.
diff --git a/ComfyUI-Manager/README.md b/ComfyUI-Manager/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a80964430da0199565aeeded881b7b6986f5639b
--- /dev/null
+++ b/ComfyUI-Manager/README.md
@@ -0,0 +1,160 @@
+# ComfyUI Manager
+
+## Installation
+
+### Installation[method1] (General installation method: ComfyUI-Manager only)
+
+To install ComfyUI-Manager in addition to an existing installation of ComfyUI, you can follow the following steps:
+
+1. cd custom_nodes
+2. git clone https://github.com/ltdrdata/ComfyUI-Manager.git
+3. Restart ComfyUI
+
+
+### Installation[method2] (Installation for portable ComfyUI version: ComfyUI-Manager only)
+
+1. Download [scripts/install-manager-for-portable-version.bat](https://github.com/ltdrdata/ComfyUI-Manager/raw/main/scripts/install-manager-for-portable-version.bat) into installed `"ComfyUI_windows_portable"` directory
+2. double click `install-manager-for-portable-version.bat` batch file
+
+
+
+
+### Installation[method3] (Installation for linux+venv: ComfyUI + ComfyUI-Manager)
+
+To install ComfyUI with ComfyUI-Manager on Linux using a venv environment, you can follow these steps:
+
+1. Download [scripts/install-comfyui-venv-linux.sh](https://github.com/ltdrdata/ComfyUI-Manager/raw/main/scripts/install-comfyui-venv-linux.sh) into empty install directory
+- ComfyUI will be installed in the subdirectory of the specified directory, and the directory will contain the generated executable script.
+2. `chmod +x install-comfyui-venv-linux.sh`
+3. `./install-comfyui-venv-linux.sh`
+
+
+You can execute ComfyUI by running either `./run_gpu.sh` or `./run_cpu.sh` depending on your system configuration.
+
+## Colab Notebook
+This repository provides Colab notebooks that allow you to install and use ComfyUI, including ComfyUI-Manager. To use ComfyUI, [click on this link](https://colab.research.google.com/github/ltdrdata/ComfyUI-Manager/blob/main/notebooks/comfyui_colab_with_manager.ipynb).
+* Support for installing ComfyUI
+* Support for basic installation of ComfyUI-Manager
+* Support for automatically installing dependencies of custom nodes upon restarting Colab notebooks.
+
+## Changes
+* **0.18.1** `skip update check` feature added.
+ * A feature that allows quickly opening windows in environments where update checks take a long time.
+* **0.17.1** Bug fix for the issue where enable/disable of the web extension was not working. Compatibility patch for StableSwarmUI.
+ * Requires latest version of ComfyUI (Revision: 1240)
+* **0.17** Support preview method setting feature.
+* **0.14** Support robust update.
+* **0.13** Support additional 'pip' section for install spec.
+* **0.12** Better installation support for Windows.
+* **0.9** Support keyword search in installer menu.
+* **V0.7.1** Bug fix for the issue where updates were not being applied on Windows.
+ * **For those who have been using versions 0.6, please perform a manual git pull in the custom_nodes/ComfyUI-Manager directory.**
+* **V0.7** To address the issue of a slow list refresh, separate the fetch update and update check processes.
+* **V0.6** Support extension installation for missing nodes.
+* **V0.5** Removed external git program dependencies.
+
+
+## How To Use
+
+1. Click "Manager" button on main menu
+
+
+
+
+2. If you click on 'Install Custom Nodes' or 'Install Models', an installer dialog will open.
+
+
+* When the 'Use local DB' feature is enabled, the application will utilize the data stored locally on your device, rather than retrieving node/model information over the internet
+
+* The ```Fetch Updates``` menu retrieves update data for custom nodes locally. Actual updates are applied by clicking the ```Update``` button in the ```Install Custom Nodes``` menu.
+
+3. Click 'Install' or 'Try Install' button.
+
+
+
+
+
+* Installed: This item is already installed.
+* Install: Clicking this button will install the item.
+* Try Install: This is a custom node of which installation information cannot be confirmed. Click the button to try installing it.
+
+
+## Custom node support guide
+
+* Currently, the system operates by cloning the git repository and sequentially installing the dependencies listed in requirements.txt using pip, followed by invoking the install.py script. In the future, we plan to discuss and determine the specifications for supporting custom nodes.
+
+* Please submit a pull request to update either the custom-node-list.json or model-list.json file.
+
+* The scanner currently provides a detection function for missing nodes, which is capable of detecting nodes described by the following two patterns.
+ * Or you can provide manually `node_list.js` file.
+
+```
+NODE_CLASS_MAPPINGS = {
+ "ExecutionSwitch": ExecutionSwitch,
+ "ExecutionBlocker": ExecutionBlocker,
+ ...
+}
+
+NODE_CLASS_MAPPINGS.update({
+ "UniFormer-SemSegPreprocessor": Uniformer_SemSegPreprocessor,
+ "SemSegPreprocessor": Uniformer_SemSegPreprocessor,
+})
+```
+
+* **Special purpose files** (optional)
+ * `node_list.js` - When your custom nodes pattern of NODE_CLASS_MAPPINGS is not conventional, it is used to manually provide a list of nodes for reference. ([example](https://github.com/melMass/comfy_mtb/raw/main/node_list.json))
+ * `requirements.txt` - When installing, this pip requirements will be installed automatically
+ * `install.py` - When installing, it is automatically called
+ * `uninstall.py` - When uninstalling, it is automatically called
+ * `disable.py` - When disabled, it is automatically called
+ * When installing a custom node setup `.js` file, it is recommended to write this script for disabling.
+ * `enable.py` - When enabled, it is automatically called
+ * **All scripts are executed from the root path of the corresponding custom node.**
+
+## Support of missing nodes installation
+
+
+
+* When you click on the ```Install Missing Custom Nodes``` button in the menu, it displays a list of extension nodes that contain nodes not currently present in the workflow.
+
+
+
+* Currently, support is not available for custom nodes that can only be downloaded through civitai.
+
+* [ComfyUI_Custom_Nodes_AlekPet](https://github.com/AlekPet/ComfyUI_Custom_Nodes_AlekPet)
+
+
+## Troubleshooting
+* If updating ComfyUI-Manager itself fails, please go to the **ComfyUI-Manager** directory and execute the command `git update-ref refs/remotes/origin/main a361cc1 && git fetch --all && git pull`.
+ * Alternatively, download the update-fix.py script from [update-fix.py](https://github.com/ltdrdata/ComfyUI-Manager/raw/main/scripts/update-fix.py) and place it in the ComfyUI-Manager directory. Then, run it using your Python command.
+ For the portable version, use `..\..\..\python_embedded\python.exe update-fix.py`.
+* If Controlnet is set to skip_v1 true, nodes like `CannyEdgePreprocessor` will appear as extensions of missing nodes, but simply installing them is not enough, and direct modification of the user's config.yaml is required.
+* For cases where nodes like `PreviewTextNode` from `ComfyUI_Custom_Nodes_AlekPet` are only supported as front-end nodes, we currently do not provide missing nodes for them.
+* Currently, `vid2vid` is not being updated, causing compatibility issues.
+
+
+## TODO: Unconventional form of custom node list
+
+* https://github.com/bmad4ever/ComfyUI-Bmad-Custom-Nodes
+* https://github.com/diontimmer/Sample-Diffusion-ComfyUI-Extension
+* https://github.com/laksjdjf/pfg-ComfyUI
+* https://github.com/senshilabs/NINJA-plugin
+* https://github.com/failfa-st/comfyui-extensions
+
+
+## Roadmap
+
+- [x] category/keyword filter
+- [x] Automatic recognition of missing custom nodes
+- [x] Automatic installation suggestion of missing custom nodes
+- [ ] installation from git url
+- [ ] 3rd party repository
+- [ ] Specification of custom nodes
+- [ ] Specification scanner
+- [ ] Search extension by node name
+- [ ] workflow downloader
+
+
+# Disclaimer
+
+* This extension simply provides the convenience of installing custom nodes and does not guarantee their proper functioning.
diff --git a/ComfyUI-Manager/__init__.py b/ComfyUI-Manager/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..19b121c602cf2ea2471dba987b1cafb0cf833fbd
--- /dev/null
+++ b/ComfyUI-Manager/__init__.py
@@ -0,0 +1,975 @@
+import configparser
+import shutil
+import folder_paths
+import os, sys
+import subprocess
+
+try:
+ import git
+except:
+ my_path = os.path.dirname(__file__)
+ requirements_path = os.path.join(my_path, "requirements.txt")
+
+ print(f"## ComfyUI-Manager: installing dependencies")
+
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', '-r', requirements_path])
+
+ try:
+ import git
+ except:
+ print(f"## [ERROR] ComfyUI-Manager: Attempting to reinstall dependencies using an alternative method.")
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', '--user', '-r', requirements_path])
+
+ try:
+ import git
+ except:
+ print(f"## [ERROR] ComfyUI-Manager: Failed to install the GitPython package in the correct Python environment. Please install it manually in the appropriate environment. (You can seek help at https://app.element.io/#/room/%23comfyui_space%3Amatrix.org)")
+
+ print(f"## ComfyUI-Manager: installing dependencies done.")
+
+
+sys.path.append('../..')
+
+from torchvision.datasets.utils import download_url
+
+# ensure .js
+print("### Loading: ComfyUI-Manager (V0.19.1)")
+
+comfy_ui_required_revision = 1240
+comfy_ui_revision = "Unknown"
+
+comfy_path = os.path.dirname(folder_paths.__file__)
+custom_nodes_path = os.path.join(comfy_path, 'custom_nodes')
+js_path = os.path.join(comfy_path, "web", "extensions")
+
+comfyui_manager_path = os.path.dirname(__file__)
+local_db_model = os.path.join(comfyui_manager_path, "model-list.json")
+local_db_alter = os.path.join(comfyui_manager_path, "alter-list.json")
+local_db_custom_node_list = os.path.join(comfyui_manager_path, "custom-node-list.json")
+local_db_extension_node_mappings = os.path.join(comfyui_manager_path, "extension-node-map.json")
+git_script_path = os.path.join(os.path.dirname(__file__), "git_helper.py")
+
+startup_script_path = os.path.join(comfyui_manager_path, "startup-scripts")
+config_path = os.path.join(os.path.dirname(__file__), "config.ini")
+cached_config = None
+
+
+from comfy.cli_args import args
+import latent_preview
+
+
+def write_config():
+ config = configparser.ConfigParser()
+ config['default'] = {
+ 'preview_method': get_current_preview_method(),
+ }
+ with open(config_path, 'w') as configfile:
+ config.write(configfile)
+
+
+def read_config():
+ try:
+ config = configparser.ConfigParser()
+ config.read(config_path)
+ default_conf = config['default']
+
+ return {
+ 'preview_method': default_conf['preview_method']
+ }
+
+ except Exception:
+ return {'preview_method': get_current_preview_method()}
+
+
+def get_config():
+ global cached_config
+
+ if cached_config is None:
+ cached_config = read_config()
+
+ return cached_config
+
+
+def get_current_preview_method():
+ if args.preview_method == latent_preview.LatentPreviewMethod.Auto:
+ return "auto"
+ elif args.preview_method == latent_preview.LatentPreviewMethod.Latent2RGB:
+ return "latent2rgb"
+ elif args.preview_method == latent_preview.LatentPreviewMethod.TAESD:
+ return "taesd"
+ else:
+ return "none"
+
+
+def set_preview_method(method):
+ if method == 'auto':
+ args.preview_method = latent_preview.LatentPreviewMethod.Auto
+ elif method == 'latent2rgb':
+ args.preview_method = latent_preview.LatentPreviewMethod.Latent2RGB
+ elif method == 'taesd':
+ args.preview_method = latent_preview.LatentPreviewMethod.TAESD
+ else:
+ args.preview_method = latent_preview.LatentPreviewMethod.NoPreviews
+
+ get_config()['preview_method'] = args.preview_method
+
+
+set_preview_method(get_config()['preview_method'])
+
+
+def try_install_script(url, repo_path, install_cmd):
+ int_comfyui_revision = 0
+
+ if type(comfy_ui_revision) == int:
+ int_comfyui_revision = comfy_ui_revision
+ elif comfy_ui_revision.isdigit():
+ int_comfyui_revision = int(comfy_ui_revision)
+
+ if platform.system() == "Windows" and int_comfyui_revision >= comfy_ui_required_revision:
+ if not os.path.exists(startup_script_path):
+ os.makedirs(startup_script_path)
+
+ script_path = os.path.join(startup_script_path, "install-scripts.txt")
+ with open(script_path, "a") as file:
+ obj = [repo_path] + install_cmd
+ file.write(f"{obj}\n")
+
+ return True
+ else:
+ code = subprocess.run(install_cmd, cwd=repo_path)
+
+ if platform.system() == "Windows":
+ try:
+ if int(comfy_ui_revision) < comfy_ui_required_revision:
+ print("\n\n###################################################################")
+ print(f"[WARN] ComfyUI-Manager: Your ComfyUI version ({comfy_ui_revision}) is too old. Please update to the latest version.")
+ print(f"[WARN] The extension installation feature may not work properly in the current installed ComfyUI version on Windows environment.")
+ print("###################################################################\n\n")
+ except:
+ pass
+
+ if code.returncode != 0:
+ print(f"install script failed: {url}")
+ return False
+
+def print_comfyui_version():
+ global comfy_ui_revision
+ try:
+ repo = git.Repo(os.path.dirname(folder_paths.__file__))
+
+ comfy_ui_revision = len(list(repo.iter_commits('HEAD')))
+ current_branch = repo.active_branch.name
+ git_hash = repo.head.commit.hexsha
+
+ try:
+ if int(comfy_ui_revision) < comfy_ui_required_revision:
+ print(f"\n\n## [WARN] ComfyUI-Manager: Your ComfyUI version ({comfy_ui_revision}) is too old. Please update to the latest version. ##\n\n")
+ except:
+ pass
+
+ if current_branch == "master":
+ print(f"### ComfyUI Revision: {comfy_ui_revision} [{git_hash[:8]}]")
+ else:
+ print(f"### ComfyUI Revision: {comfy_ui_revision} on '{current_branch}' [{git_hash[:8]}]")
+ except:
+ print("### ComfyUI Revision: UNKNOWN (The currently installed ComfyUI is not a Git repository)")
+
+
+print_comfyui_version()
+
+
+# use subprocess to avoid file system lock by git (Windows)
+def __win_check_git_update(path, do_fetch=False):
+ if do_fetch:
+ command = [sys.executable, git_script_path, "--fetch", path]
+ else:
+ command = [sys.executable, git_script_path, "--check", path]
+
+ process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+ output, _ = process.communicate()
+ output = output.decode('utf-8').strip()
+
+ if "CUSTOM NODE CHECK: True" in output:
+ process.wait()
+ return True
+ else:
+ process.wait()
+ return False
+
+
+def __win_check_git_pull(path):
+ command = [sys.executable, git_script_path, "--pull", path]
+ process = subprocess.Popen(command)
+ process.wait()
+
+
+def git_repo_has_updates(path, do_fetch=False):
+ # Check if the path is a git repository
+ if not os.path.exists(os.path.join(path, '.git')):
+ raise ValueError('Not a git repository')
+
+ if platform.system() == "Windows":
+ return __win_check_git_update(path, do_fetch)
+ else:
+ # Fetch the latest commits from the remote repository
+ repo = git.Repo(path)
+
+ current_branch = repo.active_branch
+ branch_name = current_branch.name
+
+ remote_name = 'origin'
+ remote = repo.remote(name=remote_name)
+
+ if do_fetch:
+ remote.fetch()
+
+ # Get the current commit hash and the commit hash of the remote branch
+ commit_hash = repo.head.commit.hexsha
+ remote_commit_hash = repo.refs[f'{remote_name}/{branch_name}'].object.hexsha
+
+ # Compare the commit hashes to determine if the local repository is behind the remote repository
+ if commit_hash != remote_commit_hash:
+ # Get the commit dates
+ commit_date = repo.head.commit.committed_datetime
+ remote_commit_date = repo.refs[f'{remote_name}/{branch_name}'].object.committed_datetime
+
+ # Compare the commit dates to determine if the local repository is behind the remote repository
+ if commit_date < remote_commit_date:
+ return True
+
+ return False
+
+
+def git_pull(path):
+ # Check if the path is a git repository
+ if not os.path.exists(os.path.join(path, '.git')):
+ raise ValueError('Not a git repository')
+
+ # Pull the latest changes from the remote repository
+ if platform.system() == "Windows":
+ return __win_check_git_pull(path)
+ else:
+ repo = git.Repo(path)
+ if repo.is_dirty():
+ repo.git.stash()
+
+ origin = repo.remote(name='origin')
+ origin.pull(rebase=True)
+ repo.git.submodule('update', '--init', '--recursive')
+
+ repo.close()
+
+ return True
+
+
+async def get_data(uri):
+ print(f"FECTH DATA from: {uri}")
+ if uri.startswith("http"):
+ async with aiohttp.ClientSession() as session:
+ async with session.get(uri) as resp:
+ json_text = await resp.text()
+ else:
+ with open(uri, "r") as f:
+ json_text = f.read()
+
+ json_obj = json.loads(json_text)
+ return json_obj
+
+
+def setup_js():
+ # remove garbage
+ old_js_path = os.path.join(comfy_path, "web", "extensions", "core", "comfyui-manager.js")
+ if os.path.exists(old_js_path):
+ os.remove(old_js_path)
+
+ # setup js
+ js_dest_path = os.path.join(js_path, "comfyui-manager")
+ if not os.path.exists(js_dest_path):
+ os.makedirs(js_dest_path)
+ js_src_path = os.path.join(comfyui_manager_path, "js", "comfyui-manager.js")
+ shutil.copy(js_src_path, js_dest_path)
+
+setup_js()
+
+
+# Expand Server api
+
+import server
+from aiohttp import web
+import aiohttp
+import json
+import zipfile
+import urllib.request
+
+
+def get_model_dir(data):
+ if data['save_path'] != 'default':
+ base_model = os.path.join(folder_paths.models_dir, data['save_path'])
+ else:
+ model_type = data['type']
+ if model_type == "checkpoints":
+ base_model = folder_paths.folder_names_and_paths["checkpoints"][0][0]
+ elif model_type == "unclip":
+ base_model = folder_paths.folder_names_and_paths["checkpoints"][0][0]
+ elif model_type == "VAE":
+ base_model = folder_paths.folder_names_and_paths["vae"][0][0]
+ elif model_type == "lora":
+ base_model = folder_paths.folder_names_and_paths["loras"][0][0]
+ elif model_type == "T2I-Adapter":
+ base_model = folder_paths.folder_names_and_paths["controlnet"][0][0]
+ elif model_type == "T2I-Style":
+ base_model = folder_paths.folder_names_and_paths["controlnet"][0][0]
+ elif model_type == "controlnet":
+ base_model = folder_paths.folder_names_and_paths["controlnet"][0][0]
+ elif model_type == "clip_vision":
+ base_model = folder_paths.folder_names_and_paths["clip_vision"][0][0]
+ elif model_type == "gligen":
+ base_model = folder_paths.folder_names_and_paths["gligen"][0][0]
+ elif model_type == "upscale":
+ base_model = folder_paths.folder_names_and_paths["upscale_models"][0][0]
+ elif model_type == "embeddings":
+ base_model = folder_paths.folder_names_and_paths["embeddings"][0][0]
+ else:
+ base_model = "etc"
+
+ return base_model
+
+
+def get_model_path(data):
+ base_model = get_model_dir(data)
+ return os.path.join(base_model, data['filename'])
+
+
+def check_a_custom_node_installed(item, do_fetch=False, do_update_check=True):
+ item['installed'] = 'None'
+
+ if item['install_type'] == 'git-clone' and len(item['files']) == 1:
+ dir_name = os.path.splitext(os.path.basename(item['files'][0]))[0].replace(".git", "")
+ dir_path = os.path.join(custom_nodes_path, dir_name)
+ if os.path.exists(dir_path):
+ try:
+ if do_update_check and git_repo_has_updates(dir_path, do_fetch):
+ item['installed'] = 'Update'
+ else:
+ item['installed'] = 'True'
+ except:
+ item['installed'] = 'True'
+
+ elif os.path.exists(dir_path + ".disabled"):
+ item['installed'] = 'Disabled'
+
+ else:
+ item['installed'] = 'False'
+
+ elif item['install_type'] == 'copy' and len(item['files']) == 1:
+ dir_name = os.path.basename(item['files'][0])
+
+ if item['files'][0].endswith('.py'):
+ base_path = custom_nodes_path
+ elif 'js_path' in item:
+ base_path = os.path.join(js_path, item['js_path'])
+ else:
+ base_path = js_path
+
+ file_path = os.path.join(base_path, dir_name)
+ if os.path.exists(file_path):
+ item['installed'] = 'True'
+ elif os.path.exists(file_path + ".disabled"):
+ item['installed'] = 'Disabled'
+ else:
+ item['installed'] = 'False'
+
+
+def check_custom_nodes_installed(json_obj, do_fetch=False, do_update_check=True):
+ for item in json_obj['custom_nodes']:
+ check_a_custom_node_installed(item, do_fetch, do_update_check)
+
+
+@server.PromptServer.instance.routes.get("/customnode/getmappings")
+async def fetch_customnode_mappings(request):
+ if request.rel_url.query["mode"] == "local":
+ uri = local_db_extension_node_mappings
+ else:
+ uri = 'https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/extension-node-map.json'
+
+ json_obj = await get_data(uri)
+
+ return web.json_response(json_obj, content_type='application/json')
+
+
+@server.PromptServer.instance.routes.get("/customnode/fetch_updates")
+async def fetch_updates(request):
+ try:
+ if request.rel_url.query["mode"] == "local":
+ uri = local_db_custom_node_list
+ else:
+ uri = 'https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json'
+
+ json_obj = await get_data(uri)
+ check_custom_nodes_installed(json_obj, True)
+
+ update_exists = any('custom_nodes' in json_obj and 'installed' in node and node['installed'] == 'Update' for node in
+ json_obj['custom_nodes'])
+
+ if update_exists:
+ return web.Response(status=201)
+
+ return web.Response(status=200)
+ except:
+ return web.Response(status=400)
+
+
+@server.PromptServer.instance.routes.get("/customnode/getlist")
+async def fetch_customnode_list(request):
+ if "skip_update" in request.rel_url.query and request.rel_url.query["skip_update"] == "true":
+ skip_update = True
+ else:
+ skip_update = False
+
+ if request.rel_url.query["mode"] == "local":
+ uri = local_db_custom_node_list
+ else:
+ uri = 'https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json'
+
+ json_obj = await get_data(uri)
+ check_custom_nodes_installed(json_obj, False, not skip_update)
+
+ return web.json_response(json_obj, content_type='application/json')
+
+
+@server.PromptServer.instance.routes.get("/alternatives/getlist")
+async def fetch_alternatives_list(request):
+ if "skip_update" in request.rel_url.query and request.rel_url.query["skip_update"] == "true":
+ skip_update = True
+ else:
+ skip_update = False
+
+ if request.rel_url.query["mode"] == "local":
+ uri1 = local_db_alter
+ uri2 = local_db_custom_node_list
+ else:
+ uri1 = 'https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/alter-list.json'
+ uri2 = 'https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json'
+
+ alter_json = await get_data(uri1)
+ custom_node_json = await get_data(uri2)
+
+ fileurl_to_custom_node = {}
+ for item in custom_node_json['custom_nodes']:
+ for fileurl in item['files']:
+ fileurl_to_custom_node[fileurl] = item
+
+ for item in alter_json['items']:
+ fileurl = item['id']
+ if fileurl in fileurl_to_custom_node:
+ custom_node = fileurl_to_custom_node[fileurl]
+ check_a_custom_node_installed(custom_node, not skip_update)
+ item['custom_node'] = custom_node
+
+ return web.json_response(alter_json, content_type='application/json')
+
+
+def check_model_installed(json_obj):
+ for item in json_obj['models']:
+ item['installed'] = 'None'
+
+ model_path = get_model_path(item)
+
+ if model_path is not None:
+ if os.path.exists(model_path):
+ item['installed'] = 'True'
+ else:
+ item['installed'] = 'False'
+
+
+@server.PromptServer.instance.routes.get("/externalmodel/getlist")
+async def fetch_externalmodel_list(request):
+ if request.rel_url.query["mode"] == "local":
+ uri = local_db_model
+ else:
+ uri = 'https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/model-list.json'
+
+ json_obj = await get_data(uri)
+ check_model_installed(json_obj)
+
+ return web.json_response(json_obj, content_type='application/json')
+
+
+def unzip_install(files):
+ temp_filename = 'manager-temp.zip'
+ for url in files:
+ try:
+ headers = {
+ 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
+
+ req = urllib.request.Request(url, headers=headers)
+ response = urllib.request.urlopen(req)
+ data = response.read()
+
+ with open(temp_filename, 'wb') as f:
+ f.write(data)
+
+ with zipfile.ZipFile(temp_filename, 'r') as zip_ref:
+ zip_ref.extractall(custom_nodes_path)
+
+ os.remove(temp_filename)
+ except Exception as e:
+ print(f"Install(unzip) error: {url} / {e}")
+ return False
+
+ print("Installation was successful.")
+ return True
+
+
+def download_url_with_agent(url, save_path):
+ try:
+ headers = {
+ 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
+
+ req = urllib.request.Request(url, headers=headers)
+ response = urllib.request.urlopen(req)
+ data = response.read()
+
+ if not os.path.exists(os.path.dirname(save_path)):
+ os.makedirs(os.path.dirname(save_path))
+
+ with open(save_path, 'wb') as f:
+ f.write(data)
+
+ except Exception as e:
+ print(f"Download error: {url} / {e}")
+ return False
+
+ print("Installation was successful.")
+ return True
+
+
+def copy_install(files, js_path_name=None):
+ for url in files:
+ try:
+ if url.endswith(".py"):
+ download_url(url, custom_nodes_path)
+ else:
+ path = os.path.join(js_path, js_path_name) if js_path_name is not None else js_path
+ if not os.path.exists(path):
+ os.makedirs(path)
+ download_url(url, path)
+
+ except Exception as e:
+ print(f"Install(copy) error: {url} / {e}")
+ return False
+
+ print("Installation was successful.")
+ return True
+
+
+def copy_uninstall(files, js_path_name=None):
+ for url in files:
+ dir_name = os.path.basename(url)
+ base_path = custom_nodes_path if url.endswith('.py') else os.path.join(js_path, js_path_name)
+ file_path = os.path.join(base_path, dir_name)
+
+ try:
+ if os.path.exists(file_path):
+ os.remove(file_path)
+ elif os.path.exists(file_path + ".disabled"):
+ os.remove(file_path + ".disabled")
+ except Exception as e:
+ print(f"Uninstall(copy) error: {url} / {e}")
+ return False
+
+ print("Uninstallation was successful.")
+ return True
+
+
+def copy_set_active(files, is_disable, js_path_name=None):
+ if is_disable:
+ action_name = "Disable"
+ else:
+ action_name = "Enable"
+
+ for url in files:
+ dir_name = os.path.basename(url)
+ base_path = custom_nodes_path if url.endswith('.py') else os.path.join(js_path, js_path_name)
+ file_path = os.path.join(base_path, dir_name)
+
+ try:
+ if is_disable:
+ current_name = file_path
+ new_name = file_path + ".disabled"
+ else:
+ current_name = file_path + ".disabled"
+ new_name = file_path
+
+ os.rename(current_name, new_name)
+
+ except Exception as e:
+ print(f"{action_name}(copy) error: {url} / {e}")
+
+ return False
+
+ print(f"{action_name} was successful.")
+ return True
+
+
+def execute_install_script(url, repo_path):
+ install_script_path = os.path.join(repo_path, "install.py")
+ requirements_path = os.path.join(repo_path, "requirements.txt")
+
+ if os.path.exists(requirements_path):
+ print(f"Install: pip packages")
+ install_cmd = [sys.executable, "-m", "pip", "install", "-r", "requirements.txt"]
+ try_install_script(url, repo_path, install_cmd)
+
+ if os.path.exists(install_script_path):
+ print(f"Install: install script")
+ install_cmd = [sys.executable, "install.py"]
+ try_install_script(url, repo_path, install_cmd)
+
+ return True
+
+
+def gitclone_install(files):
+ print(f"install: {files}")
+ for url in files:
+ try:
+ print(f"Download: git clone '{url}'")
+ repo_name = os.path.splitext(os.path.basename(url))[0]
+ repo_path = os.path.join(custom_nodes_path, repo_name)
+
+ # Clone the repository from the remote URL
+ if platform.system() == 'Windows':
+ process = subprocess.Popen([sys.executable, git_script_path, "--clone", custom_nodes_path, url])
+ process.wait()
+ else:
+ repo = git.Repo.clone_from(url, repo_path, recursive=True)
+ repo.git.clear_cache()
+ repo.close()
+
+ if not execute_install_script(url, repo_path):
+ return False
+
+ except Exception as e:
+ print(f"Install(git-clone) error: {url} / {e}")
+ return False
+
+ print("Installation was successful.")
+ return True
+
+
+import platform
+import subprocess
+import time
+
+
+def rmtree(path):
+ retry_count = 3
+
+ while True:
+ try:
+ retry_count -= 1
+
+ if platform.system() == "Windows":
+ subprocess.check_call(['attrib', '-R', path + '\\*', '/S'])
+ shutil.rmtree(path)
+
+ return True
+
+ except Exception as ex:
+ print(f"ex: {ex}")
+ time.sleep(3)
+
+ if retry_count < 0:
+ raise ex
+
+ print(f"Uninstall retry({retry_count})")
+
+
+def gitclone_uninstall(files):
+ import shutil
+ import os
+
+ print(f"uninstall: {files}")
+ for url in files:
+ try:
+ dir_name = os.path.splitext(os.path.basename(url))[0].replace(".git", "")
+ dir_path = os.path.join(custom_nodes_path, dir_name)
+
+ # safety check
+ if dir_path == '/' or dir_path[1:] == ":/" or dir_path == '':
+ print(f"Uninstall(git-clone) error: invalid path '{dir_path}' for '{url}'")
+ return False
+
+ install_script_path = os.path.join(dir_path, "uninstall.py")
+ disable_script_path = os.path.join(dir_path, "disable.py")
+ if os.path.exists(install_script_path):
+ uninstall_cmd = [sys.executable, "uninstall.py"]
+ code = subprocess.run(uninstall_cmd, cwd=dir_path)
+
+ if code.returncode != 0:
+ print(f"An error occurred during the execution of the uninstall.py script. Only the '{dir_path}' will be deleted.")
+ elif os.path.exists(disable_script_path):
+ disable_script = [sys.executable, "disable.py"]
+ code = subprocess.run(disable_script, cwd=dir_path)
+ if code.returncode != 0:
+ print(f"An error occurred during the execution of the disable.py script. Only the '{dir_path}' will be deleted.")
+
+ if os.path.exists(dir_path):
+ rmtree(dir_path)
+ elif os.path.exists(dir_path + ".disabled"):
+ rmtree(dir_path + ".disabled")
+ except Exception as e:
+ print(f"Uninstall(git-clone) error: {url} / {e}")
+ return False
+
+ print("Uninstallation was successful.")
+ return True
+
+
+def gitclone_set_active(files, is_disable):
+ import os
+
+ if is_disable:
+ action_name = "Disable"
+ else:
+ action_name = "Enable"
+
+ print(f"{action_name}: {files}")
+ for url in files:
+ try:
+ dir_name = os.path.splitext(os.path.basename(url))[0].replace(".git", "")
+ dir_path = os.path.join(custom_nodes_path, dir_name)
+
+ # safey check
+ if dir_path == '/' or dir_path[1:] == ":/" or dir_path == '':
+ print(f"{action_name}(git-clone) error: invalid path '{dir_path}' for '{url}'")
+ return False
+
+ if is_disable:
+ current_path = dir_path
+ new_path = dir_path + ".disabled"
+ else:
+ current_path = dir_path + ".disabled"
+ new_path = dir_path
+
+ os.rename(current_path, new_path)
+
+ if is_disable:
+ if os.path.exists(os.path.join(new_path, "disable.py")):
+ disable_script = [sys.executable, "disable.py"]
+ try_install_script(url, new_path, disable_script)
+ else:
+ if os.path.exists(os.path.join(new_path, "enable.py")):
+ enable_script = [sys.executable, "enable.py"]
+ try_install_script(url, new_path, enable_script)
+
+ except Exception as e:
+ print(f"{action_name}(git-clone) error: {url} / {e}")
+ return False
+
+ print(f"{action_name} was successful.")
+ return True
+
+
+def gitclone_update(files):
+ import os
+
+ print(f"Update: {files}")
+ for url in files:
+ try:
+ repo_name = os.path.splitext(os.path.basename(url))[0].replace(".git", "")
+ repo_path = os.path.join(custom_nodes_path, repo_name)
+ git_pull(repo_path)
+
+ if not execute_install_script(url, repo_path):
+ return False
+
+ except Exception as e:
+ print(f"Update(git-clone) error: {url} / {e}")
+ return False
+
+ print("Update was successful.")
+ return True
+
+
+@server.PromptServer.instance.routes.post("/customnode/install")
+async def install_custom_node(request):
+ json_data = await request.json()
+
+ install_type = json_data['install_type']
+
+ print(f"Install custom node '{json_data['title']}'")
+
+ res = False
+
+ if len(json_data['files']) == 0:
+ return web.Response(status=400)
+
+ if install_type == "unzip":
+ res = unzip_install(json_data['files'])
+
+ if install_type == "copy":
+ js_path_name = json_data['js_path'] if 'js_path' in json_data else None
+ res = copy_install(json_data['files'], js_path_name)
+
+ elif install_type == "git-clone":
+ res = gitclone_install(json_data['files'])
+
+ if 'pip' in json_data:
+ for pname in json_data['pip']:
+ install_cmd = [sys.executable, "-m", "pip", "install", pname]
+ try_install_script(json_data['files'][0], ".", install_cmd)
+
+ if res:
+ print(f"After restarting ComfyUI, please refresh the browser.")
+ return web.json_response({}, content_type='application/json')
+
+ return web.Response(status=400)
+
+
+@server.PromptServer.instance.routes.post("/customnode/uninstall")
+async def install_custom_node(request):
+ json_data = await request.json()
+
+ install_type = json_data['install_type']
+
+ print(f"Uninstall custom node '{json_data['title']}'")
+
+ res = False
+
+ if install_type == "copy":
+ js_path_name = json_data['js_path'] if 'js_path' in json_data else None
+ res = copy_uninstall(json_data['files'], js_path_name)
+
+ elif install_type == "git-clone":
+ res = gitclone_uninstall(json_data['files'])
+
+ if res:
+ print(f"After restarting ComfyUI, please refresh the browser.")
+ return web.json_response({}, content_type='application/json')
+
+ return web.Response(status=400)
+
+
+@server.PromptServer.instance.routes.post("/customnode/update")
+async def install_custom_node(request):
+ json_data = await request.json()
+
+ install_type = json_data['install_type']
+
+ print(f"Update custom node '{json_data['title']}'")
+
+ res = False
+
+ if install_type == "git-clone":
+ res = gitclone_update(json_data['files'])
+
+ if res:
+ print(f"After restarting ComfyUI, please refresh the browser.")
+ return web.json_response({}, content_type='application/json')
+
+ return web.Response(status=400)
+
+
+@server.PromptServer.instance.routes.get("/comfyui_manager/update_comfyui")
+async def install_custom_node(request):
+ print(f"Update ComfyUI")
+
+ try:
+ repo_path = os.path.dirname(folder_paths.__file__)
+
+ if not os.path.exists(os.path.join(repo_path, '.git')):
+ print(f"ComfyUI update fail: The installed ComfyUI does not have a Git repository.")
+ return web.Response(status=400)
+
+ # version check
+ repo = git.Repo(repo_path)
+
+ current_branch = repo.active_branch
+ branch_name = current_branch.name
+
+ remote_name = 'origin'
+ remote = repo.remote(name=remote_name)
+ remote.fetch()
+
+ commit_hash = repo.head.commit.hexsha
+ remote_commit_hash = repo.refs[f'{remote_name}/{branch_name}'].object.hexsha
+
+ if commit_hash != remote_commit_hash:
+ git_pull(repo_path)
+ execute_install_script("ComfyUI", repo_path)
+ return web.Response(status=201)
+ else:
+ return web.Response(status=200)
+ except Exception as e:
+ print(f"ComfyUI update fail: {e}")
+ pass
+
+ return web.Response(status=400)
+
+
+@server.PromptServer.instance.routes.post("/customnode/toggle_active")
+async def install_custom_node(request):
+ json_data = await request.json()
+
+ install_type = json_data['install_type']
+ is_disabled = json_data['installed'] == "Disabled"
+
+ print(f"Update custom node '{json_data['title']}'")
+
+ res = False
+
+ if install_type == "git-clone":
+ res = gitclone_set_active(json_data['files'], not is_disabled)
+ elif install_type == "copy":
+ res = copy_set_active(json_data['files'], not is_disabled, json_data.get('js_path', None))
+
+ if res:
+ return web.json_response({}, content_type='application/json')
+
+ return web.Response(status=400)
+
+
+@server.PromptServer.instance.routes.post("/model/install")
+async def install_model(request):
+ json_data = await request.json()
+
+ model_path = get_model_path(json_data)
+
+ res = False
+
+ try:
+ if model_path is not None:
+ print(f"Install model '{json_data['name']}' into '{model_path}'")
+
+ if json_data['url'].startswith('https://github.com') or json_data['url'].startswith('https://huggingface.co'):
+ model_dir = get_model_dir(json_data)
+ download_url(json_data['url'], model_dir)
+ return web.json_response({}, content_type='application/json')
+ else:
+ res = download_url_with_agent(json_data['url'], model_path)
+ else:
+ print(f"Model installation error: invalid model type - {json_data['type']}")
+
+ if res:
+ return web.json_response({}, content_type='application/json')
+ except Exception as e:
+ print(f"[ERROR] {e}")
+ pass
+
+ return web.Response(status=400)
+
+
+@server.PromptServer.instance.routes.get("/manager/preview_method")
+async def preview_method(request):
+ if "value" in request.rel_url.query:
+ set_preview_method(request.rel_url.query['value'])
+ write_config()
+ else:
+ return web.Response(text=get_current_preview_method(), status=200)
+
+ return web.Response(status=200)
+
+
+NODE_CLASS_MAPPINGS = {}
+__all__ = ['NODE_CLASS_MAPPINGS']
diff --git a/ComfyUI-Manager/alter-list.json b/ComfyUI-Manager/alter-list.json
new file mode 100644
index 0000000000000000000000000000000000000000..22c7abe56805a6177c62a29b5c4195c6e72f0729
--- /dev/null
+++ b/ComfyUI-Manager/alter-list.json
@@ -0,0 +1,134 @@
+{
+ "items": [
+ {
+ "id":"https://github.com/Fannovel16/comfy_controlnet_preprocessors",
+ "tags":"controlnet",
+ "description": "This extension provides preprocessor nodes for using controlnet."
+ },
+ {
+ "id":"https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/sampler_tonemap.py",
+ "tags":"Dynamic Thresholding, DT, CFG",
+ "description": "Increasing the CFG prevents the degradation of color accuracy."
+ },
+ {
+ "id":"https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/reference_only.py",
+ "tags":"controlnet, reference only",
+ "description": "This node provides functionality corresponding to Reference only in Controlnet."
+ },
+ {
+ "id":"https://github.com/ltdrdata/ComfyUI-Impact-Pack",
+ "tags":"ddetailer, adetailer, ddsd, DD, loopback scaler, prompt, wildcard",
+ "description": "To implement the feature of automatically detecting faces and enhancing details, various detection nodes and detailers provided by the Impact Pack can be applied. Similarly to Loopback Scaler, it also provides various custom workflows that can apply Ksampler while gradually scaling up."
+ },
+ {
+ "id":"https://github.com/biegert/ComfyUI-CLIPSeg/raw/main/custom_nodes/clipseg.py",
+ "tags":"ddsd",
+ "description": "This extension provides a feature that generates segment masks on an image using a text prompt. When used in conjunction with Impact Pack, it enables applications such as DDSD."
+ },
+ {
+ "id":"https://github.com/BadCafeCode/masquerade-nodes-comfyui",
+ "tags":"ddetailer",
+ "description": "This extension provides a way to recognize and enhance masks for faces similar to Impact Pack."
+ },
+ {
+ "id":"https://github.com/BlenderNeko/ComfyUI_Cutoff",
+ "tags":"cutoff",
+ "description": "By using this extension, prompts like 'blue hair' can be prevented from interfering with other prompts by blocking the attribute 'blue' from being used in prompts other than 'hair'."
+ },
+ {
+ "id":"https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb",
+ "tags":"prompt, weight",
+ "description": "There are differences in the processing methods of prompts, such as weighting and scheduling, between A1111 and ComfyUI. With this extension, various settings can be used to implement prompt processing methods similar to A1111. As this feature is also integrated into ComfyUI Cutoff, please download the Cutoff extension if you plan to use it in conjunction with Cutoff."
+ },
+ {
+ "id":"https://github.com/shiimizu/ComfyUI_smZNodes",
+ "tags":"prompt, weight",
+ "description": "There are differences in the processing methods of prompts, such as weighting and scheduling, between A1111 and ComfyUI. This extension helps to reproduce the same embedding as A1111."
+ },
+ {
+ "id":"https://github.com/BlenderNeko/ComfyUI_Noise",
+ "tags":"img2img alt, random",
+ "description": "The extension provides an unsampler that reverses the sampling process, allowing for a function similar to img2img alt to be implemented. Furthermore, ComfyUI uses CPU's Random instead of GPU's Random for better reproducibility compared to A1111. This extension provides the ability to use GPU's Random for Latent Noise. However, since GPU's Random may vary depending on the GPU model, reproducibility on different devices cannot be guaranteed."
+ },
+ {
+ "id":"https://github.com/BlenderNeko/ComfyUI_SeeCoder",
+ "tags":"seecoder, prompt-free-diffusion",
+ "description": "The extension provides seecoder feature."
+ },
+ {
+ "id":"https://github.com/lilly1987/ComfyUI_node_Lilly",
+ "tags":"prompt, wildcard",
+ "description": "This extension provides features such as a wildcard function that randomly selects prompts belonging to a category and the ability to directly load lora from prompts."
+ },
+ {
+ "id":"https://github.com/Davemane42/ComfyUI_Dave_CustomNode",
+ "tags":"latent couple",
+ "description": "ComfyUI already provides the ability to composite latents by default. However, this extension makes it more convenient to use by visualizing the composite area."
+ },
+ {
+ "id":"https://github.com/LEv145/images-grid-comfy-plugin",
+ "tags":"X/Y Plot",
+ "description": "This tool provides a viewer node that allows for checking multiple outputs in a grid, similar to the X/Y Plot extension."
+ },
+ {
+ "id":"https://github.com/gamert/ComfyUI_tagger",
+ "tags":"deepbooru, clip interrogation",
+ "description": "This extension generates clip text by taking an image as input and using the Deepbooru model."
+ },
+ {
+ "id":"https://github.com/szhublox/ambw_comfyui",
+ "tags":"supermerger",
+ "description": "This node takes two models, merges individual blocks together at various ratios, and automatically rates each merge, keeping the ratio with the highest score. "
+ },
+ {
+ "id":"https://github.com/ltdrdata/ComfyUI-tomeSD-installer",
+ "tags":"tomesd",
+ "description": "This extension is a ComfyUI wrapper for 'tomesd' that improves generation speed through token merging. Note:In vanilla ComfyUI, the TomePatchModel node is provided as a built-in feature."
+ },
+ {
+ "id":"https://github.com/ssitu/ComfyUI_UltimateSDUpscale",
+ "tags":"upscaler, Ultimate SD Upscale",
+ "description": "ComfyUI nodes for the Ultimate Stable Diffusion Upscale script by Coyote-A. Uses the same script used in the A1111 extension to hopefully replicate images generated using the A1111 webui."
+ },
+ {
+ "id":"https://github.com/dawangraoming/ComfyUI_ksampler_gpu/raw/main/ksampler_gpu.py",
+ "tags":"random, noise",
+ "description": "A1111 provides KSampler that uses GPU-based random noise. This extension offers KSampler utilizing GPU-based random noise."
+ },
+ {
+ "id":"https://github.com/space-nuko/nui-suite",
+ "tags":"prompt, dynamic prompt",
+ "description": "This extension provides nodes with the functionality of dynamic prompts."
+ },
+ {
+ "id":"https://github.com/melMass/comfy_mtb",
+ "tags":"roop",
+ "description": "This extension provides bunch of nodes including roop"
+ },
+ {
+ "id":"https://github.com/ssitu/ComfyUI_roop",
+ "tags":"roop",
+ "description": "This extension provides nodes for the roop A1111 webui script."
+ },
+ {
+ "id":"https://github.com/asagi4/comfyui-prompt-control",
+ "tags":"prompt, prompt editing",
+ "description": "This extension provides the ability to use prompts like
a [large::0.1] [cat|dog:0.05] [::0.5] [in a park:in space:0.4]
"
+ },
+ {
+ "id":"https://github.com/adieyal/comfyui-dynamicprompts",
+ "tags":"prompt, dynamic prompt",
+ "description": "This extension is a port of sd-dynamic-prompt to ComfyUI."
+ },
+ {
+ "id":"https://github.com/kwaroran/abg-comfyui",
+ "tags":"abg, background remover",
+ "description": "A Anime Background Remover node for comfyui, based on this hf space, works same as AGB extention in automatic1111."
+ },
+ {
+ "id":"https://github.com/Gourieff/comfyui-reactor-node",
+ "tags":"reactor, sd-webui-roop-nsfw",
+ "description": "This is a ported version of ComfyUI for the sd-webui-roop-nsfw extension."
+ }
+ ]
+}
\ No newline at end of file
diff --git a/ComfyUI-Manager/custom-node-list.json b/ComfyUI-Manager/custom-node-list.json
new file mode 100644
index 0000000000000000000000000000000000000000..af1c62cd7b29089d40791c172687f8b3ff404a0e
--- /dev/null
+++ b/ComfyUI-Manager/custom-node-list.json
@@ -0,0 +1,1155 @@
+{
+ "custom_nodes": [
+ {
+ "author": "Dr.Lt.Data",
+ "title": "ComfyUI-Manager",
+ "reference": "https://github.com/ltdrdata/ComfyUI-Manager",
+ "files": [
+ "https://github.com/ltdrdata/ComfyUI-Manager"
+ ],
+ "install_type": "git-clone",
+ "description": "ComfyUI-Manager itself is also a custom node."
+ },
+ {
+ "author": "Dr.Lt.Data",
+ "title": "ComfyUI Impact Pack",
+ "reference": "https://github.com/ltdrdata/ComfyUI-Impact-Pack",
+ "files": [
+ "https://github.com/ltdrdata/ComfyUI-Impact-Pack"
+ ],
+ "install_type": "git-clone",
+ "description": "This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler.
NOTE:MMDetDetectorProvider and other legacy nodes are disabled by default. If you want to activate these nodes and use them, please edit the impact-pack.ini file in the ComfyUI-Impact-Pack directory and change 'mmdet_skip = True' to 'mmdet_skip = False.'
"
+ },
+ {
+ "author": "comfyanonymous",
+ "title": "ComfyUI_experiments/sampler_tonemap",
+ "reference": "https://github.com/comfyanonymous/ComfyUI_experiments",
+ "files": [
+ "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/sampler_tonemap.py"
+ ],
+ "install_type": "copy",
+ "description": "ModelSamplerTonemapNoiseTest a node that makes the sampler use a simple tonemapping algorithm to tonemap the noise. It will let you use higher CFG without breaking the image. To using higher CFG lower the multiplier value. Similar to Dynamic Thresholding extension of A1111."
+ },
+ {
+ "author": "comfyanonymous",
+ "title": "ComfyUI_experiments/sampler_rescalecfg",
+ "reference": "https://github.com/comfyanonymous/ComfyUI_experiments",
+ "files": [
+ "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/sampler_rescalecfg.py"
+ ],
+ "install_type": "copy",
+ "description": "RescaleClassifierFreeGuidance improves the problem of images being degraded by high CFG.To using higher CFG lower the multiplier value. Similar to Dynamic Thresholding extension of A1111. (reference paper)"
+ },
+ {
+ "author": "comfyanonymous",
+ "title": "ComfyUI_experiments/advanced_model_merging",
+ "reference": "https://github.com/comfyanonymous/ComfyUI_experiments",
+ "files": [
+ "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/advanced_model_merging.py"
+ ],
+ "install_type": "copy",
+ "description": "This provides a detailed model merge feature based on block weight. ModelMergeBlock, in vanilla ComfyUI, allows for adjusting the ratios of input/middle/output layers, but this node provides ratio adjustments for all blocks within each layer."
+ },
+ {
+ "author": "comfyanonymous",
+ "title": "ComfyUI_experiments/reference_only",
+ "reference": "https://github.com/comfyanonymous/ComfyUI_experiments",
+ "files": [
+ "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/reference_only.py"
+ ],
+ "install_type": "copy",
+ "description": "This node provides functionality corresponding to Reference only in Controlnet."
+ },
+ {
+ "author": "Fannovel16",
+ "title": "ControlNet Preprocessors",
+ "reference": "https://github.com/Fannovel16/comfy_controlnet_preprocessors",
+ "files": [
+ "https://github.com/Fannovel16/comfy_controlnet_preprocessors"
+ ],
+ "install_type": "git-clone",
+ "description": "ControlNet Preprocessors. (To use this extension, you need to download the required model file from Install Models)
NOTE: To use nodes belonging to controlnet v1 such as Canny_Edge_Preprocessor, MIDAS_Depth_Map_Preprocessor, Uniformer_SemSegPreprocessor, etc., you need to copy the config.yaml.example file to config.yaml and change skip_v1: True to skip_v1: False.
"
+ },
+ {
+ "author": "Fannovel16",
+ "title": "ComfyUI-Frame-Interpolation",
+ "reference": "https://github.com/Fannovel16/ComfyUI-Frame-Interpolation",
+ "files": [
+ "https://github.com/Fannovel16/ComfyUI-Frame-Interpolation"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: KSampler Gradually Adding More Denoise (efficient)"
+ },
+ {
+ "author": "biegert",
+ "title": "CLIPSeg",
+ "reference": "https://github.com/biegert/ComfyUI-CLIPSeg",
+ "files": [
+ "https://github.com/biegert/ComfyUI-CLIPSeg/raw/main/custom_nodes/clipseg.py"
+ ],
+ "install_type": "copy",
+ "description": "The CLIPSeg node generates a binary mask for a given input image and text prompt."
+ },
+ {
+ "author": "BlenderNeko",
+ "title": "ComfyUI Cutoff",
+ "reference": "https://github.com/BlenderNeko/ComfyUI_Cutoff",
+ "files": [
+ "https://github.com/BlenderNeko/ComfyUI_Cutoff"
+ ],
+ "install_type": "git-clone",
+ "description": "These custom nodes provides features that allow for better control over the effects of the text prompt."
+ },
+ {
+ "author": "BlenderNeko",
+ "title": "Advanced CLIP Text Encode",
+ "reference": "https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb",
+ "files": [
+ "https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb"
+ ],
+ "install_type": "git-clone",
+ "description": "Advanced CLIP Text Encode (if you need A1111 like prompt. you need this. But Cutoff node includes this feature, already.)"
+ },
+ {
+ "author": "BlenderNeko",
+ "title": "ComfyUI Noise",
+ "reference": "https://github.com/BlenderNeko/ComfyUI_Noise",
+ "files": [
+ "https://github.com/BlenderNeko/ComfyUI_Noise"
+ ],
+ "install_type": "git-clone",
+ "description": "This extension contains 6 nodes for ComfyUI that allows for more control and flexibility over the noise."
+ },
+ {
+ "author": "BlenderNeko",
+ "title": "Tiled sampling for ComfyUI",
+ "reference": "https://github.com/BlenderNeko/ComfyUI_TiledKSampler",
+ "files": [
+ "https://github.com/BlenderNeko/ComfyUI_TiledKSampler"
+ ],
+ "install_type": "git-clone",
+ "description": "This extension contains a tiled sampler for ComfyUI. It allows for denoising larger images by splitting it up into smaller tiles and denoising these. It tries to minimize any seams for showing up in the end result by gradually denoising all tiles one step at the time and randomizing tile positions for every step."
+ },
+ {
+ "author": "BlenderNeko",
+ "title": "(WIP) SeeCoder",
+ "reference": "https://github.com/BlenderNeko/ComfyUI_SeeCoder",
+ "files": [
+ "https://github.com/BlenderNeko/ComfyUI_SeeCoder"
+ ],
+ "install_type": "git-clone",
+ "description": "It provides the capability to generate CLIP from an image input, unlike unCLIP, which works in all models. (To use this extension, you need to download the required model file from Install Models)"
+ },
+ {
+ "author": "LucianoCirino",
+ "title": "Efficiency Nodes for ComfyUI",
+ "reference": "https://github.com/LucianoCirino/efficiency-nodes-comfyui",
+ "files": [
+ "https://github.com/LucianoCirino/efficiency-nodes-comfyui"
+ ],
+ "install_type": "git-clone",
+ "description": "A collection of ComfyUI custom nodes to help streamline workflows and reduce total node count."
+ },
+ {
+ "author": "Derfuu",
+ "title": "Derfuu_ComfyUI_ModdedNodes",
+ "reference": "https://github.com/Derfuu/Derfuu_ComfyUI_ModdedNodes",
+ "files": [
+ "https://github.com/Derfuu/Derfuu_ComfyUI_ModdedNodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Automate calculation depending on image sizes or something you want."
+ },
+ {
+ "author": "paulo-coronado",
+ "title": "comfy_clip_blip_node",
+ "reference": "https://github.com/paulo-coronado/comfy_clip_blip_node",
+ "files": [
+ "https://github.com/paulo-coronado/comfy_clip_blip_node"
+ ],
+ "install_type": "git-clone",
+ "apt_dependency": [
+ "rustc",
+ "cargo"
+ ],
+ "description": "CLIPTextEncodeBLIP: This custom node provides a CLIP Encoder that is capable of receiving images as input."
+ },
+ {
+ "author": "Davemane42",
+ "title": "Visual Area Conditioning / Latent composition",
+ "reference": "https://github.com/Davemane42/ComfyUI_Dave_CustomNode",
+ "files": [
+ "https://github.com/Davemane42/ComfyUI_Dave_CustomNode"
+ ],
+ "install_type": "git-clone",
+ "description": "This tool provides custom nodes that allow visualization and configuration of area conditioning and latent composite."
+ },
+ {
+ "author": "WASasquatch",
+ "title": "WAS Node Suite",
+ "reference": "https://github.com/WASasquatch/was-node-suite-comfyui",
+ "pip": ["numba"],
+ "files": [
+ "https://github.com/WASasquatch/was-node-suite-comfyui"
+ ],
+ "install_type": "git-clone",
+ "description": "A node suite for ComfyUI with many new nodes, such as image processing, text processing, and more."
+ },
+ {
+ "author": "omar92",
+ "title": "Quality of life Suit:V2",
+ "reference": "https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92",
+ "files": [
+ "https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92"
+ ],
+ "install_type": "git-clone",
+ "description": "openAI suite, String suite, Latent Tools, Image Tools: These custom nodes provide expanded functionality for image and string processing, latent processing, as well as the ability to interface with models such as ChatGPT/DallE-2."
+ },
+ {
+ "author": "lilly1987",
+ "title": "simple wildcard for ComfyUI",
+ "reference": "https://github.com/lilly1987/ComfyUI_node_Lilly",
+ "files": [
+ "https://github.com/lilly1987/ComfyUI_node_Lilly"
+ ],
+ "install_type": "git-clone",
+ "description": "These custom nodes provides a feature to insert arbitrary inputs through wildcards in the prompt. Additionally, this tool provides features that help simplify workflows, such as VAELoaderDecoder and SimplerSample."
+ },
+ {
+ "author": "sylym",
+ "title": "Vid2vid",
+ "reference": "https://github.com/sylym/comfy_vid2vid",
+ "files": [
+ "https://github.com/sylym/comfy_vid2vid"
+ ],
+ "install_type": "git-clone",
+ "description": "A node suite for ComfyUI that allows you to load image sequence and generate new image sequence with different styles or content."
+ },
+ {
+ "author": "EllangoK",
+ "title": "ComfyUI-post-processing-nodes",
+ "reference": "https://github.com/EllangoK/ComfyUI-post-processing-nodes",
+ "files": [
+ "https://github.com/EllangoK/ComfyUI-post-processing-nodes"
+ ],
+ "install_type": "git-clone",
+ "description": "A collection of post processing nodes for ComfyUI, simply download this repo and drag."
+ },
+ {
+ "author": "LEv145",
+ "title": "ImagesGrid",
+ "reference": "https://github.com/LEv145/images-grid-comfy-plugin",
+ "files": [
+ "https://github.com/LEv145/images-grid-comfy-plugin"
+ ],
+ "install_type": "git-clone",
+ "description": "This tool provides a viewer node that allows for checking multiple outputs in a grid, similar to the X/Y Plot extension."
+ },
+ {
+ "author": "diontimmer",
+ "title": "ComfyUI-Vextra-Nodes",
+ "reference": "https://github.com/diontimmer/ComfyUI-Vextra-Nodes",
+ "files": [
+ "https://github.com/diontimmer/ComfyUI-Vextra-Nodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: Pixel Sort, Swap Color Mode, Solid Color, Glitch This, Add Text To Image, Play Sound, Prettify Prompt, Generate Noise, Flatten Colors"
+ },
+ {
+ "author": "hnmr293",
+ "title": "ComfyUI-nodes-hnmr",
+ "reference": "https://github.com/hnmr293/ComfyUI-nodes-hnmr",
+ "files": [
+ "https://github.com/hnmr293/ComfyUI-nodes-hnmr"
+ ],
+ "install_type": "git-clone",
+ "description": "Provide various custom nodes for Latent, Sampling, Model, Loader, Image, Text"
+ },
+ {
+ "author": "BadCafeCode",
+ "title": "Masquerade Nodes",
+ "reference": "https://github.com/BadCafeCode/masquerade-nodes-comfyui",
+ "files": [
+ "https://github.com/BadCafeCode/masquerade-nodes-comfyui"
+ ],
+ "install_type": "git-clone",
+ "description": "This is a node pack for ComfyUI, primarily dealing with masks."
+ },
+ {
+ "author": "guoyk93",
+ "title": "y.k.'s ComfyUI node suite",
+ "reference": "https://github.com/guoyk93/yk-node-suite-comfyui",
+ "files": [
+ "https://github.com/guoyk93/yk-node-suite-comfyui"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: YKImagePadForOutpaint, YKMaskToImage"
+ },
+ {
+ "author": "Jcd1230",
+ "title": "Rembg Background Removal Node for ComfyUI",
+ "reference": "https://github.com/Jcd1230/rembg-comfyui-node",
+ "files": [
+ "https://github.com/Jcd1230/rembg-comfyui-node"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: Image Remove Background (rembg)"
+ },
+ {
+ "author": "gamert",
+ "title": "ComfyUI_tagger",
+ "reference": "https://github.com/gamert/ComfyUI_tagger",
+ "files": [
+ "https://github.com/gamert/ComfyUI_tagger"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: CLIPTextEncodeTaggerDD, ImageTaggerDD"
+ },
+ {
+ "author": "YinBailiang",
+ "title": "MergeBlockWeighted_fo_ComfyUI",
+ "reference": "https://github.com/YinBailiang/MergeBlockWeighted_fo_ComfyUI",
+ "files": [
+ "https://github.com/YinBailiang/MergeBlockWeighted_fo_ComfyUI"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: MergeBlockWeighted"
+ },
+ {
+ "author": "trojblue",
+ "title": "trNodes",
+ "reference": "https://github.com/trojblue/trNodes",
+ "files": [
+ "https://github.com/trojblue/trNodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: image_layering, color_correction, model_router"
+ },
+ {
+ "author": "szhublox",
+ "title": "Auto-MBW",
+ "reference": "https://github.com/szhublox/ambw_comfyui",
+ "files": [
+ "https://github.com/szhublox/ambw_comfyui"
+ ],
+ "install_type": "git-clone",
+ "description": "Auto-MBW for ComfyUI loosely based on sdweb-auto-MBW. Nodes: auto merge block weighted"
+ },
+ {
+ "author": "city96",
+ "title": "ComfyUI_NetDist",
+ "reference": "https://github.com/city96/ComfyUI_NetDist",
+ "files": [
+ "https://github.com/city96/ComfyUI_NetDist"
+ ],
+ "install_type": "git-clone",
+ "description": "Run ComfyUI workflows on multiple local GPUs/networked machines. Nodes: Remote images, Local Remote control"
+ },
+ {
+ "author": "Kaharos94",
+ "title": "ComfyUI-Saveaswebp",
+ "reference": "https://github.com/Kaharos94/ComfyUI-Saveaswebp",
+ "files": [
+ "https://github.com/Kaharos94/ComfyUI-Saveaswebp"
+ ],
+ "install_type": "git-clone",
+ "description": "Save a picture as Webp file in Comfy + Workflow loading"
+ },
+ {
+ "author": "chenbaiyujason",
+ "title": "sc-node-comfyui",
+ "reference": "https://github.com/chenbaiyujason/sc-node-comfyui",
+ "files": [
+ "https://github.com/chenbaiyujason/sc-node-comfyui"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes for GPT interaction and text manipulation"
+ },
+ {
+ "author": "SLAPaper",
+ "title": "ComfyUI-Image-Selector",
+ "reference": "https://github.com/SLAPaper/ComfyUI-Image-Selector",
+ "files": [
+ "https://github.com/SLAPaper/ComfyUI-Image-Selector"
+ ],
+ "install_type": "git-clone",
+ "description": "A custom node for ComfyUI, which can select one or some of images from a batch."
+ },
+ {
+ "author": "flyingshutter",
+ "title": "As_ComfyUI_CustomNodes",
+ "reference": "https://github.com/flyingshutter/As_ComfyUI_CustomNodes",
+ "files": [
+ "https://github.com/flyingshutter/As_ComfyUI_CustomNodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Manipulation nodes for Image, Latent"
+ },
+ {
+ "author": "Zuellni",
+ "title": "Zuellni/ComfyUI-Custom-Nodes",
+ "reference": "https://github.com/Zuellni/ComfyUI-Custom-Nodes",
+ "files": [
+ "https://github.com/Zuellni/ComfyUI-Custom-Nodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: DeepFloyd, Filter, Select, Save, Decode, Encode, Repeat, Noise, Noise"
+ },
+ {
+ "author": "AlekPet",
+ "title": "AlekPet/ComfyUI_Custom_Nodes_AlekPet",
+ "reference": "https://github.com/AlekPet/ComfyUI_Custom_Nodes_AlekPet",
+ "files": [
+ "https://github.com/AlekPet/ComfyUI_Custom_Nodes_AlekPet"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: PoseNode, TranslateCLIPTextEncodeNode"
+ },
+ {
+ "author": "pythongosssss",
+ "title": "ComfyUI WD 1.4 Tagger",
+ "reference": "https://github.com/pythongosssss/ComfyUI-WD14-Tagger",
+ "files": [
+ "https://github.com/pythongosssss/ComfyUI-WD14-Tagger"
+ ],
+ "install_type": "git-clone",
+ "description": "A ComfyUI extension allowing the interrogation of booru tags from images."
+ },
+ {
+ "author": "strimmlarn",
+ "title": "ComfyUI_Strimmlarns_aesthetic_score",
+ "reference": "https://github.com/strimmlarn/ComfyUI_Strimmlarns_aesthetic_score",
+ "js_path": "strimmlarn",
+ "files": [
+ "https://github.com/strimmlarn/ComfyUI_Strimmlarns_aesthetic_score"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: CalculateAestheticScore, LoadAesteticModel, AesthetlcScoreSorter, ScoreToNumber"
+ },
+ {
+ "author": "tinyterra",
+ "title": "tinyterraNodes",
+ "reference": "https://github.com/tinyterra/ComfyUI_tinyterraNodes",
+ "files": [
+ "https://github.com/TinyTerra/ComfyUI_tinyterraNodes.git"
+ ],
+ "install_type": "git-clone",
+ "nodename_pattern": "^ttN ",
+ "description": "Nodes: pipeLoader, pipeKSampler, assorted pipe utils, imageOutput, imageRemBG, 3x TXT Loader MultiConcat, +more"
+ },
+ {
+ "author": "Jordach",
+ "title": "comfy-plasma",
+ "reference": "https://github.com/Jordach/comfy-plasma",
+ "files": [
+ "https://github.com/Jordach/comfy-plasma"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: Plasma Noise, Random Noise, Greyscale Noise, Pink Noise, Brown Noise, Plasma KSampler"
+ },
+ {
+ "author": "bvhari",
+ "title": "ImageProcessing",
+ "reference": "https://github.com/bvhari/ComfyUI_ImageProcessing",
+ "files": [
+ "https://github.com/bvhari/ComfyUI_ImageProcessing"
+ ],
+ "install_type": "git-clone",
+ "description": "ComfyUI custom nodes to apply various image processing techniques."
+ },
+ {
+ "author": "bvhari",
+ "title": "LatentToRGB",
+ "reference": "https://github.com/bvhari/ComfyUI_LatentToRGB",
+ "files": [
+ "https://github.com/bvhari/ComfyUI_LatentToRGB"
+ ],
+ "install_type": "git-clone",
+ "description": "ComfyUI custom node to convert latent to RGB."
+ },
+ {
+ "author": "ssitu",
+ "title": "UltimateSDUpscale",
+ "reference": "https://github.com/ssitu/ComfyUI_UltimateSDUpscale",
+ "files": [
+ "https://github.com/ssitu/ComfyUI_UltimateSDUpscale"
+ ],
+ "install_type": "git-clone",
+ "description": "ComfyUI nodes for the Ultimate Stable Diffusion Upscale script by Coyote-A."
+ },
+ {
+ "author": "ssitu",
+ "title": "NestedNodeBuilder",
+ "reference": "https://github.com/ssitu/ComfyUI_NestedNodeBuilder",
+ "files": [
+ "https://github.com/ssitu/ComfyUI_NestedNodeBuilder"
+ ],
+ "install_type": "git-clone",
+ "description": "This extension provides the ability to combine multiple nodes into a single node."
+ },
+ {
+ "author": "ssitu",
+ "title": "Restart Sampling",
+ "reference": "https://github.com/ssitu/ComfyUI_restart_sampling",
+ "files": [
+ "https://github.com/ssitu/ComfyUI_restart_sampling"
+ ],
+ "install_type": "git-clone",
+ "description": "Unofficial ComfyUI nodes for restart sampling based on the paper 'Restart Sampling for Improving Generative Processes' [paper] [repo]"
+ },
+ {
+ "author": "ssitu",
+ "title": "ComfyUI roop",
+ "reference": "https://github.com/ssitu/ComfyUI_roop",
+ "files": [
+ "https://github.com/ssitu/ComfyUI_roop"
+ ],
+ "install_type": "git-clone",
+ "description": "ComfyUI nodes for the roop A1111 webui script."
+ },
+ {
+ "author": "space-nuko",
+ "title": "Disco Diffusion",
+ "reference": "https://github.com/space-nuko/ComfyUI-Disco-Diffusion",
+ "files": [
+ "https://github.com/space-nuko/ComfyUI-Disco-Diffusion"
+ ],
+ "install_type": "git-clone",
+ "description": "Modularized version of Disco Diffusion for use with ComfyUI."
+ },
+ {
+ "author": "space-nuko",
+ "title": "OpenPose Editor",
+ "reference": "https://github.com/space-nuko/ComfyUI-OpenPose-Editor",
+ "files": [
+ "https://github.com/space-nuko/ComfyUI-OpenPose-Editor"
+ ],
+ "install_type": "git-clone",
+ "description": "A port of the openpose-editor extension for stable-diffusion-webui. NOTE: Requires this ComfyUI patch to work correctly"
+ },
+ {
+ "author": "space-nuko",
+ "title": "nui suite",
+ "reference": "https://github.com/space-nuko/nui-suite",
+ "files": [
+ "https://github.com/space-nuko/nui-suite"
+ ],
+ "install_type": "git-clone",
+ "description": "NODES: Dynamic Prompts Text Encode, Feeling Lucky Text Encode, Output String"
+ },
+ {
+ "author": "Nourepide",
+ "title": "Allor Plugin",
+ "reference": "https://github.com/Nourepide/ComfyUI-Allor",
+ "files": [
+ "https://github.com/Nourepide/ComfyUI-Allor"
+ ],
+ "install_type": "git-clone",
+ "description": "Allor is a plugin for ComfyUI with an emphasis on transparency and performance.
NOTE: If you do not disable the default node override feature in the settings, the built-in nodes, namely ImageScale and ImageScaleBy nodes, will be disabled. (ref: Configutation)
"
+ },
+ {
+ "author": "pythongosssss",
+ "title": "pythongosssss/ComfyUI-Custom-Scripts",
+ "reference": "https://github.com/pythongosssss/ComfyUI-Custom-Scripts",
+ "files": [
+ "https://github.com/pythongosssss/ComfyUI-Custom-Scripts"
+ ],
+ "install_type": "git-clone",
+ "description": "This extension provides: Auto Arrange Graph, Workflow SVG, Favicon Status, Image Feed, Latent Upscale By, Lock Nodes & Groups, Lora Subfolders, Preset Text, Show Text, Touch Support, Link Render Mode, Locking, Node Finder, Quick Nodes, Show Image On Menu, Show Text, Workflow Managements, Custom Widget Default Values"
+ },
+ {
+ "author": "melMass",
+ "title": "MTB Nodes",
+ "reference": "https://github.com/melMass/comfy_mtb",
+ "files": [
+ "https://github.com/melMass/comfy_mtb"
+ ],
+ "nodename_pattern": "\\(mtb\\)$",
+ "install_type": "git-clone",
+ "description": "NODES: Face Swap, Film Interpolation, Latent Lerp, Int To Number, Bounding Box, Crop, Uncrop, ImageBlur, Denoise, ImageCompare, RGV to HSV, HSV to RGB, Color Correct, Modulo, Deglaze Image, Smart Step, ..."
+ },
+ {
+ "author": "xXAdonesXx",
+ "title": "NodeGPT",
+ "reference": "https://github.com/xXAdonesXx/NodeGPT",
+ "files": [
+ "https://github.com/xXAdonesXx/NodeGPT/raw/main/Textnode.py"
+ ],
+ "install_type": "copy",
+ "description": "ComfyUI Extension Nodes for Automated Text Generation."
+ },
+ {
+ "author": "asd417",
+ "title": "CheckpointTomeLoader",
+ "reference": "https://github.com/asd417/tomeSD_for_Comfy",
+ "files": [
+ "https://github.com/ltdrdata/ComfyUI-tomeSD-installer"
+ ],
+ "install_type": "git-clone",
+ "description": "tomeSD(https://github.com/dbolya/tomesd) applied to ComfyUI stable diffusion UI using custom node. Note:In vanilla ComfyUI, the TomePatchModel node is provided as a built-in feature."
+ },
+ {
+ "author": "RockOfFire",
+ "title": "ComfyUI_Comfyroll_CustomNodes",
+ "reference": "https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes",
+ "files": [
+ "https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes"
+ ],
+ "install_type": "git-clone",
+ "description": "These nodes were made for use in the Comfyroll Template Workflows."
+ },
+ {
+ "author": "bmad4ever",
+ "title": "ComfyUI-Bmad-DirtyUndoRedo",
+ "reference": "https://github.com/bmad4ever/ComfyUI-Bmad-DirtyUndoRedo",
+ "files": [
+ "https://github.com/bmad4ever/ComfyUI-Bmad-DirtyUndoRedo"
+ ],
+ "install_type": "git-clone",
+ "description": "ComfyUI extension that adds undo (and redo) functionality."
+ },
+ {
+ "author": "FizzleDorf",
+ "title": "FizzNodes",
+ "reference": "https://github.com/FizzleDorf/ComfyUI_FizzNodes",
+ "files": [
+ "https://github.com/FizzleDorf/ComfyUI_FizzNodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Custom animation and utility nodes for Comfyui."
+ },
+ {
+ "author": "FizzleDorf",
+ "title": "AIT",
+ "reference": "https://github.com/FizzleDorf/AIT",
+ "files": [
+ "https://github.com/FizzleDorf/AIT"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: Load AITemplate, Load AITemplate (ControlNet), VAE Decode (AITemplate), VAE Encode (AITemplate), VAE Encode (AITemplate, Inpaint). Experimental usage of AITemplate"
+ },
+ {
+ "author": "filipemeneses",
+ "title": "Pixelization",
+ "reference": "https://github.com/filipemeneses/comfy_pixelization",
+ "files": [
+ "https://github.com/filipemeneses/comfy_pixelization"
+ ],
+ "install_type": "git-clone",
+ "description": "ComfyUI node that pixelizes images."
+ },
+ {
+ "author": "shiimizu",
+ "title": "smZNodes",
+ "reference": "https://github.com/shiimizu/ComfyUI_smZNodes",
+ "files": [
+ "https://github.com/shiimizu/ComfyUI_smZNodes"
+ ],
+ "install_type": "git-clone",
+ "description": "NODES: CLIP Text Encode++. Achieve identical embeddings from stable-diffusion-webui for ComfyUI."
+ },
+ {
+ "author": "ZaneA",
+ "title": "ImageReward",
+ "reference": "https://github.com/ZaneA/ComfyUI-ImageReward",
+ "files": [
+ "https://github.com/ZaneA/ComfyUI-ImageReward"
+ ],
+ "install_type": "git-clone",
+ "description": "NODES: ImageRewardLoader, ImageRewardScore"
+ },
+ {
+ "author": "SeargeDP",
+ "title": "SeargeSDXL",
+ "reference": "https://github.com/SeargeDP/SeargeSDXL",
+ "files": [
+ "https://github.com/SeargeDP/SeargeSDXL"
+ ],
+ "install_type": "git-clone",
+ "description": "Custom nodes for easier use of SDXL in ComfyUI including an img2img workflow that utilizes both the base and refiner checkpoints."
+ },
+ {
+ "author": "cubiq",
+ "title": "Simple Math",
+ "reference": "https://github.com/cubiq/ComfyUI_SimpleMath",
+ "files": [
+ "https://github.com/cubiq/ComfyUI_SimpleMath"
+ ],
+ "install_type": "git-clone",
+ "description": "custom node for ComfyUI to perform simple math operations"
+ },
+ {
+ "author": "shockz0rz",
+ "title": "InterpolateEverything",
+ "reference": "https://github.com/shockz0rz/ComfyUI_InterpolateEverything",
+ "files": [
+ "https://github.com/shockz0rz/ComfyUI_InterpolateEverything"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: Interpolate Poses, Interpolate Lineart, ... Custom nodes for interpolating between, well, everything in the Stable Diffusion ComfyUI."
+ },
+ {
+ "author": "yolanother",
+ "title": "DTAIComfyPromptAgent",
+ "reference": "https://github.com/yolanother/DTAIComfyPromptAgent",
+ "files": [
+ "https://github.com/yolanother/DTAIComfyPromptAgent"
+ ],
+ "install_type": "git-clone",
+ "description": "This script provides a prompt agent node for the Comfy UI stable diffusion client."
+ },
+ {
+ "author": "sipherxyz",
+ "title": "comfyui-art-venture",
+ "reference": "https://github.com/sipherxyz/comfyui-art-venture",
+ "files": [
+ "https://github.com/sipherxyz/comfyui-art-venture"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: ImagesConcat, LoadImageFromUrl, AV_UploadImage"
+ },
+ {
+ "author": "SOELexicon",
+ "title": "LexMSDBNodes",
+ "reference": "https://github.com/SOELexicon/ComfyUI-LexMSDBNodes",
+ "files": [
+ "https://github.com/SOELexicon/ComfyUI-LexMSDBNodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: MSSqlTableNode, MSSqlSelectNode. This extension provides custom nodes to interact with MSSQL."
+ },
+ {
+ "author": "pants007",
+ "title": "pants",
+ "reference": "https://github.com/pants007/comfy-pants",
+ "files": [
+ "https://github.com/pants007/comfy-pants"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: Make Square Node, Interrogate Node, TextEncodeAIO"
+ },
+ {
+ "author": "evanspearman",
+ "title": "ComfyMath",
+ "reference": "https://github.com/evanspearman/ComfyMath",
+ "files": [
+ "https://github.com/evanspearman/ComfyMath"
+ ],
+ "install_type": "git-clone",
+ "description": "Provides Math Nodes for ComfyUI. Boolean Logic, Integer Arithmetic, Floating Point Arithmetic and Functions, Vec2, Vec3, and Vec4 Arithmetic and Functions"
+ },
+ {
+ "author": "civitai",
+ "title": "comfy-nodes",
+ "reference": "https://github.com/civitai/comfy-nodes",
+ "files": [
+ "https://github.com/civitai/comfy-nodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: CivitAI_Loaders. Load Checkpoints, and LORA models directly from CivitAI API."
+ },
+ {
+ "author": "andersxa",
+ "title": "CLIP Directional Prompt Attention",
+ "reference": "https://github.com/andersxa/comfyui-PromptAttention",
+ "files": [
+ "https://github.com/andersxa/comfyui-PromptAttention"
+ ],
+ "pip": ["scikit-learn", "matplotlib"],
+ "install_type": "git-clone",
+ "description": "Nodes: CLIP Directional Prompt Attention Encode. Direction prompt attention tries to solve the problem of contextual words (or parts of the prompt) having an effect on much later or irrelevant parts of the prompt."
+ },
+ {
+ "author": "ArtVentureX",
+ "title": "AnimateDiff",
+ "reference": "https://github.com/ArtVentureX/comfyui-animatediff",
+ "files": [
+ "https://github.com/ArtVentureX/comfyui-animatediff"
+ ],
+ "install_type": "git-clone",
+ "description": "AnimateDiff integration for ComfyUI, adapts from sd-webui-animatediff.
You only need to download one of mm_sd_v14.ckpt | mm_sd_v15.ckpt. Put the model weights under ComfyUI/custom_nodes/comfyui-animatediff/models. DO NOT change model filename.
"
+ },
+ {
+ "author": "twri",
+ "title": "SDXL Prompt Styler",
+ "reference": "https://github.com/twri/sdxl_prompt_styler",
+ "files": [
+ "https://github.com/twri/sdxl_prompt_styler"
+ ],
+ "install_type": "git-clone",
+ "description": "SDXL Prompt Styler is a node that enables you to style prompts based on predefined templates stored in a JSON file."
+ },
+ {
+ "author": "daxthin",
+ "title": "facedetailer",
+ "reference": "https://github.com/daxthin/facedetailer",
+ "files": [
+ "https://github.com/daxthin/facedetailer"
+ ],
+ "install_type": "git-clone",
+ "description": "Face Detailer is a custom node for the 'ComfyUI' framework inspired by !After Detailer extension from auto1111, it allows you to detect faces using Mediapipe and YOLOv8n to create masks for the detected faces."
+ },
+ {
+ "author": "asagi4",
+ "title": "ComfyUI prompt control",
+ "reference": "https://github.com/asagi4/comfyui-prompt-control",
+ "files": [
+ "https://github.com/asagi4/comfyui-prompt-control"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes for convenient prompt editing. The aim is to make basic generations in ComfyUI completely prompt-controllable."
+ },
+ {
+ "author": "jamesWalker55",
+ "title": "ComfyUI - P2LDGAN Node",
+ "reference": "https://github.com/jamesWalker55/comfyui-p2ldgan",
+ "files": [
+ "https://github.com/jamesWalker55/comfyui-p2ldgan"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: P2LDGAN. This integrates P2LDGAN into ComfyUI. P2LDGAN extracts lineart from input images.
To use this extension, you need to download the p2ldgan model and save it in the ComfyUI/custom_nodes/comfyui-p2ldgan/checkpoints directory.
"
+ },
+ {
+ "author": "adieyal",
+ "title": "DynamicPrompts Custom Nodes",
+ "reference": "https://github.com/adieyal/comfyui-dynamicprompts",
+ "files": [
+ "https://github.com/adieyal/comfyui-dynamicprompts"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: Random Prompts, Combinatorial Prompts, I'm Feeling Lucky, Magic Prompt, Jinja2 Templates. ComfyUI-DynamicPrompts is a custom nodes library that integrates into your existing ComfyUI Library. It provides nodes that enable the use of Dynamic Prompts in your ComfyUI."
+ },
+ {
+ "author": "mihaiiancu",
+ "title": "mihaiiancu/Inpaint",
+ "reference": "https://github.com/mihaiiancu/ComfyUI_Inpaint",
+ "files": [
+ "https://github.com/mihaiiancu/ComfyUI_Inpaint"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: InpaintMediapipe. This node provides a simple interface to inpaint."
+ },
+ {
+ "author": "kwaroran",
+ "title": "abg-comfyui",
+ "reference": "https://github.com/kwaroran/abg-comfyui",
+ "files": [
+ "https://github.com/kwaroran/abg-comfyui"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: Remove Image Background (abg). A Anime Background Remover node for comfyui, based on this hf space, works same as AGB extention in automatic1111."
+ },
+ {
+ "author": "bash-j",
+ "title": "Mikey Nodes",
+ "reference": "https://github.com/bash-j/mikey_nodes",
+ "files": [
+ "https://github.com/bash-j/mikey_nodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: Prompt With Style, Prompt With SDXL, Resize Image for SDXL, Save Image With Prompt Data, HaldCLUT, Empty Latent Ratio Select/Custom SDXL"
+ },
+ {
+ "author": "failfa.st",
+ "title": "failfast-comfyui-extensions",
+ "reference": "https://github.com/failfa-st/failfast-comfyui-extensions",
+ "files": [
+ "https://github.com/failfa-st/failfast-comfyui-extensions"
+ ],
+ "install_type": "git-clone",
+ "description": "node color customization, custom colors, dot reroutes, link rendering options, straight lines, group freezing, node pinning, automated arrangement of nodes, copy image"
+ },
+ {
+ "author": "Pfaeff",
+ "title": "pfaeff-comfyui",
+ "reference": "https://github.com/Pfaeff/pfaeff-comfyui",
+ "files": [
+ "https://github.com/Pfaeff/pfaeff-comfyui"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: AstropulsePixelDetector, BackgroundRemover, ImagePadForBetterOutpaint, InpaintingPipelineLoader, Inpainting, ..."
+ },
+ {
+ "author": "wallish77",
+ "title": "wlsh_nodes",
+ "reference": "https://github.com/wallish77/wlsh_nodes",
+ "files": [
+ "https://github.com/wallish77/wlsh_nodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: Checkpoint Loader with Name, Save Prompt Info, Outpaint to Image, CLIP Positive-Negative, SDXL Quick Empty Latent, Empty Latent by Ratio, Time String, SDXL Steps, SDXL Resolutions ..."
+ },
+ {
+ "author": "Kosinkadink",
+ "title": "ComfyUI-Advanced-ControlNet",
+ "reference": "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet",
+ "files": [
+ "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: ControlNetLoaderAdvanced, DiffControlNetLoaderAdvanced, ScaledSoftControlNetWeights, SoftControlNetWeights, CustomControlNetWeights, SoftT2IAdapterWeights, CustomT2IAdapterWeights"
+ },
+ {
+ "author": "Gourieff",
+ "title": "ReActor Node 0.1.0 for ComfyUI",
+ "reference": "https://github.com/Gourieff/comfyui-reactor-node",
+ "files": [
+ "https://github.com/Gourieff/comfyui-reactor-node"
+ ],
+ "install_type": "git-clone",
+ "description": "The Fast and Simple 'roop-like' Face Swap Extension Node for ComfyUI, based on ReActor (ex Roop-GE) SD-WebUI Face Swap Extension"
+ },
+ {
+ "author": "imb101",
+ "title": "FaceSwap",
+ "reference": "https://github.com/imb101/ComfyUI-FaceSwap",
+ "files": [
+ "https://github.com/imb101/ComfyUI-FaceSwap"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes:FaceSwapNode. Very basic custom node to enable face swapping in ComfyUI. (roop)"
+ },
+ {
+ "author": "Chaoses-Ib",
+ "title": "ComfyUI_Ib_CustomNodes",
+ "reference": "https://github.com/Chaoses-Ib/ComfyUI_Ib_CustomNodes",
+ "files": [
+ "https://github.com/Chaoses-Ib/ComfyUI_Ib_CustomNodes"
+ ],
+ "install_type": "git-clone",
+ "description": "Nodes: LoadImageFromPath. Load Image From Path loads the image from the source path and does not have such problems."
+ },
+ {
+ "author": "taabata",
+ "title": "Syrian Falcon Nodes",
+ "reference": "https://github.com/taabata/Comfy_Syrian_Falcon_Nodes",
+ "files": [
+ "https://github.com/taabata/Comfy_Syrian_Falcon_Nodes/raw/main/SyrianFalconNodes.py"
+ ],
+ "install_type": "copy",
+ "description": "Nodes:Prompt editing, Word as Image"
+ },
+ {
+ "author": "Ser-Hilary",
+ "title": "SDXL_sizing",
+ "reference": "https://github.com/Ser-Hilary/SDXL_sizing",
+ "files": [
+ "https://github.com/Ser-Hilary/SDXL_sizing/raw/main/conditioning_sizing_for_SDXL.py"
+ ],
+ "install_type": "copy",
+ "description": "Nodes:sizing_node. Size calculation node related to image size in prompts supported by SDXL."
+ },
+ {
+ "author": "ailex000",
+ "title": "Image Gallery",
+ "reference": "https://github.com/ailex000/ComfyUI-Extensions",
+ "js_path": "image-gallery",
+ "files": [
+ "https://github.com/ailex000/ComfyUI-Extensions/raw/main/image-gallery/imageGallery.js"
+ ],
+ "install_type": "copy",
+ "description": "Custom javascript extensions for better UX for ComfyUI. Supported nodes: PreviewImage, SaveImage. Double click on image to open."
+ },
+ {
+ "author": "rock-land",
+ "title": "graphNavigator",
+ "reference": "https://github.com/rock-land/graphNavigator",
+ "js_path": "graphNavigator",
+ "files": [
+ "https://github.com/rock-land/graphNavigator/raw/main/graphNavigator/graphNavigator.js"
+ ],
+ "install_type": "copy",
+ "description": "ComfyUI Web Extension for saving views and navigating graphs."
+ },
+ {
+ "author": "diffus3",
+ "title": "diffus3/ComfyUI-extensions",
+ "reference": "https://github.com/diffus3/ComfyUI-extensions",
+ "js_path": "diffus3",
+ "files": [
+ "https://github.com/diffus3/ComfyUI-extensions/raw/main/multiReroute/multireroute.js",
+ "https://github.com/diffus3/ComfyUI-extensions/raw/main/setget/setget.js"
+ ],
+ "install_type": "copy",
+ "description": "Extensions: subgraph, setget, multiReroute"
+ },
+ {
+ "author": "m957ymj75urz",
+ "title": "m957ymj75urz/ComfyUI-Custom-Nodes",
+ "reference": "https://github.com/m957ymj75urz/ComfyUI-Custom-Nodes",
+ "js_path": "m957ymj75urz",
+ "files": [
+ "https://github.com/m957ymj75urz/ComfyUI-Custom-Nodes/raw/main/clip-text-encode-split/clip_text_encode_split.py",
+ "https://github.com/m957ymj75urz/ComfyUI-Custom-Nodes/raw/main/colors/colors.js"
+ ],
+ "install_type": "copy",
+ "description": "Nodes: RawText, RawTextCLIPEncode, RawTextCombine, RawTextReplace, Extension: m957ymj75urz.colors"
+ },
+ {
+ "author": "Bikecicle",
+ "title": "Waveform Extensions",
+ "reference": "https://github.com/Bikecicle/ComfyUI-Waveform-Extensions",
+ "files": [
+ "https://github.com/Bikecicle/ComfyUI-Waveform-Extensions/raw/main/EXT_AudioManipulation.py",
+ "https://github.com/Bikecicle/ComfyUI-Waveform-Extensions/raw/main/EXT_VariationUtils.py"
+ ],
+ "install_type": "copy",
+ "description": "Some additional audio utilites for use on top of Sample Diffusion ComfyUI Extension"
+ },
+ {
+ "author": "dawangraoming",
+ "title": "KSampler GPU",
+ "reference": "https://github.com/dawangraoming/ComfyUI_ksampler_gpu",
+ "files": [
+ "https://github.com/dawangraoming/ComfyUI_ksampler_gpu/raw/main/ksampler_gpu.py"
+ ],
+ "install_type": "copy",
+ "description": "KSampler is provided, based on GPU random noise"
+ },
+ {
+ "author": "fitCorder",
+ "title": "fcSuite",
+ "reference": "https://github.com/fitCorder/fcSuite",
+ "files": [
+ "https://github.com/fitCorder/fcSuite/raw/main/fcSuite.py"
+ ],
+ "install_type": "copy",
+ "description": "fcFloatMatic is a custom module, that when configured correctly will increment through the lines generating you loras at different strengths. The JSON file will load the config."
+ },
+ {
+ "author": "lrzjason",
+ "title": "ComfyUIJasonNode",
+ "reference": "https://github.com/lrzjason/ComfyUIJasonNode",
+ "files": [
+ "https://github.com/lrzjason/ComfyUIJasonNode/raw/main/SDXLMixSampler.py",
+ "https://github.com/lrzjason/ComfyUIJasonNode/raw/main/LatentByRatio.py"
+ ],
+ "install_type": "copy",
+ "description": "Nodes:SDXLMixSampler, LatentByRatio"
+ },
+ {
+ "author": "alpertunga-bile",
+ "title": "prompt-generator",
+ "reference": "https://github.com/alpertunga-bile/prompt-generator-comfyui",
+ "files": [
+ "https://github.com/alpertunga-bile/prompt-generator-comfyui/raw/master/prompt_generator.py"
+ ],
+ "install_type": "copy",
+ "description": "Nodes:Prompt Generator. Custom prompt generator node for ComfyUI."
+ },
+ {
+ "author": "lordgasmic",
+ "title": "Wildcards",
+ "reference": "https://github.com/lordgasmic/ComfyUI-Wildcards",
+ "files": [
+ "https://github.com/lordgasmic/ComfyUI-Wildcards/raw/master/wildcards.py"
+ ],
+ "install_type": "copy",
+ "description": "Nodes:CLIPTextEncodeWithWildcards. This wildcard node is a wildcard node that operates based on the seed."
+ },
+ {
+ "author": "Beinsezii",
+ "title": "bsz-cui-extras",
+ "reference": "https://github.com/Beinsezii/bsz-cui-extras",
+ "files": [
+ "https://github.com/Beinsezii/bsz-cui-extras/raw/master/custom_nodes/bsz-auto-hires.py"
+ ],
+ "install_type": "copy",
+ "description": "Contains 3 nodes each with a different means to the same end result. These nodes are designed to automatically calculate the appropriate latent sizes when performing a 'Hi Res Fix' style workflow."
+ },
+ {
+ "author": "throttlekitty",
+ "title": "SDXLCustomAspectRatio",
+ "reference": "https://github.com/throttlekitty/SDXLCustomAspectRatio",
+ "files": [
+ "https://raw.githubusercontent.com/throttlekitty/SDXLCustomAspectRatio/main/SDXLAspectRatio.py"
+ ],
+ "install_type": "copy",
+ "description": "A quick and easy ComfyUI custom node for setting SDXL-friendly aspect ratios."
+ },
+ {
+ "author": "s1dlx",
+ "title": "comfy_meh",
+ "reference": "https://github.com/s1dlx/comfy_meh",
+ "files": [
+ "https://github.com/s1dlx/comfy_meh/raw/main/meh.py"
+ ],
+ "install_type": "copy",
+ "description": "Advanced merging methods."
+ },
+ {
+ "author": "tudal",
+ "title": "Hakkun-ComfyUI-nodes",
+ "reference": "https://github.com/tudal/Hakkun-ComfyUI-nodes",
+ "files": [
+ "https://github.com/tudal/Hakkun-ComfyUI-nodes/raw/main/hakkun_nodes.py"
+ ],
+ "install_type": "copy",
+ "description": "Nodes: Prompt parser. ComfyUI extra nodes. Mostly prompt parsing."
+ },
+ {
+ "author": "SadaleNet",
+ "title": "ComfyUI A1111-like Prompt Custom Node Solution",
+ "reference": "https://github.com/SadaleNet/CLIPTextEncodeA1111-ComfyUI",
+ "files": [
+ "https://github.com/SadaleNet/CLIPTextEncodeA1111-ComfyUI/raw/master/custom_nodes/clip_text_encoder_a1111.py"
+ ],
+ "install_type": "copy",
+ "description": "Nodes: CLIPTextEncodeA1111, RerouteTextForCLIPTextEncodeA1111."
+ },
+ {
+ "author": "theally",
+ "title": "TheAlly's Custom Nodes",
+ "reference": "https://civitai.com/models/19625?modelVersionId=23296",
+ "files": [
+ "https://civitai.com/api/download/models/25114",
+ "https://civitai.com/api/download/models/24679",
+ "https://civitai.com/api/download/models/24154",
+ "https://civitai.com/api/download/models/23884",
+ "https://civitai.com/api/download/models/23649",
+ "https://civitai.com/api/download/models/23467",
+ "https://civitai.com/api/download/models/23296"
+ ],
+ "install_type": "unzip",
+ "description": "Custom nodes for ComfyUI by TheAlly."
+ },
+ {
+ "author": "xss",
+ "title": "Custom Nodes by xss",
+ "reference": "https://civitai.com/models/24869/comfyui-custom-nodes-by-xss",
+ "files": [
+ "https://civitai.com/api/download/models/32717",
+ "https://civitai.com/api/download/models/47776",
+ "https://civitai.com/api/download/models/29772",
+ "https://civitai.com/api/download/models/31618",
+ "https://civitai.com/api/download/models/31591",
+ "https://civitai.com/api/download/models/29773",
+ "https://civitai.com/api/download/models/29774",
+ "https://civitai.com/api/download/models/29755",
+ "https://civitai.com/api/download/models/29750"
+ ],
+ "install_type": "unzip",
+ "description": "Various image processing nodes."
+ },
+ {
+ "author": "AIrjen",
+ "title": "One Button Prompt",
+ "reference": "https://github.com/AIrjen/OneButtonPrompt",
+ "files": [
+ "https://github.com/AIrjen/OneButtonPrompt"
+ ],
+ "install_type": "git-clone",
+ "description": "One Button Prompt has a prompt generation node for beginners who have problems writing a good prompt, or advanced users who want to get inspired. It generates an entire prompt from scratch. It is random, but controlled. You simply load up the script and press generate, and let it surprise you."
+ },
+ {
+ "author": "sofuego",
+ "title": "Comfy-QR",
+ "reference": "https://gitlab.com/sofuego-comfy-nodes/comfy-qr",
+ "files": [
+ "https://gitlab.com/sofuego-comfy-nodes/comfy-qr"
+ ],
+ "install_type": "git-clone",
+ "description": "A set of ComfyUI nodes to dynamically create QR image layers for generative QR art without the hassle of leaving the webui."
