
77
+
78
+ 1) 'image': 1024x1024x3 PNG image
79
+ 2) 'depth': 1024x1024x1 PNG image
80
+ 3) 'mask': 1024x1024x1 unit16 PNG image
81
+
82
+ ⚠️ Note: Instance masks are stored as uint16 PNG images, where each pixel value corresponds to an instance ID. Many default image viewers will render them as black; for visualization, we recommend converting them to RGB with a color palette.
83
+
84
+ ### Data Splits
85
+
86
+ The dataset contains only a training split for all image collections, but provides three configs (`all`, `blender` and `sdxl`) to load the images generated by different methods.
87
+
88
+ ### Limitations
89
+
90
+ - The dataset only contains synthetic images; no real-world samples are provided.
91
+ - Stable Diffusion XL generations may contain artifacts or unrealistic structures.
92
+ - Depth annotations are derived from rendering/generation pipelines and may not reflect real-world sensor noise.
93
+
94
+ ## Usage
95
+
96
+ You can load the dataset using 🤗 Datasets:
97
+
98
+ ```python
99
+ from datasets import load_dataset
100
+
101
+ # Load all synthetic images
102
+ ds = load_dataset("ABC-iRobotics/SynWBM", name="all")
103
+
104
+ # Load only Blender-rendered images
105
+ ds_blender = load_dataset("ABC-iRobotics/SynWBM", name="blender")
106
+
107
+ # Load only SDXL-generated images
108
+ ds_sdxl = load_dataset("ABC-iRobotics/SynWBM", name="sdxl")
109
+
110
+ # Example visualization
111
+ example = ds["train"][0]
112
+ image, mask, depth = example["image"], example["mask"], example["depth"]
113
+ ```
114
+
115
+ ### Intended Use
116
+
117
+ The dataset is intended for research in:
118
+ - instance segmentation of mushrooms
119
+ - sim-to-real transfer learning
120
+ - synthetic-to-real domain adaptation
121
+ - benchmarking data generation pipelines
122
+
123
+ ## Dataset Creation
124
+
125
+ ### Curation Rationale
126
+
127
+ The dataset was created to train instance segmentation models on synthetic data for mushroom instance segmentation in real-world images.
128
+
129
+ ### Source Data
130
+
131
+ The data is generated using two methods:
132
+
133
+ - Blender-rendered images are produced from a fully procedural Blender scene, with annotations generated using the [Blender Annotation Tool (BAT)](https://github.com/ABC-iRobotics/blender_annotation_tool)
134
+ - Stable Diffusion XL images are generated using ComfyUI with a [custom workflow](https://huggingface.co/datasets/ABC-iRobotics/SynWBM/resolve/main/mushroom_workflow.json) provided in this repository.
135
+
136
+ ## Citation Information
137
+
138
+ Coming Soon
139
+
140
+ ## License
141
+ This dataset is released under [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.en.html).
SynWBM.py ADDED
@@ -0,0 +1,184 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #
2
+ # This file is part of the SynWBM distribution (https://huggingface.co/datasets/ABC-iRobotics/SynWBM).
3
+ # Copyright (c) 2023 ABC-iRobotics.
4
+ #
5
+ # This program is free software: you can redistribute it and/or modify
6
+ # it under the terms of the GNU General Public License as published by
7
+ # the Free Software Foundation, version 3.
8
+ #
9
+ # This program is distributed in the hope that it will be useful, but
10
+ # WITHOUT ANY WARRANTY; without even the implied warranty of
11
+ # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
12
+ # General Public License for more details.
13
+ #
14
+ # You should have received a copy of the GNU General Public License
15
+ # along with this program. If not, see <http://www.gnu.org/licenses/>.
16
+ #
17
+ """SynWBM dataset"""
18
+
19
+ import sys
20
+ if sys.version_info < (3, 9):
21
+ from typing import Sequence, Generator, Tuple
22
+ else:
23
+ from collections.abc import Sequence, Generator
24
+ Tuple = tuple
25
+
26
+ from typing import Optional, IO
27
+
28
+ import datasets
29
+ import itertools
30
+
31
+
32
+ # ---- Constants ----
33
+
34
+ _CITATION = """\
35
+ COMING SOON
36
+ """
37
+
38
+ _DESCRIPTION = """\
39
+ A synthetic instance segmentation dataset for white button mushrooms (Agaricus bisporus).