+ }
+ ]
+}
diff --git a/ComfyUI-Manager/extension-node-map.json b/ComfyUI-Manager/extension-node-map.json
new file mode 100644
index 0000000000000000000000000000000000000000..4118209b024cbe14b01b8c7945f19499d4a1a0d6
--- /dev/null
+++ b/ComfyUI-Manager/extension-node-map.json
@@ -0,0 +1,1365 @@
+{
+ "https://github.com/AIrjen/OneButtonPrompt": [
+ "CreatePromptVariant",
+ "OneButtonPrompt",
+ "SavePromptToFile"
+ ],
+ "https://github.com/ArtVentureX/comfyui-animatediff": [
+ "AnimateDiffCombine",
+ "AnimateDiffLoader",
+ "AnimateDiffLoader_v2",
+ "AnimateDiffUnload"
+ ],
+ "https://github.com/BadCafeCode/masquerade-nodes-comfyui": [
+ "Blur",
+ "Change Channel Count",
+ "Combine Masks",
+ "Constant Mask",
+ "Convert Color Space",
+ "Create QR Code",
+ "Create Rect Mask",
+ "Cut By Mask",
+ "Get Image Size",
+ "Image To Mask",
+ "Make Image Batch",
+ "Mask By Text",
+ "Mask Morphology",
+ "Mask To Region",
+ "MasqueradeIncrementer",
+ "Mix Color By Mask",
+ "Mix Images By Mask",
+ "Paste By Mask",
+ "Prune By Mask",
+ "Separate Mask Components",
+ "Unary Image Op",
+ "Unary Mask Op"
+ ],
+ "https://github.com/Beinsezii/bsz-cui-extras/raw/master/custom_nodes/bsz-auto-hires.py": [
+ "BSZAbsoluteHires",
+ "BSZAspectHires",
+ "BSZCombinedHires"
+ ],
+ "https://github.com/Bikecicle/ComfyUI-Waveform-Extensions/raw/main/EXT_AudioManipulation.py": [
+ "BatchJoinAudio",
+ "CutAudio",
+ "DuplicateAudio",
+ "JoinAudio",
+ "ResampleAudio",
+ "ReverseAudio",
+ "StretchAudio"
+ ],
+ "https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb": [
+ "BNK_AddCLIPSDXLParams",
+ "BNK_AddCLIPSDXLRParams",
+ "BNK_CLIPTextEncodeAdvanced",
+ "BNK_CLIPTextEncodeSDXLAdvanced"
+ ],
+ "https://github.com/BlenderNeko/ComfyUI_Cutoff": [
+ "BNK_CutoffBasePrompt",
+ "BNK_CutoffRegionsToConditioning",
+ "BNK_CutoffRegionsToConditioning_ADV",
+ "BNK_CutoffSetRegions"
+ ],
+ "https://github.com/BlenderNeko/ComfyUI_Noise": [
+ "BNK_DuplicateBatchIndex",
+ "BNK_GetSigma",
+ "BNK_InjectNoise",
+ "BNK_NoisyLatentImage",
+ "BNK_SlerpLatent",
+ "BNK_Unsampler"
+ ],
+ "https://github.com/BlenderNeko/ComfyUI_SeeCoder": [
+ "ConcatConditioning",
+ "SEECoderImageEncode"
+ ],
+ "https://github.com/BlenderNeko/ComfyUI_TiledKSampler": [
+ "BNK_TiledKSampler",
+ "BNK_TiledKSamplerAdvanced"
+ ],
+ "https://github.com/Chaoses-Ib/ComfyUI_Ib_CustomNodes": [
+ "LoadImageFromPath"
+ ],
+ "https://github.com/Davemane42/ComfyUI_Dave_CustomNode": [
+ "ABGRemover",
+ "ConditioningStretch",
+ "ConditioningUpscale",
+ "MultiAreaConditioning",
+ "MultiLatentComposite"
+ ],
+ "https://github.com/Derfuu/Derfuu_ComfyUI_ModdedNodes": [
+ "ABSNode_DF",
+ "Absolute value",
+ "Ceil",
+ "CeilNode_DF",
+ "Conditioning area scale by ratio",
+ "ConditioningSetArea with tuples",
+ "ConditioningSetAreaEXT_DF",
+ "ConditioningSetArea_DF",
+ "CosNode_DF",
+ "Cosines",
+ "Divide",
+ "DivideNode_DF",
+ "EmptyLatentImage_DF",
+ "Float",
+ "Float debug print",
+ "Float2Tuple_DF",
+ "FloatDebugPrint_DF",
+ "FloatNode_DF",
+ "Floor",
+ "FloorNode_DF",
+ "Get image size",
+ "Get latent size",
+ "GetImageSize_DF",
+ "GetLatentSize_DF",
+ "Image scale by ratio",
+ "Image scale to side",
+ "ImageScale_Ratio_DF",
+ "ImageScale_Side_DF",
+ "Int debug print",
+ "Int to float",
+ "Int to tuple",
+ "Int2Float_DF",
+ "IntDebugPrint_DF",
+ "Integer",
+ "IntegerNode_DF",
+ "Latent Scale by ratio",
+ "Latent Scale to side",
+ "LatentComposite with tuples",
+ "LatentScale_Ratio_DF",
+ "LatentScale_Side_DF",
+ "MultilineStringNode_DF",
+ "Multiply",
+ "MultiplyNode_DF",
+ "PowNode_DF",
+ "Power",
+ "Random",
+ "RandomFloat_DF",
+ "SinNode_DF",
+ "Sinus",
+ "SqrtNode_DF",
+ "Square root",
+ "String debug print",
+ "StringNode_DF",
+ "Subtract",
+ "SubtractNode_DF",
+ "Sum",
+ "SumNode_DF",
+ "TanNode_DF",
+ "Tangent",
+ "Text",
+ "Text box",
+ "Tuple",
+ "Tuple debug print",
+ "Tuple multiply",
+ "Tuple swap",
+ "Tuple to floats",
+ "Tuple to ints",
+ "Tuple2Float_DF",
+ "TupleDebugPrint_DF",
+ "TupleNode_DF"
+ ],
+ "https://github.com/EllangoK/ComfyUI-post-processing-nodes": [
+ "ArithmeticBlend",
+ "AsciiArt",
+ "Blend",
+ "Blur",
+ "CannyEdgeMask",
+ "ChromaticAberration",
+ "ColorCorrect",
+ "ColorTint",
+ "Dissolve",
+ "Dither",
+ "DodgeAndBurn",
+ "FilmGrain",
+ "Glow",
+ "HSVThresholdMask",
+ "KMeansQuantize",
+ "KuwaharaBlur",
+ "Parabolize",
+ "PencilSketch",
+ "PixelSort",
+ "Pixelize",
+ "Quantize",
+ "Sharpen",
+ "SineWave",
+ "Solarize",
+ "Vignette"
+ ],
+ "https://github.com/Fannovel16/ComfyUI-Frame-Interpolation": [
+ "KSampler Gradually Adding More Denoise (efficient)"
+ ],
+ "https://github.com/Fannovel16/comfy_controlnet_preprocessors": [
+ "AnimeLineArtPreprocessor",
+ "BAE-NormalMapPreprocessor",
+ "BinaryPreprocessor",
+ "CannyEdgePreprocessor",
+ "ColorPreprocessor",
+ "FakeScribblePreprocessor",
+ "HEDPreprocessor",
+ "InpaintPreprocessor",
+ "LeReS-DepthMapPreprocessor",
+ "LineArtPreprocessor",
+ "M-LSDPreprocessor",
+ "Manga2Anime-LineArtPreprocessor",
+ "MediaPipe-FaceMeshPreprocessor",
+ "MediaPipe-HandPosePreprocessor",
+ "MiDaS-DepthMapPreprocessor",
+ "MiDaS-NormalMapPreprocessor",
+ "OneFormer-ADE20K-SemSegPreprocessor",
+ "OneFormer-COCO-SemSegPreprocessor",
+ "OpenposePreprocessor",
+ "PiDiNetPreprocessor",
+ "ScribblePreprocessor",
+ "SemSegPreprocessor",
+ "ShufflePreprocessor",
+ "TilePreprocessor",
+ "UniFormer-SemSegPreprocessor",
+ "Zoe-DepthMapPreprocessor"
+ ],
+ "https://github.com/FizzleDorf/AIT": [
+ "AITemplateControlNetLoader",
+ "AITemplateEmptyLatentImage",
+ "AITemplateLatentUpscale",
+ "AITemplateLoader",
+ "AITemplateVAEDecode",
+ "AITemplateVAEEncode",
+ "AITemplateVAEEncodeForInpaint"
+ ],
+ "https://github.com/FizzleDorf/ComfyUI_FizzNodes": [
+ "AbsCosWave",
+ "AbsSinWave",
+ "CosWave",
+ "InvCosWave",
+ "InvSinWave",
+ "Lerp",
+ "PromptSchedule",
+ "PromptScheduleEncodeSDXL",
+ "PromptScheduleGLIGEN",
+ "PromptScheduleNodeFlow",
+ "PromptScheduleNodeFlowEnd",
+ "SawtoothWave",
+ "SinWave",
+ "SquareWave",
+ "TriangleWave",
+ "ValueSchedule"
+ ],
+ "https://github.com/Gourieff/comfyui-reactor-node": [
+ "ReActorFaceSwap"
+ ],
+ "https://github.com/Jcd1230/rembg-comfyui-node": [
+ "Image Remove Background (rembg)"
+ ],
+ "https://github.com/Jordach/comfy-plasma": [
+ "JDC_AutoContrast",
+ "JDC_BlendImages",
+ "JDC_BrownNoise",
+ "JDC_Contrast",
+ "JDC_EqualizeGrey",
+ "JDC_GaussianBlur",
+ "JDC_GreyNoise",
+ "JDC_Greyscale",
+ "JDC_ImageLoader",
+ "JDC_ImageLoaderMeta",
+ "JDC_PinkNoise",
+ "JDC_Plasma",
+ "JDC_PlasmaSampler",
+ "JDC_PowerImage",
+ "JDC_RandNoise",
+ "JDC_ResizeFactor"
+ ],
+ "https://github.com/Kaharos94/ComfyUI-Saveaswebp": [
+ "Save_as_webp"
+ ],
+ "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet": [
+ "ControlNetLoaderAdvanced",
+ "CustomControlNetWeights",
+ "CustomT2IAdapterWeights",
+ "DiffControlNetLoaderAdvanced",
+ "ScaledSoftControlNetWeights",
+ "SoftControlNetWeights",
+ "SoftT2IAdapterWeights"
+ ],
+ "https://github.com/LEv145/images-grid-comfy-plugin": [
+ "GridAnnotation",
+ "ImageCombine",
+ "ImagesGridByColumns",
+ "ImagesGridByRows",
+ "LatentCombine"
+ ],
+ "https://github.com/LucianoCirino/efficiency-nodes-comfyui": [
+ "Efficient Loader",
+ "Evaluate Floats",
+ "Evaluate Integers",
+ "Evaluate Strings",
+ "Image Overlay",
+ "Join XY Inputs of Same Type",
+ "KSampler (Efficient)",
+ "KSampler Adv. (Efficient)",
+ "LoRA Stacker",
+ "LoRA Stacker Adv.",
+ "Manual XY Entry Info",
+ "Simple Eval Examples",
+ "XY Input: CFG Scale",
+ "XY Input: Checkpoint",
+ "XY Input: Clip Skip",
+ "XY Input: Denoise",
+ "XY Input: LoRA",
+ "XY Input: LoRA Adv.",
+ "XY Input: LoRA Stacks",
+ "XY Input: Manual XY Entry",
+ "XY Input: Negative Prompt S/R",
+ "XY Input: Positive Prompt S/R",
+ "XY Input: Sampler",
+ "XY Input: Scheduler",
+ "XY Input: Seeds++ Batch",
+ "XY Input: Steps",
+ "XY Input: VAE",
+ "XY Plot"
+ ],
+ "https://github.com/Nourepide/ComfyUI-Allor": [
+ "AlphaChanelAdd",
+ "AlphaChanelAddByMask",
+ "AlphaChanelAsMask",
+ "AlphaChanelRemove",
+ "AlphaChanelRestore",
+ "ClipClamp",
+ "ClipVisionClamp",
+ "ClipVisionOutputClamp",
+ "ConditioningClamp",
+ "ControlNetClamp",
+ "GligenClamp",
+ "ImageBatchFork",
+ "ImageBatchGet",
+ "ImageBatchJoin",
+ "ImageBatchRemove",
+ "ImageClamp",
+ "ImageCompositeAbsolute",
+ "ImageCompositeAbsoluteByContainer",
+ "ImageCompositeRelative",
+ "ImageCompositeRelativeByContainer",
+ "ImageContainer",
+ "ImageContainerInheritanceAdd",
+ "ImageContainerInheritanceMax",
+ "ImageContainerInheritanceScale",
+ "ImageContainerInheritanceSum",
+ "ImageDrawArc",
+ "ImageDrawArcByContainer",
+ "ImageDrawChord",
+ "ImageDrawChordByContainer",
+ "ImageDrawEllipse",
+ "ImageDrawEllipseByContainer",
+ "ImageDrawLine",
+ "ImageDrawLineByContainer",
+ "ImageDrawPieslice",
+ "ImageDrawPiesliceByContainer",
+ "ImageDrawPolygon",
+ "ImageDrawRectangle",
+ "ImageDrawRectangleByContainer",
+ "ImageDrawRectangleRounded",
+ "ImageDrawRectangleRoundedByContainer",
+ "ImageEffectsAdjustment",
+ "ImageEffectsGrayscale",
+ "ImageEffectsLensBokeh",
+ "ImageEffectsLensChromaticAberration",
+ "ImageEffectsLensOpticAxis",
+ "ImageEffectsLensVignette",
+ "ImageEffectsLensZoomBurst",
+ "ImageEffectsNegative",
+ "ImageEffectsSepia",
+ "ImageFilterBilateralBlur",
+ "ImageFilterBlur",
+ "ImageFilterBoxBlur",
+ "ImageFilterContour",
+ "ImageFilterDetail",
+ "ImageFilterEdgeEnhance",
+ "ImageFilterEdgeEnhanceMore",
+ "ImageFilterEmboss",
+ "ImageFilterFindEdges",
+ "ImageFilterGaussianBlur",
+ "ImageFilterGaussianBlurAdvanced",
+ "ImageFilterMax",
+ "ImageFilterMedianBlur",
+ "ImageFilterMin",
+ "ImageFilterMode",
+ "ImageFilterRank",
+ "ImageFilterSharpen",
+ "ImageFilterSmooth",
+ "ImageFilterSmoothMore",
+ "ImageFilterStackBlur",
+ "ImageNoiseBeta",
+ "ImageNoiseBinomial",
+ "ImageNoiseBytes",
+ "ImageNoiseGaussian",
+ "ImageSegmentation",
+ "ImageSegmentationCustom",
+ "ImageSegmentationCustomAdvanced",
+ "ImageText",
+ "ImageTextMultiline",
+ "ImageTextMultilineOutlined",
+ "ImageTextOutlined",
+ "ImageTransformCropAbsolute",
+ "ImageTransformCropCorners",
+ "ImageTransformCropRelative",
+ "ImageTransformPaddingAbsolute",
+ "ImageTransformPaddingRelative",
+ "ImageTransformResizeAbsolute",
+ "ImageTransformResizeRelative",
+ "ImageTransformRotate",
+ "ImageTransformTranspose",
+ "LatentClamp",
+ "MaskClamp",
+ "ModelClamp",
+ "StyleModelClamp",
+ "UpscaleModelClamp",
+ "VaeClamp"
+ ],
+ "https://github.com/Pfaeff/pfaeff-comfyui": [
+ "AstropulsePixelDetector",
+ "BackgroundRemover",
+ "ImagePadForBetterOutpaint",
+ "Inpainting",
+ "InpaintingPipelineLoader"
+ ],
+ "https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes": [
+ "CR Apply ControlNet",
+ "CR Aspect Ratio",
+ "CR Aspect Ratio SDXL",
+ "CR Clip Input Switch",
+ "CR Color Tint",
+ "CR Conditioning Input Switch",
+ "CR ControlNet Input Switch",
+ "CR Image Input Switch",
+ "CR Image Input Switch (4 way)",
+ "CR Image Output",
+ "CR Image Pipe Edit",
+ "CR Image Pipe In",
+ "CR Image Pipe Out",
+ "CR Image Size",
+ "CR Integer Multiple",
+ "CR Latent Input Switch",
+ "CR Load LoRA",
+ "CR Model Input Switch",
+ "CR Module Input",
+ "CR Module Output",
+ "CR Module Pipe Loader",
+ "CR Pipe Switch",
+ "CR SDXL Base Prompt Encoder",
+ "CR SDXL Prompt Mixer",
+ "CR SDXL Style Text",
+ "CR Seed to Int"
+ ],
+ "https://github.com/SLAPaper/ComfyUI-Image-Selector": [
+ "ImageDuplicator",
+ "ImageSelector",
+ "LatentDuplicator",
+ "LatentSelector"
+ ],
+ "https://github.com/SOELexicon/ComfyUI-LexMSDBNodes": [
+ "MSSqlSelectNode",
+ "MSSqlTableNode"
+ ],
+ "https://github.com/SadaleNet/CLIPTextEncodeA1111-ComfyUI/raw/master/custom_nodes/clip_text_encoder_a1111.py": [
+ "CLIPTextEncodeA1111",
+ "RerouteTextForCLIPTextEncodeA1111"
+ ],
+ "https://github.com/SeargeDP/SeargeSDXL": [
+ "SeargeCheckpointLoader",
+ "SeargeConditioningMuxer5",
+ "SeargeFloatConstant",
+ "SeargeFloatMath",
+ "SeargeFloatPair",
+ "SeargeGenerated1",
+ "SeargeImageSave",
+ "SeargeInput1",
+ "SeargeInput2",
+ "SeargeInput3",
+ "SeargeInput4",
+ "SeargeInput5",
+ "SeargeInput6",
+ "SeargeInput7",
+ "SeargeIntegerConstant",
+ "SeargeIntegerMath",
+ "SeargeIntegerPair",
+ "SeargeIntegerScaler",
+ "SeargeLatentMuxer3",
+ "SeargeLoraLoader",
+ "SeargeOutput1",
+ "SeargeOutput2",
+ "SeargeOutput3",
+ "SeargeOutput4",
+ "SeargeOutput5",
+ "SeargeOutput6",
+ "SeargeOutput7",
+ "SeargeParameterProcessor",
+ "SeargePromptCombiner",
+ "SeargePromptText",
+ "SeargeSDXLBasePromptEncoder",
+ "SeargeSDXLImage2ImageSampler",
+ "SeargeSDXLPromptEncoder",
+ "SeargeSDXLRefinerPromptEncoder",
+ "SeargeSDXLSampler",
+ "SeargeStylePreprocessor",
+ "SeargeUpscaleModelLoader",
+ "SeargeVAELoader"
+ ],
+ "https://github.com/Ser-Hilary/SDXL_sizing/raw/main/conditioning_sizing_for_SDXL.py": [
+ "get_aspect_from_image",
+ "get_aspect_from_ints",
+ "sizing_node",
+ "sizing_node_basic",
+ "sizing_node_unparsed"
+ ],
+ "https://github.com/TinyTerra/ComfyUI_tinyterraNodes.git": [
+ "ttN busIN",
+ "ttN busOUT",
+ "ttN concat",
+ "ttN debugInput",
+ "ttN float",
+ "ttN hiresfixScale",
+ "ttN imageOutput",
+ "ttN imageREMBG",
+ "ttN int",
+ "ttN pipe2BASIC",
+ "ttN pipe2DETAILER",
+ "ttN pipeEDIT",
+ "ttN pipeIN",
+ "ttN pipeKSampler",
+ "ttN pipeKSamplerAdvanced",
+ "ttN pipeLoader",
+ "ttN pipeLoaderSDXL",
+ "ttN pipeOUT",
+ "ttN seed",
+ "ttN seedDebug",
+ "ttN text",
+ "ttN text3BOX_3WAYconcat",
+ "ttN text7BOX_concat",
+ "ttN textDebug",
+ "ttN xyPlot"
+ ],
+ "https://github.com/WASasquatch/was-node-suite-comfyui": [
+ "BLIP Analyze Image",
+ "BLIP Model Loader",
+ "Blend Latents",
+ "Bounded Image Blend",
+ "Bounded Image Blend with Mask",
+ "Bounded Image Crop",
+ "Bounded Image Crop with Mask",
+ "CLIP Input Switch",
+ "CLIP Vision Input Switch",
+ "CLIPSeg Batch Masking",
+ "CLIPSeg Masking",
+ "CLIPSeg Model Loader",
+ "CLIPTextEncode (BlenderNeko Advanced + NSP)",
+ "CLIPTextEncode (NSP)",
+ "Cache Node",
+ "Checkpoint Loader",
+ "Checkpoint Loader (Simple)",
+ "Conditioning Input Switch",
+ "Constant Number",
+ "Control Net Model Input Switch",
+ "Convert Masks to Images",
+ "Create Grid Image",
+ "Create Morph Image",
+ "Create Morph Image from Path",
+ "Create Video from Path",
+ "Debug Number to Console",
+ "Dictionary to Console",
+ "Diffusers Hub Model Down-Loader",
+ "Diffusers Model Loader",
+ "Export API",
+ "Image Analyze",
+ "Image Aspect Ratio",
+ "Image Batch",
+ "Image Blank",
+ "Image Blend",
+ "Image Blend by Mask",
+ "Image Blending Mode",
+ "Image Bloom Filter",
+ "Image Bounds",
+ "Image Canny Filter",
+ "Image Chromatic Aberration",
+ "Image Color Palette",
+ "Image Crop Face",
+ "Image Crop Location",
+ "Image Crop Square Location",
+ "Image Displacement Warp",
+ "Image Dragan Photography Filter",
+ "Image Edge Detection Filter",
+ "Image Film Grain",
+ "Image Filter Adjustments",
+ "Image Flip",
+ "Image Generate Gradient",
+ "Image Gradient Map",
+ "Image High Pass Filter",
+ "Image History Loader",
+ "Image Input Switch",
+ "Image Levels Adjustment",
+ "Image Load",
+ "Image Median Filter",
+ "Image Mix RGB Channels",
+ "Image Monitor Effects Filter",
+ "Image Nova Filter",
+ "Image Padding",
+ "Image Paste Crop",
+ "Image Paste Crop by Location",
+ "Image Paste Face",
+ "Image Perlin Noise",
+ "Image Perlin Power Fractal",
+ "Image Pixelate",
+ "Image Power Noise",
+ "Image Rembg (Remove Background)",
+ "Image Remove Background (Alpha)",
+ "Image Remove Color",
+ "Image Resize",
+ "Image Rotate",
+ "Image Rotate Hue",
+ "Image SSAO (Ambient Occlusion)",
+ "Image SSDO (Direct Occlusion)",
+ "Image Save",
+ "Image Seamless Texture",
+ "Image Select Channel",
+ "Image Select Color",
+ "Image Shadows and Highlights",
+ "Image Size to Number",
+ "Image Stitch",
+ "Image Style Filter",
+ "Image Threshold",
+ "Image Tiled",
+ "Image Transpose",
+ "Image Voronoi Noise Filter",
+ "Image fDOF Filter",
+ "Image to Latent Mask",
+ "Image to Noise",
+ "Image to Seed",
+ "Images to RGB",
+ "Inset Image Bounds",
+ "Integer place counter",
+ "KSampler (WAS)",
+ "KSampler Cycle",
+ "LangSAM Masking",
+ "LangSAM Model Loader",
+ "Latent Input Switch",
+ "Latent Noise Injection",
+ "Latent Size to Number",
+ "Latent Upscale by Factor (WAS)",
+ "Load Cache",
+ "Load Image Batch",
+ "Load Lora",
+ "Load Text File",
+ "Logic Boolean",
+ "Lora Loader",
+ "Mask Arbitrary Region",
+ "Mask Batch",
+ "Mask Batch to Mask",
+ "Mask Ceiling Region",
+ "Mask Crop Dominant Region",
+ "Mask Crop Minority Region",
+ "Mask Crop Region",
+ "Mask Dilate Region",
+ "Mask Dominant Region",
+ "Mask Erode Region",
+ "Mask Fill Holes",
+ "Mask Floor Region",
+ "Mask Gaussian Region",
+ "Mask Invert",
+ "Mask Minority Region",
+ "Mask Paste Region",
+ "Mask Smooth Region",
+ "Mask Threshold Region",
+ "Masks Add",
+ "Masks Combine Batch",
+ "Masks Combine Regions",
+ "Masks Subtract",
+ "MiDaS Depth Approximation",
+ "MiDaS Mask Image",
+ "MiDaS Model Loader",
+ "Model Input Switch",
+ "Number Counter",
+ "Number Input Condition",
+ "Number Input Switch",
+ "Number Multiple Of",
+ "Number Operation",
+ "Number PI",
+ "Number to Float",
+ "Number to Int",
+ "Number to Seed",
+ "Number to String",
+ "Number to Text",
+ "Prompt Multiple Styles Selector",
+ "Prompt Styles Selector",
+ "Random Number",
+ "SAM Image Mask",
+ "SAM Model Loader",
+ "SAM Parameters",
+ "SAM Parameters Combine",
+ "Samples Passthrough (Stat System)",
+ "Save Text File",
+ "Seed",
+ "String to Text",
+ "Tensor Batch to Image",
+ "Text Add Token by Input",
+ "Text Add Tokens",
+ "Text Compare",
+ "Text Concatenate",
+ "Text Dictionary Update",
+ "Text File History Loader",
+ "Text Find and Replace",
+ "Text Find and Replace Input",
+ "Text Find and Replace by Dictionary",
+ "Text Input Switch",
+ "Text List",
+ "Text List Concatenate",
+ "Text Load Line From File",
+ "Text Multiline",
+ "Text Parse A1111 Embeddings",
+ "Text Parse Noodle Soup Prompts",
+ "Text Parse Tokens",
+ "Text Random Line",
+ "Text Random Prompt",
+ "Text String",
+ "Text String Truncate",
+ "Text to Conditioning",
+ "Text to Console",
+ "Text to Number",
+ "Text to String",
+ "True Random.org Number Generator",
+ "Upscale Model Loader",
+ "Upscale Model Switch",
+ "VAE Input Switch",
+ "Video Dump Frames",
+ "Write to GIF",
+ "Write to Video",
+ "unCLIP Checkpoint Loader"
+ ],
+ "https://github.com/YinBailiang/MergeBlockWeighted_fo_ComfyUI": [
+ "MergeBlockWeighted"
+ ],
+ "https://github.com/ZaneA/ComfyUI-ImageReward": [
+ "ImageRewardLoader",
+ "ImageRewardScore"
+ ],
+ "https://github.com/adieyal/comfyui-dynamicprompts": [
+ "DPCombinatorialGenerator",
+ "DPFeelingLucky",
+ "DPJinja",
+ "DPMagicPrompt",
+ "DPRandomGenerator"
+ ],
+ "https://github.com/andersxa/comfyui-PromptAttention": [
+ "CLIPAttentionMaskEncode"
+ ],
+ "https://github.com/asagi4/comfyui-prompt-control": [
+ "CondLinearInterpolate",
+ "ConditioningCutoff",
+ "EditableCLIPEncode",
+ "FilterSchedule",
+ "JinjaRender",
+ "LoRAScheduler",
+ "PromptControlSimple",
+ "PromptToSchedule",
+ "ScheduleToCond",
+ "ScheduleToModel",
+ "StringConcat"
+ ],
+ "https://github.com/bash-j/mikey_nodes": [
+ "AddMetaData",
+ "Batch Resize Image for SDXL",
+ "Empty Latent Ratio Custom SDXL",
+ "Empty Latent Ratio Select SDXL",
+ "HaldCLUT",
+ "Prompt With SDXL",
+ "Prompt With Style",
+ "Prompt With Style V2",
+ "Prompt With Style V3",
+ "Resize Image for SDXL",
+ "Save Image With Prompt Data",
+ "SaveMetaData",
+ "Style Conditioner",
+ "Upscale Tile Calculator",
+ "VAE Decode 6GB SDXL (deprecated)"
+ ],
+ "https://github.com/biegert/ComfyUI-CLIPSeg/raw/main/custom_nodes/clipseg.py": [
+ "CLIPSeg",
+ "CombineSegMasks"
+ ],
+ "https://github.com/bvhari/ComfyUI_ImageProcessing": [
+ "BilateralFilter",
+ "Brightness",
+ "Gamma",
+ "Hue",
+ "Saturation",
+ "SigmoidCorrection",
+ "UnsharpMask"
+ ],
+ "https://github.com/bvhari/ComfyUI_LatentToRGB": [
+ "LatentToRGB"
+ ],
+ "https://github.com/chenbaiyujason/sc-node-comfyui": [
+ "8 Combine Text String",
+ "Builder Text String",
+ "Clean Gradio",
+ "Combine GPT Prompt",
+ "Combine Text String",
+ "Get Gradio",
+ "Multiple Combine GPT Prompt",
+ "Multiple Post to GPT",
+ "Multiple Text String",
+ "One GPT Builder",
+ "One Post to GPT",
+ "Out Gradio",
+ "Prompt Preview",
+ "SCSCCLIPTextEncode",
+ "SCSearch and Replace",
+ "SCText to Console",
+ "Single Text String",
+ "String to ASCII",
+ "Verb One Post to GPT"
+ ],
+ "https://github.com/city96/ComfyUI_NetDist": [
+ "FetchRemote",
+ "QueueRemote"
+ ],
+ "https://github.com/civitai/comfy-nodes": [
+ "CivitAI_Checkpoint_Loader",
+ "CivitAI_Lora_Loader"
+ ],
+ "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/advanced_model_merging.py": [
+ "ModelMergeBlockNumber"
+ ],
+ "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/reference_only.py": [
+ "ReferenceOnlySimple"
+ ],
+ "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/sampler_rescalecfg.py": [
+ "RescaleClassifierFreeGuidanceTest"
+ ],
+ "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/sampler_tonemap.py": [
+ "ModelSamplerTonemapNoiseTest"
+ ],
+ "https://github.com/cubiq/ComfyUI_SimpleMath": [
+ "SimpleMath"
+ ],
+ "https://github.com/dawangraoming/ComfyUI_ksampler_gpu/raw/main/ksampler_gpu.py": [
+ "KSamplerAdvancedGPU",
+ "KSamplerGPU"
+ ],
+ "https://github.com/daxthin/facedetailer": [
+ "DZ_Face_Detailer"
+ ],
+ "https://github.com/diontimmer/ComfyUI-Vextra-Nodes": [
+ "Add Text To Image",
+ "Apply Instagram Filter",
+ "Create Solid Color",
+ "Flatten Colors",
+ "Generate Noise Image",
+ "GlitchThis Effect",
+ "Hue Rotation",
+ "Load Picture Index",
+ "Pixel Sort",
+ "Play Sound At Execution",
+ "Prettify Prompt Using distilgpt2",
+ "Swap Color Mode"
+ ],
+ "https://github.com/evanspearman/ComfyMath": [
+ "CM_BoolBinaryOperation",
+ "CM_BoolToInt",
+ "CM_BoolUnaryOperation",
+ "CM_BreakoutVec2",
+ "CM_BreakoutVec3",
+ "CM_BreakoutVec4",
+ "CM_ComposeVec2",
+ "CM_ComposeVec3",
+ "CM_ComposeVec4",
+ "CM_FloatBinaryCondition",
+ "CM_FloatBinaryOperation",
+ "CM_FloatToInt",
+ "CM_FloatToNumber",
+ "CM_FloatUnaryCondition",
+ "CM_FloatUnaryOperation",
+ "CM_IntBinaryCondition",
+ "CM_IntBinaryOperation",
+ "CM_IntToBool",
+ "CM_IntToFloat",
+ "CM_IntToNumber",
+ "CM_IntUnaryCondition",
+ "CM_IntUnaryOperation",
+ "CM_NumberBinaryCondition",
+ "CM_NumberBinaryOperation",
+ "CM_NumberToFloat",
+ "CM_NumberToInt",
+ "CM_NumberUnaryCondition",
+ "CM_NumberUnaryOperation",
+ "CM_Vec2BinaryCondition",
+ "CM_Vec2BinaryOperation",
+ "CM_Vec2ScalarOperation",
+ "CM_Vec2ToScalarBinaryOperation",
+ "CM_Vec2ToScalarUnaryOperation",
+ "CM_Vec2UnaryCondition",
+ "CM_Vec2UnaryOperation",
+ "CM_Vec3BinaryCondition",
+ "CM_Vec3BinaryOperation",
+ "CM_Vec3ScalarOperation",
+ "CM_Vec3ToScalarBinaryOperation",
+ "CM_Vec3ToScalarUnaryOperation",
+ "CM_Vec3UnaryCondition",
+ "CM_Vec3UnaryOperation",
+ "CM_Vec4BinaryCondition",
+ "CM_Vec4BinaryOperation",
+ "CM_Vec4ScalarOperation",
+ "CM_Vec4ToScalarBinaryOperation",
+ "CM_Vec4ToScalarUnaryOperation",
+ "CM_Vec4UnaryCondition",
+ "CM_Vec4UnaryOperation"
+ ],
+ "https://github.com/filipemeneses/comfy_pixelization": [
+ "Pixelization"
+ ],
+ "https://github.com/fitCorder/fcSuite/raw/main/fcSuite.py": [
+ "fcFloat",
+ "fcFloatMatic",
+ "fcInteger"
+ ],
+ "https://github.com/flyingshutter/As_ComfyUI_CustomNodes": [
+ "BatchIndex_AS",
+ "ImageMixMasked_As",
+ "ImageToMask_AS",
+ "Increment_AS",
+ "Int2Any_AS",
+ "LatentAdd_AS",
+ "LatentMixMasked_As",
+ "LatentMix_AS",
+ "LatentToImages_AS",
+ "LoadLatent_AS",
+ "MapRange_AS",
+ "MaskToImage_AS",
+ "Math_AS",
+ "Number2Float_AS",
+ "Number2Int_AS",
+ "Number_AS",
+ "SaveLatent_AS",
+ "TextToImage_AS"
+ ],
+ "https://github.com/gamert/ComfyUI_tagger": [
+ "CLIPTextEncodeTaggerDD",
+ "ImageTaggerDD",
+ "LoadImage_Tagger",
+ "PromptDD"
+ ],
+ "https://github.com/guoyk93/yk-node-suite-comfyui": [
+ "YKImagePadForOutpaint",
+ "YKMaskToImage"
+ ],
+ "https://github.com/hnmr293/ComfyUI-nodes-hnmr": [
+ "CLIPIter",
+ "Dict2Model",
+ "GridImage",
+ "ImageBlend2",
+ "KSamplerOverrided",
+ "KSamplerSetting",
+ "KSamplerXYZ",
+ "LatentToHist",
+ "LatentToImage",
+ "ModelIter",
+ "RandomLatentImage",
+ "SaveStateDict",
+ "SaveText",
+ "StateDictLoader",
+ "StateDictMerger",
+ "StateDictMergerBlockWeighted",
+ "StateDictMergerBlockWeightedMulti",
+ "VAEDecodeBatched",
+ "VAEEncodeBatched",
+ "VAEIter"
+ ],
+ "https://github.com/imb101/ComfyUI-FaceSwap": [
+ "FaceSwapNode"
+ ],
+ "https://github.com/kwaroran/abg-comfyui": [
+ "Remove Image Background (abg)"
+ ],
+ "https://github.com/lilly1987/ComfyUI_node_Lilly": [
+ "CheckpointLoaderSimpleText",
+ "LoraLoaderText",
+ "LoraLoaderTextRandom",
+ "Random_Sampler",
+ "VAELoaderDecode"
+ ],
+ "https://github.com/lordgasmic/ComfyUI-Wildcards/raw/master/wildcards.py": [
+ "CLIPTextEncodeWithWildcards"
+ ],
+ "https://github.com/lrzjason/ComfyUIJasonNode/raw/main/SDXLMixSampler.py": [
+ "SDXLMixSampler"
+ ],
+ "https://github.com/ltdrdata/ComfyUI-Impact-Pack": [
+ "BasicPipeToDetailerPipe",
+ "BboxDetectorCombined",
+ "BboxDetectorCombined_v2",
+ "BboxDetectorForEach",
+ "BboxDetectorSEGS",
+ "BitwiseAndMask",
+ "BitwiseAndMaskForEach",
+ "CLIPSegDetectorProvider",
+ "CfgScheduleHookProvider",
+ "CombineRegionalPrompts",
+ "DenoiseScheduleHookProvider",
+ "DetailerForEach",
+ "DetailerForEachDebug",
+ "DetailerForEachDebugPipe",
+ "DetailerForEachPipe",
+ "DetailerPipeToBasicPipe",
+ "EditBasicPipe",
+ "EditDetailerPipe",
+ "EmptySegs",
+ "FaceDetailer",
+ "FaceDetailerPipe",
+ "FromBasicPipe",
+ "FromBasicPipe_v2",
+ "FromDetailerPipe",
+ "FromDetailerPipe_v2",
+ "ImageMaskSwitch",
+ "ImageReceiver",
+ "ImageSender",
+ "ImpactCompare",
+ "ImpactConditionalBranch",
+ "ImpactConditionalStopIteration",
+ "ImpactFloat",
+ "ImpactImageInfo",
+ "ImpactInt",
+ "ImpactKSamplerAdvancedBasicPipe",
+ "ImpactKSamplerBasicPipe",
+ "ImpactLogger",
+ "ImpactMinMax",
+ "ImpactNeg",
+ "ImpactSEGSConcat",
+ "ImpactSEGSLabelFilter",
+ "ImpactSEGSOrderedFilter",
+ "ImpactSEGSRangeFilter",
+ "ImpactSEGSToMaskList",
+ "ImpactValueReceiver",
+ "ImpactValueSender",
+ "ImpactWildcardProcessor",
+ "IterativeImageUpscale",
+ "IterativeLatentUpscale",
+ "KSamplerAdvancedProvider",
+ "KSamplerProvider",
+ "LatentPixelScale",
+ "LatentReceiver",
+ "LatentSender",
+ "LatentSwitch",
+ "LoadConditioning",
+ "MMDetDetectorProvider",
+ "MMDetLoader",
+ "MaskPainter",
+ "MaskToSEGS",
+ "MasksToMaskList",
+ "ONNXDetectorProvider",
+ "ONNXDetectorSEGS",
+ "PixelKSampleHookCombine",
+ "PixelKSampleUpscalerProvider",
+ "PixelKSampleUpscalerProviderPipe",
+ "PixelTiledKSampleUpscalerProvider",
+ "PixelTiledKSampleUpscalerProviderPipe",
+ "PreviewBridge",
+ "ReencodeLatent",
+ "ReencodeLatentPipe",
+ "RegionalPrompt",
+ "RegionalSampler",
+ "SAMDetectorCombined",
+ "SAMDetectorSegmented",
+ "SAMLoader",
+ "SEGEdit",
+ "SEGPick",
+ "SEGSDetailer",
+ "SEGSPaste",
+ "SEGSPreview",
+ "SEGSSwitch",
+ "SEGSToImageList",
+ "SaveConditioning",
+ "SegmDetectorCombined",
+ "SegmDetectorCombined_v2",
+ "SegmDetectorForEach",
+ "SegmDetectorSEGS",
+ "Segs & Mask",
+ "Segs & Mask ForEach",
+ "SegsMaskCombine",
+ "SegsToCombinedMask",
+ "SubtractMask",
+ "SubtractMaskForEach",
+ "TiledKSamplerProvider",
+ "ToBasicPipe",
+ "ToBinaryMask",
+ "ToDetailerPipe",
+ "TwoAdvancedSamplersForMask",
+ "TwoSamplersForMask",
+ "TwoSamplersForMaskUpscalerProvider",
+ "TwoSamplersForMaskUpscalerProviderPipe",
+ "UltralyticsDetectorProvider"
+ ],
+ "https://github.com/ltdrdata/ComfyUI-tomeSD-installer": [
+ "CheckpointTomeLoader",
+ "TestNode"
+ ],
+ "https://github.com/m957ymj75urz/ComfyUI-Custom-Nodes/raw/main/clip-text-encode-split/clip_text_encode_split.py": [
+ "RawText",
+ "RawTextCombine",
+ "RawTextEncode",
+ "RawTextReplace"
+ ],
+ "https://github.com/melMass/comfy_mtb": [
+ "Animation Builder (mtb)",
+ "Bbox (mtb)",
+ "Bbox From Mask (mtb)",
+ "Blur (mtb)",
+ "Color Correct (mtb)",
+ "Colored Image (mtb)",
+ "Concat Images (mtb)",
+ "Crop (mtb)",
+ "Debug (mtb)",
+ "Deep Bump (mtb)",
+ "Export To Prores (mtb)",
+ "Face Swap (mtb)",
+ "Film Interpolation (mtb)",
+ "Fit Number (mtb)",
+ "Float To Number (mtb)",
+ "Get Batch From History (mtb)",
+ "Image Compare (mtb)",
+ "Image Premultiply (mtb)",
+ "Image Remove Background Rembg (mtb)",
+ "Image Resize Factor (mtb)",
+ "Int To Bool (mtb)",
+ "Int To Number (mtb)",
+ "Latent Lerp (mtb)",
+ "Latent Noise (mtb)",
+ "Latent Transform (mtb)",
+ "Load Face Enhance Model (mtb)",
+ "Load Face Swap Model (mtb)",
+ "Load Film Model (mtb)",
+ "Load Image From Url (mtb)",
+ "Load Image Sequence (mtb)",
+ "Mask To Image (mtb)",
+ "Qr Code (mtb)",
+ "Restore Face (mtb)",
+ "Save Gif (mtb)",
+ "Save Image Grid (mtb)",
+ "Save Image Sequence (mtb)",
+ "Smart Step (mtb)",
+ "String Replace (mtb)",
+ "Styles Loader (mtb)",
+ "Text To Image (mtb)",
+ "Uncrop (mtb)"
+ ],
+ "https://github.com/mihaiiancu/ComfyUI_Inpaint": [
+ "InpaintMediapipe"
+ ],
+ "https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92": [
+ "CLIPStringEncode _O",
+ "Chat completion _O",
+ "ChatGPT Simple _O",
+ "ChatGPT _O",
+ "ChatGPT compact _O",
+ "Chat_Completion _O",
+ "Chat_Message _O",
+ "Chat_Message_fromString _O",
+ "Concat Text _O",
+ "ConcatRandomNSP_O",
+ "Debug String _O",
+ "Debug Text _O",
+ "Debug Text route _O",
+ "Edit_image _O",
+ "Equation1param _O",
+ "Equation2params _O",
+ "GetImage_(Width&Height) _O",
+ "GetLatent_(Width&Height) _O",
+ "ImageScaleFactor _O",
+ "ImageScaleFactorSimple _O",
+ "LatentUpscaleFactor _O",
+ "LatentUpscaleFactorSimple _O",
+ "LatentUpscaleMultiply",
+ "Note _O",
+ "RandomNSP _O",
+ "Replace Text _O",
+ "String _O",
+ "Text _O",
+ "Text2Image _O",
+ "Trim Text _O",
+ "VAEDecodeParallel _O",
+ "combine_chat_messages _O",
+ "compine_chat_messages _O",
+ "concat Strings _O",
+ "create image _O",
+ "create_image _O",
+ "debug Completeion _O",
+ "debug messages_O",
+ "float _O",
+ "floatToInt _O",
+ "floatToText _O",
+ "int _O",
+ "intToFloat _O",
+ "load_openAI _O",
+ "replace String _O",
+ "replace String advanced _O",
+ "saveTextToFile _O",
+ "seed _O",
+ "selectLatentFromBatch _O",
+ "string2Image _O",
+ "trim String _O",
+ "variation_image _O"
+ ],
+ "https://github.com/pants007/comfy-pants": [
+ "CLIPInterrogator",
+ "CLIPTextEncodeAIO",
+ "Image Make Square"
+ ],
+ "https://github.com/paulo-coronado/comfy_clip_blip_node": [
+ "CLIPTextEncodeBLIP",
+ "CLIPTextEncodeBLIP-2",
+ "Example"
+ ],
+ "https://github.com/pythongosssss/ComfyUI-Custom-Scripts": [
+ "CheckpointLoader|pysssss",
+ "ConstrainImage|pysssss",
+ "LoraLoader|pysssss",
+ "MathExpression|pysssss",
+ "MultiPrimitive|pysssss",
+ "ReroutePrimitive|pysssss",
+ "ShowText|pysssss",
+ "StringFunction|pysssss"
+ ],
+ "https://github.com/pythongosssss/ComfyUI-WD14-Tagger": [
+ "WD14Tagger|pysssss"
+ ],
+ "https://github.com/s1dlx/comfy_meh/raw/main/meh.py": [
+ "MergingExecutionHelper"
+ ],
+ "https://github.com/shiimizu/ComfyUI_smZNodes": [
+ "smZ CLIPTextEncode"
+ ],
+ "https://github.com/shockz0rz/ComfyUI_InterpolateEverything": [
+ "OpenposePreprocessorInterpolate"
+ ],
+ "https://github.com/sipherxyz/comfyui-art-venture": [
+ "AV_UploadImage",
+ "ImagesConcat",
+ "LoadImageFromUrl"
+ ],
+ "https://github.com/space-nuko/ComfyUI-Disco-Diffusion": [
+ "DiscoDiffusion_DiscoDiffusion",
+ "DiscoDiffusion_DiscoDiffusionExtraSettings",
+ "DiscoDiffusion_GuidedDiffusionLoader",
+ "DiscoDiffusion_OpenAICLIPLoader"
+ ],
+ "https://github.com/space-nuko/ComfyUI-OpenPose-Editor": [
+ "Nui.OpenPoseEditor"
+ ],
+ "https://github.com/space-nuko/nui-suite": [
+ "Nui.DynamicPromptsTextGen",
+ "Nui.FeelingLuckyTextGen",
+ "Nui.OutputString"
+ ],
+ "https://github.com/ssitu/ComfyUI_UltimateSDUpscale": [
+ "UltimateSDUpscale",
+ "UltimateSDUpscaleNoUpscale"
+ ],
+ "https://github.com/ssitu/ComfyUI_restart_sampling": [
+ "KRestartSampler",
+ "KRestartSamplerSimple"
+ ],
+ "https://github.com/ssitu/ComfyUI_roop": [
+ "roop"
+ ],
+ "https://github.com/strimmlarn/ComfyUI_Strimmlarns_aesthetic_score": [
+ "AesthetlcScoreSorter",
+ "CalculateAestheticScore",
+ "LoadAesteticModel",
+ "ScoreToNumber"
+ ],
+ "https://github.com/sylym/comfy_vid2vid": [
+ "CheckpointLoaderSimpleSequence",
+ "DdimInversionSequence",
+ "KSamplerSequence",
+ "LoadImageMaskSequence",
+ "LoadImageSequence",
+ "LoraLoaderSequence",
+ "SetLatentNoiseSequence",
+ "TrainUnetSequence",
+ "VAEEncodeForInpaintSequence"
+ ],
+ "https://github.com/szhublox/ambw_comfyui": [
+ "Auto Merge Block Weighted"
+ ],
+ "https://github.com/taabata/Comfy_Syrian_Falcon_Nodes/raw/main/SyrianFalconNodes.py": [
+ "CompositeImage",
+ "KSamplerAdvancedCustom",
+ "LoopBack",
+ "QRGenerate",
+ "WordAsImage"
+ ],
+ "https://github.com/trojblue/trNodes": [
+ "JpgConvertNode",
+ "trColorCorrection",
+ "trLayering",
+ "trRouter",
+ "trRouterLonger"
+ ],
+ "https://github.com/tudal/Hakkun-ComfyUI-nodes/raw/main/hakkun_nodes.py": [
+ "Any Converter",
+ "Calculate Upscale",
+ "Image size to string",
+ "Multi Text Merge",
+ "Prompt Parser",
+ "Random Line",
+ "Random Line 4"
+ ],
+ "https://github.com/twri/sdxl_prompt_styler": [
+ "SDXLPromptStyler"
+ ],
+ "https://github.com/wallish77/wlsh_nodes": [
+ "Alternating KSampler (WLSH)",
+ "CLIP Positive-Negative (WLSH)",
+ "CLIP Positive-Negative w/Text (WLSH)",
+ "Checkpoint Loader w/Name (WLSH)",
+ "Empty Latent by Ratio (WLSH)",
+ "Generate Edge Mask (WLSH)",
+ "Generate Face Mask (WLSH)",
+ "Image Save with Prompt Data (WLSH)",
+ "Image Save with Prompt File (WLSH)",
+ "Image Scale By Factor (WLSH)",
+ "KSamplerAdvanced (WLSH)",
+ "Multiply Integer (WLSH)",
+ "Outpaint to Image (WLSH)",
+ "Resolutions by Ratio (WLSH)",
+ "SDXL Quick Empty Latent (WLSH)",
+ "SDXL Quick Image Scale (WLSH)",
+ "SDXL Resolutions (WLSH)",
+ "SDXL Steps (WLSH)",
+ "Save Positive Prompt File (WLSH)",
+ "Save Prompt Info (WLSH)",
+ "Seed to Number (WLSH)",
+ "Time String (WLSH)",
+ "Upscale by Factor with Model (WLSH)",
+ "VAE Encode for Inpaint Padding (WLSH)"
+ ],
+ "https://github.com/xXAdonesXx/NodeGPT/raw/main/Textnode.py": [
+ "CombineInput",
+ "CostumeAgent_1",
+ "CostumeAgent_2",
+ "CostumeMaster_1",
+ "Image_generation_Conditioning",
+ "Memory_Excel",
+ "Model_1",
+ "TextCombine",
+ "TextGenerator",
+ "TextInput",
+ "TextOutput"
+ ],
+ "https://github.com/yolanother/DTAIComfyPromptAgent": [
+ "DTPromptAgent",
+ "DTPromptAgentString"
+ ],
+ "https://gitlab.com/sofuego-comfy-nodes/comfy-qr": [
+ "comfy-qr-by-image-size",
+ "comfy-qr-by-module-size"
+ ],
+ "https://raw.githubusercontent.com/throttlekitty/SDXLCustomAspectRatio/main/SDXLAspectRatio.py": [
+ "SDXLAspectRatio"
+ ]
+}
\ No newline at end of file
diff --git a/ComfyUI-Manager/git_helper.py b/ComfyUI-Manager/git_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..4df83e07135abe9ac2554041d6e19348067b823a
--- /dev/null
+++ b/ComfyUI-Manager/git_helper.py
@@ -0,0 +1,72 @@
+import sys
+import os
+import git
+
+def gitclone(custom_nodes_path, url):
+ repo_name = os.path.splitext(os.path.basename(url))[0]
+ repo_path = os.path.join(custom_nodes_path, repo_name)
+
+ # Clone the repository from the remote URL
+ repo = git.Repo.clone_from(url, repo_path, recursive=True)
+ repo.git.clear_cache()
+ repo.close()
+
+def gitcheck(path, do_fetch=False):
+ # Fetch the latest commits from the remote repository
+ repo = git.Repo(path)
+
+ current_branch = repo.active_branch
+ branch_name = current_branch.name
+
+ remote_name = 'origin'
+ remote = repo.remote(name=remote_name)
+
+ if do_fetch:
+ remote.fetch()
+
+ # Get the current commit hash and the commit hash of the remote branch
+ commit_hash = repo.head.commit.hexsha
+ remote_commit_hash = repo.refs[f'{remote_name}/{branch_name}'].object.hexsha
+
+ # Compare the commit hashes to determine if the local repository is behind the remote repository
+ if commit_hash != remote_commit_hash:
+ # Get the commit dates
+ commit_date = repo.head.commit.committed_datetime
+ remote_commit_date = repo.refs[f'{remote_name}/{branch_name}'].object.committed_datetime
+
+ # Compare the commit dates to determine if the local repository is behind the remote repository
+ if commit_date < remote_commit_date:
+ print("CUSTOM NODE CHECK: True")
+ else:
+ print("CUSTOM NODE CHECK: False")
+
+def gitpull(path):
+ # Check if the path is a git repository
+ if not os.path.exists(os.path.join(path, '.git')):
+ raise ValueError('Not a git repository')
+
+ # Pull the latest changes from the remote repository
+ repo = git.Repo(path)
+ if repo.is_dirty():
+ repo.git.stash()
+
+ origin = repo.remote(name='origin')
+ origin.pull(rebase=True)
+ repo.git.submodule('update', '--init', '--recursive')
+
+ repo.close()
+
+try:
+ if sys.argv[1] == "--clone":
+ gitclone(sys.argv[2], sys.argv[3])
+ elif sys.argv[1] == "--check":
+ gitcheck(sys.argv[2], False)
+ elif sys.argv[1] == "--fetch":
+ gitcheck(sys.argv[2], True)
+ elif sys.argv[1] == "--pull":
+ gitpull(sys.argv[2])
+ sys.exit(0)
+except:
+ sys.exit(-1)
+
+
diff --git a/ComfyUI-Manager/js/comfyui-manager.js b/ComfyUI-Manager/js/comfyui-manager.js
new file mode 100644
index 0000000000000000000000000000000000000000..c8fe8aeb8bb2df5a691b10098d9cfc47bdabfcfb
--- /dev/null
+++ b/ComfyUI-Manager/js/comfyui-manager.js
@@ -0,0 +1,1501 @@
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js"
+import { ComfyDialog, $el } from "../../scripts/ui.js";
+import {ComfyWidgets} from "../../scripts/widgets.js";
+
+var update_comfyui_button = null;
+var fetch_updates_button = null;
+
+async function getCustomnodeMappings() {
+ var mode = "url";
+ if(ManagerMenuDialog.instance.local_mode_checkbox.checked)
+ mode = "local";
+
+ const response = await api.fetchApi(`/customnode/getmappings?mode=${mode}`);
+
+ const data = await response.json();
+ return data;
+}
+
+async function getUnresolvedNodesInComponent() {
+ try {
+ var mode = "url";
+ if(ManagerMenuDialog.instance.local_mode_checkbox.checked)
+ mode = "local";
+
+ const response = await api.fetchApi(`/component/get_unresolved`);
+
+ const data = await response.json();
+ return data.nodes;
+ }
+ catch {
+ return [];
+ }
+}
+
+async function getCustomNodes() {
+ var mode = "url";
+ if(ManagerMenuDialog.instance.local_mode_checkbox.checked)
+ mode = "local";
+
+ var skip_update = "";
+ if(ManagerMenuDialog.instance.update_check_checkbox.checked)
+ skip_update = "&skip_update=true";
+
+ const response = await api.fetchApi(`/customnode/getlist?mode=${mode}${skip_update}`);
+
+ const data = await response.json();
+ return data;
+}
+
+async function getAlterList() {
+ var mode = "url";
+ if(ManagerMenuDialog.instance.local_mode_checkbox.checked)
+ mode = "local";
+
+ var skip_update = "";
+ if(ManagerMenuDialog.instance.update_check_checkbox.checked)
+ skip_update = "&skip_update=true";
+
+ const response = await api.fetchApi(`/alternatives/getlist?mode=${mode}${skip_update}`);
+
+ const data = await response.json();
+ return data;
+}
+
+async function getModelList() {
+ var mode = "url";
+ if(ManagerMenuDialog.instance.local_mode_checkbox.checked)
+ mode = "local";
+
+ const response = await api.fetchApi(`/externalmodel/getlist?mode=${mode}`);
+
+ const data = await response.json();
+ return data;
+}
+
+async function install_custom_node(target, caller, mode) {
+ if(caller) {
+ caller.startInstall(target);
+
+ try {
+ const response = await api.fetchApi(`/customnode/${mode}`, {
+ method: 'POST',
+ headers: { 'Content-Type': 'application/json' },
+ body: JSON.stringify(target)
+ });
+
+ if(response.status == 400) {
+ app.ui.dialog.show(`${mode} failed: ${target.title}`);
+ app.ui.dialog.element.style.zIndex = 9999;
+ return false;
+ }
+
+ const status = await response.json();
+ app.ui.dialog.close();
+ target.installed = 'True';
+ return true;
+ }
+ catch(exception) {
+ app.ui.dialog.show(`${mode} failed: ${target.title} / ${exception}`);
+ app.ui.dialog.element.style.zIndex = 9999;
+ return false;
+ }
+ finally {
+ await caller.invalidateControl();
+ caller.updateMessage('
To apply the installed/disabled/enabled custom node, please restart ComfyUI.');
+ }
+ }
+}
+
+async function updateComfyUI() {
+ update_comfyui_button.innerText = "Updating ComfyUI...";
+ update_comfyui_button.disabled = true;
+ update_comfyui_button.style.backgroundColor = "gray";
+
+ try {
+ const response = await api.fetchApi('/comfyui_manager/update_comfyui');
+
+ if(response.status == 400) {
+ app.ui.dialog.show('Failed to update ComfyUI');
+ app.ui.dialog.element.style.zIndex = 9999;
+ return false;
+ }
+
+ if(response.status == 201) {
+ app.ui.dialog.show('ComfyUI has been successfully updated.');
+ app.ui.dialog.element.style.zIndex = 9999;
+ }
+ else {
+ app.ui.dialog.show('ComfyUI is already up to date with the latest version.');
+ app.ui.dialog.element.style.zIndex = 9999;
+ }
+
+ return true;
+ }
+ catch(exception) {
+ app.ui.dialog.show(`Failed to update ComfyUI / ${exception}`);
+ app.ui.dialog.element.style.zIndex = 9999;
+ return false;
+ }
+ finally {
+ update_comfyui_button.disabled = false;
+ update_comfyui_button.innerText = "Update ComfyUI";
+ update_comfyui_button.style.backgroundColor = "";
+ }
+}
+
+async function fetchUpdates(update_check_checkbox) {
+ fetch_updates_button.innerText = "Fetching updates...";
+ fetch_updates_button.disabled = true;
+ fetch_updates_button.style.backgroundColor = "gray";
+
+ try {
+ var mode = "url";
+ if(ManagerMenuDialog.instance.local_mode_checkbox.checked)
+ mode = "local";
+
+ const response = await api.fetchApi(`/customnode/fetch_updates?mode=${mode}`);
+
+ if(response.status == 400) {
+ app.ui.dialog.show('Failed to fetch updates.');
+ app.ui.dialog.element.style.zIndex = 9999;
+ return false;
+ }
+
+ if(response.status == 201) {
+ app.ui.dialog.show('There is an updated extension available.');
+ app.ui.dialog.element.style.zIndex = 9999;
+ update_check_checkbox.checked = false;
+ }
+ else {
+ app.ui.dialog.show('All extensions are already up-to-date with the latest versions.');
+ app.ui.dialog.element.style.zIndex = 9999;
+ }
+
+ return true;
+ }
+ catch(exception) {
+ app.ui.dialog.show(`Failed to update custom nodes / ${exception}`);
+ app.ui.dialog.element.style.zIndex = 9999;
+ return false;
+ }
+ finally {
+ fetch_updates_button.disabled = false;
+ fetch_updates_button.innerText = "Fetch Updates";
+ fetch_updates_button.style.backgroundColor = "";
+ }
+}
+
+async function install_model(target) {
+ if(ModelInstaller.instance) {
+ ModelInstaller.instance.startInstall(target);
+
+ try {
+ const response = await api.fetchApi('/model/install', {
+ method: 'POST',
+ headers: { 'Content-Type': 'application/json' },
+ body: JSON.stringify(target)
+ });
+
+ const status = await response.json();
+ app.ui.dialog.close();
+ target.installed = 'True';
+ return true;
+ }
+ catch(exception) {
+ app.ui.dialog.show(`Install failed: ${target.title} / ${exception}`);
+ app.ui.dialog.element.style.zIndex = 9999;
+ return false;
+ }
+ finally {
+ await ModelInstaller.instance.invalidateControl();
+ ModelInstaller.instance.updateMessage("
To apply the installed model, please click the 'Refresh' button on the main menu.");
+ }
+ }
+}
+
+
+// -----
+class CustomNodesInstaller extends ComfyDialog {
+ static instance = null;
+
+ install_buttons = [];
+ message_box = null;
+ data = null;
+
+ clear() {
+ this.install_buttons = [];
+ this.message_box = null;
+ this.data = null;
+ }
+
+ constructor() {
+ super();
+ this.search_keyword = '';
+ this.element = $el("div.comfy-modal", { parent: document.body }, []);
+ }
+
+ startInstall(target) {
+ const self = CustomNodesInstaller.instance;
+
+ self.updateMessage(`
Installing '${target.title}'`);
+
+ for(let i in self.install_buttons) {
+ self.install_buttons[i].disabled = true;
+ self.install_buttons[i].style.backgroundColor = 'gray';
+ }
+ }
+
+ apply_searchbox(data) {
+ let keyword = this.search_box.value.toLowerCase();
+ for(let i in this.grid_rows) {
+ let data = this.grid_rows[i].data;
+ let content = data.author.toLowerCase() + data.description.toLowerCase() + data.title.toLowerCase();
+
+ if(this.filter && this.filter != '*') {
+ if(this.filter != data.installed) {
+ this.grid_rows[i].control.style.display = 'none';
+ continue;
+ }
+ }
+
+ if(keyword == "")
+ this.grid_rows[i].control.style.display = null;
+ else if(content.includes(keyword)) {
+ this.grid_rows[i].control.style.display = null;
+ }
+ else {
+ this.grid_rows[i].control.style.display = 'none';
+ }
+ }
+ }
+
+ async filter_missing_node(data) {
+ const mappings = await getCustomnodeMappings();
+
+
+ // build regex->url map
+ const regex_to_url = [];
+ for (let i in data) {
+ if(data[i]['nodename_pattern']) {
+ let item = {regex: new RegExp(data[i].nodename_pattern), url: data[i].files[0]};
+ regex_to_url.push(item);
+ }
+ }
+
+ // build name->url map
+ const name_to_url = {};
+ for (const url in mappings) {
+ const names = mappings[url];
+ for(const name in names) {
+ name_to_url[names[name]] = url;
+ }
+ }
+
+ const registered_nodes = new Set();
+ for (let i in LiteGraph.registered_node_types) {
+ registered_nodes.add(LiteGraph.registered_node_types[i].type);
+ }
+
+ const missing_nodes = new Set();
+ const nodes = app.graph.serialize().nodes;
+ for (let i in nodes) {
+ const node_type = nodes[i].type;
+ if (!registered_nodes.has(node_type)) {
+ const url = name_to_url[node_type.trim()];
+ if(url)
+ missing_nodes.add(url);
+ else {
+ for(let j in regex_to_url) {
+ if(regex_to_url[j].regex.test(node_type)) {
+ missing_nodes.add(regex_to_url[j].url);
+ }
+ }
+ }
+ }
+ }
+
+ let unresolved_nodes = await getUnresolvedNodesInComponent();
+ for (let i in unresolved_nodes) {
+ let node_type = unresolved_nodes[i];
+ const url = name_to_url[node_type];
+ if(url)
+ missing_nodes.add(url);
+ }
+
+ return data.filter(node => node.files.some(file => missing_nodes.has(file)));
+ }
+
+ async invalidateControl() {
+ this.clear();
+
+ // splash
+ while (this.element.children.length) {
+ this.element.removeChild(this.element.children[0]);
+ }
+
+ const msg = $el('div', {id:'custom-message'},
+ [$el('br'),
+ 'The custom node DB is currently being updated, and updates to custom nodes are being checked for.',
+ $el('br'),
+ 'NOTE: Update only checks for extensions that have been fetched.',
+ $el('br')]);
+ msg.style.height = '100px';
+ msg.style.verticalAlign = 'middle';
+ msg.style.color = "var(--fg-color)";
+
+ this.element.appendChild(msg);
+
+ // invalidate
+ this.data = (await getCustomNodes()).custom_nodes;
+
+ if(this.is_missing_node_mode)
+ this.data = await this.filter_missing_node(this.data);
+
+ this.element.removeChild(msg);
+
+ while (this.element.children.length) {
+ this.element.removeChild(this.element.children[0]);
+ }
+
+ this.createHeaderControls();
+ await this.createGrid();
+ this.apply_searchbox(this.data);
+ this.createBottomControls();
+ }
+
+ updateMessage(msg) {
+ this.message_box.innerHTML = msg;
+ }
+
+ async createGrid() {
+ var grid = document.createElement('table');
+ grid.setAttribute('id', 'custom-nodes-grid');
+
+ grid.style.position = "relative";
+ grid.style.display = "inline-block";
+ grid.style.width = "100%"
+
+ var headerRow = document.createElement('tr');
+ var header1 = document.createElement('th');
+ header1.innerHTML = ' ID ';
+ header1.style.width = "20px";
+ var header2 = document.createElement('th');
+ header2.innerHTML = 'Author';
+ header2.style.width = "150px";
+ var header3 = document.createElement('th');
+ header3.innerHTML = 'Name';
+ header3.style.width = "200px";
+ var header4 = document.createElement('th');
+ header4.innerHTML = 'Description';
+ header4.style.width = "500px";
+ var header5 = document.createElement('th');
+ header5.innerHTML = 'Install';
+ header5.style.width = "130px";
+ headerRow.appendChild(header1);
+ headerRow.appendChild(header2);
+ headerRow.appendChild(header3);
+ headerRow.appendChild(header4);
+ headerRow.appendChild(header5);
+
+ headerRow.style.backgroundColor = "Black";
+ headerRow.style.color = "White";
+ headerRow.style.textAlign = "center";
+ headerRow.style.width = "100%";
+ headerRow.style.padding = "0";
+ grid.appendChild(headerRow);
+
+ this.grid_rows = {};
+
+ if(this.data)
+ for (var i = 0; i < this.data.length; i++) {
+ const data = this.data[i];
+ var dataRow = document.createElement('tr');
+ var data1 = document.createElement('td');
+ data1.style.textAlign = "center";
+ data1.innerHTML = i+1;
+ var data2 = document.createElement('td');
+ data2.style.maxWidth = "100px";
+ data2.textContent = ` ${data.author}`;
+ data2.style.whiteSpace = "nowrap";
+ data2.style.overflow = "hidden";
+ data2.style.textOverflow = "ellipsis";
+ var data3 = document.createElement('td');
+ data3.style.maxWidth = "200px";
+ data3.style.wordWrap = "break-word";
+ data3.innerHTML = ` ${data.title}`;
+ var data4 = document.createElement('td');
+ data4.innerHTML = data.description;
+ var data5 = document.createElement('td');
+ data5.style.textAlign = "center";
+
+ var installBtn = document.createElement('button');
+ var installBtn2 = null;
+ var installBtn3 = null;
+
+ this.install_buttons.push(installBtn);
+
+ switch(data.installed) {
+ case 'Disabled':
+ installBtn3 = document.createElement('button');
+ installBtn3.innerHTML = 'Enable';
+ installBtn3.style.backgroundColor = 'blue';
+ installBtn3.style.color = 'white';
+ this.install_buttons.push(installBtn3);
+
+ installBtn.innerHTML = 'Uninstall';
+ installBtn.style.backgroundColor = 'red';
+ break;
+ case 'Update':
+ installBtn2 = document.createElement('button');
+ installBtn2.innerHTML = 'Update';
+ installBtn2.style.backgroundColor = 'blue';
+ installBtn2.style.color = 'white';
+ this.install_buttons.push(installBtn2);
+
+ installBtn3 = document.createElement('button');
+ installBtn3.innerHTML = 'Disable';
+ installBtn3.style.backgroundColor = 'MediumSlateBlue';
+ installBtn3.style.color = 'white';
+ this.install_buttons.push(installBtn3);
+
+ installBtn.innerHTML = 'Uninstall';
+ installBtn.style.backgroundColor = 'red';
+ break;
+ case 'True':
+ installBtn3 = document.createElement('button');
+ installBtn3.innerHTML = 'Disable';
+ installBtn3.style.backgroundColor = 'MediumSlateBlue';
+ installBtn3.style.color = 'white';
+ this.install_buttons.push(installBtn3);
+
+ installBtn.innerHTML = 'Uninstall';
+ installBtn.style.backgroundColor = 'red';
+ break;
+ case 'False':
+ installBtn.innerHTML = 'Install';
+ installBtn.style.backgroundColor = 'black';
+ installBtn.style.color = 'white';
+ break;
+ default:
+ installBtn.innerHTML = 'Try Install';
+ installBtn.style.backgroundColor = 'Gray';
+ installBtn.style.color = 'white';
+ }
+
+ if(installBtn2 != null) {
+ installBtn2.style.width = "120px";
+ installBtn2.addEventListener('click', function() {
+ install_custom_node(data, CustomNodesInstaller.instance, 'update');
+ });
+
+ data5.appendChild(installBtn2);
+ }
+
+ if(installBtn3 != null) {
+ installBtn3.style.width = "120px";
+ installBtn3.addEventListener('click', function() {
+ install_custom_node(data, CustomNodesInstaller.instance, 'toggle_active');
+ });
+
+ data5.appendChild(installBtn3);
+ }
+
+ installBtn.style.width = "120px";
+ installBtn.addEventListener('click', function() {
+ if(this.innerHTML == 'Uninstall') {
+ if (confirm(`Are you sure uninstall ${data.title}?`)) {
+ install_custom_node(data, CustomNodesInstaller.instance, 'uninstall');
+ }
+ }
+ else {
+ install_custom_node(data, CustomNodesInstaller.instance, 'install');
+ }
+ });
+
+ data5.appendChild(installBtn);
+
+ dataRow.style.backgroundColor = "var(--bg-color)";
+ dataRow.style.color = "var(--fg-color)";
+ dataRow.style.textAlign = "left";
+
+ dataRow.appendChild(data1);
+ dataRow.appendChild(data2);
+ dataRow.appendChild(data3);
+ dataRow.appendChild(data4);
+ dataRow.appendChild(data5);
+ grid.appendChild(dataRow);
+
+ this.grid_rows[i] = {data:data, control:dataRow};
+ }
+
+ const panel = document.createElement('div');
+ panel.style.height = "400px";
+ panel.style.width = "1000px";
+ panel.style.overflowY = "scroll";
+
+ panel.appendChild(grid);
+ this.element.appendChild(panel);
+ }
+
+ createFilterCombo() {
+ let combo = document.createElement("select");
+
+ combo.style.cssFloat = "left";
+ combo.style.fontSize = "14px";
+ combo.style.padding = "4px";
+ combo.style.background = "black";
+ combo.style.marginLeft = "2px";
+ combo.style.width = "199px";
+ combo.id = `combo-manger-filter`;
+ combo.style.borderRadius = "15px";
+
+ let items =
+ [
+ { value:'*', text:'Filter: all' },
+ { value:'Disabled', text:'Filter: disabled' },
+ { value:'Update', text:'Filter: update' },
+ { value:'True', text:'Filter: installed' },
+ { value:'False', text:'Filter: not-installed' },
+ ];
+
+ items.forEach(item => {
+ const option = document.createElement("option");
+ option.value = item.value;
+ option.text = item.text;
+ combo.appendChild(option);
+ });
+
+ let self = this;
+ combo.addEventListener('change', function(event) {
+ self.filter = event.target.value;
+ self.apply_searchbox();
+ });
+
+ if(self.filter) {
+ combo.value = self.filter;
+ }
+
+ return combo;
+ }
+
+ createHeaderControls() {
+ let self = this;
+ this.search_box = $el('input', {type:'text', id:'manager-customnode-search-box', placeholder:'input search keyword', value:this.search_keyword}, []);
+ this.search_box.style.height = "25px";
+ this.search_box.onkeydown = (event) => {
+ if (event.key === 'Enter') {
+ self.search_keyword = self.search_box.value;
+ self.apply_searchbox();
+ }
+ if (event.key === 'Escape') {
+ self.search_keyword = self.search_box.value;
+ self.apply_searchbox();
+ }
+ };
+
+
+ let search_button = document.createElement("button");
+ search_button.innerHTML = "Search";
+ search_button.onclick = () => {
+ self.search_keyword = self.search_box.value;
+ self.apply_searchbox();
+ };
+ search_button.style.display = "inline-block";
+
+ let filter_control = this.createFilterCombo();
+ filter_control.style.display = "inline-block";
+
+ let cell = $el('td', {width:'100%'}, [filter_control, this.search_box, ' ', search_button]);
+ let search_control = $el('table', {width:'100%'},
+ [
+ $el('tr', {}, [cell])
+ ]
+ );
+
+ cell.style.textAlign = "right";
+
+ this.element.appendChild(search_control);
+ }
+
+ async createBottomControls() {
+ let close_button = document.createElement("button");
+ close_button.innerHTML = "Close";
+ close_button.onclick = () => { this.close(); }
+ close_button.style.display = "inline-block";
+
+ this.message_box = $el('div', {id:'custom-installer-message'}, [$el('br'), '']);
+ this.message_box.style.height = '60px';
+ this.message_box.style.verticalAlign = 'middle';
+
+ this.element.appendChild(this.message_box);
+ this.element.appendChild(close_button);
+ }
+
+ async show(is_missing_node_mode) {
+ this.is_missing_node_mode = is_missing_node_mode;
+ try {
+ this.invalidateControl();
+
+ this.element.style.display = "block";
+ }
+ catch(exception) {
+ app.ui.dialog.show(`Failed to get custom node list. / ${exception}`);
+ }
+ }
+}
+
+// -----
+class AlternativesInstaller extends ComfyDialog {
+ static instance = null;
+
+ install_buttons = [];
+ message_box = null;
+ data = null;
+
+ clear() {
+ this.install_buttons = [];
+ this.message_box = null;
+ this.data = null;
+ }
+
+ constructor() {
+ super();
+ this.search_keyword = '';
+ this.element = $el("div.comfy-modal", { parent: document.body }, []);
+ }
+
+ startInstall(target) {
+ const self = AlternativesInstaller.instance;
+
+ self.updateMessage(`
Installing '${target.title}'`);
+
+ for(let i in self.install_buttons) {
+ self.install_buttons[i].disabled = true;
+ self.install_buttons[i].style.backgroundColor = 'gray';
+ }
+ }
+
+ apply_searchbox(data) {
+ let keyword = this.search_box.value.toLowerCase();
+ for(let i in this.grid_rows) {
+ let data1 = this.grid_rows[i].data;
+ let data2 = data1.custom_node;
+ let content = data1.tags.toLowerCase() + data1.description.toLowerCase() + data2.author.toLowerCase() + data2.description.toLowerCase() + data2.title.toLowerCase();
+
+ if(this.filter && this.filter != '*') {
+ if(this.filter != data2.installed) {
+ this.grid_rows[i].control.style.display = 'none';
+ continue;
+ }
+ }
+
+ if(keyword == "")
+ this.grid_rows[i].control.style.display = null;
+ else if(content.includes(keyword)) {
+ this.grid_rows[i].control.style.display = null;
+ }
+ else {
+ this.grid_rows[i].control.style.display = 'none';
+ }
+ }
+ }
+
+ async invalidateControl() {
+ this.clear();
+
+ // splash
+ while (this.element.children.length) {
+ this.element.removeChild(this.element.children[0]);
+ }
+
+ const msg = $el('div', {id:'custom-message'},
+ [$el('br'),
+ 'The custom node DB is currently being updated, and updates to custom nodes are being checked for.',
+ $el('br'),
+ 'NOTE: Update only checks for extensions that have been fetched.',
+ $el('br')]);
+ msg.style.height = '100px';
+ msg.style.verticalAlign = 'middle';
+ this.element.appendChild(msg);
+
+ // invalidate
+ this.data = (await getAlterList()).items;
+
+ this.element.removeChild(msg);
+
+ while (this.element.children.length) {
+ this.element.removeChild(this.element.children[0]);
+ }
+
+ this.createHeaderControls();
+ await this.createGrid();
+ this.apply_searchbox(this.data);
+ this.createBottomControls();
+ }
+
+ updateMessage(msg) {
+ this.message_box.innerHTML = msg;
+ }
+
+ async createGrid() {
+ var grid = document.createElement('table');
+ grid.setAttribute('id', 'alternatives-grid');
+
+ grid.style.position = "relative";
+ grid.style.display = "inline-block";
+ grid.style.width = "100%"
+
+ var headerRow = document.createElement('tr');
+ var header1 = document.createElement('th');
+ header1.innerHTML = ' ID ';
+ header1.style.width = "20px";
+ var header2 = document.createElement('th');
+ header2.innerHTML = 'Tags';
+ header2.style.width = "200px";
+ var header3 = document.createElement('th');
+ header3.innerHTML = 'Author';
+ header3.style.width = "150px";
+ var header4 = document.createElement('th');
+ header4.innerHTML = 'Title';
+ header4.style.width = "200px";
+ var header5 = document.createElement('th');
+ header5.innerHTML = 'Description';
+ header5.style.width = "500px";
+ var header6 = document.createElement('th');
+ header6.innerHTML = 'Install';
+ header6.style.width = "130px";
+ headerRow.appendChild(header1);
+ headerRow.appendChild(header2);
+ headerRow.appendChild(header3);
+ headerRow.appendChild(header4);
+ headerRow.appendChild(header5);
+ headerRow.appendChild(header6);
+
+ headerRow.style.backgroundColor = "Black";
+ headerRow.style.color = "White";
+ headerRow.style.textAlign = "center";
+ headerRow.style.width = "100%";
+ headerRow.style.padding = "0";
+ grid.appendChild(headerRow);
+
+ this.grid_rows = {};
+
+ if(this.data)
+ for (var i = 0; i < this.data.length; i++) {
+ const data = this.data[i];
+ var dataRow = document.createElement('tr');
+ var data1 = document.createElement('td');
+ data1.style.textAlign = "center";
+ data1.innerHTML = i+1;
+ var data2 = document.createElement('td');
+ data2.innerHTML = ` ${data.tags}`;
+ var data3 = document.createElement('td');
+ var data4 = document.createElement('td');
+ if(data.custom_node) {
+ data3.innerHTML = ` ${data.custom_node.author}`;
+ data4.innerHTML = ` ${data.custom_node.title}`;
+ }
+ else {
+ data3.innerHTML = ` Unknown`;
+ data4.innerHTML = ` Unknown`;
+ }
+ var data5 = document.createElement('td');
+ data5.innerHTML = data.description;
+ var data6 = document.createElement('td');
+ data6.style.textAlign = "center";
+
+ if(data.custom_node) {
+ var installBtn = document.createElement('button');
+ var installBtn2 = null;
+ var installBtn3 = null;
+
+ this.install_buttons.push(installBtn);
+
+ switch(data.custom_node.installed) {
+ case 'Disabled':
+ installBtn3 = document.createElement('button');
+ installBtn3.innerHTML = 'Enable';
+ installBtn3.style.backgroundColor = 'blue';
+ installBtn3.style.color = 'white';
+ this.install_buttons.push(installBtn3);
+
+ installBtn.innerHTML = 'Uninstall';
+ installBtn.style.backgroundColor = 'red';
+ installBtn.style.color = 'white';
+ break;
+ case 'Update':
+ installBtn2 = document.createElement('button');
+ installBtn2.innerHTML = 'Update';
+ installBtn2.style.backgroundColor = 'blue';
+ installBtn2.style.color = 'white';
+ this.install_buttons.push(installBtn2);
+
+ installBtn3 = document.createElement('button');
+ installBtn3.innerHTML = 'Disable';
+ installBtn3.style.backgroundColor = 'MediumSlateBlue';
+ installBtn3.style.color = 'white';
+ this.install_buttons.push(installBtn3);
+
+ installBtn.innerHTML = 'Uninstall';
+ installBtn.style.backgroundColor = 'red';
+ installBtn.style.color = 'white';
+ break;
+ case 'True':
+ installBtn3 = document.createElement('button');
+ installBtn3.innerHTML = 'Disable';
+ installBtn3.style.backgroundColor = 'MediumSlateBlue';
+ installBtn3.style.color = 'white';
+ this.install_buttons.push(installBtn3);
+
+ installBtn.innerHTML = 'Uninstall';
+ installBtn.style.backgroundColor = 'red';
+ installBtn.style.color = 'white';
+ break;
+ case 'False':
+ installBtn.innerHTML = 'Install';
+ installBtn.style.backgroundColor = 'black';
+ installBtn.style.color = 'white';
+ break;
+ default:
+ installBtn.innerHTML = 'Try Install';
+ installBtn.style.backgroundColor = 'Gray';
+ installBtn.style.color = 'white';
+ }
+
+ if(installBtn2 != null) {
+ installBtn2.style.width = "120px";
+ installBtn2.addEventListener('click', function() {
+ install_custom_node(data.custom_node, AlternativesInstaller.instance, 'update');
+ });
+
+ data6.appendChild(installBtn2);
+ }
+
+ if(installBtn3 != null) {
+ installBtn3.style.width = "120px";
+ installBtn3.addEventListener('click', function() {
+ install_custom_node(data, CustomNodesInstaller.instance, 'toggle_active');
+ });
+
+ data6.appendChild(installBtn3);
+ }
+
+
+ installBtn.style.width = "120px";
+ installBtn.addEventListener('click', function() {
+ if(this.innerHTML == 'Uninstall') {
+ if (confirm(`Are you sure uninstall ${data.title}?`)) {
+ install_custom_node(data.custom_node, AlternativesInstaller.instance, 'uninstall');
+ }
+ }
+ else {
+ install_custom_node(data.custom_node, AlternativesInstaller.instance, 'install');
+ }
+ });
+
+ data6.appendChild(installBtn);
+ }
+
+ dataRow.style.backgroundColor = "var(--bg-color)";
+ dataRow.style.color = "var(--fg-color)";
+ dataRow.style.textAlign = "left";
+
+ dataRow.appendChild(data1);
+ dataRow.appendChild(data2);
+ dataRow.appendChild(data3);
+ dataRow.appendChild(data4);
+ dataRow.appendChild(data5);
+ dataRow.appendChild(data6);
+ grid.appendChild(dataRow);
+
+ this.grid_rows[i] = {data:data, control:dataRow};
+ }
+
+ const panel = document.createElement('div');
+ panel.style.height = "400px";
+ panel.style.width = "1000px";
+ panel.style.overflowY = "scroll";
+
+ panel.appendChild(grid);
+ this.element.appendChild(panel);
+ }
+
+ createFilterCombo() {
+ let combo = document.createElement("select");
+
+ combo.style.cssFloat = "left";
+ combo.style.fontSize = "14px";
+ combo.style.padding = "4px";
+ combo.style.background = "black";
+ combo.style.marginLeft = "2px";
+ combo.style.width = "199px";
+ combo.id = `combo-manger-filter`;
+ combo.style.borderRadius = "15px";
+
+ let items =
+ [
+ { value:'*', text:'Filter: all' },
+ { value:'Disabled', text:'Filter: disabled' },
+ { value:'Update', text:'Filter: update' },
+ { value:'True', text:'Filter: installed' },
+ { value:'False', text:'Filter: not-installed' },
+ ];
+
+ items.forEach(item => {
+ const option = document.createElement("option");
+ option.value = item.value;
+ option.text = item.text;
+ combo.appendChild(option);
+ });
+
+ let self = this;
+ combo.addEventListener('change', function(event) {
+ self.filter = event.target.value;
+ self.apply_searchbox();
+ });
+
+ if(self.filter) {
+ combo.value = self.filter;
+ }
+
+ return combo;
+ }
+
+ createHeaderControls() {
+ let self = this;
+ this.search_box = $el('input', {type:'text', id:'manager-alternode-search-box', placeholder:'input search keyword', value:this.search_keyword}, []);
+ this.search_box.style.height = "25px";
+ this.search_box.onkeydown = (event) => {
+ if (event.key === 'Enter') {
+ self.search_keyword = self.search_box.value;
+ self.apply_searchbox();
+ }
+ if (event.key === 'Escape') {
+ self.search_keyword = self.search_box.value;
+ self.apply_searchbox();
+ }
+ };
+
+ let search_button = document.createElement("button");
+ search_button.innerHTML = "Search";
+ search_button.onclick = () => {
+ self.search_keyword = self.search_box.value;
+ self.apply_searchbox();
+ };
+ search_button.style.display = "inline-block";
+
+ let filter_control = this.createFilterCombo();
+ filter_control.style.display = "inline-block";
+
+ let cell = $el('td', {width:'100%'}, [filter_control, this.search_box, ' ', search_button]);
+ let search_control = $el('table', {width:'100%'},
+ [
+ $el('tr', {}, [cell])
+ ]
+ );
+
+ cell.style.textAlign = "right";
+ this.element.appendChild(search_control);
+ }
+
+ async createBottomControls() {
+ var close_button = document.createElement("button");
+ close_button.innerHTML = "Close";
+ close_button.onclick = () => { this.close(); }
+ close_button.style.display = "inline-block";
+
+ this.message_box = $el('div', {id:'alternatives-installer-message'}, [$el('br'), '']);
+ this.message_box.style.height = '60px';
+ this.message_box.style.verticalAlign = 'middle';
+
+ this.element.appendChild(this.message_box);
+ this.element.appendChild(close_button);
+ }
+
+ async show() {
+ try {
+ this.invalidateControl();
+ this.element.style.display = "block";
+ }
+ catch(exception) {
+ app.ui.dialog.show(`Failed to get alternatives list. / ${exception}`);
+ console.error(exception);
+ }
+ }
+}
+
+
+// -----------
+class ModelInstaller extends ComfyDialog {
+ static instance = null;
+
+ install_buttons = [];
+ message_box = null;
+ data = null;
+
+ clear() {
+ this.install_buttons = [];
+ this.message_box = null;
+ this.data = null;
+ }
+
+ constructor() {
+ super();
+ this.search_keyword = '';
+ this.element = $el("div.comfy-modal", { parent: document.body }, []);
+ }
+
+ createControls() {
+ return [
+ $el("button", {
+ type: "button",
+ textContent: "Close",
+ onclick: () => { this.close(); }
+ })
+ ];
+ }
+
+ startInstall(target) {
+ const self = ModelInstaller.instance;
+
+ self.updateMessage(`
Installing '${target.name}'`);
+
+ for(let i in self.install_buttons) {
+ self.install_buttons[i].disabled = true;
+ self.install_buttons[i].style.backgroundColor = 'gray';
+ }
+ }
+
+ apply_searchbox(data) {
+ let keyword = this.search_box.value.toLowerCase();
+ for(let i in this.grid_rows) {
+ let data = this.grid_rows[i].data;
+ let content = data.name.toLowerCase() + data.type.toLowerCase() + data.base.toLowerCase() + data.description.toLowerCase();
+
+ if(this.filter && this.filter != '*') {
+ if(this.filter != data.installed) {
+ this.grid_rows[i].control.style.display = 'none';
+ continue;
+ }
+ }
+
+ if(keyword == "")
+ this.grid_rows[i].control.style.display = null;
+ else if(content.includes(keyword)) {
+ this.grid_rows[i].control.style.display = null;
+ }
+ else {
+ this.grid_rows[i].control.style.display = 'none';
+ }
+ }
+ }
+
+ async invalidateControl() {
+ this.clear();
+ this.data = (await getModelList()).models;
+
+ while (this.element.children.length) {
+ this.element.removeChild(this.element.children[0]);
+ }
+
+ await this.createHeaderControls();
+
+ if(this.search_keyword) {
+ this.search_box.value = this.search_keyword;
+ }
+
+ await this.createGrid();
+ await this.createBottomControls();
+
+ this.apply_searchbox(this.data);
+ }
+
+ updateMessage(msg) {
+ this.message_box.innerHTML = msg;
+ }
+
+ async createGrid(models_json) {
+ var grid = document.createElement('table');
+ grid.setAttribute('id', 'external-models-grid');
+
+ grid.style.position = "relative";
+ grid.style.display = "inline-block";
+ grid.style.width = "100%"
+
+ var headerRow = document.createElement('tr');
+ var header1 = document.createElement('th');
+ header1.innerHTML = ' ID ';
+ header1.style.width = "20px";
+ var header2 = document.createElement('th');
+ header2.innerHTML = 'Type';
+ header2.style.width = "100px";
+ var header3 = document.createElement('th');
+ header3.innerHTML = 'Base';
+ header3.style.width = "100px";
+ var header4 = document.createElement('th');
+ header4.innerHTML = 'Name';
+ header4.style.width = "200px";
+ var header5 = document.createElement('th');
+ header5.innerHTML = 'Filename';
+ header5.style.width = "250px";
+ header5.style.tableLayout = "fixed";
+ var header6 = document.createElement('th');
+ header6.innerHTML = 'description';
+ header6.style.width = "380px";
+ var header_down = document.createElement('th');
+ header_down.innerHTML = 'Download';
+ header_down.style.width = "50px";
+
+ headerRow.appendChild(header1);
+ headerRow.appendChild(header2);
+ headerRow.appendChild(header3);
+ headerRow.appendChild(header4);
+ headerRow.appendChild(header5);
+ headerRow.appendChild(header6);
+ headerRow.appendChild(header_down);
+
+ headerRow.style.backgroundColor = "Black";
+ headerRow.style.color = "White";
+ headerRow.style.textAlign = "center";
+ headerRow.style.width = "100%";
+ headerRow.style.padding = "0";
+ grid.appendChild(headerRow);
+
+ this.grid_rows = {};
+
+ if(this.data)
+ for (var i = 0; i < this.data.length; i++) {
+ const data = this.data[i];
+ var dataRow = document.createElement('tr');
+ var data1 = document.createElement('td');
+ data1.style.textAlign = "center";
+ data1.innerHTML = i+1;
+ var data2 = document.createElement('td');
+ data2.innerHTML = ` ${data.type}`;
+ var data3 = document.createElement('td');
+ data3.innerHTML = ` ${data.base}`;
+ var data4 = document.createElement('td');
+ data4.innerHTML = ` ${data.name}`;
+ var data5 = document.createElement('td');
+ data5.innerHTML = ` ${data.filename}`;
+ data5.style.wordBreak = "break-all";
+ var data6 = document.createElement('td');
+ data6.innerHTML = data.description;
+ data6.style.wordBreak = "break-all";
+ var data_install = document.createElement('td');
+ var installBtn = document.createElement('button');
+ data_install.style.textAlign = "center";
+
+ installBtn.innerHTML = 'Install';
+ this.install_buttons.push(installBtn);
+
+ switch(data.installed) {
+ case 'True':
+ installBtn.innerHTML = 'Installed';
+ installBtn.style.backgroundColor = 'green';
+ installBtn.style.color = 'white';
+ installBtn.disabled = true;
+ break;
+ default:
+ installBtn.innerHTML = 'Install';
+ installBtn.style.backgroundColor = 'black';
+ installBtn.style.color = 'white';
+ break;
+ }
+
+ installBtn.style.width = "100px";
+
+ installBtn.addEventListener('click', function() {
+ install_model(data);
+ });
+
+ data_install.appendChild(installBtn);
+
+ dataRow.style.backgroundColor = "var(--bg-color)";
+ dataRow.style.color = "var(--fg-color)";
+ dataRow.style.textAlign = "left";
+
+ dataRow.appendChild(data1);
+ dataRow.appendChild(data2);
+ dataRow.appendChild(data3);
+ dataRow.appendChild(data4);
+ dataRow.appendChild(data5);
+ dataRow.appendChild(data6);
+ dataRow.appendChild(data_install);
+ grid.appendChild(dataRow);
+
+ this.grid_rows[i] = {data:data, control:dataRow};
+ }
+
+ const panel = document.createElement('div');
+ panel.style.height = "400px";
+ panel.style.width = "1050px";
+ panel.style.overflowY = "scroll";
+
+ panel.appendChild(grid);
+ this.element.appendChild(panel);
+ }
+
+ createFilterCombo() {
+ let combo = document.createElement("select");
+
+ combo.style.cssFloat = "left";
+ combo.style.fontSize = "14px";
+ combo.style.padding = "4px";
+ combo.style.background = "black";
+ combo.style.marginLeft = "2px";
+ combo.style.width = "199px";
+ combo.id = `combo-manger-filter`;
+ combo.style.borderRadius = "15px";
+
+ let items =
+ [
+ { value:'*', text:'Filter: all' },
+ { value:'True', text:'Filter: installed' },
+ { value:'False', text:'Filter: not-installed' },
+ ];
+
+ items.forEach(item => {
+ const option = document.createElement("option");
+ option.value = item.value;
+ option.text = item.text;
+ combo.appendChild(option);
+ });
+
+ let self = this;
+ combo.addEventListener('change', function(event) {
+ self.filter = event.target.value;
+ self.apply_searchbox();
+ });
+
+ return combo;
+ }
+
+ createHeaderControls() {
+ let self = this;
+ this.search_box = $el('input', {type:'text', id:'manager-model-search-box', placeholder:'input search keyword', value:this.search_keyword}, []);
+ this.search_box.style.height = "25px";
+ this.search_box.onkeydown = (event) => {
+ if (event.key === 'Enter') {
+ self.search_keyword = self.search_box.value;
+ self.apply_searchbox();
+ }
+ if (event.key === 'Escape') {
+ self.search_keyword = self.search_box.value;
+ self.apply_searchbox();
+ }
+ };
+
+ let search_button = document.createElement("button");
+ search_button.innerHTML = "Search";
+ search_button.onclick = () => {
+ self.search_keyword = self.search_box.value;
+ self.apply_searchbox();
+ };
+ search_button.style.display = "inline-block";
+
+ let filter_control = this.createFilterCombo();
+ filter_control.style.display = "inline-block";
+
+ let cell = $el('td', {width:'100%'}, [filter_control, this.search_box, ' ', search_button]);
+ let search_control = $el('table', {width:'100%'},
+ [
+ $el('tr', {}, [cell])
+ ]
+ );
+
+ cell.style.textAlign = "right";
+ this.element.appendChild(search_control);
+ }
+
+ async createBottomControls() {
+ var close_button = document.createElement("button");
+ close_button.innerHTML = "Close";
+ close_button.onclick = () => { this.close(); }
+ close_button.style.display = "inline-block";
+
+ this.message_box = $el('div', {id:'custom-download-message'}, [$el('br'), '']);
+ this.message_box.style.height = '60px';
+ this.message_box.style.verticalAlign = 'middle';
+
+ this.element.appendChild(this.message_box);
+ this.element.appendChild(close_button);
+ }
+
+ async show() {
+ try {
+ this.invalidateControl();
+ this.element.style.display = "block";
+ }
+ catch(exception) {
+ app.ui.dialog.show(`Failed to get external model list. / ${exception}`);
+ }
+ }
+}
+
+
+// -----------
+class ManagerMenuDialog extends ComfyDialog {
+ static instance = null;
+ local_mode_checkbox = null;
+
+ createButtons() {
+ this.local_mode_checkbox = $el("input",{type:'checkbox', id:"use_local_db"},[])
+ const checkbox_text = $el("label",{},[" Use local DB"])
+ checkbox_text.style.color = "var(--fg-color)";
+ checkbox_text.style.marginRight = "10px";
+
+ this.update_check_checkbox = $el("input",{type:'checkbox', id:"skip_update_check"},[])
+ const uc_checkbox_text = $el("label",{},[" Skip update check"])
+ uc_checkbox_text.style.color = "var(--fg-color)";
+ this.update_check_checkbox.checked = true;
+
+ update_comfyui_button =
+ $el("button", {
+ type: "button",
+ textContent: "Update ComfyUI",
+ onclick:
+ () => updateComfyUI()
+ });
+
+ fetch_updates_button =
+ $el("button", {
+ type: "button",
+ textContent: "Fetch Updates",
+ onclick:
+ () => fetchUpdates(this.update_check_checkbox)
+ });
+
+ let preview_combo = document.createElement("select");
+ preview_combo.appendChild($el('option', {value:'auto', text:'Preview method: Auto'}, []));
+ preview_combo.appendChild($el('option', {value:'taesd', text:'Preview method: TAESD'}, []));
+ preview_combo.appendChild($el('option', {value:'latent2rgb', text:'Preview method: Latent2RGB'}, []));
+ preview_combo.appendChild($el('option', {value:'none', text:'Preview method: None'}, []));
+
+ api.fetchApi('/manager/preview_method')
+ .then(response => response.text())
+ .then(data => { preview_combo.value = data; })
+
+ preview_combo.addEventListener('change', function(event) {
+ api.fetchApi(`/manager/preview_method?value=${event.target.value}`);
+ });
+
+ const res =
+ [
+ $el("tr.td", {width:"100%"}, [$el("font", {size:6, color:"white"}, [`ComfyUI Manager Menu`])]),
+ $el("br", {}, []),
+ $el("div", {}, [this.local_mode_checkbox, checkbox_text, this.update_check_checkbox, uc_checkbox_text]),
+ $el("br", {}, []),
+ $el("button", {
+ type: "button",
+ textContent: "Install Custom Nodes",
+ onclick:
+ () => {
+ if(!CustomNodesInstaller.instance)
+ CustomNodesInstaller.instance = new CustomNodesInstaller(app);
+ CustomNodesInstaller.instance.show(false);
+ }
+ }),
+
+ $el("button", {
+ type: "button",
+ textContent: "Install Missing Custom Nodes",
+ onclick:
+ () => {
+ if(!CustomNodesInstaller.instance)
+ CustomNodesInstaller.instance = new CustomNodesInstaller(app);
+ CustomNodesInstaller.instance.show(true);
+ }
+ }),
+
+ $el("button", {
+ type: "button",
+ textContent: "Install Models",
+ onclick:
+ () => {
+ if(!ModelInstaller.instance)
+ ModelInstaller.instance = new ModelInstaller(app);
+ ModelInstaller.instance.show();
+ }
+ }),
+
+ $el("br", {}, []),
+ update_comfyui_button,
+ fetch_updates_button,
+
+ $el("br", {}, []),
+ $el("button", {
+ type: "button",
+ textContent: "Alternatives of A1111",
+ onclick:
+ () => {
+ if(!AlternativesInstaller.instance)
+ AlternativesInstaller.instance = new AlternativesInstaller(app);
+ AlternativesInstaller.instance.show();
+ }
+ }),
+
+ $el("br", {}, []),
+ $el("button", {
+ type: "button",
+ textContent: "ComfyUI Community Manual",
+ onclick: () => { window.open("https://blenderneko.github.io/ComfyUI-docs/", "comfyui-community-manual"); }
+ }),
+
+ $el("br", {}, []),
+ $el("hr", {width: "100%"}, []),
+ preview_combo,
+ $el("hr", {width: "100%"}, []),
+ $el("br", {}, []),
+
+ $el("button", {
+ type: "button",
+ textContent: "Close",
+ onclick: () => this.close()
+ }),
+ $el("br", {}, []),
+ ];
+
+ res[0].style.padding = "10px 10px 0 10px";
+ res[0].style.backgroundColor = "black";
+ res[0].style.textAlign = "center";
+ res[0].style.height = "45px";
+ return res;
+ }
+
+ constructor() {
+ super();
+ this.element = $el("div.comfy-modal", { parent: document.body },
+ [ $el("div.comfy-modal-content",
+ [...this.createButtons()]),
+ ]);
+ }
+
+ show() {
+ this.element.style.display = "block";
+ }
+}
+
+app.registerExtension({
+ name: "Comfy.ManagerMenu",
+
+ async setup() {
+ const menu = document.querySelector(".comfy-menu");
+ const separator = document.createElement("hr");
+
+ separator.style.margin = "20px 0";
+ separator.style.width = "100%";
+ menu.append(separator);
+
+ const managerButton = document.createElement("button");
+ managerButton.textContent = "Manager";
+ managerButton.onclick = () => {
+ if(!ManagerMenuDialog.instance)
+ ManagerMenuDialog.instance = new ManagerMenuDialog();
+ ManagerMenuDialog.instance.show();
+ }
+ menu.append(managerButton);
+ }
+});
diff --git a/ComfyUI-Manager/misc/custom-nodes.png b/ComfyUI-Manager/misc/custom-nodes.png
new file mode 100644
index 0000000000000000000000000000000000000000..41bf039606a250065431ca194437a71f3ffa542a
--- /dev/null
+++ b/ComfyUI-Manager/misc/custom-nodes.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa77fff6d703080986675340c9d109ebf48ee39ab1913ab6d2a92a318c881712
+size 152760
diff --git a/ComfyUI-Manager/misc/main.png b/ComfyUI-Manager/misc/main.png
new file mode 100644
index 0000000000000000000000000000000000000000..f766cb8c663120b63002d881243ab6181a669981
--- /dev/null
+++ b/ComfyUI-Manager/misc/main.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5aad57871895f3ab22d567e644246de5268b34074e4032e37d0745f280401633
+size 29165
diff --git a/ComfyUI-Manager/misc/menu.jpg b/ComfyUI-Manager/misc/menu.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4de126395bf66525b6ca600b4a25f7dd25391282
--- /dev/null
+++ b/ComfyUI-Manager/misc/menu.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b950e7fa6d626f8dfa0d18fe20a2a48e2bb13c5e59f4e30ce60c3bb6121fdbb0
+size 50964
diff --git a/ComfyUI-Manager/misc/missing-list.png b/ComfyUI-Manager/misc/missing-list.png
new file mode 100644
index 0000000000000000000000000000000000000000..11fe5bc68761505fb780c50cdf84a857aa0b93ff
--- /dev/null
+++ b/ComfyUI-Manager/misc/missing-list.png
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+{
+ "models": [
+ {
+ "name": "TAESDXL Decoder",
+ "type": "VAE",
+ "base": "TAESD",
+ "save_path": "vae_approx",
+ "description": "(SDXL Verison) To view the preview in high quality while running samples in ComfyUI, you will need this model.",
+ "reference": "https://github.com/madebyollin/taesd",
+ "filename": "taesdxl_decoder.pth",
+ "url": "https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth"
+ },
+ {
+ "name": "TAESDXL Encoder",
+ "type": "VAE",
+ "base": "TAESD",
+ "save_path": "vae_approx",
+ "description": "(SDXL Verison) To view the preview in high quality while running samples in ComfyUI, you will need this model.",
+ "reference": "https://github.com/madebyollin/taesd",
+ "filename": "taesdxl_encoder.pth",
+ "url": "https://github.com/madebyollin/taesd/raw/main/taesdxl_encoder.pth"
+ },
+ {
+ "name": "TAESD Decoder",
+ "type": "VAE",
+ "base": "TAESD",
+ "save_path": "vae_approx",
+ "description": "To view the preview in high quality while running samples in ComfyUI, you will need this model.",
+ "reference": "https://github.com/madebyollin/taesd",
+ "filename": "taesd_decoder.pth",
+ "url": "https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth"
+ },
+ {
+ "name": "TAESD Encoder",
+ "type": "VAE",
+ "base": "TAESD",
+ "save_path": "vae_approx",
+ "description": "To view the preview in high quality while running samples in ComfyUI, you will need this model.",
+ "reference": "https://github.com/madebyollin/taesd",
+ "filename": "taesd_encoder.pth",
+ "url": "https://github.com/madebyollin/taesd/raw/main/taesd_encoder.pth"
+ },
+ {
+ "name": "RealESRGAN x2",
+ "type": "upscale",
+ "base": "upscale",
+ "save_path": "default",
+ "description": "RealESRGAN x2 upscaler model",
+ "reference": "https://huggingface.co/sberbank-ai/Real-ESRGAN",
+ "filename": "RealESRGAN_x2.pth",
+ "url": "https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth"
+ },
+ {
+ "name": "RealESRGAN x4",
+ "type": "upscale",
+ "base": "upscale",
+ "save_path": "default",
+ "description": "RealESRGAN x4 upscaler model",
+ "reference": "https://huggingface.co/sberbank-ai/Real-ESRGAN",
+ "filename": "RealESRGAN_x4.pth",
+ "url": "https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth"
+ },
+ {
+ "name": "4x-UltraSharp",
+ "type": "upscale",
+ "base": "upscale",
+ "save_path": "default",
+ "description": "4x-UltraSharp upscaler model",
+ "reference": "https://upscale.wiki/wiki/Model_Database",
+ "filename": "4x-UltraSharp.pth",
+ "url": "https://huggingface.co/datasets/Kizi-Art/Upscale/resolve/fa98e357882a23b8e7928957a39462fbfaee1af5/4x-UltraSharp.pth"
+ },
+ {
+ "name": "Inswapper (face swap)",
+ "type": "insightface",
+ "base" : "inswapper",
+ "save_path": "insightface",
+ "description": "Checkpoint of the insightface swapper model (used by Comfy-Roop and comfy_mtb)",
+ "reference": "https://huggingface.co/deepinsight/inswapper/",
+ "filename": "inswapper_128.onnx",
+ "url": "https://huggingface.co/deepinsight/inswapper/resolve/main/inswapper_128.onnx"
+ },
+ {
+ "name": "Deepbump",
+ "type": "deepbump",
+ "base": "deepbump",
+ "save_path": "deepbump",
+ "description": "Checkpoint of the deepbump model to generate height and normal maps textures from an image (requires comfy_mtb)",
+ "reference": "https://github.com/HugoTini/DeepBump",
+ "filename": "deepbump256.onnx",
+ "url": "https://github.com/HugoTini/DeepBump/raw/master/deepbump256.onnx"
+ },
+ {
+ "name": "GFPGAN 1.3",
+ "type": "face_restore",
+ "base": "face_restore",
+ "save_path": "face_restore",
+ "description": "Face restoration",
+ "reference": "https://github.com/TencentARC/GFPGAN",
+ "filename": "GFPGANv1.3.pth",
+ "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth"
+ },
+ {
+ "name": "GFPGAN 1.4",
+ "type": "face_restore",
+ "base": "face_restore",
+ "save_path": "face_restore",
+ "description": "Face restoration",
+ "reference": "https://github.com/TencentARC/GFPGAN",
+ "filename": "GFPGANv1.4.pth",
+ "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
+ },
+ {
+ "name": "RestoreFormer",
+ "type": "face_restore",
+ "base": "face_restore",
+ "save_path": "face_restore",
+ "description": "Face restoration",
+ "reference": "https://github.com/TencentARC/GFPGAN",
+ "filename": "RestoreFormer.pth",
+ "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth"
+ },
+ {
+ "name": "negative_hand Negative Embedding",
+ "type": "embeddings",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "If you use this embedding with negatives, you can solve the issue of damaging your hands.",
+ "reference": "https://civitai.com/models/56519/negativehand-negative-embedding",
+ "filename": "negative_hand-neg.pt",
+ "url": "https://civitai.com/api/download/models/60938"
+ },
+ {
+ "name": "bad_prompt Negative Embedding",
+ "type": "embeddings",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "The idea behind this embedding was to somehow train the negative prompt as an embedding, thus unifying the basis of the negative prompt into one word or embedding.",
+ "reference": "https://civitai.com/models/55700/badprompt-negative-embedding",
+ "filename": "bad_prompt_version2-neg.pt",
+ "url": "https://civitai.com/api/download/models/60095"
+ },
+ {
+ "name": "Deep Negative V1.75",
+ "type": "embeddings",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "These embedding learn what disgusting compositions and color patterns are, including faulty human anatomy, offensive color schemes, upside-down spatial structures, and more. Placing it in the negative can go a long way to avoiding these things.",
+ "reference": "https://civitai.com/models/4629/deep-negative-v1x",
+ "filename": "ng_deepnegative_v1_75t.pt",
+ "url": "https://civitai.com/api/download/models/5637"
+ },
+ {
+ "name": "EasyNegative",
+ "type": "embeddings",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "This embedding should be used in your NEGATIVE prompt. Adjust the strength as desired (seems to scale well without any distortions), the strength required may vary based on positive and negative prompts.",
+ "reference": "https://civitai.com/models/7808/easynegative",
+ "filename": "easynegative.safetensors",
+ "url": "https://civitai.com/api/download/models/9208"
+ },
+ {
+ "name": "sd_xl_base_1.0_0.9vae.safetensors",
+ "type": "checkpoints",
+ "base": "SDXL",
+ "save_path": "default",
+ "description": "Stable Diffusion XL base model (VAE 0.9)",
+ "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0",
+ "filename": "sd_xl_base_1.0_0.9vae.safetensors",
+ "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors"
+ },
+ {
+ "name": "sd_xl_base_1.0.safetensors",
+ "type": "checkpoints",
+ "base": "SDXL",
+ "save_path": "default",
+ "description": "Stable Diffusion XL base model",
+ "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0",
+ "filename": "sd_xl_base_1.0.safetensors",
+ "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors"
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+ "name": "sd_xl_refiner_1.0_0.9vae.safetensors",
+ "type": "checkpoints",
+ "base": "SDXL",
+ "save_path": "default",
+ "description": "Stable Diffusion XL refiner model (VAE 0.9)",
+ "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0",
+ "filename": "sd_xl_refiner_1.0_0.9vae.safetensors",
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+ },
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+ "name": "stable-diffusion-xl-refiner-1.0",
+ "type": "checkpoints",
+ "base": "SDXL",
+ "save_path": "default",
+ "description": "Stable Diffusion XL refiner model",
+ "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0",
+ "filename": "sd_xl_refiner_1.0.safetensors",
+ "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors"
+ },
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+ "name": "sd_xl_offset_example-lora_1.0.safetensors",
+ "type": "lora",
+ "base": "SDXL",
+ "save_path": "default",
+ "description": "Stable Diffusion XL offset LoRA",
+ "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0",
+ "filename": "sd_xl_offset_example-lora_1.0.safetensors",
+ "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors"
+ },
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+ "name": "v1-5-pruned-emaonly.ckpt",
+ "type": "checkpoints",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Stable Diffusion 1.5 base model",
+ "reference": "https://huggingface.co/runwayml/stable-diffusion-v1-5",
+ "filename": "v1-5-pruned-emaonly.ckpt",
+ "url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt"
+ },
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+ "name": "v2-1_512-ema-pruned.safetensors",
+ "type": "checkpoints",
+ "base": "SD2",
+ "save_path": "default",
+ "description": "Stable Diffusion 2 base model (512)",
+ "reference": "https://huggingface.co/stabilityai/stable-diffusion-2-1-base",
+ "filename": "v2-1_512-ema-pruned.safetensors",
+ "url": "https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors"
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+ "name": "v2-1_768-ema-pruned.safetensors",
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+ "base": "SD2",
+ "save_path": "default",
+ "description": "Stable Diffusion 2 base model (768)",
+ "reference": "https://huggingface.co/stabilityai/stable-diffusion-2-1",
+ "filename": "v2-1_768-ema-pruned.safetensors",
+ "url": "https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors"
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+ "name": "AbyssOrangeMix2 (hard)",
+ "type": "checkpoints",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "AbyssOrangeMix2 - hard version (anime style)",
+ "reference": "https://huggingface.co/WarriorMama777/OrangeMixs",
+ "filename": "AbyssOrangeMix2_hard.safetensors",
+ "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors"
+ },
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+ "name": "AbyssOrangeMix3 A1",
+ "type": "checkpoints",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "AbyssOrangeMix3 - A1 (anime style)",
+ "reference": "https://huggingface.co/WarriorMama777/OrangeMixs",
+ "filename": "AOM3A1_orangemixs.safetensors",
+ "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors"
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+ "name": "AbyssOrangeMix3 A3",
+ "type": "checkpoints",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "AbyssOrangeMix - A3 (anime style)",
+ "reference": "https://huggingface.co/WarriorMama777/OrangeMixs",
+ "filename": "AOM3A3_orangemixs.safetensors",
+ "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors"
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+ "description": "Anything v3 (anime style)",
+ "reference": "https://huggingface.co/Linaqruf/anything-v3.0",
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+ "url": "https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors"
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+ "base": "SD2.1",
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+ "description": "Mix model (SD2.1 unCLIP + illuminatiDiffusionV1_v11)",
+ "reference": "https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP",
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+ "base": "SD2.1",
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+ "description": "Mix model (SD2.1 unCLIP + Waifu Diffusion 1.5)",
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+ "url": "https://civitai.com/api/download/models/10350"
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+ "url": "https://civitai.com/api/download/models/10638"
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+ "name": "T2I-Adapter (depth)",
+ "type": "T2I-Adapter",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "ControlNet T2I-Adapter for depth",
+ "reference": "https://huggingface.co/TencentARC/T2I-Adapter",
+ "filename": "t2iadapter_depth_sd14v1.pth",
+ "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth"
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+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "ControlNet T2I-Adapter for seg",
+ "reference": "https://huggingface.co/TencentARC/T2I-Adapter",
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+ "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth"
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+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "ControlNet T2I-Adapter for sketch",
+ "reference": "https://huggingface.co/TencentARC/T2I-Adapter",
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+ "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth"
+ },
+ {
+ "name": "T2I-Adapter (keypose)",
+ "type": "T2I-Adapter",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "ControlNet T2I-Adapter for keypose",
+ "reference": "https://huggingface.co/TencentARC/T2I-Adapter",
+ "filename": "t2iadapter_keypose_sd14v1.pth",
+ "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth"
+ },
+ {
+ "name": "T2I-Adapter (openpose)",
+ "type": "T2I-Adapter",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "ControlNet T2I-Adapter for openpose",
+ "reference": "https://huggingface.co/TencentARC/T2I-Adapter",
+ "filename": "t2iadapter_openpose_sd14v1.pth",
+ "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth"
+ },
+ {
+ "name": "T2I-Adapter (color)",
+ "type": "T2I-Adapter",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "ControlNet T2I-Adapter for color",
+ "reference": "https://huggingface.co/TencentARC/T2I-Adapter",
+ "filename": "t2iadapter_color_sd14v1.pth",
+ "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth"
+ },
+ {
+ "name": "T2I-Adapter (canny)",
+ "type": "T2I-Adapter",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "ControlNet T2I-Adapter for canny",
+ "reference": "https://huggingface.co/TencentARC/T2I-Adapter",
+ "filename": "t2iadapter_canny_sd14v1.pth",
+ "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth"
+ },
+ {
+ "name": "T2I-Style model",
+ "type": "T2I-Style",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "ControlNet T2I-Adapter style model. Need to download CLIPVision model.",
+ "reference": "https://huggingface.co/TencentARC/T2I-Adapter",
+ "filename": "t2iadapter_style_sd14v1.pth",
+ "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth"
+ },
+ {
+ "name": "CLIPVision model",
+ "type": "clip_vision",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "CLIPVision model (needed for styles model)",
+ "reference": "https://huggingface.co/openai/clip-vit-large-patch14",
+ "filename": "pytorch_model.bin",
+ "url": "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin"
+ },
+ {
+ "name": "ControlNet-v1-1 (ip2p; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (ip2p)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11e_sd15_ip2p_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (shuffle; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (shuffle)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11e_sd15_shuffle_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (canny; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (canny)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11p_sd15_canny_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (depth; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (depth)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11f1p_sd15_depth_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (inpaint; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (inpaint)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11p_sd15_inpaint_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (lineart; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (lineart)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11p_sd15_lineart_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (mlsd; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (mlsd)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11p_sd15_mlsd_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (normalbae; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (normalbae)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11p_sd15_normalbae_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (openpose; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (openpose)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11p_sd15_openpose_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (scribble; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (scribble)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11p_sd15_scribble_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (seg; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (seg)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11p_sd15_seg_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (softedge; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (softedge)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11p_sd15_softedge_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (anime; fp16)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (anime)",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11p_sd15s2_lineart_anime_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (tile; fp16; v11u)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (tile) / v11u",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11u_sd15_tile_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors"
+ },
+ {
+ "name": "ControlNet-v1-1 (tile; fp16; v11f1e)",
+ "type": "controlnet",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (tile) / v11f1e
You need to this model for Tiled Resample",
+ "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors",
+ "filename": "control_v11f1e_sd15_tile_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors"
+ },
+ {
+ "name": "GLIGEN textbox (fp16; pruned)",
+ "type": "gligen",
+ "base": "SD1.5",
+ "save_path": "default",
+ "description": "GLIGEN textbox model",
+ "reference": "https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors",
+ "filename": "gligen_sd14_textbox_pruned_fp16.safetensors",
+ "url": "https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors"
+ },
+ {
+ "name": "ViT-H SAM model",
+ "type": "sam",
+ "base": "SAM",
+ "save_path": "sams",
+ "description": "Segmenty Anything SAM model (ViT-H)",
+ "reference": "https://github.com/facebookresearch/segment-anything#model-checkpoints",
+ "filename": "sam_vit_h_4b8939.pth",
+ "url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
+ },
+ {
+ "name": "ViT-L SAM model",
+ "type": "sam",
+ "base": "SAM",
+ "save_path": "sams",
+ "description": "Segmenty Anything SAM model (ViT-L)",
+ "reference": "https://github.com/facebookresearch/segment-anything#model-checkpoints",
+ "filename": "sam_vit_l_0b3195.pth",
+ "url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth"
+ },
+ {
+ "name": "ViT-B SAM model",
+ "type": "sam",
+ "base": "SAM",
+ "save_path": "sams",
+ "description": "Segmenty Anything SAM model (ViT-B)",
+ "reference": "https://github.com/facebookresearch/segment-anything#model-checkpoints",
+ "filename": "sam_vit_b_01ec64.pth",
+ "url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth"
+ },
+ {
+ "name": "seecoder v1.0",
+ "type": "seecoder",
+ "base": "SEECODER",
+ "save_path": "seecoders",
+ "description": "SeeCoder model",
+ "reference": "https://huggingface.co/shi-labs/prompt-free-diffusion/tree/main/pretrained/pfd/seecoder",
+ "filename": "seecoder-v1-0.safetensors",
+ "url": "https://huggingface.co/shi-labs/prompt-free-diffusion/resolve/main/pretrained/pfd/seecoder/seecoder-v1-0.safetensors"
+ },
+ {
+ "name": "seecoder pa v1.0",
+ "type": "seecoder",
+ "base": "SEECODER",
+ "save_path": "seecoders",
+ "description": "SeeCoder model",
+ "reference": "https://huggingface.co/shi-labs/prompt-free-diffusion/tree/main/pretrained/pfd/seecoder",
+ "filename": "seecoder-pa-v1-0.safetensors",
+ "url": "https://huggingface.co/shi-labs/prompt-free-diffusion/resolve/main/pretrained/pfd/seecoder/seecoder-pa-v1-0.safetensors"
+ },
+ {
+ "name": "seecoder anime v1.0",
+ "type": "seecoder",
+ "base": "SEECODER",
+ "save_path": "seecoders",
+ "description": "SeeCoder model",
+ "reference": "https://huggingface.co/shi-labs/prompt-free-diffusion/tree/main/pretrained/pfd/seecoder",
+ "filename": "seecoder-anime-v1-0.safetensors",
+ "url": "https://huggingface.co/shi-labs/prompt-free-diffusion/resolve/main/pretrained/pfd/seecoder/seecoder-anime-v1-0.safetensors"
+ },
+ {
+ "name": "face_yolov8m (bbox)",
+ "type": "Ultralytics",
+ "base": "Ultralytics",
+ "save_path": "ultralytics/bbox",
+ "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.",
+ "reference": "https://huggingface.co/Bingsu/adetailer/tree/main",
+ "filename": "face_yolov8m.pt",
+ "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8m.pt"
+ },
+ {
+ "name": "face_yolov8n (bbox)",
+ "type": "Ultralytics",
+ "base": "Ultralytics",
+ "save_path": "ultralytics/bbox",
+ "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.",
+ "reference": "https://huggingface.co/Bingsu/adetailer/tree/main",
+ "filename": "face_yolov8n.pt",
+ "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8n.pt"
+ },
+ {
+ "name": "face_yolov8n_v2 (bbox)",
+ "type": "Ultralytics",
+ "base": "Ultralytics",
+ "save_path": "ultralytics/bbox",
+ "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.",
+ "reference": "https://huggingface.co/Bingsu/adetailer/tree/main",
+ "filename": "face_yolov8n_v2.pt",
+ "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8n_v2.pt"
+ },
+ {
+ "name": "face_yolov8s (bbox)",
+ "type": "Ultralytics",
+ "base": "Ultralytics",
+ "save_path": "ultralytics/bbox",
+ "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.",
+ "reference": "https://huggingface.co/Bingsu/adetailer/tree/main",
+ "filename": "face_yolov8s.pt",
+ "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8s.pt"
+ },
+ {
+ "name": "hand_yolov8n (bbox)",
+ "type": "Ultralytics",
+ "base": "Ultralytics",
+ "save_path": "ultralytics/bbox",
+ "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.",
+ "reference": "https://huggingface.co/Bingsu/adetailer/tree/main",
+ "filename": "hand_yolov8n.pt",
+ "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/hand_yolov8n.pt"
+ },
+ {
+ "name": "hand_yolov8s (bbox)",
+ "type": "Ultralytics",
+ "base": "Ultralytics",
+ "save_path": "ultralytics/bbox",
+ "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.",
+ "reference": "https://huggingface.co/Bingsu/adetailer/tree/main",
+ "filename": "hand_yolov8s.pt",
+ "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/hand_yolov8s.pt"
+ },
+ {
+ "name": "person_yolov8m (segm)",
+ "type": "Ultralytics",
+ "base": "Ultralytics",
+ "save_path": "ultralytics/segm",
+ "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.",
+ "reference": "https://huggingface.co/Bingsu/adetailer/tree/main",
+ "filename": "person_yolov8m-seg.pt",
+ "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8m-seg.pt"
+ },
+ {
+ "name": "person_yolov8n (segm)",
+ "type": "Ultralytics",
+ "base": "Ultralytics",
+ "save_path": "ultralytics/segm",
+ "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.",
+ "reference": "https://huggingface.co/Bingsu/adetailer/tree/main",
+ "filename": "person_yolov8n-seg.pt",
+ "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8n-seg.pt"
+ },
+ {
+ "name": "person_yolov8s (segm)",
+ "type": "Ultralytics",
+ "base": "Ultralytics",
+ "save_path": "ultralytics/segm",
+ "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.",
+ "reference": "https://huggingface.co/Bingsu/adetailer/tree/main",
+ "filename": "person_yolov8s-seg.pt",
+ "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8s-seg.pt"
+ },
+ {
+ "name": "deepfashion2_yolov8s (segm)",
+ "type": "Ultralytics",
+ "base": "Ultralytics",
+ "save_path": "ultralytics/segm",
+ "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.",
+ "reference": "https://huggingface.co/Bingsu/adetailer/tree/main",
+ "filename": "deepfashion2_yolov8s-seg.pt",
+ "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/deepfashion2_yolov8s-seg.pt"
+ }
+ ]
+}
diff --git a/ComfyUI-Manager/notebooks/comfyui_colab_with_manager.ipynb b/ComfyUI-Manager/notebooks/comfyui_colab_with_manager.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c191a11aa5e77d65826350dd6494b086361727d4
--- /dev/null
+++ b/ComfyUI-Manager/notebooks/comfyui_colab_with_manager.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{"id":"aaaaaaaaaa"},"source":["Git clone the repo and install the requirements. (ignore the pip errors about protobuf)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bbbbbbbbbb"},"outputs":[],"source":["#@title Environment Setup\n","\n","from pathlib import Path\n","\n","OPTIONS = {}\n","\n","USE_GOOGLE_DRIVE = False #@param {type:\"boolean\"}\n","UPDATE_COMFY_UI = True #@param {type:\"boolean\"}\n","USE_COMFYUI_MANAGER = True #@param {type:\"boolean\"}\n","INSTALL_CUSTOM_NODES_DEPENDENCIES = True #@param {type:\"boolean\"}\n","OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n","OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n","OPTIONS['USE_COMFYUI_MANAGER'] = USE_COMFYUI_MANAGER\n","OPTIONS['INSTALL_CUSTOM_NODES_DEPENDENCIES'] = INSTALL_CUSTOM_NODES_DEPENDENCIES\n","\n","current_dir = !pwd\n","WORKSPACE = f\"{current_dir[0]}/ComfyUI\"\n","\n","if OPTIONS['USE_GOOGLE_DRIVE']:\n"," !echo \"Mounting Google Drive...\"\n"," %cd /\n","\n"," from google.colab import drive\n"," drive.mount('/content/drive')\n","\n"," WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n"," %cd /content/drive/MyDrive\n","\n","![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI\n","%cd $WORKSPACE\n","\n","if OPTIONS['UPDATE_COMFY_UI']:\n"," !echo -= Updating ComfyUI =-\n"," !git pull\n","\n","!echo -= Install dependencies =-\n","!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117\n","\n","if OPTIONS['USE_COMFYUI_MANAGER']:\n"," %cd custom_nodes\n"," ![ ! -d ComfyUI-Manager ] && echo -= Initial setup ComfyUI-Manager =- && git clone https://github.com/ltdrdata/ComfyUI-Manager\n"," %cd ComfyUI-Manager\n"," !git pull\n","\n","%cd $WORKSPACE\n","\n","if OPTIONS['INSTALL_CUSTOM_NODES_DEPENDENCIES']:\n"," !echo -= Install custom nodes dependencies =-\n"," ![ -f \"custom_nodes/ComfyUI-Manager/scripts/colab-dependencies.py\" ] && python \"custom_nodes/ComfyUI-Manager/scripts/colab-dependencies.py\"\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"cccccccccc"},"source":["Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dddddddddd"},"outputs":[],"source":["# Checkpoints\n","\n","### SDXL\n","### I recommend these workflow examples: https://comfyanonymous.github.io/ComfyUI_examples/sdxl/\n","\n","#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P ./models/checkpoints/\n","#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors -P ./models/checkpoints/\n","\n","\n","# SD1.5\n","!wget -c https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/\n","\n","# SD2\n","#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n","#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n","\n","# Some SD1.5 anime style\n","#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n","#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P ./models/checkpoints/\n","#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P ./models/checkpoints/\n","#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n","\n","# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n","#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors -P ./models/checkpoints/\n","\n","\n","# unCLIP models\n","#!wget -c https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors -P ./models/checkpoints/\n","#!wget -c https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors -P ./models/checkpoints/\n","\n","\n","# VAE\n","!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n","#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n","#!wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P ./models/vae/\n","\n","\n","# Loras\n","#!wget -c https://civitai.com/api/download/models/10350 -O ./models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v\n","#!wget -c https://civitai.com/api/download/models/10638 -O ./models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x\n","#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors -P ./models/loras/ #SDXL offset noise lora\n","\n","\n","# T2I-Adapter\n","#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/controlnet/\n","#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/controlnet/\n","#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/controlnet/\n","#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/controlnet/\n","#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/controlnet/\n","#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/controlnet/\n","#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/controlnet/\n","\n","# T2I Styles Model\n","#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P ./models/style_models/\n","\n","# CLIPVision model (needed for styles model)\n","#!wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O ./models/clip_vision/clip_vit14.bin\n","\n","\n","# ControlNet\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors -P ./models/controlnet/\n","#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors -P ./models/controlnet/\n","\n","\n","# Controlnet Preprocessor nodes by Fannovel16\n","#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n","\n","\n","# GLIGEN\n","#!wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/\n","\n","\n","# ESRGAN upscale model\n","#!wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/\n","#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n","#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"kkkkkkkkkkkkkk"},"source":["### Run ComfyUI with localtunnel (Recommended Way)\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jjjjjjjjjjjjj"},"outputs":[],"source":["!npm install -g localtunnel\n","\n","import subprocess\n","import threading\n","import time\n","import socket\n","import urllib.request\n","\n","def iframe_thread(port):\n"," while True:\n"," time.sleep(0.5)\n"," sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n"," result = sock.connect_ex(('127.0.0.1', port))\n"," if result == 0:\n"," break\n"," sock.close()\n"," print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n","\n"," print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n"," p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n"," for line in p.stdout:\n"," print(line.decode(), end='')\n","\n","\n","threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n","\n","!python main.py --dont-print-server"]},{"cell_type":"markdown","metadata":{"id":"gggggggggg"},"source":["### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n","\n","You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n","\n","If you want to open it in another window use the link.\n","\n","Note that some UI features like live image previews won't work because the colab iframe blocks websockets."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hhhhhhhhhh"},"outputs":[],"source":["import threading\n","import time\n","import socket\n","def iframe_thread(port):\n"," while True:\n"," time.sleep(0.5)\n"," sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n"," result = sock.connect_ex(('127.0.0.1', port))\n"," if result == 0:\n"," break\n"," sock.close()\n"," from google.colab import output\n"," output.serve_kernel_port_as_iframe(port, height=1024)\n"," print(\"to open it in a window you can open this link here:\")\n"," output.serve_kernel_port_as_window(port)\n","\n","threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n","\n","!python main.py --dont-print-server"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"1HnIQphOu1EScR7P5m5aptRHGahn42dtP","timestamp":1690352987368},{"file_id":"1thcWyDbx3i__qJm5gHhErolK9UNO5uSd","timestamp":1690352936536},{"file_id":"1a5iNqa6a0kU3fcktIDoRAcdR_muaG5it","timestamp":1690260043089}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
diff --git a/ComfyUI-Manager/prestartup_script.py b/ComfyUI-Manager/prestartup_script.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f4dcc405c058c8109bd581d41387a7e1ea0d0eb
--- /dev/null
+++ b/ComfyUI-Manager/prestartup_script.py
@@ -0,0 +1,36 @@
+import os
+import subprocess
+
+script_list_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "startup-scripts", "install-scripts.txt")
+
+# Check if script_list_path exists
+if os.path.exists(script_list_path):
+ print("\n#######################################################################")
+ print("[ComfyUI-Manager] Starting dependency installation/(de)activation for the extension\n")
+
+ executed = set()
+ # Read each line from the file and convert it to a list using eval
+ with open(script_list_path, 'r') as file:
+ for line in file:
+ if line in executed:
+ continue
+
+ executed.add(line)
+
+ try:
+ script = eval(line)
+ print(f"\n## Execute install/(de)activation script for '{script[0]}'")
+ code = subprocess.run(script[1:], cwd=script[0])
+
+ if code.returncode != 0:
+ print(f"install/(de)activation script failed: {script[0]}")
+ except Exception as e:
+ print(f"[ERROR] Failed to execute install/(de)activation script: {line} / {e}")
+
+ # Remove the script_list_path file
+ if os.path.exists(script_list_path):
+ os.remove(script_list_path)
+
+ print("\n[ComfyUI-Manager] Startup script completed.")
+ print("#######################################################################\n")
+
diff --git a/ComfyUI-Manager/requirements.txt b/ComfyUI-Manager/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5779f39618b6a471f6d62c0dccfe9952e08fa49e
--- /dev/null
+++ b/ComfyUI-Manager/requirements.txt
@@ -0,0 +1 @@
+GitPython
\ No newline at end of file
diff --git a/ComfyUI-Manager/scan.sh b/ComfyUI-Manager/scan.sh
new file mode 100644
index 0000000000000000000000000000000000000000..53fb9039ec92f1edc802c731b95ce2af7cb29ce9
--- /dev/null
+++ b/ComfyUI-Manager/scan.sh
@@ -0,0 +1,3 @@
+source ../../venv/bin/activate
+rm .tmp/*.py
+python scanner.py
\ No newline at end of file
diff --git a/ComfyUI-Manager/scanner.py b/ComfyUI-Manager/scanner.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f8442529666f41b2e693a239b4aaa77ddf40a2a
--- /dev/null
+++ b/ComfyUI-Manager/scanner.py
@@ -0,0 +1,247 @@
+import re
+import os
+import json
+from git import Repo
+from torchvision.datasets.utils import download_url
+
+
+def scan_in_file(filename):
+ try:
+ with open(filename, encoding='utf-8') as file:
+ code = file.read()
+ except UnicodeDecodeError:
+ with open(filename, encoding='cp949') as file:
+ code = file.read()
+
+ pattern = r"NODE_CLASS_MAPPINGS\s*=\s*{([^}]*)}"
+ regex = re.compile(pattern, re.MULTILINE | re.DOTALL)
+
+ nodes = set()
+ class_dict = {}
+
+ pattern2 = r'NODE_CLASS_MAPPINGS\["(.*?)"\]'
+ keys = re.findall(pattern2, code)
+ for key in keys:
+ nodes.add(key.strip())
+
+ pattern3 = r'NODE_CLASS_MAPPINGS\[\'(.*?)\'\]'
+ keys = re.findall(pattern3, code)
+ for key in keys:
+ nodes.add(key.strip())
+
+ matches = regex.findall(code)
+ for match in matches:
+ dict_text = match
+
+ key_value_pairs = re.findall(r"\"([^\"]*)\"\s*:\s*([^,\n]*)", dict_text)
+ for key, value in key_value_pairs:
+ class_dict[key.strip()] = value.strip()
+
+ key_value_pairs = re.findall(r"'([^']*)'\s*:\s*([^,\n]*)", dict_text)
+ for key, value in key_value_pairs:
+ class_dict[key.strip()] = value.strip()
+
+ for key, value in class_dict.items():
+ nodes.add(key.strip())
+
+ update_pattern = r"NODE_CLASS_MAPPINGS.update\s*\({([^}]*)}\)"
+ update_match = re.search(update_pattern, code)
+ if update_match:
+ update_dict_text = update_match.group(1)
+ update_key_value_pairs = re.findall(r"\"([^\"]*)\"\s*:\s*([^,\n]*)", update_dict_text)
+ for key, value in update_key_value_pairs:
+ class_dict[key.strip()] = value.strip()
+ nodes.add(key.strip())
+
+ return nodes
+
+def get_py_file_paths(dirname):
+ file_paths = []
+
+ for root, dirs, files in os.walk(dirname):
+ if ".git" in root or "__pycache__" in root:
+ continue
+
+ for file in files:
+ if file.endswith(".py"):
+ file_path = os.path.join(root, file)
+ file_paths.append(file_path)
+
+ return file_paths
+
+
+def get_nodes(target_dir):
+ py_files = []
+ directories = []
+
+ for item in os.listdir(target_dir):
+ if ".git" in item or "__pycache__" in item:
+ continue
+
+ path = os.path.abspath(os.path.join(target_dir, item))
+
+ if os.path.isfile(path) and item.endswith(".py"):
+ py_files.append(path)
+ elif os.path.isdir(path):
+ directories.append(path)
+
+ return py_files, directories
+
+
+def get_git_urls_from_json(json_file):
+ with open(json_file) as file:
+ data = json.load(file)
+
+ custom_nodes = data.get('custom_nodes', [])
+ git_clone_files = []
+ for node in custom_nodes:
+ if node.get('install_type') == 'git-clone':
+ files = node.get('files', [])
+ if files:
+ git_clone_files.append(files[0])
+
+ return git_clone_files
+
+
+def get_py_urls_from_json(json_file):
+ with open(json_file) as file:
+ data = json.load(file)
+
+ custom_nodes = data.get('custom_nodes', [])
+ py_files = []
+ for node in custom_nodes:
+ if node.get('install_type') == 'copy':
+ files = node.get('files', [])
+ if files:
+ py_files.append(files[0])
+
+ return py_files
+
+
+def clone_or_pull_git_repository(git_url):
+ repo_name = git_url.split("/")[-1].split(".")[0]
+ repo_dir = os.path.join(os.getcwd(), ".tmp", repo_name)
+
+ if os.path.exists(repo_dir):
+ try:
+ repo = Repo(repo_dir)
+ origin = repo.remote(name="origin")
+ origin.pull(rebase=True)
+ repo.git.submodule('update', '--init', '--recursive')
+ print(f"Pulling {repo_name}...")
+ except Exception as e:
+ print(f"Pulling {repo_name} failed: {e}")
+ else:
+ try:
+ repo = Repo.clone_from(git_url, repo_dir, recursive=True)
+ print(f"Cloning {repo_name}...")
+ except Exception as e:
+ print(f"Cloning {repo_name} failed: {e}")
+
+
+def update_custom_nodes():
+ tmp_dir = os.path.join(os.getcwd(), ".tmp")
+ if not os.path.exists(tmp_dir):
+ os.makedirs(tmp_dir)
+
+ node_info = {}
+
+ git_urls = get_git_urls_from_json('custom-node-list.json')
+
+ for url in git_urls:
+ name = os.path.basename(url)
+ if name.endswith(".git"):
+ name = name[:-4]
+
+ node_info[name] = url
+ clone_or_pull_git_repository(url)
+
+ py_urls = get_py_urls_from_json('custom-node-list.json')
+
+ for url in py_urls:
+ name = os.path.basename(url)
+ if name.endswith(".py"):
+ node_info[name] = url
+
+ try:
+ download_url(url, ".tmp")
+ except:
+ print(f"[ERROR] Cannot download '{url}'")
+
+ return node_info
+
+
+def gen_json(node_info):
+ # scan from .py file
+ node_files, node_dirs = get_nodes(".tmp")
+
+ data = {}
+ for dirname in node_dirs:
+ py_files = get_py_file_paths(dirname)
+
+ nodes = set()
+ for py in py_files:
+ nodes.update(scan_in_file(py))
+
+ dirname = os.path.basename(dirname)
+
+ if len(nodes) > 0:
+ nodes = list(nodes)
+ nodes.sort()
+
+ if dirname in node_info:
+ git_url = node_info[dirname]
+ data[git_url] = nodes
+ else:
+ print(f"WARN: {dirname} is removed from custom-node-list.json")
+
+ for file in node_files:
+ nodes = scan_in_file(file)
+
+ if len(nodes) > 0:
+ nodes = list(nodes)
+ nodes.sort()
+
+ file = os.path.basename(file)
+
+ if file in node_info:
+ url = node_info[file]
+ data[url] = nodes
+ else:
+ print(f"Missing info: {url}")
+
+ # scan from node_list.json file
+ extensions = [name for name in os.listdir('.tmp') if os.path.isdir(os.path.join('.tmp', name))]
+
+ for extension in extensions:
+ node_list_json_path = os.path.join('.tmp', extension, 'node_list.json')
+ if os.path.exists(node_list_json_path):
+ git_url = node_info[extension]
+
+ with open(node_list_json_path, 'r') as f:
+ node_list_json = json.load(f)
+
+ if git_url not in data:
+ nodes = set()
+ else:
+ nodes = set(data[git_url])
+
+ for x, desc in node_list_json.items():
+ nodes.add(x.strip())
+
+ nodes = list(nodes)
+ nodes.sort()
+ data[git_url] = nodes
+
+ json_path = f"extension-node-map.json"
+ with open(json_path, "w") as file:
+ json.dump(data, file, indent=4, sort_keys=True)
+
+
+print("### ComfyUI Manager Node Scanner ###")
+
+print("\n# Updating extensions\n")
+updated_node_info = update_custom_nodes()
+
+print("\n# 'extension-node-map.json' file is generated.\n")
+gen_json(updated_node_info)
\ No newline at end of file
diff --git a/ComfyUI-Manager/scripts/colab-dependencies.py b/ComfyUI-Manager/scripts/colab-dependencies.py
new file mode 100644
index 0000000000000000000000000000000000000000..76d25153d5af0f27bc8970a521377be5bb924128
--- /dev/null
+++ b/ComfyUI-Manager/scripts/colab-dependencies.py
@@ -0,0 +1,34 @@
+import os
+import subprocess
+
+
+def get_enabled_subdirectories_with_files(base_directory):
+ subdirs_with_files = []
+ for subdir in os.listdir(base_directory):
+ full_path = os.path.join(base_directory, subdir)
+ if os.path.isdir(full_path) and not subdir.endswith(".disabled") and not subdir.startswith('.') and subdir != '__pycache__':
+ print(f"## Install dependencies for '{subdir}'")
+ requirements_file = os.path.join(full_path, "requirements.txt")
+ install_script = os.path.join(full_path, "install.py")
+
+ if os.path.isfile(requirements_file) and os.path.isfile(install_script):
+ subdirs_with_files.append((full_path, requirements_file, install_script))
+
+ return subdirs_with_files
+
+
+def install_requirements(requirements_file_path):
+ subprocess.run(["pip", "install", "-r", requirements_file_path])
+
+
+def run_install_script(install_script_path):
+ subprocess.run(["python", install_script_path])
+
+
+custom_nodes_directory = "custom_nodes"
+subdirs_with_files = get_enabled_subdirectories_with_files(custom_nodes_directory)
+
+
+for subdir, requirements_file, install_script in subdirs_with_files:
+ install_requirements(requirements_file)
+ run_install_script(install_script)
diff --git a/ComfyUI-Manager/scripts/install-comfyui-venv-linux.sh b/ComfyUI-Manager/scripts/install-comfyui-venv-linux.sh
new file mode 100644
index 0000000000000000000000000000000000000000..be473dc66f8eeb36c48d409945eb5ae83a030171
--- /dev/null
+++ b/ComfyUI-Manager/scripts/install-comfyui-venv-linux.sh
@@ -0,0 +1,21 @@
+git clone https://github.com/comfyanonymous/ComfyUI
+cd ComfyUI/custom_nodes
+git clone https://github.com/ltdrdata/ComfyUI-Manager
+cd ..
+python -m venv venv
+source venv/bin/activate
+python -m pip install -r requirements.txt
+python -m pip install -r custom_nodes/ComfyUI-Manager/requirements.txt
+python -m pip install torchvision
+cd ..
+echo "#!/bin/bash" > run_gpu.sh
+echo "cd ComfyUI" >> run_gpu.sh
+echo "source venv/bin/activate" >> run_gpu.sh
+echo "python main.py --preview-method auto" >> run_gpu.sh
+chmod +x run_gpu.sh
+
+echo "#!/bin/bash" > run_cpu.sh
+echo "cd ComfyUI" >> run_cpu.sh
+echo "source venv/bin/activate" >> run_cpu.sh
+echo "python main.py --preview-method auto --cpu" >> run_cpu.sh
+chmod +x run_cpu.sh
diff --git a/ComfyUI-Manager/scripts/install-manager-for-portable-version.bat b/ComfyUI-Manager/scripts/install-manager-for-portable-version.bat
new file mode 100644
index 0000000000000000000000000000000000000000..7b067dfd770d197ccd68e760087536552223f260
--- /dev/null
+++ b/ComfyUI-Manager/scripts/install-manager-for-portable-version.bat
@@ -0,0 +1,2 @@
+.\python_embeded\python.exe -s -m pip install gitpython
+.\python_embeded\python.exe -c "import git; git.Repo.clone_from('https://github.com/ltdrdata/ComfyUI-Manager', './ComfyUI/custom_nodes/ComfyUI-Manager')"
diff --git a/ComfyUI-Manager/scripts/update-fix.py b/ComfyUI-Manager/scripts/update-fix.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2ac10074607544d0b9cdaf4372e43c7f62bb8d0
--- /dev/null
+++ b/ComfyUI-Manager/scripts/update-fix.py
@@ -0,0 +1,12 @@
+import git
+
+commit_hash = "a361cc1"
+
+repo = git.Repo('.')
+
+if repo.is_dirty():
+ repo.git.stash()
+
+repo.git.update_ref("refs/remotes/origin/main", commit_hash)
+repo.remotes.origin.fetch()
+repo.git.pull("origin", "main")
diff --git a/ComfyUI_Comfyroll_CustomNodes/Comfyroll_Nodes.py b/ComfyUI_Comfyroll_CustomNodes/Comfyroll_Nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b29edb8e75470ac7383da3f7dc6e909e37bd321
--- /dev/null
+++ b/ComfyUI_Comfyroll_CustomNodes/Comfyroll_Nodes.py
@@ -0,0 +1,859 @@
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+# Comfyroll Custom Nodes by RockOfFire and Akatsuzi https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes #
+# for ComfyUI https://github.com/comfyanonymous/ComfyUI #
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+import torch
+import numpy as np
+from PIL import Image, ImageEnhance
+from PIL.PngImagePlugin import PngInfo
+import os
+import sys
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
+
+import comfy.sd
+import comfy.utils
+import comfy.model_management
+
+import folder_paths
+import json
+from nodes import MAX_RESOLUTION
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_InputImages:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Input": ("INT", {"default": 1, "min": 1, "max": 2}),
+ "image1": ("IMAGE",),
+ "image2": ("IMAGE",)
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ OUTPUT_NODE = True
+ FUNCTION = "InputImages"
+
+ CATEGORY = "Comfyroll/Logic"
+
+ def InputImages(self, Input, image1, image2):
+ if Input == 1:
+ return (image1, )
+ else:
+ return (image2, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_InputImages_4way:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Input": ("INT", {"default": 1, "min": 1, "max": 4}),
+ "image1": ("IMAGE",),
+ },
+ "optional": {
+ "image2": ("IMAGE",),
+ "image3": ("IMAGE",),
+ "image4": ("IMAGE",),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ OUTPUT_NODE = True
+ FUNCTION = "InputImages_4"
+
+ CATEGORY = "Comfyroll/Logic"
+
+ def InputImages_4(self, Input, image1, image2=None, image3=None, image4=None):
+ if Input == 1:
+ return (image1, )
+ elif Input == 2:
+ return (image2, )
+ elif Input == 3:
+ return (image3, )
+ else:
+ return (image4, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_InputLatents:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Input": ("INT", {"default": 1, "min": 1, "max": 2}),
+ "latent1": ("LATENT",),
+ "latent2": ("LATENT",)
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ OUTPUT_NODE = True
+ FUNCTION = "InputLatents"
+
+ CATEGORY = "Comfyroll/Logic"
+
+ def InputLatents(self, Input, latent1, latent2):
+ if Input == 1:
+ return (latent1, )
+ else:
+ return (latent2, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_InputConditioning:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Input": ("INT", {"default": 1, "min": 1, "max": 2}),
+ "conditioning1": ("CONDITIONING",),
+ "conditioning2": ("CONDITIONING",)
+ }
+ }
+
+ RETURN_TYPES = ("CONDITIONING",)
+ OUTPUT_NODE = True
+ FUNCTION = "InputConditioning"
+
+ CATEGORY = "Comfyroll/Logic"
+
+ def InputConditioning(self, Input, conditioning1, conditioning2):
+ if Input == 1:
+ return (conditioning1, )
+ else:
+ return (conditioning2, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_InputClip:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Input": ("INT", {"default": 1, "min": 1, "max": 2}),
+ "clip1": ("CLIP",),
+ "clip2": ("CLIP",)
+ }
+ }
+
+ RETURN_TYPES = ("CLIP",)
+ OUTPUT_NODE = True
+ FUNCTION = "InputClip"
+
+ CATEGORY = "Comfyroll/Logic"
+
+ def InputClip(self, Input, clip1, clip2):
+ if Input == 1:
+ return (clip1, )
+ else:
+ return (clip2, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_InputModel:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Input": ("INT", {"default": 1, "min": 1, "max": 2}),
+ "model1": ("MODEL",),
+ "model2": ("MODEL",)
+ }
+ }
+
+ RETURN_TYPES = ("MODEL",)
+ OUTPUT_NODE = True
+ FUNCTION = "InputModel"
+
+ CATEGORY = "Comfyroll/Logic"
+
+ def InputModel(self, Input, model1, model2):
+ if Input == 1:
+ return (model1, )
+ else:
+ return (model2, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_InputControlNet:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Input": ("INT", {"default": 1, "min": 1, "max": 2}),
+ "control_net1": ("CONTROL_NET",),
+ "control_net2": ("CONTROL_NET",)
+ }
+ }
+
+ RETURN_TYPES = ("CONTROL_NET",)
+ OUTPUT_NODE = True
+ FUNCTION = "InputControlNet"
+
+ CATEGORY = "Comfyroll/Logic"
+
+ def InputControlNet(self, Input, control_net1, control_net2):
+ if Input == 1:
+ return (control_net1, )
+ else:
+ return (control_net2, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_LoraLoader:
+ def __init__(self):
+ self.loaded_lora = None
+
+ @classmethod
+ def INPUT_TYPES(s):
+ file_list = folder_paths.get_filename_list("loras")
+ file_list.insert(0, "None")
+ return {"required": { "model": ("MODEL",),
+ "clip": ("CLIP", ),
+ "switch": ([
+ "On",
+ "Off"],),
+ "lora_name": (file_list, ),
+ "strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+ "strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+ }}
+ RETURN_TYPES = ("MODEL", "CLIP")
+ FUNCTION = "load_lora"
+
+ CATEGORY = "Comfyroll/IO"
+
+ def load_lora(self, model, clip, switch, lora_name, strength_model, strength_clip):
+ if strength_model == 0 and strength_clip == 0:
+ return (model, clip)
+
+ if switch == "Off" or lora_name == "None":
+ return (model, clip)
+
+ lora_path = folder_paths.get_full_path("loras", lora_name)
+ lora = None
+ if self.loaded_lora is not None:
+ if self.loaded_lora[0] == lora_path:
+ lora = self.loaded_lora[1]
+ else:
+ del self.loaded_lora
+
+ if lora is None:
+ lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+ self.loaded_lora = (lora_path, lora)
+
+ model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
+ return (model_lora, clip_lora)
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_ApplyControlNet:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"conditioning": ("CONDITIONING", ),
+ "control_net": ("CONTROL_NET", ),
+ "image": ("IMAGE", ),
+ "switch": ([
+ "On",
+ "Off"],),
+ "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})
+ }}
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "apply_controlnet"
+
+ CATEGORY = "Comfyroll/Conditioning"
+
+ def apply_controlnet(self, conditioning, control_net, image, switch, strength):
+ if strength == 0 or switch == "Off":
+ return (conditioning, )
+
+ c = []
+ control_hint = image.movedim(-1,1)
+ for t in conditioning:
+ n = [t[0], t[1].copy()]
+ c_net = control_net.copy().set_cond_hint(control_hint, strength)
+ if 'control' in t[1]:
+ c_net.set_previous_controlnet(t[1]['control'])
+ n[1]['control'] = c_net
+ c.append(n)
+ return (c, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_ImageSize_Float:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "width": ("INT", {"default": 512, "min": 64, "max": 2048}),
+ "height": ("INT", {"default": 512, "min": 64, "max": 2048}),
+ "upscale_factor": ("FLOAT", {"default": 1, "min": 1, "max": 2000})
+ }
+ }
+ RETURN_TYPES = ("INT", "INT", "FLOAT")
+ #RETURN_NAMES = ("Width", "Height")
+ FUNCTION = "ImageSize_Float"
+
+ CATEGORY = "Comfyroll/Image"
+
+ def ImageSize_Float(self, width, height, upscale_factor):
+ return(width, height, upscale_factor)
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_ImageOutput:
+ def __init__(self):
+ self.output_dir = folder_paths.get_output_directory()
+ self.type = "output"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"images": ("IMAGE", ),
+ "output_type": (["Preview", "Save"],),
+ "filename_prefix": ("STRING", {"default": "ComfyUI"})},
+ "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+ }
+
+ RETURN_TYPES = ()
+ FUNCTION = "save_images"
+
+ OUTPUT_NODE = True
+
+ CATEGORY = "Comfyroll/Test"
+
+ def save_images(self, images, filename_prefix="ComfyUI", output_type = "Preview", prompt=None, extra_pnginfo=None):
+ def map_filename(filename):
+ prefix_len = len(os.path.basename(filename_prefix))
+ prefix = filename[:prefix_len + 1]
+ try:
+ digits = int(filename[prefix_len + 1:].split('_')[0])
+ except:
+ digits = 0
+ return (digits, prefix)
+
+ def compute_vars(input):
+ input = input.replace("%width%", str(images[0].shape[1]))
+ input = input.replace("%height%", str(images[0].shape[0]))
+ return input
+
+ if output_type == "Save":
+ self.output_dir = folder_paths.get_output_directory()
+ self.type = "output"
+ elif output_type == "Preview":
+ self.output_dir = folder_paths.get_temp_directory()
+ self.type = "temp"
+
+ filename_prefix = compute_vars(filename_prefix)
+
+ subfolder = os.path.dirname(os.path.normpath(filename_prefix))
+ filename = os.path.basename(os.path.normpath(filename_prefix))
+
+ full_output_folder = os.path.join(self.output_dir, subfolder)
+
+ if os.path.commonpath((self.output_dir, os.path.abspath(full_output_folder))) != self.output_dir:
+ print("Saving image outside the output folder is not allowed.")
+ return {}
+
+ try:
+ counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
+ except ValueError:
+ counter = 1
+ except FileNotFoundError:
+ os.makedirs(full_output_folder, exist_ok=True)
+ counter = 1
+
+ results = list()
+ for image in images:
+ i = 255. * image.cpu().numpy()
+ img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+ metadata = PngInfo()
+ if prompt is not None:
+ metadata.add_text("prompt", json.dumps(prompt))
+ if extra_pnginfo is not None:
+ for x in extra_pnginfo:
+ metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+ file = f"{filename}_{counter:05}_.png"
+ img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
+ results.append({
+ "filename": file,
+ "subfolder": subfolder,
+ "type": self.type
+ })
+ counter += 1
+
+ return { "ui": { "images": results } }
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class CR_Int_Multiple_Of:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "integer": ("INT", {"default": 1, "min": -18446744073709551615, "max": 18446744073709551615}),
+ "multiple": ("FLOAT", {"default": 8, "min": 1, "max": 18446744073709551615}),
+ }
+ }
+
+ RETURN_TYPES =("INT",)
+ FUNCTION = "int_multiple_of"
+
+ CATEGORY = "Comfyroll/Math"
+
+ def int_multiple_of(self, integer, multiple=8):
+ if multiple == 0:
+ return (int(integer), )
+ integer = integer * multiple
+ return (int(integer), )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_AspectRatio:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "width": ("INT", {"default": 512, "min": 64, "max": 2048}),
+ "height": ("INT", {"default": 512, "min": 64, "max": 2048}),
+ "aspect_ratio": (["custom", "1:1 square 512x512", "1:1 square 1024x1024", "2:3 portrait 512x768", "3:4 portrait 512x682", "3:2 landscape 768x512", "4:3 landscape 682x512", "16:9 cinema 910x512", "2:1 cinema 1024x512"],),
+ "swap_dimensions": (["Off", "On"],),
+ "upscale_factor1": ("FLOAT", {"default": 1, "min": 1, "max": 2000}),
+ "upscale_factor2": ("FLOAT", {"default": 1, "min": 1, "max": 2000}),
+ "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
+ }
+ }
+ RETURN_TYPES = ("INT", "INT", "FLOAT", "FLOAT", "INT")
+ #RETURN_NAMES = ("Width", "Height")
+ FUNCTION = "Aspect_Ratio"
+
+ CATEGORY = "Comfyroll/Image"
+
+ def Aspect_Ratio(self, width, height, aspect_ratio, swap_dimensions, upscale_factor1, upscale_factor2, batch_size):
+ if swap_dimensions == "Off":
+ if aspect_ratio == "2:3 portrait 512x768":
+ width, height = 512, 768
+ elif aspect_ratio == "3:2 landscape 768x512":
+ width, height = 768, 512
+ elif aspect_ratio == "1:1 square 512x512":
+ width, height = 512, 512
+ elif aspect_ratio == "1:1 square 1024x1024":
+ width, height = 1024, 1024
+ elif aspect_ratio == "16:9 cinema 910x512":
+ width, height = 910, 512
+ elif aspect_ratio == "3:4 portrait 512x682":
+ width, height = 512, 682
+ elif aspect_ratio == "4:3 landscape 682x512":
+ width, height = 682, 512
+ elif aspect_ratio == "2:1 cinema 1024x512":
+ width, height = 1024, 512
+ return(width, height, upscale_factor1, upscale_factor2, batch_size)
+ elif swap_dimensions == "On":
+ if aspect_ratio == "2:3 portrait 512x768":
+ width, height = 512, 768
+ elif aspect_ratio == "3:2 landscape 768x512":
+ width, height = 768, 512
+ elif aspect_ratio == "1:1 square 512x512":
+ width, height = 512, 512
+ elif aspect_ratio == "1:1 square 1024x1024":
+ width, height = 1024, 1024
+ elif aspect_ratio == "16:9 cinema 910x512":
+ width,height = 910, 512
+ elif aspect_ratio == "3:4 portrait 512x682":
+ width, height = 512, 682
+ elif aspect_ratio == "4:3 landscape 682x512":
+ width, height = 682, 512
+ elif aspect_ratio == "2:1 cinema 1024x512":
+ width, height = 1024, 512
+ return(height, width, upscale_factor1, upscale_factor2, batch_size)
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_AspectRatio_SDXL:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "width": ("INT", {"default": 1024, "min": 64, "max": 2048}),
+ "height": ("INT", {"default": 1024, "min": 64, "max": 2048}),
+ "aspect_ratio": (["custom", "square 1024x1024", "portrait 896x1152", "portrait 832x1216", "portrait 768x1344", "portrait 640 x 1536", "landscape 1152x896", "landscape 1216x832", "landscape 1344x768", "landscape 1536x640"],),
+ "swap_dimensions": (["Off", "On"],),
+ "upscale_factor1": ("FLOAT", {"default": 1, "min": 1, "max": 2000}),
+ "upscale_factor2": ("FLOAT", {"default": 1, "min": 1, "max": 2000}),
+ "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
+ }
+ }
+ RETURN_TYPES = ("INT", "INT", "FLOAT", "FLOAT", "INT")
+ #RETURN_NAMES = ("Width", "Height")
+ FUNCTION = "Aspect_Ratio"
+
+ CATEGORY = "Comfyroll/SDXL"
+
+ def Aspect_Ratio(self, width, height, aspect_ratio, swap_dimensions, upscale_factor1, upscale_factor2, batch_size):
+ if aspect_ratio == "square 1024x1024":
+ width, height = 1024, 1024
+ elif aspect_ratio == "portrait 896x1152":
+ width, height = 896, 1152
+ elif aspect_ratio == "portrait 832x1216":
+ width, height = 822, 1216
+ elif aspect_ratio == "portrait 768x1344":
+ width, height = 768, 1344
+ elif aspect_ratio == "portrait 640 x 1536":
+ width, height = 640, 1536
+ elif aspect_ratio == "landscape 1152x896":
+ width, height = 1152, 896
+ elif aspect_ratio == "landscape 1152x896":
+ width, height = 682, 512
+ elif aspect_ratio == "landscape 1216x832":
+ width, height = 1216, 832
+ elif aspect_ratio == "landscape 1344x768":
+ width, height = 1152, 896
+ elif aspect_ratio == "landscape 1536x640":
+ width, height = 1536, 640
+
+ if swap_dimensions == "On":
+ return(height, width, upscale_factor1, upscale_factor2, batch_size,)
+ else:
+ return(width, height, upscale_factor1, upscale_factor2, batch_size,)
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_SeedToInt:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "seed": ("SEED", ),
+ }
+ }
+
+ RETURN_TYPES = ("INT",)
+ FUNCTION = "seed_to_int"
+
+ CATEGORY = "Comfyroll/Number"
+
+ def seed_to_int(self, seed):
+ return (seed.get('seed'),)
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class Comfyroll_Color_Tint:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "strength": ("FLOAT", {
+ "default": 1.0,
+ "min": 0.1,
+ "max": 1.0,
+ "step": 0.1
+ }),
+ "mode": (["white", "black", "sepia", "red", "green", "blue", "cyan", "magenta", "yellow", "purple", "orange", "warm", "cool", "lime", "navy", "vintage", "rose", "teal", "maroon", "peach", "lavender", "olive"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "color_tint"
+
+ CATEGORY = "Comfyroll/Image"
+
+ def color_tint(self, image: torch.Tensor, strength: float, mode: str = "sepia"):
+ if strength == 0:
+ return (image,)
+
+ sepia_weights = torch.tensor([0.2989, 0.5870, 0.1140]).view(1, 1, 1, 3).to(image.device)
+
+ mode_filters = {
+ "white": torch.tensor([1.0, 1.0, 1.0]),
+ "black": torch.tensor([0, 0, 0]),
+ "sepia": torch.tensor([1.0, 0.8, 0.6]),
+ "red": torch.tensor([1.0, 0.6, 0.6]),
+ "green": torch.tensor([0.6, 1.0, 0.6]),
+ "blue": torch.tensor([0.6, 0.8, 1.0]),
+ "cyan": torch.tensor([0.6, 1.0, 1.0]),
+ "magenta": torch.tensor([1.0, 0.6, 1.0]),
+ "yellow": torch.tensor([1.0, 1.0, 0.6]),
+ "purple": torch.tensor([0.8, 0.6, 1.0]),
+ "orange": torch.tensor([1.0, 0.7, 0.3]),
+ "warm": torch.tensor([1.0, 0.9, 0.7]),
+ "cool": torch.tensor([0.7, 0.9, 1.0]),
+ "lime": torch.tensor([0.7, 1.0, 0.3]),
+ "navy": torch.tensor([0.3, 0.4, 0.7]),
+ "vintage": torch.tensor([0.9, 0.85, 0.7]),
+ "rose": torch.tensor([1.0, 0.8, 0.9]),
+ "teal": torch.tensor([0.3, 0.8, 0.8]),
+ "maroon": torch.tensor([0.7, 0.3, 0.5]),
+ "peach": torch.tensor([1.0, 0.8, 0.6]),
+ "lavender": torch.tensor([0.8, 0.6, 1.0]),
+ "olive": torch.tensor([0.6, 0.7, 0.4]),
+ }
+
+ scale_filter = mode_filters[mode].view(1, 1, 1, 3).to(image.device)
+
+ grayscale = torch.sum(image * sepia_weights, dim=-1, keepdim=True)
+ tinted = grayscale * scale_filter
+
+ result = tinted * strength + image * (1 - strength)
+ return (result,)
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class ComfyRoll_prompt_mixer:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required":{
+ },
+ "optional":{
+ "prompt_positive": ("STRING", {"multiline": True, "default": "BASE_POSITIVE"}),
+ "prompt_negative": ("STRING", {"multiline": True, "default": "BASE_NEGATIVE"}),
+ "style_positive": ("STRING", {"multiline": True, "default": "REFINER_POSTIVE"}),
+ "style_negative": ("STRING", {"multiline": True, "default": "REFINER_NEGATIVE"}),
+ "preset": (["preset 1", "preset 2", "preset 3", "preset 4", "preset 5"],),
+ },
+ }
+
+ RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING", "STRING", )
+ RETURN_NAMES = ("pos_g", "pos_l", "pos_r", "neg_g", "neg_l", "neg_r", )
+ FUNCTION = "mixer"
+
+ CATEGORY = "Comfyroll/SDXL"
+
+ def mixer(self, prompt_positive, prompt_negative, style_positive, style_negative, preset):
+ if preset == "preset 1":
+ pos_g = prompt_positive
+ pos_l = prompt_positive
+ pos_r = prompt_positive
+ neg_g = prompt_negative
+ neg_l = prompt_negative
+ neg_r = prompt_negative
+ elif preset == "preset 2":
+ pos_g = prompt_positive
+ pos_l = style_positive
+ pos_r = prompt_positive
+ neg_g = prompt_negative
+ neg_l = style_negative
+ neg_r = prompt_negative
+ elif preset == "preset 3":
+ pos_g = style_positive
+ pos_l = prompt_positive
+ pos_r = style_positive
+ neg_g = style_negative
+ neg_l = prompt_negative
+ neg_r = style_negative
+ elif preset == "preset 4":
+ pos_g = prompt_positive + style_positive
+ pos_l = prompt_positive + style_positive
+ pos_r = prompt_positive + style_positive
+ neg_g = prompt_negative + style_negative
+ neg_l = prompt_negative + style_negative
+ neg_r = prompt_negative + style_negative
+ elif preset == "preset 5":
+ pos_g = prompt_positive
+ pos_l = prompt_positive
+ pos_r = style_positive
+ neg_g = prompt_negative
+ neg_l = prompt_negative
+ neg_r = style_negative
+ return (pos_g, pos_l, pos_r, neg_g, neg_l, neg_r, )
+
+
+
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+
+class Comfyroll_SDXLStyleText:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {
+ "positive_style": ("STRING", {"default": "POS_STYLE", "multiline": True}),
+ "negative_style": ("STRING", {"default": "NEG_STYLE", "multiline": True}),
+ },
+ }
+
+ RETURN_TYPES = ("STRING", "STRING", )
+ RETURN_NAMES = ("positive_prompt_text_l", "negative_prompt_text_l" )
+ FUNCTION = "get_value"
+
+ CATEGORY = "Comfyroll/SDXL"
+
+ def get_value(self, positive_style, negative_style):
+ return (positive_style, negative_style,)
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class Comfyroll_SDXLBasePromptEncoder:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {
+ "base_clip": ("CLIP", ),
+ "pos_g": ("STRING", {"multiline": True, "default": "POS_G"}),
+ "pos_l": ("STRING", {"multiline": True, "default": "POS_L"}),
+ "neg_g": ("STRING", {"multiline": True, "default": "NEG_G"}),
+ "neg_l": ("STRING", {"multiline": True, "default": "NEG_L"}),
+ "preset": (["preset A", "preset B", "preset C"],),
+ "base_width": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
+ "base_height": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
+ "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
+ "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
+ "target_width": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
+ "target_height": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
+ },
+ }
+
+ RETURN_TYPES = ("CONDITIONING", "CONDITIONING", )
+ RETURN_NAMES = ("base_positive", "base_negative", )
+ FUNCTION = "encode"
+
+ CATEGORY = "Comfyroll/SDXL"
+
+ def encode(self, base_clip, pos_g, pos_l, neg_g, neg_l, base_width, base_height, crop_w, crop_h, target_width, target_height, preset,):
+ empty = base_clip.tokenize("")
+
+ # positive prompt
+ tokens1 = base_clip.tokenize(pos_g)
+ tokens1["l"] = base_clip.tokenize(pos_l)["l"]
+
+ if len(tokens1["l"]) != len(tokens1["g"]):
+ while len(tokens1["l"]) < len(tokens1["g"]):
+ tokens1["l"] += empty["l"]
+ while len(tokens1["l"]) > len(tokens1["g"]):
+ tokens1["g"] += empty["g"]
+
+ cond1, pooled1 = base_clip.encode_from_tokens(tokens1, return_pooled=True)
+ res1 = [[cond1, {"pooled_output": pooled1, "width": base_width, "height": base_height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
+
+ # negative prompt
+ tokens2 = base_clip.tokenize(neg_g)
+ tokens2["l"] = base_clip.tokenize(neg_l)["l"]
+
+ if len(tokens2["l"]) != len(tokens2["g"]):
+ while len(tokens2["l"]) < len(tokens2["g"]):
+ tokens2["l"] += empty["l"]
+ while len(tokens2["l"]) > len(tokens2["g"]):
+ tokens2["g"] += empty["g"]
+
+ cond2, pooled2 = base_clip.encode_from_tokens(tokens2, return_pooled=True)
+ res2 = [[cond2, {"pooled_output": pooled2, "width": base_width, "height": base_height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
+
+ # positive style
+ tokens2 = base_clip.tokenize(pos_l)
+ tokens2["l"] = base_clip.tokenize(neg_l)["l"]
+
+ if len(tokens2["l"]) != len(tokens2["g"]):
+ while len(tokens2["l"]) < len(tokens2["g"]):
+ tokens2["l"] += empty["l"]
+ while len(tokens2["l"]) > len(tokens2["g"]):
+ tokens2["g"] += empty["g"]
+
+ cond2, pooled2 = base_clip.encode_from_tokens(tokens2, return_pooled=True)
+ res3 = [[cond2, {"pooled_output": pooled2, "width": base_width, "height": base_height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
+
+ # negative style
+ tokens2 = base_clip.tokenize(neg_l)
+ tokens2["l"] = base_clip.tokenize(neg_l)["l"]
+
+ if len(tokens2["l"]) != len(tokens2["g"]):
+ while len(tokens2["l"]) < len(tokens2["g"]):
+ tokens2["l"] += empty["l"]
+ while len(tokens2["l"]) > len(tokens2["g"]):
+ tokens2["g"] += empty["g"]
+
+ cond2, pooled2 = base_clip.encode_from_tokens(tokens2, return_pooled=True)
+ res4 = [[cond2, {"pooled_output": pooled2, "width": base_width, "height": base_height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
+
+ if preset == "preset A":
+ base_positive = res1
+ base_negative = res2
+ elif preset == "preset B":
+ base_positive = res3
+ base_negative = res4
+ elif preset == "preset C":
+ base_positive = res1 + res3
+ base_negative = res2 + res4
+
+ return (base_positive, base_negative, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+
+
+'''
+NODE_CLASS_MAPPINGS = {
+ "CR Image Input Switch": ComfyRoll_InputImages,
+ "CR Image Input Switch (4 way)": ComfyRoll_InputImages_4way,
+ "CR Latent Input Switch": ComfyRoll_InputLatents,
+ "CR Conditioning Input Switch": ComfyRoll_InputConditioning,
+ "CR Clip Input Switch": ComfyRoll_InputClip,
+ "CR Model Input Switch": ComfyRoll_InputModel,
+ "CR ControlNet Input Switch": ComfyRoll_InputControlNet,
+ "CR Load LoRA": ComfyRoll_LoraLoader,
+ "CR Apply ControlNet": ComfyRoll_ApplyControlNet,
+ "CR Image Size": ComfyRoll_ImageSize_Float,
+ "CR Image Output": ComfyRoll_ImageOutput,
+ "CR Integer Multiple": CR_Int_Multiple_Of,
+ "CR Aspect Ratio": ComfyRoll_AspectRatio,
+ "CR Aspect Ratio SDXL": ComfyRoll_AspectRatio_SDXL,
+ "CR Seed to Int": ComfyRoll_SeedToInt,
+ "CR Color Tint": Comfyroll_Color_Tint,
+ "CR SDXL Prompt Mixer": ComfyRoll_prompt_mixer,
+ "CR SDXL Style Text": Comfyroll_SDXLStyleText,
+ "CR SDXL Base Prompt Encoder": Comfyroll_SDXLBasePromptEncoder,
+}
+'''
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+# Credits #
+# WASasquatch https://github.com/WASasquatch/was-node-suite-comfyui #
+# hnmr293 https://github.com/hnmr293/ComfyUI-nodes-hnmr #
+# SeargeDP https://github.com/SeargeDP/SeargeSDXL
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
diff --git a/ComfyUI_Comfyroll_CustomNodes/Comfyroll_Pipe_Nodes.py b/ComfyUI_Comfyroll_CustomNodes/Comfyroll_Pipe_Nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a10ec4f39e8700558963a52ab9fd593e60b38b9
--- /dev/null
+++ b/ComfyUI_Comfyroll_CustomNodes/Comfyroll_Pipe_Nodes.py
@@ -0,0 +1,271 @@
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+# Comfyroll Pipe Nodes by Akatsuzi https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes #
+# for ComfyUI https://github.com/comfyanonymous/ComfyUI #
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+import os
+import sys
+import json
+import torch
+import comfy.sd
+import comfy.utils
+import numpy as np
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class module_pipe_loader:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ #"model": ("MODEL",),
+ },
+ "optional": {
+ "model": ("MODEL",),
+ "pos": ("CONDITIONING",),
+ "neg": ("CONDITIONING",),
+ "latent": ("LATENT",),
+ "vae": ("VAE",),
+ "clip": ("CLIP",),
+ "controlnet": ("CONTROL_NET",),
+ "image": ("IMAGE",),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
+ },
+ }
+
+ RETURN_TYPES = ("PIPE_LINE", )
+ RETURN_NAMES = ("pipe", )
+ FUNCTION = "flush"
+
+ CATEGORY = "Comfyroll/Module"
+
+ def flush(self, model=0, pos=0, neg=0, latent=0, vae=0, clip=0, controlnet=0, image=0, seed=0):
+ pipe_line = (model, pos, neg, latent, vae, clip, controlnet, image, seed)
+ return (pipe_line, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class module_input:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {"pipe": ("PIPE_LINE",)},
+ }
+
+ RETURN_TYPES = ("PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "CONTROL_NET", "IMAGE", "INT")
+ RETURN_NAMES = ("pipe", "model", "pos", "neg", "latent", "vae", "clip", "controlnet", "image", "seed")
+ FUNCTION = "flush"
+
+ CATEGORY = "Comfyroll/Module"
+
+ def flush(self, pipe):
+ model, pos, neg, latent, vae, clip, controlnet, image, seed = pipe
+ return pipe, model, pos, neg, latent, vae, clip, controlnet, image, seed
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class module_output:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"pipe": ("PIPE_LINE",)},
+ "optional": {
+ "model": ("MODEL",),
+ "pos": ("CONDITIONING",),
+ "neg": ("CONDITIONING",),
+ "latent": ("LATENT",),
+ "vae": ("VAE",),
+ "clip": ("CLIP",),
+ "controlnet": ("CONTROL_NET",),
+ "image": ("IMAGE",),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
+ },
+ }
+
+ RETURN_TYPES = ("PIPE_LINE", )
+ RETURN_NAMES = ("pipe", )
+ FUNCTION = "flush"
+
+ CATEGORY = "Comfyroll/Module"
+
+ def flush(self, pipe, model=None, pos=None, neg=None, latent=None, vae=None, clip=None, controlnet=None, image=None, seed=None):
+ new_model, new_pos, new_neg, new_latent, new_vae, new_clip, new_controlnet, new_image, new_seed = pipe
+
+ if model is not None:
+ new_model = model
+
+ if pos is not None:
+ new_pos = pos
+
+ if neg is not None:
+ new_neg = neg
+
+ if latent is not None:
+ new_latent = latent
+
+ if vae is not None:
+ new_vae = vae
+
+ if clip is not None:
+ new_clip = clip
+
+ if controlnet is not None:
+ new_controlnet = controlnet
+
+ if image is not None:
+ new_image = image
+
+ if seed is not None:
+ new_seed = seed
+
+ pipe = new_model, new_pos, new_neg, new_latent, new_vae, new_clip, new_controlnet, new_image, new_seed
+ return (pipe, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class image_pipe_in:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ #"model": ("MODEL",),
+ },
+ "optional": {
+ "image": ("IMAGE",),
+ "width": ("INT", {"default": 512, "min": 64, "max": 2048}),
+ "height": ("INT", {"default": 512, "min": 64, "max": 2048}),
+ "upscale_factor": ("FLOAT", {"default": 1, "min": 1, "max": 2000})
+ },
+ }
+
+ RETURN_TYPES = ("PIPE_LINE", )
+ RETURN_NAMES = ("pipe", )
+ FUNCTION = "flush"
+
+ CATEGORY = "Comfyroll/Module"
+
+ def flush(self, image=0, width=0, height=0, upscale_factor=0):
+ pipe_line = (image, width, height, upscale_factor)
+ return (pipe_line, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class image_pipe_edit:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"pipe": ("PIPE_LINE",)},
+ "optional": {
+ "image": ("IMAGE",),
+ "width": ("INT", {"default": 512, "min": 64, "max": 2048}),
+ "height": ("INT", {"default": 512, "min": 64, "max": 2048}),
+ "upscale_factor": ("FLOAT", {"default": 1, "min": 1, "max": 2000})
+ },
+ }
+
+ RETURN_TYPES = ("PIPE_LINE", )
+ RETURN_NAMES = ("pipe", )
+ FUNCTION = "flush"
+
+ CATEGORY = "Comfyroll/Module"
+
+ def flush(self, pipe, image=None, width=None, height=None, upscale_factor=None):
+ new_image, new_width, new_height, new_upscale_factor = pipe
+
+ if image is not None:
+ new_image = image
+
+ if width is not None:
+ new_width = width
+
+ if height is not None:
+ new_height = height
+
+ if upscale_factor is not None:
+ new_upscale_factor = upscale_factor
+
+ pipe = new_image, new_width, new_height, new_upscale_factor
+ return (pipe, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class image_pipe_out:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {"pipe": ("PIPE_LINE",)},
+ }
+
+ RETURN_TYPES = ("PIPE_LINE", "IMAGE", "INT", "INT", "FLOAT",)
+ RETURN_NAMES = ("pipe", "image", "width", "height", "upscale_factor")
+ FUNCTION = "flush"
+
+ CATEGORY = "Comfyroll/Module"
+
+ def flush(self, pipe):
+ #if switch == "Off":
+ #return (pipe, )
+ #else:
+ image, width, height, upscale_factor = pipe
+ return pipe, image, width, height, upscale_factor
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+
+class input_switch_pipe:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Input": ("INT", {"default": 1, "min": 1, "max": 2}),
+ "pipe1": ("PIPE_LINE",),
+ "pipe2": ("PIPE_LINE",)
+ }
+ }
+
+ RETURN_TYPES = ("PIPE_LINE",)
+ OUTPUT_NODE = True
+ FUNCTION = "InputSwitchPipe"
+
+ CATEGORY = "Comfyroll/Module"
+
+ def InputSwitchPipe(self, Input, pipe1, pipe2):
+ if Input == 1:
+ return (pipe1, )
+ else:
+ return (pipe2, )
+
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+'''
+NODE_CLASS_MAPPINGS_2 = {
+ "CR Module Pipe Loader": module_pipe_loader,
+ "CR Module Input": module_input,
+ "CR Module Output": module_output,
+ "CR Image Pipe In": image_pipe_in,
+ "CR Image Pipe Edit": image_pipe_edit,
+ "CR Image Pipe Out": image_pipe_out,
+ "CR Pipe Switch": input_switch_pipe,
+}
+'''
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
+# Credits
+# TinyTerra https://github.com/TinyTerra/ComfyUI_tinyterraNodes #
+#---------------------------------------------------------------------------------------------------------------------------------------------------#
diff --git a/ComfyUI_Comfyroll_CustomNodes/README.md b/ComfyUI_Comfyroll_CustomNodes/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d004a9d98fa9d43355e5404aa18cbec7b67439af
--- /dev/null
+++ b/ComfyUI_Comfyroll_CustomNodes/README.md
@@ -0,0 +1,82 @@
+# Comfyroll Custom Nodes
+
+These nodes were originally made for use in the Comfyroll Template Workflows.
+
+[ComfyUI Template Workflows](https://civitai.com/models/59806/comfyroll-template-workflows)
+
+[Comfyroll Pro Templates](https://civitai.com/models/85619/comfyroll-pro-template)
+
+The nodes can be used in any ComfyUI workflow.
+
+# Installation
+
+If you have a previous version of the Comfyroll nodes from the Comfyroll Worflow Templates download, please delete this before installing these nodes.
+
+1. cd custom_nodes
+2. git clone https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes.git
+3. Restart ComfyUI
+
+You can also install the nodes using the following methods:
+* install using [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager)
+* download from [CivitAI](https://civitai.com/models/87609/comfyroll-custom-nodes-for-comfyui)
+
+# List of Custom Nodes
+
+__Logic__
+* CR Image Input Switch
+* CR Image Input Switch (4 way)
+* CR Latent Input Switch
+* CR Conditioning Input Switch
+* CR Clip Input Switch
+* CR Model Input Switch
+* CR ControlNet Input Switch
+
+__IO__
+* CR Load LoRA
+
+__Maths__
+* CR Integer Multiple
+
+__Number__
+* CR Seed to Int
+
+__Image__
+* CR Image Size
+* CR Aspect Ratio
+* CR Color Tint
+
+__Conditioning__
+* CR Apply ControlNet
+
+__SDXL__
+* CR Aspect Ratio SDXL
+* CR SDXL Prompt Mixer
+* CR SDXL Style Text
+
+__Module__
+* CR Module Pipe Loader
+* CR Module Input
+* CR Module Output
+* CR Image Pipe In
+* CR Image Pipe Edit
+* CR Image Pipe Out
+* CR Pipe Switch
+
+
+
+
+
+
+
+
+
+# Credits
+
+comfyanonymous/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI.
+
+WASasquatch/[was-node-suite-comfyui](https://github.com/WASasquatch/was-node-suite-comfyui) - A powerful custom node extensions of ComfyUI.
+
+TinyTerra/[ComfyUI_tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) - A selection of nodes for Stable Diffusion ComfyUI
+
+hnmr293/[ComfyUI-nodes-hnmr](https://github.com/hnmr293/ComfyUI-nodes-hnmr) - ComfyUI custom nodes - merge, grid (aka xyz-plot) and others
+
diff --git a/ComfyUI_Comfyroll_CustomNodes/__init__.py b/ComfyUI_Comfyroll_CustomNodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b50de882ae6eb888a16f90773e004b82a49e3e3a
--- /dev/null
+++ b/ComfyUI_Comfyroll_CustomNodes/__init__.py
@@ -0,0 +1,35 @@
+from .Comfyroll_Nodes import *
+from .Comfyroll_Pipe_Nodes import *
+
+NODE_CLASS_MAPPINGS = {
+ "CR Module Pipe Loader": module_pipe_loader,
+ "CR Module Input": module_input,
+ "CR Module Output": module_output,
+ "CR Image Pipe In": image_pipe_in,
+ "CR Image Pipe Edit": image_pipe_edit,
+ "CR Image Pipe Out": image_pipe_out,
+ "CR Pipe Switch": input_switch_pipe,
+ "CR Image Input Switch": ComfyRoll_InputImages,
+ "CR Image Input Switch (4 way)": ComfyRoll_InputImages_4way,
+ "CR Latent Input Switch": ComfyRoll_InputLatents,
+ "CR Conditioning Input Switch": ComfyRoll_InputConditioning,
+ "CR Clip Input Switch": ComfyRoll_InputClip,
+ "CR Model Input Switch": ComfyRoll_InputModel,
+ "CR ControlNet Input Switch": ComfyRoll_InputControlNet,
+ "CR Load LoRA": ComfyRoll_LoraLoader,
+ "CR Apply ControlNet": ComfyRoll_ApplyControlNet,
+ "CR Image Size": ComfyRoll_ImageSize_Float,
+ "CR Image Output": ComfyRoll_ImageOutput,
+ "CR Integer Multiple": CR_Int_Multiple_Of,
+ "CR Aspect Ratio": ComfyRoll_AspectRatio,
+ "CR Aspect Ratio SDXL": ComfyRoll_AspectRatio_SDXL,
+ "CR Seed to Int": ComfyRoll_SeedToInt,
+ "CR Color Tint": Comfyroll_Color_Tint,
+ "CR SDXL Prompt Mixer": ComfyRoll_prompt_mixer,
+ "CR SDXL Style Text": Comfyroll_SDXLStyleText,
+ "CR SDXL Base Prompt Encoder": Comfyroll_SDXLBasePromptEncoder,
+}
+
+__all__ = ['NODE_CLASS_MAPPINGS']
+
+print("\033[34mComfyroll Custom Nodes: \033[92mLoaded\033[0m")
diff --git a/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image1.png b/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image1.png
new file mode 100644
index 0000000000000000000000000000000000000000..a0bbaffb7fe9323b973a0242524677e3328840a7
--- /dev/null
+++ b/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5f44791610e6a619423d8cdc8c99ba84372c0c0f897c129a684bc28f39f5159
+size 156949
diff --git a/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image2.jpg b/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image2.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0f508cce456d29ed98a9d0930e6b4ea35a29f946
--- /dev/null
+++ b/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image2.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9608d07ae62f76d882f7db90f49af7cc7f8383cfbb752cbd0c05a8e3be3f5ec5
+size 26245
diff --git a/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image3.JPG b/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image3.JPG
new file mode 100644
index 0000000000000000000000000000000000000000..dd968f9f3c7efdd4d5292eb85698ae417835dbba
--- /dev/null
+++ b/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image3.JPG
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa827470be4a9023e2e7330ddd45ff11b42aa19209a0c2bcf40d18675eabc32b
+size 66908
diff --git a/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image4.JPG b/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image4.JPG
new file mode 100644
index 0000000000000000000000000000000000000000..eeb75d388bb65bf8da696aac8ef0f5d89cdde65b
--- /dev/null
+++ b/ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image4.JPG
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65f31bb4780780c083ea9ee2e8e52628297e531793b99d317349bc3de0a4bc9b
+size 57043
diff --git a/ComfyUI_Cutoff-master/LICENSE b/ComfyUI_Cutoff-master/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7
--- /dev/null
+++ b/ComfyUI_Cutoff-master/LICENSE
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
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+
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+
+ 12. No Surrender of Others' Freedom.
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+to attach them to the start of each source file to most effectively
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+ (at your option) any later version.
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+ but WITHOUT ANY WARRANTY; without even the implied warranty of
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+ along with this program. If not, see .
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
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+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
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+might be different; for a GUI interface, you would use an "about box".
+
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+if any, to sign a "copyright disclaimer" for the program, if necessary.
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+.
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+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
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+Public License instead of this License. But first, please read
+.
diff --git a/ComfyUI_Cutoff-master/README.md b/ComfyUI_Cutoff-master/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..575bad2dc1f5abc9c6bc9e0f08543eafd9b5782f
--- /dev/null
+++ b/ComfyUI_Cutoff-master/README.md
@@ -0,0 +1,43 @@
+# Cutoff for ComfyUI
+
+
+
+### what is cutoff?
+
+[cutoff](https://github.com/hnmr293/sd-webui-cutoff) is a script/extension for the Automatic1111 webui that lets users limit the effect certain attributes have on specified subsets of the prompt. I.e. when the prompt is `a cute girl, white shirt with green tie, red shoes, blue hair, yellow eyes, pink skirt`, cutoff lets you specify that the word blue belongs to the hair and not the shoes, and green tot he tie and not the skirt, etc. This is an implementation of cutoff in the form of 3 nodes that can be used in [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
+
+### how does this work?
+
+When you provide stable diffusion with some text, that text gets tokenized and CLIP creates a vector (embedding) for each token in the text. So if we have a prompt containing "blue hair, yellow eyes" some of the vectors coming out of CLIP will correspond to the "blue hair" part, and some to the "yellow eyes". When CLIP does this it tries to take the context of the entire sentence into consideration. Unfortunately CLIP isn't always as great at figuring out that the "blue" in "blue hair" should really only modify the noun "hair" and not the noun "eyes" a bit further in the sentence.
+
+So how do we deal with this? we can mask out the tokens corresponding to "blue" and ask CLIP to create another embedding. In this new embedding we have a set of vectors corresponding to "yellow eyes" that are not affected by "blue", because blue wasn't part of the tokens. If we then take the difference between our original vectors and these new vectors we now have a direction we can travel in for the eyes to become more affected by "yellow" and less by "blue". If we do this for all the color relations in text we can travel to an embedding where each of these relations are more isolated. Of course this effect isn't limited to just colors.
+
+### ComfyUI nodes
+To achieve all of this, the following 4 nodes are introduced:
+
+**Cutoff BasePrompt:** this node takes the full original prompt
+
+**Cutoff Set Region:** this node sets a "region" of influence for specific target words, and comes with the following inputs:
+- region\_text: defines the set of tokens that the target words should affect, this should be a part of the original prompt. It is possible to define multiple regions in a single CLIPSetRegion node by stating every region on a new line.
+- target\_text: defines the set of tokens that will be masked off (i.e. the tokens we wish to limit to the region) this is a space separated list of words. If you want to match a sequence of words use underscores instead of spaces, e.g. "a\_series\_of\_connected\_tokens". If you want to match a word that actually contains underscores escape the underscore, e.g. "the\\_target\\_tokens". You can target textual inversion embeddings using the default syntax but do note that any underscores in the name of the embedding have to be escaped in this input field.
+- weight: how far to travel in the direction of the isolated vector
+
+**Cutoff Regions To Conditioning:** this node converts the base prompt and regions into an actual conditioning to be used in the rest of ComfyUI, and comes with the following inputs:
+- mask\_token: the token to be used for masking. If left blank it will default to the `` token. If the string converts to multiple tokens it will give a warning in the console and only use the first token in the list.
+- strict_mask: When 0.0 the specified target tokens will not affect the other specified areas but do affect anything outside of those areas. When set to 1.0 the specified target tokens will only affect their own region.
+- start\_from\_masked: When 0.0 the starting point to travel from is the original prompt. When set to 1.0 the starting point to travel from is the completely masked off prompt. Note that specifically when all region weights are 1.0 there is no difference between the two
+
+**Cutoff Regions To Conditioning (ADV):** provides the same functionality as the above node but also provides options on how to interpret prompt weighting. More on these settings can be found [here](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb).
+
+You can find these nodes under `conditioning>cutoff`
+
+### SDXL
+
+The nodes won't throw any errors when used with SDXL, but at least for 0.9 I didn't found it to be working that well.