40
+ The dataset incorporates rendered and generated synthetic images for training mushroom segmentation models.
41
+ """
42
+
43
+ _HOMEPAGE = "https://huggingface.co/datasets/ABC-iRobotics/SynWBM"
44
+
45
+ _LICENSE = "GNU General Public License v3.0"
46
+
47
+ _LATEST_VERSIONS = {
48
+ "all": "1.0.0",
49
+ "blender": "1.0.0",
50
+ "sdxl": "1.0.0",
51
+ }
52
+
53
+ BASE_URL = "https://huggingface.co/datasets/ABC-iRobotics/SynWBM/resolve/main/"
54
+
55
+
56
+
57
+ # ---- SynWBM dataset Configs ----
58
+
59
+ class SynWBMDatasetConfig(datasets.BuilderConfig):
60
+ """BuilderConfig for SynWBM dataset."""
61
+
62
+ def __init__(self, name: str, base_urls: Sequence[str], images_txt: str, version: Optional[str] = None, **kwargs):
63
+ _version = _LATEST_VERSIONS[name] if version is None else version
64
+ super(SynWBMDatasetConfig, self).__init__(version=datasets.Version(_version), name=name, **kwargs)
65
+ with open(images_txt, 'r') as f:
66
+ image_list = f.readlines()
67
+ img_urls = []
68
+ depth_urls = []
69
+ mask_urls = []
70
+ for base_url in base_urls:
71
+ img_urls.extend([base_url + image.strip() for image in image_list])
72
+ depth_urls.extend([BASE_URL + "depths/" + image.strip() for image in image_list])
73
+ mask_urls.extend([BASE_URL + "masks/" + image.strip() for image in image_list])
74
+
75
+ self._imgs_urls = img_urls
76
+ self._depth_urls = depth_urls
77
+ self._masks_urls = mask_urls
78
+
79
+
80
+ @property
81
+ def features(self):
82
+ return datasets.Features(
83
+ {
84
+ "image": datasets.Image(),
85
+ "depth": datasets.Image(),
86
+ "mask": datasets.Image(),
87
+ }
88
+ )
89
+
90
+ @property
91
+ def supervised_keys(self):
92
+ return None
93
+
94
+
95
+
96
+ # ---- SynWBM dataset Loader ----
97
+
98
+ class SynWBMDataset(datasets.GeneratorBasedBuilder):
99
+ """SynWBM dataset."""
100
+
101
+ BUILDER_CONFIG_CLASS = SynWBMDatasetConfig
102
+ BUILDER_CONFIGS = [
103
+ SynWBMDatasetConfig(
104
+ name = "all",
105
+ description = "All images",
106
+ base_urls = [
107
+ BASE_URL + "rendered/",
108
+ BASE_URL + "generated/"
109
+ ],
110
+ images_txt = "images.txt"
111
+ ),
112
+ SynWBMDatasetConfig(
113
+ name = "blender",
114
+ description = "Synthetic images rendered using Blender",
115
+ base_urls = [
116
+ BASE_URL + "rendered/"
117
+ ],
118
+ images_txt = "images.txt"
119
+ ),
120
+ SynWBMDatasetConfig(
121
+ name = "sdxl",
122
+ description = "Synthetic images generated by Stable Diffusion XL",
123
+ base_urls = [
124
+ BASE_URL + "generated/"
125
+ ],
126
+ images_txt = "images.txt"
127
+ ),
128
+ ]
129
+ DEFAULT_WRITER_BATCH_SIZE = 10
130
+
131
+ def _info(self):
132
+ return datasets.DatasetInfo(
133
+ description=_DESCRIPTION,
134
+ features=self.config.features,
135
+ supervised_keys=self.config.supervised_keys,
136
+ homepage=_HOMEPAGE,
137
+ license=_LICENSE,
138
+ citation=_CITATION,
139
+ version=self.config.version,
140
+ )
141
+
142
+ def _split_generators(self, dl_manager):
143
+ imgs_paths = dl_manager.download(self.config._imgs_urls)
144
+ depths_paths = dl_manager.download(self.config._depth_urls)
145
+ masks_paths = dl_manager.download(self.config._masks_urls)
146
+
147
+ imgs_gen = itertools.chain.from_iterable([dl_manager.iter_archive(path) for path in imgs_paths])
148
+ depths_gen = itertools.chain.from_iterable([dl_manager.iter_archive(path) for path in depths_paths])
149
+ masks_gen = itertools.chain.from_iterable([dl_manager.iter_archive(path) for path in masks_paths])
150
+
151
+ return [
152
+ datasets.SplitGenerator(
153
+ name=datasets.Split.TRAIN,
154
+ gen_kwargs={
155
+ "images": imgs_gen,
156
+ "depths": depths_gen,
157
+ "masks": masks_gen,
158
+ },
159
+ ),
160
+ ]
161
+
162
+ def _generate_examples(
163
+ self,
164
+ images: Generator[Tuple[str,IO], None, None],
165
+ depths: Generator[Tuple[str,IO], None, None],
166
+ masks: Generator[Tuple[str,IO], None, None],
167
+ ):
168
+ for i, (img_info, depth_info, mask_info) in enumerate(zip(images, depths, masks)):
169
+ img_file_path, img_file_obj = img_info
170
+ depth_file_path, depth_file_obj = depth_info
171
+ mask_file_path, mask_file_obj = mask_info
172
+
173
+ img_bytes = img_file_obj.read()
174
+ depth_bytes = depth_file_obj.read()
175
+ mask_bytes = mask_file_obj.read()
176
+ img_file_obj.close()