+
+Finally, Here are some example images that you can load into ComfyUI:
+
+
+
+
+
\ No newline at end of file
diff --git a/ComfyUI_Cutoff-master/__init__.py b/ComfyUI_Cutoff-master/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5edb4438ee14248288b842919cbc71babf9c637a
--- /dev/null
+++ b/ComfyUI_Cutoff-master/__init__.py
@@ -0,0 +1,3 @@
+from .cutoff import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
\ No newline at end of file
diff --git a/ComfyUI_Cutoff-master/adv_encode.py b/ComfyUI_Cutoff-master/adv_encode.py
new file mode 100644
index 0000000000000000000000000000000000000000..f193d61a183fadaa62113f5d792329b1cd937b85
--- /dev/null
+++ b/ComfyUI_Cutoff-master/adv_encode.py
@@ -0,0 +1,290 @@
+import torch
+import numpy as np
+import itertools
+from math import gcd
+
+from comfy import model_management
+from comfy.sdxl_clip import SDXLClipModel
+
+def _grouper(n, iterable):
+ it = iter(iterable)
+ while True:
+ chunk = list(itertools.islice(it, n))
+ if not chunk:
+ return
+ yield chunk
+
+def _norm_mag(w, n):
+ d = w - 1
+ return 1 + np.sign(d) * np.sqrt(np.abs(d)**2 / n)
+ #return np.sign(w) * np.sqrt(np.abs(w)**2 / n)
+
+def divide_length(word_ids, weights):
+ sums = dict(zip(*np.unique(word_ids, return_counts=True)))
+ sums[0] = 1
+ weights = [[_norm_mag(w, sums[id]) if id != 0 else 1.0
+ for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
+ return weights
+
+def shift_mean_weight(word_ids, weights):
+ delta = 1 - np.mean([w for x, y in zip(weights, word_ids) for w, id in zip(x,y) if id != 0])
+ weights = [[w if id == 0 else w+delta
+ for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
+ return weights
+
+def scale_to_norm(weights, word_ids, w_max):
+ top = np.max(weights)
+ w_max = min(top, w_max)
+ weights = [[w_max if id == 0 else (w/top) * w_max
+ for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
+ return weights
+
+def from_zero(weights, base_emb):
+ weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device)
+ weight_tensor = weight_tensor.reshape(1,-1,1).expand(base_emb.shape)
+ return base_emb * weight_tensor
+
+def mask_word_id(tokens, word_ids, target_id, mask_token):
+ new_tokens = [[mask_token if wid == target_id else t
+ for t, wid in zip(x,y)] for x,y in zip(tokens, word_ids)]
+ mask = np.array(word_ids) == target_id
+ return (new_tokens, mask)
+
+def batched_clip_encode(tokens, length, encode_func, num_chunks):
+ embs = []
+ for e in _grouper(32, tokens):
+ enc, pooled = encode_func(e)
+ enc = enc.reshape((len(e), length, -1))
+ embs.append(enc)
+ embs = torch.cat(embs)
+ embs = embs.reshape((len(tokens) // num_chunks, length * num_chunks, -1))
+ return embs
+
+def from_masked(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266):
+ pooled_base = base_emb[0,length-1:length,:]
+ wids, inds = np.unique(np.array(word_ids).reshape(-1), return_index=True)
+ weight_dict = dict((id,w)
+ for id,w in zip(wids ,np.array(weights).reshape(-1)[inds])
+ if w != 1.0)
+
+ if len(weight_dict) == 0:
+ return torch.zeros_like(base_emb), base_emb[0,length-1:length,:]
+
+ weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device)
+ weight_tensor = weight_tensor.reshape(1,-1,1).expand(base_emb.shape)
+
+ #m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0)
+ #TODO: find most suitable masking token here
+ m_token = (m_token, 1.0)
+
+ ws = []
+ masked_tokens = []
+ masks = []
+
+ #create prompts
+ for id, w in weight_dict.items():
+ masked, m = mask_word_id(tokens, word_ids, id, m_token)
+ masked_tokens.extend(masked)
+
+ m = torch.tensor(m, dtype=base_emb.dtype, device=base_emb.device)
+ m = m.reshape(1,-1,1).expand(base_emb.shape)
+ masks.append(m)
+
+ ws.append(w)
+
+ #batch process prompts
+ embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens))
+ masks = torch.cat(masks)
+
+ embs = (base_emb.expand(embs.shape) - embs)
+ pooled = embs[0,length-1:length,:]
+
+ embs *= masks
+ embs = embs.sum(axis=0, keepdim=True)
+
+ pooled_start = pooled_base.expand(len(ws), -1)
+ ws = torch.tensor(ws).reshape(-1,1).expand(pooled_start.shape)
+ pooled = (pooled - pooled_start) * (ws - 1)
+ pooled = pooled.mean(axis=0, keepdim=True)
+
+ return ((weight_tensor - 1) * embs), pooled_base + pooled
+
+def mask_inds(tokens, inds, mask_token):
+ clip_len = len(tokens[0])
+ inds_set = set(inds)
+ new_tokens = [[mask_token if i*clip_len + j in inds_set else t
+ for j, t in enumerate(x)] for i, x in enumerate(tokens)]
+ return new_tokens
+
+def down_weight(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266):
+ w, w_inv = np.unique(weights,return_inverse=True)
+
+ if np.sum(w < 1) == 0:
+ return base_emb, tokens, base_emb[0,length-1:length,:]
+ #m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0)
+ #using the comma token as a masking token seems to work better than aos tokens for SD 1.x
+ m_token = (m_token, 1.0)
+
+ masked_tokens = []
+
+ masked_current = tokens
+ for i in range(len(w)):
+ if w[i] >= 1:
+ continue
+ masked_current = mask_inds(masked_current, np.where(w_inv == i)[0], m_token)
+ masked_tokens.extend(masked_current)
+
+ embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens))
+ embs = torch.cat([base_emb, embs])
+ w = w[w<=1.0]
+ w_mix = np.diff([0] + w.tolist())
+ w_mix = torch.tensor(w_mix, dtype=embs.dtype, device=embs.device).reshape((-1,1,1))
+
+ weighted_emb = (w_mix * embs).sum(axis=0, keepdim=True)
+ return weighted_emb, masked_current, weighted_emb[0,length-1:length,:]
+
+def scale_emb_to_mag(base_emb, weighted_emb):
+ norm_base = torch.linalg.norm(base_emb)
+ norm_weighted = torch.linalg.norm(weighted_emb)
+ embeddings_final = (norm_base / norm_weighted) * weighted_emb
+ return embeddings_final
+
+def recover_dist(base_emb, weighted_emb):
+ fixed_std = (base_emb.std() / weighted_emb.std()) * (weighted_emb - weighted_emb.mean())
+ embeddings_final = fixed_std + (base_emb.mean() - fixed_std.mean())
+ return embeddings_final
+
+def A1111_renorm(base_emb, weighted_emb):
+ embeddings_final = (base_emb.mean() / weighted_emb.mean()) * weighted_emb
+ return embeddings_final
+
+def advanced_encode_from_tokens(tokenized, token_normalization, weight_interpretation, encode_func, m_token=266, length=77, w_max=1.0, return_pooled=False, apply_to_pooled=False):
+ tokens = [[t for t,_,_ in x] for x in tokenized]
+ weights = [[w for _,w,_ in x] for x in tokenized]
+ word_ids = [[wid for _,_,wid in x] for x in tokenized]
+
+ #weight normalization
+ #====================
+
+ #distribute down/up weights over word lengths
+ if token_normalization.startswith("length"):
+ weights = divide_length(word_ids, weights)
+
+ #make mean of word tokens 1
+ if token_normalization.endswith("mean"):
+ weights = shift_mean_weight(word_ids, weights)
+
+ #weight interpretation
+ #=====================
+ pooled = None
+
+ if weight_interpretation == "comfy":
+ weighted_tokens = [[(t,w) for t, w in zip(x, y)] for x, y in zip(tokens, weights)]
+ weighted_emb, pooled_base = encode_func(weighted_tokens)
+ pooled = pooled_base
+ else:
+ unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokenized]
+ base_emb, pooled_base = encode_func(unweighted_tokens)
+
+ if weight_interpretation == "A1111":
+ weighted_emb = from_zero(weights, base_emb)
+ weighted_emb = A1111_renorm(base_emb, weighted_emb)
+ pooled = pooled_base
+
+ if weight_interpretation == "compel":
+ pos_tokens = [[(t,w) if w >= 1.0 else (t,1.0) for t, w in zip(x, y)] for x, y in zip(tokens, weights)]
+ weighted_emb, _ = encode_func(pos_tokens)
+ weighted_emb, _, pooled = down_weight(pos_tokens, weights, word_ids, weighted_emb, length, encode_func)
+
+ if weight_interpretation == "comfy++":
+ weighted_emb, tokens_down, _ = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
+ weights = [[w if w > 1.0 else 1.0 for w in x] for x in weights]
+ #unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokens_down]
+ embs, pooled = from_masked(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
+ weighted_emb += embs
+
+ if weight_interpretation == "down_weight":
+ weights = scale_to_norm(weights, word_ids, w_max)
+ weighted_emb, _, pooled = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
+
+ if return_pooled:
+ if apply_to_pooled:
+ return weighted_emb, pooled
+ else:
+ return weighted_emb, pooled_base
+ return weighted_emb, None
+
+def encode_token_weights_g(model, token_weight_pairs):
+ return model.clip_g.encode_token_weights(token_weight_pairs)
+
+def encode_token_weights_l(model, token_weight_pairs):
+ l_out, _ = model.clip_l.encode_token_weights(token_weight_pairs)
+ return l_out, None
+
+def encode_token_weights(model, token_weight_pairs, encode_func):
+ if model.layer_idx is not None:
+ model.cond_stage_model.clip_layer(model.layer_idx)
+
+ model_management.load_model_gpu(model.patcher)
+ return encode_func(model.cond_stage_model, token_weight_pairs)
+
+def prepareXL(embs_l, embs_g, pooled, clip_balance):
+ l_w = 1 - max(0, clip_balance - .5) * 2
+ g_w = 1 - max(0, .5 - clip_balance) * 2
+ if embs_l is not None:
+ return torch.cat([embs_l * l_w, embs_g * g_w], dim=-1), pooled
+ else:
+ return embs_g, pooled
+
+def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True):
+ tokenized = clip.tokenize(text, return_word_ids=True)
+ if isinstance(tokenized, dict):
+ embs_l = None
+ embs_g = None
+ pooled = None
+ if 'l' in tokenized and isinstance(clip.cond_stage_model, SDXLClipModel):
+ embs_l, _ = advanced_encode_from_tokens(tokenized['l'],
+ token_normalization,
+ weight_interpretation,
+ lambda x: encode_token_weights(clip, x, encode_token_weights_l),
+ w_max=w_max,
+ return_pooled=False)
+ if 'g' in tokenized:
+ embs_g, pooled = advanced_encode_from_tokens(tokenized['g'],
+ token_normalization,
+ weight_interpretation,
+ lambda x: encode_token_weights(clip, x, encode_token_weights_g),
+ w_max=w_max,
+ return_pooled=True,
+ apply_to_pooled=apply_to_pooled)
+ return prepareXL(embs_l, embs_g, pooled, clip_balance)
+ else:
+ return advanced_encode_from_tokens(tokenized,
+ token_normalization,
+ weight_interpretation,
+ lambda x: (clip.encode_from_tokens(x), None),
+ w_max=w_max)
+def advanced_encode_XL(clip, text1, text2, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True):
+ tokenized1 = clip.tokenize(text1, return_word_ids=True)
+ tokenized2 = clip.tokenize(text2, return_word_ids=True)
+
+ embs_l, _ = advanced_encode_from_tokens(tokenized1['l'],
+ token_normalization,
+ weight_interpretation,
+ lambda x: encode_token_weights(clip, x, encode_token_weights_l),
+ w_max=w_max,
+ return_pooled=False)
+
+ embs_g, pooled = advanced_encode_from_tokens(tokenized2['g'],
+ token_normalization,
+ weight_interpretation,
+ lambda x: encode_token_weights(clip, x, encode_token_weights_g),
+ w_max=w_max,
+ return_pooled=True,
+ apply_to_pooled=apply_to_pooled)
+
+ gcd_num = gcd(embs_l.shape[1], embs_g.shape[1])
+ repeat_l = int((embs_g.shape[1] / gcd_num) * embs_l.shape[1])
+ repeat_g = int((embs_l.shape[1] / gcd_num) * embs_g.shape[1])
+
+ return prepareXL(embs_l.expand((-1,repeat_l,-1)), embs_g.expand((-1,repeat_g,-1)), pooled, clip_balance)
diff --git a/ComfyUI_Cutoff-master/cutoff.py b/ComfyUI_Cutoff-master/cutoff.py
new file mode 100644
index 0000000000000000000000000000000000000000..09388e276c25fc1c49f76ab3fd9c9a7e9c8330e8
--- /dev/null
+++ b/ComfyUI_Cutoff-master/cutoff.py
@@ -0,0 +1,300 @@
+import torch
+import copy
+import re
+import warnings
+
+import numpy as np
+
+from .adv_encode import advanced_encode_from_tokens, encode_token_weights_g, encode_token_weights_l, encode_token_weights, prepareXL
+from comfy.sdxl_clip import SDXLClipModel
+
+#sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
+
+def replace_embeddings(max_token, prompt, replacements=None):
+
+ if replacements is None:
+ emb_lookup = []
+ else:
+ emb_lookup = replacements.copy()
+ max_token += len(emb_lookup)
+
+ def get_replacement(embedding):
+ for e, n in emb_lookup:
+ if torch.equal(embedding, e):
+ return n
+ return None
+
+ tokens = []
+ for x in prompt:
+ row = []
+ for i in range(len(x)):
+ emb = x[i][0]
+ if not torch.is_tensor(emb):
+ row.append(emb)
+ else:
+ n = get_replacement(emb)
+ if n is not None:
+ row.append(n)
+ else:
+ max_token += 1
+ row.append(max_token)
+ emb_lookup.append((emb,max_token))
+ tokens.append(row)
+ tokens = np.array(tokens)[:,1:-1].reshape(-1)
+ return (tokens, emb_lookup)
+
+def unpad_prompt(end_token, prompt):
+ res = np.trim_zeros(prompt, 'b')
+ return np.trim_zeros(res-end_token, 'b')+end_token
+
+class CLIPRegionsBasePrompt:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}}
+ RETURN_TYPES = ("CLIPREGION",)
+ FUNCTION = "init_prompt"
+
+ CATEGORY = "conditioning/cutoff"
+
+ def init_prompt(self, clip, text):
+ tokens = clip.tokenize(text, return_word_ids=True)
+ return ({
+ "clip" : clip,
+ "base_tokens" : tokens,
+ "regions" : [],
+ "targets" : [],
+ "weights" : [],
+ },)
+
+def get_sublists(super_list, sub_list):
+ positions = []
+ for candidate_ind in (i for i,e in enumerate(super_list) if e==sub_list[0]):
+ if super_list[candidate_ind:candidate_ind+len(sub_list)] == sub_list:
+ positions.append(candidate_ind)
+ return positions
+
+class CLIPSetRegion:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"clip_regions": ("CLIPREGION", ),
+ "region_text": ("STRING", {"multiline": True}),
+ "target_text": ("STRING", {"multiline": False}),
+ "weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.05})}}
+ RETURN_TYPES = ("CLIPREGION",)
+ FUNCTION = "add_clip_region"
+
+ CATEGORY = "conditioning/cutoff"
+
+ def add_clip_region(self, clip_regions, region_text, target_text, weight):
+ clip = clip_regions["clip"]
+ tokenizer = clip.tokenizer
+ base_tokens = clip_regions["base_tokens"]
+ if isinstance(base_tokens, dict):
+ base_tokens = base_tokens['g']
+ if hasattr(tokenizer, 'clip_g'):
+ tokenizer = tokenizer.clip_g
+ region_outputs = []
+ target_outputs = []
+
+ #strip input strings
+ region_text = region_text.strip()
+ target_text = target_text.strip()
+
+
+ endtoken = tokenizer.end_token
+
+ prompt_tokens, emb_lookup = replace_embeddings(endtoken, base_tokens)
+
+ for rt in region_text.split('\n'):
+ region_tokens = tokenizer.tokenize_with_weights(rt)
+ region_tokens, _ = replace_embeddings(endtoken, region_tokens, emb_lookup)
+ region_tokens = unpad_prompt(endtoken, region_tokens).tolist()
+
+ #calc region mask
+ region_length = len(region_tokens)
+ regions = get_sublists(list(prompt_tokens), region_tokens)
+
+ region_mask = np.zeros(len(prompt_tokens))
+ for r in regions:
+ region_mask[r:r+region_length] = 1
+ region_mask = region_mask.reshape(-1,tokenizer.max_length-2)
+ region_mask = np.pad(region_mask, pad_width=((0,0),(1,1)), mode='constant', constant_values=0)
+ region_mask = region_mask.reshape(1, -1)
+ region_outputs.append(region_mask)
+
+ #calc target mask
+ targets = []
+ for target in target_text.split(" "):
+ # deal with underscores
+ target = re.sub(r"(? 1:
+ warnings.warn("mask_token does not map to a single token, using the first token instead")
+ mask_token = mask_token[0]
+
+ #calc global target mask
+ global_target_mask = np.any(np.stack(clip_regions["targets"]), axis=0).astype(int)
+
+ #calc global region mask
+ global_region_mask = np.any(np.stack(clip_regions["regions"]), axis=0).astype(float)
+ regions_sum = np.sum(np.stack(clip_regions["regions"]), axis=0)
+ regions_normalized = np.divide(1, regions_sum, out=np.zeros_like(regions_sum), where=regions_sum!=0)
+
+ #calc base embedding
+ base_embedding_full, pool = encode_from_tokens(clip, base_weighted_tokens, token_normalization, weight_interpretation, True)
+ base_embedding_masked = encode_from_tokens(clip, create_masked_prompt(base_weighted_tokens, global_target_mask, mask_token), token_normalization, weight_interpretation)
+ base_embedding_start = base_embedding_full * (1-start_from_masked) + base_embedding_masked * start_from_masked
+ base_embedding_outer = base_embedding_full * (1-strict_mask) + base_embedding_masked * strict_mask
+
+ region_embeddings = []
+ for region, target, weight in zip (clip_regions["regions"],clip_regions["targets"],clip_regions["weights"]):
+ region_masking = torch.tensor(regions_normalized * region * weight, dtype=base_embedding_full.dtype, device=base_embedding_full.device).unsqueeze(-1)
+
+ region_emb = encode_from_tokens(clip, create_masked_prompt(base_weighted_tokens, global_target_mask - target, mask_token), token_normalization, weight_interpretation)
+ region_emb -= base_embedding_start
+ region_emb *= region_masking
+
+ region_embeddings.append(region_emb)
+ region_embeddings = torch.stack(region_embeddings).sum(axis=0)
+
+ embeddings_final_mask = torch.tensor(global_region_mask, dtype=base_embedding_full.dtype, device=base_embedding_full.device).unsqueeze(-1)
+ embeddings_final = base_embedding_start * embeddings_final_mask + base_embedding_outer * (1 - embeddings_final_mask)
+ embeddings_final += region_embeddings
+ return ([[embeddings_final, {"pooled_output": pool}]], )
+
+
+class CLIPRegionsToConditioning:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"clip_regions": ("CLIPREGION", ),
+ "mask_token": ("STRING", {"multiline": False, "default" : ""}),
+ "strict_mask": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05}),
+ "start_from_masked": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05})}}
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "finalize"
+
+ CATEGORY = "conditioning/cutoff"
+
+ def finalize(self, clip_regions, mask_token, strict_mask, start_from_masked):
+ return finalize_clip_regions(clip_regions, mask_token, strict_mask, start_from_masked)
+
+class CLIPRegionsToConditioningADV:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"clip_regions": ("CLIPREGION", ),
+ "mask_token": ("STRING", {"multiline": False, "default" : ""}),
+ "strict_mask": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05}),
+ "start_from_masked": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05}),
+ "token_normalization": (["none", "mean", "length", "length+mean"],),
+ "weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],),
+ }}
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "finalize"
+
+ CATEGORY = "conditioning/cutoff"
+
+ def finalize(self, clip_regions, mask_token, strict_mask, start_from_masked, token_normalization, weight_interpretation):
+ return finalize_clip_regions(clip_regions, mask_token, strict_mask, start_from_masked, token_normalization, weight_interpretation)
+
+
+NODE_CLASS_MAPPINGS = {
+ "BNK_CutoffBasePrompt": CLIPRegionsBasePrompt,
+ "BNK_CutoffSetRegions": CLIPSetRegion,
+ "BNK_CutoffRegionsToConditioning": CLIPRegionsToConditioning,
+ "BNK_CutoffRegionsToConditioning_ADV": CLIPRegionsToConditioningADV,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "BNK_CutoffBasePrompt": "Cutoff Base Prompt",
+ "BNK_CutoffSetRegions": "Cutoff Set Regions",
+ "BNK_CutoffRegionsToConditioning": "Cutoff Regions To Conditioning",
+ "BNK_CutoffRegionsToConditioning_ADV": "Cutoff Regions To Conditioning (ADV)",
+}
\ No newline at end of file
diff --git a/ComfyUI_Cutoff-master/examples/ComfyUI_00671_.png b/ComfyUI_Cutoff-master/examples/ComfyUI_00671_.png
new file mode 100644
index 0000000000000000000000000000000000000000..6108d7dc5deb17fc1faea1282601418c497fed16
--- /dev/null
+++ b/ComfyUI_Cutoff-master/examples/ComfyUI_00671_.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:308795b4e00e0e77b58ed28b0ed8be9fc5d273091f0fda1ee1df3ce3419bdf96
+size 1345356
diff --git a/ComfyUI_Cutoff-master/examples/ComfyUI_00672_.png b/ComfyUI_Cutoff-master/examples/ComfyUI_00672_.png
new file mode 100644
index 0000000000000000000000000000000000000000..0347775a72140448e6603d9afa53d108d4075851
--- /dev/null
+++ b/ComfyUI_Cutoff-master/examples/ComfyUI_00672_.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b783eb5c123066581264b1b9b65137fba9a8a6c3b54ddf5253f16cdd2b6f570b
+size 1304628
diff --git a/ComfyUI_Cutoff-master/examples/ComfyUI_00673_.png b/ComfyUI_Cutoff-master/examples/ComfyUI_00673_.png
new file mode 100644
index 0000000000000000000000000000000000000000..a5deba7e14a249d600aafe62a33ba8e658edb90e
--- /dev/null
+++ b/ComfyUI_Cutoff-master/examples/ComfyUI_00673_.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b46c74312fd3732268d58a76ace998881c20ce8cabc6d1672ec5b6a24b29ef9f
+size 1462752
diff --git a/ComfyUI_Cutoff-master/examples/ComfyUI_00674_.png b/ComfyUI_Cutoff-master/examples/ComfyUI_00674_.png
new file mode 100644
index 0000000000000000000000000000000000000000..51f8a2c4ddea459d45759bdff595d28099a75e48
--- /dev/null
+++ b/ComfyUI_Cutoff-master/examples/ComfyUI_00674_.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ba98fba8f098d04c9aa7d4a3474cb891577f5eb260679484353395120591bbc
+size 1649576
diff --git a/ComfyUI_Cutoff-master/examples/screenshot.png b/ComfyUI_Cutoff-master/examples/screenshot.png
new file mode 100644
index 0000000000000000000000000000000000000000..1ee11de1c2cbb00b8f2b14ec6582998ad9f0605a
--- /dev/null
+++ b/ComfyUI_Cutoff-master/examples/screenshot.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f822a21ae408f9df0684a97a90b28bad9a1749367bab711ce98ef0dd891b03eb
+size 728521
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Custom/Types.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Custom/Types.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ed01ad55ad2b8232b4ca93ee2c70f42d2e722d6
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Custom/Types.py
@@ -0,0 +1,94 @@
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_VARIABLE
+
+import sys
+
+class FloatNode:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value": field.FLOAT,
+ },
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ CATEGORY = TREE_VARIABLE
+ FUNCTION = "get_value"
+
+ def get_value(self, Value):
+ return (Value,)
+
+
+class IntegerNode:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value": ("FLOAT", {
+ "default": 1,
+ "min": -sys.float_info.max,
+ "max": sys.float_info.max,
+ "step": 1
+ },
+ )
+ },
+ }
+
+ RETURN_TYPES = ("INT",)
+ CATEGORY = TREE_VARIABLE
+ FUNCTION = "get_value"
+
+ def get_value(self, Value):
+ return (int(Value),)
+
+
+class StringNode:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Text": field.STRING,
+ }
+ }
+
+ RETURN_TYPES = ("STRING",)
+ FUNCTION = "get_value"
+ CATEGORY = TREE_VARIABLE
+
+ def get_value(self, Text):
+ return (Text,)
+
+
+class MultilineStringNode:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Text": ("STRING", {
+ "default": "",
+ "multiline": True,
+ }),
+ }
+ }
+
+ RETURN_TYPES = ("STRING",)
+ FUNCTION = "get_value"
+ CATEGORY = TREE_VARIABLE
+
+ def get_value(self, Text):
+ return (Text,)
+
+
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Custom/__init__.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Custom/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03a367a7b1e8d968cc174e8974c3b5cc339ac3df
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Custom/__init__.py
@@ -0,0 +1,9 @@
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Custom.Types as TypeNodes
+#
+# NODE_CLASS_MAPPINGS = {
+# "FloatNode_DF": TypeNodes.FloatNode,
+# "IntegerNode_DF": TypeNodes.IntegerNode,
+# "StringNode_DF": TypeNodes.StringNode,
+# "TupleNode_DF": TypeNodes.TupleNode,
+# "MultilineStringNode_DF": TypeNodes.MultilineStringNode,
+# }
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Debug/Debug.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Debug/Debug.py
new file mode 100644
index 0000000000000000000000000000000000000000..25473aadec4b887f4a4e17cf6c7a950a65508748
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Debug/Debug.py
@@ -0,0 +1,94 @@
+import sys
+
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_DEBUG
+
+class DebugNodeFloat:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Prefix": field.STRING,
+ "Value": field.FLOAT,
+ }
+ }
+
+ RETURN_TYPES = ()
+ CATEGORY = TREE_DEBUG
+ FUNCTION = "print_values"
+ OUTPUT_NODE = True
+
+ def print_values(self, Value, Prefix):
+ print(f"{Prefix}: {Value}", sep="\n")
+ return (None,)
+
+
+class DebugNodeInt:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Prefix": field.STRING,
+ "Value": field.INT,
+ }
+ }
+
+ RETURN_TYPES = ()
+ CATEGORY = TREE_DEBUG
+ FUNCTION = "print_values"
+ OUTPUT_NODE = True
+
+ def print_values(self, Value, Prefix):
+ print(f"{Prefix}: {Value}", sep="\n")
+ return (None,)
+
+
+class DebugNodeTuple:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Prefix": field.STRING,
+ "Values": ("TUPLE",),
+ }
+ }
+
+ RETURN_TYPES = ()
+ CATEGORY = TREE_DEBUG
+ FUNCTION = "print_values"
+ OUTPUT_NODE = True
+
+ def print_values(self, Values, Prefix):
+ print(f"{Prefix}: {Values}", sep="\n")
+ return (None,)
+
+class DebugNodeString:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Prefix": field.STRING,
+ "Text": field.STRING,
+ }
+ }
+
+ RETURN_TYPES = ()
+ CATEGORY = TREE_DEBUG
+ FUNCTION = "print_values"
+ OUTPUT_NODE = True
+
+ def print_values(self, Text, Prefix):
+ print(f"{Prefix}: {Text}", sep="\n")
+ return (None,)
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Debug/__init__.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Debug/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfc6b9db579c3c85cf05e15770ab1b2ad2cb1b92
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Debug/__init__.py
@@ -0,0 +1,7 @@
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Debug.Debug as DebugNodes
+#
+# NODE_CLASS_MAPPINGS = {
+# "FloatDebugPrint_DF": DebugNodes.DebugNodeFloat,
+# "IntDebugPrint_DF": DebugNodes.DebugNodeInt,
+# "TupleDebugPrint_DF": DebugNodes.DebugNodeTuple,
+# }
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/Converters.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/Converters.py
new file mode 100644
index 0000000000000000000000000000000000000000..05f0a3223b3c4838a32aa00f3ee1acdc01344244
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/Converters.py
@@ -0,0 +1,89 @@
+import math
+
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_CONVERTERS
+
+
+class Int2Float:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value": field.INT,
+ }
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "get_value"
+ CATEGORY = TREE_CONVERTERS
+
+ def get_value(self, Value):
+ return (float(Value),)
+
+
+class CeilNode:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value": field.FLOAT,
+ }
+ }
+
+ RETURN_TYPES = ("INT",)
+ FUNCTION = "get_value"
+ CATEGORY = TREE_CONVERTERS
+
+ def get_value(self, Value):
+ total = int(math.ceil(Value))
+ return (total,)
+
+
+class FloorNode:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value": field.FLOAT,
+ }
+ }
+
+ RETURN_TYPES = ("INT",)
+ FUNCTION = "get_value"
+ CATEGORY = TREE_CONVERTERS
+
+ def get_value(self, Value):
+ total = int(math.floor(Value))
+ return (total,)
+
+
+class ABSNode:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "Value": field.FLOAT,
+ "negative_out": ([False, True],)
+ }
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "abs_val"
+ CATEGORY = TREE_CONVERTERS
+
+ def abs_val(self, Value, Get_negative):
+ if Get_negative:
+ return (-abs(Value),)
+ return (abs(Value),)
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/GetSizes.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/GetSizes.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bf018ce17143fbdac58e14e9cf99407ac948227
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/GetSizes.py
@@ -0,0 +1,47 @@
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.sizes as sizes
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_FUNCTIONS
+
+
+class GetLatentSize:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "latent": ("LATENT",),
+ "original": ([False, True],),
+ }
+ }
+
+ RETURN_TYPES = ("INT", "INT", "TUPLE",)
+ CATEGORY = TREE_FUNCTIONS
+
+ FUNCTION = 'get_size'
+
+ def get_size(self, latent, original):
+ size = sizes.get_latent_size(latent, original)
+ return (size, size[0], size[1],)
+
+
+class GetImageSize:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ }
+ }
+
+ RETURN_TYPES = ("INT", "INT", "TUPLE",)
+ CATEGORY = TREE_FUNCTIONS
+
+ FUNCTION = 'get_size'
+
+ def get_size(self, image):
+ size = sizes.get_image_size(image)
+ return (size, size[0], size[1],)
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/Random.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/Random.py
new file mode 100644
index 0000000000000000000000000000000000000000..74e47c6207fac320325786426b60e932a4a09a3a
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/Random.py
@@ -0,0 +1,28 @@
+import numpy.random
+
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_FUNCTIONS
+
+
+class RandomValue:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value_A": field.FLOAT,
+ "Value_B": field.FLOAT,
+ }
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "get_rand"
+
+ CATEGORY = TREE_FUNCTIONS
+
+ def get_rand(self, Value_A, Value_B):
+ value = numpy.random.uniform(Value_A, Value_B)
+ return (value,)
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/Tuples.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/Tuples.py
new file mode 100644
index 0000000000000000000000000000000000000000..c166e604c7832e48b8ec886cb9e57ee560cdfde6
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/Tuples.py
@@ -0,0 +1,161 @@
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+
+import math
+
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_TUPLES
+
+
+class Tuple:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value_A": field.FLOAT,
+ "Value_B": field.FLOAT,
+ "round": (["No", "Yes", "Ceil", "Floor"],),
+ }
+ }
+
+ RETURN_TYPES = ("TUPLE",)
+ CATEGORY = TREE_TUPLES
+
+ FUNCTION = 'get_tuple'
+
+ def get_tuple(self, Value_A=0, Value_B=0, round="No"):
+ match round:
+ case "No":
+ return ((Value_A, Value_B),)
+ case "Yes":
+ return ((int(Value_A), int(Value_B)),)
+ case "Ceil":
+ return ((math.ceil(Value_A), math.ceil(Value_B)),)
+ case "Floor":
+ return ((math.floor(Value_A), math.floor(Value_B)),)
+
+
+class Int2Tuple:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value_A": field.INT,
+ "Value_B": field.INT,
+ }
+ }
+
+ RETURN_TYPES = ("TUPLE",)
+ CATEGORY = TREE_TUPLES
+
+ FUNCTION = 'get_tuple'
+
+ def get_tuple(self, Value_A=0, Value_B=0):
+ return ((Value_A, Value_B),)
+
+
+class Tuple2Float:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Tuple": ("TUPLE",),
+ }
+ }
+
+ RETURN_TYPES = ("FLOAT", "FLOAT",)
+ CATEGORY = TREE_TUPLES
+
+ FUNCTION = 'get_tuple'
+
+ def get_tuple(self, Tuple):
+ return (Tuple[0], Tuple[1],)
+
+class Tuple2Int:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Tuple": ("TUPLE",),
+ }
+ }
+
+ RETURN_TYPES = ("INT", "INT",)
+ CATEGORY = TREE_TUPLES
+
+ FUNCTION = 'get_tuple'
+
+ def get_tuple(self, Tuple):
+ return (int(Tuple[0]), int(Tuple[1]),)
+
+
+class FlipTuple:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Tuple": ("TUPLE",)
+ }
+ }
+
+ RETURN_TYPES = ("TUPLE",)
+ FUNCTION = "flip"
+ CATEGORY = TREE_TUPLES
+
+ def flip(self, Tuple):
+ return ((Tuple[1], Tuple[0]),)
+
+
+
+# class MergeTuples:
+# def __init__(self):
+# pass
+#
+# @classmethod
+# def INPUT_TYPES(cls):
+# return {
+# "required": {
+# "TUPLE_A": ("TUPLE",),
+# "TUPLE_B": ("TUPLE",),
+# }
+# }
+#
+# RETURN_TYPES = ("TUPLE",)
+# FUNCTION = "merge"
+# CATEGORY = TREE_TUPLES
+#
+# def merge(self, TUPLE_A, TUPLE_B):
+# return ((TUPLE_A, TUPLE_B),)
+
+
+# class Tuple2Tuple:
+# def __init__(self):
+# pass
+#
+# @classmethod
+# def INPUT_TYPES(cls):
+# return {
+# "required": {
+# "TUPLE": ("TUPLE",),
+# }
+# }
+#
+# RETURN_TYPES = ("TUPLE", "TUPLE",)
+# FUNCTION = "spl"
+# CATEGORY = TREE_TUPLES
+#
+# def spl(self, TUPLE):
+# return (TUPLE[0], TUPLE[1],)
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/__init__.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..93834f6a0664ce50fc06d9cf93b4219f7c906679
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Functions/__init__.py
@@ -0,0 +1,21 @@
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Functions.Converters as ConvNodes
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Functions.GetSizes as GetSizes
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Functions.Random as RendNodes
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Functions.Tuples as TupleNodes
+#
+#
+# NODE_CLASS_MAPPINGS = {
+# "RandomFloat_DF": RendNodes.RandomValue,
+#
+# "Float2Tuple_DF": TupleNodes.Float2Tuple,
+# "Tuple2Float_DF": TupleNodes.Tuple2Float,
+#
+# "Int2Float_DF": ConvNodes.Int2Float,
+# "CeilNode_DF": ConvNodes.CeilNode,
+# "FloorNode_DF": ConvNodes.FloorNode,
+# "ABSNode_DF": ConvNodes.ABSNode,
+#
+# "GetLatentSize_DF": GetSizes.GetLatentSize,
+# "GetImageSize_DF": GetSizes.GetImageSize,
+#
+# }
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Math/SimpleMath.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Math/SimpleMath.py
new file mode 100644
index 0000000000000000000000000000000000000000..81ccb0a88bf8b1b6a5686c1d72dc2bf488cf9b71
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Math/SimpleMath.py
@@ -0,0 +1,135 @@
+import math
+
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_MATH
+
+
+class MultiplyNode:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value_A": field.FLOAT,
+ "Value_B": field.FLOAT,
+ },
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "multiply"
+ CATEGORY = TREE_MATH
+
+ def multiply(self, Value_A, Value_B):
+ total = float(Value_A * Value_B)
+ return (total,)
+
+
+class DivideNode:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Numerator": field.FLOAT,
+ "Denominator": field.FLOAT,
+ },
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "divide"
+ CATEGORY = TREE_MATH
+
+ def divide(self, Numerator, Denominator):
+ total = float(Numerator / Denominator)
+ return (total,)
+
+
+class SumNode:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value_A": field.FLOAT,
+ "Value_B": field.FLOAT,
+ },
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "sum"
+ CATEGORY = TREE_MATH
+
+ def sum(self, Value_A, Value_B):
+ total = float(Value_A + Value_B)
+ return (total,)
+
+
+class SubtractNode:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value_A": field.FLOAT,
+ "Value_B": field.FLOAT,
+ },
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "sub"
+ CATEGORY = TREE_MATH
+
+ def sub(self, Value_A, Value_B):
+ total = float(Value_A - Value_B)
+ return (total,)
+
+
+class PowNode:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value": field.FLOAT,
+ "Exponent": field.FLOAT,
+ },
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "pow"
+ CATEGORY = TREE_MATH
+
+ def pow(self, Value, Exponent):
+ total = math.pow(Value, Exponent)
+ return (total,)
+
+
+class SquareRootNode:
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "Value": field.FLOAT,
+ },
+ }
+
+ RETURN_TYPES = ("FLOAT", "FLOAT",)
+ FUNCTION = "sqrt"
+ CATEGORY = TREE_MATH
+
+ def sqrt(self, Value):
+ total = math.sqrt(Value)
+ return (total, -total,)
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Math/Trigonometry.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Math/Trigonometry.py
new file mode 100644
index 0000000000000000000000000000000000000000..7743addb55262ef9cb2ff6ba8c26d84cbc30a7a4
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Math/Trigonometry.py
@@ -0,0 +1,87 @@
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_TRIGONOMETRY
+
+import math
+
+class SinNode:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "value": field.FLOAT,
+ "type_": (["RAD", "DEG"],),
+ "arcSin": ([False, True],)
+ }
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "get_value"
+ CATEGORY = TREE_TRIGONOMETRY
+
+ def get_value(self, value, type_="RAD", arcSin=False):
+ if type_ == "DEG":
+ value = math.radians(value)
+ if arcSin == True:
+ value = math.asin(value)
+ else:
+ value = math.sin(value)
+ return (value,)
+
+
+class CosNode:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "value": field.FLOAT,
+ "type_": (["RAD", "DEG"],),
+ "arcCos": ([False, True],)
+ }
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "get_value"
+ CATEGORY = TREE_TRIGONOMETRY
+
+ def get_value(self, value, type_="RAD", arcCos=False):
+ if type_ == "DEG":
+ value = math.radians(value)
+ if arcCos == True:
+ value = math.acos(value)
+ else:
+ value = math.cos(value)
+ return (value,)
+
+
+class tgNode:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "value": field.FLOAT,
+ "type_": (["RAD", "DEG"],),
+ "arcTan": ([False, True],)
+ }
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "get_value"
+ CATEGORY = TREE_TRIGONOMETRY
+
+ def get_value(self, value, type_="RAD", arcTan=False):
+ if type_ == "DEG":
+ value = math.radians(value)
+ if arcTan == True:
+ value = math.atan(value)
+ else:
+ value = math.tan(value)
+ return (value,)
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Math/__init__.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Math/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9716090b6c8479f924205220aa094258cc9a427c
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Math/__init__.py
@@ -0,0 +1,16 @@
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Math.SimpleMath as Math_A
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Math.Trigonometry as Math_B
+#
+#
+# NODE_CLASS_MAPPINGS = {
+# "SumNode_DF": Math_A.SumNode,
+# "SubtractNode_DF": Math_A.SubtractNode,
+# "MultiplyNode_DF": Math_A.MultiplyNode,
+# "DivideNode_DF": Math_A.DivideNode,
+# "PowNode_DF": Math_A.PowNode,
+# "SqrtNode_DF": Math_A.SquareRootNode,
+#
+# "SinNode_DF": Math_B.SinNode,
+# "CosNode_DF": Math_B.CosNode,
+# "TanNode_DF": Math_B.tgNode,
+# }
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/Condotionig.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/Condotionig.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2fad84549bf02faefa27f2fb12a8b33022cb2a3
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/Condotionig.py
@@ -0,0 +1,51 @@
+import math
+
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_COND
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.sizes as sizes
+
+
+class ConditioningAreaScale_Ratio:
+ def __init__(self):
+ pass
+
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "conditioning": ('CONDITIONING',),
+ "modifier": field.FLOAT,
+ "strength_modifier": field.FLOAT,
+ }
+ }
+
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "resize"
+ CATEGORY = TREE_COND
+
+ def resize(self, conditioning, modifier, strength_modifier, min_sigma=0.0, max_sigma=99.0):
+ c = []
+
+ for t in conditioning:
+ n = [t[0], t[1].copy()]
+
+ try:
+ size, offset = sizes.get_conditioning_size(n[1])
+ except:
+ c.append(n)
+ continue
+
+ height = int(size["h"] * 8 * modifier)
+ width = int(size["w"] * 8 * modifier)
+
+ y = int(offset["y"] * 8 * modifier)
+ x = int(offset["x"] * 8 * modifier)
+
+ n[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
+ n[1]['strength'] *= strength_modifier
+ n[1]['min_sigma'] = min_sigma
+ n[1]['max_sigma'] = max_sigma
+ c.append(n)
+
+ return (c,)
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/Images.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/Images.py
new file mode 100644
index 0000000000000000000000000000000000000000..298b2b9c5a923c30f2dd0e7378b958710bb69053
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/Images.py
@@ -0,0 +1,90 @@
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.sizes import get_image_size
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_IMAGES
+
+import math
+import comfy.utils
+
+
+class ImageScale_Ratio:
+ upscale_methods = ["nearest-exact", "bilinear", "area"]
+ crop_methods = ["disabled", "center"]
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "upscale_by": field.FLOAT,
+ "upscale_method": (cls.upscale_methods,),
+ "crop": (cls.crop_methods,)}}
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "upscale"
+
+ CATEGORY = TREE_IMAGES
+
+ def upscale(self, image, upscale_method, upscale_by, crop):
+ size = get_image_size(image)
+
+ width_B = int(size[0])
+ height_B = int(size[1])
+
+ samples = image.movedim(-1, 1)
+
+ height = math.ceil(height_B * upscale_by)
+ width = math.ceil(width_B * upscale_by)
+ cls = comfy.utils.common_upscale(samples, width, height, upscale_method, crop)
+ cls = cls.movedim(1, -1)
+ return (cls,)
+
+
+class ImageScale_Side:
+ upscale_methods = ["nearest-exact", "bilinear", "area"]
+ crop_methods = ["disabled", "center"]
+
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "side_length": field.INT,
+ "side": (["Width", "Height"],),
+ "upscale_method": (cls.upscale_methods,),
+ "crop": (cls.crop_methods,)}}
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "upscale"
+
+ CATEGORY = TREE_IMAGES
+
+ def upscale(self, image, upscale_method, side_length, side, crop):
+ samples = image.movedim(-1, 1)
+
+ size = get_image_size(image)
+
+ width_B = int(size[0])
+ height_B = int(size[1])
+
+ width = width_B
+ height = height_B
+
+ if side == "Width":
+ heigh_ratio = height_B / width_B
+ width = side_length
+ height = heigh_ratio * width
+ elif side == "Height":
+ width_ratio = width_B / height_B
+ height = side_length
+ width = width_ratio * height
+
+ width = math.ceil(width)
+ height = math.ceil(height)
+
+ cls = comfy.utils.common_upscale(samples, width, height, upscale_method, crop)
+ cls = cls.movedim(1, -1)
+ return (cls,)
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/Latents.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/Latents.py
new file mode 100644
index 0000000000000000000000000000000000000000..1887a4b3f24cc57ab082efced82f508d914bfd85
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/Latents.py
@@ -0,0 +1,123 @@
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_LATENTS
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.sizes as sizes
+
+import math
+import torch
+import comfy.utils
+
+# DO NOT USE, MAY BREAK ALL
+# class EmptyLatentImage:
+# def __init__(self, device="cpu"):
+# self.device = device
+#
+# @classmethod
+# def INPUT_TYPES(cls):
+# return {
+# "required": {
+# "size_tuple": ("TUPLE",),
+# "batch_size": field.INT,
+# }
+# }
+#
+# RETURN_TYPES = ("LATENT",)
+# FUNCTION = "generate"
+# CATEGORY = TREE_LATENTS
+#
+# def generate(self, size_tuple, batch_size=1):
+# width = int(size_tuple[0])
+# height = int(size_tuple[1])
+#
+# latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+# return ({"samples": latent},)
+
+
+class LatentScale_Ratio:
+ scale_methods = (["nearest-exact", "bilinear", "area"],)
+ crop_methods = (["disabled", "center"],)
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "latent": ("LATENT",),
+ "modifier": field.FLOAT,
+ "scale_method": cls.scale_methods,
+ "crop": cls.crop_methods,
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "scale"
+ CATEGORY = TREE_LATENTS
+
+ def scale(self, latent, scale_method, crop, modifier):
+
+ size = sizes.get_latent_size(latent, True)
+
+ lat_width = size[0]
+ lat_height = size[1]
+ #
+ # width = latent["samples"][2]
+ # height = latent["samples"][3]
+
+ cls = latent.copy()
+ cls["samples"] = comfy.utils.common_upscale(latent["samples"], lat_width // 8, lat_height // 8, scale_method, crop)
+ return (cls,)
+
+
+class LatentScale_Side:
+ upscale_methods = ["nearest-exact", "bilinear", "area"]
+ crop_methods = ["disabled", "center"]
+
+ def __init__(self) -> None:
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "latent": ("LATENT",),
+ "side_length": field.INT,
+ "side": (["Width", "Height"],),
+ "scale_method": (cls.upscale_methods,),
+ "crop": (cls.crop_methods,)}}
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "upscale"
+
+ CATEGORY = TREE_LATENTS
+
+ def upscale(self, latent, side_length, side, scale_method, crop):
+
+ size = sizes.get_latent_size(latent, True)
+
+ lat_width = size[0]
+ lat_height = size[1]
+
+ width = lat_width
+ height = lat_height
+
+ if side == "Width":
+ heigh_ratio = lat_height / lat_width
+ width = side_length
+ height = heigh_ratio * width
+ elif side == "Height":
+ width_ratio = lat_width / lat_height
+ height = side_length
+ width = width_ratio * height
+
+ width = math.ceil(width)
+ height = math.ceil(height)
+
+ cls = latent.copy()
+ cls["samples"] = comfy.utils.common_upscale(latent["samples"], width // 8, height // 8, scale_method, crop)
+ return (cls,)
+
+ # 3rd option with both sides manually
+
+
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/__init__.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/StandardInputs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/TuplesUsed/Conditioning.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/TuplesUsed/Conditioning.py
new file mode 100644
index 0000000000000000000000000000000000000000..f98a44c4e403c155908d75221b32d618caf15ef1
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/TuplesUsed/Conditioning.py
@@ -0,0 +1,41 @@
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_TUPLE_CONDITIONING
+
+
+class ConditioningSetArea:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "conditioning": ("CONDITIONING",),
+ "size_tuple": ("TUPLE",),
+ "offset_tuple": ("TUPLE",),
+ "strength": field.FLOAT,
+ }
+ }
+
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "append"
+ CATEGORY = TREE_TUPLE_CONDITIONING
+
+ def append(self, conditioning, size_tuple, offset_tuple, strength, min_sigma=0.0, max_sigma=99.0):
+
+ width = int(size_tuple[0])
+ height = int(size_tuple[1])
+
+ x = int(offset_tuple[0])
+ y = int(offset_tuple[1])
+
+ c = []
+ for t in conditioning:
+ n = [t[0], t[1].copy()]
+ # n[1]["area"].movedim(-1, 1)
+ n[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
+ n[1]['strength'] = strength
+ n[1]['min_sigma'] = min_sigma
+ n[1]['max_sigma'] = max_sigma
+ c.append(n)
+ return (c,)
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/TuplesUsed/Latents.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/TuplesUsed/Latents.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e989cf4b7af2a6c2a64f437f7d9572615fd4dbf
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/TuplesUsed/Latents.py
@@ -0,0 +1,60 @@
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+
+from custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.tree import TREE_TUPLE_LATENTS
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.sizes as sizes
+
+import torch
+
+class LatentComposite:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "samples_to": ("LATENT",),
+ "samples_from": ("LATENT",),
+ "position_tuple": ("TUPLE",),
+ "feather": field.INT,
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "compose"
+ CATEGORY = TREE_TUPLE_LATENTS
+
+ def compose(self, samples_from, samples_to, position_tuple, feather):
+ x_off = int(position_tuple[0] // 8)
+ y_off = int(position_tuple[1] // 8)
+ feather = feather // 8
+
+ samples_out = samples_to.copy()
+ samples = samples_to["samples"].clone()
+
+ samples_to = samples_to["samples"]
+ samples_from = samples_from["samples"]
+
+ if feather == 0:
+ samples[:, :, y_off:y_off + samples_from.shape[2], x_off:x_off + samples_from.shape[3]] = \
+ samples_from[:, :, :samples_to.shape[2] - y_off, :samples_to.shape[3] - x_off]
+ else:
+ samples_from = samples_from[:, :, :samples_to.shape[2] - y_off, :samples_to.shape[3] - x_off]
+ mask = torch.ones_like(samples_from)
+ for t in range(feather):
+ if y_off != 0:
+ mask[:, :, t:1 + t, :] *= ((1.0 / feather) * (t + 1))
+
+ if y_off + samples_from.shape[2] < samples_to.shape[2]:
+ mask[:, :, mask.shape[2] - 1 - t: mask.shape[2] - t, :] *= ((1.0 / feather) * (t + 1))
+ if x_off != 0:
+ mask[:, :, :, t:1 + t] *= ((1.0 / feather) * (t + 1))
+ if x_off + samples_from.shape[3] < samples_to.shape[3]:
+ mask[:, :, :, mask.shape[3] - 1 - t: mask.shape[3] - t] *= ((1.0 / feather) * (t + 1))
+ rev_mask = torch.ones_like(mask) - mask
+ samples[:, :, y_off:y_off + samples_from.shape[2], x_off:x_off + samples_from.shape[3]] = \
+ samples_from[:, :, :samples_to.shape[2] - y_off, :samples_to.shape[3] - x_off] * \
+ mask + samples[:, :, y_off:y_off + samples_from.shape[2], x_off:x_off + samples_from.shape[3]] *\
+ rev_mask
+ samples_out["samples"] = samples
+ return (samples_out,)
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/TuplesUsed/__init__.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/TuplesUsed/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/__init__.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b38224eedf293a3e973a5f796554c6a2619b3474
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/Nodes/Modded/__init__.py
@@ -0,0 +1,16 @@
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Modded.Images as ImageNodes
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Modded.Latents as LatentNodes
+# import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Modded.Condotionig as CondNodes
+#
+#
+# NODE_CLASS_MAPPINGS = {
+# "EmptyLatentImage_DF": LatentNodes.EmptyLatentImage,
+# "LatentScale_Ratio_DF": LatentNodes.LatentScale_Ratio,
+# "LatentScale_Side_DF": LatentNodes.LatentScale_Side,
+#
+# "ImageScale_Ratio_DF": ImageNodes.ImageScale_Ratio,
+# "ImageScale_Side_DF": ImageNodes.ImageScale_Side,
+#
+# "ConditioningSetArea_DF": CondNodes.ConditioningSetArea_MOD,
+# "ConditioningSetAreaEXT_DF": CondNodes.ConditioningSetAreaExt_MOD,
+# }
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/Nodes/__init__.py b/Derfuu_ComfyUI_ModdedNodes/Nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Derfuu_ComfyUI_ModdedNodes/README.md b/Derfuu_ComfyUI_ModdedNodes/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f96529f089abed0405f4fff0207f501c8348deec
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/README.md
@@ -0,0 +1,67 @@
+# Derfuu_ComfyUI_ModdedNodes
+
+- **ComfyUI**: [LINK](https://github.com/comfyanonymous/ComfyUI)
+- **CivitAI post**: [LINK](https://civitai.com/models/21558/comfyui-derfuu-math-and-modded-nodes)
+
+## Small description
+- Automate calculation depending on image sizes or something you want
+- easier(or not) editing multiple values of various nodes
+- Math
+- Modded scalers
+
+## Used:
+- model: hPANTYHOSENEKO (can't find link, sorry) <_<
+- vae: kl-f8-anime2
+- embedding: [verybadimagenegative-6400](https://civitai.com/models/11772/verybadimagenegative)
+
+# Previews:
+- Only composed latents:
+
+
+- Composed latents with area modifiers:
+
+
+- All included nodes:
+
+
+## Workflow example:
+
+
+# Nodes descriptions
+- Variables:
+ - Float - mainly used to calculation
+ - Integer - used to set width/height and offsets mainly, also provides converting of float values
+ - Text - input field for text
+ - Text box - same, but multiline
+- Debug nodes: **prints values in console**
+ - DebugFloat
+ - DebugInt
+ - DebugText
+ - DebugTuple
+- Functional:
+ - Random - gives random value within threshold
+ - Get image size - return image size like: Width, Height and (Width, Height) in one value as tuple
+ - Get latent size - return latent size like: Width, Height and (Width, Height) in one value as tuple
+ - NOTE: Original values are 8 times smaller
+ -Converters - converts one type to another
+ - Int to float
+ - Ceil - rounding up float value ex: 1.01 --> 2
+ - Floor - rounding down float value ex: 1.99 --> 1
+ - Absolute - return only positive (or negative) value on your choice (default - positive)
+- Tuples (dead fetch?)
+- Maths
+ - sum - (A + B)
+ - cubtract - (A - B)
+ - multiply (A * B)
+ - divide - (A / B)
+ - power - (A * A * A...) B times
+ - square root - (√A)
+- Modded:
+ - Conditioning
+ - Condition area scale - multiplies size of condion area
+ - Latents
+ - Latent scale by ratio - multiplies size of latent
+ - Latent scale to size - scale size of latent to length of selected side
+ - Image
+ - Image scale by ratio - multiplies size of latent
+ - Image scale to size - scale size of image to length of selected side
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/__init__.py b/Derfuu_ComfyUI_ModdedNodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9652f26cb3fac7031a975ea6aa1b9059710155a3
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/__init__.py
@@ -0,0 +1,72 @@
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Custom.Types as TypeNodes
+
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Debug.Debug as DebugNodes
+
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Functions.Converters as ConvNodes
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Functions.GetSizes as GetSizes
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Functions.Random as RandNodes
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Functions.Tuples as TupleNodes
+
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Math.SimpleMath as SMath
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Math.Trigonometry as TMath
+
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Modded.StandardInputs.Images as St_ImageNodes
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Modded.StandardInputs.Latents as St_LatentNodes
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Modded.StandardInputs.Condotionig as St_CondNodes
+
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Modded.TuplesUsed.Latents as Tu_LatentNodes
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.Nodes.Modded.TuplesUsed.Conditioning as Tu_CondNodes
+
+
+NODE_CLASS_MAPPINGS = {
+ "Float": TypeNodes.FloatNode, # Return float Value
+ "Integer": TypeNodes.IntegerNode, # Return int Value
+ "Text": TypeNodes.StringNode, # IDK where to use this... yet
+ "Text box": TypeNodes.MultilineStringNode, # This too
+
+ # NOTE: if input values are not changed, they don't print in console, same to random
+ "Float debug print": DebugNodes.DebugNodeFloat,
+ "Int debug print": DebugNodes.DebugNodeInt,
+ "Tuple debug print": DebugNodes.DebugNodeTuple,
+ "String debug print": DebugNodes.DebugNodeString,
+
+ "Random": RandNodes.RandomValue, # Return random value in range
+
+ "Tuple": TupleNodes.Tuple, # Takes floats into Tuple
+ "Int to tuple": TupleNodes.Int2Tuple, # Takes ints into Tuple
+ "Tuple to floats": TupleNodes.Tuple2Float, # Return 2 floats from Tuple
+ "Tuple to ints": TupleNodes.Tuple2Int, # Return 2 ints from Tuple
+ "Tuple swap": TupleNodes.FlipTuple, # Swap Values in tuple
+
+ "Int to float": ConvNodes.Int2Float, # Interpretation of int value as float
+ "Ceil": ConvNodes.CeilNode, # Rounds Value to next int
+ "Floor": ConvNodes.FloorNode, # Rounds Value to previous int
+ "Absolute value": ConvNodes.ABSNode, # Return absolute Value of input
+
+ "Get latent size": GetSizes.GetLatentSize, # Return size of latent
+ "Get image size": GetSizes.GetImageSize, # Return size of image
+
+ "Sum": SMath.SumNode, # Summaries 2 values
+ "Subtract": SMath.SubtractNode, # Subtracts Value_B from Value_A
+ "Multiply": SMath.MultiplyNode, # Multiplies 2 values
+ "Divide": SMath.DivideNode, # Divides Value_A on Value_B
+ "Power": SMath.PowNode, # Returns Value_A powered by Value_B
+ "Square root": SMath.SquareRootNode, # Returns square root of Value
+
+ "Sinus": TMath.SinNode, # Returns sinus of Value
+ "Cosines": TMath.CosNode, # Returns cosines of Value
+ "Tangent": TMath.tgNode, # Returns tangents of Value
+
+
+ # STANDARD MODDED
+ "Latent Scale by ratio": St_LatentNodes.LatentScale_Ratio, # Scales latent proportionally on value
+ "Latent Scale to side": St_LatentNodes.LatentScale_Side, # Proportionally scales latent to fit in side size
+ "Image scale by ratio": St_ImageNodes.ImageScale_Ratio, # Scales image proportionally on value
+ "Image scale to side": St_ImageNodes.ImageScale_Side, # Proportionally scales image to fit in side size
+ "Conditioning area scale by ratio": St_CondNodes.ConditioningAreaScale_Ratio,
+
+
+ # TUPLE MODDED
+ "ConditioningSetArea with tuples": Tu_CondNodes.ConditioningSetArea, # Compose condition on field using tuples
+ "LatentComposite with tuples": Tu_LatentNodes.LatentComposite,
+}
diff --git a/Derfuu_ComfyUI_ModdedNodes/components/__init__.py b/Derfuu_ComfyUI_ModdedNodes/components/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Derfuu_ComfyUI_ModdedNodes/components/fields.py b/Derfuu_ComfyUI_ModdedNodes/components/fields.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc1a40f1b28361be9b3adcdc8ab89b0ee7cc3d3a
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/components/fields.py
@@ -0,0 +1,13 @@
+import sys
+
+FLOAT = ("FLOAT", {"default": 1,
+ "min": -sys.float_info.max,
+ "max": sys.float_info.max,
+ "step": 0.01})
+# BOOL = ("BOOL", {"default": False})
+INT = ("INT", {"default": 1,
+ "min": -sys.maxsize,
+ "max": sys.maxsize,
+ "step": 1})
+STRING = ("STRING", {"default": ""})
+STRING_ML = ("STRING", {"multiline": True})
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/components/sizes.py b/Derfuu_ComfyUI_ModdedNodes/components/sizes.py
new file mode 100644
index 0000000000000000000000000000000000000000..025c081a3f69822577ec2bcb46ab238753239795
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/components/sizes.py
@@ -0,0 +1,20 @@
+def get_latent_size(LATENT, ORIGINAL_VALUES=False):
+ lc = LATENT.copy()
+ size = lc["samples"].shape[3], lc["samples"].shape[2]
+ if ORIGINAL_VALUES == False:
+ size = size[0] * 8, size[1] * 8
+ return size
+
+def get_image_size(IMAGE):
+ samples = IMAGE.movedim(-1, 1)
+ size = samples.shape[3], samples.shape[2]
+ # size = size.movedim(1, -1)
+ return size
+
+def get_conditioning_size(CONDITIONING):
+ size = CONDITIONING["area"]
+ width = size[1]
+ height = size[0]
+ x_offs = size[3]
+ y_offs = size[2]
+ return ({"w": width, "h": height},{"x": x_offs, "y":y_offs})
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/components/tree.py b/Derfuu_ComfyUI_ModdedNodes/components/tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..39c49e4e6e7dd6eb6fae347828779dd1d548cbd3
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/components/tree.py
@@ -0,0 +1,22 @@
+TREE_MAIN = "Derfuu_Nodes"
+
+TREE_VARIABLE = TREE_MAIN + "/Variables"
+TREE_TUPLES = TREE_MAIN + "/Tuples"
+TREE_MATH = TREE_MAIN + "/Math"
+TREE_FUNCTIONS = TREE_MAIN + "/Functions"
+TREE_MODDED = TREE_MAIN + "/Modded nodes"
+
+TREE_CONVERTERS = TREE_FUNCTIONS + "/Converters"
+
+TREE_TRIGONOMETRY = TREE_MATH + "/Trigonometry"
+
+TREE_TUPLE_MODDED = TREE_TUPLES + "/Modded nodes"
+
+TREE_TUPLE_LATENTS = TREE_TUPLE_MODDED + "/Latents"
+TREE_TUPLE_CONDITIONING = TREE_TUPLE_MODDED + "/Conditioning"
+
+TREE_COND = TREE_MODDED + "/Conditions"
+TREE_IMAGES = TREE_MODDED + "/Image"
+TREE_LATENTS = TREE_MODDED + "/Latent"
+
+TREE_DEBUG = TREE_MAIN + "/Debug"
diff --git a/Derfuu_ComfyUI_ModdedNodes/workflow_examples/0.5.0_demo.json b/Derfuu_ComfyUI_ModdedNodes/workflow_examples/0.5.0_demo.json
new file mode 100644
index 0000000000000000000000000000000000000000..d5914811be32c7b85d00b7f04f9d2a5cab2f643a
--- /dev/null
+++ b/Derfuu_ComfyUI_ModdedNodes/workflow_examples/0.5.0_demo.json
@@ -0,0 +1,6494 @@
+{
+ "last_node_id": 119,
+ "last_link_id": 236,
+ "nodes": [
+ {
+ "id": 1,
+ "type": "Integer",
+ "pos": [
+ 585.2266004328964,
+ -82.96194113798964
+ ],
+ "size": {
+ "0": 210,
+ "1": 58
+ },
+ "flags": {},
+ "order": 10,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "Value",
+ "type": "FLOAT",
+ "link": 1,
+ "widget": {
+ "name": "Value",
+ "config": [
+ "FLOAT",
+ {
+ "default": 1,
+ "min": -1.7976931348623157e+308,
+ "max": 1.7976931348623157e+308,
+ "step": 1
+ }
+ ]
+ }
+ }
+ ],
+ "outputs": [
+ {
+ "name": "INT",
+ "type": "INT",
+ "links": [
+ 43
+ ]
+ }
+ ],
+ "title": "Integer (CanvasWidth)",
+ "properties": {},
+ "widgets_values": [
+ 1366
+ ],
+ "color": "#232",
+ "bgcolor": "#353"
+ },
+ {
+ "id": 6,
+ "type": "Divide",
+ "pos": [
+ 614.2029289629653,
+ 245.9632059465576
+ ],
+ "size": {
+ "0": 210,
+ "1": 58
+ },
+ "flags": {},
+ "order": 17,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "Numerator",
+ "type": "FLOAT",
+ "link": 5,
+ "widget": {
+ "name": "Numerator",
+ "config": [
+ "FLOAT",
+ {
+ "default": 1,
+ "min": -1.7976931348623157e+308,
+ "max": 1.7976931348623157e+308,
+ "step": 0.01
+ }
+ ]
+ },
+ "slot_index": 0
+ }
+ ],
+ "outputs": [
+ {
+ "name": "FLOAT",
+ "type": "FLOAT",
+ "links": [
+ 4,
+ 6
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ 1,
+ 4
+ ]
+ },
+ {
+ "id": 13,
+ "type": "Divide",
+ "pos": [
+ 603.5720306012286,
+ 648.191039523236
+ ],
+ "size": {
+ "0": 210,
+ "1": 58
+ },
+ "flags": {},
+ "order": 12,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "Numerator",
+ "type": "FLOAT",
+ "link": 13,
+ "widget": {
+ "name": "Numerator",
+ "config": [
+ "FLOAT",
+ {
+ "default": 1,
+ "min": -1.7976931348623157e+308,
+ "max": 1.7976931348623157e+308,
+ "step": 0.01
+ }
+ ]
+ },
+ "slot_index": 0
+ }
+ ],
+ "outputs": [
+ {
+ "name": "FLOAT",
+ "type": "FLOAT",
+ "links": [
+ 12,
+ 16
+ ]
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ 1,
+ 4
+ ]
+ },
+ {
+ "id": 20,
+ "type": "Divide",
+ "pos": [
+ 598.2264210237249,
+ 852.6403308642596
+ ],
+ "size": {
+ "0": 210,
+ "1": 58
+ },
+ "flags": {},
+ "order": 29,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "Numerator",
+ "type": "FLOAT",
+ "link": 16,
+ "widget": {
+ "name": "Numerator",
+ "config": [
+ "FLOAT",
+ {
+ "default": 1,
+ "min": -1.7976931348623157e+308,
+ "max": 1.7976931348623157e+308,
+ "step": 0.01
+ }
+ ]
+ },
+ "slot_index": 0
+ }
+ ],
+ "outputs": [
+ {
+ "name": "FLOAT",
+ "type": "FLOAT",
+ "links": [
+ 17
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ 1,
+ 2
+ ]
+ },
+ {
+ "id": 17,
+ "type": "Integer",
+ "pos": [
+ 845.1264961552051,
+ 744.6298124570469
+ ],
+ "size": {
+ "0": 210,
+ "1": 58
+ },
+ "flags": {},
+ "order": 37,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "Value",
+ "type": "FLOAT",
+ "link": 14,
+ "widget": {
+ "name": "Value",
+ "config": [
+ "FLOAT",
+ {
+ "default": 1,
+ "min": -1.7976931348623157e+308,
+ "max": 1.7976931348623157e+308,
+ "step": 1
+ }
+ ]
+ },
+ "slot_index": 0
+ }
+ ],
+ "outputs": [
+ {
+ "name": "INT",
+ "type": "INT",
+ "links": [
+ 36,
+ 195
+ ]
+ }
+ ],
+ "title": "Img 2 Height",
+ "properties": {},
+ "widgets_values": [
+ 1
+ ],
+ "color": "#232",
+ "bgcolor": "#353"
+ },
+ {
+ "id": 26,
+ "type": "Integer",
+ "pos": [
+ 863.9620520675775,
+ 1198.7307812273664
+ ],
+ "size": {
+ "0": 210,
+ "1": 58
+ },
+ "flags": {},
+ "order": 40,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "Value",
+ "type": "FLOAT",
+ "link": 180,
+ "widget": {
+ "name": "Value",
+ "config": [
+ "FLOAT",
+ {
+ "default": 1,
+ "min": -1.7976931348623157e+308,
+ "max": 1.7976931348623157e+308,
+ "step": 1
+ }
+ ]
+ }
+ }
+ ],
+ "outputs": [
+ {
+ "name": "INT",
+ "type": "INT",
+ "links": [
+ 40,
+ 178,
+ 199
+ ]
+ }
+ ],
+ "title": "Img 3 Height",
+ "properties": {},
+ "widgets_values": [
+ 1
+ ],
+ "color": "#232",
+ "bgcolor": "#353"
+ },
+ {
+ "id": 27,
+ "type": "Integer",
+ "pos": [
+ 869,
+ 1408
+ ],
+ "size": {
+ "0": 210,
+ "1": 58
+ },
+ "flags": {},
+ "order": 56,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "Value",
+ "type": "FLOAT",
+ "link": 236,
+ "widget": {
+ "name": "Value",
+ "config": [
+ "FLOAT",
+ {
+ "default": 1,
+ "min": -1.7976931348623157e+308,
+ "max": 1.7976931348623157e+308,
+ "step": 1
+ }
+ ]
+ },
+ "slot_index": 0
+ }
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+}
\ No newline at end of file
diff --git a/Derfuu_ComfyUI_ModdedNodes/workflow_examples/__init__.py b/Derfuu_ComfyUI_ModdedNodes/workflow_examples/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/was-node-suite-comfyui/.gitignore b/was-node-suite-comfyui/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..6558d887c50f63798850565314266ae2a5f06d1b
--- /dev/null
+++ b/was-node-suite-comfyui/.gitignore
@@ -0,0 +1,14 @@
+
+__pycache__/__init__.cpython-310.pyc
+__pycache__/WAS_Node_Suite.cpython-310.pyc
+was_suite_settings.json
+styles.json
+was_suite_config.json
+repos/BLIP/
+workflows/
+was_history.json
+nsp_pantry.json
+cache/
+*.latent
+*.image
+*.conditioning
diff --git a/was-node-suite-comfyui/LICENSE b/was-node-suite-comfyui/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..cda3c409482e31c2a47d349f7ce01818ca903419
--- /dev/null
+++ b/was-node-suite-comfyui/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Jordan Thompson (WASasquatch)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/was-node-suite-comfyui/README.md b/was-node-suite-comfyui/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9fec2adaf3b671454f697d7b311b53c71ea72325
--- /dev/null
+++ b/was-node-suite-comfyui/README.md
@@ -0,0 +1,386 @@
+# **WAS** Node Suite [](https://colab.research.google.com/github/WASasquatch/comfyui-colab-was-node-suite/blob/main/ComfyUI_%2B_WAS_Node_Suite.ipynb) [](https://hits.seeyoufarm.com) [](https://paypal.me/ThompsonJordan?country.x=US&locale.x=en_US)
+
+
+ 
+
+
+### A node suite for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) with many new nodes, such as image processing, text processing, and more.
+
+#### [Share Workflows](https://github.com/WASasquatch/was-node-suite-comfyui/wiki/Workflow-Examples) to the workflows wiki. Preferably embedded PNGs with workflows, but JSON is OK too. [You can use this tool to add a workflow to a PNG file easily](https://colab.research.google.com/drive/1hQMjNUdhMQ3rw1Wcm3_umvmOMeS_K4s8?usp=sharing).
+#### Consider [donating to the project](https://paypal.me/ThompsonJordan?country.x=US&locale.x=en_US) to help it's continued development.
+
+# Important Updates
+
+ - **[Updated 5/29/2023]** `ASCII` **is deprecated**. The new preferred method of text node output is `STRING`. This is a change from `ASCII` so that it is more clear what data is being passed.
+ - The `was_suite_config.json` will automatically set `use_legacy_ascii_text` to `false`.
+ - [Video Nodes](https://github.com/WASasquatch/was-node-suite-comfyui#video-nodes) - There are two new video nodes, `Write to Video` and `Create Video from Path`. These are experimental nodes.
+
+# Current Nodes:
+
+
+ $\Large\color{orange}{Expand\ Node\ List}$
+
+
+
+ - BLIP Model Loader: Load a BLIP model to input into the BLIP Analyze node
+ - BLIP Analyze Image: Get a text caption from a image, or interrogate the image with a question.
+ - Model will download automatically from default URL, but you can point the download to another location/caption model in `was_suite_config`
+ - Models will be stored in `ComfyUI/models/blip/checkpoints/`
+ - SAM Model Loader: Load a SAM Segmentation model
+ - SAM Parameters: Define your SAM parameters for segmentation of a image
+ - SAM Parameters Combine: Combine SAM parameters
+ - SAM Image Mask: SAM image masking
+ - Image Bounds: Bounds a image
+ - Inset Image Bounds: Inset a image bounds
+ - Bounded Image Blend: Blend bounds image
+ - Bounded Image Blend with Mask: Blend a bounds image by mask
+ - Bounded Image Crop: Crop a bounds image
+ - Bounded Image Crop with Mask: Crop a bounds image by mask
+ - Cache Node: Cache Latnet, Tensor Batches (Image), and Conditioning to disk to use later.
+ - CLIPTextEncode (NSP): Parse noodle soups from the NSP pantry, or parse wildcards from a directory containing A1111 style wildacrds.
+ - Wildcards are in the style of `__filename__`, which also includes subdirectories like `__appearance/haircolour__` (if you noodle_key is set to `__`)
+ - You can set a custom wildcards path in `was_suite_config.json` file with key:
+ - ` "wildcards_path": "E:\\python\\automatic\\webui3\\stable-diffusion-webui\\extensions\\sd-dynamic-prompts\\wildcards"`
+ - If no path is set the wildcards dir is located at the root of WAS Node Suite as `/wildcards`
+ - CLIP Input Switch: Switch between two CLIP inputs based on a boolean switch.
+ - CLIP Vision Input Switch: Switch between two CLIP Vision inputs based on a boolean swith.
+ - Conditioning Input Switch: Switch between two conditioning inputs.
+ - Constant Number
+ - Control Net Model Input Switch: Switch between two Control Net Model inputs based on a boolean switch.
+ - Create Grid Image: Create a image grid from images at a destination with customizable glob pattern. Optional border size and color.
+ - Create Morph Image: Create a GIF/APNG animation from two images, fading between them.
+ - Create Morph Image by Path: Create a GIF/APNG animation from a path to a directory containing images, with optional pattern.
+ - Create Video from Path: Create video from images from a specified path.
+ - CLIPSeg Masking: Mask a image with CLIPSeg and return a raw mask
+ - CLIPSeg Masking Batch: Create a batch image (from image inputs) and batch mask with CLIPSeg
+ - Dictionary to Console: Print a dictionary input to the console
+ - Image Analyze
+ - Black White Levels
+ - RGB Levels
+ - Depends on `matplotlib`, will attempt to install on first run
+ - Diffusers Hub Down-Loader: Download a diffusers model from the HuggingFace Hub and load it
+ - Image SSAO (Ambient Occlusion): [Expiremental Beta Node] Create Screen Space Ambient Occlusion with a image and MiDaS depth approximation (or provided depth map).
+ - Image SSDO (Direct Occlusion): [Expiremental Beta Node] Create a Screen Space Direct Occlusion with a image input. Direct Occlusion presents you with direct lighting highliths, similar to how Ambient Occlusion finds the crevices and shadowy areas around objets.
+ - Image Aspect Ratio: Fetch image aspect ratio in float format, common format (eg 16:9), and in if the image is portrait, landscape, or square.
+ - Image Batch: Create one batch out of multiple batched tensors.
+ - Image Blank: Create a blank image in any color
+ - Image Blend by Mask: Blend two images by a mask
+ - Image Blend: Blend two images by opacity
+ - Image Blending Mode: Blend two images by various blending modes
+ - Image Bloom Filter: Apply a high-pass based bloom filter
+ - Image Canny Filter: Apply a canny filter to a image
+ - Image Chromatic Aberration: Apply chromatic aberration lens effect to a image like in sci-fi films, movie theaters, and video games
+ - Image Color Palette
+ - Generate a color palette based on the input image.
+ - Depends on `scikit-learn`, will attempt to install on first run.
+ - Supports color range of 8-256
+ - Utilizes font in `./res/` unless unavailable, then it will utilize internal better then nothing font.
+ - Image Crop Face: Crop a face out of a image
+ - **Limitations:**
+ - Sometimes no faces are found in badly generated images, or faces at angles
+ - Sometimes face crop is black, this is because the padding is too large and intersected with the image edge. Use a smaller padding size.
+ - face_recognition mode sometimes finds random things as faces. It also requires a [CUDA] GPU.
+ - Only detects one face. This is a design choice to make it's use easy.
+ - **Notes:**
+ - Detection runs in succession. If nothing is found with the selected detection cascades, it will try the next available cascades file.
+ - Image Crop Location: Crop a image to specified location in top, left, right, and bottom locations relating to the pixel dimensions of the image in X and Y coordinats.
+ - Image Crop Square Location: Crop a location by X/Y center, creating a square crop around that point.
+ - Image Displacement Warp: Warp a image by a displacement map image by a given amplitude.
+ - Image Dragan Photography Filter: Apply a Andrzej Dragan photography style to a image
+ - Image Edge Detection Filter: Detect edges in a image
+ - Image Film Grain: Apply film grain to a image
+ - Image Filter Adjustments: Apply various image adjustments to a image
+ - Image Flip: Flip a image horizontal, or vertical
+ - Image Gradient Map: Apply a gradient map to a image
+ - Image Generate Gradient: Generate a gradient map with desired stops and colors
+ - Image High Pass Filter: Apply a high frequency pass to the image returning the details
+ - Image History Loader: Load images from history based on the Load Image Batch node. Can define max history in config file. *(requires restart to show last sessions files at this time)*
+ - Image Input Switch: Switch between two image inputs
+ - Image Levels Adjustment: Adjust the levels of a image
+ - Image Load: Load a *image* from any path on the system, or a url starting with `http`
+ - Image Median Filter: Apply a median filter to a image, such as to smooth out details in surfaces
+ - Image Mix RGB Channels: Mix together RGB channels into a single iamge
+ - Image Monitor Effects Filter: Apply various monitor effects to a image
+ - Digital Distortion
+ - A digital breakup distortion effect
+ - Signal Distortion
+ - A analog signal distortion effect on vertical bands like a CRT monitor
+ - TV Distortion
+ - A TV scanline and bleed distortion effect
+ - Image Nova Filter: A image that uses a sinus frequency to break apart a image into RGB frequencies
+ - Image Perlin Noise: Generate perlin noise
+ - Image Perlin Power Fractal: Generate a perlin power fractal
+ - Image Paste Face Crop: Paste face crop back on a image at it's original location and size
+ - Features a better blending funciton than GFPGAN/CodeFormer so there shouldn't be visible seams, and coupled with Diffusion Result, looks better than GFPGAN/CodeFormer.
+ - Image Paste Crop: Paste a crop (such as from Image Crop Location) at it's original location and size utilizing the `crop_data` node input. This uses a different blending algorithm then Image Paste Face Crop, which may be desired in certain instances.
+ - Image Power Noise: Generate power-law noise
+ - frequency: The frequency parameter controls the distribution of the noise across different frequencies. In the context of Fourier analysis, higher frequencies represent fine details or high-frequency components, while lower frequencies represent coarse details or low-frequency components. Adjusting the frequency parameter can result in different textures and levels of detail in the generated noise. The specific range and meaning of the frequency parameter may vary depending on the noise type.
+ - attenuation: The attenuation parameter determines the strength or intensity of the noise. It controls how much the noise values deviate from the mean or central value. Higher values of attenuation lead to more significant variations and a stronger presence of noise, while lower values result in a smoother and less noticeable noise. The specific range and interpretation of the attenuation parameter may vary depending on the noise type.
+ - noise_type: The tyoe of Power-Law noise to generate (white, grey, pink, green, blue)
+ - Image Paste Crop by Location: Paste a crop top a custom location. This uses the same blending algorithm as Image Paste Crop.
+ - Image Pixelate: Turn a image into pixel art! Define the max number of colors, the pixelation mode, the random state, and max iterations, and max those sprites shine.
+ - Image Remove Background (Alpha): Remove the background from a image by threshold and tolerance.
+ - Image Remove Color: Remove a color from a image and replace it with another
+ - Image Resize
+ - Image Rotate: Rotate an image
+ - Image Rotate Hue: Rotate the hue of a image. A hue_shift of `0.0` would represent no change, and `1.0` would represent a full circle of the hue, and also exhibit no change.
+ - Image Save: A save image node with format support and path support.
+ - `show_history` will show previously saved images with the WAS Save Image node. ComfyUI unfortunately resizes displayed images to the same size however, so if images are in different sizes it will force them in a different size.
+ - Doesn't display images saved outside `/ComfyUI/output/`
+ - You can save as `webp` if you have webp available to you system. On windows you can get that support with this [precompiled libarary](https://storage.googleapis.com/downloads.webmproject.org/releases/webp/libwebp-1.3.0-windows-x64.zip) from the [webp project](https://developers.google.com/speed/webp/download). On linux you can run `apt-get install webp`.
+ - Image Seamless Texture: Create a seamless texture out of a image with optional tiling
+ - Image Select Channel: Select a single channel of an RGB image
+ - Image Select Color: Return the select image only on a black canvas
+ - Image Shadows and Highlights: Adjust the shadows and highlights of an image
+ - Image Size to Number: Get the `width` and `height` of an input image to use with **Number** nodes.
+ - Image Stitch: Stitch images together on different sides with optional feathering blending between them.
+ - Image Style Filter: Style a image with Pilgram instragram-like filters
+ - Depends on `pilgram` module
+ - Image Threshold: Return the desired threshold range of a image
+ - Image Tile: Split a image up into a image batch of tiles. Can be used with Tensor Batch to Image to select a individual tile from the batch.
+ - Image Transpose
+ - Image fDOF Filter: Apply a fake depth of field effect to an image
+ - Image to Latent Mask: Convert a image into a latent mask
+ - Image to Noise: Convert a image into noise, useful for init blending or init input to theme a diffusion.
+ - Images to RGB: Convert a tensor image batch to RGB if they are RGBA or some other mode.
+ - Image to Seed: Convert a image to a reproducible seed
+ - Image Voronoi Noise Filter
+ - A custom implementation of the worley voronoi noise diagram
+ - Input Switch (Disable until `*` wildcard fix)
+ - KSampler (WAS): A sampler that accepts a seed as a node inputs
+ - KSampler Cycle: A KSampler able to do HR pass loops, you can specify an upscale factor, and how many steps to achieve that factor. Accepts a upscale_model, as well as a 1x processor model. A secondary diffusion model can also be used.
+ - Load Cache: Load cached Latent, Tensor Batch (image), and Conditioning files.
+ - Load Text File
+ - Now supports outputting a dictionary named after the file, or custom input.
+ - The dictionary contains a list of all lines in the file.
+ - Load Batch Images
+ - Increment images in a folder, or fetch a single image out of a batch.
+ - Will reset it's place if the path, or pattern is changed.
+ - pattern is a glob that allows you to do things like `**/*` to get all files in the directory and subdirectory
+ or things like `*.jpg` to select only JPEG images in the directory specified.
+ - Mask to Image: Convert `MASK` to `IMAGE`
+ - Mask Batch to Mask: Return a single mask from a batch of masks
+ - Mask Invert: Invert a mask.
+ - Mask Add: Add masks together.
+ - Mask Subtract: Subtract from a mask by another.
+ - Mask Dominant Region: Return the dominant region in a mask (the largest area)
+ - Mask Minority Region: Return the smallest region in a mask (the smallest area)
+ - Mask Crop Dominant Region: Crop mask to the dominant region with optional padding in pixels
+ - Mask Crop Minority Region: Crop mask to the minority region with optional padding in pixels
+ - Mask Crop Region: Crop to dominant or minority region and return `crop_data` for pasting back. Additionally outputs region location and size for other nodes like Crop Image Location.
+ - Mask Arbitrary Region: Return a region that most closely matches the size input (size is not a direct representation of pixels, but approximate)
+ - Mask Smooth Region: Smooth the boundaries of a mask
+ - Mask Erode Region: Erode the boundaries of a mask
+ - Mask Dilate Region: Dilate the boundaries of a mask
+ - Mask Fill Region: Fill holes within the masks regions
+ - Mask Ceiling Region": Return only white pixels within a offset range.
+ - Mask Floor Region: Return the lower most pixel values as white (255)
+ - Mask Threshold Region: Apply a thresholded image between a black value and white value
+ - Mask Gaussian Region: Apply a Gaussian blur to the mask
+ - Masks Combine Masks: Combine 2 or more masks into one mask.
+ - Masks Combine Batch: Combine batched masks into one mask.
+ - Model Input Switch: Switch between two model inputs based on a boolean switch
+ - ComfyUI Loaders: A set of ComfyUI loaders that also output a string that contains the name of the model being loaded.
+ - Latent Noise Injection: Inject latent noise into a latent image
+ - Latent Size to Number: Latent sizes in tensor width/height
+ - Latent Upscale by Factor: Upscale a latent image by a factor
+ - Latent Input Switch: Switch between two latent inputs
+ - Logic Boolean: A simple `1` or `0` output to use with logic
+ - MiDaS Model Loader: Load a MiDaS model as an optional input for MiDaS Depth Approximation
+ - MiDaS Depth Approximation: Produce a depth approximation of a single image input
+ - MiDaS Mask Image: Mask a input image using MiDaS with a desired color
+ - Number Operation
+ - Number to Seed
+ - Number to Float
+ - Number Input Switch: Switch between two number inputs
+ - Number Input Condition: Compare between two inputs or against the A input
+ - Number to Int
+ - Number to String
+ - Number to Text
+ - Random Number
+ - Save Text File: Save a text string to a file
+ - Samples Passthrough (Stat System): Logs RAM, VRAM, and Disk usage to the console.
+ - Seed: Return a seed
+ - Tensor Batch to Image: Select a single image out of a latent batch for post processing with filters
+ - Text Add Tokens: Add custom tokens to parse in filenames or other text.
+ - Text Add Token by Input: Add custom token by inputs representing single **single line** name and value of the token
+ - Text Compare: Compare two strings. Returns a boolean if they are the same, a score of similarity, and the similarity or difference text.
+ - Text Concatenate: Merge two strings
+ - Text Dictionary Update: Merge two dictionaries
+ - Text File History: Show previously opened text files *(requires restart to show last sessions files at this time)*
+ - Text Find and Replace: Find and replace a substring in a string
+ - Text Find and Replace by Dictionary: Replace substrings in a ASCII text input with a dictionary.
+ - The dictionary keys are used as the key to replace, and the list of lines it contains chosen at random based on the seed.
+ - Text Input Switch: Switch between two text inputs
+ - Text List: Create a list of text strings
+ - Text Load Line From File: Load lines from a file sequentially each *batch prompt* run, or select a line index.
+ - Text Concatenate: Merge lists of strings
+ - Text Multiline: Write a multiline text string
+ - Text Parse A1111 Embeddings: Convert embeddings filenames in your prompts to `embedding:[filename]]` format based on your `/ComfyUI/models/embeddings/` files.
+ - Text Parse Noodle Soup Prompts: Parse NSP in a text input
+ - Text Parse Tokens: Parse custom tokens in text.
+ - Text Random Line: Select a random line from a text input string
+ - Text Random Prompt: Feeling lucky? Get a random prompt based on a search seed, such as "superhero"
+ - Text String: Write a single line text string value
+ - Text String Truncate: Truncate a string from the beginning or end by characters or words.
+ - Text to Conditioning: Convert a text string to conditioning.
+ - True Random.org Number Generator: Generate a truly random number online from atmospheric noise with [Random.org](https://random.org/)
+ - [Get your API key from your account page](https://accounts.random.org/)
+ - Upscale Model Input Switch: Switch between two Upscale Models inputs based on a boolean switch.
+ - Write to Morph GIF: Write a new frame to an existing GIF (or create new one) with interpolation between frames.
+ - Write to Video: Write a frame as you generate to a video (Best used with FFV1 for lossless images)
+ - VAE Input Switch: Switch between two VAE inputs based on boolean input
+
+
+
+
+
+ ### Extra Nodes
+
+ - CLIPTextEncode (BlenderNeko Advanced + NSP): Only available if you have BlenderNeko's [Advanced CLIP Text Encode](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb). Allows for NSP and Wildcard use with their advanced CLIPTextEncode.
+
+
+ ### Notes:
+
+ - **CLIPTextEncode (NSP)** and **CLIPTextEncode (BlenderNeko Advanced + NSP)**: Accept dynamic prompts in `` format. This will respect the nodes input seed to yield reproducible results like NSP and Wildcards.
+ - **CLIPTextEncode (NSP)** and **CLIPTextEncode (BlenderNeko Advanced + NSP)**: Assign variables with `$|prompt words|$` format. You can then print this word again within the prompt with the number corresponding the order you created them. So the first prompt var would be printed with `$1` and the second with `$2` and so on.
+
+---
+
+
+## Video Nodes
+
+### Codecs
+You can use codecs that are available to your ffmpeg binaries by adding their fourcc ID (in one string), and appropriate container extension to the `was_suite_config.json`
+
+Example [H264 Codecs](https://github.com/cisco/openh264/releases/tag/v1.8.0) (Defaults)
+```
+ "ffmpeg_extra_codecs": {
+ "avc1": ".mp4",
+ "h264": ".mkv"
+ }
+```
+
+### Notes
+ - For now I am only supporting **Windows** installations for video nodes.
+ - I do not have access to Mac or a stand-alone linux distro. If you get them working and want to PR a patch/directions, feel free.
+ - Video nodes require [FFMPEG](https://ffmpeg.org/download.html). You should download the proper FFMPEG binaries for you system and set the FFMPEG path in the config file.
+ - Additionally, if you want to use H264 codec need to [download OpenH264 1.8.0](https://github.com/cisco/openh264/releases/tag/v1.8.0) and place it in the root of ComfyUI (Example: `C:\ComfyUI_windows_portable`).
+ - FFV1 will complain about invalid container. You can ignore this. The resulting MKV file is readable. I have not figured out what this issue is about. Documentaion tells me to use MKV, but it's telling me it's unsupported.
+ - If you know how to resolve this, I'd love a PR
+ - `Write to Video` node should use a lossless video codec or when it copies frames, and reapplies compression, it will start expontentially ruining the starting frames run to run.
+
+---
+
+# Text Tokens
+Text tokens can be used in the Save Text File and Save Image nodes. You can also add your own custom tokens with the Text Add Tokens node.
+
+The token name can be anything excluding the `:` character to define your token. It can also be simple Regular Expressions.
+
+## Built-in Tokens
+ - [time]
+ - The current system microtime
+ - [time(`format_code`)]
+ - The current system time in human readable format. Utilizing [datetime](https://docs.python.org/3/library/datetime.html) formatting
+ - Example: `[hostname]_[time]__[time(%Y-%m-%d__%I-%M%p)]` would output: **SKYNET-MASTER_1680897261__2023-04-07__07-54PM**
+ - [hostname]
+ - The hostname of the system executing ComfyUI
+ - [user]
+ - The user that is executing ComfyUI
+
+
+
+
+ $\color{orange}{Expand\ Date\ Code\ List}$
+
+
+
+| Directive | Meaning | Example | Notes |
+| --- | --- | --- | --- |
+| %a | Weekday as locale’s abbreviated name. | Sun, Mon, …, Sat (en_US); So, Mo, …, Sa (de_DE) | (1) |
+| %A | Weekday as locale’s full name. | Sunday, Monday, …, Saturday (en_US); Sonntag, Montag, …, Samstag (de_DE) | (1) |
+| %w | Weekday as a decimal number, where 0 is Sunday and 6 is Saturday. | 0, 1, …, 6 | |
+| %d | Day of the month as a zero-padded decimal number. | 01, 02, …, 31 | (9) |
+| %b | Month as locale’s abbreviated name. | Jan, Feb, …, Dec (en_US); Jan, Feb, …, Dez (de_DE) | (1) |
+| %B | Month as locale’s full name. | January, February, …, December (en_US); Januar, Februar, …, Dezember (de_DE) | (1) |
+| %m | Month as a zero-padded decimal number. | 01, 02, …, 12 | (9) |
+| %y | Year without century as a zero-padded decimal number. | 00, 01, …, 99 | (9) |
+| %Y | Year with century as a decimal number. | 0001, 0002, …, 2013, 2014, …, 9998, 9999 | (2) |
+| %H | Hour (24-hour clock) as a zero-padded decimal number. | 00, 01, …, 23 | (9) |
+| %I | Hour (12-hour clock) as a zero-padded decimal number. | 01, 02, …, 12 | (9) |
+| %p | Locale’s equivalent of either AM or PM. | AM, PM (en_US); am, pm (de_DE) | (1), (3) |
+| %M | Minute as a zero-padded decimal number. | 00, 01, …, 59 | (9) |
+| %S | Second as a zero-padded decimal number. | 00, 01, …, 59 | (4), (9) |
+| %f | Microsecond as a decimal number, zero-padded to 6 digits. | 000000, 000001, …, 999999 | (5) |
+| %z | UTC offset in the form ±HHMM[SS[.ffffff]] (empty string if the object is naive). | (empty), +0000, -0400, +1030, +063415, -030712.345216 | (6) |
+| %Z | Time zone name (empty string if the object is naive). | (empty), UTC, GMT | (6) |
+| %j | Day of the year as a zero-padded decimal number. | 001, 002, …, 366 | (9) |
+| %U | Week number of the year (Sunday as the first day of the week) as a zero-padded decimal number. All days in a new year preceding the first Sunday are considered to be in week 0. | 00, 01, …, 53 | (7), (9) |
+| %W | Week number of the year (Monday as the first day of the week) as a zero-padded decimal number. All days in a new year preceding the first Monday are considered to be in week 0. | 00, 01, …, 53 | (7), (9) |
+| %c | Locale’s appropriate date and time representation. | Tue Aug 16 21:30:00 1988 (en_US); Di 16 Aug 21:30:00 1988 (de_DE) | (1) |
+| %x | Locale’s appropriate date representation. | 08/16/88 (None); 08/16/1988 (en_US); 16.08.1988 (de_DE) | (1) |
+| %X | Locale’s appropriate time representation. | 21:30:00 (en_US); 21:30:00 (de_DE) | (1) |
+| %% | A literal '%' character. | % | |
+
+
+
+
+
+---
+
+# Other Features
+
+### Import AUTOMATIC1111 WebUI Styles
+When using the latest builds of WAS Node Suite a `was_suite_config.json` file will be generated (if it doesn't exist). In this file you can setup a A1111 styles import.
+
+ - Run ComfyUI to generate the new `/custom-nodes/was-node-suite-comfyui/was_Suite_config.json` file.
+ - Open the `was_suite_config.json` file with a text editor.
+ - Replace the `webui_styles` value from `None` to the path of your A1111 styles file called **styles.csv**. Be sure to use double backslashes for Windows paths.
+ - Example `C:\\python\\stable-diffusion-webui\\styles.csv`
+ - Restart ComfyUI
+ - Select a style with the `Prompt Styles Node`.
+ - The first ASCII output is your positive prompt, and the second ASCII output is your negative prompt.
+
+You can set `webui_styles_persistent_update` to `true` to update the WAS Node Suite styles from WebUI every start of ComfyUI
+
+# Recommended Installation:
+If you're running on Linux, or non-admin account on windows you'll want to ensure `/ComfyUI/custom_nodes`, `was-node-suite-comfyui`, and `WAS_Node_Suite.py` has write permissions.
+
+There is now a **install.bat** you can run to install to portable if detected. Otherwise it will default to system and assume you followed ConfyUI's manual installation steps.
+
+ - Navigate to your `/ComfyUI/custom_nodes/` folder
+ - Run `git clone https://github.com/WASasquatch/was-node-suite-comfyui/`
+ - Navigate to your `was-node-suite-comfyui` folder
+ - Portable/venv:
+ - Run `path/to/ComfUI/python_embeded/python.exe -s -m pip install -r requirements.txt`
+ - With system python
+ - Run `pip install -r requirements.txt`
+ - Start ComfyUI
+ - WAS Suite should uninstall legacy nodes automatically for you.
+ - Tools will be located in the WAS Suite menu.
+
+## Alternate [Legacy] Installation:
+If you're running on Linux, or non-admin account on windows you'll want to ensure `/ComfyUI/custom_nodes`, and `WAS_Node_Suite.py` has write permissions.
+
+ - Download `WAS_Node_Suite.py`
+ - Move the file to your `/ComfyUI/custom_nodes/` folder
+ - WAS Node Suite will attempt install dependencies on it's own, but you may need to manually do so. The dependencies required are in the `requirements.txt` on this repo. See installation steps above.
+ - If this process fails attempt the following:
+ - Navigate to your `was-node-suite-comfyui` folder
+ - Portable/venv:
+ - Run `path/to/ComfUI/python_embeded/python.exe -s -m pip install -r requirements.txt`
+ - With system python
+ - Run `pip install -r requirements.txt`
+ - Start, or Restart ComfyUI
+ - WAS Suite should uninstall legacy nodes automatically for you.
+ - Tools will be located in the WAS Suite menu.
+
+This method will not install the resources required for Image Crop Face node, and you'll have to download the [./res/](https://github.com/WASasquatch/was-node-suite-comfyui/tree/main/res) folder yourself.
+
+## Installing on Colab
+Create a new cell and add the following code, then run the cell. You may need to edit the path to your `custom_nodes` folder. You can also use the [colab hosted here](https://colab.research.google.com/github/WASasquatch/comfyui-colab-was-node-suite/blob/main/ComfyUI_%2B_WAS_Node_Suite.ipynb)
+
+ - `!git clone https://github.com/WASasquatch/was-node-suite-comfyui /content/ComfyUI/custom_nodes/was-node-suite-comfyui`
+ - `!pip install -r /content/ComfyUI/custom_nodes/was-node-suite-comfyui/requirements.txt`
+ - Restart Colab Runtime (don't disconnect)
+ - Tools will be located in the WAS Suite menu.
diff --git a/was-node-suite-comfyui/WAS_Node_Suite.py b/was-node-suite-comfyui/WAS_Node_Suite.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7eb0c7c60a30a44b80d9a25fd280d21e2475a4f
--- /dev/null
+++ b/was-node-suite-comfyui/WAS_Node_Suite.py
@@ -0,0 +1,13191 @@
+# By WASasquatch (Discord: WAS#0263)
+#
+# Copyright 2023 Jordan Thompson (WASasquatch)
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to
+# deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+
+
+from PIL import Image, ImageFilter, ImageEnhance, ImageOps, ImageDraw, ImageChops, ImageFont
+from PIL.PngImagePlugin import PngInfo
+from io import BytesIO
+from typing import Optional
+from urllib.request import urlopen
+import comfy.diffusers_convert
+import comfy.samplers
+import comfy.sd
+import comfy.utils
+import comfy.clip_vision
+import model_management
+import folder_paths as comfy_paths
+import model_management
+from comfy_extras.chainner_models import model_loading
+import glob
+import hashlib
+import json
+import nodes
+import math
+import numpy as np
+from numba import jit
+import os
+import random
+import re
+import requests
+import socket
+import subprocess
+import sys
+import time
+import torch
+from tqdm import tqdm
+
+MANIFEST = {
+ "name": "WAS Node Suite",
+ "version": (2,2,2),
+ "author": "WASasquatch",
+ "project": "https://github.com/WASasquatch/was-node-suite-comfyui",
+ "description": "An extensive node suite for ComfyUI with over 180 new nodes",
+}
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "was_node_suite_comfyui"))
+sys.path.append(comfy_paths.base_path)
+
+#! SYSTEM HOOKS
+
+class cstr(str):
+ class color:
+ END = '\33[0m'
+ BOLD = '\33[1m'
+ ITALIC = '\33[3m'
+ UNDERLINE = '\33[4m'
+ BLINK = '\33[5m'
+ BLINK2 = '\33[6m'
+ SELECTED = '\33[7m'
+
+ BLACK = '\33[30m'
+ RED = '\33[31m'
+ GREEN = '\33[32m'
+ YELLOW = '\33[33m'
+ BLUE = '\33[34m'
+ VIOLET = '\33[35m'
+ BEIGE = '\33[36m'
+ WHITE = '\33[37m'
+
+ BLACKBG = '\33[40m'
+ REDBG = '\33[41m'
+ GREENBG = '\33[42m'
+ YELLOWBG = '\33[43m'
+ BLUEBG = '\33[44m'
+ VIOLETBG = '\33[45m'
+ BEIGEBG = '\33[46m'
+ WHITEBG = '\33[47m'
+
+ GREY = '\33[90m'
+ LIGHTRED = '\33[91m'
+ LIGHTGREEN = '\33[92m'
+ LIGHTYELLOW = '\33[93m'
+ LIGHTBLUE = '\33[94m'
+ LIGHTVIOLET = '\33[95m'
+ LIGHTBEIGE = '\33[96m'
+ LIGHTWHITE = '\33[97m'
+
+ GREYBG = '\33[100m'
+ LIGHTREDBG = '\33[101m'
+ LIGHTGREENBG = '\33[102m'
+ LIGHTYELLOWBG = '\33[103m'
+ LIGHTBLUEBG = '\33[104m'
+ LIGHTVIOLETBG = '\33[105m'
+ LIGHTBEIGEBG = '\33[106m'
+ LIGHTWHITEBG = '\33[107m'
+
+ @staticmethod
+ def add_code(name, code):
+ if not hasattr(cstr.color, name.upper()):
+ setattr(cstr.color, name.upper(), code)
+ else:
+ raise ValueError(f"'cstr' object already contains a code with the name '{name}'.")
+
+ def __new__(cls, text):
+ return super().__new__(cls, text)
+
+ def __getattr__(self, attr):
+ if attr.lower().startswith("_cstr"):
+ code = getattr(self.color, attr.upper().lstrip("_cstr"))
+ modified_text = self.replace(f"__{attr[1:]}__", f"{code}")
+ return cstr(modified_text)
+ elif attr.upper() in dir(self.color):
+ code = getattr(self.color, attr.upper())
+ modified_text = f"{code}{self}{self.color.END}"
+ return cstr(modified_text)
+ elif attr.lower() in dir(cstr):
+ return getattr(cstr, attr.lower())
+ else:
+ raise AttributeError(f"'cstr' object has no attribute '{attr}'")
+
+ def print(self, **kwargs):
+ print(self, **kwargs)
+
+#! MESSAGE TEMPLATES
+cstr.color.add_code("msg", f"{cstr.color.BLUE}WAS Node Suite: {cstr.color.END}")
+cstr.color.add_code("warning", f"{cstr.color.BLUE}WAS Node Suite {cstr.color.LIGHTYELLOW}Warning: {cstr.color.END}")
+cstr.color.add_code("error", f"{cstr.color.RED}WAS Node Suite {cstr.color.END}Error: {cstr.color.END}")
+
+#! GLOBALS
+NODE_FILE = os.path.abspath(__file__)
+MIDAS_INSTALLED = False
+CUSTOM_NODES_DIR = comfy_paths.folder_names_and_paths["custom_nodes"][0][0]
+MODELS_DIR = comfy_paths.models_dir
+WAS_SUITE_ROOT = os.path.dirname(NODE_FILE)
+WAS_DATABASE = os.path.join(WAS_SUITE_ROOT, 'was_suite_settings.json')
+WAS_HISTORY_DATABASE = os.path.join(WAS_SUITE_ROOT, 'was_history.json')
+WAS_CONFIG_FILE = os.path.join(WAS_SUITE_ROOT, 'was_suite_config.json')
+STYLES_PATH = os.path.join(WAS_SUITE_ROOT, 'styles.json')
+ALLOWED_EXT = ('.jpeg', '.jpg', '.png',
+ '.tiff', '.gif', '.bmp', '.webp')
+
+
+#! INSTALLATION CLEANUP
+
+# Delete legacy nodes
+legacy_was_nodes = ['fDOF_WAS.py', 'Image_Blank_WAS.py', 'Image_Blend_WAS.py', 'Image_Canny_Filter_WAS.py', 'Canny_Filter_WAS.py', 'Image_Combine_WAS.py', 'Image_Edge_Detection_WAS.py', 'Image_Film_Grain_WAS.py', 'Image_Filters_WAS.py',
+ 'Image_Flip_WAS.py', 'Image_Nova_Filter_WAS.py', 'Image_Rotate_WAS.py', 'Image_Style_Filter_WAS.py', 'Latent_Noise_Injection_WAS.py', 'Latent_Upscale_WAS.py', 'MiDaS_Depth_Approx_WAS.py', 'NSP_CLIPTextEncoder.py', 'Samplers_WAS.py']
+legacy_was_nodes_found = []
+
+if os.path.basename(CUSTOM_NODES_DIR) == 'was-node-suite-comfyui':
+ legacy_was_nodes.append('WAS_Node_Suite.py')
+
+f_disp = False
+node_path_dir = os.getcwd()+os.sep+'ComfyUI'+os.sep+'custom_nodes'+os.sep
+for f in legacy_was_nodes:
+ file = f'{node_path_dir}{f}'
+ if os.path.exists(file):
+ if not f_disp:
+ cstr("Found legacy nodes. Archiving legacy nodes...").msg.print()
+ f_disp = True
+ legacy_was_nodes_found.append(file)
+if legacy_was_nodes_found:
+ import zipfile
+ from os.path import basename
+ archive = zipfile.ZipFile(
+ f'{node_path_dir}WAS_Legacy_Nodes_Backup_{round(time.time())}.zip', "w")
+ for f in legacy_was_nodes_found:
+ archive.write(f, basename(f))
+ try:
+ os.remove(f)
+ except OSError:
+ pass
+ archive.close()
+if f_disp:
+ cstr("Legacy cleanup complete.").msg.print()
+
+#! WAS SUITE CONFIG
+
+was_conf_template = {
+ "run_requirements": True,
+ "suppress_uncomfy_warnings": True,
+ "show_startup_junk": True,
+ "show_inspiration_quote": True,
+ "text_nodes_type": "STRING",
+ "webui_styles": None,
+ "webui_styles_persistent_update": True,
+ "blip_model_url": "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth",
+ "blip_model_vqa_url": "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth",
+ "sam_model_vith_url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
+ "sam_model_vitl_url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
+ "sam_model_vitb_url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth",
+ "history_display_limit": 36,
+ "use_legacy_ascii_text": False,
+ "ffmpeg_bin_path": "/path/to/ffmpeg",
+ "ffmpeg_extra_codecs": {
+ "avc1": ".mp4",
+ "h264": ".mkv",
+ },
+ "wildcards_path": os.path.join(WAS_SUITE_ROOT, "wildcards"),
+ "wildcard_api": True,
+ }
+
+# Create, Load, or Update Config
+
+def getSuiteConfig():
+ global was_conf_template
+ try:
+ with open(WAS_CONFIG_FILE, "r") as f:
+ was_config = json.load(f)
+ except OSError as e:
+ cstr(f"Unable to load conf file at `{WAS_CONFIG_FILE}`. Using internal config template.").error.print()
+ return was_conf_template
+ except Exception as e:
+ cstr(f"Unable to load conf file at `{WAS_CONFIG_FILE}`. Using internal config template.").error.print()
+ return was_conf_template
+ return was_config
+ return was_config
+
+def updateSuiteConfig(conf):
+ try:
+ with open(WAS_CONFIG_FILE, "w", encoding='utf-8') as f:
+ json.dump(conf, f, indent=4)
+ except OSError as e:
+ print(e)
+ return False
+ except Exception as e:
+ print(e)
+ return False
+ return True
+
+if not os.path.exists(WAS_CONFIG_FILE):
+ if updateSuiteConfig(was_conf_template):
+ cstr(f'Created default conf file at `{WAS_CONFIG_FILE}`.').msg.print()
+ was_config = getSuiteConfig()
+ else:
+ cstr(f"Unable to create default conf file at `{WAS_CONFIG_FILE}`. Using internal config template.").error.print()
+ was_config = was_conf_template
+
+else:
+ was_config = getSuiteConfig()
+
+ update_config = False
+ for sett_ in was_conf_template.keys():
+ if not was_config.__contains__(sett_):
+ was_config.update({sett_: was_conf_template[sett_]})
+ update_config = True
+
+ if update_config:
+ updateSuiteConfig(was_config)
+
+# WAS Suite Locations Debug
+if was_config.__contains__('show_startup_junk'):
+ if was_config['show_startup_junk']:
+ cstr(f"Running At: {NODE_FILE}")
+ cstr(f"Running From: {WAS_SUITE_ROOT}")
+
+# Check Write Access
+if not os.access(WAS_SUITE_ROOT, os.W_OK) or not os.access(MODELS_DIR, os.W_OK):
+ cstr(f"There is no write access to `{WAS_SUITE_ROOT}` or `{MODELS_DIR}`. Write access is required!").error.print()
+ exit
+
+# SET TEXT TYPE
+TEXT_TYPE = "STRING"
+if was_config and was_config.__contains__('text_nodes_type'):
+ if was_config['text_nodes_type'].strip() != '':
+ TEXT_TYPE = was_config['text_nodes_type'].strip()
+if was_config and was_config.__contains__('use_legacy_ascii_text'):
+ if was_config['use_legacy_ascii_text']:
+ TEXT_TYPE = "ASCII"
+ cstr("use_legacy_ascii_text is `True` in `was_suite_config.json`. `ASCII` type is deprecated and the default will be `STRING` in the future.").warning.print()
+
+# Convert WebUI Styles - TODO: Convert to PromptStyles class
+if was_config.__contains__('webui_styles'):
+
+ if was_config['webui_styles'] not in [None, 'None', 'none', '']:
+
+ webui_styles_file = was_config['webui_styles']
+
+ if was_config.__contains__('webui_styles_persistent_update'):
+ styles_persist = was_config['webui_styles_persistent_update']
+ else:
+ styles_persist = True
+
+ if webui_styles_file not in [None, 'none', 'None', ''] and os.path.exists(webui_styles_file):
+
+ cstr(f"Importing styles from `{webui_styles_file}`.").msg.print()
+
+ import csv
+
+ styles = {}
+ with open(webui_styles_file, "r", encoding="utf-8-sig", newline='') as file:
+ reader = csv.DictReader(file)
+ for row in reader:
+ prompt = row.get("prompt") or row.get("text", "") # Old files
+ negative_prompt = row.get("negative_prompt", "")
+ styles[row["name"]] = {
+ "prompt": prompt,
+ "negative_prompt": negative_prompt
+ }
+
+ if styles:
+ if not os.path.exists(STYLES_PATH) or styles_persist:
+ with open(STYLES_PATH, "w", encoding='utf-8') as f:
+ json.dump(styles, f, indent=4)
+
+ del styles
+
+ cstr(f"Styles import complete.").msg.print()
+
+ else:
+ cstr(f"Styles file `{webui_styles_file}` does not exist.").error.print()
+
+
+#! SUITE SPECIFIC CLASSES & FUNCTIONS
+
+# Freeze PIP modules
+def packages(versions=False):
+ import sys
+ import subprocess
+ return [( r.decode().split('==')[0] if not versions else r.decode() ) for r in subprocess.check_output([sys.executable, '-s', '-m', 'pip', 'freeze']).split()]
+
+# Tensor to PIL
+def tensor2pil(image):
+ return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# PIL to Tensor
+def pil2tensor(image):
+ return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+# PIL Hex
+def pil2hex(image):
+ return hashlib.sha256(np.array(tensor2pil(image)).astype(np.uint16).tobytes()).hexdigest()
+
+# PIL to Mask
+def pil2mask(image):
+ image_np = np.array(image.convert("L")).astype(np.float32) / 255.0
+ mask = torch.from_numpy(image_np)
+ return 1.0 - mask
+
+# Mask to PIL
+def mask2pil(mask):
+ if mask.ndim > 2:
+ mask = mask.squeeze(0)
+ mask_np = mask.cpu().numpy().astype('uint8')
+ mask_pil = Image.fromarray(mask_np, mode="L")
+ return mask_pil
+
+# Tensor to SAM-compatible NumPy
+def tensor2sam(image):
+ # Convert tensor to numpy array in HWC uint8 format with pixel values in [0, 255]
+ sam_image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ # Transpose the image to HWC format if it's in CHW format
+ if sam_image.shape[0] == 3:
+ sam_image = np.transpose(sam_image, (1, 2, 0))
+ return sam_image
+
+# SAM-compatible NumPy to tensor
+def sam2tensor(image):
+ # Convert the image to float32 and normalize the pixel values to [0, 1]
+ float_image = image.astype(np.float32) / 255.0
+ # Transpose the image from HWC format to CHW format
+ chw_image = np.transpose(float_image, (2, 0, 1))
+ # Convert the numpy array to a tensor
+ tensor_image = torch.from_numpy(chw_image)
+ return tensor_image
+
+# Median Filter
+def medianFilter(img, diameter, sigmaColor, sigmaSpace):
+ import cv2 as cv
+ diameter = int(diameter)
+ sigmaColor = int(sigmaColor)
+ sigmaSpace = int(sigmaSpace)
+ img = img.convert('RGB')
+ img = cv.cvtColor(np.array(img), cv.COLOR_RGB2BGR)
+ img = cv.bilateralFilter(img, diameter, sigmaColor, sigmaSpace)
+ img = cv.cvtColor(np.array(img), cv.COLOR_BGR2RGB)
+ return Image.fromarray(img).convert('RGB')
+
+# Resize Image
+def resizeImage(image, max_size):
+ width, height = image.size
+ if width > height:
+ if width > max_size:
+ new_width = max_size
+ new_height = int(height * (max_size / width))
+ else:
+ if height > max_size:
+ new_height = max_size
+ new_width = int(width * (max_size / height))
+ resized_image = image.resize((new_width, new_height))
+ return resized_image
+
+# Image Seed
+def image2seed(image):
+ image_data = image.tobytes()
+ hash_object = hashlib.sha256(image_data)
+ hash_digest = hash_object.digest()
+ seed = int.from_bytes(hash_digest[:4], byteorder='big')
+ return seed
+
+
+# SHA-256 Hash
+def get_sha256(file_path):
+ sha256_hash = hashlib.sha256()
+ with open(file_path, 'rb') as file:
+ for chunk in iter(lambda: file.read(4096), b''):
+ sha256_hash.update(chunk)
+ return sha256_hash.hexdigest()
+
+# Download File
+def download_file(url, filename=None, path=None):
+ if not filename:
+ filename = url.split('/')[-1]
+ if not path:
+ path = '.'
+ save_path = os.path.join(path, filename)
+ response = requests.get(url, stream=True)
+ if response.status_code == requests.codes.ok:
+ file_size = int(response.headers.get('Content-Length', 0))
+ with open(save_path, 'wb') as file:
+ with tqdm(total=file_size, unit='B', unit_scale=True, unit_divisor=1024) as progress:
+ for chunk in response.iter_content(chunk_size=1024):
+ file.write(chunk)
+ progress.update(len(chunk))
+ print(f"Downloaded file saved at: {save_path}")
+ return True
+ elif response.status_code == requests.codes.not_found:
+ cstr("Error: File not found.").error.print()
+ else:
+ cstr(f"Error: Failed to download file. Status code: {response.status_code}").error.print()
+ return False
+
+# NSP Function
+
+def nsp_parse(text, seed=0, noodle_key='__', nspterminology=None, pantry_path=None):
+ if nspterminology is None:
+ # Fetch the NSP Pantry
+ if pantry_path is None:
+ pantry_path = os.path.join(WAS_SUITE_ROOT, 'nsp_pantry.json')
+ if not os.path.exists(pantry_path):
+ response = urlopen('https://raw.githubusercontent.com/WASasquatch/noodle-soup-prompts/main/nsp_pantry.json')
+ tmp_pantry = json.loads(response.read())
+ # Dump JSON locally
+ pantry_serialized = json.dumps(tmp_pantry, indent=4)
+ with open(pantry_path, "w") as f:
+ f.write(pantry_serialized)
+ del response, tmp_pantry
+
+ # Load local pantry
+ with open(pantry_path, 'r') as f:
+ nspterminology = json.load(f)
+
+ if seed > 0 or seed < 0:
+ random.seed(seed)
+
+ # Parse Text
+ new_text = text
+ for term in nspterminology:
+ # Target Noodle
+ tkey = f'{noodle_key}{term}{noodle_key}'
+ # How many occurrences?
+ tcount = new_text.count(tkey)
+ # Apply random results for each noodle counted
+ for _ in range(tcount):
+ new_text = new_text.replace(
+ tkey, random.choice(nspterminology[term]), 1)
+ seed += 1
+ random.seed(seed)
+
+ return new_text
+
+# Simple wildcard parser:
+
+def replace_wildcards(text, seed=None, noodle_key='__'):
+
+ def replace_nested(text, key_path_dict):
+ if re.findall(f"{noodle_key}(.+?){noodle_key}", text):
+ for key, file_path in key_path_dict.items():
+ with open(file_path, "r", encoding="utf-8") as file:
+ lines = file.readlines()
+ if lines:
+ random_line = None
+ while not random_line:
+ line = random.choice(lines).strip()
+ if not line.startswith('#') and not line.startswith('//'):
+ random_line = line
+ text = text.replace(key, random_line)
+ return text
+
+ conf = getSuiteConfig()
+ wildcard_dir = os.path.join(WAS_SUITE_ROOT, 'wildcards')
+ if not os.path.exists(wildcard_dir):
+ os.makedirs(wildcard_dir, exist_ok=True)
+ if conf.__contains__('wildcards_path'):
+ if conf['wildcards_path'] not in [None, ""]:
+ wildcard_dir = conf['wildcards_path']
+
+ cstr(f"Wildcard Path: {wildcard_dir}").msg.print()
+
+ # Set the random seed for reproducibility
+ if seed:
+ random.seed(seed)
+
+ # Create a dictionary of key to file path pairs
+ key_path_dict = {}
+ for root, dirs, files in os.walk(wildcard_dir):
+ for file in files:
+ file_path = os.path.join(root, file)
+ key = os.path.relpath(file_path, wildcard_dir).replace(os.path.sep, "/").rsplit(".", 1)[0]
+ key_path_dict[f"{noodle_key}{key}{noodle_key}"] = os.path.abspath(file_path)
+
+ # Replace keys in text with random lines from corresponding files
+ for key, file_path in key_path_dict.items():
+ with open(file_path, "r", encoding="utf-8") as file:
+ lines = file.readlines()
+ if lines:
+ random_line = None
+ while not random_line:
+ line = random.choice(lines).strip()
+ if not line.startswith('#') and not line.startswith('//'):
+ random_line = line
+ text = text.replace(key, random_line)
+
+ # Replace sub-wildacrds in result
+ text = replace_nested(text, key_path_dict)
+
+ return text
+
+# Parse Prompt Variables
+
+def parse_prompt_vars(input_string, optional_vars=None):
+ variables = optional_vars or {}
+ pattern = r"\$\|(.*?)\|\$"
+ variable_count = len(variables) + 1
+
+ def replace_variable(match):
+ nonlocal variable_count
+ variable_name = f"${variable_count}"
+ variables[variable_name] = match.group(1)
+ variable_count += 1
+ return variable_name
+
+ output_string = re.sub(pattern, replace_variable, input_string)
+
+ for variable_name, phrase in variables.items():
+ variable_pattern = re.escape(variable_name)
+ output_string = re.sub(variable_pattern, phrase, output_string)
+
+ return output_string, variables
+
+# Parse Dynamic Prompts
+
+def parse_dynamic_prompt(prompt, seed):
+ random.seed(seed)
+
+ def replace_match(match):
+ options = match.group(1).split('|')
+ return random.choice(options)
+
+ parse_prompt = re.sub(r'\<(.*?)\>', replace_match, prompt)
+ while re.search(r'\<(.*?)\>', parse_prompt):
+ parse_prompt = re.sub(r'\<(.*?)\>', replace_match, parse_prompt)
+
+ return parse_prompt
+
+# Ambient Occlusion Factor
+
+@jit(nopython=True)
+def calculate_ambient_occlusion_factor(rgb_normalized, depth_normalized, height, width, radius):
+ occlusion_array = np.zeros((height, width), dtype=np.uint8)
+
+ for y in range(height):
+ for x in range(width):
+ if radius == 0:
+ occlusion_factor = 0
+ else:
+ y_min = max(y - radius, 0)
+ y_max = min(y + radius + 1, height)
+ x_min = max(x - radius, 0)
+ x_max = min(x + radius + 1, width)
+
+ neighborhood_depth = depth_normalized[y_min:y_max, x_min:x_max]
+ neighborhood_rgb = rgb_normalized[y_min:y_max, x_min:x_max, :]
+
+ depth_diff = depth_normalized[y, x] - neighborhood_depth
+ rgb_diff = np.abs(rgb_normalized[y, x] - neighborhood_rgb)
+ occlusion_factor = np.maximum(0, depth_diff).mean() + np.maximum(0, np.sum(rgb_diff, axis=2)).mean()
+
+ occlusion_value = int(255 - occlusion_factor * 255)
+ occlusion_array[y, x] = occlusion_value
+
+ return occlusion_array
+
+# Direct Occlusion Factor
+
+@jit(nopython=True)
+def calculate_direct_occlusion_factor(rgb_normalized, depth_normalized, height, width, radius):
+ occlusion_array = np.empty((int(height), int(width)), dtype=np.uint8)
+ depth_normalized = depth_normalized[:, :, 0]
+
+ for y in range(int(height)):
+ for x in range(int(width)):
+ if radius == 0:
+ occlusion_factor = 0
+ else:
+ y_min = max(int(y - radius), 0)
+ y_max = min(int(y + radius + 1), int(height))
+ x_min = max(int(x - radius), 0)
+ x_max = min(int(x + radius + 1), int(width))
+
+ neighborhood_depth = np.zeros((y_max - y_min, x_max - x_min), dtype=np.float64)
+ neighborhood_rgb = np.empty((y_max - y_min, x_max - x_min, 3))
+
+ for i in range(y_min, y_max):
+ for j in range(x_min, x_max):
+ neighborhood_depth[i - y_min, j - x_min] = depth_normalized[i, j]
+ neighborhood_rgb[i - y_min, j - x_min, :] = rgb_normalized[i, j, :]
+
+ depth_diff = neighborhood_depth - depth_normalized[y, x]
+ rgb_diff = np.abs(neighborhood_rgb - rgb_normalized[y, x])
+ occlusion_factor = np.maximum(0, depth_diff).mean() + np.maximum(0, np.sum(np.abs(rgb_diff), axis=2)).mean()
+
+ occlusion_value = int(occlusion_factor * 255)
+ occlusion_array[y, x] = occlusion_value
+
+ occlusion_min = np.min(occlusion_array)
+ occlusion_max = np.max(occlusion_array)
+ occlusion_scaled = ((occlusion_array - occlusion_min) / (occlusion_max - occlusion_min) * 255).astype(np.uint8)
+
+ return occlusion_scaled
+
+
+class PromptStyles:
+ def __init__(self, styles_file, preview_length = 32):
+ self.styles_file = styles_file
+ self.styles = {}
+ self.preview_length = preview_length
+
+ if os.path.exists(self.styles_file):
+ with open(self.styles_file, 'r') as f:
+ self.styles = json.load(f)
+
+ def add_style(self, prompt="", negative_prompt="", auto=False, name=None):
+ if auto:
+ date_format = '%A, %d %B %Y %I:%M %p'
+ date_str = datetime.datetime.now().strftime(date_format)
+ key = None
+ if prompt.strip() != "":
+ if len(prompt) > self.preview_length:
+ length = self.preview_length
+ else:
+ length = len(prompt)
+ key = f"[{date_str}] Positive: {prompt[:length]} ..."
+ elif negative_prompt.strip() != "":
+ if len(negative_prompt) > self.preview_length:
+ length = self.preview_length
+ else:
+ length = len(negative_prompt)
+ key = f"[{date_str}] Negative: {negative_prompt[:length]} ..."
+ else:
+ cstr("At least a `prompt`, or `negative_prompt` input is required!").error.print()
+ return
+ else:
+ if name == None or str(name).strip() == "":
+ cstr("A `name` input is required when not using `auto=True`").error.print()
+ return
+ key = str(name)
+
+
+ for k, v in self.styles.items():
+ if v['prompt'] == prompt and v['negative_prompt'] == negative_prompt:
+ return
+
+ self.styles[key] = {"prompt": prompt, "negative_prompt": negative_prompt}
+
+ with open(self.styles_file, "w", encoding='utf-8') as f:
+ json.dump(self.styles, f, indent=4)
+
+ def get_prompts(self):
+ return self.styles
+
+ def get_prompt(self, prompt_key):
+ if prompt_key in self.styles:
+ return self.styles[prompt_key]['prompt'], self.styles[prompt_key]['negative_prompt']
+ else:
+ cstr(f"Prompt style `{prompt_key}` was not found!").error.print()
+ return None, None
+
+
+
+# WAS SETTINGS MANAGER
+
+class WASDatabase:
+ """
+ The WAS Suite Database Class provides a simple key-value database that stores
+ data in a flatfile using the JSON format. Each key-value pair is associated with
+ a category.
+
+ Attributes:
+ filepath (str): The path to the JSON file where the data is stored.
+ data (dict): The dictionary that holds the data read from the JSON file.
+
+ Methods:
+ insert(category, key, value): Inserts a key-value pair into the database
+ under the specified category.
+ get(category, key): Retrieves the value associated with the specified
+ key and category from the database.
+ update(category, key): Update a value associated with the specified
+ key and category from the database.
+ delete(category, key): Deletes the key-value pair associated with the
+ specified key and category from the database.
+ _save(): Saves the current state of the database to the JSON file.
+ """
+ def __init__(self, filepath):
+ self.filepath = filepath
+ try:
+ with open(filepath, 'r') as f:
+ self.data = json.load(f)
+ except FileNotFoundError:
+ self.data = {}
+
+ def catExists(self, category):
+ return self.data.__contains__(category)
+
+ def keyExists(self, category, key):
+ return self.data[category].__contains__(key)
+
+ def insert(self, category, key, value):
+ if category not in self.data:
+ self.data[category] = {}
+ self.data[category][key] = value
+ self._save()
+
+ def update(self, category, key, value):
+ if category in self.data and key in self.data[category]:
+ self.data[category][key] = value
+ self._save()
+
+ def updateCat(self, category, dictionary):
+ self.data[category].update(dictionary)
+ self._save()
+
+ def get(self, category, key):
+ return self.data.get(category, {}).get(key, None)
+
+ def getDB(self):
+ return self.data
+
+ def insertCat(self, category):
+ if self.data.__contains__(category):
+ cstr(f"The database category `{category}` already exists!").error.print()
+ return
+ self.data[category] = {}
+ self._save()
+
+ def getDict(self, category):
+ if not self.data.__contains__(category):
+ cstr(f"\033[34mWAS Node Suite\033[0m Error: The database category `{category}` does not exist!").error.print()
+ return self.data[category]
+
+ def delete(self, category, key):
+ if category in self.data and key in self.data[category]:
+ del self.data[category][key]
+ self._save()
+
+ def _save(self):
+ try:
+ with open(self.filepath, 'w') as f:
+ json.dump(self.data, f, indent=4)
+ except FileNotFoundError:
+ cstr(f"Cannot save database to file '{self.filepath}'."
+ " Storing the data in the object instead. Does the folder and node file have write permissions?").warning.print()
+
+# Initialize the settings database
+WDB = WASDatabase(WAS_DATABASE)
+
+# WAS Token Class
+
+class TextTokens:
+ def __init__(self):
+ self.WDB = WDB
+ if not self.WDB.getDB().__contains__('custom_tokens'):
+ self.WDB.insertCat('custom_tokens')
+ self.custom_tokens = self.WDB.getDict('custom_tokens')
+
+ self.tokens = {
+ '[time]': str(time.time()).replace('.','_'),
+ '[hostname]': socket.gethostname(),
+ }
+
+ if '.' in self.tokens['[time]']:
+ self.tokens['[time]'] = self.tokens['[time]'].split('.')[0]
+
+ try:
+ self.tokens['[user]'] = ( os.getlogin() if os.getlogin() else 'null' )
+ except Exception:
+ self.tokens['[user]'] = 'null'
+
+ def addToken(self, name, value):
+ self.custom_tokens.update({name: value})
+ self._update()
+
+ def removeToken (self, name):
+ self.custom_tokens.pop(name)
+ self._update()
+
+ def format_time(self, format_code):
+ return time.strftime(format_code, time.localtime(time.time()))
+
+ def parseTokens(self, text):
+ tokens = self.tokens.copy()
+
+ if self.custom_tokens:
+ tokens.update(self.custom_tokens)
+
+ # Update time
+ tokens['[time]'] = str(time.time())
+ if '.' in tokens['[time]']:
+ tokens['[time]'] = tokens['[time]'].split('.')[0]
+
+ for token, value in tokens.items():
+ if token.startswith('[time('):
+ continue
+ text = text.replace(token, value)
+
+ def replace_custom_time(match):
+ format_code = match.group(1)
+ return self.format_time(format_code)
+
+ text = re.sub(r'\[time\((.*?)\)\]', replace_custom_time, text)
+
+ return text
+
+ def _update(self):
+ self.WDB.updateCat('custom_tokens', self.custom_tokens)
+
+
+# Update image history
+
+def update_history_images(new_paths):
+ HDB = WASDatabase(WAS_HISTORY_DATABASE)
+ if HDB.catExists("History") and HDB.keyExists("History", "Images"):
+ saved_paths = HDB.get("History", "Images")
+ for path_ in saved_paths:
+ if not os.path.exists(path_):
+ saved_paths.remove(path_)
+ if isinstance(new_paths, str):
+ if new_paths in saved_paths:
+ saved_paths.remove(new_paths)
+ saved_paths.append(new_paths)
+ elif isinstance(new_paths, list):
+ for path_ in new_paths:
+ if path_ in saved_paths:
+ saved_paths.remove(path_)
+ saved_paths.append(path_)
+ HDB.update("History", "Images", saved_paths)
+ else:
+ if not HDB.catExists("History"):
+ HDB.insertCat("History")
+ if isinstance(new_paths, str):
+ HDB.insert("History", "Images", [new_paths])
+ elif isinstance(new_paths, list):
+ HDB.insert("History", "Images", new_paths)
+
+# Update output image history
+
+def update_history_output_images(new_paths):
+ HDB = WASDatabase(WAS_HISTORY_DATABASE)
+ category = "Output_Images"
+ if HDB.catExists("History") and HDB.keyExists("History", category):
+ saved_paths = HDB.get("History", category)
+ for path_ in saved_paths:
+ if not os.path.exists(path_):
+ saved_paths.remove(path_)
+ if isinstance(new_paths, str):
+ if new_paths in saved_paths:
+ saved_paths.remove(new_paths)
+ saved_paths.append(new_paths)
+ elif isinstance(new_paths, list):
+ for path_ in new_paths:
+ if path_ in saved_paths:
+ saved_paths.remove(path_)
+ saved_paths.append(path_)
+ HDB.update("History", category, saved_paths)
+ else:
+ if not HDB.catExists("History"):
+ HDB.insertCat("History")
+ if isinstance(new_paths, str):
+ HDB.insert("History", category, [new_paths])
+ elif isinstance(new_paths, list):
+ HDB.insert("History", category, new_paths)
+
+# Update text file history
+
+def update_history_text_files(new_paths):
+ HDB = WASDatabase(WAS_HISTORY_DATABASE)
+ if HDB.catExists("History") and HDB.keyExists("History", "TextFiles"):
+ saved_paths = HDB.get("History", "TextFiles")
+ for path_ in saved_paths:
+ if not os.path.exists(path_):
+ saved_paths.remove(path_)
+ if isinstance(new_paths, str):
+ if new_paths in saved_paths:
+ saved_paths.remove(new_paths)
+ saved_paths.append(new_paths)
+ elif isinstance(new_paths, list):
+ for path_ in new_paths:
+ if path_ in saved_paths:
+ saved_paths.remove(path_)
+ saved_paths.append(path_)
+ HDB.update("History", "TextFiles", saved_paths)
+ else:
+ if not HDB.catExists("History"):
+ HDB.insertCat("History")
+ if isinstance(new_paths, str):
+ HDB.insert("History", "TextFiles", [new_paths])
+ elif isinstance(new_paths, list):
+ HDB.insert("History", "TextFiles", new_paths)
+# WAS Filter Class
+
+class WAS_Tools_Class():
+ """
+ Contains various tools and filters for WAS Node Suite
+ """
+ # TOOLS
+
+ def fig2img(self, plot):
+ import io
+ buf = io.BytesIO()
+ plot.savefig(buf)
+ buf.seek(0)
+ img = Image.open(buf)
+ return img
+
+ def stitch_image(self, image_a, image_b, mode='right', fuzzy_zone=50):
+
+ def linear_gradient(start_color, end_color, size, start, end, mode='horizontal'):
+ width, height = size
+ gradient = Image.new('RGB', (width, height), end_color)
+ draw = ImageDraw.Draw(gradient)
+
+ for i in range(0, start):
+ if mode == "horizontal":
+ draw.line((i, 0, i, height-1), start_color)
+ elif mode == "vertical":
+ draw.line((0, i, width-1, i), start_color)
+
+ for i in range(start, end):
+ if mode == "horizontal":
+ curr_color = (
+ int(start_color[0] + (float(i - start) / (end - start)) * (end_color[0] - start_color[0])),
+ int(start_color[1] + (float(i - start) / (end - start)) * (end_color[1] - start_color[1])),
+ int(start_color[2] + (float(i - start) / (end - start)) * (end_color[2] - start_color[2]))
+ )
+ draw.line((i, 0, i, height-1), curr_color)
+ elif mode == "vertical":
+ curr_color = (
+ int(start_color[0] + (float(i - start) / (end - start)) * (end_color[0] - start_color[0])),
+ int(start_color[1] + (float(i - start) / (end - start)) * (end_color[1] - start_color[1])),
+ int(start_color[2] + (float(i - start) / (end - start)) * (end_color[2] - start_color[2]))
+ )
+ draw.line((0, i, width-1, i), curr_color)
+
+ for i in range(end, width if mode == 'horizontal' else height):
+ if mode == "horizontal":
+ draw.line((i, 0, i, height-1), end_color)
+ elif mode == "vertical":
+ draw.line((0, i, width-1, i), end_color)
+
+ return gradient
+
+ image_a = image_a.convert('RGB')
+ image_b = image_b.convert('RGB')
+
+ offset = int(fuzzy_zone / 2)
+ canvas_width = int(image_a.size[0] + image_b.size[0] - fuzzy_zone) if mode == 'right' or mode == 'left' else image_a.size[0]
+ canvas_height = int(image_a.size[1] + image_b.size[1] - fuzzy_zone) if mode == 'top' or mode == 'bottom' else image_a.size[1]
+ canvas = Image.new('RGB', (canvas_width, canvas_height), (0,0,0))
+
+ im_ax = 0
+ im_ay = 0
+ im_bx = 0
+ im_by = 0
+
+ image_a_mask = None
+ image_b_mask = None
+
+ if mode == 'top':
+
+ image_a_mask = linear_gradient((0,0,0), (255,255,255), image_a.size, 0, fuzzy_zone, 'vertical')
+ image_b_mask = linear_gradient((255,255,255), (0,0,0), image_b.size, int(image_b.size[1] - fuzzy_zone), image_b.size[1], 'vertical')
+ im_ay = image_b.size[1] - fuzzy_zone
+
+ elif mode == 'bottom':
+
+ image_a_mask = linear_gradient((255,255,255), (0,0,0), image_a.size, int(image_a.size[1] - fuzzy_zone), image_a.size[1], 'vertical')
+ image_b_mask = linear_gradient((0,0,0), (255,255,255), image_b.size, 0, fuzzy_zone, 'vertical').convert('L')
+ im_by = image_a.size[1] - fuzzy_zone
+
+ elif mode == 'left':
+
+ image_a_mask = linear_gradient((0,0,0), (255,255,255), image_a.size, 0, fuzzy_zone, 'horizontal')
+ image_b_mask = linear_gradient((255,255,255), (0,0,0), image_b.size, int(image_b.size[0] - fuzzy_zone), image_b.size[0], 'horizontal')
+ im_ax = image_b.size[0] - fuzzy_zone
+
+ elif mode == 'right':
+
+ image_a_mask = linear_gradient((255,255,255), (0,0,0), image_a.size, int(image_a.size[0] - fuzzy_zone), image_a.size[0], 'horizontal')
+ image_b_mask = linear_gradient((0,0,0), (255,255,255), image_b.size, 0, fuzzy_zone, 'horizontal')
+ im_bx = image_a.size[0] - fuzzy_zone
+
+ Image.Image.paste(canvas, image_a, (im_ax, im_ay), image_a_mask.convert('L'))
+ Image.Image.paste(canvas, image_b, (im_bx, im_by), image_b_mask.convert('L'))
+
+
+ return canvas
+
+
+ def morph_images(self, images, steps=10, max_size=512, loop=None, still_duration=30, duration=0.1, output_path='output', filename="morph", filetype="GIF"):
+
+ import cv2
+ import imageio
+
+ output_file = os.path.abspath(os.path.join(os.path.join(*output_path.split('/')), filename))
+ output_file += ( '.png' if filetype == 'APNG' else '.gif' )
+
+ max_width = max(im.size[0] for im in images)
+ max_height = max(im.size[1] for im in images)
+ max_aspect_ratio = max_width / max_height
+
+ def padded_images():
+ for im in images:
+ aspect_ratio = im.size[0] / im.size[1]
+ if aspect_ratio > max_aspect_ratio:
+ new_height = int(max_width / aspect_ratio)
+ padding = (max_height - new_height) // 2
+ padded_im = Image.new('RGB', (max_width, max_height), color=(0, 0, 0))
+ padded_im.paste(im.resize((max_width, new_height)), (0, padding))
+ else:
+ new_width = int(max_height * aspect_ratio)
+ padding = (max_width - new_width) // 2
+ padded_im = Image.new('RGB', (max_width, max_height), color=(0, 0, 0))
+ padded_im.paste(im.resize((new_width, max_height)), (padding, 0))
+ yield np.array(padded_im)
+
+ padded_images = list(padded_images())
+ padded_images.append(padded_images[0].copy())
+ images = padded_images
+ frames = []
+ durations = []
+
+ for i in range(len(images)-1):
+ frames.append(Image.fromarray(images[i]).convert('RGB'))
+ durations.append(still_duration)
+
+ for j in range(steps):
+ alpha = j / float(steps)
+ morph = cv2.addWeighted(images[i], 1 - alpha, images[i+1], alpha, 0)
+ frames.append(Image.fromarray(morph).convert('RGB'))
+ durations.append(duration)
+
+ frames.append(Image.fromarray(images[-1]).convert('RGB'))
+ durations.insert(0, still_duration)
+
+ if loop is not None:
+ for i in range(loop):
+ durations.insert(0, still_duration)
+ durations.append(still_duration)
+
+ try:
+ imageio.mimsave(output_file, frames, filetype, duration=durations, loop=loop)
+ except OSError as e:
+ cstr(f"Unable to save output to {output_file} due to the following error:").error.print()
+ print(e)
+ return
+ except Exception as e:
+ cstr(f"\033[34mWAS NS\033[0m Error: Unable to generate GIF due to the following error:").error.print()
+ print(e)
+
+ cstr(f"Morphing completed. Output saved as {output_file}").msg.print()
+
+ return output_file
+
+ class GifMorphWriter:
+ def __init__(self, transition_frames=30, duration_ms=100, still_image_delay_ms=2500, loop=0):
+ self.transition_frames = transition_frames
+ self.duration_ms = duration_ms
+ self.still_image_delay_ms = still_image_delay_ms
+ self.loop = loop
+
+ def write(self, image, gif_path):
+
+ import cv2
+
+ if not os.path.isfile(gif_path):
+ with Image.new("RGBA", image.size) as new_gif:
+ new_gif.paste(image.convert("RGBA"))
+ new_gif.info["duration"] = self.still_image_delay_ms
+ new_gif.save(gif_path, format="GIF", save_all=True, append_images=[], duration=self.still_image_delay_ms, loop=0)
+ cstr(f"Created new GIF animation at: {gif_path}").msg.print()
+ else:
+ with Image.open(gif_path) as gif:
+ n_frames = gif.n_frames
+ if n_frames > 0:
+ gif.seek(n_frames - 1)
+ last_frame = gif.copy()
+ else:
+ last_frame = None
+
+ end_image = image
+ steps = self.transition_frames - 1 if last_frame is not None else self.transition_frames
+
+ if last_frame is not None:
+ image = self.pad_to_size(image, last_frame.size)
+
+ frames = self.generate_transition_frames(last_frame, image, steps)
+
+ still_frame = end_image.copy()
+
+ gif_frames = []
+ for i in range(n_frames):
+ gif.seek(i)
+ gif_frame = gif.copy()
+ gif_frames.append(gif_frame)
+
+ for frame in frames:
+ frame.info["duration"] = self.duration_ms
+ gif_frames.append(frame)
+
+ still_frame.info['duration'] = self.still_image_delay_ms
+ gif_frames.append(still_frame)
+
+ gif_frames[0].save(
+ gif_path,
+ format="GIF",
+ save_all=True,
+ append_images=gif_frames[1:],
+ optimize=True,
+ loop=self.loop,
+ )
+
+ cstr(f"Edited existing GIF animation at: {gif_path}").msg.print()
+
+
+ def pad_to_size(self, image, size):
+ new_image = Image.new("RGBA", size, color=(0, 0, 0, 0))
+ x_offset = (size[0] - image.width) // 2
+ y_offset = (size[1] - image.height) // 2
+ new_image.paste(image, (x_offset, y_offset))
+ return new_image
+
+ def generate_transition_frames(self, start_frame, end_image, num_frames):
+
+ if start_frame is None:
+ return [image]
+
+ start_frame = start_frame.convert("RGBA")
+ end_image = end_image.convert("RGBA")
+
+ frames = []
+ for i in range(1, num_frames + 1):
+ weight = i / (num_frames + 1)
+ frame = Image.blend(start_frame, end_image, weight)
+ frames.append(frame)
+ return frames
+
+ class VideoWriter:
+ def __init__(self, transition_frames=30, fps=25, still_image_delay_sec=2,
+ max_size=512, codec="mp4v"):
+ conf = getSuiteConfig()
+ self.transition_frames = transition_frames
+ self.fps = fps
+ self.still_image_delay_frames = round(still_image_delay_sec * fps)
+ self.max_size = int(max_size)
+ self.valid_codecs = ["ffv1","mp4v"]
+ self.extensions = {"ffv1":".mkv","mp4v":".mp4"}
+ if conf.__contains__('ffmpeg_extra_codecs'):
+ self.add_codecs(conf['ffmpeg_extra_codecs'])
+ self.codec = codec.lower() if codec.lower() in self.valid_codecs else "mp4v"
+
+ def write(self, image, video_path):
+ import cv2
+ import os
+
+ video_path += self.extensions[self.codec]
+ end_image = self.rescale(self.pil2cv(image), self.max_size)
+
+ if os.path.isfile(video_path):
+ cap = cv2.VideoCapture(video_path)
+
+ width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+ height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+ fps = int(cap.get(cv2.CAP_PROP_FPS))
+ total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+ temp_file_path = video_path.replace(self.extensions[self.codec], '_temp'+self.extensions[self.codec])
+ fourcc = cv2.VideoWriter_fourcc(*self.codec)
+ out = cv2.VideoWriter(temp_file_path, fourcc, fps, (width, height), isColor=True)
+
+ for i in tqdm(range(total_frames), desc="Copying original frames"):
+ ret, frame = cap.read()
+ out.write(frame)
+
+ if self.transition_frames > 0:
+ cap.set(cv2.CAP_PROP_POS_FRAMES, total_frames - 1)
+ ret, last_frame = cap.read()
+ transition_frames = self.generate_transition_frames(last_frame, end_image, self.transition_frames)
+ for i, transition_frame in tqdm(enumerate(transition_frames), desc="Generating transition frames", total=self.transition_frames):
+ transition_frame_resized = cv2.resize(transition_frame, (width, height))
+ out.write(transition_frame_resized)
+
+ for i in tqdm(range(self.still_image_delay_frames), desc="Adding new frames"):
+ out.write(end_image)
+
+ cap.release()
+ out.release()
+
+ os.remove(video_path)
+ os.rename(temp_file_path, video_path)
+
+ cstr(f"Edited video at: {video_path}").msg.print()
+
+ return video_path
+
+ else:
+ fourcc = cv2.VideoWriter_fourcc(*self.codec)
+ height, width, _ = end_image.shape
+ out = cv2.VideoWriter(video_path, fourcc, self.fps, (width, height), isColor=True)
+
+ for i in tqdm(range(self.still_image_delay_frames), desc="Adding new frames"):
+ out.write(end_image)
+
+ out.release()
+
+ cstr(f"Created new video at: {video_path}").msg.print()
+
+ return video_path
+
+ return ""
+
+ def create_video(self, image_folder, video_path):
+ import cv2
+ from tqdm import tqdm
+
+ image_paths = sorted([os.path.join(image_folder, f) for f in os.listdir(image_folder)
+ if os.path.isfile(os.path.join(image_folder, f))
+ and os.path.join(image_folder, f).lower().endswith(ALLOWED_EXT)])
+
+ if len(image_paths) == 0:
+ cstr(f"No valid image files found in `{image_folder}` directory.").error.print()
+ cstr(f"The valid formats are: {', '.join(sorted(ALLOWED_EXT))}").error.print()
+ return
+
+ output_file = video_path + self.extensions[self.codec]
+ image = self.rescale(cv2.imread(image_paths[0]), self.max_size)
+ height, width = image.shape[:2]
+ fourcc = cv2.VideoWriter_fourcc(*self.codec)
+ out = cv2.VideoWriter(output_file, fourcc, self.fps, (width, height), isColor=True)
+ out.write(image)
+ for _ in range(self.still_image_delay_frames - 1):
+ out.write(image)
+
+ for i in tqdm(range(len(image_paths)), desc="Writing video frames"):
+ start_frame = cv2.imread(image_paths[i])
+ end_frame = None
+ if i+1 <= len(image_paths)-1:
+ end_frame = self.rescale(cv2.imread(image_paths[i+1]), self.max_size)
+
+ if isinstance(end_frame, np.ndarray):
+ transition_frames = self.generate_transition_frames(start_frame, end_frame, self.transition_frames)
+ transition_frames = [cv2.resize(frame, (width, height)) for frame in transition_frames]
+ for _, frame in enumerate(transition_frames):
+ out.write(frame)
+
+ for _ in range(self.still_image_delay_frames - self.transition_frames):
+ out.write(end_frame)
+
+ else:
+ out.write(start_frame)
+ for _ in range(self.still_image_delay_frames - 1):
+ out.write(start_frame)
+
+ out.release()
+
+ if os.path.exists(output_file):
+ cstr(f"Created video at: {output_file}").msg.print()
+ return output_file
+ else:
+ cstr(f"Unable to create video at: {output_file}").error.print()
+ return ""
+
+ def extract(self, video_file, output_folder, prefix='frame_', extension="png", zero_padding_digits=-1):
+ os.makedirs(output_folder, exist_ok=True)
+
+ video = cv2.VideoCapture(video_file)
+
+ fps = video.get(cv2.CAP_PROP_FPS)
+ frame_number = 0
+
+ while True:
+ success, frame = video.read()
+
+ if success:
+ if zero_padding_digits > 0:
+ frame_path = os.path.join(output_folder, f"{prefix}{frame_number:0{zero_padding_digits}}.{extension}")
+ else:
+ frame_path = os.path.join(output_folder, f"{prefix}{frame_number}.{extension}")
+
+ cv2.imwrite(frame_path, frame)
+ print(f"Saved frame {frame_number} to {frame_path}")
+ frame_number += 1
+ else:
+ break
+
+ video.release()
+
+ def rescale(self, image, max_size):
+ f1 = max_size / image.shape[1]
+ f2 = max_size / image.shape[0]
+ f = min(f1, f2)
+ dim = (int(image.shape[1] * f), int(image.shape[0] * f))
+ resized = cv2.resize(image, dim)
+ return resized
+
+ def generate_transition_frames(self, img1, img2, num_frames):
+ import cv2
+ if img1 is None and img2 is None:
+ return []
+
+ if img1 is not None and img2 is not None:
+ if img1.shape != img2.shape:
+ img2 = cv2.resize(img2, img1.shape[:2][::-1])
+ elif img1 is not None:
+ img2 = np.zeros_like(img1)
+ else:
+ img1 = np.zeros_like(img2)
+
+ height, width, _ = img2.shape
+
+ frame_sequence = []
+ for i in range(num_frames):
+ alpha = i / float(num_frames)
+ blended = cv2.addWeighted(img1, 1 - alpha, img2, alpha,
+ gamma=0.0, dtype=cv2.CV_8U)
+ frame_sequence.append(blended)
+
+ return frame_sequence
+
+ def pil2cv(self, img):
+ import cv2
+ img = np.array(img)
+ img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+ return img
+
+ def add_codecs(self, codecs):
+ if isinstance(codecs, dict):
+ codec_forcc_codes = codecs.keys()
+ self.valid_codecs.extend(codec_forcc_codes)
+ self.extensions.update(codecs)
+
+ def get_codecs(self):
+ return self.valid_codecs
+
+
+ # FILTERS
+
+ class Masking:
+
+ @staticmethod
+ def crop_dominant_region(image, padding=0):
+ from scipy.ndimage import label
+ grayscale_image = image.convert("L")
+ binary_image = grayscale_image.point(lambda x: 255 if x > 128 else 0, mode="1")
+ labeled_image, num_labels = label(np.array(binary_image))
+ largest_label = max(range(1, num_labels + 1), key=lambda i: np.sum(labeled_image == i))
+ largest_region_mask = (labeled_image == largest_label).astype(np.uint8) * 255
+ bbox = Image.fromarray(largest_region_mask, mode="L").getbbox()
+ cropped_image = image.crop(bbox)
+ size = max(cropped_image.size)
+ padded_size = size + 2 * padding
+ centered_crop = Image.new("L", (padded_size, padded_size), color="black")
+ left = (padded_size - cropped_image.width) // 2
+ top = (padded_size - cropped_image.height) // 2
+ centered_crop.paste(cropped_image, (left, top), mask=cropped_image)
+
+ return ImageOps.invert(centered_crop)
+
+ @staticmethod
+ def crop_minority_region(image, padding=0):
+ from scipy.ndimage import label
+ grayscale_image = image.convert("L")
+ binary_image = grayscale_image.point(lambda x: 255 if x > 128 else 0, mode="1")
+ labeled_image, num_labels = label(np.array(binary_image))
+ smallest_label = min(range(1, num_labels + 1), key=lambda i: np.sum(labeled_image == i))
+ smallest_region_mask = (labeled_image == smallest_label).astype(np.uint8) * 255
+ bbox = Image.fromarray(smallest_region_mask, mode="L").getbbox()
+ cropped_image = image.crop(bbox)
+ size = max(cropped_image.size)
+ padded_size = size + 2 * padding
+ centered_crop = Image.new("L", (padded_size, padded_size), color="black")
+ left = (padded_size - cropped_image.width) // 2
+ top = (padded_size - cropped_image.height) // 2
+ centered_crop.paste(cropped_image, (left, top), mask=cropped_image)
+
+ return ImageOps.invert(centered_crop)
+
+ @staticmethod
+ def crop_region(mask, region_type, padding=0):
+ from scipy.ndimage import label, find_objects
+ binary_mask = np.array(mask.convert("L")) > 0
+ bbox = mask.getbbox()
+ if bbox is None:
+ return mask, (mask.size, (0, 0, 0, 0))
+
+ bbox_width = bbox[2] - bbox[0]
+ bbox_height = bbox[3] - bbox[1]
+
+ side_length = max(bbox_width, bbox_height) + 2 * padding
+
+ center_x = (bbox[2] + bbox[0]) // 2
+ center_y = (bbox[3] + bbox[1]) // 2
+
+ crop_x = center_x - side_length // 2
+ crop_y = center_y - side_length // 2
+
+ crop_x = max(crop_x, 0)
+ crop_y = max(crop_y, 0)
+ crop_x2 = min(crop_x + side_length, mask.width)
+ crop_y2 = min(crop_y + side_length, mask.height)
+
+ cropped_mask = mask.crop((crop_x, crop_y, crop_x2, crop_y2))
+ crop_data = (cropped_mask.size, (crop_x, crop_y, crop_x2, crop_y2))
+
+ return cropped_mask, crop_data
+
+ @staticmethod
+ def dominant_region(image, threshold=128):
+ from scipy.ndimage import label
+ image = ImageOps.invert(image.convert("L"))
+ binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1")
+ l, n = label(np.array(binary_image))
+ sizes = np.bincount(l.flatten())
+ dominant = 0
+ try:
+ dominant = np.argmax(sizes[1:]) + 1
+ except ValueError:
+ pass
+ dominant_region_mask = (l == dominant).astype(np.uint8) * 255
+ result = Image.fromarray(dominant_region_mask, mode="L")
+ return result.convert("RGB")
+
+ @staticmethod
+ def minority_region(image, threshold=128):
+ from scipy.ndimage import label
+ image = image.convert("L")
+ binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1")
+ labeled_array, num_features = label(np.array(binary_image))
+ sizes = np.bincount(labeled_array.flatten())
+ smallest_region = 0
+ try:
+ smallest_region = np.argmin(sizes[1:]) + 1
+ except ValueError:
+ pass
+ smallest_region_mask = (labeled_array == smallest_region).astype(np.uint8) * 255
+ inverted_mask = Image.fromarray(smallest_region_mask, mode="L")
+ rgb_image = Image.merge("RGB", [inverted_mask, inverted_mask, inverted_mask])
+
+ return rgb_image
+
+ @staticmethod
+ def arbitrary_region(image, size, threshold=128):
+ from skimage.measure import label, regionprops
+ image = image.convert("L")
+ binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1")
+ labeled_image = label(np.array(binary_image))
+ regions = regionprops(labeled_image)
+
+ image_area = binary_image.size[0] * binary_image.size[1]
+ scaled_size = size * image_area / 10000
+
+ filtered_regions = [region for region in regions if region.area >= scaled_size]
+ if len(filtered_regions) > 0:
+ filtered_regions.sort(key=lambda region: region.area)
+ smallest_region = filtered_regions[0]
+ region_mask = (labeled_image == smallest_region.label).astype(np.uint8) * 255
+ result = Image.fromarray(region_mask, mode="L")
+ return result
+
+ return image
+
+ @staticmethod
+ def smooth_region(image, tolerance):
+ from scipy.ndimage import gaussian_filter
+ image = image.convert("L")
+ mask_array = np.array(image)
+ smoothed_array = gaussian_filter(mask_array, sigma=tolerance)
+ threshold = np.max(smoothed_array) / 2
+ smoothed_mask = np.where(smoothed_array >= threshold, 255, 0).astype(np.uint8)
+ smoothed_image = Image.fromarray(smoothed_mask, mode="L")
+ return ImageOps.invert(smoothed_image.convert("RGB"))
+
+ @staticmethod
+ def erode_region(image, iterations=1):
+ from scipy.ndimage import binary_erosion
+ image = image.convert("L")
+ binary_mask = np.array(image) > 0
+ eroded_mask = binary_erosion(binary_mask, iterations=iterations)
+ eroded_image = Image.fromarray(eroded_mask.astype(np.uint8) * 255, mode="L")
+ return ImageOps.invert(eroded_image.convert("RGB"))
+
+ @staticmethod
+ def dilate_region(image, iterations=1):
+ from scipy.ndimage import binary_dilation
+ image = image.convert("L")
+ binary_mask = np.array(image) > 0
+ dilated_mask = binary_dilation(binary_mask, iterations=iterations)
+ dilated_image = Image.fromarray(dilated_mask.astype(np.uint8) * 255, mode="L")
+ return ImageOps.invert(dilated_image.convert("RGB"))
+
+ @staticmethod
+ def fill_region(image):
+ from scipy.ndimage import binary_fill_holes
+ image = image.convert("L")
+ binary_mask = np.array(image) > 0
+ filled_mask = binary_fill_holes(binary_mask)
+ filled_image = Image.fromarray(filled_mask.astype(np.uint8) * 255, mode="L")
+ return ImageOps.invert(filled_image.convert("RGB"))
+
+ @staticmethod
+ def combine_masks(*masks):
+ if len(masks) < 1:
+ raise ValueError("\033[34mWAS NS\033[0m Error: At least one mask must be provided.")
+ dimensions = masks[0].size
+ for mask in masks:
+ if mask.size != dimensions:
+ raise ValueError("\033[34mWAS NS\033[0m Error: All masks must have the same dimensions.")
+
+ inverted_masks = [mask.convert("L") for mask in masks]
+ combined_mask = Image.new("L", dimensions, 255)
+ for mask in inverted_masks:
+ combined_mask = Image.fromarray(np.minimum(np.array(combined_mask), np.array(mask)), mode="L")
+
+ return combined_mask
+
+ @staticmethod
+ def threshold_region(image, black_threshold=0, white_threshold=255):
+ gray_image = image.convert("L")
+ mask_array = np.array(gray_image)
+ mask_array[mask_array < black_threshold] = 0
+ mask_array[mask_array > white_threshold] = 255
+ thresholded_image = Image.fromarray(mask_array, mode="L")
+ return ImageOps.invert(thresholded_image)
+
+ @staticmethod
+ def floor_region(image):
+ gray_image = image.convert("L")
+ mask_array = np.array(gray_image)
+ non_black_pixels = mask_array[mask_array > 0]
+
+ if non_black_pixels.size > 0:
+ threshold_value = non_black_pixels.min()
+ mask_array[mask_array > threshold_value] = 255 # Set whites to 255
+ mask_array[mask_array <= threshold_value] = 0 # Set blacks to 0
+
+ thresholded_image = Image.fromarray(mask_array, mode="L")
+ return ImageOps.invert(thresholded_image)
+
+ @staticmethod
+ def ceiling_region(image, offset=30):
+ if offset < 0:
+ offset = 0
+ elif offset > 255:
+ offset = 255
+ grayscale_image = image.convert("L")
+ mask_array = np.array(grayscale_image)
+ mask_array[mask_array < 255 - offset] = 0
+ mask_array[mask_array >= 250] = 255
+ filtered_image = Image.fromarray(mask_array, mode="L")
+ return ImageOps.invert(filtered_image)
+
+ @staticmethod
+ def gaussian_region(image, radius=5.0):
+ image = ImageOps.invert(image.convert("L"))
+ image = image.filter(ImageFilter.GaussianBlur(radius=int(radius)))
+ return image.convert("RGB")
+
+ # SHADOWS AND HIGHLIGHTS ADJUSTMENTS
+
+ def shadows_and_highlights(self, image, shadow_thresh=30, highlight_thresh=220, shadow_factor=0.5, highlight_factor=1.5, shadow_smooth=None, highlight_smooth=None, simplify_masks=None):
+
+ if 'pilgram' not in packages():
+ cstr("Installing pilgram...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'pilgram'])
+
+ import pilgram
+
+ alpha = None
+ if image.mode.endswith('A'):
+ alpha = image.getchannel('A')
+ image = image.convert('RGB')
+
+ grays = image.convert('L')
+
+ if shadow_smooth is not None or highlight_smooth is not None and simplify_masks is not None:
+ simplify = float(simplify_masks)
+ grays = grays.filter(ImageFilter.GaussianBlur(radius=simplify))
+
+ shadow_mask = Image.eval(grays, lambda x: 255 if x < shadow_thresh else 0)
+ highlight_mask = Image.eval(grays, lambda x: 255 if x > highlight_thresh else 0)
+
+ image_shadow = image.copy()
+ image_highlight = image.copy()
+
+ if shadow_smooth is not None:
+ shadow_mask = shadow_mask.filter(ImageFilter.GaussianBlur(radius=shadow_smooth))
+ if highlight_smooth is not None:
+ highlight_mask = highlight_mask.filter(ImageFilter.GaussianBlur(radius=highlight_smooth))
+
+ image_shadow = Image.eval(image_shadow, lambda x: x * shadow_factor)
+ image_highlight = Image.eval(image_highlight, lambda x: x * highlight_factor)
+
+ if shadow_smooth is not None:
+ shadow_mask = shadow_mask.filter(ImageFilter.GaussianBlur(radius=shadow_smooth))
+ if highlight_smooth is not None:
+ highlight_mask = highlight_mask.filter(ImageFilter.GaussianBlur(radius=highlight_smooth))
+
+ result = image.copy()
+ result.paste(image_shadow, shadow_mask)
+ result.paste(image_highlight, highlight_mask)
+ result = pilgram.css.blending.color(result, image)
+
+ if alpha:
+ result.putalpha(alpha)
+
+ return (result, shadow_mask, highlight_mask)
+
+ # DRAGAN PHOTOGRAPHY FILTER
+
+
+ def dragan_filter(self, image, saturation=1, contrast=1, sharpness=1, brightness=1, highpass_radius=3, highpass_samples=1, highpass_strength=1, colorize=True):
+
+ if 'pilgram' not in packages():
+ cstr("Installing pilgram...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'pilgram'])
+
+ import pilgram
+
+ alpha = None
+ if image.mode == 'RGBA':
+ alpha = image.getchannel('A')
+
+ grayscale_image = image if image.mode == 'L' else image.convert('L')
+
+ contrast_enhancer = ImageEnhance.Contrast(grayscale_image)
+ contrast_image = contrast_enhancer.enhance(contrast)
+
+ saturation_enhancer = ImageEnhance.Color(contrast_image) if image.mode != 'L' else None
+ saturation_image = contrast_image if saturation_enhancer is None else saturation_enhancer.enhance(saturation)
+
+ sharpness_enhancer = ImageEnhance.Sharpness(saturation_image)
+ sharpness_image = sharpness_enhancer.enhance(sharpness)
+
+ brightness_enhancer = ImageEnhance.Brightness(sharpness_image)
+ brightness_image = brightness_enhancer.enhance(brightness)
+
+ blurred_image = brightness_image.filter(ImageFilter.GaussianBlur(radius=-highpass_radius))
+ highpass_filter = ImageChops.subtract(image, blurred_image.convert('RGB'))
+ blank_image = Image.new('RGB', image.size, (127, 127, 127))
+ highpass_image = ImageChops.screen(blank_image, highpass_filter.resize(image.size))
+ if not colorize:
+ highpass_image = highpass_image.convert('L').convert('RGB')
+ highpassed_image = pilgram.css.blending.overlay(brightness_image.convert('RGB'), highpass_image)
+ for _ in range((highpass_samples if highpass_samples > 0 else 1)):
+ highpassed_image = pilgram.css.blending.overlay(highpassed_image, highpass_image)
+
+ final_image = ImageChops.blend(brightness_image.convert('RGB'), highpassed_image, highpass_strength)
+
+ if colorize:
+ final_image = pilgram.css.blending.color(final_image, image)
+
+ if alpha:
+ final_image.putalpha(alpha)
+
+ return final_image
+
+ def sparkle(self, image):
+
+ if 'pilgram' not in packages():
+ cstr("Installing pilgram...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'pilgram'])
+
+ import pilgram
+
+ image = image.convert('RGBA')
+ contrast_enhancer = ImageEnhance.Contrast(image)
+ image = contrast_enhancer.enhance(1.25)
+ saturation_enhancer = ImageEnhance.Color(image)
+ image = saturation_enhancer.enhance(1.5)
+
+ bloom = image.filter(ImageFilter.GaussianBlur(radius=20))
+ bloom = ImageEnhance.Brightness(bloom).enhance(1.2)
+ bloom.putalpha(128)
+ bloom = bloom.convert(image.mode)
+ image = Image.alpha_composite(image, bloom)
+
+ width, height = image.size
+
+ particles = Image.new('RGBA', (width, height), (0, 0, 0, 0))
+ draw = ImageDraw.Draw(particles)
+ for i in range(5000):
+ x = random.randint(0, width)
+ y = random.randint(0, height)
+ r = random.randint(0, 255)
+ g = random.randint(0, 255)
+ b = random.randint(0, 255)
+ draw.point((x, y), fill=(r, g, b, 255))
+ particles = particles.filter(ImageFilter.GaussianBlur(radius=1))
+ particles.putalpha(128)
+
+ particles2 = Image.new('RGBA', (width, height), (0, 0, 0, 0))
+ draw = ImageDraw.Draw(particles2)
+ for i in range(5000):
+ x = random.randint(0, width)
+ y = random.randint(0, height)
+ r = random.randint(0, 255)
+ g = random.randint(0, 255)
+ b = random.randint(0, 255)
+ draw.point((x, y), fill=(r, g, b, 255))
+ particles2 = particles2.filter(ImageFilter.GaussianBlur(radius=1))
+ particles2.putalpha(128)
+
+ image = pilgram.css.blending.color_dodge(image, particles)
+ image = pilgram.css.blending.lighten(image, particles2)
+
+ return image
+
+ def digital_distortion(self, image, amplitude=5, line_width=2):
+
+ im = np.array(image)
+
+ x, y, z = im.shape
+ sine_wave = amplitude * np.sin(np.linspace(-np.pi, np.pi, y))
+ sine_wave = sine_wave.astype(int)
+
+ left_distortion = np.zeros((x, y, z), dtype=np.uint8)
+ right_distortion = np.zeros((x, y, z), dtype=np.uint8)
+ for i in range(y):
+ left_distortion[:, i, :] = np.roll(im[:, i, :], -sine_wave[i], axis=0)
+ right_distortion[:, i, :] = np.roll(im[:, i, :], sine_wave[i], axis=0)
+
+ distorted_image = np.maximum(left_distortion, right_distortion)
+ scan_lines = np.zeros((x, y), dtype=np.float32)
+ scan_lines[::line_width, :] = 1
+ scan_lines = np.minimum(scan_lines * amplitude*50.0, 1) # Scale scan line values
+ scan_lines = np.tile(scan_lines[:, :, np.newaxis], (1, 1, z)) # Add channel dimension
+ distorted_image = np.where(scan_lines > 0, np.random.permutation(im), distorted_image)
+ distorted_image = np.roll(distorted_image, np.random.randint(0, y), axis=1)
+
+ distorted_image = Image.fromarray(distorted_image)
+
+ return distorted_image
+
+ def signal_distortion(self, image, amplitude):
+
+ img_array = np.array(image)
+ row_shifts = np.random.randint(-amplitude, amplitude + 1, size=img_array.shape[0])
+ distorted_array = np.zeros_like(img_array)
+
+ for y in range(img_array.shape[0]):
+ x_shift = row_shifts[y]
+ x_shift = x_shift + y % (amplitude * 2) - amplitude
+ distorted_array[y,:] = np.roll(img_array[y,:], x_shift, axis=0)
+
+ distorted_image = Image.fromarray(distorted_array)
+
+ return distorted_image
+
+ def tv_vhs_distortion(self, image, amplitude=10):
+ np_image = np.array(image)
+ offset_variance = int(image.height / amplitude)
+ row_shifts = np.random.randint(-offset_variance, offset_variance + 1, size=image.height)
+ distorted_array = np.zeros_like(np_image)
+
+ for y in range(np_image.shape[0]):
+ x_shift = row_shifts[y]
+ x_shift = x_shift + y % (offset_variance * 2) - offset_variance
+ distorted_array[y,:] = np.roll(np_image[y,:], x_shift, axis=0)
+
+ h, w, c = distorted_array.shape
+ x_scale = np.linspace(0, 1, w)
+ y_scale = np.linspace(0, 1, h)
+ x_idx = np.broadcast_to(x_scale, (h, w))
+ y_idx = np.broadcast_to(y_scale.reshape(h, 1), (h, w))
+ noise = np.random.rand(h, w, c) * 0.1
+ distortion = np.sin(x_idx * 50) * 0.5 + np.sin(y_idx * 50) * 0.5
+ distorted_array = distorted_array + distortion[:, :, np.newaxis] + noise
+
+ distorted_image = Image.fromarray(np.uint8(distorted_array))
+ distorted_image = distorted_image.resize((image.width, image.height))
+
+ image_enhance = ImageEnhance.Color(image)
+ image = image_enhance.enhance(0.5)
+
+ effect_image = ImageChops.overlay(image, distorted_image)
+ result_image = ImageChops.overlay(image, effect_image)
+ result_image = ImageChops.blend(image, result_image, 0.25)
+
+ return result_image
+
+ def gradient(self, size, mode='horizontal', colors=None, tolerance=0):
+
+ if isinstance(colors, str):
+ colors = json.loads(colors)
+
+ if colors is None:
+ colors = {0: [255, 0, 0], 50: [0, 255, 0], 100: [0, 0, 255]}
+
+ colors = {int(k): [int(c) for c in v] for k, v in colors.items()}
+
+ colors[0] = colors[min(colors.keys())]
+ colors[255] = colors[max(colors.keys())]
+
+ img = Image.new('RGB', size, color=(0, 0, 0))
+
+ color_stop_positions = sorted(colors.keys())
+ color_stop_count = len(color_stop_positions)
+ spectrum = []
+ for i in range(256):
+ start_pos = max(p for p in color_stop_positions if p <= i)
+ end_pos = min(p for p in color_stop_positions if p >= i)
+ start = colors[start_pos]
+ end = colors[end_pos]
+
+ if start_pos == end_pos:
+ factor = 0
+ else:
+ factor = (i - start_pos) / (end_pos - start_pos)
+
+ r = round(start[0] + (end[0] - start[0]) * factor)
+ g = round(start[1] + (end[1] - start[1]) * factor)
+ b = round(start[2] + (end[2] - start[2]) * factor)
+ spectrum.append((r, g, b))
+
+ draw = ImageDraw.Draw(img)
+ if mode == 'horizontal':
+ for x in range(size[0]):
+ pos = int(x * 100 / (size[0] - 1))
+ color = spectrum[pos]
+ if tolerance > 0:
+ color = tuple([round(c / tolerance) * tolerance for c in color])
+ draw.line((x, 0, x, size[1]), fill=color)
+ elif mode == 'vertical':
+ for y in range(size[1]):
+ pos = int(y * 100 / (size[1] - 1))
+ color = spectrum[pos]
+ if tolerance > 0:
+ color = tuple([round(c / tolerance) * tolerance for c in color])
+ draw.line((0, y, size[0], y), fill=color)
+
+ blur = 1.5
+ if size[0] > 512 or size[1] > 512:
+ multiplier = max(size[0], size[1]) / 512
+ if multiplier < 1.5:
+ multiplier = 1.5
+ blur = blur * multiplier
+
+ img = img.filter(ImageFilter.GaussianBlur(radius=blur))
+
+ return img
+
+
+
+
+ # Version 2 optimized based on Mark Setchell's ideas
+ def gradient_map(self, image, gradient_map, reverse=False):
+
+ # Reverse the image
+ if reverse:
+ gradient_map = gradient_map.transpose(Image.FLIP_LEFT_RIGHT)
+
+ # Convert image to Numpy array and average RGB channels
+ na = np.array(image)
+ grey = np.mean(na, axis=2).astype(np.uint8)
+
+ # Convert gradient map to Numpy array
+ cmap = np.array(gradient_map.convert('RGB'))
+
+ # Make output image, same height and width as grey image, but 3-channel RGB
+ result = np.zeros((*grey.shape, 3), dtype=np.uint8)
+
+ # Reshape grey to match the shape of result
+ grey_reshaped = grey.reshape(-1)
+
+ # Take entries from RGB gradient map according to grayscale values in image
+ np.take(cmap.reshape(-1, 3), grey_reshaped, axis=0, out=result.reshape(-1, 3))
+
+ # Convert result to PIL image
+ result_image = Image.fromarray(result)
+
+ return result_image
+
+
+ # Generate Perlin Noise (Finally in house version)
+
+ def perlin_noise(self, width, height, octaves, persistence, scale, seed=None):
+
+ @jit(nopython=True)
+ def fade(t):
+ return 6 * t**5 - 15 * t**4 + 10 * t**3
+
+
+ @jit(nopython=True)
+ def lerp(t, a, b):
+ return a + t * (b - a)
+
+
+ @jit(nopython=True)
+ def grad(hash, x, y, z):
+ h = hash & 15
+ u = x if h < 8 else y
+ v = y if h < 4 else (x if h == 12 or h == 14 else z)
+ return (u if (h & 1) == 0 else -u) + (v if (h & 2) == 0 else -v)
+
+
+ @jit(nopython=True)
+ def noise(x, y, z, p):
+ X = np.int32(np.floor(x)) & 255
+ Y = np.int32(np.floor(y)) & 255
+ Z = np.int32(np.floor(z)) & 255
+
+ x -= np.floor(x)
+ y -= np.floor(y)
+ z -= np.floor(z)
+
+ u = fade(x)
+ v = fade(y)
+ w = fade(z)
+
+ A = p[X] + Y
+ AA = p[A] + Z
+ AB = p[A + 1] + Z
+ B = p[X + 1] + Y
+ BA = p[B] + Z
+ BB = p[B + 1] + Z
+
+ return lerp(w, lerp(v, lerp(u, grad(p[AA], x, y, z), grad(p[BA], x - 1, y, z)),
+ lerp(u, grad(p[AB], x, y - 1, z), grad(p[BB], x - 1, y - 1, z))),
+ lerp(v, lerp(u, grad(p[AA + 1], x, y, z - 1), grad(p[BA + 1], x - 1, y, z - 1)),
+ lerp(u, grad(p[AB + 1], x, y - 1, z - 1), grad(p[BB + 1], x - 1, y - 1, z - 1))))
+
+ if seed:
+ random.seed(seed)
+
+ p = np.arange(256, dtype=np.int32)
+ random.shuffle(p)
+ p = np.concatenate((p, p))
+
+ noise_map = np.zeros((height, width))
+ amplitude = 1.0
+ total_amplitude = 0.0
+
+ for octave in range(octaves):
+ frequency = 2 ** octave
+ total_amplitude += amplitude
+
+ for y in range(height):
+ for x in range(width):
+ nx = x / scale * frequency
+ ny = y / scale * frequency
+ noise_value = noise(nx, ny, 0, p) * amplitude
+ current_value = noise_map[y, x]
+ noise_map[y, x] = current_value + noise_value
+
+ amplitude *= persistence
+
+ min_value = np.min(noise_map)
+ max_value = np.max(noise_map)
+ noise_map = np.interp(noise_map, (min_value, max_value), (0, 255)).astype(np.uint8)
+ image = Image.fromarray(noise_map, mode='L').convert("RGB")
+
+ return image
+
+
+ # Generate Perlin Power Fractal (Based on in-house perlin noise)
+
+ def perlin_power_fractal(self, width, height, octaves, persistence, lacunarity, exponent, scale, seed=None):
+
+ @jit(nopython=True)
+ def fade(t):
+ return 6 * t**5 - 15 * t**4 + 10 * t**3
+
+ @jit(nopython=True)
+ def lerp(t, a, b):
+ return a + t * (b - a)
+
+ @jit(nopython=True)
+ def grad(hash, x, y, z):
+ h = hash & 15
+ u = x if h < 8 else y
+ v = y if h < 4 else (x if h == 12 or h == 14 else z)
+ return (u if (h & 1) == 0 else -u) + (v if (h & 2) == 0 else -v)
+
+ @jit(nopython=True)
+ def noise(x, y, z, p):
+ X = np.int32(np.floor(x)) & 255
+ Y = np.int32(np.floor(y)) & 255
+ Z = np.int32(np.floor(z)) & 255
+
+ x -= np.floor(x)
+ y -= np.floor(y)
+ z -= np.floor(z)
+
+ u = fade(x)
+ v = fade(y)
+ w = fade(z)
+
+ A = p[X] + Y
+ AA = p[A] + Z
+ AB = p[A + 1] + Z
+ B = p[X + 1] + Y
+ BA = p[B] + Z
+ BB = p[B + 1] + Z
+
+ return lerp(w, lerp(v, lerp(u, grad(p[AA], x, y, z), grad(p[BA], x - 1, y, z)),
+ lerp(u, grad(p[AB], x, y - 1, z), grad(p[BB], x - 1, y - 1, z))),
+ lerp(v, lerp(u, grad(p[AA + 1], x, y, z - 1), grad(p[BA + 1], x - 1, y, z - 1)),
+ lerp(u, grad(p[AB + 1], x, y - 1, z - 1), grad(p[BB + 1], x - 1, y - 1, z - 1))))
+
+ if seed:
+ random.seed(seed)
+
+ p = np.arange(256, dtype=np.int32)
+ random.shuffle(p)
+ p = np.concatenate((p, p))
+
+ noise_map = np.zeros((height, width))
+ amplitude = 1.0
+ total_amplitude = 0.0
+
+ for octave in range(octaves):
+ frequency = lacunarity ** octave
+ amplitude *= persistence
+ total_amplitude += amplitude
+
+ for y in range(height):
+ for x in range(width):
+ nx = x / scale * frequency
+ ny = y / scale * frequency
+ noise_value = noise(nx, ny, 0, p) * amplitude ** exponent
+ current_value = noise_map[y, x]
+ noise_map[y, x] = current_value + noise_value
+
+ min_value = np.min(noise_map)
+ max_value = np.max(noise_map)
+ noise_map = np.interp(noise_map, (min_value, max_value), (0, 255)).astype(np.uint8)
+ image = Image.fromarray(noise_map, mode='L').convert("RGB")
+
+ return image
+
+ # Worley Noise Generator
+
+ class worley_noise:
+
+ def __init__(self, height=512, width=512, density=50, option=0, use_broadcast_ops=True):
+
+ self.height = height
+ self.width = width
+ self.density = density
+ self.use_broadcast_ops = use_broadcast_ops
+ self.image = self.generateImage(option)
+
+ def generate_points(self):
+ self.points = np.random.randint(0, (self.width, self.height), (self.density, 2))
+
+ def calculate_noise(self, option):
+ self.data = np.zeros((self.height, self.width))
+ for h in range(self.height):
+ for w in range(self.width):
+ distances = np.sqrt(np.sum((self.points - np.array([w, h])) ** 2, axis=1))
+ self.data[h, w] = np.sort(distances)[option]
+
+ def broadcast_calculate_noise(self, option):
+ xs = np.arange(self.width)
+ ys = np.arange(self.height)
+ x_dist = np.power(self.points[:, 0, np.newaxis] - xs, 2)
+ y_dist = np.power(self.points[:, 1, np.newaxis] - ys, 2)
+ d = np.sqrt(x_dist[:, :, np.newaxis] + y_dist[:, np.newaxis, :])
+ distances = np.sort(d, axis=0)
+ self.data = distances[option]
+
+ def generateImage(self, option):
+ self.generate_points()
+ if self.use_broadcast_ops:
+ self.broadcast_calculate_noise(option)
+ else:
+ self.calculate_noise(option)
+ min_val, max_val = np.min(self.data), np.max(self.data)
+ data_scaled = (self.data - min_val) / (max_val - min_val) * 255
+ data_scaled = data_scaled.astype(np.uint8)
+
+ return Image.fromarray(data_scaled).convert('RGB')
+
+ # Make Image Seamless
+
+ def make_seamless(self, image, blending=0.5, tiled=False, tiles=2):
+
+ if 'img2texture' not in packages():
+ cstr("Installing img2texture...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'git+https://github.com/WASasquatch/img2texture.git'])
+
+ from img2texture import img2tex
+ from img2texture._tiling import tile
+
+ texture = img2tex(src=image, dst=None, pct=blending, return_result=True)
+ if tiled:
+ texture = tile(source=texture, target=None, horizontal=tiles, vertical=tiles, return_result=True)
+
+ return texture
+
+ # Image Displacement Warp
+
+ def displace_image(self, image, displacement_map, amplitude):
+
+ image = image.convert('RGB')
+ displacement_map = displacement_map.convert('L')
+ width, height = image.size
+ result = Image.new('RGB', (width, height))
+
+ for y in range(height):
+ for x in range(width):
+
+ # Calculate the displacements n' stuff
+ displacement = displacement_map.getpixel((x, y))
+ displacement_amount = amplitude * (displacement / 255)
+ new_x = x + int(displacement_amount)
+ new_y = y + int(displacement_amount)
+
+ # Apply mirror reflection at edges and corners
+ if new_x < 0:
+ new_x = abs(new_x)
+ elif new_x >= width:
+ new_x = 2 * width - new_x - 1
+
+ if new_y < 0:
+ new_y = abs(new_y)
+ elif new_y >= height:
+ new_y = 2 * height - new_y - 1
+
+ if new_x < 0:
+ new_x = abs(new_x)
+ if new_y < 0:
+ new_y = abs(new_y)
+
+ if new_x >= width:
+ new_x = 2 * width - new_x - 1
+ if new_y >= height:
+ new_y = 2 * height - new_y - 1
+
+ # Consider original image color at new location for RGB results, oops
+ pixel = image.getpixel((new_x, new_y))
+ result.putpixel((x, y), pixel)
+
+ return result
+
+ # Analyze Filters
+
+ def black_white_levels(self, image):
+
+ if 'matplotlib' not in packages():
+ cstr("Installing matplotlib...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'matplotlib'])
+
+ import matplotlib.pyplot as plt
+
+ # convert to grayscale
+ image = image.convert('L')
+
+ # Calculate the histogram of grayscale intensities
+ hist = image.histogram()
+
+ # Find the minimum and maximum grayscale intensity values
+ min_val = 0
+ max_val = 255
+ for i in range(256):
+ if hist[i] > 0:
+ min_val = i
+ break
+ for i in range(255, -1, -1):
+ if hist[i] > 0:
+ max_val = i
+ break
+
+ # Create a graph of the grayscale histogram
+ plt.figure(figsize=(16, 8))
+ plt.hist(image.getdata(), bins=256, range=(0, 256), color='black', alpha=0.7)
+ plt.xlim([0, 256])
+ plt.ylim([0, max(hist)])
+ plt.axvline(min_val, color='red', linestyle='dashed')
+ plt.axvline(max_val, color='red', linestyle='dashed')
+ plt.title('Black and White Levels')
+ plt.xlabel('Intensity')
+ plt.ylabel('Frequency')
+
+ return self.fig2img(plt)
+
+ def channel_frequency(self, image):
+
+ if 'matplotlib' not in packages():
+ cstr("Installing matplotlib...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'matplotlib'])
+
+ import matplotlib.pyplot as plt
+
+ # Split the image into its RGB channels
+ r, g, b = image.split()
+
+ # Calculate the frequency of each color in each channel
+ r_freq = r.histogram()
+ g_freq = g.histogram()
+ b_freq = b.histogram()
+
+ # Create a graph to hold the frequency maps
+ fig, axs = plt.subplots(1, 3, figsize=(16, 4))
+ axs[0].set_title('Red Channel')
+ axs[1].set_title('Green Channel')
+ axs[2].set_title('Blue Channel')
+
+ # Plot the frequency of each color in each channel
+ axs[0].plot(range(256), r_freq, color='red')
+ axs[1].plot(range(256), g_freq, color='green')
+ axs[2].plot(range(256), b_freq, color='blue')
+
+ # Set the axis limits and labels
+ for ax in axs:
+ ax.set_xlim([0, 255])
+ ax.set_xlabel('Color Intensity')
+ ax.set_ylabel('Frequency')
+
+ return self.fig2img(plt)
+
+ def generate_palette(self, img, n_colors=16, cell_size=128, padding=0, font_path=None, font_size=15, mode='chart'):
+ if 'scikit-learn' not in packages():
+ cstr("Installing scikit-learn...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'scikit-learn'])
+
+ from sklearn.cluster import KMeans
+
+ img = img.resize((img.width // 2, img.height // 2), resample=Image.BILINEAR)
+ pixels = np.array(img)
+ pixels = pixels.reshape((-1, 3))
+ kmeans = KMeans(n_clusters=n_colors, random_state=0, n_init='auto').fit(pixels)
+ cluster_centers = np.uint8(kmeans.cluster_centers_)
+
+ # Get the sorted indices based on luminance
+ luminance = np.sqrt(np.dot(cluster_centers, [0.299, 0.587, 0.114]))
+ sorted_indices = np.argsort(luminance)
+
+ # Rearrange the cluster centers and luminance based on sorted indices
+ cluster_centers = cluster_centers[sorted_indices]
+ luminance = luminance[sorted_indices]
+
+ # Group colors by their individual types
+ reds = []
+ greens = []
+ blues = []
+ others = []
+
+ for i in range(n_colors):
+ color = cluster_centers[i]
+ color_type = np.argmax(color) # Find the dominant color component
+
+ if color_type == 0:
+ reds.append((color, luminance[i]))
+ elif color_type == 1:
+ greens.append((color, luminance[i]))
+ elif color_type == 2:
+ blues.append((color, luminance[i]))
+ else:
+ others.append((color, luminance[i]))
+
+ # Sort each color group by luminance
+ reds.sort(key=lambda x: x[1])
+ greens.sort(key=lambda x: x[1])
+ blues.sort(key=lambda x: x[1])
+ others.sort(key=lambda x: x[1])
+
+ # Combine the sorted color groups
+ sorted_colors = reds + greens + blues + others
+
+ if mode == 'back_to_back':
+ # Calculate the size of the palette image based on the number of colors
+ palette_width = n_colors * cell_size
+ palette_height = cell_size
+ else:
+ # Calculate the number of rows and columns based on the number of colors
+ num_rows = int(np.sqrt(n_colors))
+ num_cols = int(np.ceil(n_colors / num_rows))
+
+ # Calculate the size of the palette image based on the number of rows and columns
+ palette_width = num_cols * cell_size
+ palette_height = num_rows * cell_size
+
+ palette_size = (palette_width, palette_height)
+
+ palette = Image.new('RGB', palette_size, color='white')
+ draw = ImageDraw.Draw(palette)
+ if font_path:
+ font = ImageFont.truetype(font_path, font_size)
+ else:
+ font = ImageFont.load_default()
+
+ hex_palette = []
+ for i, (color, _) in enumerate(sorted_colors):
+ if mode == 'back_to_back':
+ cell_x = i * cell_size
+ cell_y = 0
+ else:
+ row = i % num_rows
+ col = i // num_rows
+ cell_x = col * cell_size
+ cell_y = row * cell_size
+
+ cell_width = cell_size
+ cell_height = cell_size
+
+ color = tuple(color)
+
+ cell = Image.new('RGB', (cell_width, cell_height), color=color)
+ palette.paste(cell, (cell_x, cell_y))
+
+ if mode != 'back_to_back':
+ text_x = cell_x + (cell_width / 2)
+ text_y = cell_y + cell_height + padding
+
+ draw.text((text_x + 1, text_y + 1), f"R: {color[0]} G: {color[1]} B: {color[2]}", font=font, fill='black', anchor='ms')
+ draw.text((text_x, text_y), f"R: {color[0]} G: {color[1]} B: {color[2]}", font=font, fill='white', anchor='ms')
+
+ hex_palette.append('#%02x%02x%02x' % color)
+
+ return palette, '\n'.join(hex_palette)
+
+#! IMAGE FILTER NODES
+
+# IMAGE ADJUSTMENTS NODES
+
+# IMAGE SHADOW AND HIGHLIGHT ADJUSTMENTS
+
+class WAS_Shadow_And_Highlight_Adjustment:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "shadow_threshold": ("FLOAT", {"default": 75, "min": 0.0, "max": 255.0, "step": 0.1}),
+ "shadow_factor": ("FLOAT", {"default": 1.5, "min": -12.0, "max": 12.0, "step": 0.1}),
+ "shadow_smoothing": ("FLOAT", {"default": 0.25, "min": -255.0, "max": 255.0, "step": 0.1}),
+ "highlight_threshold": ("FLOAT", {"default": 175, "min": 0.0, "max": 255.0, "step": 0.1}),
+ "highlight_factor": ("FLOAT", {"default": 0.5, "min": -12.0, "max": 12.0, "step": 0.1}),
+ "highlight_smoothing": ("FLOAT", {"default": 0.25, "min": -255.0, "max": 255.0, "step": 0.1}),
+ "simplify_isolation": ("FLOAT", {"default": 0, "min": -255.0, "max": 255.0, "step": 0.1}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE","IMAGE","IMAGE")
+ RETURN_NAMES = ("image","shadow_map","highlight_map")
+ FUNCTION = "apply_shadow_and_highlight"
+
+ CATEGORY = "WAS Suite/Image/Adjustment"
+
+ def apply_shadow_and_highlight(self, image, shadow_threshold=30, highlight_threshold=220, shadow_factor=1.5, highlight_factor=0.5, shadow_smoothing=0, highlight_smoothing=0, simplify_isolation=0):
+
+ WTools = WAS_Tools_Class()
+
+ result, shadows, highlights = WTools.shadows_and_highlights(tensor2pil(image), shadow_threshold, highlight_threshold, shadow_factor, highlight_factor, shadow_smoothing, highlight_smoothing, simplify_isolation)
+ result, shadows, highlights = WTools.shadows_and_highlights(tensor2pil(image), shadow_threshold, highlight_threshold, shadow_factor, highlight_factor, shadow_smoothing, highlight_smoothing, simplify_isolation)
+
+ return (pil2tensor(result), pil2tensor(shadows), pil2tensor(highlights) )
+
+
+# IMAGE PIXATE
+
+class WAS_Image_Pixelate:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "pixelation_size": ("FLOAT", {"default": 164, "min": 16, "max": 480, "step": 1}),
+ "num_colors": ("FLOAT", {"default": 16, "min": 2, "max": 256, "step": 1}),
+ "init_mode": (["k-means++", "random", "none"],),
+ "max_iterations": ("FLOAT", {"default": 100, "min": 1, "max": 256, "step": 1}),
+ "dither": (["False", "True"],),
+ "dither_mode": (["FloydSteinberg", "Ordered"],),
+ },
+ "optional": {
+ "color_palettes": ("LIST", {"forceInput": True}),
+ "color_palette_mode": (["Brightness", "BrightnessAndTonal", "Linear", "Tonal"],),
+ "reverse_palette":(["False","True"],),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "image_pixelate"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def image_pixelate(self, images, pixelation_size=164, num_colors=16, init_mode='random', max_iterations=100,
+ color_palettes=None, color_palette_mode="Linear", reverse_palette='False', dither='False', dither_mode='FloydSteinberg'):
+
+ if 'scikit-learn' not in packages():
+ cstr("Installing scikit-learn...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'scikit-learn'])
+
+ pixelation_size = int(pixelation_size)
+ num_colors = int(num_colors)
+ max_iterations = int(max_iterations)
+ color_palette_mode = color_palette_mode
+ dither = (dither == 'True')
+
+ color_palettes_list = []
+ if color_palettes:
+ for palette in color_palettes:
+ color_palettes_list.append([color.strip() for color in palette.splitlines() if not color.startswith('//') or not color.startswith(';')])
+
+ reverse_palette = (True if reverse_palette == 'True' else False)
+
+ return ( self.pixel_art_batch(images, pixelation_size, num_colors, init_mode, max_iterations, 42,
+ (color_palettes_list if color_palettes_list else None), color_palette_mode, reverse_palette, dither, dither_mode), )
+
+ def pixel_art_batch(self, batch, min_size, num_colors=16, init_mode='random', max_iter=100, random_state=42,
+ palette=None, palette_mode="Linear", reverse_palette=False, dither=False, dither_mode='FloydSteinberg'):
+
+ from sklearn.cluster import KMeans
+
+ hex_palette_to_rgb = lambda hex: tuple(int(hex[i:i+2], 16) for i in (0, 2, 4))
+
+ def flatten_colors(image, num_colors, init_mode='random', max_iter=100, random_state=42):
+ np_image = np.array(image)
+ pixels = np_image.reshape(-1, 3)
+ kmeans = KMeans(n_clusters=num_colors, init=init_mode, max_iter=max_iter, tol=1e-3, random_state=random_state, n_init='auto')
+ labels = kmeans.fit_predict(pixels)
+ colors = kmeans.cluster_centers_.astype(np.uint8)
+ flattened_pixels = colors[labels]
+ flattened_image = flattened_pixels.reshape(np_image.shape)
+ return Image.fromarray(flattened_image)
+
+ def dither_image(image, mode, nc):
+
+ def clamp(value, min_value=0, max_value=255):
+ return max(min(value, max_value), min_value)
+
+ def get_new_val(old_val, nc):
+ return np.round(old_val * (nc - 1)) / (nc - 1)
+
+ def fs_dither(img, nc):
+ arr = np.array(img, dtype=float) / 255
+ new_width, new_height = img.size
+
+ for ir in range(new_height):
+ for ic in range(new_width):
+ old_val = arr[ir, ic].copy()
+ new_val = get_new_val(old_val, nc)
+ arr[ir, ic] = new_val
+ err = old_val - new_val
+
+ if ic < new_width - 1:
+ arr[ir, ic + 1] += err * 7/16
+ if ir < new_height - 1:
+ if ic > 0:
+ arr[ir + 1, ic - 1] += err * 3/16
+ arr[ir + 1, ic] += err * 5/16
+ if ic < new_width - 1:
+ arr[ir + 1, ic + 1] += err / 16
+
+ carr = np.array(arr * 255, dtype=np.uint8)
+ return Image.fromarray(carr)
+
+ def ordered_dither(img, nc):
+ width, height = img.size
+ dither_matrix = [
+ [0, 8, 2, 10],
+ [12, 4, 14, 6],
+ [3, 11, 1, 9],
+ [15, 7, 13, 5]
+ ]
+ dithered_image = Image.new('RGB', (width, height))
+ num_colors = min(2 ** int(np.log2(nc)), 16)
+
+ for y in range(height):
+ for x in range(width):
+ old_pixel = img.getpixel((x, y))
+ threshold = dither_matrix[x % 4][y % 4] * num_colors
+ new_pixel = tuple(int(c * num_colors / 256) * (256 // num_colors) for c in old_pixel)
+ error = tuple(old - new for old, new in zip(old_pixel, new_pixel))
+ dithered_image.putpixel((x, y), new_pixel)
+
+ if x < width - 1:
+ neighboring_pixel = img.getpixel((x + 1, y))
+ neighboring_pixel = tuple(int(c * num_colors / 256) * (256 // num_colors) for c in neighboring_pixel)
+ neighboring_error = tuple(neighboring - new for neighboring, new in zip(neighboring_pixel, new_pixel))
+ neighboring_pixel = tuple(int(clamp(pixel + error * 7 / 16)) for pixel, error in zip(neighboring_pixel, neighboring_error))
+ img.putpixel((x + 1, y), neighboring_pixel)
+
+ if x < width - 1 and y < height - 1:
+ neighboring_pixel = img.getpixel((x + 1, y + 1))
+ neighboring_pixel = tuple(int(c * num_colors / 256) * (256 // num_colors) for c in neighboring_pixel)
+ neighboring_error = tuple(neighboring - new for neighboring, new in zip(neighboring_pixel, new_pixel))
+ neighboring_pixel = tuple(int(clamp(pixel + error * 1 / 16)) for pixel, error in zip(neighboring_pixel, neighboring_error))
+ img.putpixel((x + 1, y + 1), neighboring_pixel)
+
+ if y < height - 1:
+ neighboring_pixel = img.getpixel((x, y + 1))
+ neighboring_pixel = tuple(int(c * num_colors / 256) * (256 // num_colors) for c in neighboring_pixel)
+ neighboring_error = tuple(neighboring - new for neighboring, new in zip(neighboring_pixel, new_pixel))
+ neighboring_pixel = tuple(int(clamp(pixel + error * 5 / 16)) for pixel, error in zip(neighboring_pixel, neighboring_error))
+ img.putpixel((x, y + 1), neighboring_pixel)
+
+ if x > 0 and y < height - 1:
+ neighboring_pixel = img.getpixel((x - 1, y + 1))
+ neighboring_pixel = tuple(int(c * num_colors / 256) * (256 // num_colors) for c in neighboring_pixel)
+ neighboring_error = tuple(neighboring - new for neighboring, new in zip(neighboring_pixel, new_pixel))
+ neighboring_pixel = tuple(int(clamp(pixel + error * 3 / 16)) for pixel, error in zip(neighboring_pixel, neighboring_error))
+ img.putpixel((x - 1, y + 1), neighboring_pixel)
+
+ return dithered_image
+
+ if mode == 'FloydSteinberg':
+ return fs_dither(image, nc)
+ elif mode == 'Ordered':
+ return ordered_dither(image, nc)
+ else:
+ cstr(f"Inavlid dithering mode `{mode}` selected.").error.print()
+ return image
+
+ return image
+
+ def color_palette_from_hex_lines(image, colors, palette_mode='Linear', reverse_palette=False):
+
+ def color_distance(color1, color2):
+ r1, g1, b1 = color1
+ r2, g2, b2 = color2
+ return np.sqrt((r1 - r2)**2 + (g1 - g2)**2 + (b1 - b2)**2)
+
+ def find_nearest_color_index(color, palette):
+ distances = [color_distance(color, palette_color) for palette_color in palette]
+ return distances.index(min(distances))
+
+ def find_nearest_color_index_tonal(color, palette):
+ distances = [color_distance_tonal(color, palette_color) for palette_color in palette]
+ return distances.index(min(distances))
+
+ def find_nearest_color_index_both(color, palette):
+ distances = [color_distance_both(color, palette_color) for palette_color in palette]
+ return distances.index(min(distances))
+
+ def color_distance_tonal(color1, color2):
+ r1, g1, b1 = color1
+ r2, g2, b2 = color2
+ l1 = 0.299 * r1 + 0.587 * g1 + 0.114 * b1
+ l2 = 0.299 * r2 + 0.587 * g2 + 0.114 * b2
+ return abs(l1 - l2)
+
+ def color_distance_both(color1, color2):
+ r1, g1, b1 = color1
+ r2, g2, b2 = color2
+ l1 = 0.299 * r1 + 0.587 * g1 + 0.114 * b1
+ l2 = 0.299 * r2 + 0.587 * g2 + 0.114 * b2
+ return abs(l1 - l2) + sum(abs(c1 - c2) for c1, c2 in zip(color1, color2))
+
+ def color_distance(color1, color2):
+ return sum(abs(c1 - c2) for c1, c2 in zip(color1, color2))
+
+ color_palette = [hex_palette_to_rgb(color.lstrip('#')) for color in colors]
+
+ if reverse_palette:
+ color_palette = color_palette[::-1]
+
+ np_image = np.array(image)
+ labels = np_image.reshape(image.size[1], image.size[0], -1)
+ width, height = image.size
+ new_image = Image.new("RGB", image.size)
+
+ if palette_mode == 'Linear':
+ color_palette_indices = list(range(len(color_palette)))
+ elif palette_mode == 'Brightness':
+ color_palette_indices = sorted(range(len(color_palette)), key=lambda i: sum(color_palette[i]) / 3)
+ elif palette_mode == 'Tonal':
+ color_palette_indices = sorted(range(len(color_palette)), key=lambda i: color_distance(color_palette[i], (128, 128, 128)))
+ elif palette_mode == 'BrightnessAndTonal':
+ color_palette_indices = sorted(range(len(color_palette)), key=lambda i: (sum(color_palette[i]) / 3, color_distance(color_palette[i], (128, 128, 128))))
+ else:
+ raise ValueError(f"Unsupported mapping mode: {palette_mode}")
+
+ for x in range(width):
+ for y in range(height):
+ pixel_color = labels[y, x, :]
+
+ if palette_mode == 'Linear':
+ color_index = pixel_color[0] % len(color_palette)
+ elif palette_mode == 'Brightness':
+ color_index = find_nearest_color_index(pixel_color, [color_palette[i] for i in color_palette_indices])
+ elif palette_mode == 'Tonal':
+ color_index = find_nearest_color_index_tonal(pixel_color, [color_palette[i] for i in color_palette_indices])
+ elif palette_mode == 'BrightnessAndTonal':
+ color_index = find_nearest_color_index_both(pixel_color, [color_palette[i] for i in color_palette_indices])
+ else:
+ raise ValueError(f"Unsupported mapping mode: {palette_mode}")
+
+ color = color_palette[color_palette_indices[color_index]]
+ new_image.putpixel((x, y), color)
+
+ return new_image
+
+ pil_images = [tensor2pil(image) for image in batch]
+ pixel_art_images = []
+ original_sizes = []
+ total_images = len(pil_images)
+ for image in pil_images:
+ width, height = image.size
+ original_sizes.append((width, height))
+ if max(width, height) > min_size:
+ if width > height:
+ new_width = min_size
+ new_height = int(height * (min_size / width))
+ else:
+ new_height = min_size
+ new_width = int(width * (min_size / height))
+ pixel_art_images.append(image.resize((new_width, int(new_height)), Image.NEAREST))
+ else:
+ pixel_art_images.append(image)
+ if init_mode != 'none':
+ pixel_art_images = [flatten_colors(image, num_colors, init_mode) for image in pixel_art_images]
+ if dither:
+ pixel_art_images = [dither_image(image, dither_mode, num_colors) for image in pixel_art_images]
+ if palette:
+ pixel_art_images = [color_palette_from_hex_lines(pixel_art_image, palette[i], palette_mode, reverse_palette) for i, pixel_art_image in enumerate(pixel_art_images)]
+ else:
+ pixel_art_images = pixel_art_images
+ pixel_art_images = [image.resize(size, Image.NEAREST) for image, size in zip(pixel_art_images, original_sizes)]
+
+ tensor_images = [pil2tensor(image) for image in pixel_art_images]
+
+ batch_tensor = torch.cat(tensor_images, dim=0)
+ return batch_tensor
+
+# SIMPLE IMAGE ADJUST
+
+class WAS_Image_Filters:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "brightness": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}),
+ "contrast": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 2.0, "step": 0.01}),
+ "saturation": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01}),
+ "sharpness": ("FLOAT", {"default": 1.0, "min": -5.0, "max": 5.0, "step": 0.01}),
+ "blur": ("INT", {"default": 0, "min": 0, "max": 16, "step": 1}),
+ "gaussian_blur": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1024.0, "step": 0.1}),
+ "edge_enhance": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "detail_enhance": (["false", "true"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_filters"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def image_filters(self, image, brightness, contrast, saturation, sharpness, blur, gaussian_blur, edge_enhance, detail_enhance):
+
+
+ tensors = []
+ for img in image:
+
+ pil_image = None
+
+ # Apply NP Adjustments
+ if brightness > 0.0 or brightness < 0.0:
+ # Apply brightness
+ img = np.clip(img + brightness, 0.0, 1.0)
+
+ if contrast > 1.0 or contrast < 1.0:
+ # Apply contrast
+ img = np.clip(img * contrast, 0.0, 1.0)
+
+ # Apply PIL Adjustments
+ if saturation > 1.0 or saturation < 1.0:
+ # PIL Image
+ pil_image = tensor2pil(img)
+ # Apply saturation
+ pil_image = ImageEnhance.Color(pil_image).enhance(saturation)
+
+ if sharpness > 1.0 or sharpness < 1.0:
+ # Assign or create PIL Image
+ pil_image = pil_image if pil_image else tensor2pil(img)
+ # Apply sharpness
+ pil_image = ImageEnhance.Sharpness(pil_image).enhance(sharpness)
+
+ if blur > 0:
+ # Assign or create PIL Image
+ pil_image = pil_image if pil_image else tensor2pil(img)
+ # Apply blur
+ for _ in range(blur):
+ pil_image = pil_image.filter(ImageFilter.BLUR)
+
+ if gaussian_blur > 0.0:
+ # Assign or create PIL Image
+ pil_image = pil_image if pil_image else tensor2pil(img)
+ # Apply Gaussian blur
+ pil_image = pil_image.filter(
+ ImageFilter.GaussianBlur(radius=gaussian_blur))
+
+ if edge_enhance > 0.0:
+ # Assign or create PIL Image
+ pil_image = pil_image if pil_image else tensor2pil(img)
+ # Edge Enhancement
+ edge_enhanced_img = pil_image.filter(ImageFilter.EDGE_ENHANCE_MORE)
+ # Blend Mask
+ blend_mask = Image.new(
+ mode="L", size=pil_image.size, color=(round(edge_enhance * 255)))
+ # Composite Original and Enhanced Version
+ pil_image = Image.composite(
+ edge_enhanced_img, pil_image, blend_mask)
+ # Clean-up
+ del blend_mask, edge_enhanced_img
+
+ if detail_enhance == "true":
+ pil_image = pil_image if pil_image else tensor2pil(img)
+ pil_image = pil_image.filter(ImageFilter.DETAIL)
+
+ # Output image
+ out_image = (pil2tensor(pil_image) if pil_image else img)
+
+ tensors.append(out_image)
+
+ tensors = torch.cat(tensors, dim=0)
+
+ return (tensors, )
+
+
+# IMAGE STYLE FILTER
+
+class WAS_Image_Style_Filter:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "style": ([
+ "1977",
+ "aden",
+ "brannan",
+ "brooklyn",
+ "clarendon",
+ "earlybird",
+ "fairy tale",
+ "gingham",
+ "hudson",
+ "inkwell",
+ "kelvin",
+ "lark",
+ "lofi",
+ "maven",
+ "mayfair",
+ "moon",
+ "nashville",
+ "perpetua",
+ "reyes",
+ "rise",
+ "sci-fi",
+ "slumber",
+ "stinson",
+ "toaster",
+ "valencia",
+ "walden",
+ "willow",
+ "xpro2"
+ ],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_style_filter"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def image_style_filter(self, image, style):
+
+ # Install Pilgram
+ if 'pilgram' not in packages():
+ cstr("Installing Pilgram...").msg.print()
+ subprocess.check_call(
+ [sys.executable, '-s', '-m', 'pip', '-q', 'install', 'pilgram'])
+
+ # Import Pilgram module
+ import pilgram
+
+ # WAS Filters
+ WTools = WAS_Tools_Class()
+
+ # Apply blending
+ tensors = []
+ for img in image:
+ if style == "1977":
+ tensors.append(pil2tensor(pilgram._1977(tensor2pil(img))))
+ elif style == "aden":
+ tensors.append(pil2tensor(pilgram.aden(tensor2pil(img))))
+ elif style == "brannan":
+ tensors.append(pil2tensor(pilgram.brannan(tensor2pil(img))))
+ elif style == "brooklyn":
+ tensors.append(pil2tensor(pilgram.brooklyn(tensor2pil(img))))
+ elif style == "clarendon":
+ tensors.append(pil2tensor(pilgram.clarendon(tensor2pil(img))))
+ elif style == "earlybird":
+ tensors.append(pil2tensor(pilgram.earlybird(tensor2pil(img))))
+ elif style == "fairy tale":
+ tensors.append(pil2tensor(WTools.sparkle(tensor2pil(img))))
+ elif style == "gingham":
+ tensors.append(pil2tensor(pilgram.gingham(tensor2pil(img))))
+ elif style == "hudson":
+ tensors.append(pil2tensor(pilgram.hudson(tensor2pil(img))))
+ elif style == "inkwell":
+ tensors.append(pil2tensor(pilgram.inkwell(tensor2pil(img))))
+ elif style == "kelvin":
+ tensors.append(pil2tensor(pilgram.kelvin(tensor2pil(img))))
+ elif style == "lark":
+ tensors.append(pil2tensor(pilgram.lark(tensor2pil(img))))
+ elif style == "lofi":
+ tensors.append(pil2tensor(pilgram.lofi(tensor2pil(img))))
+ elif style == "maven":
+ tensors.append(pil2tensor(pilgram.maven(tensor2pil(img))))
+ elif style == "mayfair":
+ tensors.append(pil2tensor(pilgram.mayfair(tensor2pil(img))))
+ elif style == "moon":
+ tensors.append(pil2tensor(pilgram.moon(tensor2pil(img))))
+ elif style == "nashville":
+ tensors.append(pil2tensor(pilgram.nashville(tensor2pil(img))))
+ elif style == "perpetua":
+ tensors.append(pil2tensor(pilgram.perpetua(tensor2pil(img))))
+ elif style == "reyes":
+ tensors.append(pil2tensor(pilgram.reyes(tensor2pil(img))))
+ elif style == "rise":
+ tensors.append(pil2tensor(pilgram.rise(tensor2pil(img))))
+ elif style == "slumber":
+ tensors.append(pil2tensor(pilgram.slumber(tensor2pil(img))))
+ elif style == "stinson":
+ tensors.append(pil2tensor(pilgram.stinson(tensor2pil(img))))
+ elif style == "toaster":
+ tensors.append(pil2tensor(pilgram.toaster(tensor2pil(img))))
+ elif style == "valencia":
+ tensors.append(pil2tensor(pilgram.valencia(tensor2pil(img))))
+ elif style == "walden":
+ tensors.append(pil2tensor(pilgram.walden(tensor2pil(img))))
+ elif style == "willow":
+ tensors.append(pil2tensor(pilgram.willow(tensor2pil(img))))
+ elif style == "xpro2":
+ tensors.append(pil2tensor(pilgram.xpro2(tensor2pil(img))))
+ else:
+ tensors.append(img)
+
+ tensors = torch.cat(tensors, dim=0)
+
+ return (tensors, )
+
+
+# IMAGE CROP FACE
+
+class WAS_Image_Crop_Face:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "crop_padding_factor": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 2.0, "step": 0.01}),
+ "cascade_xml": ([
+ "lbpcascade_animeface.xml",
+ "haarcascade_frontalface_default.xml",
+ "haarcascade_frontalface_alt.xml",
+ "haarcascade_frontalface_alt2.xml",
+ "haarcascade_frontalface_alt_tree.xml",
+ "haarcascade_profileface.xml",
+ "haarcascade_upperbody.xml"
+ ],),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "CROP_DATA")
+ FUNCTION = "image_crop_face"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def image_crop_face(self, image, cascade_xml=None, crop_padding_factor=0.25):
+ return self.crop_face(tensor2pil(image), cascade_xml, crop_padding_factor)
+
+ def crop_face(self, image, cascade_name=None, padding=0.25):
+
+ import cv2
+
+ img = np.array(image.convert('RGB'))
+
+ face_location = None
+
+ cascades = [ os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'lbpcascade_animeface.xml'),
+ os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_frontalface_default.xml'),
+ os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_frontalface_alt.xml'),
+ os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_frontalface_alt2.xml'),
+ os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_frontalface_alt_tree.xml'),
+ os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_profileface.xml'),
+ os.path.join(os.path.join(WAS_SUITE_ROOT, 'res'), 'haarcascade_upperbody.xml') ]
+
+ if cascade_name:
+ for cascade in cascades:
+ if os.path.basename(cascade) == cascade_name:
+ cascades.remove(cascade)
+ cascades.insert(0, cascade)
+ break
+
+ faces = None
+ if not face_location:
+ for cascade in cascades:
+ if not os.path.exists(cascade):
+ cstr(f"Unable to find cascade XML file at `{cascade}`. Did you pull the latest files from https://github.com/WASasquatch/was-node-suite-comfyui repo?").error.print()
+ return (pil2tensor(Image.new("RGB", (512,512), (0,0,0))), False)
+ face_cascade = cv2.CascadeClassifier(cascade)
+ gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
+ if len(faces) != 0:
+ cstr(f"Face found with: {os.path.basename(cascade)}").msg.print()
+ break
+ if len(faces) == 0:
+ cstr("No faces found in the image!").warning.print()
+ return (pil2tensor(Image.new("RGB", (512,512), (0,0,0))), False)
+ else:
+ cstr("Face found with: face_recognition model").warning.print()
+ faces = face_location
+
+ # Assume there is only one face in the image
+ x, y, w, h = faces[0]
+
+ # Check if the face region aligns with the edges of the original image
+ left_adjust = max(0, -x)
+ right_adjust = max(0, x + w - img.shape[1])
+ top_adjust = max(0, -y)
+ bottom_adjust = max(0, y + h - img.shape[0])
+
+ # Check if the face region is near any edges, and if so, pad in the opposite direction
+ if left_adjust < w:
+ x += right_adjust
+ elif right_adjust < w:
+ x -= left_adjust
+ if top_adjust < h:
+ y += bottom_adjust
+ elif bottom_adjust < h:
+ y -= top_adjust
+
+ w -= left_adjust + right_adjust
+ h -= top_adjust + bottom_adjust
+
+ # Calculate padding around face
+ face_size = min(h, w)
+ y_pad = int(face_size * padding)
+ x_pad = int(face_size * padding)
+
+ # Calculate square coordinates around face
+ center_x = x + w // 2
+ center_y = y + h // 2
+ half_size = (face_size + max(x_pad, y_pad)) // 2
+ top = max(0, center_y - half_size)
+ bottom = min(img.shape[0], center_y + half_size)
+ left = max(0, center_x - half_size)
+ right = min(img.shape[1], center_x + half_size)
+
+ # Ensure square crop of the original image
+ crop_size = min(right - left, bottom - top)
+ left = center_x - crop_size // 2
+ right = center_x + crop_size // 2
+ top = center_y - crop_size // 2
+ bottom = center_y + crop_size // 2
+
+ # Crop face from original image
+ face_img = img[top:bottom, left:right, :]
+
+ # Resize image
+ size = max(face_img.copy().shape[:2])
+ pad_h = (size - face_img.shape[0]) // 2
+ pad_w = (size - face_img.shape[1]) // 2
+ face_img = cv2.copyMakeBorder(face_img, pad_h, pad_h, pad_w, pad_w, cv2.BORDER_CONSTANT, value=[0,0,0])
+ min_size = 64 # Set minimum size for padded image
+ if size < min_size:
+ size = min_size
+ face_img = cv2.resize(face_img, (size, size))
+
+ # Convert numpy array back to PIL image
+ face_img = Image.fromarray(face_img)
+
+ # Resize image to a multiple of 64
+ original_size = face_img.size
+ face_img.resize((((face_img.size[0] // 64) * 64 + 64), ((face_img.size[1] // 64) * 64 + 64)))
+
+ # Return face image and coordinates
+ return (pil2tensor(face_img.convert('RGB')), (original_size, (left, top, right, bottom)))
+
+
+# IMAGE PASTE FACE CROP
+
+class WAS_Image_Paste_Face_Crop:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "crop_image": ("IMAGE",),
+ "crop_data": ("CROP_DATA",),
+ "crop_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "crop_sharpening": ("INT", {"default": 0, "min": 0, "max": 3, "step": 1}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "IMAGE")
+ RETURN_NAMES = ("IMAGE", "MASK_IMAGE")
+ FUNCTION = "image_paste_face"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def image_paste_face(self, image, crop_image, crop_data=None, crop_blending=0.25, crop_sharpening=0):
+
+ if crop_data == False:
+ cstr("No valid crop data found!").error.print()
+ return (image, pil2tensor(Image.new("RGB", tensor2pil(image).size, (0,0,0))))
+
+ result_image, result_mask = self.paste_image(tensor2pil(image), tensor2pil(crop_image), crop_data, crop_blending, crop_sharpening)
+ return(result_image, result_mask)
+
+ def paste_image(self, image, crop_image, crop_data, blend_amount=0.25, sharpen_amount=1):
+
+ def lingrad(size, direction, white_ratio):
+ image = Image.new('RGB', size)
+ draw = ImageDraw.Draw(image)
+ if direction == 'vertical':
+ black_end = int(size[1] * (1 - white_ratio))
+ range_start = 0
+ range_end = size[1]
+ range_step = 1
+ for y in range(range_start, range_end, range_step):
+ color_ratio = y / size[1]
+ if y <= black_end:
+ color = (0, 0, 0)
+ else:
+ color_value = int(((y - black_end) / (size[1] - black_end)) * 255)
+ color = (color_value, color_value, color_value)
+ draw.line([(0, y), (size[0], y)], fill=color)
+ elif direction == 'horizontal':
+ black_end = int(size[0] * (1 - white_ratio))
+ range_start = 0
+ range_end = size[0]
+ range_step = 1
+ for x in range(range_start, range_end, range_step):
+ color_ratio = x / size[0]
+ if x <= black_end:
+ color = (0, 0, 0)
+ else:
+ color_value = int(((x - black_end) / (size[0] - black_end)) * 255)
+ color = (color_value, color_value, color_value)
+ draw.line([(x, 0), (x, size[1])], fill=color)
+
+ return image.convert("L")
+
+ crop_size, (top, left, right, bottom) = crop_data
+ crop_image = crop_image.resize(crop_size)
+
+ if sharpen_amount > 0:
+ for _ in range(int(sharpen_amount)):
+ crop_image = crop_image.filter(ImageFilter.SHARPEN)
+
+ blended_image = Image.new('RGBA', image.size, (0, 0, 0, 255))
+ blended_mask = Image.new('L', image.size, 0)
+ crop_padded = Image.new('RGBA', image.size, (0, 0, 0, 0))
+ blended_image.paste(image, (0, 0))
+ crop_padded.paste(crop_image, (top, left))
+ crop_mask = Image.new('L', crop_image.size, 0)
+
+ if top > 0:
+ gradient_image = ImageOps.flip(lingrad(crop_image.size, 'vertical', blend_amount))
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ if left > 0:
+ gradient_image = ImageOps.mirror(lingrad(crop_image.size, 'horizontal', blend_amount))
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ if right < image.width:
+ gradient_image = lingrad(crop_image.size, 'horizontal', blend_amount)
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ if bottom < image.height:
+ gradient_image = lingrad(crop_image.size, 'vertical', blend_amount)
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ crop_mask = ImageOps.invert(crop_mask)
+ blended_mask.paste(crop_mask, (top, left))
+ blended_mask = blended_mask.convert("L")
+ blended_image.paste(crop_padded, (0, 0), blended_mask)
+
+ return (pil2tensor(blended_image.convert("RGB")), pil2tensor(blended_mask.convert("RGB")))
+
+
+# IMAGE CROP LOCATION
+
+class WAS_Image_Crop_Location:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "top": ("INT", {"default":0, "max": 10000000, "min":0, "step":1}),
+ "left": ("INT", {"default":0, "max": 10000000, "min":0, "step":1}),
+ "right": ("INT", {"default":256, "max": 10000000, "min":0, "step":1}),
+ "bottom": ("INT", {"default":256, "max": 10000000, "min":0, "step":1}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "CROP_DATA")
+ FUNCTION = "image_crop_location"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def image_crop_location(self, image, top=0, left=0, right=256, bottom=256):
+ image = tensor2pil(image)
+ img_width, img_height = image.size
+
+ # Calculate the final coordinates for cropping
+ crop_top = max(top, 0)
+ crop_left = max(left, 0)
+ crop_bottom = min(bottom, img_height)
+ crop_right = min(right, img_width)
+
+ # Ensure that the cropping region has non-zero width and height
+ crop_width = crop_right - crop_left
+ crop_height = crop_bottom - crop_top
+ if crop_width <= 0 or crop_height <= 0:
+ raise ValueError("Invalid crop dimensions. Please check the values for top, left, right, and bottom.")