177
+ depth_file_obj.close()
178
+ mask_file_obj.close()
179
+
180
+ yield i, {
181
+ "image": {"path": img_file_path, "bytes": img_bytes},
182
+ "depth": {"path": depth_file_path, "bytes": depth_bytes},
183
+ "mask": {"path": mask_file_path, "bytes": mask_bytes},
184
+ }
images.txt ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 0001.tar.gz
2
+ 0002.tar.gz
3
+ 0003.tar.gz
4
+ 0004.tar.gz
5
+ 0005.tar.gz
6
+ 0006.tar.gz
7
+ 0007.tar.gz
8
+ 0008.tar.gz
9
+ 0009.tar.gz
10
+ 0010.tar.gz
11
+ 0011.tar.gz
12
+ 0012.tar.gz
13
+ 0013.tar.gz
14
+ 0014.tar.gz
15
+ 0015.tar.gz
16
+ 0016.tar.gz
17
+ 0017.tar.gz
18
+ 0018.tar.gz
19
+ 0019.tar.gz
20
+ 0020.tar.gz
mushroom_worflow.json ADDED
@@ -0,0 +1,2066 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "id": "cddfa9fa-2168-409f-be6c-8ad54459c584",
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+ "revision": 0,
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+ "last_node_id": 106,
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+ "last_link_id": 249,
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+ "nodes": [
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+ 315,
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+ ],
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+ "flags": {},
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+ "order": 14,
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+ "mode": 0,
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+ "inputs": [
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+ {
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+ "name": "ipadapter",
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+ "type": "IPADAPTER",
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+ "link": 95
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+ },
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+ {
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+ "name": "image",
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+ "type": "IMAGE",
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+ "link": 96
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+ },
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+ {
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+ "name": "mask",
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+ "shape": 7,
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+ "type": "MASK",
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+ "link": null
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+ },
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+ {
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+ "name": "clip_vision",
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+ "shape": 7,
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+ "type": "CLIP_VISION",
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+ "link": 97
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+ }
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+ ],
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+ "outputs": [
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+ {
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+ "name": "pos_embed",
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+ "shape": 3,
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+ "type": "EMBEDS",
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+ "slot_index": 0,
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+ "links": [
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+ 99
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+ ]
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+ },
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+ {
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+ "name": "neg_embed",
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+ "shape": 3,
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+ "slot_index": 1,
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+ "links": [
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+ 102
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+ ]
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+ }
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+ ],
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