+
+ # Crop the image and resize
+ crop = image.crop((crop_left, crop_top, crop_right, crop_bottom))
+ crop_data = (crop.size, (crop_left, crop_top, crop_right, crop_bottom))
+ crop = crop.resize((((crop.size[0] // 8) * 8), ((crop.size[1] // 8) * 8)))
+
+ return (pil2tensor(crop), crop_data)
+
+
+# IMAGE SQUARE CROP LOCATION
+
+class WAS_Image_Crop_Square_Location:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "x": ("INT", {"default":0, "max": 24576, "min":0, "step":1}),
+ "y": ("INT", {"default":0, "max": 24576, "min":0, "step":1}),
+ "size": ("INT", {"default":256, "max": 4096, "min":5, "step":1}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "CROP_DATA")
+ FUNCTION = "image_crop_location"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def image_crop_location(self, image, x=256, y=256, size=512):
+
+ image = tensor2pil(image)
+ img_width, img_height = image.size
+ exp_size = size // 2
+ left = max(x - exp_size, 0)
+ top = max(y - exp_size, 0)
+ right = min(x + exp_size, img_width)
+ bottom = min(y + exp_size, img_height)
+
+ if right - left < size:
+ if right < img_width:
+ right = min(right + size - (right - left), img_width)
+ elif left > 0:
+ left = max(left - (size - (right - left)), 0)
+ if bottom - top < size:
+ if bottom < img_height:
+ bottom = min(bottom + size - (bottom - top), img_height)
+ elif top > 0:
+ top = max(top - (size - (bottom - top)), 0)
+
+ crop = image.crop((left, top, right, bottom))
+
+ # Original Crop Data
+ crop_data = (crop.size, (left, top, right, bottom))
+
+ # Output resize
+ crop = crop.resize((((crop.size[0] // 8) * 8), ((crop.size[1] // 8) * 8)))
+
+ return (pil2tensor(crop), crop_data)
+
+
+# IMAGE SQUARE CROP LOCATION
+
+class WAS_Image_Tile_Batch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "num_tiles": ("INT", {"default":4, "max": 64, "min":2, "step":1}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("IMAGES",)
+ FUNCTION = "tile_image"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def tile_image(self, image, num_tiles=6):
+ image = tensor2pil(image.squeeze(0))
+ img_width, img_height = image.size
+
+ num_rows = int(num_tiles ** 0.5)
+ num_cols = (num_tiles + num_rows - 1) // num_rows
+ tile_width = img_width // num_cols
+ tile_height = img_height // num_rows
+
+ tiles = []
+ for y in range(0, img_height, tile_height):
+ for x in range(0, img_width, tile_width):
+ tile = image.crop((x, y, x + tile_width, y + tile_height))
+ tiles.append(pil2tensor(tile))
+
+ tiles = torch.stack(tiles, dim=0).squeeze(1)
+
+ return (tiles, )
+
+
+# IMAGE PASTE CROP
+
+class WAS_Image_Paste_Crop:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "crop_image": ("IMAGE",),
+ "crop_data": ("CROP_DATA",),
+ "crop_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "crop_sharpening": ("INT", {"default": 0, "min": 0, "max": 3, "step": 1}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "IMAGE")
+ FUNCTION = "image_paste_crop"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def image_paste_crop(self, image, crop_image, crop_data=None, crop_blending=0.25, crop_sharpening=0):
+
+ if crop_data == False:
+ cstr("No valid crop data found!").error.print()
+ return (image, pil2tensor(Image.new("RGB", tensor2pil(image).size, (0,0,0))))
+
+ result_image, result_mask = self.paste_image(tensor2pil(image), tensor2pil(crop_image), crop_data, crop_blending, crop_sharpening)
+
+ return (result_image, result_mask)
+
+ def paste_image(self, image, crop_image, crop_data, blend_amount=0.25, sharpen_amount=1):
+
+ def lingrad(size, direction, white_ratio):
+ image = Image.new('RGB', size)
+ draw = ImageDraw.Draw(image)
+ if direction == 'vertical':
+ black_end = int(size[1] * (1 - white_ratio))
+ range_start = 0
+ range_end = size[1]
+ range_step = 1
+ for y in range(range_start, range_end, range_step):
+ color_ratio = y / size[1]
+ if y <= black_end:
+ color = (0, 0, 0)
+ else:
+ color_value = int(((y - black_end) / (size[1] - black_end)) * 255)
+ color = (color_value, color_value, color_value)
+ draw.line([(0, y), (size[0], y)], fill=color)
+ elif direction == 'horizontal':
+ black_end = int(size[0] * (1 - white_ratio))
+ range_start = 0
+ range_end = size[0]
+ range_step = 1
+ for x in range(range_start, range_end, range_step):
+ color_ratio = x / size[0]
+ if x <= black_end:
+ color = (0, 0, 0)
+ else:
+ color_value = int(((x - black_end) / (size[0] - black_end)) * 255)
+ color = (color_value, color_value, color_value)
+ draw.line([(x, 0), (x, size[1])], fill=color)
+
+ return image.convert("L")
+
+ crop_size, (left, top, right, bottom) = crop_data
+ crop_image = crop_image.resize(crop_size)
+
+ if sharpen_amount > 0:
+ for _ in range(int(sharpen_amount)):
+ crop_image = crop_image.filter(ImageFilter.SHARPEN)
+
+ blended_image = Image.new('RGBA', image.size, (0, 0, 0, 255))
+ blended_mask = Image.new('L', image.size, 0)
+ crop_padded = Image.new('RGBA', image.size, (0, 0, 0, 0))
+ blended_image.paste(image, (0, 0))
+ crop_padded.paste(crop_image, (left, top))
+ crop_mask = Image.new('L', crop_image.size, 0)
+
+ if top > 0:
+ gradient_image = ImageOps.flip(lingrad(crop_image.size, 'vertical', blend_amount))
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ if left > 0:
+ gradient_image = ImageOps.mirror(lingrad(crop_image.size, 'horizontal', blend_amount))
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ if right < image.width:
+ gradient_image = lingrad(crop_image.size, 'horizontal', blend_amount)
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ if bottom < image.height:
+ gradient_image = lingrad(crop_image.size, 'vertical', blend_amount)
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ crop_mask = ImageOps.invert(crop_mask)
+ blended_mask.paste(crop_mask, (left, top))
+ blended_mask = blended_mask.convert("L")
+ blended_image.paste(crop_padded, (0, 0), blended_mask)
+
+ return (pil2tensor(blended_image.convert("RGB")), pil2tensor(blended_mask.convert("RGB")))
+
+
+# IMAGE PASTE CROP BY LOCATION
+
+class WAS_Image_Paste_Crop_Location:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "crop_image": ("IMAGE",),
+ "top": ("INT", {"default":0, "max": 10000000, "min":0, "step":1}),
+ "left": ("INT", {"default":0, "max": 10000000, "min":0, "step":1}),
+ "right": ("INT", {"default":256, "max": 10000000, "min":0, "step":1}),
+ "bottom": ("INT", {"default":256, "max": 10000000, "min":0, "step":1}),
+ "crop_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "crop_sharpening": ("INT", {"default": 0, "min": 0, "max": 3, "step": 1}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "IMAGE")
+ FUNCTION = "image_paste_crop_location"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def image_paste_crop_location(self, image, crop_image, top=0, left=0, right=256, bottom=256, crop_blending=0.25, crop_sharpening=0):
+
+ result_image, result_mask = self.paste_image(tensor2pil(image), tensor2pil(crop_image), top, left, right, bottom, crop_blending, crop_sharpening)
+ return (result_image, result_mask)
+
+ def paste_image(self, image, crop_image, top=0, left=0, right=256, bottom=256, blend_amount=0.25, sharpen_amount=1):
+
+ def inset_border(image, border_width=20, border_color=(0)):
+ width, height = image.size
+ bordered_image = Image.new(image.mode, (width, height), border_color)
+ bordered_image.paste(image, (0, 0))
+ draw = ImageDraw.Draw(bordered_image)
+ draw.rectangle((0, 0, width-1, height-1), outline=border_color, width=border_width)
+ return bordered_image
+
+ img_width, img_height = image.size
+
+ # Ensure that the coordinates are within the image bounds
+ top = min(max(top, 0), img_height)
+ left = min(max(left, 0), img_width)
+ bottom = min(max(bottom, 0), img_height)
+ right = min(max(right, 0), img_width)
+
+ crop_size = (right - left, bottom - top)
+ crop_img = crop_image.convert("RGB")
+ crop_img = crop_img.resize(crop_size)
+
+ if sharpen_amount > 0:
+ for _ in range(sharpen_amount):
+ crop_img = crop_img.filter(ImageFilter.SHARPEN)
+
+ if blend_amount > 1.0:
+ blend_amount = 1.0
+ elif blend_amount < 0.0:
+ blend_amount = 0.0
+ blend_ratio = (max(crop_size) / 2) * float(blend_amount)
+
+ blend = image.convert("RGBA")
+ mask = Image.new("L", image.size, 0)
+
+ mask_block = Image.new("L", crop_size, 255)
+ mask_block = inset_border(mask_block, int(blend_ratio/2), (0))
+
+ Image.Image.paste(mask, mask_block, (left, top))
+ Image.Image.paste(blend, crop_img, (left, top))
+
+ mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio/4))
+ mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio/4))
+
+ blend.putalpha(mask)
+ image = Image.alpha_composite(image.convert("RGBA"), blend)
+
+ return (pil2tensor(image.convert('RGB')), pil2tensor(mask.convert('RGB')))
+
+# IMAGE GRID IMAGE
+
+class WAS_Image_Grid_Image:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images_path": ("STRING", {"default":"./ComfyUI/input/", "multiline": False}),
+ "pattern_glob": ("STRING", {"default":"*", "multiline": False}),
+ "include_subfolders": (["false", "true"],),
+ "border_width": ("INT", {"default":3, "min": 0, "max": 100, "step":1}),
+ "number_of_columns": ("INT", {"default":6, "min": 1, "max": 24, "step":1}),
+ "max_cell_size": ("INT", {"default":256, "min":32, "max":1280, "step":1}),
+ "border_red": ("INT", {"default":0, "min": 0, "max": 255, "step":1}),
+ "border_green": ("INT", {"default":0, "min": 0, "max": 255, "step":1}),
+ "border_blue": ("INT", {"default":0, "min": 0, "max": 255, "step":1}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "create_grid_image"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def create_grid_image(self, images_path, pattern_glob="*", include_subfolders="false", number_of_columns=6,
+ max_cell_size=256, border_width=3, border_red=0, border_green=0, border_blue=0):
+
+ if not os.path.exists(images_path):
+ cstr(f"The grid image path `{images_path}` does not exist!").error.print()
+ return (pil2tensor(Image.new("RGB", (512,512), (0,0,0))),)
+
+ paths = glob.glob(os.path.join(images_path, pattern_glob), recursive=(False if include_subfolders == "false" else True))
+ image_paths = []
+ for path in paths:
+ if path.lower().endswith(ALLOWED_EXT) and os.path.exists(path):
+ image_paths.append(path)
+
+ grid_image = self.smart_grid_image(image_paths, int(number_of_columns), (int(max_cell_size), int(max_cell_size)),
+ (False if border_width <= 0 else True), (int(border_red),
+ int(border_green), int(border_blue)), int(border_width))
+
+ return (pil2tensor(grid_image),)
+
+ def smart_grid_image(self, images, cols=6, size=(256,256), add_border=False, border_color=(0,0,0), border_width=3):
+
+ # calculate row height
+ max_width, max_height = size
+ row_height = 0
+ images_resized = []
+ for image in images:
+ img = Image.open(image).convert('RGB')
+
+ img_w, img_h = img.size
+ aspect_ratio = img_w / img_h
+ if aspect_ratio > 1: # landscape
+ thumb_w = min(max_width, img_w-border_width)
+ thumb_h = thumb_w / aspect_ratio
+ else: # portrait
+ thumb_h = min(max_height, img_h-border_width)
+ thumb_w = thumb_h * aspect_ratio
+
+ # pad the image to match the maximum size and center it within the cell
+ pad_w = max_width - int(thumb_w)
+ pad_h = max_height - int(thumb_h)
+ left = pad_w // 2
+ top = pad_h // 2
+ right = pad_w - left
+ bottom = pad_h - top
+ padding = (left, top, right, bottom) # left, top, right, bottom
+ img_resized = ImageOps.expand(img.resize((int(thumb_w), int(thumb_h))), padding)
+
+ if add_border:
+ img_resized_bordered = ImageOps.expand(img_resized, border=border_width//2, fill=border_color)
+
+ images_resized.append(img_resized)
+ row_height = max(row_height, img_resized.size[1])
+ row_height = int(row_height)
+
+ # calculate the number of rows
+ total_images = len(images_resized)
+ rows = math.ceil(total_images / cols)
+
+ # create empty image to put thumbnails
+ new_image = Image.new('RGB', (cols*size[0]+(cols-1)*border_width, rows*row_height+(rows-1)*border_width), border_color)
+
+ for i, img in enumerate(images_resized):
+ if add_border:
+ border_img = ImageOps.expand(img, border=border_width//2, fill=border_color)
+ x = (i % cols) * (size[0]+border_width)
+ y = (i // cols) * (row_height+border_width)
+ if border_img.size == (size[0], size[1]):
+ new_image.paste(border_img, (x, y, x+size[0], y+size[1]))
+ else:
+ # Resize image to match size parameter
+ border_img = border_img.resize((size[0], size[1]))
+ new_image.paste(border_img, (x, y, x+size[0], y+size[1]))
+ else:
+ x = (i % cols) * (size[0]+border_width)
+ y = (i // cols) * (row_height+border_width)
+ if img.size == (size[0], size[1]):
+ new_image.paste(img, (x, y, x+img.size[0], y+img.size[1]))
+ else:
+ # Resize image to match size parameter
+ img = img.resize((size[0], size[1]))
+ new_image.paste(img, (x, y, x+size[0], y+size[1]))
+
+ new_image = ImageOps.expand(new_image, border=border_width, fill=border_color)
+
+ return new_image
+
+# IMAGE MORPH GIF
+
+class WAS_Image_Morph_GIF:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image_a": ("IMAGE",),
+ "image_b": ("IMAGE",),
+ "transition_frames": ("INT", {"default":30, "min":2, "max":60, "step":1}),
+ "still_image_delay_ms": ("FLOAT", {"default":2500.0, "min":0.1, "max":60000.0, "step":0.1}),
+ "duration_ms": ("FLOAT", {"default":0.1, "min":0.1, "max":60000.0, "step":0.1}),
+ "loops": ("INT", {"default":0, "min":0, "max":100, "step":1}),
+ "max_size": ("INT", {"default":512, "min":128, "max":1280, "step":1}),
+ "output_path": ("STRING", {"default": "./ComfyUI/output", "multiline": False}),
+ "filename": ("STRING", {"default": "morph", "multiline": False}),
+ "filetype": (["GIF", "APNG"],),
+ }
+ }
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+ RETURN_TYPES = ("IMAGE","IMAGE",TEXT_TYPE,TEXT_TYPE)
+ RETURN_NAMES = ("image_a_pass","image_b_pass","filepath_text","filename_text")
+ FUNCTION = "create_morph_gif"
+
+ CATEGORY = "WAS Suite/Animation"
+
+ def create_morph_gif(self, image_a, image_b, transition_frames=10, still_image_delay_ms=10, duration_ms=0.1, loops=0, max_size=512,
+ output_path="./ComfyUI/output", filename="morph", filetype="GIF"):
+
+ tokens = TextTokens()
+ WTools = WAS_Tools_Class()
+
+ if 'imageio' not in packages():
+ cstr("Installing imageio...").msg.print()
+ subprocess.check_call(
+ [sys.executable, '-s', '-m', 'pip', '-q', 'install', 'imageio'])
+
+ if filetype not in ["APNG", "GIF"]:
+ filetype = "GIF"
+ if output_path.strip() in [None, "", "."]:
+ output_path = "./ComfyUI/output"
+ output_path = tokens.parseTokens(os.path.join(*output_path.split('/')))
+ if not os.path.exists(output_path):
+ os.makedirs(output_path, exist_ok=True)
+
+ if image_a == None:
+ image_a = pil2tensor(Image.new("RGB", (512,512), (0,0,0)))
+ if image_b == None:
+ image_b = pil2tensor(Image.new("RGB", (512,512), (255,255,255)))
+
+ if transition_frames < 2:
+ transition_frames = 2
+ elif transition_frames > 60:
+ transition_frames = 60
+
+ if duration_ms < 0.1:
+ duration_ms = 0.1
+ elif duration_ms > 60000.0:
+ duration_ms = 60000.0
+
+ output_file = WTools.morph_images([tensor2pil(image_a), tensor2pil(image_b)], steps=int(transition_frames), max_size=int(max_size), loop=int(loops),
+ still_duration=int(still_image_delay_ms), duration=int(duration_ms), output_path=output_path,
+ filename=tokens.parseTokens(filename), filetype=filetype)
+
+ return (image_a, image_b, output_file)
+
+
+# IMAGE MORPH GIF WRITER
+
+class WAS_Image_Morph_GIF_Writer:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "transition_frames": ("INT", {"default":30, "min":2, "max":60, "step":1}),
+ "image_delay_ms": ("FLOAT", {"default":2500.0, "min":0.1, "max":60000.0, "step":0.1}),
+ "duration_ms": ("FLOAT", {"default":0.1, "min":0.1, "max":60000.0, "step":0.1}),
+ "loops": ("INT", {"default":0, "min":0, "max":100, "step":1}),
+ "max_size": ("INT", {"default":512, "min":128, "max":1280, "step":1}),
+ "output_path": ("STRING", {"default": "./ComfyUI/output", "multiline": False}),
+ "filename": ("STRING", {"default": "morph_writer", "multiline": False}),
+ }
+ }
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+ RETURN_TYPES = ("IMAGE",TEXT_TYPE,TEXT_TYPE)
+ RETURN_NAMES = ("IMAGE_PASS","filepath_text","filename_text")
+ FUNCTION = "write_to_morph_gif"
+
+ CATEGORY = "WAS Suite/Animation/Writer"
+
+ def write_to_morph_gif(self, image, transition_frames=10, image_delay_ms=10, duration_ms=0.1, loops=0, max_size=512,
+ output_path="./ComfyUI/output", filename="morph"):
+
+ if 'imageio' not in packages():
+ cstr("Installing imageio...").msg.print()
+ subprocess.check_call(
+ [sys.executable, '-s', '-m', 'pip', '-q', 'install', 'imageio'])
+
+ if output_path.strip() in [None, "", "."]:
+ output_path = "./ComfyUI/output"
+
+ if image == None:
+ image = pil2tensor(Image.new("RGB", (512,512), (0,0,0)))
+
+ if transition_frames < 2:
+ transition_frames = 2
+ elif transition_frames > 60:
+ transition_frames = 60
+
+ if duration_ms < 0.1:
+ duration_ms = 0.1
+ elif duration_ms > 60000.0:
+ duration_ms = 60000.0
+
+ tokens = TextTokens()
+ output_path = os.path.abspath(os.path.join(*tokens.parseTokens(output_path).split('/')))
+ output_file = os.path.join(output_path, tokens.parseTokens(filename)+'.gif')
+
+ if not os.path.exists(output_path):
+ os.makedirs(output_path, exist_ok=True)
+
+ WTools = WAS_Tools_Class()
+ GifMorph = WTools.GifMorphWriter(int(transition_frames), int(duration_ms), int(image_delay_ms))
+ GifMorph.write(tensor2pil(image), output_file)
+
+ return (image, output_file, filename)
+
+# IMAGE MORPH GIF BY PATH
+
+class WAS_Image_Morph_GIF_By_Path:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "transition_frames": ("INT", {"default":30, "min":2, "max":60, "step":1}),
+ "still_image_delay_ms": ("FLOAT", {"default":2500.0, "min":0.1, "max":60000.0, "step":0.1}),
+ "duration_ms": ("FLOAT", {"default":0.1, "min":0.1, "max":60000.0, "step":0.1}),
+ "loops": ("INT", {"default":0, "min":0, "max":100, "step":1}),
+ "max_size": ("INT", {"default":512, "min":128, "max":1280, "step":1}),
+ "input_path": ("STRING",{"default":"./ComfyUI", "multiline": False}),
+ "input_pattern": ("STRING",{"default":"*", "multiline": False}),
+ "output_path": ("STRING", {"default": "./ComfyUI/output", "multiline": False}),
+ "filename": ("STRING", {"default": "morph", "multiline": False}),
+ "filetype": (["GIF", "APNG"],),
+ }
+ }
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+ RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE)
+ RETURN_NAMES = ("filepath_text","filename_text")
+ FUNCTION = "create_morph_gif"
+
+ CATEGORY = "WAS Suite/Animation"
+
+ def create_morph_gif(self, transition_frames=30, still_image_delay_ms=2500, duration_ms=0.1, loops=0, max_size=512,
+ input_path="./ComfyUI/output", input_pattern="*", output_path="./ComfyUI/output", filename="morph", filetype="GIF"):
+
+ if 'imageio' not in packages():
+ cstr("Installing imageio...").msg.print()
+ subprocess.check_call(
+ [sys.executable, '-s', '-m', 'pip', '-q', 'install', 'imageio'])
+
+ if not os.path.exists(input_path):
+ cstr(f"The input_path `{input_path}` does not exist!").error.print()
+ return ("",)
+
+ images = self.load_images(input_path, input_pattern)
+ if not images:
+ cstr(f"The input_path `{input_path}` does not contain any valid images!").msg.print()
+ return ("",)
+
+ if filetype not in ["APNG", "GIF"]:
+ filetype = "GIF"
+ if output_path.strip() in [None, "", "."]:
+ output_path = "./ComfyUI/output"
+
+ if transition_frames < 2:
+ transition_frames = 2
+ elif transition_frames > 60:
+ transition_frames = 60
+
+ if duration_ms < 0.1:
+ duration_ms = 0.1
+ elif duration_ms > 60000.0:
+ duration_ms = 60000.0
+
+ tokens = TextTokens()
+ WTools = WAS_Tools_Class()
+
+ output_file = WTools.morph_images(images, steps=int(transition_frames), max_size=int(max_size), loop=int(loops), still_duration=int(still_image_delay_ms),
+ duration=int(duration_ms), output_path=tokens.parseTokens(os.path.join(*output_path.split('/'))),
+ filename=tokens.parseTokens(filename), filetype=filetype)
+
+ return (output_file,filename)
+
+
+ def load_images(self, directory_path, pattern):
+ images = []
+ for file_name in glob.glob(os.path.join(directory_path, pattern), recursive=False):
+ if file_name.lower().endswith(ALLOWED_EXT):
+ images.append(Image.open(file_name).convert("RGB"))
+ return images
+
+
+# COMBINE NODE
+
+class WAS_Image_Blending_Mode:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image_a": ("IMAGE",),
+ "image_b": ("IMAGE",),
+ "mode": ([
+ "add",
+ "color",
+ "color_burn",
+ "color_dodge",
+ "darken",
+ "difference",
+ "exclusion",
+ "hard_light",
+ "hue",
+ "lighten",
+ "multiply",
+ "overlay",
+ "screen",
+ "soft_light"
+ ],),
+ "blend_percentage": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ FUNCTION = "image_blending_mode"
+
+ CATEGORY = "WAS Suite/Image"
+
+ def image_blending_mode(self, image_a, image_b, mode='add', blend_percentage=1.0):
+
+ # Install Pilgram
+ if 'pilgram' not in packages():
+ cstr("Installing Pilgram...").msg.print()
+ subprocess.check_call(
+ [sys.executable, '-s', '-m', 'pip', '-q', 'install', 'pilgram'])
+
+ # Import Pilgram module
+ import pilgram
+
+ # Convert images to PIL
+ img_a = tensor2pil(image_a)
+ img_b = tensor2pil(image_b)
+
+ # Apply blending
+ if mode:
+ if mode == "color":
+ out_image = pilgram.css.blending.color(img_a, img_b)
+ elif mode == "color_burn":
+ out_image = pilgram.css.blending.color_burn(img_a, img_b)
+ elif mode == "color_dodge":
+ out_image = pilgram.css.blending.color_dodge(img_a, img_b)
+ elif mode == "darken":
+ out_image = pilgram.css.blending.darken(img_a, img_b)
+ elif mode == "difference":
+ out_image = pilgram.css.blending.difference(img_a, img_b)
+ elif mode == "exclusion":
+ out_image = pilgram.css.blending.exclusion(img_a, img_b)
+ elif mode == "hard_light":
+ out_image = pilgram.css.blending.hard_light(img_a, img_b)
+ elif mode == "hue":
+ out_image = pilgram.css.blending.hue(img_a, img_b)
+ elif mode == "lighten":
+ out_image = pilgram.css.blending.lighten(img_a, img_b)
+ elif mode == "multiply":
+ out_image = pilgram.css.blending.multiply(img_a, img_b)
+ elif mode == "add":
+ out_image = pilgram.css.blending.normal(img_a, img_b)
+ elif mode == "overlay":
+ out_image = pilgram.css.blending.overlay(img_a, img_b)
+ elif mode == "screen":
+ out_image = pilgram.css.blending.screen(img_a, img_b)
+ elif mode == "soft_light":
+ out_image = pilgram.css.blending.soft_light(img_a, img_b)
+ else:
+ out_image = img_a
+
+ out_image = out_image.convert("RGB")
+
+ # Blend image
+ blend_mask = Image.new(mode="L", size=img_a.size,
+ color=(round(blend_percentage * 255)))
+ blend_mask = ImageOps.invert(blend_mask)
+ out_image = Image.composite(img_a, out_image, blend_mask)
+
+ return (pil2tensor(out_image), )
+
+
+# IMAGE BLEND NODE
+
+class WAS_Image_Blend:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image_a": ("IMAGE",),
+ "image_b": ("IMAGE",),
+ "blend_percentage": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ FUNCTION = "image_blend"
+
+ CATEGORY = "WAS Suite/Image"
+
+ def image_blend(self, image_a, image_b, blend_percentage):
+
+ # Convert images to PIL
+ img_a = tensor2pil(image_a)
+ img_b = tensor2pil(image_b)
+
+ # Blend image
+ blend_mask = Image.new(mode="L", size=img_a.size,
+ color=(round(blend_percentage * 255)))
+ blend_mask = ImageOps.invert(blend_mask)
+ img_result = Image.composite(img_a, img_b, blend_mask)
+
+ del img_a, img_b, blend_mask
+
+ return (pil2tensor(img_result), )
+
+
+
+# IMAGE MONITOR DISTORTION FILTER
+
+class WAS_Image_Monitor_Distortion_Filter:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "mode": (["Digital Distortion", "Signal Distortion", "TV Distortion"],),
+ "amplitude": ("INT", {"default": 5, "min": 1, "max": 255, "step": 1}),
+ "offset": ("INT", {"default": 10, "min": 1, "max": 255, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ FUNCTION = "image_monitor_filters"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def image_monitor_filters(self, image, mode="Digital Distortion", amplitude=5, offset=5):
+
+ # Convert images to PIL
+ image = tensor2pil(image)
+
+ # WAS Filters
+ WTools = WAS_Tools_Class()
+
+ # Apply image effect
+ if mode:
+ if mode == 'Digital Distortion':
+ image = WTools.digital_distortion(image, amplitude, offset)
+ elif mode == 'Signal Distortion':
+ image = WTools.signal_distortion(image, amplitude)
+ elif mode == 'TV Distortion':
+ image = WTools.tv_vhs_distortion(image, amplitude)
+ else:
+ image = image
+
+ return (pil2tensor(image), )
+
+
+
+# IMAGE PERLIN NOISE
+
+class WAS_Image_Perlin_Noise:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "width": ("INT", {"default": 512, "max": 2048, "min": 64, "step": 1}),
+ "height": ("INT", {"default": 512, "max": 2048, "min": 64, "step": 1}),
+ "scale": ("INT", {"default": 100, "max": 2048, "min": 2, "step": 1}),
+ "octaves": ("INT", {"default": 4, "max": 8, "min": 0, "step": 1}),
+ "persistence": ("FLOAT", {"default": 0.5, "max": 100.0, "min": 0.01, "step": 0.01}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ FUNCTION = "perlin_noise"
+
+ CATEGORY = "WAS Suite/Image/Generate/Noise"
+
+ def perlin_noise(self, width, height, scale, octaves, persistence, seed):
+
+ WTools = WAS_Tools_Class()
+
+ image = WTools.perlin_noise(width, height, octaves, persistence, scale, seed)
+
+ return (pil2tensor(image), )
+
+
+# IMAGE PERLIN POWER FRACTAL
+
+class WAS_Image_Perlin_Power_Fractal:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "width": ("INT", {"default": 512, "max": 2048, "min": 64, "step": 1}),
+ "height": ("INT", {"default": 512, "max": 2048, "min": 64, "step": 1}),
+ "scale": ("INT", {"default": 100, "max": 2048, "min": 2, "step": 1}),
+ "octaves": ("INT", {"default": 4, "max": 8, "min": 0, "step": 1}),
+ "persistence": ("FLOAT", {"default": 0.5, "max": 100.0, "min": 0.01, "step": 0.01}),
+ "lacunarity": ("FLOAT", {"default": 2.0, "max": 100.0, "min": 0.01, "step": 0.01}),
+ "exponent": ("FLOAT", {"default": 2.0, "max": 100.0, "min": 0.01, "step": 0.01}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ FUNCTION = "perlin_power_fractal"
+
+ CATEGORY = "WAS Suite/Image/Generate/Noise"
+
+ def perlin_power_fractal(self, width, height, scale, octaves, persistence, lacunarity, exponent, seed):
+
+ WTools = WAS_Tools_Class()
+
+ image = WTools.perlin_power_fractal(width, height, octaves, persistence, lacunarity, exponent, scale, seed)
+
+ return (pil2tensor(image), )
+
+
+# IMAGE VORONOI NOISE FILTER
+
+class WAS_Image_Voronoi_Noise_Filter:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "width": ("INT", {"default": 512, "max": 4096, "min": 64, "step": 1}),
+ "height": ("INT", {"default": 512, "max": 4096, "min": 64, "step": 1}),
+ "density": ("INT", {"default": 50, "max": 256, "min": 10, "step": 2}),
+ "modulator": ("INT", {"default": 0, "max": 8, "min": 0, "step": 1}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ FUNCTION = "voronoi_noise_filter"
+
+ CATEGORY = "WAS Suite/Image/Generate/Noise"
+
+ def voronoi_noise_filter(self, width, height, density, modulator, seed):
+
+ WTools = WAS_Tools_Class()
+
+ image = WTools.worley_noise(height=width, width=height, density=density, option=modulator, use_broadcast_ops=True).image
+
+ return (pil2tensor(image), )
+
+# IMAGE POWER NOISE
+
+class WAS_Image_Power_Noise:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "width": ("INT", {"default": 512, "max": 4096, "min": 64, "step": 1}),
+ "height": ("INT", {"default": 512, "max": 4096, "min": 64, "step": 1}),
+ "frequency": ("FLOAT", {"default": 0.5, "max": 10.0, "min": 0.0, "step": 0.01}),
+ "attenuation": ("FLOAT", {"default": 0.5, "max": 10.0, "min": 0.0, "step": 0.01}),
+ "noise_type": (["grey", "white", "pink", "blue", "green", "mix"],),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ FUNCTION = "power_noise"
+
+ CATEGORY = "WAS Suite/Image/Generate/Noise"
+
+ def power_noise(self, width, height, frequency, attenuation, noise_type, seed):
+
+ noise_image = self.generate_power_noise(width, height, frequency, attenuation, noise_type, seed)
+
+ return (pil2tensor(noise_image), )
+
+ def generate_power_noise(self, width, height, frequency=None, attenuation=None, noise_type="white", seed=None):
+ def white_noise(width, height):
+ noise = np.random.random((height, width))
+ return noise
+
+ def grey_noise(width, height, attenuation):
+ noise = np.random.normal(0, attenuation, (height, width))
+ return noise
+
+ def pink_noise(width, height, frequency, attenuation):
+ noise = grey_noise(width, height, attenuation)
+ scale = 1.0 / (width * height)
+ f = np.fft.fftfreq(height)[:, np.newaxis] ** 2 + np.fft.fftfreq(width) ** 2
+ power = np.sqrt(f)
+ power[0, 0] = 1
+ noise = np.fft.ifft2(np.fft.fft2(noise) * power)
+ noise *= scale / noise.std()
+ return np.real(noise)
+
+ def green_noise(width, height, frequency, attenuation):
+ noise = grey_noise(width, height, attenuation)
+ scale = 1.0 / (width * height)
+ f = np.fft.fftfreq(height)[:, np.newaxis] ** 2 + np.fft.fftfreq(width) ** 2
+ power = np.sqrt(f)
+ power[0, 0] = 1
+ noise = np.fft.ifft2(np.fft.fft2(noise) / np.sqrt(power))
+ noise *= scale / noise.std()
+ return np.real(noise)
+
+ def blue_noise(width, height, frequency, attenuation):
+ noise = grey_noise(width, height, attenuation)
+ scale = 1.0 / (width * height)
+ f = np.fft.fftfreq(height)[:, np.newaxis] ** 2 + np.fft.fftfreq(width) ** 2
+ power = np.sqrt(f)
+ power[0, 0] = 1
+ noise = np.fft.ifft2(np.fft.fft2(noise) / power)
+ noise *= scale / noise.std()
+ return np.real(noise)
+
+ def blue_noise_mask(width, height, frequency, attenuation, seed, num_masks=3):
+ masks = []
+ for i in range(num_masks):
+ mask_seed = seed + i
+ np.random.seed(mask_seed)
+ mask = blue_noise(width, height, frequency, attenuation)
+ masks.append(mask)
+ return masks
+
+ def blend_noise(width, height, masks, noise_types, attenuations):
+ blended_image = Image.new("L", (width, height), color=0)
+ i = 0
+ for mask, noise_type, attenuation in zip(masks, noise_types, attenuations):
+ mask = Image.fromarray((255 * (mask - np.min(mask)) / (np.max(mask) - np.min(mask))).astype(np.uint8).real)
+ if noise_type == "white":
+ noise = white_noise(width, height)
+ noise = Image.fromarray((255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))).astype(np.uint8).real)
+ elif noise_type == "grey":
+ noise = grey_noise(width, height, attenuation)
+ noise = Image.fromarray((255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))).astype(np.uint8).real)
+ elif noise_type == "pink":
+ noise = pink_noise(width, height, frequency, attenuation)
+ noise = Image.fromarray((255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))).astype(np.uint8).real)
+ elif noise_type == "green":
+ noise = green_noise(width, height, frequency, attenuation)
+ noise = Image.fromarray((255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))).astype(np.uint8).real)
+ elif noise_type == "blue":
+ noise = blue_noise(width, height, frequency, attenuation)
+ noise = Image.fromarray((255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))).astype(np.uint8).real)
+
+ blended_image = Image.composite(blended_image, noise, mask)
+ i += 1
+
+ return np.asarray(blended_image)
+
+ def shorten_to_range(value, min_value, max_value):
+ range_length = max_value - min_value + 1
+ return ((value - min_value) % range_length) + min_value
+
+ if seed is not None:
+ if seed > 4294967294:
+ seed = shorten_to_range(seed, 0, 4294967293)
+ cstr(f"Seed too large for power noise; rescaled to: {seed}").warning.print()
+
+ np.random.seed(seed)
+
+ if noise_type == "white":
+ noise = white_noise(width, height)
+ elif noise_type == "grey":
+ noise = grey_noise(width, height, attenuation)
+ elif noise_type == "pink":
+ if frequency is None:
+ cstr("Pink noise requires a frequency value.").error.print()
+ return None
+ noise = pink_noise(width, height, frequency, attenuation)
+ elif noise_type == "green":
+ if frequency is None:
+ cstr("Green noise requires a frequency value.").error.print()
+ return None
+ noise = green_noise(width, height, frequency, attenuation)
+ elif noise_type == "blue":
+ if frequency is None:
+ cstr("Blue noise requires a frequency value.").error.print()
+ return None
+ noise = blue_noise(width, height, frequency, attenuation)
+ elif noise_type == "mix":
+ if frequency is None:
+ cstr("Mix noise requires a frequency value.").error.print()
+ return None
+ if seed is None:
+ cstr("Mix noise requires a seed value.").error.print()
+ return None
+
+ blue_noise_masks = blue_noise_mask(width, height, frequency, attenuation, seed=seed, num_masks=3)
+ noise_types = ["white", "grey", "pink", "green", "blue"]
+ attenuations = [attenuation] * len(noise_types)
+ noise = blend_noise(width, height, blue_noise_masks, noise_types, attenuations)
+ else:
+ cstr(f"Unsupported noise type `{noise_type}`").error.print()
+ return None
+ if noise_type != 'mix':
+ noise = 255 * (noise - np.min(noise)) / (np.max(noise) - np.min(noise))
+ noise_image = Image.fromarray(noise.astype(np.uint8).real)
+
+ return noise_image.convert("RGB")
+
+# IMAGE TO NOISE
+
+class WAS_Image_To_Noise:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "num_colors": ("INT", {"default": 16, "max": 256, "min": 2, "step": 2}),
+ "black_mix": ("INT", {"default": 0, "max": 20, "min": 0, "step": 1}),
+ "gaussian_mix": ("FLOAT", {"default": 0.0, "max": 1024, "min": 0, "step": 0.1}),
+ "brightness": ("FLOAT", {"default": 1.0, "max": 2.0, "min": 0.0, "step": 0.01}),
+ "output_mode": (["batch","list"],),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ OUTPUT_IS_LIST = (False,)
+ FUNCTION = "image_to_noise"
+
+ CATEGORY = "WAS Suite/Image/Generate/Noise"
+
+ def image_to_noise(self, images, num_colors, black_mix, gaussian_mix, brightness, output_mode, seed):
+
+ noise_images = []
+ for image in images:
+ noise_images.append(pil2tensor(self.image2noise(tensor2pil(image), num_colors, black_mix, brightness, gaussian_mix, seed)))
+ if output_mode == "list":
+ self.OUTPUT_IS_LIST = (True,)
+ else:
+ noise_images = torch.cat(noise_images, dim=0)
+ return (noise_images, )
+
+ def image2noise(self, image, num_colors=16, black_mix=0, brightness=1.0, gaussian_mix=0, seed=0):
+
+ random.seed(int(seed))
+ image = image.quantize(colors=num_colors)
+ image = image.convert("RGBA")
+ pixel_data = list(image.getdata())
+ random.shuffle(pixel_data)
+ randomized_image = Image.new("RGBA", image.size)
+ randomized_image.putdata(pixel_data)
+
+ width, height = image.size
+ black_noise = Image.new("RGBA", (width, height), (0, 0, 0, 0))
+
+ for _ in range(black_mix):
+ for x in range(width):
+ for y in range(height):
+ if random.randint(0,1) == 1:
+ black_noise.putpixel((x, y), (0, 0, 0, 255))
+
+ randomized_image = Image.alpha_composite(randomized_image, black_noise)
+ enhancer = ImageEnhance.Brightness(randomized_image)
+ randomized_image = enhancer.enhance(brightness)
+
+ if gaussian_mix > 0:
+ original_noise = randomized_image.copy()
+ randomized_gaussian = randomized_image.filter(ImageFilter.GaussianBlur(radius=gaussian_mix))
+ randomized_image = Image.blend(randomized_image, randomized_gaussian, 0.65)
+ randomized_image = Image.blend(randomized_image, original_noise, 0.25)
+
+ return randomized_image
+
+# IMAGE MAKE SEAMLESS
+
+class WAS_Image_Make_Seamless:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "blending": ("FLOAT", {"default": 0.4, "max": 1.0, "min": 0.0, "step": 0.01}),
+ "tiled": (["true", "false"],),
+ "tiles": ("INT", {"default": 2, "max": 6, "min": 2, "step": 2}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "make_seamless"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def make_seamless(self, images, blending, tiled, tiles):
+
+ WTools = WAS_Tools_Class()
+
+ seamless_images = []
+ for image in images:
+ seamless_images.append(pil2tensor(WTools.make_seamless(tensor2pil(image), blending, tiled, tiles)))
+
+ seamless_images = torch.cat(seamless_images, dim=0)
+
+ return (seamless_images, )
+
+
+# IMAGE DISPLACEMENT WARP
+
+class WAS_Image_Displacement_Warp:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "displacement_maps": ("IMAGE",),
+ "amplitude": ("FLOAT", {"default": 25.0, "min": -4096, "max": 4096, "step": 0.1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "displace_image"
+
+ CATEGORY = "WAS Suite/Image/Transform"
+
+ def displace_image(self, images, displacement_maps, amplitude):
+
+ WTools = WAS_Tools_Class()
+
+ displaced_images = []
+ for i in range(len(images)):
+ img = tensor2pil(images[i])
+ if i < len(displacement_maps):
+ disp = tensor2pil(displacement_maps[i])
+ else:
+ disp = tensor2pil(displacement_maps[-1])
+ disp = self.resize_and_crop(disp, img.size)
+ displaced_images.append(pil2tensor(WTools.displace_image(img, disp, amplitude)))
+
+ displaced_images = torch.cat(displaced_images, dim=0)
+
+ return (displaced_images, )
+
+
+ def resize_and_crop(self, image, target_size):
+ width, height = image.size
+ target_width, target_height = target_size
+ aspect_ratio = width / height
+ target_aspect_ratio = target_width / target_height
+
+ if aspect_ratio > target_aspect_ratio:
+ new_height = target_height
+ new_width = int(new_height * aspect_ratio)
+ else:
+ new_width = target_width
+ new_height = int(new_width / aspect_ratio)
+
+ image = image.resize((new_width, new_height))
+ left = (new_width - target_width) // 2
+ top = (new_height - target_height) // 2
+ right = left + target_width
+ bottom = top + target_height
+ image = image.crop((left, top, right, bottom))
+
+ return image
+
+# IMAGE TO BATCH
+
+class WAS_Image_Batch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "optional": {
+ "images_a": ("IMAGE",),
+ "images_b": ("IMAGE",),
+ "images_c": ("IMAGE",),
+ "images_d": ("IMAGE",),
+ # "images_e": ("IMAGE",),
+ # "images_f": ("IMAGE",),
+ # Theoretically, an infinite number of image input parameters can be added.
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ FUNCTION = "image_batch"
+ CATEGORY = "WAS Suite/Image"
+
+ def _check_image_dimensions(self, tensors, names):
+ dimensions = [tensor.shape for tensor in tensors]
+ if len(set(dimensions)) > 1:
+ mismatched_indices = [i for i, dim in enumerate(dimensions) if dim != dimensions[0]]
+ mismatched_images = [names[i] for i in mismatched_indices]
+ raise ValueError(f"WAS Image Batch Warning: Input image dimensions do not match for images: {mismatched_images}")
+
+ def image_batch(self, **kwargs):
+ batched_tensors = [kwargs[key] for key in kwargs if kwargs[key] is not None]
+ image_names = [key for key in kwargs if kwargs[key] is not None]
+
+ if not batched_tensors:
+ raise ValueError("At least one input image must be provided.")
+
+ self._check_image_dimensions(batched_tensors, image_names)
+ batched_tensors = torch.cat(batched_tensors, dim=0)
+ return (batched_tensors,)
+
+
+# MASK TO BATCH
+
+class WAS_Mask_Batch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "optional": {
+ "masks_a": ("MASK",),
+ "masks_b": ("MASK",),
+ "masks_c": ("MASK",),
+ "masks_d": ("MASK",),
+ # "masks_e": ("MASK",),
+ # "masks_f": ("MASK",),
+ # Theoretically, an infinite number of mask input parameters can be added.
+ },
+ }
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("masks",)
+ FUNCTION = "mask_batch"
+ CATEGORY = "WAS Suite/Mask"
+
+ def _check_mask_dimensions(self, tensors, names):
+ dimensions = [tensor.shape[1:] for tensor in tensors] # Exclude the batch dimension (if present)
+ if len(set(dimensions)) > 1:
+ mismatched_indices = [i for i, dim in enumerate(dimensions) if dim != dimensions[0]]
+ mismatched_masks = [names[i] for i in mismatched_indices]
+ raise ValueError(f"WAS Mask Batch Warning: Input mask dimensions do not match for masks: {mismatched_masks}")
+
+ def mask_batch(self, **kwargs):
+ batched_tensors = [kwargs[key] for key in kwargs if kwargs[key] is not None]
+ mask_names = [key for key in kwargs if kwargs[key] is not None]
+
+ if not batched_tensors:
+ raise ValueError("At least one input mask must be provided.")
+
+ self._check_mask_dimensions(batched_tensors, mask_names)
+ batched_tensors = torch.stack(batched_tensors, dim=0)
+ batched_tensors = batched_tensors.unsqueeze(1) # Add a channel dimension
+ return (batched_tensors,)
+
+# IMAGE GENERATE COLOR PALETTE
+
+class WAS_Image_Color_Palette:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "colors": ("INT", {"default": 16, "min": 8, "max": 256, "step": 1}),
+ "mode": (["Chart", "back_to_back"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE","LIST")
+ RETURN_NAMES = ("image","color_palettes")
+ FUNCTION = "image_generate_palette"
+
+ CATEGORY = "WAS Suite/Image/Analyze"
+
+ def image_generate_palette(self, image, colors=16, mode="chart"):
+
+ # WAS Filters
+ WTools = WAS_Tools_Class()
+
+ res_dir = os.path.join(WAS_SUITE_ROOT, 'res')
+ font = os.path.join(res_dir, 'font.ttf')
+
+ if not os.path.exists(font):
+ font = None
+ else:
+ if mode == "Chart":
+ cstr(f'Found font at `{font}`').msg.print()
+
+ if len(image) > 1:
+ palette_strings = []
+ palette_images = []
+ for img in image:
+ img = tensor2pil(img)
+ palette_image, palette = WTools.generate_palette(img, colors, 128, 10, font, 15, mode.lower())
+ palette_images.append(pil2tensor(palette_image))
+ palette_strings.append(palette)
+ palette_images = torch.cat(palette_images, dim=0)
+ return (palette_images, palette_strings)
+ else:
+ image = tensor2pil(image)
+ palette_image, palette = WTools.generate_palette(image, colors, 128, 10, font, 15, mode.lower())
+ return (pil2tensor(palette_image), [palette,])
+
+
+# IMAGE ANALYZE
+
+class WAS_Image_Analyze:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "mode": (["Black White Levels", "RGB Levels"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_analyze"
+
+ CATEGORY = "WAS Suite/Image/Analyze"
+
+ def image_analyze(self, image, mode='Black White Levels'):
+
+ # Convert images to PIL
+ image = tensor2pil(image)
+
+ # WAS Filters
+ WTools = WAS_Tools_Class()
+
+ # Analye Image
+ if mode:
+ if mode == 'Black White Levels':
+ image = WTools.black_white_levels(image)
+ elif mode == 'RGB Levels':
+ image = WTools.channel_frequency(image)
+ else:
+ image = image
+
+ return (pil2tensor(image), )
+
+
+# IMAGE GENERATE GRADIENT
+
+class WAS_Image_Generate_Gradient:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ gradient_stops = '''0:255,0,0
+25:255,255,255
+50:0,255,0
+75:0,0,255'''
+ return {
+ "required": {
+ "width": ("INT", {"default":512, "max": 4096, "min": 64, "step":1}),
+ "height": ("INT", {"default":512, "max": 4096, "min": 64, "step":1}),
+ "direction": (["horizontal", "vertical"],),
+ "tolerance": ("INT", {"default":0, "max": 255, "min": 0, "step":1}),
+ "gradient_stops": ("STRING", {"default": gradient_stops, "multiline": True}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_gradient"
+
+ CATEGORY = "WAS Suite/Image/Generate"
+
+ def image_gradient(self, gradient_stops, width=512, height=512, direction='horizontal', tolerance=0):
+
+ import io
+
+ # WAS Filters
+ WTools = WAS_Tools_Class()
+
+ colors_dict = {}
+ stops = io.StringIO(gradient_stops.strip().replace(' ',''))
+ for stop in stops:
+ parts = stop.split(':')
+ colors = parts[1].replace('\n','').split(',')
+ colors_dict[parts[0].replace('\n','')] = colors
+
+ image = WTools.gradient((width, height), direction, colors_dict, tolerance)
+
+ return (pil2tensor(image), )
+
+# IMAGE GRADIENT MAP
+
+class WAS_Image_Gradient_Map:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "gradient_image": ("IMAGE",),
+ "flip_left_right": (["false", "true"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_gradient_map"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def image_gradient_map(self, image, gradient_image, flip_left_right='false'):
+
+ # Convert images to PIL
+ image = tensor2pil(image)
+ gradient_image = tensor2pil(gradient_image)
+
+ # WAS Filters
+ WTools = WAS_Tools_Class()
+
+ image = WTools.gradient_map(image, gradient_image, (True if flip_left_right == 'true' else False))
+
+ return (pil2tensor(image), )
+
+
+# IMAGE TRANSPOSE
+
+class WAS_Image_Transpose:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "image_overlay": ("IMAGE",),
+ "width": ("INT", {"default": 512, "min": -48000, "max": 48000, "step": 1}),
+ "height": ("INT", {"default": 512, "min": -48000, "max": 48000, "step": 1}),
+ "X": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),
+ "Y": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),
+ "rotation": ("INT", {"default": 0, "min": -360, "max": 360, "step": 1}),
+ "feathering": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_transpose"
+
+ CATEGORY = "WAS Suite/Image/Transform"
+
+ def image_transpose(self, image: torch.Tensor, image_overlay: torch.Tensor, width: int, height: int, X: int, Y: int, rotation: int, feathering: int = 0):
+ return (pil2tensor(self.apply_transpose_image(tensor2pil(image), tensor2pil(image_overlay), (width, height), (X, Y), rotation, feathering)), )
+
+ def apply_transpose_image(self, image_bg, image_element, size, loc, rotate=0, feathering=0):
+
+ # Apply transformations to the element image
+ image_element = image_element.rotate(rotate, expand=True)
+ image_element = image_element.resize(size)
+
+ # Create a mask for the image with the faded border
+ if feathering > 0:
+ mask = Image.new('L', image_element.size, 255) # Initialize with 255 instead of 0
+ draw = ImageDraw.Draw(mask)
+ for i in range(feathering):
+ alpha_value = int(255 * (i + 1) / feathering) # Invert the calculation for alpha value
+ draw.rectangle((i, i, image_element.size[0] - i, image_element.size[1] - i), fill=alpha_value)
+ alpha_mask = Image.merge('RGBA', (mask, mask, mask, mask))
+ image_element = Image.composite(image_element, Image.new('RGBA', image_element.size, (0, 0, 0, 0)), alpha_mask)
+
+ # Create a new image of the same size as the base image with an alpha channel
+ new_image = Image.new('RGBA', image_bg.size, (0, 0, 0, 0))
+ new_image.paste(image_element, loc)
+
+ # Paste the new image onto the base image
+ image_bg = image_bg.convert('RGBA')
+ image_bg.paste(new_image, (0, 0), new_image)
+
+ return image_bg
+
+
+
+# IMAGE RESCALE
+
+class WAS_Image_Rescale:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "mode": (["rescale", "resize"],),
+ "supersample": (["true", "false"],),
+ "resampling": (["lanczos", "nearest", "bilinear", "bicubic"],),
+ "rescale_factor": ("FLOAT", {"default": 2, "min": 0.01, "max": 16.0, "step": 0.01}),
+ "resize_width": ("INT", {"default": 1024, "min": 1, "max": 48000, "step": 1}),
+ "resize_height": ("INT", {"default": 1536, "min": 1, "max": 48000, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_rescale"
+
+ CATEGORY = "WAS Suite/Image/Transform"
+
+ def image_rescale(self, image, mode="rescale", supersample='true', resampling="lanczos", rescale_factor=2, resize_width=1024, resize_height=1024):
+ scaled_images = []
+ for img in image:
+ scaled_images.append(pil2tensor(self.apply_resize_image(tensor2pil(img), mode, supersample, rescale_factor, resize_width, resize_height, resampling)))
+ scaled_images = torch.cat(scaled_images, dim=0)
+
+ return (scaled_images, )
+
+ def apply_resize_image(self, image: Image.Image, mode='scale', supersample='true', factor: int = 2, width: int = 1024, height: int = 1024, resample='bicubic'):
+
+ # Get the current width and height of the image
+ current_width, current_height = image.size
+
+ # Calculate the new width and height based on the given mode and parameters
+ if mode == 'rescale':
+ new_width, new_height = int(
+ current_width * factor), int(current_height * factor)
+ else:
+ new_width = width if width % 8 == 0 else width + (8 - width % 8)
+ new_height = height if height % 8 == 0 else height + \
+ (8 - height % 8)
+
+ # Define a dictionary of resampling filters
+ resample_filters = {
+ 'nearest': 0,
+ 'bilinear': 2,
+ 'bicubic': 3,
+ 'lanczos': 1
+ }
+
+ # Apply supersample
+ if supersample == 'true':
+ image = image.resize((new_width * 8, new_height * 8), resample=Image.Resampling(resample_filters[resample]))
+
+ # Resize the image using the given resampling filter
+ resized_image = image.resize((new_width, new_height), resample=Image.Resampling(resample_filters[resample]))
+
+ return resized_image
+
+
+# LOAD IMAGE BATCH
+
+class WAS_Load_Image_Batch:
+ def __init__(self):
+ self.HDB = WASDatabase(WAS_HISTORY_DATABASE)
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "mode": (["single_image", "incremental_image"],),
+ "index": ("INT", {"default": 0, "min": 0, "max": 150000, "step": 1}),
+ "label": ("STRING", {"default": 'Batch 001', "multiline": False}),
+ "path": ("STRING", {"default": '', "multiline": False}),
+ "pattern": ("STRING", {"default": '*', "multiline": False}),
+ "allow_RGBA_output": (["false","true"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",TEXT_TYPE)
+ RETURN_NAMES = ("image","filename_text")
+ FUNCTION = "load_batch_images"
+
+ CATEGORY = "WAS Suite/IO"
+
+ def load_batch_images(self, path, pattern='*', index=0, mode="single_image", label='Batch 001', allow_RGBA_output='false'):
+
+ allow_RGBA_output = (allow_RGBA_output == 'true')
+
+ if not os.path.exists(path):
+ return (None, )
+ fl = self.BatchImageLoader(path, label, pattern)
+ new_paths = fl.image_paths
+ if mode == 'single_image':
+ image, filename = fl.get_image_by_id(index)
+ if image == None:
+ cstr(f"No valid image was found for the inded `{index}`").error.print()
+ return (None, None)
+ else:
+ image, filename = fl.get_next_image()
+ if image == None:
+ cstr(f"No valid image was found for the next ID. Did you remove images from the source directory?").error.print()
+ return (None, None)
+
+ # Update history
+ update_history_images(new_paths)
+
+ if not allow_RGBA_output:
+ image = image.convert("RGB")
+
+ return (pil2tensor(image), filename)
+
+ class BatchImageLoader:
+ def __init__(self, directory_path, label, pattern):
+ self.WDB = WDB
+ self.image_paths = []
+ self.load_images(directory_path, pattern)
+ self.image_paths.sort()
+ stored_directory_path = self.WDB.get('Batch Paths', label)
+ stored_pattern = self.WDB.get('Batch Patterns', label)
+ if stored_directory_path != directory_path or stored_pattern != pattern:
+ self.index = 0
+ self.WDB.insert('Batch Counters', label, 0)
+ self.WDB.insert('Batch Paths', label, directory_path)
+ self.WDB.insert('Batch Patterns', label, pattern)
+ else:
+ self.index = self.WDB.get('Batch Counters', label)
+ self.label = label
+
+ def load_images(self, directory_path, pattern):
+ for file_name in glob.glob(os.path.join(directory_path, pattern), recursive=True):
+ if file_name.lower().endswith(ALLOWED_EXT):
+ abs_file_path = os.path.abspath(file_name)
+ self.image_paths.append(abs_file_path)
+
+ def get_image_by_id(self, image_id):
+ if image_id < 0 or image_id >= len(self.image_paths):
+ cstr(f"Invalid image index `{image_id}`").error.print()
+ return
+ i = Image.open(self.image_paths[image_id])
+ i = ImageOps.exif_transpose(i)
+ return (i, os.path.basename(self.image_paths[image_id]))
+
+ def get_next_image(self):
+ if self.index >= len(self.image_paths):
+ self.index = 0
+ image_path = self.image_paths[self.index]
+ self.index += 1
+ if self.index == len(self.image_paths):
+ self.index = 0
+ cstr(f'{cstr.color.YELLOW}{self.label}{cstr.color.END} Index: {self.index}').msg.print()
+ self.WDB.insert('Batch Counters', self.label, self.index)
+ i = Image.open(image_path)
+ i = ImageOps.exif_transpose(i)
+ return (i, os.path.basename(image_path))
+
+ def get_current_image(self):
+ if self.index >= len(self.image_paths):
+ self.index = 0
+ image_path = self.image_paths[self.index]
+ return os.path.basename(image_path)
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ if kwargs['mode'] != 'single_image':
+ return float("NaN")
+ else:
+ fl = WAS_Load_Image_Batch.BatchImageLoader(kwargs['path'], kwargs['label'], kwargs['pattern'])
+ filename = fl.get_current_image()
+ image = os.path.join(kwargs['path'], filename)
+ sha = get_sha256(image)
+ return sha
+
+
+# IMAGE HISTORY NODE
+
+class WAS_Image_History:
+ def __init__(self):
+ self.HDB = WASDatabase(WAS_HISTORY_DATABASE)
+ self.conf = getSuiteConfig()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ HDB = WASDatabase(WAS_HISTORY_DATABASE)
+ conf = getSuiteConfig()
+ paths = ['No History']
+ if HDB.catExists("History") and HDB.keyExists("History", "Images"):
+ history_paths = HDB.get("History", "Images")
+ if conf.__contains__('history_display_limit'):
+ history_paths = history_paths[-conf['history_display_limit']:]
+ paths = []
+ for path_ in history_paths:
+ paths.append(os.path.join('...'+os.sep+os.path.basename(os.path.dirname(path_)), os.path.basename(path_)))
+
+ return {
+ "required": {
+ "image": (paths,),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",TEXT_TYPE)
+ RETURN_NAMES = ("image","filename_text")
+ FUNCTION = "image_history"
+
+ CATEGORY = "WAS Suite/History"
+
+ def image_history(self, image):
+ self.HDB = WASDatabase(WAS_HISTORY_DATABASE)
+ paths = {}
+ if self.HDB.catExists("History") and self.HDB.keyExists("History", "Images"):
+ history_paths = self.HDB.get("History", "Images")
+ for path_ in history_paths:
+ paths.update({os.path.join('...'+os.sep+os.path.basename(os.path.dirname(path_)), os.path.basename(path_)): path_})
+ if os.path.exists(paths[image]) and paths.__contains__(image):
+ return (pil2tensor(Image.open(paths[image]).convert('RGB')), os.path.basename(paths[image]))
+ else:
+ cstr(f"The image `{image}` does not exist!").error.print()
+ return (pil2tensor(Image.new('RGB', (512,512), (0, 0, 0, 0))), 'null')
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+# IMAGE PADDING
+
+class WAS_Image_Stitch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image_a": ("IMAGE",),
+ "image_b": ("IMAGE",),
+ "stitch": (["top", "left", "bottom", "right"],),
+ "feathering": ("INT", {"default": 50, "min": 0, "max": 2048, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_stitching"
+
+ CATEGORY = "WAS Suite/Image/Transform"
+
+ def image_stitching(self, image_a, image_b, stitch="right", feathering=50):
+
+ valid_stitches = ["top", "left", "bottom", "right"]
+ if stitch not in valid_stitches:
+ cstr(f"The stitch mode `{stitch}` is not valid. Valid sitch modes are {', '.join(valid_stitches)}").error.print()
+ if feathering > 2048:
+ cstr(f"The stitch feathering of `{feathering}` is too high. Please choose a value between `0` and `2048`").error.print()
+
+ WTools = WAS_Tools_Class();
+
+ stitched_image = WTools.stitch_image(tensor2pil(image_a), tensor2pil(image_b), stitch, feathering)
+
+ return (pil2tensor(stitched_image), )
+
+
+
+# IMAGE PADDING
+
+class WAS_Image_Padding:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "feathering": ("INT", {"default": 120, "min": 0, "max": 2048, "step": 1}),
+ "feather_second_pass": (["true", "false"],),
+ "left_padding": ("INT", {"default": 512, "min": 8, "max": 48000, "step": 1}),
+ "right_padding": ("INT", {"default": 512, "min": 8, "max": 48000, "step": 1}),
+ "top_padding": ("INT", {"default": 512, "min": 8, "max": 48000, "step": 1}),
+ "bottom_padding": ("INT", {"default": 512, "min": 8, "max": 48000, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE", "IMAGE")
+ FUNCTION = "image_padding"
+
+ CATEGORY = "WAS Suite/Image/Transform"
+
+ def image_padding(self, image, feathering, left_padding, right_padding, top_padding, bottom_padding, feather_second_pass=True):
+ padding = self.apply_image_padding(tensor2pil(
+ image), left_padding, right_padding, top_padding, bottom_padding, feathering, second_pass=True)
+ return (pil2tensor(padding[0]), pil2tensor(padding[1]))
+
+ def apply_image_padding(self, image, left_pad=100, right_pad=100, top_pad=100, bottom_pad=100, feather_radius=50, second_pass=True):
+ # Create a mask for the feathered edge
+ mask = Image.new('L', image.size, 255)
+ draw = ImageDraw.Draw(mask)
+
+ # Draw black rectangles at each edge of the image with the specified feather radius
+ draw.rectangle((0, 0, feather_radius*2, image.height), fill=0)
+ draw.rectangle((image.width-feather_radius*2, 0,
+ image.width, image.height), fill=0)
+ draw.rectangle((0, 0, image.width, feather_radius*2), fill=0)
+ draw.rectangle((0, image.height-feather_radius*2,
+ image.width, image.height), fill=0)
+
+ # Blur the mask to create a smooth gradient between the black shapes and the white background
+ mask = mask.filter(ImageFilter.GaussianBlur(radius=feather_radius))
+
+ # Apply mask if second_pass is False, apply both masks if second_pass is True
+ if second_pass:
+
+ # Create a second mask for the additional feathering pass
+ mask2 = Image.new('L', image.size, 255)
+ draw2 = ImageDraw.Draw(mask2)
+
+ # Draw black rectangles at each edge of the image with a smaller feather radius
+ feather_radius2 = int(feather_radius / 4)
+ draw2.rectangle((0, 0, feather_radius2*2, image.height), fill=0)
+ draw2.rectangle((image.width-feather_radius2*2, 0,
+ image.width, image.height), fill=0)
+ draw2.rectangle((0, 0, image.width, feather_radius2*2), fill=0)
+ draw2.rectangle((0, image.height-feather_radius2*2,
+ image.width, image.height), fill=0)
+
+ # Blur the mask to create a smooth gradient between the black shapes and the white background
+ mask2 = mask2.filter(
+ ImageFilter.GaussianBlur(radius=feather_radius2))
+
+ feathered_im = Image.new('RGBA', image.size, (0, 0, 0, 0))
+ feathered_im.paste(image, (0, 0), mask)
+ feathered_im.paste(image, (0, 0), mask)
+
+ # Apply the second mask to the feathered image
+ feathered_im.paste(image, (0, 0), mask2)
+ feathered_im.paste(image, (0, 0), mask2)
+
+ else:
+
+ # Apply the fist maskk
+ feathered_im = Image.new('RGBA', image.size, (0, 0, 0, 0))
+ feathered_im.paste(image, (0, 0), mask)
+
+ # Calculate the new size of the image with padding added
+ new_size = (feathered_im.width + left_pad + right_pad,
+ feathered_im.height + top_pad + bottom_pad)
+
+ # Create a new transparent image with the new size
+ new_im = Image.new('RGBA', new_size, (0, 0, 0, 0))
+
+ # Paste the feathered image onto the new image with the padding
+ new_im.paste(feathered_im, (left_pad, top_pad))
+
+ # Create Padding Mask
+ padding_mask = Image.new('L', new_size, 0)
+
+ # Create a mask where the transparent pixels have a gradient
+ gradient = [(int(255 * (1 - p[3] / 255)) if p[3] != 0 else 255)
+ for p in new_im.getdata()]
+ padding_mask.putdata(gradient)
+
+ # Save the new image with alpha channel as a PNG file
+ return (new_im, padding_mask.convert('RGB'))
+
+
+# IMAGE THRESHOLD NODE
+
+class WAS_Image_Threshold:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_threshold"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def image_threshold(self, image, threshold=0.5):
+ return (pil2tensor(self.apply_threshold(tensor2pil(image), threshold)), )
+
+ def apply_threshold(self, input_image, threshold=0.5):
+ # Convert the input image to grayscale
+ grayscale_image = input_image.convert('L')
+
+ # Apply the threshold to the grayscale image
+ threshold_value = int(threshold * 255)
+ thresholded_image = grayscale_image.point(
+ lambda x: 255 if x >= threshold_value else 0, mode='L')
+
+ return thresholded_image
+
+
+# IMAGE CHROMATIC ABERRATION NODE
+
+class WAS_Image_Chromatic_Aberration:
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "red_offset": ("INT", {"default": 2, "min": -255, "max": 255, "step": 1}),
+ "green_offset": ("INT", {"default": -1, "min": -255, "max": 255, "step": 1}),
+ "blue_offset": ("INT", {"default": 1, "min": -255, "max": 255, "step": 1}),
+ "intensity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "fade_radius": ("INT", {"default": 12, "min": 0, "max": 1024, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_chromatic_aberration"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def image_chromatic_aberration(self, image, red_offset=4, green_offset=2, blue_offset=0, intensity=1, fade_radius=12):
+ return (pil2tensor(self.apply_chromatic_aberration(tensor2pil(image), red_offset, green_offset, blue_offset, intensity, fade_radius)), )
+
+ def apply_chromatic_aberration(self, img, r_offset, g_offset, b_offset, intensity, fade_radius):
+
+ def lingrad(size, direction, white_ratio):
+ image = Image.new('RGB', size)
+ draw = ImageDraw.Draw(image)
+ if direction == 'vertical':
+ black_end = size[1] - white_ratio
+ range_start = 0
+ range_end = size[1]
+ range_step = 1
+ for y in range(range_start, range_end, range_step):
+ color_ratio = y / size[1]
+ if y <= black_end:
+ color = (0, 0, 0)
+ else:
+ color_value = int(((y - black_end) / (size[1] - black_end)) * 255)
+ color = (color_value, color_value, color_value)
+ draw.line([(0, y), (size[0], y)], fill=color)
+ elif direction == 'horizontal':
+ black_end = size[0] - white_ratio
+ range_start = 0
+ range_end = size[0]
+ range_step = 1
+ for x in range(range_start, range_end, range_step):
+ color_ratio = x / size[0]
+ if x <= black_end:
+ color = (0, 0, 0)
+ else:
+ color_value = int(((x - black_end) / (size[0] - black_end)) * 255)
+ color = (color_value, color_value, color_value)
+ draw.line([(x, 0), (x, size[1])], fill=color)
+
+ return image.convert("L")
+
+ def create_fade_mask(size, fade_radius):
+ mask = Image.new("L", size, 255)
+
+ left = ImageOps.invert(lingrad(size, 'horizontal', int(fade_radius * 2)))
+ right = left.copy().transpose(Image.FLIP_LEFT_RIGHT)
+ top = ImageOps.invert(lingrad(size, 'vertical', int(fade_radius *2)))
+ bottom = top.copy().transpose(Image.FLIP_TOP_BOTTOM)
+
+ # Multiply masks with the original mask image
+ mask = ImageChops.multiply(mask, left)
+ mask = ImageChops.multiply(mask, right)
+ mask = ImageChops.multiply(mask, top)
+ mask = ImageChops.multiply(mask, bottom)
+ mask = ImageChops.multiply(mask, mask)
+
+ return mask
+
+ # split the channels of the image
+ r, g, b = img.split()
+
+ # apply the offset to each channel
+ r_offset_img = ImageChops.offset(r, r_offset, 0)
+ g_offset_img = ImageChops.offset(g, 0, g_offset)
+ b_offset_img = ImageChops.offset(b, 0, b_offset)
+
+ # merge the channels with the offsets
+ merged = Image.merge("RGB", (r_offset_img, g_offset_img, b_offset_img))
+
+ # create fade masks for blending
+ fade_mask = create_fade_mask(img.size, fade_radius)
+
+ # merge the blended channels back into an RGB image
+ result = Image.composite(merged, img, fade_mask).convert("RGB")
+
+ return result
+
+
+# IMAGE BLOOM FILTER
+
+class WAS_Image_Bloom_Filter:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "radius": ("FLOAT", {"default": 10, "min": 0.0, "max": 1024, "step": 0.1}),
+ "intensity": ("FLOAT", {"default": 1, "min": 0.0, "max": 1.0, "step": 0.1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_bloom"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def image_bloom(self, image, radius=0.5, intensity=1.0):
+ return (pil2tensor(self.apply_bloom_filter(tensor2pil(image), radius, intensity)), )
+
+ def apply_bloom_filter(self, input_image, radius, bloom_factor):
+ # Apply a blur filter to the input image
+ blurred_image = input_image.filter(
+ ImageFilter.GaussianBlur(radius=radius))
+
+ # Subtract the blurred image from the input image to create a high-pass filter
+ high_pass_filter = ImageChops.subtract(input_image, blurred_image)
+
+ # Create a blurred version of the bloom filter
+ bloom_filter = high_pass_filter.filter(
+ ImageFilter.GaussianBlur(radius=radius*2))
+
+ # Adjust brightness and levels of bloom filter
+ bloom_filter = ImageEnhance.Brightness(bloom_filter).enhance(2.0)
+
+ # Multiply the bloom image with the bloom factor
+ bloom_filter = ImageChops.multiply(bloom_filter, Image.new('RGB', input_image.size, (int(
+ 255 * bloom_factor), int(255 * bloom_factor), int(255 * bloom_factor))))
+
+ # Multiply the bloom filter with the original image using the bloom factor
+ blended_image = ImageChops.screen(input_image, bloom_filter)
+
+ return blended_image
+
+
+# IMAGE ROTATE HUE
+
+class WAS_Image_Rotate_Hue:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "hue_shift": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "rotate_hue"
+
+ CATEGORY = "WAS Suite/Image/Adjustment"
+
+ def rotate_hue(self, image, hue_shift=0.0):
+ if hue_shift > 1.0 or hue_shift < 0.0:
+ cstr(f"The hue_shift `{cstr.color.LIGHTYELLOW}{hue_shift}{cstr.color.END}` is out of range. Valid range is {cstr.color.BOLD}0.0 - 1.0{cstr.color.END}").error.print()
+ hue_shift = 0.0
+ return (pil2tensor(self.rotate_hue(tensor2pil(image), hue_shift)), )
+
+ def rotate_hue(self, image, hue_shift=0.0):
+ import colorsys
+ def rgb_to_hls(r, g, b):
+ h, l, s = colorsys.rgb_to_hls(r / 255, g / 255, b / 255)
+ return h, l, s
+ def hls_to_rgb(h, l, s):
+ r, g, b = colorsys.hls_to_rgb(h, l, s)
+ return int(r * 255), int(g * 255), int(b * 255)
+
+ rotated_image = Image.new("RGB", image.size)
+ for x in range(image.width):
+ for y in range(image.height):
+ r, g, b = image.getpixel((x, y))
+ h, l, s = rgb_to_hls(r, g, b)
+ h = (h + hue_shift) % 1.0
+ r, g, b = hls_to_rgb(h, l, s)
+ rotated_image.putpixel((x, y), (r, g, b))
+
+ return rotated_image
+
+
+# IMAGE REMOVE COLOR
+
+class WAS_Image_Remove_Color:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "target_red": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
+ "target_green": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
+ "target_blue": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
+ "replace_red": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
+ "replace_green": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
+ "replace_blue": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
+ "clip_threshold": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_remove_color"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def image_remove_color(self, image, clip_threshold=10, target_red=255, target_green=255, target_blue=255, replace_red=255, replace_green=255, replace_blue=255):
+ return (pil2tensor(self.apply_remove_color(tensor2pil(image), clip_threshold, (target_red, target_green, target_blue), (replace_red, replace_green, replace_blue))), )
+
+ def apply_remove_color(self, image, threshold=10, color=(255, 255, 255), rep_color=(0, 0, 0)):
+ # Create a color image with the same size as the input image
+ color_image = Image.new('RGB', image.size, color)
+
+ # Calculate the difference between the input image and the color image
+ diff_image = ImageChops.difference(image, color_image)
+
+ # Convert the difference image to grayscale
+ gray_image = diff_image.convert('L')
+
+ # Apply a threshold to the grayscale difference image
+ mask_image = gray_image.point(lambda x: 255 if x > threshold else 0)
+
+ # Invert the mask image
+ mask_image = ImageOps.invert(mask_image)
+
+ # Apply the mask to the original image
+ result_image = Image.composite(
+ Image.new('RGB', image.size, rep_color), image, mask_image)
+
+ return result_image
+
+
+# IMAGE REMOVE BACKGROUND
+
+class WAS_Remove_Background:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "mode": (["background", "foreground"],),
+ "threshold": ("INT", {"default": 127, "min": 0, "max": 255, "step": 1}),
+ "threshold_tolerance": ("INT", {"default": 2, "min": 1, "max": 24, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "image_remove_background"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def image_remove_background(self, images, mode='background', threshold=127, threshold_tolerance=2):
+ return (self.remove_background(images, mode, threshold, threshold_tolerance), )
+
+ def remove_background(self, image, mode, threshold, threshold_tolerance):
+ images = []
+ image = [tensor2pil(img) for img in image]
+ for img in image:
+ grayscale_image = img.convert('L')
+ if mode == 'background':
+ grayscale_image = ImageOps.invert(grayscale_image)
+ threshold = 255 - threshold # adjust the threshold for "background" mode
+ blurred_image = grayscale_image.filter(
+ ImageFilter.GaussianBlur(radius=threshold_tolerance))
+ binary_image = blurred_image.point(
+ lambda x: 0 if x < threshold else 255, '1')
+ mask = binary_image.convert('L')
+ inverted_mask = ImageOps.invert(mask)
+ transparent_image = img.copy()
+ transparent_image.putalpha(inverted_mask)
+ images.append(pil2tensor(transparent_image))
+ batch = torch.cat(images, dim=0)
+
+ return batch
+
+# IMAGE REMBG
+
+class WAS_Remove_Rembg:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "transparency": (["true","false"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "image_rembg"
+
+ CATEGORY = "WAS Suite/Image/AI"
+
+ def image_rembg(self, images, transparency="true"):
+
+ if "rembg" not in packages():
+ cstr("Installing `rembg`...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'rembg'])
+
+ from rembg import remove
+
+ os.environ['U2NET_HOME'] = os.path.join(MODELS_DIR, 'rembg')
+ os.makedirs(os.environ['U2NET_HOME'], exist_ok=True)
+
+ batch_tensor = []
+ for image in images:
+ image = tensor2pil(image)
+ batch_tensor.append(pil2tensor(remove(image).convert(('RGBA' if transparency == 'true' else 'RGB'))))
+ batch_tensor = torch.cat(batch_tensor, dim=0)
+
+ return (batch_tensor,)
+
+
+# IMAGE BLEND MASK NODE
+
+class WAS_Image_Blend_Mask:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image_a": ("IMAGE",),
+ "image_b": ("IMAGE",),
+ "mask": ("IMAGE",),
+ "blend_percentage": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_blend_mask"
+
+ CATEGORY = "WAS Suite/Image"
+
+ def image_blend_mask(self, image_a, image_b, mask, blend_percentage):
+
+ # Convert images to PIL
+ img_a = tensor2pil(image_a)
+ img_b = tensor2pil(image_b)
+ mask = ImageOps.invert(tensor2pil(mask).convert('L'))
+
+ # Mask image
+ masked_img = Image.composite(img_a, img_b, mask.resize(img_a.size))
+
+ # Blend image
+ blend_mask = Image.new(mode="L", size=img_a.size,
+ color=(round(blend_percentage * 255)))
+ blend_mask = ImageOps.invert(blend_mask)
+ img_result = Image.composite(img_a, masked_img, blend_mask)
+
+ del img_a, img_b, blend_mask, mask
+
+ return (pil2tensor(img_result), )
+
+
+# IMAGE BLANK NOE
+
+
+class WAS_Image_Blank:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 1}),
+ "height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 1}),
+ "red": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
+ "green": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
+ "blue": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
+ }
+ }
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "blank_image"
+
+ CATEGORY = "WAS Suite/Image"
+
+ def blank_image(self, width, height, red, green, blue):
+
+ # Ensure multiples
+ width = (width // 8) * 8
+ height = (height // 8) * 8
+
+ # Blend image
+ blank = Image.new(mode="RGB", size=(width, height),
+ color=(red, green, blue))
+
+ return (pil2tensor(blank), )
+
+
+# IMAGE HIGH PASS
+
+class WAS_Image_High_Pass_Filter:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "radius": ("INT", {"default": 10, "min": 1, "max": 500, "step": 1}),
+ "strength": ("FLOAT", {"default": 1.5, "min": 0.0, "max": 255.0, "step": 0.1}),
+ "color_output": (["true", "false"],),
+ "neutral_background": (["true", "false"],),
+ }
+ }
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "high_pass"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def high_pass(self, images, radius=10, strength=1.5, color_output="true", neutral_background="true"):
+
+ batch_tensor = []
+ for image in images:
+ batch_tensor.append(pil2tensor(self.apply_hpf(tensor2pil(image), radius, strength, color_output, neutral_background)))
+ batch_tensor = torch.cat(batch_tensor, dim=0)
+
+ return (batch_tensor, )
+
+ def apply_hpf(self, img, radius=10, strength=1.5, color_output="true", neutral_background="true"):
+ # PIL to numpy
+ img_arr = np.array(img).astype('float')
+
+ # Apply a Gaussian blur with the given radius
+ blurred_arr = np.array(img.filter(
+ ImageFilter.GaussianBlur(radius=radius))).astype('float')
+
+ # Apply the High Pass Filter
+ hpf_arr = img_arr - blurred_arr
+ hpf_arr = np.clip(hpf_arr * strength, 0, 255).astype('uint8')
+
+ if color_output == "true":
+ # Create a color high pass image by applying the HPF to each channel separately
+ r = Image.fromarray(hpf_arr[:, :, 0], mode='L')
+ g = Image.fromarray(hpf_arr[:, :, 1], mode='L')
+ b = Image.fromarray(hpf_arr[:, :, 2], mode='L')
+ high_pass = Image.merge('RGB', (r, g, b))
+ else:
+ # Create a grayscale high pass image by converting the filtered array to mode 'L'
+ high_pass = Image.fromarray(hpf_arr, mode='L')
+
+ if neutral_background == "true":
+ # Apply neutral background
+ neutral_bg = Image.new("RGB", high_pass.size, (128, 128, 128))
+ high_pass = ImageChops.screen(neutral_bg, high_pass)
+
+ # Return the high pass image
+ return high_pass
+
+
+# IMAGE LEVELS NODE
+
+class WAS_Image_Levels:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "black_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 255.0, "step": 0.1}),
+ "mid_level": ("FLOAT", {"default": 127.5, "min": 0.0, "max": 255.0, "step": 0.1}),
+ "white_level": ("FLOAT", {"default": 255, "min": 0.0, "max": 255.0, "step": 0.1}),
+ }
+ }
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "apply_image_levels"
+
+ CATEGORY = "WAS Suite/Image/Adjustment"
+
+ def apply_image_levels(self, image, black_level, mid_level, white_level):
+
+ # Convert image to PIL
+ tensor_images = []
+ for img in image:
+ img = tensor2pil(img)
+ levels = self.AdjustLevels(black_level, mid_level, white_level)
+ tensor_images.append(pil2tensor(levels.adjust(img)))
+ tensor_images = torch.cat(tensor_images, dim=0)
+
+ # Return adjust image tensor
+ return (tensor_images, )
+
+
+ class AdjustLevels:
+ def __init__(self, min_level, mid_level, max_level):
+ self.min_level = min_level
+ self.mid_level = mid_level
+ self.max_level = max_level
+
+ def adjust(self, im):
+
+ im_arr = np.array(im)
+ im_arr[im_arr < self.min_level] = self.min_level
+ im_arr = (im_arr - self.min_level) * \
+ (255 / (self.max_level - self.min_level))
+ im_arr[im_arr < 0] = 0
+ im_arr[im_arr > 255] = 255
+ im_arr = im_arr.astype(np.uint8)
+
+ im = Image.fromarray(im_arr)
+ im = ImageOps.autocontrast(im, cutoff=self.max_level)
+
+ return im
+
+
+# FILM GRAIN NODE
+
+class WAS_Film_Grain:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "density": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 1.0, "step": 0.01}),
+ "intensity": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 1.0, "step": 0.01}),
+ "highlights": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 255.0, "step": 0.01}),
+ "supersample_factor": ("INT", {"default": 4, "min": 1, "max": 8, "step": 1})
+ }
+ }
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "film_grain"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def film_grain(self, image, density, intensity, highlights, supersample_factor):
+ return (pil2tensor(self.apply_film_grain(tensor2pil(image), density, intensity, highlights, supersample_factor)), )
+
+ def apply_film_grain(self, img, density=0.1, intensity=1.0, highlights=1.0, supersample_factor=4):
+ """
+ Apply grayscale noise with specified density, intensity, and highlights to a PIL image.
+ """
+ img_gray = img.convert('L')
+ original_size = img.size
+ img_gray = img_gray.resize(
+ ((img.size[0] * supersample_factor), (img.size[1] * supersample_factor)), Image.Resampling(2))
+ num_pixels = int(density * img_gray.size[0] * img_gray.size[1])
+
+ noise_pixels = []
+ for i in range(num_pixels):
+ x = random.randint(0, img_gray.size[0]-1)
+ y = random.randint(0, img_gray.size[1]-1)
+ noise_pixels.append((x, y))
+
+ for x, y in noise_pixels:
+ value = random.randint(0, 255)
+ img_gray.putpixel((x, y), value)
+
+ img_noise = img_gray.convert('RGB')
+ img_noise = img_noise.filter(ImageFilter.GaussianBlur(radius=0.125))
+ img_noise = img_noise.resize(original_size, Image.Resampling(1))
+ img_noise = img_noise.filter(ImageFilter.EDGE_ENHANCE_MORE)
+ img_final = Image.blend(img, img_noise, intensity)
+ enhancer = ImageEnhance.Brightness(img_final)
+ img_highlights = enhancer.enhance(highlights)
+
+ # Return the final image
+ return img_highlights
+
+
+# IMAGE FLIP NODE
+
+class WAS_Image_Flip:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "mode": (["horizontal", "vertical",],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "image_flip"
+
+ CATEGORY = "WAS Suite/Image/Transform"
+
+ def image_flip(self, images, mode):
+
+ batch_tensor = []
+ for image in images:
+ image = tensor2pil(image)
+ if mode == 'horizontal':
+ image = image.transpose(0)
+ if mode == 'vertical':
+ image = image.transpose(1)
+ batch_tensor.append(pil2tensor(image))
+ batch_tensor = torch.cat(batch_tensor, dim=0)
+
+ return (batch_tensor, )
+
+
+class WAS_Image_Rotate:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "mode": (["transpose", "internal",],),
+ "rotation": ("INT", {"default": 0, "min": 0, "max": 360, "step": 90}),
+ "sampler": (["nearest", "bilinear", "bicubic"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "image_rotate"
+
+ CATEGORY = "WAS Suite/Image/Transform"
+
+ def image_rotate(self, images, mode, rotation, sampler):
+
+ batch_tensor = []
+ for image in images:
+ # PIL Image
+ image = tensor2pil(image)
+
+ # Check rotation
+ if rotation > 360:
+ rotation = int(360)
+ if (rotation % 90 != 0):
+ rotation = int((rotation//90)*90)
+
+ # Set Sampler
+ if sampler:
+ if sampler == 'nearest':
+ sampler = Image.NEAREST
+ elif sampler == 'bicubic':
+ sampler = Image.BICUBIC
+ elif sampler == 'bilinear':
+ sampler = Image.BILINEAR
+ else:
+ sampler == Image.BILINEAR
+
+ # Rotate Image
+ if mode == 'internal':
+ image = image.rotate(rotation, sampler)
+ else:
+ rot = int(rotation / 90)
+ for _ in range(rot):
+ image = image.transpose(2)
+
+ batch_tensor.append(pil2tensor(image))
+
+ batch_tensor = torch.cat(batch_tensor, dim=0)
+
+ return (batch_tensor, )
+
+
+# IMAGE NOVA SINE FILTER
+
+class WAS_Image_Nova_Filter:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "amplitude": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.001}),
+ "frequency": ("FLOAT", {"default": 3.14, "min": 0.0, "max": 100.0, "step": 0.001}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "nova_sine"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def nova_sine(self, image, amplitude, frequency):
+
+ # Convert image to numpy
+ img = tensor2pil(image)
+
+ # Convert the image to a numpy array
+ img_array = np.array(img)
+
+ # Define a sine wave function
+ def sine(x, freq, amp):
+ return amp * np.sin(2 * np.pi * freq * x)
+
+ # Calculate the sampling frequency of the image
+ resolution = img.info.get('dpi') # PPI
+ physical_size = img.size # pixels
+
+ if resolution is not None:
+ # Convert PPI to pixels per millimeter (PPM)
+ ppm = 25.4 / resolution
+ physical_size = tuple(int(pix * ppm) for pix in physical_size)
+
+ # Set the maximum frequency for the sine wave
+ max_freq = img.width / 2
+
+ # Ensure frequency isn't outside visual representable range
+ if frequency > max_freq:
+ frequency = max_freq
+
+ # Apply levels to the image using the sine function
+ for i in range(img_array.shape[0]):
+ for j in range(img_array.shape[1]):
+ for k in range(img_array.shape[2]):
+ img_array[i, j, k] = int(
+ sine(img_array[i, j, k]/255, frequency, amplitude) * 255)
+
+ return (torch.from_numpy(img_array.astype(np.float32) / 255.0).unsqueeze(0), )
+
+
+# IMAGE CANNY FILTER
+
+
+class WAS_Canny_Filter:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "enable_threshold": (['false', 'true'],),
+ "threshold_low": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "threshold_high": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "canny_filter"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def canny_filter(self, images, threshold_low, threshold_high, enable_threshold):
+
+ if enable_threshold == 'false':
+ threshold_low = None
+ threshold_high = None
+
+ batch_tensor = []
+ for image in images:
+
+ image_canny = Image.fromarray(self.Canny_detector(
+ 255. * image.cpu().numpy().squeeze(), threshold_low, threshold_high)).convert('RGB')
+
+ batch_tensor.append(pil2tensor(image_canny))
+
+ batch_tensor = torch.cat(batch_tensor, dim=0)
+
+ return (pil2tensor(image_canny), )
+
+ def Canny_detector(self, img, weak_th=None, strong_th=None):
+
+ import cv2
+
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ img = cv2.GaussianBlur(img, (5, 5), 1.4)
+ gx = cv2.Sobel(np.float32(img), cv2.CV_64F, 1, 0, 3) # type: ignore
+ gy = cv2.Sobel(np.float32(img), cv2.CV_64F, 0, 1, 3) # type: ignore
+
+ mag, ang = cv2.cartToPolar(gx, gy, angleInDegrees=True)
+
+ mag_max = np.max(mag)
+ if not weak_th:
+ weak_th = mag_max * 0.1
+ if not strong_th:
+ strong_th = mag_max * 0.5
+
+ height, width = img.shape
+
+ for i_x in range(width):
+ for i_y in range(height):
+
+ grad_ang = ang[i_y, i_x]
+ grad_ang = abs(
+ grad_ang-180) if abs(grad_ang) > 180 else abs(grad_ang)
+
+ neighb_1_x, neighb_1_y = -1, -1
+ neighb_2_x, neighb_2_y = -1, -1
+
+ if grad_ang <= 22.5:
+ neighb_1_x, neighb_1_y = i_x-1, i_y
+ neighb_2_x, neighb_2_y = i_x + 1, i_y
+
+ elif grad_ang > 22.5 and grad_ang <= (22.5 + 45):
+ neighb_1_x, neighb_1_y = i_x-1, i_y-1
+ neighb_2_x, neighb_2_y = i_x + 1, i_y + 1
+ elif grad_ang > (22.5 + 45) and grad_ang <= (22.5 + 90):
+ neighb_1_x, neighb_1_y = i_x, i_y-1
+ neighb_2_x, neighb_2_y = i_x, i_y + 1
+ elif grad_ang > (22.5 + 90) and grad_ang <= (22.5 + 135):
+ neighb_1_x, neighb_1_y = i_x-1, i_y + 1
+ neighb_2_x, neighb_2_y = i_x + 1, i_y-1
+ elif grad_ang > (22.5 + 135) and grad_ang <= (22.5 + 180):
+ neighb_1_x, neighb_1_y = i_x-1, i_y
+ neighb_2_x, neighb_2_y = i_x + 1, i_y
+ if width > neighb_1_x >= 0 and height > neighb_1_y >= 0:
+ if mag[i_y, i_x] < mag[neighb_1_y, neighb_1_x]:
+ mag[i_y, i_x] = 0
+ continue
+
+ if width > neighb_2_x >= 0 and height > neighb_2_y >= 0:
+ if mag[i_y, i_x] < mag[neighb_2_y, neighb_2_x]:
+ mag[i_y, i_x] = 0
+
+ weak_ids = np.zeros_like(img)
+ strong_ids = np.zeros_like(img)
+ ids = np.zeros_like(img)
+
+ for i_x in range(width):
+ for i_y in range(height):
+
+ grad_mag = mag[i_y, i_x]
+
+ if grad_mag < weak_th:
+ mag[i_y, i_x] = 0
+ elif strong_th > grad_mag >= weak_th:
+ ids[i_y, i_x] = 1
+ else:
+ ids[i_y, i_x] = 2
+
+ return mag
+
+# IMAGE EDGE DETECTION
+
+class WAS_Image_Edge:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "mode": (["normal", "laplacian"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_edges"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def image_edges(self, image, mode):
+
+ # Convert image to PIL
+ image = tensor2pil(image)
+
+ # Detect edges
+ if mode:
+ if mode == "normal":
+ image = image.filter(ImageFilter.FIND_EDGES)
+ elif mode == "laplacian":
+ image = image.filter(ImageFilter.Kernel((3, 3), (-1, -1, -1, -1, 8,
+ -1, -1, -1, -1), 1, 0))
+ else:
+ image = image
+
+ return (torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0), )
+
+
+# IMAGE FDOF NODE
+
+class WAS_Image_fDOF:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "depth": ("IMAGE",),
+ "mode": (["mock", "gaussian", "box"],),
+ "radius": ("INT", {"default": 8, "min": 1, "max": 128, "step": 1}),
+ "samples": ("INT", {"default": 1, "min": 1, "max": 3, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "fdof_composite"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def fdof_composite(self, image, depth, radius, samples, mode):
+
+ import cv2 as cv
+
+ # Convert tensor to a PIL Image
+ tensor_images = []
+ for i in range(len(image)):
+ if i <= len(image):
+ img = tensor2pil(image[i])
+ else:
+ img = tensor2pil(image[-1])
+ if i <= len(depth):
+ dpth = tensor2pil(depth[i])
+ else:
+ dpth = tensor2pil(depth[-1])
+ tensor_images.append(pil2tensor(self.portraitBlur(img, dpth, radius, samples, mode)))
+ tensor_images = torch.cat(tensor_images, dim=0)
+
+ return (tensor_images, )
+
+ def portraitBlur(self, img, mask, radius, samples, mode='mock'):
+ mask = mask.resize(img.size).convert('L')
+ bimg: Optional[Image.Image] = None
+ if mode == 'mock':
+ bimg = medianFilter(img, radius, (radius * 1500), 75)
+ elif mode == 'gaussian':
+ bimg = img.filter(ImageFilter.GaussianBlur(radius=radius))
+ elif mode == 'box':
+ bimg = img.filter(ImageFilter.BoxBlur(radius))
+ else:
+ return
+ bimg.convert(img.mode)
+ rimg: Optional[Image.Image] = None
+ if samples > 1:
+ for i in range(samples):
+ if not rimg:
+ rimg = Image.composite(img, bimg, mask)
+ else:
+ rimg = Image.composite(rimg, bimg, mask)
+ else:
+ rimg = Image.composite(img, bimg, mask).convert('RGB')
+
+ return rimg
+
+
+# IMAGE DRAGAN PHOTOGRAPHY FILTER
+
+class WAS_Dragon_Filter:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "saturation": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 16.0, "step": 0.01}),
+ "contrast": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 16.0, "step": 0.01}),
+ "brightness": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 16.0, "step": 0.01}),
+ "sharpness": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 6.0, "step": 0.01}),
+ "highpass_radius": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 255.0, "step": 0.01}),
+ "highpass_samples": ("INT", {"default": 1, "min": 0, "max": 6.0, "step": 1}),
+ "highpass_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}),
+ "colorize": (["true","false"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "apply_dragan_filter"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def apply_dragan_filter(self, image, saturation, contrast, sharpness, brightness, highpass_radius, highpass_samples, highpass_strength, colorize):
+
+ WTools = WAS_Tools_Class()
+
+ tensor_images = []
+ for img in image:
+ tensor_images.append(pil2tensor(WTools.dragan_filter(tensor2pil(img), saturation, contrast, sharpness, brightness, highpass_radius, highpass_samples, highpass_strength, colorize)))
+ tensor_images = torch.cat(tensor_images, dim=0)
+
+ return (tensor_images, )
+
+
+
+# IMAGE MEDIAN FILTER NODE
+
+class WAS_Image_Median_Filter:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "diameter": ("INT", {"default": 2.0, "min": 0.1, "max": 255, "step": 1}),
+ "sigma_color": ("FLOAT", {"default": 10.0, "min": -255.0, "max": 255.0, "step": 0.1}),
+ "sigma_space": ("FLOAT", {"default": 10.0, "min": -255.0, "max": 255.0, "step": 0.1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "apply_median_filter"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def apply_median_filter(self, image, diameter, sigma_color, sigma_space):
+
+ tensor_images = []
+ for img in image:
+ img = tensor2pil(img)
+ # Apply Median Filter effect
+ tensor_images.append(pil2tensor(medianFilter(img, diameter, sigma_color, sigma_space)))
+ tensor_images = torch.cat(tensor_images, dim=0)
+
+ return (tensor_images, )
+
+# IMAGE SELECT COLOR
+
+
+class WAS_Image_Select_Color:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "red": ("INT", {"default": 255.0, "min": 0.0, "max": 255.0, "step": 0.1}),
+ "green": ("INT", {"default": 255.0, "min": 0.0, "max": 255.0, "step": 0.1}),
+ "blue": ("INT", {"default": 255.0, "min": 0.0, "max": 255.0, "step": 0.1}),
+ "variance": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "select_color"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def select_color(self, image, red=255, green=255, blue=255, variance=10):
+
+ image = self.color_pick(tensor2pil(image), red, green, blue, variance)
+
+ return (pil2tensor(image), )
+
+ def color_pick(self, image, red=255, green=255, blue=255, variance=10):
+ # Convert image to RGB mode
+ image = image.convert('RGB')
+
+ # Create a new black image of the same size as the input image
+ selected_color = Image.new('RGB', image.size, (0, 0, 0))
+
+ # Get the width and height of the image
+ width, height = image.size
+
+ # Loop through every pixel in the image
+ for x in range(width):
+ for y in range(height):
+ # Get the color of the pixel
+ pixel = image.getpixel((x, y))
+ r, g, b = pixel
+
+ # Check if the pixel is within the specified color range
+ if ((r >= red-variance) and (r <= red+variance) and
+ (g >= green-variance) and (g <= green+variance) and
+ (b >= blue-variance) and (b <= blue+variance)):
+ # Set the pixel in the selected_color image to the RGB value of the pixel
+ selected_color.putpixel((x, y), (r, g, b))
+
+ # Return the selected color image
+ return selected_color
+
+# IMAGE CONVERT TO CHANNEL
+
+
+class WAS_Image_Select_Channel:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "channel": (['red', 'green', 'blue'],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "select_channel"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def select_channel(self, image, channel='red'):
+
+ image = self.convert_to_single_channel(tensor2pil(image), channel)
+
+ return (pil2tensor(image), )
+
+ def convert_to_single_channel(self, image, channel='red'):
+
+ # Convert to RGB mode to access individual channels
+ image = image.convert('RGB')
+
+ # Extract the desired channel and convert to greyscale
+ if channel == 'red':
+ channel_img = image.split()[0].convert('L')
+ elif channel == 'green':
+ channel_img = image.split()[1].convert('L')
+ elif channel == 'blue':
+ channel_img = image.split()[2].convert('L')
+ else:
+ raise ValueError(
+ "Invalid channel option. Please choose 'red', 'green', or 'blue'.")
+
+ # Convert the greyscale channel back to RGB mode
+ channel_img = Image.merge(
+ 'RGB', (channel_img, channel_img, channel_img))
+
+ return channel_img
+
+
+
+# IMAGE MERGE RGB CHANNELS
+
+class WAS_Image_RGB_Merge:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "red_channel": ("IMAGE",),
+ "green_channel": ("IMAGE",),
+ "blue_channel": ("IMAGE",),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "merge_channels"
+
+ CATEGORY = "WAS Suite/Image/Process"
+
+ def merge_channels(self, red_channel, green_channel, blue_channel):
+
+ # Apply mix rgb channels
+ image = self.mix_rgb_channels(tensor2pil(red_channel).convert('L'), tensor2pil(
+ green_channel).convert('L'), tensor2pil(blue_channel).convert('L'))
+
+ return (pil2tensor(image), )
+
+ def mix_rgb_channels(self, red, green, blue):
+ # Create an empty image with the same size as the channels
+ width, height = red.size
+ merged_img = Image.new('RGB', (width, height))
+
+ # Merge the channels into the new image
+ merged_img = Image.merge('RGB', (red, green, blue))
+
+ return merged_img
+
+# IMAGE Ambient Occlusion
+
+class WAS_Image_Ambient_Occlusion:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "depth_images": ("IMAGE",),
+ "strength": ("FLOAT", {"min": 0.0, "max": 5.0, "default": 1.0, "step": 0.01}),
+ "radius": ("FLOAT", {"min": 0.01, "max": 1024, "default": 30, "step": 0.01}),
+ "ao_blur": ("FLOAT", {"min": 0.01, "max": 1024, "default": 2.5, "step": 0.01}),
+ "specular_threshold": ("INT", {"min":0, "max": 255, "default": 25, "step": 1}),
+ "enable_specular_masking": (["True", "False"],),
+ "tile_size": ("INT", {"min": 1, "max": 512, "default": 1, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE","IMAGE","IMAGE")
+ RETURN_NAMES = ("composited_images", "ssao_images", "specular_mask_images")
+ FUNCTION = "ambient_occlusion"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def ambient_occlusion(self, images, depth_images, strength, radius, ao_blur, specular_threshold, enable_specular_masking, tile_size):
+
+ enable_specular_masking = (enable_specular_masking == 'True')
+ composited = []
+ occlusions = []
+ speculars = []
+ for i, image in enumerate(images):
+ cstr(f"Processing SSAO image {i+1}/{len(images)} ...").msg.print()
+ composited_image, occlusion_image, specular_mask = self.create_ambient_occlusion(
+ tensor2pil(image),
+ tensor2pil(depth_images[(i if len(depth_images) >= i else -1)]),
+ strength=strength,
+ radius=radius,
+ ao_blur=ao_blur,
+ spec_threshold=specular_threshold,
+ enable_specular_masking=enable_specular_masking,
+ tile_size=tile_size
+ )
+ composited.append(pil2tensor(composited_image))
+ occlusions.append(pil2tensor(occlusion_image))
+ speculars.append(pil2tensor(specular_mask))
+
+ composited = torch.cat(composited, dim=0)
+ occlusions = torch.cat(occlusions, dim=0)
+ speculars = torch.cat(speculars, dim=0)
+
+ return ( composited, occlusions, speculars )
+
+ def process_tile(self, tile_rgb, tile_depth, tile_x, tile_y, radius):
+ tile_occlusion = calculate_ambient_occlusion_factor(tile_rgb, tile_depth, tile_rgb.shape[0], tile_rgb.shape[1], radius)
+ return tile_x, tile_y, tile_occlusion
+
+
+ def create_ambient_occlusion(self, rgb_image, depth_image, strength=1.0, radius=30, ao_blur=5, spec_threshold=200, enable_specular_masking=False, tile_size=1):
+
+ import concurrent.futures
+
+ if depth_image.size != rgb_image.size:
+ depth_image = depth_image.resize(rgb_image.size)
+ rgb_normalized = np.array(rgb_image, dtype=np.float32) / 255.0
+ depth_normalized = np.array(depth_image, dtype=np.float32) / 255.0
+
+ height, width, _ = rgb_normalized.shape
+
+ if tile_size <= 1:
+ print("Processing single-threaded AO (highest quality) ...")
+ occlusion_array = calculate_ambient_occlusion_factor(rgb_normalized, depth_normalized, height, width, radius)
+ else:
+ tile_size = ((tile_size if tile_size <= 8 else 8) if tile_size > 1 else 1)
+ num_tiles_x = (width - 1) // tile_size + 1
+ num_tiles_y = (height - 1) // tile_size + 1
+
+ occlusion_array = np.zeros((height, width), dtype=np.uint8)
+
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ futures = []
+
+ with tqdm(total=num_tiles_y * num_tiles_x) as pbar:
+ for tile_y in range(num_tiles_y):
+ for tile_x in range(num_tiles_x):
+ tile_left = tile_x * tile_size
+ tile_upper = tile_y * tile_size
+ tile_right = min(tile_left + tile_size, width)
+ tile_lower = min(tile_upper + tile_size, height)
+
+ tile_rgb = rgb_normalized[tile_upper:tile_lower, tile_left:tile_right]
+ tile_depth = depth_normalized[tile_upper:tile_lower, tile_left:tile_right]
+
+ future = executor.submit(process_tile, tile_rgb, tile_depth, tile_x, tile_y, radius)
+ futures.append(future)
+
+ for future in concurrent.futures.as_completed(futures):
+ tile_x, tile_y, tile_occlusion = future.result()
+ tile_left = tile_x * tile_size
+ tile_upper = tile_y * tile_size
+ tile_right = min(tile_left + tile_size, width)
+ tile_lower = min(tile_upper + tile_size, height)
+
+ occlusion_array[tile_upper:tile_lower, tile_left:tile_right] = tile_occlusion
+
+ pbar.update(1)
+
+ occlusion_array = (occlusion_array * strength).clip(0, 255).astype(np.uint8)
+
+ occlusion_image = Image.fromarray(occlusion_array, mode='L')
+ occlusion_image = occlusion_image.filter(ImageFilter.GaussianBlur(radius=ao_blur))
+ occlusion_image = occlusion_image.filter(ImageFilter.SMOOTH)
+ occlusion_image = ImageChops.multiply(occlusion_image, ImageChops.multiply(occlusion_image, occlusion_image))
+
+ mask = rgb_image.convert('L')
+ mask = mask.point(lambda x: x > spec_threshold, mode='1')
+ mask = mask.convert("RGB")
+ mask = mask.filter(ImageFilter.GaussianBlur(radius=2.5)).convert("L")
+
+ if enable_specular_masking:
+ occlusion_image = Image.composite(Image.new("L", rgb_image.size, 255), occlusion_image, mask)
+ occlsuion_result = ImageChops.multiply(rgb_image, occlusion_image.convert("RGB"))
+
+ return occlsuion_result, occlusion_image, mask
+
+# IMAGE Direct Occlusion
+
+class WAS_Image_Direct_Occlusion:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "depth_images": ("IMAGE",),
+ "strength": ("FLOAT", {"min": 0.0, "max": 5.0, "default": 1.0, "step": 0.01}),
+ "radius": ("FLOAT", {"min": 0.01, "max": 1024, "default": 30, "step": 0.01}),
+ "specular_threshold": ("INT", {"min":0, "max": 255, "default": 128, "step": 1}),
+ "colored_occlusion": (["True", "False"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE","IMAGE","IMAGE", "IMAGE")
+ RETURN_NAMES = ("composited_images", "ssdo_images", "ssdo_image_masks", "light_source_image_masks")
+ FUNCTION = "direct_occlusion"
+
+ CATEGORY = "WAS Suite/Image/Filter"
+
+ def direct_occlusion(self, images, depth_images, strength, radius, specular_threshold, colored_occlusion):
+
+ composited = []
+ occlusions = []
+ occlusion_masks = []
+ light_sources = []
+ for i, image in enumerate(images):
+ cstr(f"Processing SSDO image {i+1}/{len(images)} ...").msg.print()
+ composited_image, occlusion_image, occlusion_mask, light_source = self.create_direct_occlusion(
+ tensor2pil(image),
+ tensor2pil(depth_images[(i if len(depth_images) >= i else -1)]),
+ strength=strength,
+ radius=radius,
+ threshold=specular_threshold,
+ colored=True
+ )
+ composited.append(pil2tensor(composited_image))
+ occlusions.append(pil2tensor(occlusion_image))
+ occlusion_masks.append(pil2tensor(occlusion_mask))
+ light_sources.append(pil2tensor(light_source))
+
+ composited = torch.cat(composited, dim=0)
+ occlusions = torch.cat(occlusions, dim=0)
+ occlusion_masks = torch.cat(occlusion_masks, dim=0)
+ light_sources = torch.cat(light_sources, dim=0)
+
+ return ( composited, occlusions, occlusion_masks, light_sources )
+
+ def find_light_source(self, rgb_normalized, threshold):
+ from skimage.measure import regionprops
+ from skimage import measure
+ rgb_uint8 = (rgb_normalized * 255).astype(np.uint8)
+ rgb_to_grey = Image.fromarray(rgb_uint8, mode="RGB")
+ dominant = self.dominant_region(rgb_to_grey, threshold)
+ grayscale_image = np.array(dominant.convert("L"), dtype=np.float32) / 255.0
+ regions = measure.label(grayscale_image > 0)
+
+ if np.max(regions) > 0:
+ region_sums = measure.regionprops(regions, intensity_image=grayscale_image)
+ brightest_region = max(region_sums, key=lambda r: r.mean_intensity)
+ light_y, light_x = brightest_region.centroid
+ light_mask = (regions == brightest_region.label).astype(np.uint8)
+ light_mask_cluster = light_mask
+ else:
+ light_x, light_y = np.nan, np.nan
+ light_mask_cluster = np.zeros_like(dominant, dtype=np.uint8)
+ return light_mask_cluster, light_x, light_y
+
+
+ def dominant_region(self, image, threshold=128):
+ from scipy.ndimage import label
+ image = ImageOps.invert(image.convert("L"))
+ binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1")
+ l, n = label(np.array(binary_image))
+ sizes = np.bincount(l.flatten())
+ dominant = 0
+ try:
+ dominant = np.argmax(sizes[1:]) + 1
+ except ValueError:
+ pass
+ dominant_region_mask = (l == dominant).astype(np.uint8) * 255
+ result = Image.fromarray(dominant_region_mask, mode="L")
+ return result.convert("RGB")
+
+ def create_direct_occlusion(self, rgb_image, depth_image, strength=1.0, radius=10, threshold=200, colored=False):
+ rgb_normalized = np.array(rgb_image, dtype=np.float32) / 255.0
+ depth_normalized = np.array(depth_image, dtype=np.float32) / 255.0
+ height, width, _ = rgb_normalized.shape
+ light_mask, light_x, light_y = self.find_light_source(rgb_normalized, threshold)
+ occlusion_array = calculate_direct_occlusion_factor(rgb_normalized, depth_normalized, height, width, radius)
+ #occlusion_scaled = (occlusion_array / np.max(occlusion_array) * 255).astype(np.uint8)
+ occlusion_scaled = ((occlusion_array - np.min(occlusion_array)) / (np.max(occlusion_array) - np.min(occlusion_array)) * 255).astype(np.uint8)
+ occlusion_image = Image.fromarray(occlusion_scaled, mode="L")
+ occlusion_image = occlusion_image.filter(ImageFilter.GaussianBlur(radius=0.5))
+ occlusion_image = occlusion_image.filter(ImageFilter.SMOOTH_MORE)
+
+ if colored:
+ occlusion_result = Image.composite(
+ Image.new("RGB", rgb_image.size, (0, 0, 0)),
+ rgb_image,
+ occlusion_image
+ )
+ occlusion_result = ImageOps.autocontrast(occlusion_result, cutoff=(0, strength))
+ else:
+ occlusion_result = Image.blend(occlusion_image, occlusion_image, strength)
+
+ light_image = ImageOps.invert(Image.fromarray(light_mask * 255, mode="L"))
+
+ direct_occlusion_image = ImageChops.screen(rgb_image, occlusion_result.convert("RGB"))
+
+ return direct_occlusion_image, occlusion_result, occlusion_image, light_image
+
+# EXPORT API
+
+class WAS_Export_API:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "save_prompt_api": (["true","true"],),
+ "output_path": ("STRING", {"default": "./ComfyUI/output/", "multiline": False}),
+ "filename_prefix": ("STRING", {"default": "ComfyUI_Prompt"}),
+ "filename_delimiter": ("STRING", {"default":"_"}),
+ "filename_number_padding": ("INT", {"default":4, "min":2, "max":9, "step":1}),
+ },
+ "hidden": {
+ "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
+ }
+ }
+
+ OUTPUT_NODE = True
+ RETURN_TYPES = ()
+ FUNCTION = "export_api"
+
+ CATEGORY = "WAS Suite/Debug"
+
+ def export_api(self, output_path=None, filename_prefix="ComfyUI", filename_number_padding=4,
+ filename_delimiter='_', prompt=None, extra_pnginfo=None, save_prompt_api="true"):
+ delimiter = filename_delimiter
+ number_padding = filename_number_padding if filename_number_padding > 1 else 4
+
+ tokens = TextTokens()
+
+ if output_path in [None, '', "none", "."]:
+ output_path = comfy_paths.output_directory
+ else:
+ output_path = tokens.parseTokens(output_path)
+
+ pattern = f"{re.escape(filename_prefix)}{re.escape(filename_delimiter)}(\\d{{{number_padding}}})"
+ existing_counters = [
+ int(re.search(pattern, filename).group(1))
+ for filename in os.listdir(output_path)
+ if re.match(pattern, filename)
+ ]
+ existing_counters.sort(reverse=True)
+
+ if existing_counters:
+ counter = existing_counters[0] + 1
+ else:
+ counter = 1
+
+ file = f"{filename_prefix}{filename_delimiter}{counter:0{number_padding}}.json"
+ output_file = os.path.abspath(os.path.join(output_path, file))
+
+ if prompt:
+ prompt_json = json.dumps(prompt, indent=4)
+ cstr("Prompt API JSON").msg.print()
+ print(prompt_json)
+
+ if save_prompt_api == "true":
+
+ with open(output_file, 'w') as f:
+ f.write(prompt_json)
+
+ cstr(f"Output file path: {output_file}").msg.print()
+
+ return {"ui": {"string": prompt_json}}
+
+
+# Image Save (NSP Compatible)
+# Originally From ComfyUI/nodes.py
+
+class WAS_Image_Save:
+ def __init__(self):
+ self.output_dir = comfy_paths.output_directory
+ self.type = os.path.basename(self.output_dir)
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE", ),
+ "output_path": ("STRING", {"default": '[time(%Y-%m-%d)]', "multiline": False}),
+ "filename_prefix": ("STRING", {"default": "ComfyUI"}),
+ "filename_delimiter": ("STRING", {"default":"_"}),
+ "filename_number_padding": ("INT", {"default":4, "min":1, "max":9, "step":1}),
+ "filename_number_start": (["false", "true"],),
+ "extension": (['png', 'jpeg', 'gif', 'tiff', 'webp'], ),
+ "quality": ("INT", {"default": 100, "min": 1, "max": 100, "step": 1}),
+ "lossless_webp": (["false", "true"],),
+ "overwrite_mode": (["false", "prefix_as_filename"],),
+ "show_history": (["false", "true"],),
+ "show_history_by_prefix": (["true", "false"],),
+ "embed_workflow": (["true", "false"],),
+ "show_previews": (["true", "false"],),
+ },
+ "hidden": {
+ "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
+ },
+ }
+
+ RETURN_TYPES = ()
+ FUNCTION = "was_save_images"
+
+ OUTPUT_NODE = True
+
+ CATEGORY = "WAS Suite/IO"
+
+ def was_save_images(self, images, output_path='', filename_prefix="ComfyUI", filename_delimiter='_',
+ extension='png', quality=100, lossless_webp="false", prompt=None, extra_pnginfo=None,
+ overwrite_mode='false', filename_number_padding=4, filename_number_start='false',
+ show_history='false', show_history_by_prefix="true", embed_workflow="true",
+ show_previews="true"):
+
+ delimiter = filename_delimiter
+ number_padding = filename_number_padding
+ lossless_webp = (lossless_webp == "true")
+
+ # Define token system
+ tokens = TextTokens()
+
+ original_output = self.output_dir
+ # Parse prefix tokens
+ filename_prefix = tokens.parseTokens(filename_prefix)
+
+ # Setup output path
+ if output_path in [None, '', "none", "."]:
+ output_path = self.output_dir
+ else:
+ output_path = tokens.parseTokens(output_path)
+ if not os.path.isabs(output_path):
+ output_path = os.path.join(self.output_dir, output_path)
+ base_output = os.path.basename(output_path)
+ if output_path.endswith("ComfyUI/output") or output_path.endswith("ComfyUI\output"):
+ base_output = ""
+
+ # Check output destination
+ if output_path.strip() != '':
+ if not os.path.exists(output_path.strip()):
+ cstr(f'The path `{output_path.strip()}` specified doesn\'t exist! Creating directory.').warning.print()
+ os.makedirs(output_path, exist_ok=True)
+
+ # Find existing counter values
+ if filename_number_start == 'true':
+ pattern = f"(\\d{{{filename_number_padding}}}){re.escape(delimiter)}{re.escape(filename_prefix)}"
+ else:
+ pattern = f"{re.escape(filename_prefix)}{re.escape(delimiter)}(\\d{{{filename_number_padding}}})"
+ existing_counters = [
+ int(re.search(pattern, filename).group(1))
+ for filename in os.listdir(output_path)
+ if re.match(pattern, os.path.basename(filename))
+ ]
+ existing_counters.sort(reverse=True)
+
+ # Set initial counter value
+ if existing_counters:
+ counter = existing_counters[0] + 1
+ else:
+ counter = 1
+
+ # Set initial counter value
+ if existing_counters:
+ counter = existing_counters[0] + 1
+ else:
+ counter = 1
+
+ # Set Extension
+ file_extension = '.' + extension
+ if file_extension not in ALLOWED_EXT:
+ cstr(f"The extension `{extension}` is not valid. The valid formats are: {', '.join(sorted(ALLOWED_EXT))}").error.print()
+ file_extension = "png"
+
+ results = list()
+ for image in images:
+ i = 255. * image.cpu().numpy()
+ img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+
+ metadata = PngInfo()
+ if embed_workflow == 'true':
+ if prompt is not None:
+ metadata.add_text("prompt", json.dumps(prompt))
+ if extra_pnginfo is not None:
+ for x in extra_pnginfo:
+ metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+ if overwrite_mode == 'prefix_as_filename':
+ file = f"{filename_prefix}{file_extension}"
+ else:
+ if filename_number_start == 'true':
+ file = f"{counter:0{number_padding}}{delimiter}{filename_prefix}{file_extension}"
+ else:
+ file = f"{filename_prefix}{delimiter}{counter:0{number_padding}}{file_extension}"
+ if os.path.exists(os.path.join(output_path, file)):
+ counter += 1
+ try:
+ output_file = os.path.abspath(os.path.join(output_path, file))
+ if extension == 'png':
+ img.save(output_file,
+ pnginfo=metadata, optimize=True)
+ elif extension == 'webp':
+ img.save(output_file, quality=quality)
+ elif extension == 'jpeg':
+ img.save(output_file,
+ quality=quality, optimize=True)
+ elif extension == 'tiff':
+ img.save(output_file,
+ quality=quality, optimize=True)
+ elif extension == 'webp':
+ img.save(output_file, quality=quality,
+ lossless=lossless_webp, exif=metadata)
+
+ cstr(f"Image file saved to: {output_file}").msg.print()
+
+ if show_history != 'true' and show_previews == 'true':
+ subfolder = self.get_subfolder_path(output_file, original_output)
+ results.append({
+ "filename": file,
+ "subfolder": subfolder,
+ "type": self.type
+ })
+
+ # Update the output image history
+ update_history_output_images(output_file)
+
+ except OSError as e:
+ cstr(f'Unable to save file to: {output_file}').error.print()
+ print(e)
+ except Exception as e:
+ cstr('Unable to save file due to the to the following error:').error.print()
+ print(e)
+
+ if overwrite_mode == 'false':
+ counter += 1
+
+ filtered_paths = []
+ if show_history == 'true' and show_previews == 'true':
+ HDB = WASDatabase(WAS_HISTORY_DATABASE)
+ conf = getSuiteConfig()
+ if HDB.catExists("History") and HDB.keyExists("History", "Output_Images"):
+ history_paths = HDB.get("History", "Output_Images")
+ else:
+ history_paths = None
+
+ if history_paths:
+
+ for image_path in history_paths:
+ image_subdir = self.get_subfolder_path(image_path, self.output_dir)
+ current_subdir = self.get_subfolder_path(output_file, self.output_dir)
+ if not os.path.exists(image_path):
+ continue
+ if show_history_by_prefix == 'true' and image_subdir != current_subdir:
+ continue
+ if show_history_by_prefix == 'true' and not os.path.basename(image_path).startswith(filename_prefix):
+ continue
+ filtered_paths.append(image_path)
+
+ if conf.__contains__('history_display_limit'):
+ filtered_paths = filtered_paths[-conf['history_display_limit']:]
+
+ filtered_paths.reverse()
+
+ if filtered_paths:
+ for image_path in filtered_paths:
+ subfolder = self.get_subfolder_path(image_path, self.output_dir)
+ image_data = {
+ "filename": os.path.basename(image_path),
+ "subfolder": subfolder,
+ "type": self.type
+ }
+ results.append(image_data)
+
+ if show_previews == 'true':
+ return {"ui": {"images": results}}
+ else:
+ return {"ui": {"images": []}}
+
+
+ def get_subfolder_path(self, image_path, output_path):
+ output_parts = output_path.strip(os.sep).split(os.sep)
+ image_parts = image_path.strip(os.sep).split(os.sep)
+ common_parts = os.path.commonprefix([output_parts, image_parts])
+ subfolder_parts = image_parts[len(common_parts):]
+ subfolder_path = os.sep.join(subfolder_parts[:-1])
+ return subfolder_path
+
+
+# LOAD IMAGE NODE
+class WAS_Load_Image:
+
+ def __init__(self):
+ self.input_dir = comfy_paths.input_directory
+ self.HDB = WASDatabase(WAS_HISTORY_DATABASE)
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image_path": ("STRING", {"default": './ComfyUI/input/example.png', "multiline": False}),
+ "RGBA": (["false","true"],),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "MASK", TEXT_TYPE)
+ RETURN_NAMES = ("image", "mask", "filename_text")
+ FUNCTION = "load_image"
+
+ CATEGORY = "WAS Suite/IO"
+
+ def load_image(self, image_path, RGBA='false'):
+
+ RGBA = (RGBA == 'true')
+
+ if image_path.startswith('http'):
+ from io import BytesIO
+ i = self.download_image(image_path)
+ else:
+ try:
+ i = Image.open(image_path)
+ except OSError:
+ cstr(f"The image `{image_path.strip()}` specified doesn't exist!").error.print()
+ i = Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0))
+ if not i:
+ return
+
+ # Update history
+ update_history_images(image_path)
+
+ if not RGBA:
+ image = i.convert('RGB')
+ image = np.array(image).astype(np.float32) / 255.0
+ image = torch.from_numpy(image)[None,]
+
+ if 'A' in i.getbands():
+ mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+ mask = 1. - torch.from_numpy(mask)
+ else:
+ mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+
+ return (image, mask, os.path.basename(image_path))
+
+ def download_image(self, url):
+ try:
+ response = requests.get(url)
+ response.raise_for_status()
+ img = Image.open(BytesIO(response.content))
+ return img
+ except requests.exceptions.HTTPError as errh:
+ cstr(f"HTTP Error: ({url}): {errh}").error.print()
+ except requests.exceptions.ConnectionError as errc:
+ cstr(f"Connection Error: ({url}): {errc}").error.print()
+ except requests.exceptions.Timeout as errt:
+ cstr(f"Timeout Error: ({url}): {errt}").error.print()
+ except requests.exceptions.RequestException as err:
+ cstr(f"Request Exception: ({url}): {err}").error.print()
+
+ @classmethod
+ def IS_CHANGED(cls, image_path):
+ if image_path.startswith('http'):
+ return float("NaN")
+ m = hashlib.sha256()
+ with open(image_path, 'rb') as f:
+ m.update(f.read())
+ return m.digest().hex()
+
+
+# IMAGES TO RGB
+
+class WAS_Images_To_RGB:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "images_to_rgb"
+
+ CATEGORY = "WAS Suite/Image"
+
+ def images_to_rgb(self, images):
+
+ tensors = []
+ for image in images:
+ tensors.append(pil2tensor(tensor2pil(image).convert("RGB")))
+ tensors = torch.cat(tensors, dim=0)
+
+ return (tensors,)
+
+
+# MASK BATCH TO MASK
+
+class WAS_Mask_Batch_to_Single_Mask:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "batch_number": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("MASK",)
+ FUNCTION = "mask_batch_to_mask"
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ def mask_batch_to_mask(self, masks=[], batch_number=0):
+ count = 0
+ for _ in masks:
+ if batch_number == count:
+ tensor = masks[batch_number][0]
+ return (tensor,)
+ count += 1
+
+ cstr(f"Batch number `{batch_number}` is not defined, returning last image").error.print()
+ last_tensor = masks[-1][0]
+ return (last_tensor,)
+
+# TENSOR BATCH TO IMAGE
+
+class WAS_Tensor_Batch_to_Image:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images_batch": ("IMAGE",),
+ "batch_image_number": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "tensor_batch_to_image"
+
+ CATEGORY = "WAS Suite/Latent/Transform"
+
+ def tensor_batch_to_image(self, images_batch=[], batch_image_number=0):
+
+ count = 0
+ for _ in images_batch:
+ if batch_image_number == count:
+ return (images_batch[batch_image_number].unsqueeze(0), )
+ count = count+1
+
+ cstr(f"Batch number `{batch_image_number}` is not defined, returning last image").error.print()
+ return (images_batch[-1].unsqueeze(0), )
+
+
+#! LATENT NODES
+
+# IMAGE TO MASK
+
+class WAS_Image_To_Mask:
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "channel": (["alpha", "red", "green", "blue"], ),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "image_to_mask"
+
+ def image_to_mask(self, images, channel):
+ mask_images = []
+ for image in images:
+
+ image = tensor2pil(image).convert("RGBA")
+ r, g, b, a = image.split()
+ if channel == "red":
+ channel_image = r
+ elif channel == "green":
+ channel_image = g
+ elif channel == "blue":
+ channel_image = b
+ elif channel == "alpha":
+ channel_image = a
+
+ mask = torch.from_numpy(np.array(channel_image.convert("L")).astype(np.float32) / 255.0)
+ mask_images.append(mask)
+
+ return (torch.cat(mask_images, dim=0), )
+
+
+# MASK TO IMAGE
+
+class WAS_Mask_To_Image:
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("IMAGES",)
+
+ FUNCTION = "mask_to_image"
+
+ def mask_to_image(self, masks):
+ if masks.ndim == 4:
+ # If input has shape [N, C, H, W]
+ tensor = masks.permute(0, 2, 3, 1)
+ tensor_rgb = torch.cat([tensor] * 3, dim=-1)
+ return (tensor_rgb,)
+ elif masks.ndim == 2:
+ # If input has shape [H, W]
+ tensor = masks.unsqueeze(0).unsqueeze(-1)
+ tensor_rgb = torch.cat([tensor] * 3, dim=-1)
+ return (tensor_rgb,)
+ else:
+ cstr("Invalid input shape. Expected [N, C, H, W] or [H, W].").error.print()
+ return masks
+
+
+# MASK CROP DOMINANT REGION
+
+class WAS_Mask_Crop_Dominant_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "padding": ("INT", {"default": 24, "min": 0, "max": 4096, "step": 1}),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "crop_dominant_region"
+
+ def crop_dominant_region(self, masks, padding=24):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+ region_mask = self.WT.Masking.crop_dominant_region(mask_pil, padding)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_pil = Image.fromarray(np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+ region_mask = self.WT.Masking.crop_dominant_region(mask_pil, padding)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+# MASK CROP MINORITY REGION
+
+class WAS_Mask_Crop_Minority_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "padding": ("INT", {"default": 24, "min": 0, "max": 4096, "step": 1}),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "crop_minority_region"
+
+ def crop_minority_region(self, masks, padding=24):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+ region_mask = self.WT.Masking.crop_minority_region(mask_pil, padding)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_pil = Image.fromarray(np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+ region_mask = self.WT.Masking.crop_minority_region(mask_pil, padding)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+# MASK CROP REGION
+
+class WAS_Mask_Crop_Region:
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "mask": ("MASK",),
+ "padding": ("INT",{"default": 24, "min": 0, "max": 4096, "step": 1}),
+ "region_type": (["dominant", "minority"],),
+ }
+ }
+
+ RETURN_TYPES = ("MASK", "CROP_DATA", "INT", "INT", "INT", "INT", "INT", "INT")
+ RETURN_NAMES = ("cropped_mask", "crop_data", "top_int", "left_int", "right_int", "bottom_int", "width_int", "height_int")
+ FUNCTION = "mask_crop_region"
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ def mask_crop_region(self, mask, padding=24, region_type="dominant"):
+
+ mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+ region_mask, crop_data = self.WT.Masking.crop_region(mask_pil, region_type, padding)
+ region_tensor = pil2mask(ImageOps.invert(region_mask)).unsqueeze(0).unsqueeze(1)
+
+ (width, height), (left, top, right, bottom) = crop_data
+
+ return (region_tensor, crop_data, top, left, right, bottom, width, height)
+
+
+# IMAGE PASTE CROP
+
+class WAS_Mask_Paste_Region:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "mask": ("MASK",),
+ "crop_mask": ("MASK",),
+ "crop_data": ("CROP_DATA",),
+ "crop_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "crop_sharpening": ("INT", {"default": 0, "min": 0, "max": 3, "step": 1}),
+ }
+ }
+
+ RETURN_TYPES = ("MASK", "MASK")
+ FUNCTION = "mask_paste_region"
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ def mask_paste_region(self, mask, crop_mask, crop_data=None, crop_blending=0.25, crop_sharpening=0):
+
+ if crop_data == False:
+ cstr("No valid crop data found!").error.print()
+ return( pil2mask(Image.new("L", (512, 512), 0)).unsqueeze(0).unsqueeze(1),
+ pil2mask(Image.new("L", (512, 512), 0)).unsqueeze(0).unsqueeze(1) )
+
+ mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+ mask_crop_pil = Image.fromarray(np.clip(255. * crop_mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+ result_mask, result_crop_mask = self.paste_image(mask_pil, mask_crop_pil, crop_data, crop_blending, crop_sharpening)
+
+ return (pil2mask(result_mask).unsqueeze(0).unsqueeze(1), pil2mask(result_crop_mask).unsqueeze(0).unsqueeze(1))
+
+ def paste_image(self, image, crop_image, crop_data, blend_amount=0.25, sharpen_amount=1):
+
+ def lingrad(size, direction, white_ratio):
+ image = Image.new('RGB', size)
+ draw = ImageDraw.Draw(image)
+ if direction == 'vertical':
+ black_end = int(size[1] * (1 - white_ratio))
+ range_start = 0
+ range_end = size[1]
+ range_step = 1
+ for y in range(range_start, range_end, range_step):
+ color_ratio = y / size[1]
+ if y <= black_end:
+ color = (0, 0, 0)
+ else:
+ color_value = int(((y - black_end) / (size[1] - black_end)) * 255)
+ color = (color_value, color_value, color_value)
+ draw.line([(0, y), (size[0], y)], fill=color)
+ elif direction == 'horizontal':
+ black_end = int(size[0] * (1 - white_ratio))
+ range_start = 0
+ range_end = size[0]
+ range_step = 1
+ for x in range(range_start, range_end, range_step):
+ color_ratio = x / size[0]
+ if x <= black_end:
+ color = (0, 0, 0)
+ else:
+ color_value = int(((x - black_end) / (size[0] - black_end)) * 255)
+ color = (color_value, color_value, color_value)
+ draw.line([(x, 0), (x, size[1])], fill=color)
+
+ return image.convert("L")
+
+ crop_size, (left, top, right, bottom) = crop_data
+ crop_image = crop_image.resize(crop_size)
+
+ if sharpen_amount > 0:
+ for _ in range(int(sharpen_amount)):
+ crop_image = crop_image.filter(ImageFilter.SHARPEN)
+
+ blended_image = Image.new('RGBA', image.size, (0, 0, 0, 255))
+ blended_mask = Image.new('L', image.size, 0) # Update to 'L' mode for MASK image
+ crop_padded = Image.new('RGBA', image.size, (0, 0, 0, 0))
+ blended_image.paste(image, (0, 0))
+ crop_padded.paste(crop_image, (left, top))
+ crop_mask = Image.new('L', crop_image.size, 0)
+
+ if top > 0:
+ gradient_image = ImageOps.flip(lingrad(crop_image.size, 'vertical', blend_amount))
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ if left > 0:
+ gradient_image = ImageOps.mirror(lingrad(crop_image.size, 'horizontal', blend_amount))
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ if right < image.width:
+ gradient_image = lingrad(crop_image.size, 'horizontal', blend_amount)
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ if bottom < image.height:
+ gradient_image = lingrad(crop_image.size, 'vertical', blend_amount)
+ crop_mask = ImageChops.screen(crop_mask, gradient_image)
+
+ crop_mask = ImageOps.invert(crop_mask)
+ blended_mask.paste(crop_mask, (left, top))
+ blended_mask = blended_mask.convert("L")
+ blended_image.paste(crop_padded, (0, 0), blended_mask)
+
+ return (ImageOps.invert(blended_image.convert("RGB")).convert("L"), ImageOps.invert(blended_mask.convert("RGB")).convert("L"))
+
+
+
+
+# MASK DOMINANT REGION
+
+class WAS_Mask_Dominant_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "threshold": ("INT", {"default":128, "min":0, "max":255, "step":1}),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "dominant_region"
+
+ def dominant_region(self, masks, threshold=128):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+ region_mask = self.WT.Masking.dominant_region(mask_pil, threshold)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_pil = Image.fromarray(np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+ region_mask = self.WT.Masking.dominant_region(mask_pil, threshold)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+# MASK MINORITY REGION
+
+class WAS_Mask_Minority_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "threshold": ("INT", {"default":128, "min":0, "max":255, "step":1}),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "minority_region"
+
+ def minority_region(self, masks, threshold=128):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.minority_region(pil_image, threshold)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.minority_region(pil_image, threshold)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+
+# MASK ARBITRARY REGION
+
+class WAS_Mask_Arbitrary_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "size": ("INT", {"default":256, "min":1, "max":4096, "step":1}),
+ "threshold": ("INT", {"default":128, "min":0, "max":255, "step":1}),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "arbitrary_region"
+
+ def arbitrary_region(self, masks, size=256, threshold=128):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.arbitrary_region(pil_image, size, threshold)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.arbitrary_region(pil_image, size, threshold)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+# MASK SMOOTH REGION
+
+class WAS_Mask_Smooth_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "sigma": ("FLOAT", {"default":5.0, "min":0.0, "max":128.0, "step":0.1}),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "smooth_region"
+
+ def smooth_region(self, masks, sigma=128):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.smooth_region(pil_image, sigma)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.smooth_region(pil_image, sigma)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+# MASK ERODE REGION
+
+class WAS_Mask_Erode_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "iterations": ("INT", {"default":5, "min":1, "max":64, "step":1}),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "erode_region"
+
+ def erode_region(self, masks, iterations=5):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.erode_region(pil_image, iterations)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.erode_region(pil_image, iterations)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+# MASKS SUBTRACT
+
+class WAS_Mask_Subtract:
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks_a": ("MASK",),
+ "masks_b": ("MASK",),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "subtract_masks"
+
+ def subtract_masks(self, masks_a, masks_b):
+ subtracted_masks = torch.clamp(masks_a - masks_b, 0, 255)
+ return (subtracted_masks,)
+
+# MASKS ADD
+
+class WAS_Mask_Add:
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks_a": ("MASK",),
+ "masks_b": ("MASK",),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "add_masks"
+
+ def add_masks(self, masks_a, masks_b):
+ if masks_a.ndim > 2 and masks_b.ndim > 2:
+ added_masks = masks_a + masks_b
+ else:
+ added_masks = torch.clamp(masks_a.unsqueeze(1) + masks_b.unsqueeze(1), 0, 255)
+ added_masks = added_masks.squeeze(1)
+ return (added_masks,)
+
+# MASKS ADD
+
+class WAS_Mask_Invert:
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "add_masks"
+
+ def add_masks(self, masks):
+ return (1. - masks,)
+
+# MASK DILATE REGION
+
+class WAS_Mask_Dilate_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "iterations": ("INT", {"default":5, "min":1, "max":64, "step":1}),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "dilate_region"
+
+ def dilate_region(self, masks, iterations=5):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.dilate_region(pil_image, iterations)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.dilate_region(pil_image, iterations)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+# MASK FILL REGION
+
+class WAS_Mask_Fill_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "fill_region"
+
+ def fill_region(self, masks):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.fill_region(pil_image)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.fill_region(pil_image)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+# MASK THRESHOLD
+
+class WAS_Mask_Threshold_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "black_threshold": ("INT",{"default":75, "min":0, "max": 255, "step": 1}),
+ "white_threshold": ("INT",{"default":175, "min":0, "max": 255, "step": 1}),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "threshold_region"
+
+ def threshold_region(self, masks, black_threshold=75, white_threshold=255):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.threshold_region(pil_image, black_threshold, white_threshold)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.threshold_region(pil_image, black_threshold, white_threshold)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+# MASK FLOOR REGION
+
+class WAS_Mask_Floor_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "floor_region"
+
+ def floor_region(self, masks):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.floor_region(pil_image)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.floor_region(pil_image)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+# MASK CEILING REGION
+
+class WAS_Mask_Ceiling_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "ceiling_region"
+
+ def ceiling_region(self, masks):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.ceiling_region(pil_image)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.ceiling_region(pil_image)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+# MASK GAUSSIAN REGION
+
+class WAS_Mask_Gaussian_Region:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ "radius": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 1024, "step": 0.1}),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+ RETURN_NAMES = ("MASKS",)
+
+ FUNCTION = "gaussian_region"
+
+ def gaussian_region(self, masks, radius=5.0):
+ if masks.ndim > 3:
+ regions = []
+ for mask in masks:
+ mask_np = np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.gaussian_region(pil_image, radius)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ regions.append(region_tensor)
+ regions_tensor = torch.cat(regions, dim=0)
+ return (regions_tensor,)
+ else:
+ mask_np = np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+ pil_image = Image.fromarray(mask_np, mode="L")
+ region_mask = self.WT.Masking.gaussian_region(pil_image, radius)
+ region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
+ return (region_tensor,)
+
+
+# MASK COMBINE
+
+class WAS_Mask_Combine:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "mask_a": ("MASK",),
+ "mask_b": ("MASK",),
+ },
+ "optional": {
+ "mask_c": ("MASK",),
+ "mask_d": ("MASK",),
+ "mask_e": ("MASK",),
+ "mask_f": ("MASK",),
+ }
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+
+ FUNCTION = "combine_masks"
+
+ def combine_masks(self, mask_a, mask_b, mask_c=None, mask_d=None, mask_e=None, mask_f=None):
+ masks = [mask_a, mask_b]
+ if mask_c:
+ masks.append(mask_c)
+ if mask_d:
+ masks.append(mask_d)
+ if mask_e:
+ masks.append(mask_e)
+ if mask_f:
+ masks.append(mask_f)
+ combined_mask = torch.sum(torch.stack(masks, dim=0), dim=0)
+ combined_mask = torch.clamp(combined_mask, 0, 1) # Ensure values are between 0 and 1
+ return (combined_mask, )
+
+class WAS_Mask_Combine_Batch:
+
+ def __init__(self):
+ self.WT = WAS_Tools_Class()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "masks": ("MASK",),
+ },
+ }
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ RETURN_TYPES = ("MASK",)
+
+ FUNCTION = "combine_masks"
+
+ def combine_masks(self, masks):
+ combined_mask = torch.sum(torch.stack([mask.unsqueeze(0) for mask in masks], dim=0), dim=0)
+ combined_mask = torch.clamp(combined_mask, 0, 1) # Ensure values are between 0 and 1
+ return (combined_mask, )
+
+# LATENT UPSCALE NODE
+
+class WAS_Latent_Upscale:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {"samples": ("LATENT",), "mode": (["area", "bicubic", "bilinear", "nearest"],),
+ "factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.01}),
+ "align": (["true", "false"], )}}
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "latent_upscale"
+
+ CATEGORY = "WAS Suite/Latent/Transform"
+
+ def latent_upscale(self, samples, mode, factor, align):
+ valid_modes = ["area", "bicubic", "bilinear", "nearest"]
+ if mode not in valid_modes:
+ cstr(f"Invalid interpolation mode `{mode}` selected. Valid modes are: {', '.join(valid_modes)}").error.print()
+ return (s, )
+ align = True if align == 'true' else False
+ if not isinstance(factor, float) or factor <= 0:
+ cstr(f"The input `factor` is `{factor}`, but should be a positive or negative float.").error.print()
+ return (s, )
+ s = samples.copy()
+ shape = s['samples'].shape
+ size = tuple(int(round(dim * factor)) for dim in shape[-2:])
+ if mode in ['linear', 'bilinear', 'bicubic', 'trilinear']:
+ s["samples"] = torch.nn.functional.interpolate(
+ s['samples'], size=size, mode=mode, align_corners=align)
+ else:
+ s["samples"] = torch.nn.functional.interpolate(s['samples'], size=size, mode=mode)
+ return (s,)
+
+# LATENT NOISE INJECTION NODE
+
+
+class WAS_Latent_Noise:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "samples": ("LATENT",),
+ "noise_std": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01}),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "inject_noise"
+
+ CATEGORY = "WAS Suite/Latent/Generate"
+
+ def inject_noise(self, samples, noise_std):
+ s = samples.copy()
+ noise = torch.randn_like(s["samples"]) * noise_std
+ s["samples"] = s["samples"] + noise
+ return (s,)
+
+
+
+# MIDAS DEPTH APPROXIMATION NODE
+
+class MiDaS_Model_Loader:
+ def __init__(self):
+ self.midas_dir = os.path.join(MODELS_DIR, 'midas')
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "midas_model": (["DPT_Large", "DPT_Hybrid"],),
+ },
+ }
+
+ RETURN_TYPES = ("MIDAS_MODEL",)
+ RETURN_NAMES = ("midas_model",)
+ FUNCTION = "load_midas_model"
+
+ CATEGORY = "WAS Suite/Loaders"
+
+ def load_midas_model(self, midas_model):
+
+ global MIDAS_INSTALLED
+
+ if not MIDAS_INSTALLED:
+ self.install_midas()
+
+ if midas_model == 'DPT_Large':
+ model_name = 'dpt_large_384.pt'
+ elif midas_model == 'DPT_Hybrid':
+ model_name = 'dpt_hybrid_384.pt'
+ else:
+ model_name = 'dpt_large_384.pt'
+
+ model_path = os.path.join(self.midas_dir, 'checkpoints'+os.sep+model_name)
+
+ torch.hub.set_dir(self.midas_dir)
+ if os.path.exists(model_path):
+ cstr(f"Loading MiDaS Model from `{model_path}`").msg.print()
+ midas_type = model_path
+ else:
+ cstr("Downloading and loading MiDaS Model...").msg.print()
+ midas = torch.hub.load("intel-isl/MiDaS", midas_model, trust_repo=True)
+ device = torch.device("cpu")
+
+ cstr(f"MiDaS is using passive device `{device}` until in use.").msg.print()
+
+ midas.to(device)
+ midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
+ transform = midas_transforms.dpt_transform
+
+ return ( (midas, transform), )
+
+ def install_midas(self):
+ global MIDAS_INSTALLED
+ if 'timm' not in packages():
+ cstr("Installing timm...").msg.print()
+ subprocess.check_call(
+ [sys.executable, '-s', '-m', 'pip', '-q', 'install', 'timm'])
+ MIDAS_INSTALLED = True
+
+
+# MIDAS DEPTH APPROXIMATION NODE
+
+class MiDaS_Depth_Approx:
+ def __init__(self):
+ self.midas_dir = os.path.join(MODELS_DIR, 'midas')
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "use_cpu": (["false", "true"],),
+ "midas_type": (["DPT_Large", "DPT_Hybrid"],),
+ "invert_depth": (["false", "true"],),
+ },
+ "optional": {
+ "midas_model": ("MIDAS_MODEL",),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("images",)
+ FUNCTION = "midas_approx"
+
+ CATEGORY = "WAS Suite/Image/AI"
+
+ def midas_approx(self, image, use_cpu, midas_type, invert_depth, midas_model=None):
+
+ global MIDAS_INSTALLED
+
+ if not MIDAS_INSTALLED:
+ self.install_midas()
+
+ import cv2 as cv
+
+ if midas_model:
+
+ midas = midas_model[0]
+ transform = midas_model[1]
+ device = torch.device("cuda") if torch.cuda.is_available() and use_cpu == 'false' else torch.device("cpu")
+ cstr(f"MiDaS is using device: {device}").msg.print()
+ midas.to(device).eval()
+
+ else:
+
+ if midas_model == 'DPT_Large':
+ model_name = 'dpt_large_384.pt'
+ elif midas_model == 'DPT_Hybrid':
+ model_name = 'dpt_hybrid_384.pt'
+ else:
+ model_name = 'dpt_large_384.pt'
+
+ model_path = os.path.join(self.midas_dir, 'checkpoints'+os.sep+model_name)
+
+ torch.hub.set_dir(self.midas_dir)
+ if os.path.exists(model_path):
+ cstr(f"Loading MiDaS Model from `{model_path}`").msg.print()
+ midas_type = model_path
+ else:
+ cstr("Downloading and loading MiDaS Model...").msg.print()
+ midas = torch.hub.load("intel-isl/MiDaS", midas_type, trust_repo=True)
+
+ cstr(f"MiDaS is using device: {device}").msg.print()
+
+ midas.to(device).eval()
+ midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
+
+ transform = midas_transforms.dpt_transform
+
+ tensor_images = []
+ for i, img in enumerate(image):
+
+ img = np.array(tensor2pil(img))
+
+ img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
+ input_batch = transform(img).to(device)
+
+ cstr(f"Approximating depth for image {i+1}/{len(image)}").msg.print()
+
+ with torch.no_grad():
+ prediction = midas(input_batch)
+ prediction = torch.nn.functional.interpolate(
+ prediction.unsqueeze(1),
+ size=img.shape[:2],
+ mode="bicubic",
+ align_corners=False,
+ ).squeeze()
+
+
+ # Normalize and convert to uint8
+ min_val = torch.min(prediction)
+ max_val = torch.max(prediction)
+ prediction = (prediction - min_val) / (max_val - min_val)
+ prediction = (prediction * 255).clamp(0, 255).round().cpu().numpy().astype(np.uint8)
+
+ depth = Image.fromarray(prediction)
+
+ # Invert depth map
+ if invert_depth == 'true':
+ depth = ImageOps.invert(depth)
+
+ tensor_images.append(pil2tensor(depth.convert("RGB")))
+
+ tensor_images = torch.cat(tensor_images, dim=0)
+ if not midas_model:
+ del midas, device, midas_transforms
+ del midas, transform, img, input_batch, prediction
+
+ return (tensor_images, )
+
+ def install_midas(self):
+ global MIDAS_INSTALLED
+ if 'timm' not in packages():
+ cstr("Installing timm...").msg.print()
+ subprocess.check_call(
+ [sys.executable, '-s', '-m', 'pip', '-q', 'install', 'timm'])
+ MIDAS_INSTALLED = True
+
+# MIDAS REMOVE BACKGROUND/FOREGROUND NODE
+
+
+class MiDaS_Background_Foreground_Removal:
+ def __init__(self):
+ self.midas_dir = os.path.join(MODELS_DIR, 'midas')
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "use_cpu": (["false", "true"],),
+ "midas_model": (["DPT_Large", "DPT_Hybrid", "DPT_Small"],),
+ "remove": (["background", "foregroud"],),
+ "threshold": (["false", "true"],),
+ "threshold_low": ("FLOAT", {"default": 10, "min": 0, "max": 255, "step": 1}),
+ "threshold_mid": ("FLOAT", {"default": 200, "min": 0, "max": 255, "step": 1}),
+ "threshold_high": ("FLOAT", {"default": 210, "min": 0, "max": 255, "step": 1}),
+ "smoothing": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 16.0, "step": 0.01}),
+ "background_red": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
+ "background_green": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
+ "background_blue": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE", "IMAGE")
+ FUNCTION = "midas_remove"
+
+ CATEGORY = "WAS Suite/Image/AI"
+
+ def midas_remove(self,
+ image,
+ midas_model,
+ use_cpu='false',
+ remove='background',
+ threshold='false',
+ threshold_low=0,
+ threshold_mid=127,
+ threshold_high=255,
+ smoothing=0.25,
+ background_red=0,
+ background_green=0,
+ background_blue=0):
+
+ global MIDAS_INSTALLED
+
+ if not MIDAS_INSTALLED:
+ self.install_midas()
+
+ import cv2 as cv
+
+ # Convert the input image tensor to a numpy and PIL Image
+ i = 255. * image.cpu().numpy().squeeze()
+ img = i
+ # Original image
+ img_original = tensor2pil(image).convert('RGB')
+
+ cstr("Downloading and loading MiDaS Model...").msg.print()
+ torch.hub.set_dir(self.midas_dir)
+ midas = torch.hub.load("intel-isl/MiDaS", midas_model, trust_repo=True)
+ device = torch.device("cuda") if torch.cuda.is_available(
+ ) and use_cpu == 'false' else torch.device("cpu")
+
+ cstr(f"MiDaS is using device: {device}").msg.print()
+
+ midas.to(device).eval()
+ midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
+
+ if midas_model == "DPT_Large" or midas_model == "DPT_Hybrid":
+ transform = midas_transforms.dpt_transform
+ else:
+ transform = midas_transforms.small_transform
+
+ img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
+ input_batch = transform(img).to(device)
+
+ cstr("Approximating depth from image.").msg.print()
+
+ with torch.no_grad():
+ prediction = midas(input_batch)
+ prediction = torch.nn.functional.interpolate(
+ prediction.unsqueeze(1),
+ size=img.shape[:2],
+ mode="bicubic",
+ align_corners=False,
+ ).squeeze()
+
+ # Invert depth map
+ if remove == 'foreground':
+ depth = (255 - prediction.cpu().numpy().astype(np.uint8))
+ depth = depth.astype(np.float32)
+ else:
+ depth = prediction.cpu().numpy().astype(np.float32)
+ depth = depth * 255 / (np.max(depth)) / 255
+ depth = Image.fromarray(np.uint8(depth * 255))
+
+ # Threshold depth mask
+ if threshold == 'true':
+ levels = self.AdjustLevels(
+ threshold_low, threshold_mid, threshold_high)
+ depth = levels.adjust(depth.convert('RGB')).convert('L')
+ if smoothing > 0:
+ depth = depth.filter(ImageFilter.GaussianBlur(radius=smoothing))
+ depth = depth.resize(img_original.size).convert('L')
+
+ # Validate background color arguments
+ background_red = int(background_red) if isinstance(
+ background_red, (int, float)) else 0
+ background_green = int(background_green) if isinstance(
+ background_green, (int, float)) else 0
+ background_blue = int(background_blue) if isinstance(
+ background_blue, (int, float)) else 0
+
+ # Create background color tuple
+ background_color = (background_red, background_green, background_blue)
+
+ # Create background image
+ background = Image.new(
+ mode="RGB", size=img_original.size, color=background_color)
+
+ # Composite final image
+ result_img = Image.composite(img_original, background, depth)
+
+ del midas, device, midas_transforms
+ del transform, img, img_original, input_batch, prediction
+
+ return (pil2tensor(result_img), pil2tensor(depth.convert('RGB')))
+
+ class AdjustLevels:
+ def __init__(self, min_level, mid_level, max_level):
+ self.min_level = min_level
+ self.mid_level = mid_level
+ self.max_level = max_level
+
+ def adjust(self, im):
+ # load the image
+
+ # convert the image to a numpy array
+ im_arr = np.array(im)
+
+ # apply the min level adjustment
+ im_arr[im_arr < self.min_level] = self.min_level
+
+ # apply the mid level adjustment
+ im_arr = (im_arr - self.min_level) * \
+ (255 / (self.max_level - self.min_level))
+ im_arr[im_arr < 0] = 0
+ im_arr[im_arr > 255] = 255
+ im_arr = im_arr.astype(np.uint8)
+
+ # apply the max level adjustment
+ im = Image.fromarray(im_arr)
+ im = ImageOps.autocontrast(im, cutoff=self.max_level)
+
+ return im
+
+ def install_midas(self):
+ global MIDAS_INSTALLED
+ if 'timm' not in packages():
+ cstr("Installing timm...").msg.print()
+ subprocess.check_call(
+ [sys.executable, '-s', '-m', 'pip', '-q', 'install', 'timm'])
+ MIDAS_INSTALLED = True
+
+
+#! CONDITIONING NODES
+
+
+# NSP CLIPTextEncode NODE
+
+class WAS_NSP_CLIPTextEncoder:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "mode": (["Noodle Soup Prompts", "Wildcards"],),
+ "noodle_key": ("STRING", {"default": '__', "multiline": False}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "text": ("STRING", {"multiline": True}),
+ "clip": ("CLIP",),
+ }
+ }
+
+ OUTPUT_NODE = True
+ RETURN_TYPES = ("CONDITIONING", TEXT_TYPE, TEXT_TYPE)
+ RETURN_NAMES = ("conditioning", "parsed_text", "raw_text")
+ FUNCTION = "nsp_encode"
+
+ CATEGORY = "WAS Suite/Conditioning"
+
+ def nsp_encode(self, clip, text, mode="Noodle Soup Prompts", noodle_key='__', seed=0):
+
+ if mode == "Noodle Soup Prompts":
+ new_text = nsp_parse(text, seed, noodle_key)
+ else:
+ new_text = replace_wildcards(text, (None if seed == 0 else seed), noodle_key)
+
+ new_text = parse_dynamic_prompt(new_text, seed)
+ new_text, text_vars = parse_prompt_vars(new_text)
+ cstr(f"CLIPTextEncode Prased Prompt:\n {new_text}").msg.print()
+ CLIPTextEncode = nodes.CLIPTextEncode()
+ encoded = CLIPTextEncode.encode(clip=clip, text=new_text)
+
+ return (encoded[0], new_text, text, { "ui": { "string": new_text } })
+
+
+#! SAMPLING NODES
+
+# KSAMPLER
+
+class WAS_KSampler:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required":
+
+ {"model": ("MODEL", ),
+ "seed": ("SEED", ),
+ "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+ "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
+ "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+ "positive": ("CONDITIONING", ),
+ "negative": ("CONDITIONING", ),
+ "latent_image": ("LATENT", ),
+ "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ }}
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "sample"
+
+ CATEGORY = "WAS Suite/Sampling"
+
+ def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
+ return nodes.common_ksampler(model, seed['seed'], steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
+
+# KSampler Cycle
+
+class WAS_KSampler_Cycle:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "model": ("MODEL",),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+ "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
+ "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+ "positive": ("CONDITIONING", ),
+ "negative": ("CONDITIONING", ),
+ "latent_image": ("LATENT", ),
+ "tiled_vae": (["disable", "enable"], ),
+ "latent_upscale": (["disable","nearest-exact", "bilinear", "area", "bicubic", "bislerp"],),
+ "upscale_factor": ("FLOAT", {"default":2.0, "min": 0.1, "max": 8.0, "step": 0.1}),
+ "upscale_cycles": ("INT", {"default": 2, "min": 2, "max": 12, "step": 1}),
+ "starting_denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "cycle_denoise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "scale_denoise": (["enable", "disable"],),
+ "scale_sampling": (["bilinear", "bicubic", "nearest", "lanczos"],),
+ "vae": ("VAE",),
+ },
+ "optional": {
+ "secondary_model": ("MODEL",),
+ "secondary_start_cycle": ("INT", {"default": 2, "min": 2, "max": 16, "step": 1}),
+ "upscale_model": ("UPSCALE_MODEL",),
+ "processor_model": ("UPSCALE_MODEL",),
+ "pos_additive": ("CONDITIONING",),
+ "neg_additive": ("CONDITIONING",),
+ "pos_add_mode": (["increment", "decrement"],),
+ "pos_add_strength": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 1.0, "step": 0.01}),
+ "pos_add_strength_scaling": (["enable", "disable"],),
+ "pos_add_strength_cutoff": ("FLOAT", {"default": 2.0, "min": 0.01, "max": 10.0, "step": 0.01}),
+ "neg_add_mode": (["increment", "decrement"],),
+ "neg_add_strength": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 1.0, "step": 0.01}),
+ "neg_add_strength_scaling": (["enable", "disable"],),
+ "neg_add_strength_cutoff": ("FLOAT", {"default": 2.0, "min": 0.01, "max": 10.0, "step": 0.01}),
+ "sharpen_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+ "sharpen_radius": ("INT", {"default": 2, "min": 1, "max": 12, "step": 1}),
+ "steps_scaling": (["enable", "disable"],),
+ "steps_control": (["decrement", "increment"],),
+ "steps_scaling_value": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
+ "steps_cutoff": ("INT", {"default": 20, "min": 4, "max": 1000, "step": 1}),
+ "denoise_cutoff": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 1.0, "step": 0.01}),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ RETURN_NAMES = ("latent(s)",)
+ FUNCTION = "sample"
+
+ CATEGORY = "WAS Suite/Sampling"
+
+ def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, tiled_vae, latent_upscale, upscale_factor,
+ upscale_cycles, starting_denoise, cycle_denoise, scale_denoise, scale_sampling, vae, secondary_model=None, secondary_start_cycle=None,
+ pos_additive=None, pos_add_mode=None, pos_add_strength=None, pos_add_strength_scaling=None, pos_add_strength_cutoff=None,
+ neg_additive=None, neg_add_mode=None, neg_add_strength=None, neg_add_strength_scaling=None, neg_add_strength_cutoff=None,
+ upscale_model=None, processor_model=None, sharpen_strength=0, sharpen_radius=2, steps_scaling=None, steps_control=None,
+ steps_scaling_value=None, steps_cutoff=None, denoise_cutoff=0.25):
+
+ upscale_steps = upscale_cycles
+ division_factor = upscale_steps if steps >= upscale_steps else steps
+ current_upscale_factor = upscale_factor ** (1 / (division_factor - 1))
+ tiled_vae = (tiled_vae == "enable")
+ scale_denoise = (scale_denoise == "enable")
+ pos_add_strength_scaling = (pos_add_strength_scaling == "enable")
+ neg_add_strength_scaling = (neg_add_strength_scaling == "enable")
+ steps_scaling = (steps_scaling == "enable")
+ run_model = model
+ secondary_switched = False
+
+ for i in range(division_factor):
+
+ cstr(f"Cycle Pass {i+1}/{division_factor}").msg.print()
+
+ if scale_denoise:
+ denoise = (
+ ( round(cycle_denoise * (2 ** (-(i-1))), 2) if i > 0 else cycle_denoise )
+ if i > 0 else round(starting_denoise, 2)
+ )
+ else:
+ denoise = round((cycle_denoise if i > 0 else starting_denoise), 2)
+
+ if denoise < denoise_cutoff and scale_denoise:
+ denoise = denoise_cutoff
+
+ if i >= (secondary_start_cycle - 1) and secondary_model and not secondary_switched:
+ run_model = secondary_model
+ denoise = cycle_denoise
+ model = None
+ secondary_switched = True
+
+ if steps_scaling and i > 0:
+
+ steps = (
+ steps + steps_scaling_value
+ if steps_control == 'increment'
+ else steps - steps_scaling_value
+ )
+ steps = (
+ ( steps
+ if steps <= steps_cutoff
+ else steps_cutoff )
+ if steps_control == 'increment'
+ else ( steps
+ if steps >= steps_cutoff
+ else steps_cutoff )
+ )
+
+ print("Steps:", steps)
+ print("Denoise:", denoise)
+
+ if pos_additive:
+
+ pos_strength = 0.0 if i <= 0 else pos_add_strength
+
+ if pos_add_mode == 'increment':
+ pos_strength = (
+ ( round(pos_add_strength * (2 ** (i-1)), 2)
+ if i > 0
+ else pos_add_strength )
+ if pos_add_strength_scaling
+ else pos_add_strength
+ )
+ pos_strength = (
+ pos_add_strength_cutoff
+ if pos_strength > pos_add_strength_cutoff
+ else pos_strength
+ )
+ else:
+ pos_strength = (
+ ( round(pos_add_strength / (2 ** (i-1)), 2)
+ if i > 0
+ else pos_add_strength )
+ if pos_add_strength_scaling
+ else pos_add_strength
+ )
+ pos_strength = (
+ pos_add_strength_cutoff
+ if pos_strength < pos_add_strength_cutoff
+ else pos_strength
+ )
+ comb = nodes.ConditioningAverage()
+ positive = comb.addWeighted(pos_additive, positive, pos_strength)[0]
+ print("Positive Additive Strength:", pos_strength)
+
+ if neg_additive:
+
+ neg_strength = 0.0 if i <= 0 else pos_add_strength
+
+ if neg_add_mode == 'increment':
+ neg_strength = (
+ ( round(neg_add_strength * (2 ** (i-1)), 2)
+ if i > 0
+ else neg_add_strength )
+ if neg_add_strength_scaling
+ else neg_add_strength
+ )
+ neg_strength = (
+ neg_add_strength_cutoff
+ if neg_strength > neg_add_strength_cutoff
+ else neg_strength
+ )
+ else:
+ neg_strength = (
+ ( round(neg_add_strength / (2 ** (i-1)), 2)
+ if i > 0
+ else neg_add_strength )
+ if neg_add_strength_scaling
+ else neg_add_strength
+ )
+ neg_strength = (
+ neg_add_strength_cutoff
+ if neg_strength < neg_add_strength_cutoff
+ else neg_strength
+ )
+
+ comb = nodes.ConditioningAverage()
+ negative = comb.addWeighted(neg_additive, negative, neg_strength)[0]
+ print("Negative Additive Strength:", neg_strength)
+
+ if i != 0:
+ latent_image = latent_image_result
+
+ samples = nodes.common_ksampler(
+ run_model,
+ seed,
+ steps,
+ cfg,
+ sampler_name,
+ scheduler,
+ positive,
+ negative,
+ latent_image,
+ denoise=denoise,
+ )
+
+ # Upscale
+ if i < division_factor - 1:
+
+ tensors = None
+ upscaler = None
+
+ resample_filters = {
+ 'nearest': 0,
+ 'bilinear': 2,
+ 'bicubic': 3,
+ 'lanczos': 1
+ }
+
+ if latent_upscale == 'disable':
+
+ if tiled_vae:
+ tensors = vae.decode_tiled(samples[0]['samples'])
+ else:
+ tensors = vae.decode(samples[0]['samples'])
+
+ if processor_model or upscale_model:
+
+ from comfy_extras import nodes_upscale_model
+ upscaler = nodes_upscale_model.ImageUpscaleWithModel()
+
+ if processor_model:
+
+ original_size = tensor2pil(tensors[0]).size
+ upscaled_tensors = upscaler.upscale(processor_model, tensors)
+ tensor_images = []
+ for tensor in upscaled_tensors[0]:
+ pil = tensor2pil(tensor)
+ if pil.size[0] != original_size[0] or pil.size[1] != original_size[1]:
+ pil = pil.resize((original_size[0], original_size[1]), Image.Resampling(resample_filters[scale_sampling]))
+ if sharpen_strength != 0.0:
+ pil = self.unsharp_filter(pil, sharpen_radius, sharpen_strength)
+ tensor_images.append(pil2tensor(pil))
+
+ tensor_images = torch.cat(tensor_images, dim=0)
+
+ if upscale_model:
+
+ if processor_model:
+ tensors = tensor_images
+ del tensor_images
+
+ original_size = tensor2pil(tensors[0]).size
+ new_width = round(original_size[0] * current_upscale_factor)
+ new_height = round(original_size[1] * current_upscale_factor)
+ new_width = int(round(new_width / 32) * 32)
+ new_height = int(round(new_height / 32) * 32)
+ upscaled_tensors = upscaler.upscale(upscale_model, tensors)
+ tensor_images = []
+ for tensor in upscaled_tensors[0]:
+ tensor = pil2tensor(tensor2pil(tensor).resize((new_width, new_height), Image.Resampling(resample_filters[scale_sampling])))
+ size = max(tensor2pil(tensor).size)
+ if sharpen_strength != 0.0:
+ tensor = pil2tensor(self.unsharp_filter(tensor2pil(tensor), sharpen_radius, sharpen_strength))
+ tensor_images.append(tensor)
+
+ tensor_images = torch.cat(tensor_images, dim=0)
+
+ else:
+
+ tensor_images = []
+ scale = WAS_Image_Rescale()
+ for tensor in tensors:
+ tensor = scale.image_rescale(tensor.unsqueeze(0), "rescale", "true", scale_sampling, current_upscale_factor, 0, 0)[0]
+ size = max(tensor2pil(tensor).size)
+ if sharpen_strength > 0.0:
+ tensor = pil2tensor(self.unsharp_filter(tensor2pil(tensor), sharpen_radius, sharpen_strength))
+ tensor_images.append(tensor)
+ tensor_images = torch.cat(tensor_images, dim=0)
+
+ if tiled_vae:
+ latent_image_result = {"samples": vae.encode_tiled(self.vae_encode_crop_pixels(tensor_images)[:,:,:,:3])}
+ else:
+ latent_image_result = {"samples": vae.encode(self.vae_encode_crop_pixels(tensor_images)[:,:,:,:3])}
+
+ else:
+
+ upscaler = nodes.LatentUpscaleBy()
+ latent_image_result = upscaler.upscale(samples[0], latent_upscale, current_upscale_factor)[0]
+
+ else:
+
+ latent_image_result = samples[0]
+
+ return (latent_image_result, )
+
+ @staticmethod
+ def vae_encode_crop_pixels(pixels):
+ x = (pixels.shape[1] // 8) * 8
+ y = (pixels.shape[2] // 8) * 8
+ if pixels.shape[1] != x or pixels.shape[2] != y:
+ x_offset = (pixels.shape[1] % 8) // 2
+ y_offset = (pixels.shape[2] % 8) // 2
+ pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
+ return pixels
+
+ @staticmethod
+ def unsharp_filter(image, radius=2, amount=1.0):
+ from skimage.filters import unsharp_mask
+ img_array = np.array(image)
+ img_array = img_array / 255.0
+ sharpened = unsharp_mask(img_array, radius=radius, amount=amount, channel_axis=2)
+ sharpened = (sharpened * 255.0).astype(np.uint8)
+ sharpened_pil = Image.fromarray(sharpened)
+
+ return sharpened_pil
+
+
+# Latent Blend
+
+class WAS_Blend_Latents:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "latent_a": ("LATENT",),
+ "latent_b": ("LATENT",),
+ "operation": (["add", "multiply", "divide", "subtract", "overlay", "hard_light", "soft_light", "screen", "linear_dodge", "difference", "exclusion", "random"],),
+ "blend": ("FLOAT", {"default": 0.5, "min": 0.01, "max": 1.0, "step": 0.01}),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "latent_blend"
+
+ CATEGORY = "WAS Suite/Latent"
+
+ def latent_blend(self, latent_a, latent_b, operation, blend):
+ return ( {"samples": self.blend_latents(latent_a['samples'], latent_b['samples'], operation, blend)}, )
+
+ def blend_latents(self, latent1, latent2, mode='add', blend_percentage=0.5):
+
+ def overlay_blend(latent1, latent2, blend_factor):
+ low = 2 * latent1 * latent2
+ high = 1 - 2 * (1 - latent1) * (1 - latent2)
+ blended_latent = (latent1 * blend_factor) * low + (latent2 * blend_factor) * high
+ return blended_latent
+
+ def screen_blend(latent1, latent2, blend_factor):
+ inverted_latent1 = 1 - latent1
+ inverted_latent2 = 1 - latent2
+ blended_latent = 1 - (inverted_latent1 * inverted_latent2 * (1 - blend_factor))
+ return blended_latent
+
+ def difference_blend(latent1, latent2, blend_factor):
+ blended_latent = abs(latent1 - latent2) * blend_factor
+ return blended_latent
+
+ def exclusion_blend(latent1, latent2, blend_factor):
+ blended_latent = (latent1 + latent2 - 2 * latent1 * latent2) * blend_factor
+ return blended_latent
+
+ def hard_light_blend(latent1, latent2, blend_factor):
+ blended_latent = torch.where(latent2 < 0.5, 2 * latent1 * latent2, 1 - 2 * (1 - latent1) * (1 - latent2)) * blend_factor
+ return blended_latent
+
+ def linear_dodge_blend(latent1, latent2, blend_factor):
+ blended_latent = torch.clamp(latent1 + latent2, 0, 1) * blend_factor
+ return blended_latent
+
+ def soft_light_blend(latent1, latent2, blend_factor):
+ low = 2 * latent1 * latent2 + latent1 ** 2 - 2 * latent1 * latent2 * latent1
+ high = 2 * latent1 * (1 - latent2) + torch.sqrt(latent1) * (2 * latent2 - 1)
+ blended_latent = (latent1 * blend_factor) * low + (latent2 * blend_factor) * high
+ return blended_latent
+
+ def random_noise(latent1, latent2, blend_factor):
+ noise1 = torch.randn_like(latent1)
+ noise2 = torch.randn_like(latent2)
+ noise1 = (noise1 - noise1.min()) / (noise1.max() - noise1.min())
+ noise2 = (noise2 - noise2.min()) / (noise2.max() - noise2.min())
+ blended_noise = (latent1 * blend_factor) * noise1 + (latent2 * blend_factor) * noise2
+ blended_noise = torch.clamp(blended_noise, 0, 1)
+ return blended_noise
+
+ blend_factor1 = blend_percentage
+ blend_factor2 = 1 - blend_percentage
+
+ if mode == 'add':
+ blended_latent = (latent1 * blend_factor1) + (latent2 * blend_factor2)
+ elif mode == 'multiply':
+ blended_latent = (latent1 * blend_factor1) * (latent2 * blend_factor2)
+ elif mode == 'divide':
+ blended_latent = (latent1 * blend_factor1) / (latent2 * blend_factor2)
+ elif mode == 'subtract':
+ blended_latent = (latent1 * blend_factor1) - (latent2 * blend_factor2)
+ elif mode == 'overlay':
+ blended_latent = overlay_blend(latent1, latent2, blend_factor1)
+ elif mode == 'screen':
+ blended_latent = screen_blend(latent1, latent2, blend_factor1)
+ elif mode == 'difference':
+ blended_latent = difference_blend(latent1, latent2, blend_factor1)
+ elif mode == 'exclusion':
+ blended_latent = exclusion_blend(latent1, latent2, blend_factor1)
+ elif mode == 'hard_light':
+ blended_latent = hard_light_blend(latent1, latent2, blend_factor1)
+ elif mode == 'linear_dodge':
+ blended_latent = linear_dodge_blend(latent1, latent2, blend_factor1)
+ elif mode == 'soft_light':
+ blended_latent = soft_light_blend(latent1, latent2, blend_factor1)
+ elif mode == 'random':
+ blended_latent = random_noise(latent1, latent2, blend_factor1)
+ else:
+ raise ValueError("Unsupported blending mode. Please choose from 'add', 'multiply', 'divide', 'subtract', 'overlay', 'screen', 'difference', 'exclusion', 'hard_light', 'linear_dodge', 'soft_light', 'custom_noise'.")
+
+ return blended_latent
+
+
+
+# SEED NODE
+
+class WAS_Seed:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required":
+ {"seed": ("INT", {"default": 0, "min": 0,
+ "max": 0xffffffffffffffff})}
+ }
+
+ RETURN_TYPES = ("SEED",)
+ FUNCTION = "seed"
+
+ CATEGORY = "WAS Suite/Number"
+
+ def seed(self, seed):
+ return ({"seed": seed, }, )
+
+
+# IMAGE SEED
+
+class WAS_Image_To_Seed:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "images": ("IMAGE",),
+ }
+ }
+
+ RETURN_TYPES = ("INT",)
+ OUTPUT_IS_LIST = (True,)
+
+ FUNCTION = "image_to_seed"
+ CATEGORY = "WAS Suite/Image/Analyze"
+
+ def image_to_seed(self, images):
+
+ seeds = []
+ for image in images:
+ image = tensor2pil(image)
+ seeds.append(image2seed(image))
+
+ return (seeds, )
+
+
+#! TEXT NODES
+
+class WAS_Prompt_Styles_Selector:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ style_list = []
+ if os.path.exists(STYLES_PATH):
+ with open(STYLES_PATH, "r") as f:
+ if len(f.readlines()) != 0:
+ f.seek(0)
+ data = f.read()
+ styles = json.loads(data)
+ for style in styles.keys():
+ style_list.append(style)
+ if not style_list:
+ style_list.append("None")
+ return {
+ "required": {
+ "style": (style_list,),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE)
+ RETURN_NAMES = ("positive_string", "negative_string")
+ FUNCTION = "load_style"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def load_style(self, style):
+
+ styles = {}
+ if os.path.exists(STYLES_PATH):
+ with open(STYLES_PATH, 'r') as data:
+ styles = json.load(data)
+ else:
+ cstr(f"The styles file does not exist at `{STYLES_PATH}`. Unable to load styles! Have you imported your AUTOMATIC1111 WebUI styles?").error.print()
+
+ if styles and style != None or style != 'None':
+ prompt = styles[style]['prompt']
+ negative_prompt = styles[style]['negative_prompt']
+ else:
+ prompt = ''
+ negative_prompt = ''
+
+ return (prompt, negative_prompt)
+
+class WAS_Prompt_Multiple_Styles_Selector:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ style_list = []
+ if os.path.exists(STYLES_PATH):
+ with open(STYLES_PATH, "r") as f:
+ if len(f.readlines()) != 0:
+ f.seek(0)
+ data = f.read()
+ styles = json.loads(data)
+ for style in styles.keys():
+ style_list.append(style)
+ if not style_list:
+ style_list.append("None")
+ return {
+ "required": {
+ "style1": (style_list,),
+ "style2": (style_list,),
+ "style3": (style_list,),
+ "style4": (style_list,),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE)
+ RETURN_NAMES = ("positive_string", "negative_string")
+ FUNCTION = "load_style"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def load_style(self, style1, style2, style3, style4):
+ styles = {}
+ if os.path.exists(STYLES_PATH):
+ with open(STYLES_PATH, 'r') as data:
+ styles = json.load(data)
+ else:
+ cstr(f"The styles file does not exist at `{STYLES_PATH}`. Unable to load styles! Have you imported your AUTOMATIC1111 WebUI styles?").error.print()
+ return ('', '')
+
+ # Check if the selected styles exist in the loaded styles dictionary
+ selected_styles = [style1, style2, style3, style4]
+ for style in selected_styles:
+ if style not in styles:
+ print(f"Style '{style}' was not found in the styles file.")
+ return ('', '')
+
+ prompt = ""
+ negative_prompt = ""
+
+ # Concatenate the prompts and negative prompts of the selected styles
+ for style in selected_styles:
+ prompt += styles[style]['prompt'] + " "
+ negative_prompt += styles[style]['negative_prompt'] + " "
+
+ return (prompt.strip(), negative_prompt.strip())
+
+# Text Multiline Node
+
+class WAS_Text_Multiline:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": ("STRING", {"default": '', "multiline": True}),
+ }
+ }
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "text_multiline"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def text_multiline(self, text):
+ import io
+ new_text = []
+ for line in io.StringIO(text):
+ if not line.strip().startswith('#'):
+ if not line.strip().startswith("\n"):
+ line = line.replace("\n", '')
+ new_text.append(line)
+ new_text = "\n".join(new_text)
+ return (new_text, )
+
+# Text List Node
+
+class WAS_Text_List_Concatenate:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "list_a": ("LIST", {"forceInput": True}),
+ "list_b": ("LIST", {"forceInput": True}),
+ },
+ "optional": {
+ "list_c": ("LIST", {"forceInput": True}),
+ "list_d": ("LIST", {"forceInput": True}),
+ }
+ }
+ RETURN_TYPES = ("LIST",)
+ FUNCTION = "text_concatenate_list"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def text_concatenate_list(self, list_a, list_b=None, list_c=None, list_d=None):
+
+ text_list = list_a + list_b
+
+ if list_c:
+ text_list + list_c
+ if list_d:
+ text_list + list_d
+
+ return (text_list,)
+
+# Text List Concatenate Node
+
+class WAS_Text_List:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text_a": ("STRING", {"forceInput": True}),
+ },
+ "optional": {
+ "text_b": ("STRING", {"forceInput": True}),
+ "text_c": ("STRING", {"forceInput": True}),
+ "text_d": ("STRING", {"forceInput": True}),
+ "text_e": ("STRING", {"forceInput": True}),
+ "text_f": ("STRING", {"forceInput": True}),
+ "text_g": ("STRING", {"forceInput": True}),
+ }
+ }
+ RETURN_TYPES = ("LIST",)
+ FUNCTION = "text_as_list"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def text_as_list(self, text_a, text_b=None, text_c=None, text_d=None, text_e=None, text_f=None, text_g=None):
+
+ text_list = [text_a,]
+
+ if text_b:
+ text_list.append(text_b)
+ if text_c:
+ text_list.append(text_c)
+ if text_d:
+ text_list.append(text_d)
+ if text_e:
+ text_list.append(text_e)
+ if text_f:
+ text_list.append(text_f)
+ if text_g:
+ text_list.append(text_g)
+
+ return (text_list,)
+
+# Text Parse Embeddings
+
+class WAS_Text_Parse_Embeddings_By_Name:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ }
+ }
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "text_parse_embeddings"
+
+ CATEGORY = "WAS Suite/Text/Parse"
+
+ def text_parse_embeddings(self, text):
+ return (self.convert_a1111_embeddings(text), )
+
+ def convert_a1111_embeddings(self, text):
+ for embeddings_path in comfy_paths.folder_names_and_paths["embeddings"][0]:
+ for filename in os.listdir(embeddings_path):
+ basename, ext = os.path.splitext(filename)
+ pattern = re.compile(r'\b{}\b'.format(re.escape(basename)))
+ replacement = 'embedding:{}'.format(basename)
+ text = re.sub(pattern, replacement, text)
+
+ return text
+
+
+# Text Dictionary Concatenate
+
+class WAS_Dictionary_Update:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "dictionary_a": ("DICT", ),
+ "dictionary_b": ("DICT", ),
+ },
+ "optional": {
+ "dictionary_c": ("DICT", ),
+ "dictionary_d": ("DICT", ),
+ }
+ }
+ RETURN_TYPES = ("DICT",)
+ FUNCTION = "dictionary_update"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def dictionary_update(self, dictionary_a, dictionary_b, dictionary_c=None, dictionary_d=None):
+ return_dictionary = {**dictionary_a, **dictionary_b}
+ if dictionary_c is not None:
+ return_dictionary = {**return_dictionary, **dictionary_c}
+ if dictionary_d is not None:
+ return_dictionary = {**return_dictionary, **dictionary_d}
+ return (return_dictionary, )
+
+
+# Text String Node
+
+class WAS_Text_String:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": ("STRING", {"default": '', "multiline": False}),
+ },
+ "optional": {
+ "text_b": ("STRING", {"default": '', "multiline": False}),
+ "text_c": ("STRING", {"default": '', "multiline": False}),
+ "text_d": ("STRING", {"default": '', "multiline": False}),
+ }
+ }
+ RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE,TEXT_TYPE,TEXT_TYPE)
+ FUNCTION = "text_string"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def text_string(self, text='', text_b='', text_c='', text_d=''):
+ return (text, text_b, text_c, text_d)
+
+
+# Text String Truncation
+
+class WAS_Text_String_Truncate:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": ("STRING", {"forceInput": True}),
+ "truncate_by": (["characters", "words"],),
+ "truncate_from": (["end", "beginning"],),
+ "truncate_to": ("INT", {"default": 10, "min": 1, "max": 99999999, "step": 1}),
+ },
+ "optional": {
+ "text_b": ("STRING", {"forceInput": True}),
+ "text_c": ("STRING", {"forceInput": True}),
+ "text_d": ("STRING", {"forceInput": True}),
+ }
+ }
+ RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE,TEXT_TYPE,TEXT_TYPE)
+ FUNCTION = "truncate_string"
+
+ CATEGORY = "WAS Suite/Text/Operations"
+
+ def truncate_string(self, text, truncate_by, truncate_from, truncate_to, text_b='', text_c='', text_d=''):
+ return (
+ self.truncate(text, truncate_to, truncate_from, truncate_by),
+ self.truncate(text_b, truncate_to, truncate_from, truncate_by),
+ self.truncate(text_c, truncate_to, truncate_from, truncate_by),
+ self.truncate(text_d, truncate_to, truncate_from, truncate_by),
+ )
+
+ def truncate(self, string, max_length, mode='end', truncate_by='characters'):
+ if mode not in ['beginning', 'end']:
+ cstr("Invalid mode. 'mode' must be either 'beginning' or 'end'.").error.print()
+ mode = "end"
+ if truncate_by not in ['characters', 'words']:
+ cstr("Invalid truncate_by. 'truncate_by' must be either 'characters' or 'words'.").error.print()
+ if truncate_by == 'characters':
+ if mode == 'beginning':
+ return string[:max_length]
+ else:
+ return string[-max_length:]
+ words = string.split()
+ if mode == 'beginning':
+ return ' '.join(words[:max_length])
+ else:
+ return ' '.join(words[-max_length:])
+
+
+
+# Text Compare Strings
+
+class WAS_Text_Compare:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text_a": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "text_b": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "mode": (["similarity","difference"],),
+ "tolerance": ("FLOAT", {"default":0.0,"min":0.0,"max":1.0,"step":0.01}),
+ }
+ }
+ RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE,"NUMBER","NUMBER",TEXT_TYPE)
+ RETURN_NAMES = ("TEXT_A_PASS","TEXT_B_PASS","BOOL_NUMBER","SCORE_NUMBER","COMPARISON_TEXT")
+ FUNCTION = "text_compare"
+
+ CATEGORY = "WAS Suite/Text/Search"
+
+ def text_compare(self, text_a='', text_b='', mode='similarity', tolerance=0.0):
+
+ boolean = ( 1 if text_a == text_b else 0 )
+ sim = self.string_compare(text_a, text_b, tolerance, ( True if mode == 'difference' else False ))
+ score = float(sim[0])
+ sim_result = ' '.join(sim[1][::-1])
+ sim_result = ' '.join(sim_result.split())
+
+ return (text_a, text_b, boolean, score, sim_result)
+
+ def string_compare(self, str1, str2, threshold=1.0, difference_mode=False):
+ m = len(str1)
+ n = len(str2)
+ if difference_mode:
+ dp = [[0 for x in range(n+1)] for x in range(m+1)]
+ for i in range(m+1):
+ for j in range(n+1):
+ if i == 0:
+ dp[i][j] = j
+ elif j == 0:
+ dp[i][j] = i
+ elif str1[i-1] == str2[j-1]:
+ dp[i][j] = dp[i-1][j-1]
+ else:
+ dp[i][j] = 1 + min(dp[i][j-1], # Insert
+ dp[i-1][j], # Remove
+ dp[i-1][j-1]) # Replace
+ diff_indices = []
+ i, j = m, n
+ while i > 0 and j > 0:
+ if str1[i-1] == str2[j-1]:
+ i -= 1
+ j -= 1
+ else:
+ diff_indices.append(i-1)
+ i, j = min((i, j-1), (i-1, j))
+ diff_indices.reverse()
+ words = []
+ start_idx = 0
+ for i in diff_indices:
+ if str1[i] == " ":
+ words.append(str1[start_idx:i])
+ start_idx = i+1
+ words.append(str1[start_idx:m])
+ difference_score = 1 - ((dp[m][n] - len(words)) / max(m, n))
+ return (difference_score, words[::-1])
+ else:
+ dp = [[0 for x in range(n+1)] for x in range(m+1)]
+ similar_words = set()
+ for i in range(m+1):
+ for j in range(n+1):
+ if i == 0:
+ dp[i][j] = j
+ elif j == 0:
+ dp[i][j] = i
+ elif str1[i-1] == str2[j-1]:
+ dp[i][j] = dp[i-1][j-1]
+ if i > 1 and j > 1 and str1[i-2] == ' ' and str2[j-2] == ' ':
+ word1_start = i-2
+ word2_start = j-2
+ while word1_start > 0 and str1[word1_start-1] != " ":
+ word1_start -= 1
+ while word2_start > 0 and str2[word2_start-1] != " ":
+ word2_start -= 1
+ word1 = str1[word1_start:i-1]
+ word2 = str2[word2_start:j-1]
+ if word1 in str2 or word2 in str1:
+ if word1 not in similar_words:
+ similar_words.add(word1)
+ if word2 not in similar_words:
+ similar_words.add(word2)
+ else:
+ dp[i][j] = 1 + min(dp[i][j-1], # Insert
+ dp[i-1][j], # Remove
+ dp[i-1][j-1]) # Replace
+ if dp[i][j] <= threshold and i > 0 and j > 0:
+ word1_start = max(0, i-dp[i][j])
+ word2_start = max(0, j-dp[i][j])
+ word1_end = i
+ word2_end = j
+ while word1_start > 0 and str1[word1_start-1] != " ":
+ word1_start -= 1
+ while word2_start > 0 and str2[word2_start-1] != " ":
+ word2_start -= 1
+ while word1_end < m and str1[word1_end] != " ":
+ word1_end += 1
+ while word2_end < n and str2[word2_end] != " ":
+ word2_end += 1
+ word1 = str1[word1_start:word1_end]
+ word2 = str2[word2_start:word2_end]
+ if word1 in str2 or word2 in str1:
+ if word1 not in similar_words:
+ similar_words.add(word1)
+ if word2 not in similar_words:
+ similar_words.add(word2)
+ if(max(m,n) == 0):
+ similarity_score = 1
+ else:
+ similarity_score = 1 - (dp[m][n]/max(m,n))
+ return (similarity_score, list(similar_words))
+
+
+# Text Random Line
+
+class WAS_Text_Random_Line:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "text_random_line"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def text_random_line(self, text, seed):
+ lines = text.split("\n")
+ random.seed(seed)
+ choice = random.choice(lines)
+ return (choice, )
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+
+# Text Concatenate
+
+class WAS_Text_Concatenate:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text_a": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "text_b": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "linebreak_addition": (['false','true'], ),
+ },
+ "optional": {
+ "text_c": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "text_d": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "text_concatenate"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def text_concatenate(self, text_a, text_b, text_c=None, text_d=None, linebreak_addition='false'):
+ return_text = text_a + ("\n" if linebreak_addition == 'true' else '') + text_b
+ if text_c:
+ return_text = return_text + ("\n" if linebreak_addition == 'true' else '') + text_c
+ if text_d:
+ return_text = return_text + ("\n" if linebreak_addition == 'true' else '') + text_d
+ return (return_text, )
+
+
+# Text Search and Replace
+
+class WAS_Search_and_Replace:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "find": ("STRING", {"default": '', "multiline": False}),
+ "replace": ("STRING", {"default": '', "multiline": False}),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "text_search_and_replace"
+
+ CATEGORY = "WAS Suite/Text/Search"
+
+ def text_search_and_replace(self, text, find, replace):
+ return (self.replace_substring(text, find, replace), )
+
+ def replace_substring(self, text, find, replace):
+ import re
+ text = re.sub(find, replace, text)
+ return text
+
+
+# Text Search and Replace
+
+class WAS_Search_and_Replace_Input:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "find": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "replace": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "text_search_and_replace"
+
+ CATEGORY = "WAS Suite/Text/Search"
+
+ def text_search_and_replace(self, text, find, replace):
+
+ # Parse Text
+ new_text = text
+ tcount = new_text.count(find)
+ for _ in range(tcount):
+ new_text = new_text.replace(find, replace, 1)
+
+ return (new_text, )
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+
+
+# Text Search and Replace By Dictionary
+
+class WAS_Search_and_Replace_Dictionary:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "dictionary": ("DICT",),
+ "replacement_key": ("STRING", {"default": "__", "multiline": False}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "text_search_and_replace_dict"
+
+ CATEGORY = "WAS Suite/Text/Search"
+
+ def text_search_and_replace_dict(self, text, dictionary, replacement_key, seed):
+
+ random.seed(seed)
+
+ # Parse Text
+ new_text = text
+
+ for term in dictionary.keys():
+ tkey = f'{replacement_key}{term}{replacement_key}'
+ tcount = new_text.count(tkey)
+ for _ in range(tcount):
+ new_text = new_text.replace(tkey, random.choice(dictionary[term]), 1)
+ if seed > 0 or seed < 0:
+ seed = seed + 1
+ random.seed(seed)
+
+ return (new_text, )
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+
+# Text Parse NSP
+
+class WAS_Text_Parse_NSP:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "mode": (["Noodle Soup Prompts", "Wildcards"],),
+ "noodle_key": ("STRING", {"default": '__', "multiline": False}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ }
+ }
+
+ OUTPUT_NODE = True
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "text_parse_nsp"
+
+ CATEGORY = "WAS Suite/Text/Parse"
+
+ def text_parse_nsp(self, text, mode="Noodle Soup Prompts", noodle_key='__', seed=0):
+
+ if mode == "Noodle Soup Prompts":
+
+ new_text = nsp_parse(text, seed, noodle_key)
+ cstr(f"Text Parse NSP:\n{new_text}").msg.print()
+
+ else:
+
+ new_text = replace_wildcards(text, (None if seed == 0 else seed), noodle_key)
+ cstr(f"CLIPTextEncode Wildcards:\n{new_text}").msg.print()
+
+ return (new_text, )
+
+
+# TEXT SEARCH AND REPLACE
+
+class WAS_Text_Save:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": ("STRING", {"forceInput": True}),
+ "path": ("STRING", {"default": './ComfyUI/output/[time(%Y-%m-%d)]', "multiline": False}),
+ "filename_prefix": ("STRING", {"default": "ComfyUI"}),
+ "filename_delimiter": ("STRING", {"default":"_"}),
+ "filename_number_padding": ("INT", {"default":4, "min":2, "max":9, "step":1}),
+
+ }
+ }
+
+ OUTPUT_NODE = True
+ RETURN_TYPES = ()
+ FUNCTION = "save_text_file"
+ CATEGORY = "WAS Suite/IO"
+
+ def save_text_file(self, text, path, filename_prefix='ComfyUI', filename_delimiter='_', filename_number_padding=4):
+
+ tokens = TextTokens()
+ path = tokens.parseTokens(path)
+ filename_prefix = tokens.parseTokens(filename_prefix)
+
+ if not os.path.exists(path):
+ cstr(f"The path `{path}` doesn't exist! Creating it...").warning.print()
+ try:
+ os.makedirs(path, exist_ok=True)
+ except OSError as e:
+ cstr(f"The path `{path}` could not be created! Is there write access?\n{e}").error.print()
+
+ if text.strip() == '':
+ cstr(f"There is no text specified to save! Text is empty.").error.print()
+
+ delimiter = filename_delimiter
+ number_padding = int(filename_number_padding)
+ file_extension = '.txt'
+ filename = self.generate_filename(path, filename_prefix, delimiter, number_padding, file_extension)
+ file_path = os.path.join(path, filename)
+
+ self.writeTextFile(file_path, text)
+
+ update_history_text_files(file_path)
+
+ return (text, { "ui": { "string": text } } )
+
+ def generate_filename(self, path, prefix, delimiter, number_padding, extension):
+ pattern = f"{re.escape(prefix)}{re.escape(delimiter)}(\\d{{{number_padding}}})"
+ existing_counters = [
+ int(re.search(pattern, filename).group(1))
+ for filename in os.listdir(path)
+ if re.match(pattern, filename)
+ ]
+ existing_counters.sort(reverse=True)
+
+ if existing_counters:
+ counter = existing_counters[0] + 1
+ else:
+ counter = 1
+
+ filename = f"{prefix}{delimiter}{counter:0{number_padding}}{extension}"
+ while os.path.exists(os.path.join(path, filename)):
+ counter += 1
+ filename = f"{prefix}{delimiter}{counter:0{number_padding}}{extension}"
+
+ return filename
+
+ def writeTextFile(self, file, content):
+ try:
+ with open(file, 'w', encoding='utf-8', newline='\n') as f:
+ f.write(content)
+ except OSError:
+ cstr(f"Unable to save file `{file}`").error.print()
+
+
+
+# TEXT FILE HISTORY NODE
+
+class WAS_Text_File_History:
+ def __init__(self):
+ self.HDB = WASDatabase(WAS_HISTORY_DATABASE)
+ self.conf = getSuiteConfig()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ HDB = WASDatabase(WAS_HISTORY_DATABASE)
+ conf = getSuiteConfig()
+ paths = ['No History',]
+ if HDB.catExists("History") and HDB.keyExists("History", "TextFiles"):
+ history_paths = HDB.get("History", "TextFiles")
+ if conf.__contains__('history_display_limit'):
+ history_paths = history_paths[-conf['history_display_limit']:]
+ paths = []
+ for path_ in history_paths:
+ paths.append(os.path.join('...'+os.sep+os.path.basename(os.path.dirname(path_)), os.path.basename(path_)))
+
+ return {
+ "required": {
+ "file": (paths,),
+ "dictionary_name": ("STRING", {"default": '[filename]', "multiline": True}),
+ },
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,"DICT")
+ FUNCTION = "text_file_history"
+
+ CATEGORY = "WAS Suite/History"
+
+ def text_file_history(self, file=None, dictionary_name='[filename]]'):
+ file_path = file.strip()
+ filename = ( os.path.basename(file_path).split('.', 1)[0]
+ if '.' in os.path.basename(file_path) else os.path.basename(file_path) )
+ if dictionary_name != '[filename]' or dictionary_name not in [' ', '']:
+ filename = dictionary_name
+ if not os.path.exists(file_path):
+ cstr(f"The path `{file_path}` specified cannot be found.").error.print()
+ return ('', {filename: []})
+ with open(file_path, 'r', encoding="utf-8", newline='\n') as file:
+ text = file.read()
+
+ # Write to file history
+ update_history_text_files(file_path)
+
+ import io
+ lines = []
+ for line in io.StringIO(text):
+ if not line.strip().startswith('#'):
+ if not line.strip().startswith("\n"):
+ line = line.replace("\n", '')
+ lines.append(line.replace("\n",''))
+ dictionary = {filename: lines}
+
+ return ("\n".join(lines), dictionary)
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+# TEXT TO CONDITIONIONG
+
+class WAS_Text_to_Conditioning:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "clip": ("CLIP",),
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ }
+ }
+
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "text_to_conditioning"
+
+ CATEGORY = "WAS Suite/Text/Operations"
+
+ def text_to_conditioning(self, clip, text):
+ return ([[clip.encode(text), {}]], )
+
+
+# TEXT PARSE TOKENS
+
+class WAS_Text_Parse_Tokens:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "text_parse_tokens"
+
+ CATEGORY = "WAS Suite/Text/Tokens"
+
+ def text_parse_tokens(self, text):
+ # Token Parser
+ tokens = TextTokens()
+ return (tokens.parseTokens(text), )
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+# TEXT ADD TOKENS
+
+
+class WAS_Text_Add_Tokens:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "tokens": ("STRING", {"default": "[hello]: world", "multiline": True}),
+ "print_current_tokens": (["false", "true"],),
+ }
+ }
+
+ RETURN_TYPES = ()
+ FUNCTION = "text_add_tokens"
+ OUTPUT_NODE = True
+ CATEGORY = "WAS Suite/Text/Tokens"
+
+ def text_add_tokens(self, tokens, print_current_tokens="false"):
+
+ import io
+
+ # Token Parser
+ tk = TextTokens()
+
+ # Parse out Tokens
+ for line in io.StringIO(tokens):
+ parts = line.split(':')
+ token = parts[0].strip()
+ token_value = parts[1].strip()
+ tk.addToken(token, token_value)
+
+ # Current Tokens
+ if print_current_tokens == "true":
+ cstr(f'Current Custom Tokens:').msg.print()
+ print(json.dumps(tk.custom_tokens, indent=4))
+
+ return tokens
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+
+# TEXT ADD TOKEN BY INPUT
+
+
+class WAS_Text_Add_Token_Input:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "token_name": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "token_value": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "print_current_tokens": (["false", "true"],),
+ }
+ }
+
+ RETURN_TYPES = ()
+ FUNCTION = "text_add_token"
+ OUTPUT_NODE = True
+ CATEGORY = "WAS Suite/Text/Tokens"
+
+ def text_add_token(self, token_name, token_value, print_current_tokens="false"):
+
+ if token_name.strip() == '':
+ cstr(f'A `token_name` is required for a token; token name provided is empty.').error.print()
+ pass
+
+ # Token Parser
+ tk = TextTokens()
+
+ # Add Tokens
+ tk.addToken(token_name, token_value)
+
+ # Current Tokens
+ if print_current_tokens == "true":
+ cstr(f'Current Custom Tokens:').msg.print()
+ print(json.dumps(tk.custom_tokens, indent=4))
+
+ return (token_name, token_value)
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+
+
+# TEXT TO CONSOLE
+
+class WAS_Text_to_Console:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "label": ("STRING", {"default": f'Text Output', "multiline": False}),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ OUTPUT_NODE = True
+ FUNCTION = "text_to_console"
+
+ CATEGORY = "WAS Suite/Debug"
+
+ def text_to_console(self, text, label):
+ if label.strip() != '':
+ cstr(f'\033[33m{label}\033[0m:\n{text}\n').msg.print()
+ else:
+ cstr(f"\033[33mText to Console\033[0m:\n{text}\n").msg.print()
+ return (text, )
+
+# DICT TO CONSOLE
+
+class WAS_Dictionary_To_Console:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "dictionary": ("DICT",),
+ "label": ("STRING", {"default": f'Dictionary Output', "multiline": False}),
+ }
+ }
+
+ RETURN_TYPES = ("DICT",)
+ OUTPUT_NODE = True
+ FUNCTION = "text_to_console"
+
+ CATEGORY = "WAS Suite/Debug"
+
+ def text_to_console(self, dictionary, label):
+ if label.strip() != '':
+ print(f'\033[34mWAS Node Suite \033[33m{label}\033[0m:\n')
+ from pprint import pprint
+ pprint(dictionary, indent=4)
+ print('')
+ else:
+ cstr(f"\033[33mText to Console\033[0m:\n")
+ pprint(dictionary, indent=4)
+ print('')
+ return (dictionary, )
+
+
+# LOAD TEXT FILE
+
+class WAS_Text_Load_From_File:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "file_path": ("STRING", {"default": '', "multiline": False}),
+ "dictionary_name": ("STRING", {"default": '[filename]', "multiline": False}),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,"DICT")
+ FUNCTION = "load_file"
+
+ CATEGORY = "WAS Suite/IO"
+
+ def load_file(self, file_path='', dictionary_name='[filename]]'):
+
+ filename = ( os.path.basename(file_path).split('.', 1)[0]
+ if '.' in os.path.basename(file_path) else os.path.basename(file_path) )
+ if dictionary_name != '[filename]':
+ filename = dictionary_name
+ if not os.path.exists(file_path):
+ cstr(f"The path `{file_path}` specified cannot be found.").error.print()
+ return ('', {filename: []})
+ with open(file_path, 'r', encoding="utf-8", newline='\n') as file:
+ text = file.read()
+
+ # Write to file history
+ update_history_text_files(file_path)
+
+ import io
+ lines = []
+ for line in io.StringIO(text):
+ if not line.strip().startswith('#'):
+ if ( not line.strip().startswith("\n")
+ or not line.strip().startswith("\r")
+ or not line.strip().startswith("\r\n") ):
+ line = line.replace("\n", '').replace("\r",'').replace("\r\n",'')
+ lines.append(line.replace("\n",'').replace("\r",'').replace("\r\n",''))
+ dictionary = {filename: lines}
+
+ return ("\n".join(lines), dictionary)
+
+# TEXT LOAD FROM FILE
+
+class WAS_Text_Load_Line_From_File:
+ def __init__(self):
+ self.HDB = WASDatabase(WAS_HISTORY_DATABASE)
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "file_path": ("STRING", {"default": '', "multiline": False}),
+ "dictionary_name": ("STRING", {"default": '[filename]', "multiline": False}),
+ "label": ("STRING", {"default": 'TextBatch', "multiline": False}),
+ "mode": (["automatic", "index"],),
+ "index": ("INT", {"default": 0, "min": 0, "step": 1}),
+ },
+ "optional": {
+ "multiline_text": (TEXT_TYPE, {"forceInput": True}),
+ }
+ }
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ if kwargs['mode'] != 'index':
+ return float("NaN")
+ else:
+ m = hashlib.sha256()
+ if os.path.exists(kwargs['file_path']):
+ with open(kwargs['file_path'], 'rb') as f:
+ m.update(f.read())
+ return m.digest().hex()
+ else:
+ return False
+
+ RETURN_TYPES = (TEXT_TYPE, "DICT")
+ RETURN_NAMES = ("line_text", "dictionary")
+ FUNCTION = "load_file"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def load_file(self, file_path='', dictionary_name='[filename]', label='TextBatch',
+ mode='automatic', index=0, multiline_text=None):
+ if multiline_text is not None:
+ lines = multiline_text.strip().split('\n')
+ if mode == 'index':
+ if index < 0 or index >= len(lines):
+ cstr(f"Invalid line index `{index}`").error.print()
+ return ('', {dictionary_name: []})
+ line = lines[index]
+ else:
+ line_index = self.HDB.get('TextBatch Counters', label)
+ if line_index is None:
+ line_index = 0
+ line = lines[line_index % len(lines)]
+ self.HDB.insert('TextBatch Counters', label, line_index + 1)
+ return (line, {dictionary_name: lines})
+
+ if file_path == '':
+ cstr("No file path specified.").error.print()
+ return ('', {dictionary_name: []})
+
+ if not os.path.exists(file_path):
+ cstr(f"The path `{file_path}` specified cannot be found.").error.print()
+ return ('', {dictionary_name: []})
+
+ file_list = self.TextFileLoader(file_path, label)
+ line, lines = None, []
+ if mode == 'automatic':
+ line, lines = file_list.get_next_line()
+ elif mode == 'index':
+ if index >= len(file_list.lines):
+ index = index % len(file_list.lines)
+ line, lines = file_list.get_line_by_index(index)
+ if line is None:
+ cstr("No valid line was found. The file may be empty or all lines have been read.").error.print()
+ return ('', {dictionary_name: []})
+ file_list.store_index()
+ update_history_text_files(file_path)
+
+ return (line, {dictionary_name: lines})
+
+ class TextFileLoader:
+ def __init__(self, file_path, label):
+ self.WDB = WDB
+ self.file_path = file_path
+ self.lines = []
+ self.index = 0
+ self.load_file(file_path, label)
+
+ def load_file(self, file_path, label):
+ stored_file_path = self.WDB.get('TextBatch Paths', label)
+ stored_index = self.WDB.get('TextBatch Counters', label)
+ if stored_file_path != file_path:
+ self.index = 0
+ self.WDB.insert('TextBatch Counters', label, 0)
+ self.WDB.insert('TextBatch Paths', label, file_path)
+ else:
+ self.index = stored_index
+ with open(file_path, 'r', encoding="utf-8", newline='\n') as file:
+ self.lines = [line.strip() for line in file]
+
+ def get_line_index(self):
+ return self.index
+
+ def set_line_index(self, index):
+ self.index = index
+ self.WDB.insert('TextBatch Counters', 'TextBatch', self.index)
+
+ def get_next_line(self):
+ if self.index >= len(self.lines):
+ self.index = 0
+ line = self.lines[self.index]
+ self.index += 1
+ if self.index == len(self.lines):
+ self.index = 0
+ cstr(f'{cstr.color.YELLOW}TextBatch{cstr.color.END} Index: {self.index}').msg.print()
+ return line, self.lines
+
+ def get_line_by_index(self, index):
+ if index < 0 or index >= len(self.lines):
+ cstr(f"Invalid line index `{index}`").error.print()
+ return None, []
+ self.index = index
+ line = self.lines[self.index]
+ cstr(f'{cstr.color.YELLOW}TextBatch{cstr.color.END} Index: {self.index}').msg.print()
+ return line, self.lines
+
+ def store_index(self):
+ self.WDB.insert('TextBatch Counters', 'TextBatch', self.index)
+
+
+class WAS_Text_To_String:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ }
+ }
+
+ RETURN_TYPES = ("STRING",)
+ FUNCTION = "text_to_string"
+
+ CATEGORY = "WAS Suite/Text/Operations"
+
+ def text_to_string(self, text):
+ return (text, )
+
+class WAS_Text_To_Number:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER",)
+ FUNCTION = "text_to_number"
+
+ CATEGORY = "WAS Suite/Text/Operations"
+
+ def text_to_number(self, text):
+ if text.replace(".", "").isnumeric():
+ number = float(text)
+ else:
+ number = int(text)
+ return (number, )
+
+
+class WAS_String_To_Text:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "string": ("STRING", {}),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "string_to_text"
+
+ CATEGORY = "WAS Suite/Text/Operations"
+
+ def string_to_text(self, string):
+ return (string, )
+
+# Random Prompt
+
+class WAS_Text_Random_Prompt:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "search_seed": ("STRING", {"multiline": False}),
+ }
+ }
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "random_prompt"
+
+ CATEGORY = "WAS Suite/Text"
+
+ def random_prompt(self, search_seed=None):
+ if search_seed in ['', ' ']:
+ search_seed = None
+ return (self.search_lexica_art(search_seed), )
+
+ def search_lexica_art(self, query=None):
+ if not query:
+ query = random.choice(["portrait","landscape","anime","superhero","animal","nature","scenery"])
+ url = f"https://lexica.art/api/v1/search?q={query}"
+ try:
+ response = requests.get(url)
+ data = response.json()
+ images = data.get("images", [])
+ if not images:
+ return "404 not found error"
+ random_image = random.choice(images)
+ prompt = random_image.get("prompt")
+ except Exception:
+ cstr("Unable to establish connection to Lexica API.").error.print()
+ prompt = "404 not found error"
+
+ return prompt
+
+# BLIP Model Loader
+
+class WAS_BLIP_Model_Loader:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "blip_model": (["caption", "interrogate"], ),
+ }
+ }
+
+ RETURN_TYPES = ("BLIP_MODEL",)
+ FUNCTION = "blip_model"
+
+ CATEGORY = "WAS Suite/Loaders"
+
+ def blip_model(self, blip_model):
+
+ if ( 'timm' not in packages()
+ or 'transformers' not in packages()
+ or 'GitPython' not in packages()
+ or 'fairscale' not in packages() ):
+ cstr(f"Modules or packages are missing to use BLIP models. Please run the `{os.path.join(WAS_SUITE_ROOT, 'requirements.txt')}` through ComfyUI's ptyhon executable.").error.print()
+ exit
+
+ if 'transformers==4.26.1' not in packages(True):
+ cstr(f"`transformers==4.26.1` is required for BLIP models. Please run the `{os.path.join(WAS_SUITE_ROOT, 'requirements.txt')}` through ComfyUI's ptyhon executable.").error.print()
+ exit
+
+ blip_dir = os.path.join(WAS_SUITE_ROOT, 'repos'+os.sep+'BLIP')
+
+ if not os.path.exists(blip_dir):
+ from git.repo.base import Repo
+ cstr("Installing BLIP...").msg.print()
+ Repo.clone_from('https://github.com/WASasquatch/BLIP-Python', os.path.join(WAS_SUITE_ROOT, 'repos'+os.sep+'BLIP'))
+
+ sys.path.append(blip_dir)
+
+ device = 'cpu'
+ conf = getSuiteConfig()
+ size = 384
+
+ if blip_model == 'caption':
+
+ from models.blip import blip_decoder
+
+ blip_dir = os.path.join(MODELS_DIR, 'blip')
+ if not os.path.exists(blip_dir):
+ os.makedirs(blip_dir, exist_ok=True)
+
+ torch.hub.set_dir(blip_dir)
+
+ if conf.__contains__('blip_model_url'):
+ model_url = conf['blip_model_url']
+ else:
+ model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'
+
+ model = blip_decoder(pretrained=model_url, image_size=size, vit='base')
+ model.eval()
+ model = model.to(device)
+
+ elif blip_model == 'interrogate':
+
+ from models.blip_vqa import blip_vqa
+
+ blip_dir = os.path.join(MODELS_DIR, 'blip')
+ if not os.path.exists(blip_dir):
+ os.makedirs(blip_dir, exist_ok=True)
+
+ torch.hub.set_dir(blip_dir)
+
+ if conf.__contains__('blip_model_vqa_url'):
+ model_url = conf['blip_model_vqa_url']
+ else:
+ model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
+
+ model = blip_vqa(pretrained=model_url, image_size=size, vit='base')
+ model.eval()
+ model = model.to(device)
+
+ result = ( model, blip_model )
+
+ return ( result, )
+
+
+
+# BLIP CAPTION IMAGE
+
+class WAS_BLIP_Analyze_Image:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "mode": (["caption", "interrogate"], ),
+ "question": ("STRING", {"default": "What does the background consist of?", "multiline": True}),
+ },
+ "optional": {
+ "blip_model": ("BLIP_MODEL",)
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "blip_caption_image"
+
+ CATEGORY = "WAS Suite/Text/AI"
+
+ def blip_caption_image(self, image, mode, question, blip_model=None):
+
+ def transformImage(input_image, image_size, device):
+ raw_image = input_image.convert('RGB')
+ raw_image = raw_image.resize((image_size, image_size))
+ transform = transforms.Compose([
+ transforms.Resize(raw_image.size, interpolation=InterpolationMode.BICUBIC),
+ transforms.ToTensor(),
+ transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+ ])
+ image = transform(raw_image).unsqueeze(0).to(device)
+ return image.view(1, -1, image_size, image_size) # Change the shape of the output tensor
+
+ sys.path.append(os.path.join(WAS_SUITE_ROOT, 'repos'+os.sep+'BLIP'))
+
+ from torchvision import transforms
+ from torchvision.transforms.functional import InterpolationMode
+
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+ conf = getSuiteConfig()
+ image = tensor2pil(image)
+ size = 384
+ tensor = transformImage(image, size, device)
+
+ if blip_model:
+ mode = blip_model[1]
+
+ if mode == 'caption':
+
+ if blip_model:
+ model = blip_model[0].to(device)
+ else:
+ from models.blip import blip_decoder
+
+ blip_dir = os.path.join(MODELS_DIR, 'blip')
+ if not os.path.exists(blip_dir):
+ os.makedirs(blip_dir, exist_ok=True)
+
+ torch.hub.set_dir(blip_dir)
+
+ if conf.__contains__('blip_model_url'):
+ model_url = conf['blip_model_url']
+ else:
+ model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'
+
+ model = blip_decoder(pretrained=model_url, image_size=size, vit='base')
+ model.eval()
+ model = model.to(device)
+
+ with torch.no_grad():
+ caption = model.generate(tensor, sample=False, num_beams=6, max_length=74, min_length=20)
+ # nucleus sampling
+ #caption = model.generate(tensor, sample=True, top_p=0.9, max_length=75, min_length=10)
+ cstr(f"\033[33mBLIP Caption:\033[0m {caption[0]}").msg.print()
+ return (caption[0], )
+
+ elif mode == 'interrogate':
+
+ if blip_model:
+ model = blip_model[0].to(device)
+ else:
+ from models.blip_vqa import blip_vqa
+
+ blip_dir = os.path.join(MODELS_DIR, 'blip')
+ if not os.path.exists(blip_dir):
+ os.makedirs(blip_dir, exist_ok=True)
+
+ torch.hub.set_dir(blip_dir)
+
+ if conf.__contains__('blip_model_vqa_url'):
+ model_url = conf['blip_model_vqa_url']
+ else:
+ model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
+
+ model = blip_vqa(pretrained=model_url, image_size=size, vit='base')
+ model.eval()
+ model = model.to(device)
+
+ with torch.no_grad():
+ answer = model(tensor, question, train=False, inference='generate')
+ cstr(f"\033[33m BLIP Answer:\033[0m {answer[0]}").msg.print()
+ return (answer[0], )
+
+ else:
+ cstr(f"The selected mode `{mode}` is not a valid selection!").error.print()
+ return ('Invalid BLIP mode!', )
+
+
+# CLIPSeg Model Loader
+
+class WAS_CLIPSeg_Model_Loader:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "model": ("STRING", {"default": "CIDAS/clipseg-rd64-refined", "multiline": False}),
+ },
+ }
+
+ RETURN_TYPES = ("CLIPSEG_MODEL",)
+ RETURN_NAMES = ("clipseg_model",)
+ FUNCTION = "clipseg_model"
+
+ CATEGORY = "WAS Suite/Loaders"
+
+ def clipseg_model(self, model):
+ from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+
+ cache = os.path.join(MODELS_DIR, 'clipseg')
+
+ inputs = CLIPSegProcessor.from_pretrained(model, cache_dir=cache)
+ model = CLIPSegForImageSegmentation.from_pretrained(model, cache_dir=cache)
+
+ return ( (inputs, model), )
+
+# CLIPSeg Node
+
+class WAS_CLIPSeg:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "text": ("STRING", {"default":"", "multiline": False}),
+ },
+ "optional": {
+ "clipseg_model": ("CLIPSEG_MODEL",),
+ }
+ }
+
+ RETURN_TYPES = ("MASK", "IMAGE")
+ RETURN_NAMES = ("MASK", "MASK_IMAGE")
+ FUNCTION = "CLIPSeg_image"
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ def CLIPSeg_image(self, image, text=None, clipseg_model=None):
+ from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+
+ image = tensor2pil(image)
+ cache = os.path.join(MODELS_DIR, 'clipseg')
+
+ if clipseg_model:
+ inputs = clipseg_model[0]
+ model = clipseg_model[1]
+ else:
+ inputs = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined", cache_dir=cache)
+ model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined", cache_dir=cache)
+
+ with torch.no_grad():
+ result = model(**inputs(text=text, images=image, padding=True, return_tensors="pt"))
+
+ tensor = torch.sigmoid(result[0])
+ mask = 1. - (tensor - tensor.min()) / tensor.max()
+ mask = mask.unsqueeze(0)
+ mask = tensor2pil(mask).convert("L")
+ mask = mask.resize(image.size)
+
+ return (pil2mask(mask), pil2tensor(ImageOps.invert(mask.convert("RGB"))))
+
+# CLIPSeg Node
+
+class WAS_CLIPSeg_Batch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image_a": ("IMAGE",),
+ "image_b": ("IMAGE",),
+ "text_a": ("STRING", {"default":"", "multiline": False}),
+ "text_b": ("STRING", {"default":"", "multiline": False}),
+ },
+ "optional": {
+ "image_c": ("IMAGE",),
+ "image_d": ("IMAGE",),
+ "image_e": ("IMAGE",),
+ "image_f": ("IMAGE",),
+ "text_c": ("STRING", {"default":"", "multiline": False}),
+ "text_d": ("STRING", {"default":"", "multiline": False}),
+ "text_e": ("STRING", {"default":"", "multiline": False}),
+ "text_f": ("STRING", {"default":"", "multiline": False}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "MASK", "IMAGE")
+ RETURN_NAMES = ("IMAGES_BATCH", "MASKS_BATCH", "MASK_IMAGES_BATCH")
+ FUNCTION = "CLIPSeg_images"
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ def CLIPSeg_images(self, image_a, image_b, text_a, text_b, image_c=None, image_d=None,
+ image_e=None, image_f=None, text_c=None, text_d=None, text_e=None, text_f=None):
+ from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+ import torch.nn.functional as F
+
+ images_pil = [tensor2pil(image_a), tensor2pil(image_b)]
+
+ if image_c is not None:
+ if image_c.shape[-2:] != image_a.shape[-2:]:
+ cstr("Size of image_c is different from image_a.").error.print()
+ return
+ images_pil.append(tensor2pil(image_c))
+ if image_d is not None:
+ if image_d.shape[-2:] != image_a.shape[-2:]:
+ cstr("Size of image_d is different from image_a.").error.print()
+ return
+ images_pil.append(tensor2pil(image_d))
+ if image_e is not None:
+ if image_e.shape[-2:] != image_a.shape[-2:]:
+ cstr("Size of image_e is different from image_a.").error.print()
+ return
+ images_pil.append(tensor2pil(image_e))
+ if image_f is not None:
+ if image_f.shape[-2:] != image_a.shape[-2:]:
+ cstr("Size of image_f is different from image_a.").error.print()
+ return
+ images_pil.append(tensor2pil(image_f))
+
+ images_tensor = [torch.from_numpy(np.array(img.convert("RGB")).astype(np.float32) / 255.0).unsqueeze(0) for img in images_pil]
+ images_tensor = torch.cat(images_tensor, dim=0)
+
+ prompts = [text_a, text_b]
+ if text_c:
+ prompts.append(text_c)
+ if text_d:
+ prompts.append(text_d)
+ if text_e:
+ prompts.append(text_e)
+ if text_f:
+ prompts.append(text_f)
+
+ cache = os.path.join(MODELS_DIR, 'clipseg')
+
+ inputs = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined", cache_dir=cache)
+ model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined", cache_dir=cache)
+
+ with torch.no_grad():
+ result = model(**inputs(text=prompts, images=images_pil, padding=True, return_tensors="pt"))
+
+ masks = []
+ mask_images = []
+ for i, res in enumerate(result.logits):
+ tensor = torch.sigmoid(res)
+ mask = 1. - (tensor - tensor.min()) / tensor.max()
+ mask = mask.unsqueeze(0)
+ mask = tensor2pil(mask).convert("L")
+ mask = mask.resize(images_pil[0].size)
+ mask_batch = pil2mask(mask)
+
+ masks.append(mask_batch.unsqueeze(0).unsqueeze(1))
+ mask_images.append(pil2tensor(ImageOps.invert(mask.convert("RGB"))).squeeze(0))
+
+ masks_tensor = torch.cat(masks, dim=0)
+ mask_images_tensor = torch.stack(mask_images, dim=0)
+
+ del inputs, model, result, tensor, masks, mask_images, images_pil
+
+ return (images_tensor, masks_tensor, mask_images_tensor)
+
+
+# Lang SAM Model Loader
+# TODO: Fix Lang SAM Dependency Issues
+
+class WAS_Lang_SAM_Model_Loader:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "model_type": (["vit_b", "vit_l", "vit_h"],),
+ }
+ }
+
+ RETURN_TYPES = ("LANG_SAM_MODEL",)
+ RETURN_NAMES = ("lang_sam_model",)
+
+ CATEGORY = "WAS Suite/Loaders"
+
+ FUNCTION = "lang_sam_model"
+
+ def lang_sam_model(self, model_type):
+
+ from lang_sam import LangSAM
+
+ model = LangSAM(model_type=model_type)
+ model.device = 'cpu'
+
+ return ( model, )
+
+
+# Lang SAM Masking
+
+class WAS_Lang_SAM_Masking:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "lang_sam_model": ("LANG_SAM_MODEL",),
+ "image": ("IMAGE",),
+ "prompt": ("STRING", {"default": "", "multiline": False}),
+ }
+ }
+
+ RETURN_TYPES = ("LANG_SAM_MODEL",)
+ RETURN_NAMES = ("lang_sam_model",)
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ FUNCTION = "lang_sam_masking"
+
+ def lang_sam_masking(self, lang_sam_model, image, prompt):
+
+ lang_sam_model.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+ masks, boxes, phrases, logits = lang_sam_model.predict(tensor2pil(image), prompt)
+
+ print(masks)
+ print(boxes)
+ print(logits)
+
+ return ( LandSAM(), )
+
+
+# SAM MODEL LOADER
+
+class WAS_SAM_Model_Loader:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "model_size": (["ViT-H (91M)", "ViT-L (308M)", "ViT-B (636M)"], ),
+ }
+ }
+
+ RETURN_TYPES = ("SAM_MODEL",)
+ FUNCTION = "sam_load_model"
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ def sam_load_model(self, model_size):
+ conf = getSuiteConfig()
+
+ model_filename_mapping = {
+ "ViT-H (91M)": "sam_vit_h_4b8939.pth",
+ "ViT-L (308M)": "sam_vit_l_0b3195.pth",
+ "ViT-B (636M)": "sam_vit_b_01ec64.pth",
+ }
+
+ model_url_mapping = {
+ "ViT-H (91M)": conf['sam_model_vith_url'] if conf.__contains__('sam_model_vith_url') else r"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
+ "ViT-L (308M)": conf['sam_model_vitl_url'] if conf.__contains__('sam_model_vitl_url') else r"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
+ "ViT-B (636M)": conf['sam_model_vitb_url'] if conf.__contains__('sam_model_vitb_url') else r"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth",
+ }
+
+ model_url = model_url_mapping[model_size]
+ model_filename = model_filename_mapping[model_size]
+
+ if ( 'GitPython' not in packages() ):
+ cstr("Installing SAM dependencies...").error.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'gitpython'])
+
+ if not os.path.exists(os.path.join(WAS_SUITE_ROOT, 'repos'+os.sep+'SAM')):
+ from git.repo.base import Repo
+ cstr("Installing SAM...").msg.print()
+ Repo.clone_from('https://github.com/facebookresearch/segment-anything', os.path.join(WAS_SUITE_ROOT, 'repos'+os.sep+'SAM'))
+
+ sys.path.append(os.path.join(WAS_SUITE_ROOT, 'repos'+os.sep+'SAM'))
+
+ sam_dir = os.path.join(MODELS_DIR, 'sam')
+ if not os.path.exists(sam_dir):
+ os.makedirs(sam_dir, exist_ok=True)
+
+ sam_file = os.path.join(sam_dir, model_filename)
+ if not os.path.exists(sam_file):
+ cstr("Selected SAM model not found. Downloading...").msg.print()
+ r = requests.get(model_url, allow_redirects=True)
+ open(sam_file, 'wb').write(r.content)
+
+ from segment_anything import build_sam
+ sam_model = build_sam(checkpoint=sam_file)
+
+ return (sam_model, )
+
+
+# SAM PARAMETERS
+class WAS_SAM_Parameters:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "points": ("STRING", {"default": "[128, 128]; [0, 0]", "multiline": False}),
+ "labels": ("STRING", {"default": "[1, 0]", "multiline": False}),
+ }
+ }
+
+ RETURN_TYPES = ("SAM_PARAMETERS",)
+ FUNCTION = "sam_parameters"
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ def sam_parameters(self, points, labels):
+ parameters = {
+ "points": np.asarray(np.matrix(points)),
+ "labels": np.array(np.matrix(labels))[0]
+ }
+
+ return (parameters,)
+
+
+# SAM COMBINE PARAMETERS
+class WAS_SAM_Combine_Parameters:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "sam_parameters_a": ("SAM_PARAMETERS",),
+ "sam_parameters_b": ("SAM_PARAMETERS",),
+ }
+ }
+
+ RETURN_TYPES = ("SAM_PARAMETERS",)
+ FUNCTION = "sam_combine_parameters"
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ def sam_combine_parameters(self, sam_parameters_a, sam_parameters_b):
+ parameters = {
+ "points": np.concatenate(
+ (sam_parameters_a["points"],
+ sam_parameters_b["points"]),
+ axis=0
+ ),
+ "labels": np.concatenate(
+ (sam_parameters_a["labels"],
+ sam_parameters_b["labels"])
+ )
+ }
+
+ return (parameters,)
+
+
+# SAM IMAGE MASK
+class WAS_SAM_Image_Mask:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "sam_model": ("SAM_MODEL",),
+ "sam_parameters": ("SAM_PARAMETERS",),
+ "image": ("IMAGE",),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "MASK",)
+ FUNCTION = "sam_image_mask"
+
+ CATEGORY = "WAS Suite/Image/Masking"
+
+ def sam_image_mask(self, sam_model, sam_parameters, image):
+ image = tensor2sam(image)
+ points = sam_parameters["points"]
+ labels = sam_parameters["labels"]
+
+ from segment_anything import SamPredictor
+
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ sam_model.to(device=device)
+
+ predictor = SamPredictor(sam_model)
+ predictor.set_image(image)
+
+ masks, scores, logits = predictor.predict(
+ point_coords=points,
+ point_labels=labels,
+ multimask_output=False
+ )
+
+ sam_model.to(device='cpu')
+
+ mask = np.expand_dims(masks, axis=-1)
+
+ image = np.repeat(mask, 3, axis=-1)
+ image = torch.from_numpy(image)
+
+ mask = torch.from_numpy(mask)
+ mask = mask.squeeze(2)
+ mask = mask.squeeze().to(torch.float32)
+
+ return (image, mask, )
+
+
+# IMAGE BOUNDS
+class WAS_Image_Bounds:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE_BOUNDS",)
+ FUNCTION = "image_bounds"
+
+ CATEGORY = "WAS Suite/Image/Bound"
+
+ def image_bounds(self, image):
+ _, height, width, _ = image.shape
+
+ image_bounds = [0, height - 1, 0, width - 1]
+
+ return (image_bounds,)
+
+
+# INSET IMAGE BOUNDS
+class WAS_Inset_Image_Bounds:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "image_bounds": ("IMAGE_BOUNDS",),
+ "inset_left": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}),
+ "inset_right": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}),
+ "inset_top": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}),
+ "inset_bottom": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE_BOUNDS",)
+ FUNCTION = "inset_image_bounds"
+
+ CATEGORY = "WAS Suite/Image/Bound"
+
+ def inset_image_bounds(self, image_bounds, inset_left, inset_right, inset_top, inset_bottom):
+ # Unpack the image bounds
+ rmin, rmax, cmin, cmax = image_bounds
+
+ # Apply insets
+ rmin = rmin + inset_top
+ rmax = rmax - inset_bottom
+ cmin = cmin + inset_left
+ cmax = cmax - inset_right
+
+ # Check if the resulting bounds are valid
+ if rmin > rmax or cmin > cmax:
+ cstr("Invalid insets provided. Please make sure the insets do not exceed the image bounds.").error.print()
+ return
+
+ image_bounds = [rmin, rmax, cmin, cmax]
+
+ return (image_bounds,)
+
+
+# WAS BOUNDED IMAGE BLEND
+class WAS_Bounded_Image_Blend:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "target": ("IMAGE",),
+ "target_bounds": ("IMAGE_BOUNDS",),
+ "source": ("IMAGE",),
+ "blend_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
+ "feathering": ("INT", {"default": 16, "min": 0, "max": 0xffffffffffffffff}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "bounded_image_blend"
+
+ CATEGORY = "WAS Suite/Image/Bound"
+
+ def bounded_image_blend(self, target, target_bounds, source, blend_factor, feathering):
+ # Convert PyTorch tensors to PIL images
+ target_pil = Image.fromarray((target.squeeze(0).cpu().numpy() * 255).clip(0, 255).astype(np.uint8))
+ source_pil = Image.fromarray((source.squeeze(0).cpu().numpy() * 255).astype(np.uint8))
+
+ # Extract the target bounds
+ rmin, rmax, cmin, cmax = target_bounds
+
+ # Calculate the dimensions of the target bounds
+ width = cmax - cmin + 1
+ height = rmax - rmin + 1
+
+ # Resize the source image to match the dimensions of the target bounds
+ source_resized = source_pil.resize((width, height), Image.ANTIALIAS)
+
+ # Create the blend mask with the same size as the target image
+ blend_mask = Image.new('L', target_pil.size, 0)
+
+ # Create the feathered mask portion the size of the target bounds
+ if feathering > 0:
+ inner_mask = Image.new('L', (width - (2 * feathering), height - (2 * feathering)), 255)
+ inner_mask = ImageOps.expand(inner_mask, border=feathering, fill=0)
+ inner_mask = inner_mask.filter(ImageFilter.GaussianBlur(radius=feathering))
+ else:
+ inner_mask = Image.new('L', (width, height), 255)
+
+ # Paste the feathered mask portion into the blend mask at the target bounds position
+ blend_mask.paste(inner_mask, (cmin, rmin))
+
+ # Create a blank image with the same size and mode as the target
+ source_positioned = Image.new(target_pil.mode, target_pil.size)
+
+ # Paste the source image onto the blank image using the target bounds
+ source_positioned.paste(source_resized, (cmin, rmin))
+
+ # Create a blend mask using the blend_mask and blend factor
+ blend_mask = blend_mask.point(lambda p: p * blend_factor).convert('L')
+
+ # Blend the source and target images using the blend mask
+ result = Image.composite(source_positioned, target_pil, blend_mask)
+
+ # Convert the result back to a PyTorch tensor
+ result = torch.from_numpy(np.array(result).astype(np.float32) / 255).unsqueeze(0)
+
+ return (result,)
+
+
+# BOUNDED IMAGE CROP
+class WAS_Bounded_Image_Crop:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "image_bounds": ("IMAGE_BOUNDS",),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "bounded_image_crop"
+
+ CATEGORY = "WAS Suite/Image/Bound"
+
+ def bounded_image_crop(self, image, image_bounds):
+ # Unpack the image bounds
+ rmin, rmax, cmin, cmax = image_bounds
+
+ # Check if the provided bounds are valid
+ if rmin > rmax or cmin > cmax:
+ cstr("Invalid bounds provided. Please make sure the bounds are within the image dimensions.").error.print()
+
+ # Crop the image using the provided bounds and return it
+ return (image[:, rmin:rmax+1, cmin:cmax+1, :],)
+
+
+# WAS BOUNDED IMAGE BLEND WITH MASK
+class WAS_Bounded_Image_Blend_With_Mask:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "target": ("IMAGE",),
+ "target_mask": ("MASK",),
+ "target_bounds": ("IMAGE_BOUNDS",),
+ "source": ("IMAGE",),
+ "blend_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
+ "feathering": ("INT", {"default": 16, "min": 0, "max": 0xffffffffffffffff}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "bounded_image_blend_with_mask"
+
+ CATEGORY = "WAS Suite/Image/Bound"
+
+ def bounded_image_blend_with_mask(self, target, target_mask, target_bounds, source, blend_factor, feathering):
+ # Convert PyTorch tensors to PIL images
+ target_pil = Image.fromarray((target.squeeze(0).cpu().numpy() * 255).clip(0, 255).astype(np.uint8))
+ target_mask_pil = Image.fromarray((target_mask.cpu().numpy() * 255).astype(np.uint8), mode='L')
+ source_pil = Image.fromarray((source.squeeze(0).cpu().numpy() * 255).astype(np.uint8))
+
+ # Extract the target bounds
+ rmin, rmax, cmin, cmax = target_bounds
+
+ # Create a blank image with the same size and mode as the target
+ source_positioned = Image.new(target_pil.mode, target_pil.size)
+
+ # Paste the source image onto the blank image using the target bounds
+ source_positioned.paste(source_pil, (cmin, rmin))
+
+ # Create a blend mask using the target mask and blend factor
+ blend_mask = target_mask_pil.point(lambda p: p * blend_factor).convert('L')
+
+ # Apply feathering (Gaussian blur) to the blend mask if feather_amount is greater than 0
+ if feathering > 0:
+ blend_mask = blend_mask.filter(ImageFilter.GaussianBlur(radius=feathering))
+
+ # Blend the source and target images using the blend mask
+ result = Image.composite(source_positioned, target_pil, blend_mask)
+
+ # Convert the result back to a PyTorch tensor
+ result_tensor = torch.from_numpy(np.array(result).astype(np.float32) / 255).unsqueeze(0)
+
+ return (result_tensor,)
+
+
+# WAS BOUNDED IMAGE CROP WITH MASK
+class WAS_Bounded_Image_Crop_With_Mask:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "mask": ("MASK",),
+ "padding_left": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}),
+ "padding_right": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}),
+ "padding_top": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}),
+ "padding_bottom": ("INT", {"default": 64, "min": 0, "max": 0xffffffffffffffff}),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "IMAGE_BOUNDS",)
+ FUNCTION = "bounded_image_crop_with_mask"
+
+ CATEGORY = "WAS Suite/Image/Bound"
+
+ def bounded_image_crop_with_mask(self, image, mask, padding_left, padding_right, padding_top, padding_bottom):
+ # Get the bounding box coordinates of the mask
+ rows = torch.any(mask, axis=1)
+ cols = torch.any(mask, axis=0)
+ rmin, rmax = torch.where(rows)[0][[0, -1]]
+ cmin, cmax = torch.where(cols)[0][[0, -1]]
+
+ # Apply padding
+ rmin = max(rmin - padding_top, 0)
+ rmax = min(rmax + padding_bottom, mask.shape[0] - 1)
+ cmin = max(cmin - padding_left, 0)
+ cmax = min(cmax + padding_right, mask.shape[1] - 1)
+
+ bounds = [rmin, rmax, cmin, cmax]
+
+ # Crop the image using the computed coordinates and return it
+ return (image[:, rmin:rmax+1, cmin:cmax+1, :], bounds,)
+
+
+#! NUMBERS
+
+
+# RANDOM NUMBER
+
+class WAS_Random_Number:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number_type": (["integer", "float", "bool"],),
+ "minimum": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615}),
+ "maximum": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER", "FLOAT", "INT")
+ FUNCTION = "return_randm_number"
+
+ CATEGORY = "WAS Suite/Number"
+
+ def return_randm_number(self, minimum, maximum, seed, number_type='integer'):
+
+ # Set Generator Seed
+ random.seed(seed)
+
+ # Return random number
+ if number_type:
+ if number_type == 'integer':
+ number = random.randint(minimum, maximum)
+ elif number_type == 'float':
+ number = random.uniform(minimum, maximum)
+ elif number_type == 'bool':
+ number = random.random()
+ else:
+ return
+
+ # Return number
+ return (number, float(number), int(number))
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+# TRUE RANDOM NUMBER
+
+class WAS_True_Random_Number:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "api_key": ("STRING",{"default":"00000000-0000-0000-0000-000000000000", "multiline": False}),
+ "minimum": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615}),
+ "maximum": ("FLOAT", {"default": 10000000, "min": -18446744073709551615, "max": 18446744073709551615}),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER", "FLOAT", "INT")
+ FUNCTION = "return_true_randm_number"
+
+ CATEGORY = "WAS Suite/Number"
+
+ def return_true_randm_number(self, api_key=None, minimum=0, maximum=10):
+
+ # Get Random Number
+ number = self.get_random_numbers(api_key=api_key, minimum=minimum, maximum=maximum)[0]
+
+ # Return number
+ return (number, )
+
+ def get_random_numbers(self, api_key=None, amount=1, minimum=0, maximum=10):
+ '''Get random number(s) from random.org'''
+ if api_key in [None, '00000000-0000-0000-0000-000000000000', '']:
+ cstr("No API key provided! A valid RANDOM.ORG API key is required to use `True Random.org Number Generator`").error.print()
+ return [0]
+
+ url = "https://api.random.org/json-rpc/2/invoke"
+ headers = {"Content-Type": "application/json"}
+ payload = {
+ "jsonrpc": "2.0",
+ "method": "generateIntegers",
+ "params": {
+ "apiKey": api_key,
+ "n": amount,
+ "min": minimum,
+ "max": maximum,
+ "replacement": True,
+ "base": 10
+ },
+ "id": 1
+ }
+
+ response = requests.post(url, headers=headers, data=json.dumps(payload))
+ if response.status_code == 200:
+ data = response.json()
+ if "result" in data:
+ return data["result"]["random"]["data"], float(data["result"]["random"]["data"]), int(data["result"]["random"]["data"])
+
+ return [0]
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+
+# CONSTANT NUMBER
+
+class WAS_Constant_Number:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number_type": (["integer", "float", "bool"],),
+ "number": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615}),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER", "FLOAT", "INT")
+ FUNCTION = "return_constant_number"
+
+ CATEGORY = "WAS Suite/Number"
+
+ def return_constant_number(self, number_type, number):
+
+ # Return number
+ if number_type:
+ if number_type == 'integer':
+ return (int(number), )
+ elif number_type == 'integer':
+ return (float(number), )
+ elif number_type == 'bool':
+ return ((1 if int(number) > 0 else 0), )
+ else:
+ return (number, float(number), int(number) )
+
+# INCREMENT NUMBER
+
+class WAS_Number_Counter:
+ def __init__(self):
+ self.counters = {}
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number_type": (["integer", "float"],),
+ "mode": (["increment", "decrement"],),
+ "start": ("FLOAT", {"default": 0, "min": -18446744073709551615, "max": 18446744073709551615, "step": 0.01}),
+ "step": ("FLOAT", {"default": 1, "min": 0, "max": 99999, "step": 0.01}),
+ },
+ "optional": {
+ "reset_bool": ("NUMBER",),
+ },
+ "hidden": {
+ "unique_id": "UNIQUE_ID",
+ }
+ }
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+ RETURN_TYPES = ("NUMBER", "FLOAT", "INT")
+ RETURN_NAMES = ("number", "float", "int")
+ FUNCTION = "increment_number"
+
+ CATEGORY = "WAS Suite/Number"
+
+ def increment_number(self, number_type, mode, start, step, unique_id, reset_bool=0):
+
+ print(unique_id)
+
+ counter = int(start) if mode == 'integer' else start
+ if self.counters.__contains__(unique_id):
+ counter = self.counters[unique_id]
+
+ if round(reset_bool) >= 1:
+ counter = start
+
+ if mode == 'increment':
+ counter += step
+ else:
+ counter -= step
+
+ self.counters[unique_id] = counter
+
+ result = int(counter) if number_type == 'integer' else float(counter)
+
+ return ( result, float(counter), int(counter) )
+
+
+
+
+# NUMBER TO SEED
+
+class WAS_Number_To_Seed:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("SEED",)
+ FUNCTION = "number_to_seed"
+
+ CATEGORY = "WAS Suite/Number/Operations"
+
+ def number_to_seed(self, number):
+ return ({"seed": number, }, )
+
+
+# NUMBER TO INT
+
+class WAS_Number_To_Int:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("INT",)
+ FUNCTION = "number_to_int"
+
+ CATEGORY = "WAS Suite/Number/Operations"
+
+ def number_to_int(self, number):
+ return (int(number), )
+
+
+
+# NUMBER TO FLOAT
+
+class WAS_Number_To_Float:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("FLOAT",)
+ FUNCTION = "number_to_float"
+
+ CATEGORY = "WAS Suite/Number/Operations"
+
+ def number_to_float(self, number):
+ return (float(number), )
+
+
+
+# INT TO NUMBER
+
+class WAS_Int_To_Number:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "int_input": ("INT",),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER",)
+ FUNCTION = "int_to_number"
+
+ CATEGORY = "WAS Suite/Number/Operations"
+
+ def int_to_number(self, int_input):
+ return (int(int_input), )
+
+
+
+# NUMBER TO FLOAT
+
+class WAS_Float_To_Number:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "float_input": ("FLOAT",),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER",)
+ FUNCTION = "float_to_number"
+
+ CATEGORY = "WAS Suite/Number/Operations"
+
+ def float_to_number(self, float_input):
+ return ( float(float_input), )
+
+
+# NUMBER TO STRING
+
+class WAS_Number_To_String:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("STRING",)
+ FUNCTION = "number_to_string"
+
+ CATEGORY = "WAS Suite/Number/Operations"
+
+ def number_to_string(self, number):
+ return ( str(number), )
+
+# NUMBER TO STRING
+
+class WAS_Number_To_Text:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "number_to_text"
+
+ CATEGORY = "WAS Suite/Number/Operations"
+
+ def number_to_text(self, number):
+ return ( str(number), )
+
+
+# NUMBER PI
+
+class WAS_Number_PI:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {}
+ }
+
+ RETURN_TYPES = ("NUMBER", "FLOAT")
+ FUNCTION = "number_pi"
+
+ CATEGORY = "WAS Suite/Number"
+
+ def number_pi(self):
+ return (math.pi, math.pi)
+
+# Boolean
+
+class WAS_Boolean:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "boolean_number": ("INT", {"default":1, "min":0, "max":1, "step":1}),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER","INT")
+ FUNCTION = "return_boolean"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def return_boolean(self, boolean_number=1):
+ return (int(round(boolean_number)), int(round(boolean_number)))
+
+# NUMBER OPERATIONS
+
+
+class WAS_Number_Operation:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number_a": ("NUMBER",),
+ "number_b": ("NUMBER",),
+ "operation": (["addition", "subtraction", "division", "floor division", "multiplication", "exponentiation", "modulus", "greater-than", "greater-than or equals", "less-than", "less-than or equals", "equals", "does not equal"],),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER", "FLOAT", "INT")
+ FUNCTION = "math_operations"
+
+ CATEGORY = "WAS Suite/Number/Operations"
+
+ def math_operations(self, number_a, number_b, operation="addition"):
+
+ # Return random number
+ if operation:
+ if operation == 'addition':
+ result = (number_a + number_b)
+ return result, result, int(result)
+ elif operation == 'subtraction':
+ result = (number_a - number_b)
+ return result, result, int(result)
+ elif operation == 'division':
+ result = (number_a / number_b)
+ return result, result, int(result)
+ elif operation == 'floor division':
+ result = (number_a // number_b)
+ return result, result, int(result)
+ elif operation == 'multiplication':
+ result = (number_a * number_b)
+ return result, result, int(result)
+ elif operation == 'exponentiation':
+ result = (number_a ** number_b)
+ return result, result, int(result)
+ elif operation == 'modulus':
+ result = (number_a % number_b)
+ return result, result, int(result)
+ elif operation == 'greater-than':
+ result = +(number_a > number_b)
+ return result, result, int(result)
+ elif operation == 'greater-than or equals':
+ result = +(number_a >= number_b)
+ return result, result, int(result)
+ elif operation == 'less-than':
+ result = +(number_a < number_b)
+ return result, result, int(result)
+ elif operation == 'less-than or equals':
+ result = +(number_a <= number_b)
+ return result, result, int(result)
+ elif operation == 'equals':
+ result = +(number_a == number_b)
+ return result, result, int(result)
+ elif operation == 'does not equal':
+ result = +(number_a != number_b)
+ return result, result, int(result)
+ else:
+ return (number_a, number_a, int(number_a))
+
+# NUMBER MULTIPLE OF
+
+class WAS_Number_Multiple_Of:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number": ("NUMBER",),
+ "multiple": ("INT", {"default": 8, "min": -18446744073709551615, "max": 18446744073709551615}),
+ }
+ }
+
+ RETURN_TYPES =("NUMBER", "FLOAT", "INT")
+ FUNCTION = "number_multiple_of"
+
+ CATEGORY = "WAS Suite/Number/Functions"
+
+ def number_multiple_of(self, number, multiple=8):
+ if number % multiple != 0:
+ return ((number // multiple) * multiple + multiple, )
+ return (number, number, int(number))
+
+
+#! MISC
+
+# Image Width and Height to Number
+
+class WAS_Image_Size_To_Number:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER", "NUMBER", "FLOAT", "FLOAT", "INT", "INT")
+ RETURN_NAMES = ("width_num", "height_num", "width_float", "height_float", "width_int", "height_int")
+ FUNCTION = "image_width_height"
+
+ CATEGORY = "WAS Suite/Number/Operations"
+
+ def image_width_height(self, image):
+ image = tensor2pil(image)
+ if image.size:
+ return( image.size[0], image.size[1], float(image.size[0]), float(image.size[1]), image.size[0], image.size[1] )
+ return ( 0, 0, 0, 0, 0, 0)
+
+
+# Latent Width and Height to Number
+
+class WAS_Latent_Size_To_Number:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "samples": ("LATENT",),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER", "NUMBER", "FLOAT", "FLOAT", "INT", "INT")
+ RETURN_NAMES = ("tensor_w_num","tensor_h_num")
+ FUNCTION = "latent_width_height"
+
+ CATEGORY = "WAS Suite/Number/Operations"
+
+ def latent_width_height(self, samples):
+ size_dict = {}
+ i = 0
+ for tensor in samples['samples'][0]:
+ if not isinstance(tensor, torch.Tensor):
+ cstr(f'Input should be a torch.Tensor').error.print()
+ shape = tensor.shape
+ tensor_height = shape[-2]
+ tensor_width = shape[-1]
+ size_dict.update({i:[tensor_width, tensor_height]})
+ return ( size_dict[0][0], size_dict[0][1], float(size_dict[0][0]), float(size_dict[0][1]), size_dict[0][0], size_dict[0][1] )
+
+
+# LATENT INPUT SWITCH
+
+class WAS_Latent_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "latent_a": ("LATENT",),
+ "latent_b": ("LATENT",),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "latent_input_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def latent_input_switch(self, latent_a, latent_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (latent_a, )
+ else:
+ return (latent_b, )
+
+# NUMBER INPUT CONDITION
+
+class WAS_Number_Input_Condition:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number_a": ("NUMBER",),
+ "number_b": ("NUMBER",),
+ "return_boolean": (["false", "true"],),
+ "comparison": (["and", "or", "greater-than", "greater-than or equals", "less-than", "less-than or equals", "equals", "does not equal", "divisible by", "if A odd", "if A even", "if A prime", "factor of"],),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER", "FLOAT", "INT")
+ FUNCTION = "number_input_condition"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def number_input_condition(self, number_a, number_b, return_boolean="false", comparison="greater-than"):
+
+ if comparison:
+ if return_boolean == 'true':
+ if comparison == 'and':
+ result = 1 if number_a != 0 and number_b != 0 else 0
+ elif comparison == 'or':
+ result = 1 if number_a != 0 or number_b != 0 else 0
+ elif comparison == 'greater-than':
+ result = 1 if number_a > number_b else 0
+ elif comparison == 'greater-than or equals':
+ result = 1 if number_a >= number_b else 0
+ elif comparison == 'less-than':
+ result = 1 if number_a < number_b else 0
+ elif comparison == 'less-than or equals':
+ result = 1 if number_a <= number_b else 0
+ elif comparison == 'equals':
+ result = 1 if number_a == number_b else 0
+ elif comparison == 'does not equal':
+ result = 1 if number_a != number_b else 0
+ elif comparison == 'divisible by':
+ result = 1 if number_b % number_a == 0 else 0
+ elif comparison == 'if A odd':
+ result = 1 if number_a % 2 != 0 else 0
+ elif comparison == 'if A even':
+ result = 1 if number_a % 2 == 0 else 0
+ elif comparison == 'if A prime':
+ result = 1 if self.is_prime(number_a) else 0
+ elif comparison == 'factor of':
+ result = 1 if number_b % number_a == 0 else 0
+ else:
+ result = 0
+ else:
+ if comparison == 'and':
+ result = number_a if number_a != 0 and number_b != 0 else number_b
+ elif comparison == 'or':
+ result = number_a if number_a != 0 or number_b != 0 else number_b
+ elif comparison == 'greater-than':
+ result = number_a if number_a > number_b else number_b
+ elif comparison == 'greater-than or equals':
+ result = number_a if number_a >= number_b else number_b
+ elif comparison == 'less-than':
+ result = number_a if number_a < number_b else number_b
+ elif comparison == 'less-than or equals':
+ result = number_a if number_a <= number_b else number_b
+ elif comparison == 'equals':
+ result = number_a if number_a == number_b else number_b
+ elif comparison == 'does not equal':
+ result = number_a if number_a != number_b else number_b
+ elif comparison == 'divisible by':
+ result = number_a if number_b % number_a == 0 else number_b
+ elif comparison == 'if A odd':
+ result = number_a if number_a % 2 != 0 else number_b
+ elif comparison == 'if A even':
+ result = number_a if number_a % 2 == 0 else number_b
+ elif comparison == 'if A prime':
+ result = number_a if self.is_prime(number_a) else number_b
+ elif comparison == 'factor of':
+ result = number_a if number_b % number_a == 0 else number_b
+ else:
+ result = number_a
+
+ return (result, float(result), int(result))
+
+ def is_prime(self, n):
+ if n <= 1:
+ return False
+ elif n <= 3:
+ return True
+ elif n % 2 == 0 or n % 3 == 0:
+ return False
+ i = 5
+ while i * i <= n:
+ if n % i == 0 or n % (i + 2) == 0:
+ return False
+ i += 6
+ return True
+
+# ASPECT RATIO
+
+class WAS_Image_Aspect_Ratio:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {},
+ "optional": {
+ "image": ("IMAGE",),
+ "width": ("NUMBER",),
+ "height": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER", "FLOAT", "NUMBER", TEXT_TYPE, TEXT_TYPE)
+ RETURN_NAMES = ("aspect_number", "aspect_float", "is_landscape_bool", "aspect_ratio_common", "aspect_type")
+ FUNCTION = "aspect"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def aspect(self, boolean_number=1, image=None, width=None, height=None):
+
+ if width and height:
+ width = width; height = height
+ elif image is not None:
+ width, height = tensor2pil(image).size
+ else:
+ raise Exception("WAS_Image_Aspect_Ratio must have width and height provided if no image tensori supplied.")
+
+ aspect_ratio = width / height
+ aspect_type = "landscape" if aspect_ratio > 1 else "portrait" if aspect_ratio < 1 else "square"
+
+ landscape_bool = 0
+ if aspect_type == "landscape":
+ landscape_bool = 1
+
+ gcd = math.gcd(width, height)
+ gcd_w = width // gcd
+ gcd_h = height // gcd
+ aspect_ratio_common = f"{gcd_w}:{gcd_h}"
+
+ return aspect_ratio, aspect_ratio, landscape_bool, aspect_ratio_common, aspect_type
+
+
+# NUMBER INPUT SWITCH
+
+class WAS_Number_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number_a": ("NUMBER",),
+ "number_b": ("NUMBER",),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER", "FLOAT", "INT")
+ FUNCTION = "number_input_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def number_input_switch(self, number_a, number_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (number_a, float(number_a), int(number_a))
+ else:
+ return (number_b, float(number_b), int(number_b))
+
+
+# IMAGE INPUT SWITCH
+
+class WAS_Image_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image_a": ("IMAGE",),
+ "image_b": ("IMAGE",),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "image_input_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def image_input_switch(self, image_a, image_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (image_a, )
+ else:
+ return (image_b, )
+
+# CONDITIONING INPUT SWITCH
+
+class WAS_Conditioning_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "conditioning_a": ("CONDITIONING",),
+ "conditioning_b": ("CONDITIONING",),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "conditioning_input_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def conditioning_input_switch(self, conditioning_a, conditioning_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (conditioning_a, )
+ else:
+ return (conditioning_b, )
+
+# MODEL INPUT SWITCH
+
+class WAS_Model_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "model_a": ("MODEL",),
+ "model_b": ("MODEL",),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("MODEL",)
+ FUNCTION = "model_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def model_switch(self, model_a, model_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (model_a, )
+ else:
+ return (model_b, )
+
+# VAE INPUT SWITCH
+
+class WAS_VAE_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "vae_a": ("VAE",),
+ "vae_b": ("VAE",),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("VAE",)
+ FUNCTION = "vae_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def vae_switch(self, vae_a, vae_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (vae_a, )
+ else:
+ return (vae_b, )
+
+# CLIP INPUT SWITCH
+
+class WAS_CLIP_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "clip_a": ("CLIP",),
+ "clip_b": ("CLIP",),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("CLIP",)
+ FUNCTION = "clip_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def clip_switch(self, clip_a, clip_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (clip_a, )
+ else:
+ return (clip_b, )
+
+# UPSCALE MODEL INPUT SWITCH
+
+class WAS_Upscale_Model_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "upscale_model_a": ("UPSCALE_MODEL",),
+ "upscale_model_b": ("UPSCALE_MODEL",),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("UPSCALE_MODEL",)
+ FUNCTION = "upscale_model_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def upscale_model_switch(self, upscale_model_a, upscale_model_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (upscale_model_a, )
+ else:
+ return (upscale_model_b, )
+
+
+# CONTROL NET INPUT SWITCH
+
+class WAS_Control_Net_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "control_net_a": ("CONTROL_NET",),
+ "control_net_b": ("CONTROL_NET",),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("CONTROL_NET",)
+ FUNCTION = "control_net_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def control_net_switch(self, control_net_a, control_net_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (control_net_a, )
+ else:
+ return (control_net_b, )
+
+# CLIP VISION INPUT SWITCH
+
+class WAS_CLIP_Vision_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "clip_vision_a": ("CLIP_VISION",),
+ "clip_vision_b": ("CLIP_VISION",),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = ("CLIP_VISION",)
+ FUNCTION = "clip_vision_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def clip_vision_switch(self, clip_vision_a, clip_vision_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (clip_vision_a, )
+ else:
+ return (clip_vision_b)
+
+# TEXT INPUT SWITCH
+
+class WAS_Text_Input_Switch:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "text_a": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "text_b": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
+ "boolean_number": ("NUMBER",),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,)
+ FUNCTION = "text_input_switch"
+
+ CATEGORY = "WAS Suite/Logic"
+
+ def text_input_switch(self, text_a, text_b, boolean_number=1):
+
+ if int(round(boolean_number)) == 1:
+ return (text_a, )
+ else:
+ return (text_b, )
+
+
+# DEBUG INPUT TO CONSOLE
+
+
+class WAS_Debug_Number_to_Console:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "number": ("NUMBER",),
+ "label": ("STRING", {"default": 'Debug to Console', "multiline": False}),
+ }
+ }
+
+ RETURN_TYPES = ("NUMBER",)
+ OUTPUT_NODE = True
+ FUNCTION = "debug_to_console"
+
+ CATEGORY = "WAS Suite/Debug"
+
+ def debug_to_console(self, number, label):
+ if label.strip() != '':
+ cstr(f'\033[33m{label}\033[0m:\n{number}\n').msg.print()
+ else:
+ cstr(f'\033[33mDebug to Console\033[0m:\n{number}\n').msg.print()
+ return (number, )
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+
+# CUSTOM COMFYUI NODES
+
+class WAS_Checkpoint_Loader:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "config_name": (comfy_paths.get_filename_list("configs"), ),
+ "ckpt_name": (comfy_paths.get_filename_list("checkpoints"), )}}
+ RETURN_TYPES = ("MODEL", "CLIP", "VAE", TEXT_TYPE)
+ RETURN_NAMES = ("MODEL", "CLIP", "VAE", "NAME_STRING")
+ FUNCTION = "load_checkpoint"
+
+ CATEGORY = "WAS Suite/Loaders/Advanced"
+
+ def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
+ config_path = comfy_paths.get_full_path("configs", config_name)
+ ckpt_path = comfy_paths.get_full_path("checkpoints", ckpt_name)
+ out = comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=comfy_paths.get_folder_paths("embeddings"))
+ return (out[0], out[1], out[2], os.path.splitext(os.path.basename(ckpt_name))[0])
+
+class WAS_Checkpoint_Loader:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "config_name": (comfy_paths.get_filename_list("configs"), ),
+ "ckpt_name": (comfy_paths.get_filename_list("checkpoints"), )}}
+ RETURN_TYPES = ("MODEL", "CLIP", "VAE", TEXT_TYPE)
+ RETURN_NAMES = ("MODEL", "CLIP", "VAE", "NAME_STRING")
+ FUNCTION = "load_checkpoint"
+
+ CATEGORY = "WAS Suite/Loaders/Advanced"
+
+ def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
+ config_path = comfy_paths.get_full_path("configs", config_name)
+ ckpt_path = comfy_paths.get_full_path("checkpoints", ckpt_name)
+ out = comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=comfy_paths.get_folder_paths("embeddings"))
+ return (out[0], out[1], out[2], os.path.splitext(os.path.basename(ckpt_name))[0])
+
+class WAS_Diffusers_Hub_Model_Loader:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "repo_id": ("STRING", {"multiline":False}),
+ "revision": ("STRING", {"default": "None", "multiline":False})}}
+ RETURN_TYPES = ("MODEL", "CLIP", "VAE", TEXT_TYPE)
+ RETURN_NAMES = ("MODEL", "CLIP", "VAE", "NAME_STRING")
+ FUNCTION = "load_hub_checkpoint"
+
+ CATEGORY = "WAS Suite/Loaders/Advanced"
+
+ def load_hub_checkpoint(self, repo_id=None, revision=None):
+ if revision in ["", "None", "none", None]:
+ revision = None
+ model_path = comfy_paths.get_folder_paths("diffusers")[0]
+ self.download_diffusers_model(repo_id, model_path, revision)
+ diffusersLoader = nodes.DiffusersLoader()
+ model, clip, vae = diffusersLoader.load_checkpoint(os.path.join(model_path, repo_id))
+ return (model, clip, vae, repo_id)
+
+ def download_diffusers_model(self, repo_id, local_dir, revision=None):
+ if 'huggingface-hub' not in packages():
+ cstr("Installing `huggingface_hub` ...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'huggingface_hub'])
+ from huggingface_hub import snapshot_download
+ model_path = os.path.join(local_dir, repo_id)
+ ignore_patterns = ["*.ckpt","*.safetensors","*.onnx"]
+ snapshot_download(repo_id=repo_id, repo_type="model", local_dir=model_path, revision=revision, use_auth_token=False, ignore_patterns=ignore_patterns)
+
+class WAS_Checkpoint_Loader_Simple:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "ckpt_name": (comfy_paths.get_filename_list("checkpoints"), ),
+ }}
+ RETURN_TYPES = ("MODEL", "CLIP", "VAE", TEXT_TYPE)
+ RETURN_NAMES = ("MODEL", "CLIP", "VAE", "NAME_STRING")
+ FUNCTION = "load_checkpoint"
+
+ CATEGORY = "WAS Suite/Loaders"
+
+ def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
+ ckpt_path = comfy_paths.get_full_path("checkpoints", ckpt_name)
+ out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=comfy_paths.get_folder_paths("embeddings"))
+ return (out[0], out[1], out[2], os.path.splitext(os.path.basename(ckpt_name))[0])
+
+class WAS_Diffusers_Loader:
+ @classmethod
+ def INPUT_TYPES(cls):
+ paths = []
+ for search_path in comfy_paths.get_folder_paths("diffusers"):
+ if os.path.exists(search_path):
+ paths += next(os.walk(search_path))[1]
+ return {"required": {"model_path": (paths,), }}
+ RETURN_TYPES = ("MODEL", "CLIP", "VAE", TEXT_TYPE)
+ RETURN_NAMES = ("MODEL", "CLIP", "VAE", "NAME_STRING")
+ FUNCTION = "load_checkpoint"
+
+ CATEGORY = "WAS Suite/Loaders/Advanced"
+
+ def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
+ for search_path in comfy_paths.get_folder_paths("diffusers"):
+ if os.path.exists(search_path):
+ paths = next(os.walk(search_path))[1]
+ if model_path in paths:
+ model_path = os.path.join(search_path, model_path)
+ break
+
+ out = comfy.diffusers_convert.load_diffusers(model_path, fp16=model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=comfy_paths.get_folder_paths("embeddings"))
+ return (out[0], out[1], out[2], os.path.basename(model_path))
+
+
+class WAS_unCLIP_Checkpoint_Loader:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "ckpt_name": (comfy_paths.get_filename_list("checkpoints"), ),
+ }}
+ RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION", "STRING")
+ RETURN_NAMES = ("MODEL", "CLIP", "VAE", "CLIP_VISION", "NAME_STRING")
+ FUNCTION = "load_checkpoint"
+
+ CATEGORY = "WAS Suite/Loaders"
+
+ def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
+ ckpt_path = comfy_paths.get_full_path("checkpoints", ckpt_name)
+ out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=comfy_paths.get_folder_paths("embeddings"))
+ return (out[0], out[1], out[2], out[3], os.path.splitext(os.path.basename(ckpt_name))[0])
+
+class WAS_Lora_Loader:
+ def __init__(self):
+ self.loaded_lora = None;
+
+ @classmethod
+ def INPUT_TYPES(s):
+ file_list = comfy_paths.get_filename_list("loras")
+ file_list.insert(0, "None")
+ return {"required": { "model": ("MODEL",),
+ "clip": ("CLIP", ),
+ "lora_name": (file_list, ),
+ "strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+ "strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+ }}
+ RETURN_TYPES = ("MODEL", "CLIP", TEXT_TYPE)
+ RETURN_NAMES = ("MODEL", "CLIP", "NAME_STRING")
+ FUNCTION = "load_lora"
+
+ CATEGORY = "WAS Suite/Loaders"
+
+ def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
+ if strength_model == 0 and strength_clip == 0:
+ return (model, clip)
+
+ lora_path = comfy_paths.get_full_path("loras", lora_name)
+ lora = None
+ if self.loaded_lora is not None:
+ if self.loaded_lora[0] == lora_path:
+ lora = self.loaded_lora[1]
+ else:
+ temp = self.loaded_lora
+ self.loaded_lora = None
+ del temp
+
+ if lora is None:
+ lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+ self.loaded_lora = (lora_path, lora)
+
+ model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
+ return (model_lora, clip_lora, os.path.splitext(os.path.basename(lora_name))[0])
+
+class WAS_Upscale_Model_Loader:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "model_name": (comfy_paths.get_filename_list("upscale_models"), ),
+ }}
+ RETURN_TYPES = ("UPSCALE_MODEL",TEXT_TYPE)
+ RETURN_NAMES = ("UPSCALE_MODEL","MODEL_NAME_TEXT")
+ FUNCTION = "load_model"
+
+ CATEGORY = "WAS Suite/Loaders"
+
+ def load_model(self, model_name):
+ model_path = comfy_paths.get_full_path("upscale_models", model_name)
+ sd = comfy.utils.load_torch_file(model_path)
+ out = model_loading.load_state_dict(sd).eval()
+ return (out,model_name)
+
+# VIDEO WRITER
+
+class WAS_Video_Writer:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ WTools = WAS_Tools_Class()
+ v = WTools.VideoWriter()
+ codecs = []
+ for codec in v.get_codecs():
+ codecs.append(codec.upper())
+ codecs = sorted(codecs)
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "transition_frames": ("INT", {"default":30, "min":0, "max":120, "step":1}),
+ "image_delay_sec": ("FLOAT", {"default":2.5, "min":0.1, "max":60000.0, "step":0.1}),
+ "fps": ("INT", {"default":30, "min":1, "max":60.0, "step":1}),
+ "max_size": ("INT", {"default":512, "min":128, "max":1920, "step":1}),
+ "output_path": ("STRING", {"default": "./ComfyUI/output", "multiline": False}),
+ "filename": ("STRING", {"default": "comfy_writer", "multiline": False}),
+ "codec": (codecs,),
+ }
+ }
+
+ #@classmethod
+ #def IS_CHANGED(cls, **kwargs):
+ # return float("NaN")
+
+ RETURN_TYPES = ("IMAGE",TEXT_TYPE,TEXT_TYPE)
+ RETURN_NAMES = ("IMAGE_PASS","filepath_text","filename_text")
+ FUNCTION = "write_video"
+
+ CATEGORY = "WAS Suite/Animation/Writer"
+
+ def write_video(self, image, transition_frames=10, image_delay_sec=10, fps=30, max_size=512,
+ output_path="./ComfyUI/output", filename="morph", codec="H264"):
+
+ conf = getSuiteConfig()
+ if not conf.__contains__('ffmpeg_bin_path'):
+ cstr(f"Unable to use MP4 Writer because the `ffmpeg_bin_path` is not set in `{WAS_CONFIG_FILE}`").error.print()
+ return (image,"","")
+
+ if conf.__contains__('ffmpeg_bin_path'):
+ if conf['ffmpeg_bin_path'] != "/path/to/ffmpeg":
+ sys.path.append(conf['ffmpeg_bin_path'])
+ os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "rtsp_transport;udp"
+ os.environ['OPENCV_FFMPEG_BINARY'] = conf['ffmpeg_bin_path']
+
+ if output_path.strip() in [None, "", "."]:
+ output_path = "./ComfyUI/output"
+
+ if image == None:
+ image = pil2tensor(Image.new("RGB", (512,512), (0,0,0)))
+
+ if transition_frames < 0:
+ transition_frames = 0
+ elif transition_frames > 60:
+ transition_frames = 60
+
+ if fps < 1:
+ fps = 1
+ elif fps > 60:
+ fps = 60
+
+ image = self.rescale_image(tensor2pil(image), max_size)
+
+ tokens = TextTokens()
+ output_path = os.path.abspath(os.path.join(*tokens.parseTokens(output_path).split('/')))
+ output_file = os.path.join(output_path, tokens.parseTokens(filename))
+
+ if not os.path.exists(output_path):
+ os.makedirs(output_path, exist_ok=True)
+
+ WTools = WAS_Tools_Class()
+ MP4Writer = WTools.VideoWriter(int(transition_frames), int(fps), int(image_delay_sec), max_size, codec)
+ path = MP4Writer.write(image, output_file)
+
+ return (pil2tensor(image), path, filename)
+
+ def rescale_image(self, image, max_dimension):
+ width, height = image.size
+ if width > max_dimension or height > max_dimension:
+ scaling_factor = max(width, height) / max_dimension
+ new_width = int(width / scaling_factor)
+ new_height = int(height / scaling_factor)
+ image = image.resize((new_width, new_height), Image.Resampling(1))
+ return image
+
+# VIDEO CREATOR
+
+class WAS_Create_Video_From_Path:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ WTools = WAS_Tools_Class()
+ v = WTools.VideoWriter()
+ codecs = []
+ for codec in v.get_codecs():
+ codecs.append(codec.upper())
+ codecs = sorted(codecs)
+ return {
+ "required": {
+ "transition_frames": ("INT", {"default":30, "min":0, "max":120, "step":1}),
+ "image_delay_sec": ("FLOAT", {"default":2.5, "min":0.01, "max":60000.0, "step":0.01}),
+ "fps": ("INT", {"default":30, "min":1, "max":60.0, "step":1}),
+ "max_size": ("INT", {"default":512, "min":128, "max":1920, "step":1}),
+ "input_path": ("STRING", {"default": "./ComfyUI/input", "multiline": False}),
+ "output_path": ("STRING", {"default": "./ComfyUI/output", "multiline": False}),
+ "filename": ("STRING", {"default": "comfy_video", "multiline": False}),
+ "codec": (codecs,),
+ }
+ }
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+ RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE)
+ RETURN_NAMES = ("filepath_text","filename_text")
+ FUNCTION = "create_video_from_path"
+
+ CATEGORY = "WAS Suite/Animation"
+
+ def create_video_from_path(self, transition_frames=10, image_delay_sec=10, fps=30, max_size=512,
+ input_path="./ComfyUI/input", output_path="./ComfyUI/output", filename="morph", codec="H264"):
+
+ conf = getSuiteConfig()
+ if not conf.__contains__('ffmpeg_bin_path'):
+ cstr(f"Unable to use MP4 Writer because the `ffmpeg_bin_path` is not set in `{WAS_CONFIG_FILE}`").error.print()
+ return ("","")
+
+ if conf.__contains__('ffmpeg_bin_path'):
+ if conf['ffmpeg_bin_path'] != "/path/to/ffmpeg":
+ sys.path.append(conf['ffmpeg_bin_path'])
+ os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "rtsp_transport;udp"
+ os.environ['OPENCV_FFMPEG_BINARY'] = conf['ffmpeg_bin_path']
+
+ if output_path.strip() in [None, "", "."]:
+ output_path = "./ComfyUI/output"
+
+ if transition_frames < 0:
+ transition_frames = 0
+ elif transition_frames > 60:
+ transition_frames = 60
+
+ if fps < 1:
+ fps = 1
+ elif fps > 60:
+ fps = 60
+
+ tokens = TextTokens()
+
+ # Check if output_path is an absolute path
+ if not os.path.isabs(output_path):
+ output_path = os.path.abspath(os.path.join(*tokens.parseTokens(output_path).split('/')))
+
+ output_file = os.path.join(output_path, tokens.parseTokens(filename))
+
+ if not os.path.exists(output_path):
+ os.makedirs(output_path, exist_ok=True)
+
+ WTools = WAS_Tools_Class()
+ MP4Writer = WTools.VideoWriter(int(transition_frames), int(fps), int(image_delay_sec), max_size, codec)
+ path = MP4Writer.create_video(input_path, output_file)
+
+ return (path, filename)
+
+# VIDEO FRAME DUMP
+
+class WAS_Video_Frame_Dump:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "video_path": ("STRING", {"default":"./ComfyUI/input/MyVideo.mp4", "multiline":False}),
+ "output_path": ("STRING", {"default": "./ComfyUI/input/MyVideo", "multiline": False}),
+ "prefix": ("STRING", {"default": "frame_", "multiline": False}),
+ "filenumber_digits": ("INT", {"default":4, "min":-1, "max":8, "step":1}),
+ "extension": (["png","jpg","gif","tiff"],),
+ }
+ }
+
+ @classmethod
+ def IS_CHANGED(cls, **kwargs):
+ return float("NaN")
+
+ RETURN_TYPES = (TEXT_TYPE,"NUMBER")
+ RETURN_NAMES = ("output_path","processed_count")
+ FUNCTION = "dump_video_frames"
+
+ CATEGORY = "WAS Suite/Animation"
+
+ def dump_video_frames(self, video_path, output_path, prefix="fame_", extension="png",filenumber_digits=-1):
+
+ conf = getSuiteConfig()
+ if not conf.__contains__('ffmpeg_bin_path'):
+ cstr(f"Unable to use dump frames because the `ffmpeg_bin_path` is not set in `{WAS_CONFIG_FILE}`").error.print()
+ return ("",0)
+
+ if conf.__contains__('ffmpeg_bin_path'):
+ if conf['ffmpeg_bin_path'] != "/path/to/ffmpeg":
+ sys.path.append(conf['ffmpeg_bin_path'])
+ os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "rtsp_transport;udp"
+ os.environ['OPENCV_FFMPEG_BINARY'] = conf['ffmpeg_bin_path']
+
+ if output_path.strip() in [None, "", "."]:
+ output_path = "./ComfyUI/input/frames"
+
+ tokens = TextTokens()
+ output_path = os.path.abspath(os.path.join(*tokens.parseTokens(output_path).split('/')))
+ prefix = tokens.parseTokens(prefix)
+
+ WTools = WAS_Tools_Class()
+ MP4Writer = WTools.VideoWriter()
+ processed = MP4Writer.extract(video_path, output_path, prefix, extension,filenumber_digits)
+
+ return (output_path, processed)
+
+# CACHING
+
+class WAS_Cache:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "latent_suffix": ("STRING", {"default": str(random.randint(999999, 99999999))+"_cache", "multiline":False}),
+ "image_suffix": ("STRING", {"default": str(random.randint(999999, 99999999))+"_cache", "multiline":False}),
+ "conditioning_suffix": ("STRING", {"default": str(random.randint(999999, 99999999))+"_cache", "multiline":False}),
+ },
+ "optional": {
+ "output_path": ("STRING", {"default": os.path.join(WAS_SUITE_ROOT, 'cache'), "multiline": False}),
+ "latent": ("LATENT",),
+ "image": ("IMAGE",),
+ "conditioning": ("CONDITIONING",),
+ }
+ }
+
+ RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE,TEXT_TYPE)
+ RETURN_NAMES = ("latent_filename","image_filename","conditioning_filename")
+ FUNCTION = "cache_input"
+ OUTPUT_NODE = True
+
+ CATEGORY = "WAS Suite/IO"
+
+ def cache_input(self, latent_suffix="_cache", image_suffix="_cache", conditioning_suffix="_cache", output_path=None, latent=None, image=None, conditioning=None):
+
+ if 'joblib' not in packages():
+ cstr("Installing joblib...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'joblib'])
+
+ import joblib
+
+ output = os.path.join(WAS_SUITE_ROOT, 'cache')
+ if output_path:
+ if output_path.strip() not in ['', 'none', 'None']:
+ output = output_path
+ if not os.path.isabs(output):
+ output = os.path.abspath(output)
+ if not os.path.exists(output):
+ os.makedirs(output, exist_ok=True)
+
+ l_filename = ""
+ i_filename = ""
+ c_filename = ""
+
+ tokens = TextTokens()
+ output = tokens.parseTokens(output)
+
+ if latent != None:
+ l_filename = f'{tokens.parseTokens(latent_suffix)}.latent'
+ out_file = os.path.join(output, l_filename)
+ joblib.dump(latent, out_file)
+ cstr(f"Latent saved to: {out_file}").msg.print()
+
+ if image != None:
+ i_filename = f'{tokens.parseTokens(image_suffix)}.image'
+ out_file = os.path.join(output, i_filename)
+ joblib.dump(image, out_file)
+ cstr(f"Tensor batch saved to: {out_file}").msg.print()
+
+ if conditioning != None:
+ c_filename = f'{tokens.parseTokens(conditioning_suffix)}.conditioning'
+ out_file = os.path.join(output, c_filename)
+ joblib.dump(conditioning, os.path.join(output, out_file))
+ cstr(f"Conditioning saved to: {out_file}").msg.print()
+
+ return (l_filename, i_filename, c_filename)
+
+
+class WAS_Load_Cache:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "latent_path": ("STRING", {"default": "", "multiline":False}),
+ "image_path": ("STRING", {"default": "", "multiline":False}),
+ "conditioning_path": ("STRING", {"default": "", "multiline":False}),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT","IMAGE","CONDITIONING")
+ RETURN_NAMES = ("LATENT","IMAGE","CONDITIONING")
+ FUNCTION = "load_cache"
+
+ CATEGORY = "WAS Suite/IO"
+
+ def load_cache(self, latent_path=None, image_path=None, conditioning_path=None):
+
+ if 'joblib' not in packages():
+ cstr("Installing joblib...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', 'joblib'])
+
+ import joblib
+
+ input_path = os.path.join(WAS_SUITE_ROOT, 'cache')
+
+ latent = None
+ image = None
+ conditioning = None
+
+ if latent_path not in ["",None]:
+ if os.path.exists(latent_path):
+ latent = joblib.load(latent_path)
+ else:
+ cstr(f"Unable to locate cache file {latent_path}").error.print()
+
+ if image_path not in ["",None]:
+ if os.path.exists(image_path):
+ image = joblib.load(image_path)
+ else:
+ cstr(f"Unable to locate cache file {image_path}").msg.print()
+
+ if conditioning_path not in ["",None]:
+ if os.path.exists(conditioning_path):
+ conditioning = joblib.load(conditioning_path)
+ else:
+ cstr(f"Unable to locate cache file {conditioning_path}").error.print()
+
+ return (latent, image, conditioning)
+
+
+# SAMPLES PASS STAT SYSTEM
+
+class WAS_Samples_Passthrough_Stat_System:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "samples": ("LATENT",),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ RETURN_NAMES = ("samples",)
+ FUNCTION = "stat_system"
+
+ CATEGORY = "WAS Suite/Debug"
+
+ def stat_system(self, samples):
+
+ log = ""
+ for stat in self.get_system_stats():
+ log += stat + "\n"
+
+ cstr("\n"+log).msg.print()
+
+ return (samples,)
+
+ def get_system_stats(self):
+
+ import psutil
+
+ # RAM
+ ram = psutil.virtual_memory()
+ ram_used = ram.used / (1024 ** 3)
+ ram_total = ram.total / (1024 ** 3)
+ ram_stats = f"Used RAM: {ram_used:.2f} GB / Total RAM: {ram_total:.2f} GB"
+
+ # VRAM (with PyTorch)
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ vram_used = torch.cuda.memory_allocated(device) / (1024 ** 3)
+ vram_total = torch.cuda.get_device_properties(device).total_memory / (1024 ** 3)
+ vram_stats = f"Used VRAM: {vram_used:.2f} GB / Total VRAM: {vram_total:.2f} GB"
+
+ # Hard Drive Space
+ hard_drive = psutil.disk_usage("/")
+ used_space = hard_drive.used / (1024 ** 3)
+ total_space = hard_drive.total / (1024 ** 3)
+ hard_drive_stats = f"Used Space: {used_space:.2f} GB / Total Space: {total_space:.2f} GB"
+
+ return [ram_stats, vram_stats, hard_drive_stats]
+
+# Class to count the number of places on an integer
+
+class WAS_Integer_Place_Counter:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "int_input": ("INT", {"default": 0, "min": 0, "max": 10000000, "step": 1}),
+ }
+ }
+ RETURN_TYPES = ("INT",)
+ RETURN_NAMES = ("INT_PLACES",)
+ FUNCTION = "count_places"
+
+ CATEGORY = "WAS Suite/Integer"
+
+ def count_places(self, int_input):
+ output = len(str(int_input))
+ cstr("\nInteger Places Count: "+str(output)).msg.print()
+ return (output,)
+
+
+# NODE MAPPING
+NODE_CLASS_MAPPINGS = {
+ "BLIP Model Loader": WAS_BLIP_Model_Loader,
+ "Blend Latents": WAS_Blend_Latents,
+ "Cache Node": WAS_Cache,
+ "Checkpoint Loader": WAS_Checkpoint_Loader,
+ "Checkpoint Loader (Simple)": WAS_Checkpoint_Loader_Simple,
+ "CLIPTextEncode (NSP)": WAS_NSP_CLIPTextEncoder,
+ "CLIP Input Switch": WAS_CLIP_Input_Switch,
+ "CLIP Vision Input Switch": WAS_CLIP_Vision_Input_Switch,
+ "Conditioning Input Switch": WAS_Conditioning_Input_Switch,
+ "Constant Number": WAS_Constant_Number,
+ "Create Grid Image": WAS_Image_Grid_Image,
+ "Create Morph Image": WAS_Image_Morph_GIF,
+ "Create Morph Image from Path": WAS_Image_Morph_GIF_By_Path,
+ "Create Video from Path": WAS_Create_Video_From_Path,
+ "CLIPSeg Masking": WAS_CLIPSeg,
+ "CLIPSeg Model Loader": WAS_CLIPSeg_Model_Loader,
+ "CLIPSeg Batch Masking": WAS_CLIPSeg_Batch,
+ "Convert Masks to Images": WAS_Mask_To_Image,
+ "Control Net Model Input Switch": WAS_Control_Net_Input_Switch,
+ "Debug Number to Console": WAS_Debug_Number_to_Console,
+ "Dictionary to Console": WAS_Dictionary_To_Console,
+ "Diffusers Model Loader": WAS_Diffusers_Loader,
+ "Diffusers Hub Model Down-Loader": WAS_Diffusers_Hub_Model_Loader,
+ "Export API": WAS_Export_API,
+ "Latent Input Switch": WAS_Latent_Input_Switch,
+ "Load Cache": WAS_Load_Cache,
+ "Logic Boolean": WAS_Boolean,
+ "Lora Loader": WAS_Lora_Loader,
+ "Image SSAO (Ambient Occlusion)": WAS_Image_Ambient_Occlusion,
+ "Image SSDO (Direct Occlusion)": WAS_Image_Direct_Occlusion,
+ "Image Analyze": WAS_Image_Analyze,
+ "Image Aspect Ratio": WAS_Image_Aspect_Ratio,
+ "Image Batch": WAS_Image_Batch,
+ "Image Blank": WAS_Image_Blank,
+ "Image Blend by Mask": WAS_Image_Blend_Mask,
+ "Image Blend": WAS_Image_Blend,
+ "Image Blending Mode": WAS_Image_Blending_Mode,
+ "Image Bloom Filter": WAS_Image_Bloom_Filter,
+ "Image Canny Filter": WAS_Canny_Filter,
+ "Image Chromatic Aberration": WAS_Image_Chromatic_Aberration,
+ "Image Color Palette": WAS_Image_Color_Palette,
+ "Image Crop Face": WAS_Image_Crop_Face,
+ "Image Crop Location": WAS_Image_Crop_Location,
+ "Image Crop Square Location": WAS_Image_Crop_Square_Location,
+ "Image Displacement Warp": WAS_Image_Displacement_Warp,
+ "Image Paste Face": WAS_Image_Paste_Face_Crop,
+ "Image Paste Crop": WAS_Image_Paste_Crop,
+ "Image Paste Crop by Location": WAS_Image_Paste_Crop_Location,
+ "Image Pixelate": WAS_Image_Pixelate,
+ "Image Power Noise": WAS_Image_Power_Noise,
+ "Image Dragan Photography Filter": WAS_Dragon_Filter,
+ "Image Edge Detection Filter": WAS_Image_Edge,
+ "Image Film Grain": WAS_Film_Grain,
+ "Image Filter Adjustments": WAS_Image_Filters,
+ "Image Flip": WAS_Image_Flip,
+ "Image Gradient Map": WAS_Image_Gradient_Map,
+ "Image Generate Gradient": WAS_Image_Generate_Gradient,
+ "Image High Pass Filter": WAS_Image_High_Pass_Filter,
+ "Image History Loader": WAS_Image_History,
+ "Image Input Switch": WAS_Image_Input_Switch,
+ "Image Levels Adjustment": WAS_Image_Levels,
+ "Image Load": WAS_Load_Image,
+ "Image Median Filter": WAS_Image_Median_Filter,
+ "Image Mix RGB Channels": WAS_Image_RGB_Merge,
+ "Image Monitor Effects Filter": WAS_Image_Monitor_Distortion_Filter,
+ "Image Nova Filter": WAS_Image_Nova_Filter,
+ "Image Padding": WAS_Image_Padding,
+ "Image Perlin Noise": WAS_Image_Perlin_Noise,
+ "Image Rembg (Remove Background)": WAS_Remove_Rembg,
+ "Image Perlin Power Fractal": WAS_Image_Perlin_Power_Fractal,
+ "Image Remove Background (Alpha)": WAS_Remove_Background,
+ "Image Remove Color": WAS_Image_Remove_Color,
+ "Image Resize": WAS_Image_Rescale,
+ "Image Rotate": WAS_Image_Rotate,
+ "Image Rotate Hue": WAS_Image_Rotate_Hue,
+ "Image Save": WAS_Image_Save,
+ "Image Seamless Texture": WAS_Image_Make_Seamless,
+ "Image Select Channel": WAS_Image_Select_Channel,
+ "Image Select Color": WAS_Image_Select_Color,
+ "Image Shadows and Highlights": WAS_Shadow_And_Highlight_Adjustment,
+ "Image Size to Number": WAS_Image_Size_To_Number,
+ "Image Stitch": WAS_Image_Stitch,
+ "Image Style Filter": WAS_Image_Style_Filter,
+ "Image Threshold": WAS_Image_Threshold,
+ "Image Tiled": WAS_Image_Tile_Batch,
+ "Image Transpose": WAS_Image_Transpose,
+ "Image fDOF Filter": WAS_Image_fDOF,
+ "Image to Latent Mask": WAS_Image_To_Mask,
+ "Image to Noise": WAS_Image_To_Noise,
+ "Images to RGB": WAS_Images_To_RGB,
+ "Image to Seed": WAS_Image_To_Seed,
+ "Integer place counter": WAS_Integer_Place_Counter,
+ "Image Voronoi Noise Filter": WAS_Image_Voronoi_Noise_Filter,
+ "KSampler (WAS)": WAS_KSampler,
+ "KSampler Cycle": WAS_KSampler_Cycle,
+ "Latent Noise Injection": WAS_Latent_Noise,
+ "Latent Size to Number": WAS_Latent_Size_To_Number,
+ "Latent Upscale by Factor (WAS)": WAS_Latent_Upscale,
+ "Load Image Batch": WAS_Load_Image_Batch,
+ "Load Text File": WAS_Text_Load_From_File,
+ "Load Lora": WAS_Lora_Loader,
+ "Masks Add": WAS_Mask_Add,
+ "Masks Subtract": WAS_Mask_Subtract,
+ "Mask Arbitrary Region": WAS_Mask_Arbitrary_Region,
+ "Mask Batch to Mask": WAS_Mask_Batch_to_Single_Mask,
+ "Mask Batch": WAS_Mask_Batch,
+ "Mask Ceiling Region": WAS_Mask_Ceiling_Region,
+ "Mask Crop Dominant Region": WAS_Mask_Crop_Dominant_Region,
+ "Mask Crop Minority Region": WAS_Mask_Crop_Minority_Region,
+ "Mask Crop Region": WAS_Mask_Crop_Region,
+ "Mask Paste Region": WAS_Mask_Paste_Region,
+ "Mask Dilate Region": WAS_Mask_Dilate_Region,
+ "Mask Dominant Region": WAS_Mask_Dominant_Region,
+ "Mask Erode Region": WAS_Mask_Erode_Region,
+ "Mask Fill Holes": WAS_Mask_Fill_Region,
+ "Mask Floor Region": WAS_Mask_Floor_Region,
+ "Mask Gaussian Region": WAS_Mask_Gaussian_Region,
+ "Mask Invert": WAS_Mask_Invert,
+ "Mask Minority Region": WAS_Mask_Minority_Region,
+ "Mask Smooth Region": WAS_Mask_Smooth_Region,
+ "Mask Threshold Region": WAS_Mask_Threshold_Region,
+ "Masks Combine Regions": WAS_Mask_Combine,
+ "Masks Combine Batch": WAS_Mask_Combine_Batch,
+ "MiDaS Model Loader": MiDaS_Model_Loader,
+ "MiDaS Depth Approximation": MiDaS_Depth_Approx,
+ "MiDaS Mask Image": MiDaS_Background_Foreground_Removal,
+ "Model Input Switch": WAS_Model_Input_Switch,
+ "Number Counter": WAS_Number_Counter,
+ "Number Operation": WAS_Number_Operation,
+ "Number to Float": WAS_Number_To_Float,
+ "Number Input Switch": WAS_Number_Input_Switch,
+ "Number Input Condition": WAS_Number_Input_Condition,
+ "Number Multiple Of": WAS_Number_Multiple_Of,
+ "Number PI": WAS_Number_PI,
+ "Number to Int": WAS_Number_To_Int,
+ "Number to Seed": WAS_Number_To_Seed,
+ "Number to String": WAS_Number_To_String,
+ "Number to Text": WAS_Number_To_Text,
+ "Prompt Styles Selector": WAS_Prompt_Styles_Selector,
+ "Prompt Multiple Styles Selector": WAS_Prompt_Multiple_Styles_Selector,
+ "Random Number": WAS_Random_Number,
+ "Save Text File": WAS_Text_Save,
+ "Seed": WAS_Seed,
+ "Tensor Batch to Image": WAS_Tensor_Batch_to_Image,
+ "BLIP Analyze Image": WAS_BLIP_Analyze_Image,
+ "SAM Model Loader": WAS_SAM_Model_Loader,
+ "SAM Parameters": WAS_SAM_Parameters,
+ "SAM Parameters Combine": WAS_SAM_Combine_Parameters,
+ "SAM Image Mask": WAS_SAM_Image_Mask,
+ #"LangSAM Model Loader": WAS_Lang_SAM_Model_Loader,
+ #"LangSAM Masking": WAS_Lang_SAM_Masking,
+ "Samples Passthrough (Stat System)": WAS_Samples_Passthrough_Stat_System,
+ "String to Text": WAS_String_To_Text,
+ "Image Bounds": WAS_Image_Bounds,
+ "Inset Image Bounds": WAS_Inset_Image_Bounds,
+ "Bounded Image Blend": WAS_Bounded_Image_Blend,
+ "Bounded Image Blend with Mask": WAS_Bounded_Image_Blend_With_Mask,
+ "Bounded Image Crop": WAS_Bounded_Image_Crop,
+ "Bounded Image Crop with Mask": WAS_Bounded_Image_Crop_With_Mask,
+ "Text Dictionary Update": WAS_Dictionary_Update,
+ "Text Add Tokens": WAS_Text_Add_Tokens,
+ "Text Add Token by Input": WAS_Text_Add_Token_Input,
+ "Text Compare": WAS_Text_Compare,
+ "Text Concatenate": WAS_Text_Concatenate,
+ "Text File History Loader": WAS_Text_File_History,
+ "Text Find and Replace by Dictionary": WAS_Search_and_Replace_Dictionary,
+ "Text Find and Replace Input": WAS_Search_and_Replace_Input,
+ "Text Find and Replace": WAS_Search_and_Replace,
+ "Text Input Switch": WAS_Text_Input_Switch,
+ "Text List": WAS_Text_List,
+ "Text List Concatenate": WAS_Text_List_Concatenate,
+ "Text Load Line From File": WAS_Text_Load_Line_From_File,
+ "Text Multiline": WAS_Text_Multiline,
+ "Text Parse A1111 Embeddings": WAS_Text_Parse_Embeddings_By_Name,
+ "Text Parse Noodle Soup Prompts": WAS_Text_Parse_NSP,
+ "Text Parse Tokens": WAS_Text_Parse_Tokens,
+ "Text Random Line": WAS_Text_Random_Line,
+ "Text Random Prompt": WAS_Text_Random_Prompt,
+ "Text String": WAS_Text_String,
+ "Text to Conditioning": WAS_Text_to_Conditioning,
+ "Text to Console": WAS_Text_to_Console,
+ "Text to Number": WAS_Text_To_Number,
+ "Text to String": WAS_Text_To_String,
+ "Text String Truncate": WAS_Text_String_Truncate,
+ "True Random.org Number Generator": WAS_True_Random_Number,
+ "unCLIP Checkpoint Loader": WAS_unCLIP_Checkpoint_Loader,
+ "Upscale Model Loader": WAS_Upscale_Model_Loader,
+ "Upscale Model Switch": WAS_Upscale_Model_Input_Switch,
+ "Write to GIF": WAS_Image_Morph_GIF_Writer,
+ "Write to Video": WAS_Video_Writer,
+ "VAE Input Switch": WAS_VAE_Input_Switch,
+ "Video Dump Frames": WAS_Video_Frame_Dump,
+}
+
+#! EXTRA NODES
+
+# Check for BlenderNeko's Advanced CLIP Text Encode repo
+BKAdvCLIP_dir = os.path.join(CUSTOM_NODES_DIR, "ComfyUI_ADV_CLIP_emb")
+if os.path.exists(BKAdvCLIP_dir):
+
+ cstr(f"BlenderNeko\'s Advanced CLIP Text Encode found, attempting to enable `CLIPTextEncode` support.").msg.print()
+
+ class WAS_AdvancedCLIPTextEncode:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "mode": (["Noodle Soup Prompts", "Wildcards"],),
+ "noodle_key": ("STRING", {"default": '__', "multiline": False}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "clip": ("CLIP", ),
+ "token_normalization": (["none", "mean", "length", "length+mean"],),
+ "weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],),
+ "text": ("STRING", {"multiline": True}),
+ }
+ }
+
+ RETURN_TYPES = ("CONDITIONING", TEXT_TYPE, TEXT_TYPE)
+ RETURN_NAMES = ("conditioning", "parsed_text", "raw_text")
+ OUTPUT_NODE = True
+ FUNCTION = "encode"
+ CATEGORY = "WAS Suite/Conditioning"
+
+ DESCRIPTION = "A node based on Blenderneko's Advanced CLIP Text Encode. This version adds the ability to use Noodle Soup Prompts and Wildcards. Wildcards are stored in WAS Node Suite root under the folder 'wildcards'. You can create the folder if it doesn't exist and move your wildcards into it."
+ URL = {
+ "Example Workflow": "https://github.com/WASasquatch/was-node-suite-comfyui",
+ }
+ IMAGES = [
+ "https://i.postimg.cc/Jh4N2h5r/CLIPText-Encode-BLK-plus-NSP.png",
+ ]
+
+ def encode(self, clip, text, token_normalization, weight_interpretation, seed=0, mode="Noodle Soup Prompts", noodle_key="__"):
+
+ BKAdvCLIP_dir = os.path.join(CUSTOM_NODES_DIR, "ComfyUI_ADV_CLIP_emb")
+ sys.path.append(BKAdvCLIP_dir)
+
+ from ComfyUI_ADV_CLIP_emb.nodes import AdvancedCLIPTextEncode
+
+ if mode == "Noodle Soup Prompts":
+ new_text = nsp_parse(text, int(seed), noodle_key)
+ else:
+ new_text = replace_wildcards(text, (None if seed == 0 else seed), noodle_key)
+
+ new_text = parse_dynamic_prompt(new_text, seed)
+ new_text, text_vars = parse_prompt_vars(new_text)
+ cstr(f"CLIPTextEncode Prased Prompt:\n {new_text}").msg.print()
+
+ encode = AdvancedCLIPTextEncode().encode(clip, new_text, token_normalization, weight_interpretation)
+ return ([[encode[0][0][0], encode[0][0][1]]], new_text, text, { "ui": { "string": new_text } } )
+
+
+ NODE_CLASS_MAPPINGS.update({"CLIPTextEncode (BlenderNeko Advanced + NSP)": WAS_AdvancedCLIPTextEncode})
+
+ if NODE_CLASS_MAPPINGS.__contains__("CLIPTextEncode (BlenderNeko Advanced + NSP)"):
+ cstr('`CLIPTextEncode (BlenderNeko Advanced + NSP)` node enabled under `WAS Suite/Conditioning` menu.').msg.print()
+
+# opencv-python-headless handling
+if 'opencv-python' in packages() or 'opencv-python-headless' in packages():
+ try:
+ import cv2
+ build_info = ' '.join(cv2.getBuildInformation().split())
+ if "FFMPEG: YES" in build_info:
+ if was_config.__contains__('show_startup_junk'):
+ if was_config['show_startup_junk']:
+ cstr("OpenCV Python FFMPEG support is enabled").msg.print()
+ if was_config.__contains__('ffmpeg_bin_path'):
+ if was_config['ffmpeg_bin_path'] == "/path/to/ffmpeg":
+ cstr(f"`ffmpeg_bin_path` is not set in `{WAS_CONFIG_FILE}` config file. Will attempt to use system ffmpeg binaries if available.").warning.print()
+ else:
+ if was_config.__contains__('show_startup_junk'):
+ if was_config['show_startup_junk']:
+ cstr(f"`ffmpeg_bin_path` is set to: {was_config['ffmpeg_bin_path']}").msg.print()
+ else:
+ cstr(f"OpenCV Python FFMPEG support is not enabled\033[0m. OpenCV Python FFMPEG support, and FFMPEG binaries is required for video writing.").warning.print()
+ except ImportError:
+ cstr("OpenCV Python module cannot be found. Attempting install...").warning.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'uninstall', 'opencv-python', 'opencv-python-headless[ffmpeg]'])
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'opencv-python-headless[ffmpeg]'])
+ try:
+ import cv2
+ cstr("OpenCV Python installed.").msg.print()
+ except ImportError:
+ cstr("OpenCV Python module still cannot be imported. There is a system conflict.").error.print()
+else:
+ cstr("Installing `opencv-python-headless` ...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'opencv-python-headless[ffmpeg]'])
+ try:
+ import cv2
+ cstr("OpenCV Python installed.").msg.print()
+ except ImportError:
+ cstr("OpenCV Python module still cannot be imported. There is a system conflict.").error.print()
+
+# scipy handling
+if 'scipy' not in packages():
+ cstr("Installing `scipy` ...").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'scipy'])
+ try:
+ import scipy
+ except ImportError as e:
+ cstr("Unable to import tools for certain masking procedures.").msg.print()
+ print(e)
+
+# scikit-image handling
+try:
+ import skimage
+except ImportError as e:
+ cstr("Installing `scikit-image`....").msg.print()
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'uninstall', 'scikit-image'])
+ subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'scikit-image'])
+ import skimage
+
+was_conf = getSuiteConfig()
+
+# Suppress warnings
+if was_conf.__contains__('suppress_uncomfy_warnings'):
+ if was_conf['suppress_uncomfy_warnings']:
+ import warnings
+ warnings.filterwarnings("ignore", category=UserWarning, module="safetensors")
+ warnings.filterwarnings("ignore", category=UserWarning, module="torch")
+ warnings.filterwarnings("ignore", category=UserWarning, module="transformers")
+
+# Well we got here, we're as loaded as we're gonna get.
+print(" ".join([cstr("Finished.").msg, cstr("Loaded").green, cstr(len(NODE_CLASS_MAPPINGS.keys())).end, cstr("nodes successfully.").green]))
+
+show_quotes = True
+if was_conf.__contains__('show_inspiration_quote'):
+ if was_conf['show_inspiration_quote'] == False:
+ show_quotes = False
+if show_quotes:
+ art_quotes = [
+ # ARTISTIC INSPIRATION QUOTES
+ '\033[93m"Every artist was first an amateur."\033[0m\033[3m - Ralph Waldo Emerson',
+ '\033[93m"Art is not freedom from discipline, but disciplined freedom."\033[0m\033[3m - John F. Kennedy',
+ '\033[93m"Art enables us to find ourselves and lose ourselves at the same time."\033[0m\033[3m - Thomas Merton',
+ '\033[93m"Art is the most intense mode of individualism that the world has known."\033[0m\033[3m - Oscar Wilde',
+ '\033[93m"The purpose of art is washing the dust of daily life off our souls."\033[0m\033[3m - Pablo Picasso',
+ '\033[93m"Art is the lie that enables us to realize the truth."\033[0m\033[3m - Pablo Picasso',
+ '\033[93m"Art is not what you see, but what you make others see."\033[0m\033[3m - Edgar Degas',
+ '\033[93m"Every artist dips his brush in his own soul, and paints his own nature into his pictures."\033[0m\033[3m - Henry Ward Beecher',
+ '\033[93m"Art is the stored honey of the human soul."\033[0m\033[3m - Theodore Dreiser',
+ '\033[93m"Creativity takes courage."\033[0m\033[3m - Henri Matisse',
+ '\033[93m"Art should disturb the comfortable and comfort the disturbed." - Cesar Cruz',
+ '\033[93m"Art is the most beautiful of all lies."\033[0m\033[3m - Claude Debussy',
+ '\033[93m"Art is the journey of a free soul."\033[0m\033[3m - Alev Oguz',
+ '\033[93m"The artist\'s world is limitless. It can be found anywhere, far from where he lives or a few feet away. It is always on his doorstep."\033[0m\033[3m - Paul Strand',
+ '\033[93m"Art is not a thing; it is a way."\033[0m\033[3m - Elbert Hubbard',
+ '\033[93m"Art is the lie that enables us to recognize the truth."\033[0m\033[3m - Friedrich Nietzsche',
+ '\033[93m"Art is the triumph over chaos."\033[0m\033[3m - John Cheever',
+ '\033[93m"Art is the lie that enables us to realize the truth."\033[0m\033[3m - Pablo Picasso',
+ '\033[93m"Art is the only way to run away without leaving home."\033[0m\033[3m - Twyla Tharp',
+ '\033[93m"Art is the most powerful tool we have to connect with the world and express our individuality."\033[0m\033[3m - Unknown',
+ '\033[93m"Art is not about making something perfect, it\'s about making something meaningful."\033[0m\033[3m - Unknown',
+ '\033[93m"Art is the voice of the soul, expressing what words cannot."\033[0m\033[3m - Unknown',
+ '\033[93m"Art is the bridge that connects imagination to reality."\033[0m\033[3m - Unknown',
+ '\033[93m"Art is the language of the heart and the window to the soul."\033[0m\033[3m - Unknown',
+ '\033[93m"Art is the magic that brings beauty into the world."\033[0m\033[3m - Unknown',
+ '\033[93m"Art is the freedom to create, explore, and inspire."\033[0m\033[3m - Unknown',
+ '\033[93m"Art is the mirror that reflects the beauty within us."\033[0m\033[3m - Unknown',
+ '\033[93m"Art is the universal language that transcends boundaries and speaks to all."\033[0m\033[3m - Unknown',
+ '\033[93m"Art is the light that shines even in the darkest corners."\033[0m\033[3m - Unknown',
+ '\033[93m"Art is the soul made visible."\033[0m\033[3m - George Crook',
+ '\033[93m"Art is the breath of life."\033[0m\033[3m - Liza Donnelly',
+ '\033[93m"Art is a harmony parallel with nature."\033[0m\033[3m - Paul Cézanne',
+ '\033[93m"Art is the daughter of freedom."\033[0m\033[3m - Friedrich Schiller',
+ # GENERAL INSPIRATION QUOTES
+ '\033[93m"Believe you can and you\'re halfway there."\033[0m\033[3m - Theodore Roosevelt',
+ '\033[93m"The only way to do great work is to love what you do."\033[0m\033[3m - Steve Jobs',
+ '\033[93m"Success is not final, failure is not fatal: It is the courage to continue that counts."\033[0m\033[3m - Winston Churchill',
+ '\033[93m"Your time is limited, don\'t waste it living someone else\'s life."\033[0m\033[3m - Steve Jobs',
+ '\033[93m"The future belongs to those who believe in the beauty of their dreams."\033[0m\033[3m - Eleanor Roosevelt',
+ '\033[93m"Success is not the key to happiness. Happiness is the key to success."\033[0m\033[3m - Albert Schweitzer',
+ '\033[93m"The best way to predict the future is to create it."\033[0m\033[3m - Peter Drucker',
+ '\033[93m"Don\'t watch the clock; do what it does. Keep going."\033[0m\033[3m - Sam Levenson',
+ '\033[93m"Believe in yourself, take on your challenges, and dig deep within yourself to conquer fears."\033[0m\033[3m - Chantal Sutherland',
+ '\033[93m"Challenges are what make life interesting and overcoming them is what makes life meaningful."\033[0m\033[3m - Joshua J. Marine',
+ '\033[93m"Opportunities don\'t happen. You create them."\033[0m\033[3m - Chris Grosser',
+ '\033[93m"Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work."\033[0m\033[3m - Steve Jobs',
+ '\033[93m"The harder I work, the luckier I get."\033[0m\033[3m - Samuel Goldwyn',
+ '\033[93m"Don\'t be pushed around by the fears in your mind. Be led by the dreams in your heart."\033[0m\033[3m - Roy T. Bennett',
+ '\033[93m"Believe in yourself, and the rest will fall into place."\033[0m\033[3m - Unknown',
+ '\033[93m"Life is 10% what happens to us and 90% how we react to it."\033[0m\033[3m - Charles R. Swindoll',
+ '\033[93m"Success is not just about making money. It\'s about making a difference."\033[0m\033[3m - Unknown',
+ '\033[93m"The only limit to our realization of tomorrow will be our doubts of today."\033[0m\033[3m - Franklin D. Roosevelt',
+ '\033[93m"Great minds discuss ideas; average minds discuss events; small minds discuss people."\033[0m\033[3m - Eleanor Roosevelt',
+ '\033[93m"The future depends on what you do today."\033[0m\033[3m - Mahatma Gandhi',
+ '\033[93m"Don\'t be afraid to give up the good to go for the great."\033[0m\033[3m - John D. Rockefeller',
+ '\033[93m"Success usually comes to those who are too busy to be looking for it."\033[0m\033[3m - Henry David Thoreau',
+ '\033[93m"The secret to getting ahead is getting started."\033[0m\033[3m - Mark Twain',
+ '\033[93m"Every great dream begins with a dreamer."\033[0m\033[3m - Harriet Tubman',
+ '\033[93m"Do not wait for the opportunity. Create it."\033[0m\033[3m - George Bernard Shaw',
+ '\033[93m"Your time is now. Start where you are and never stop."\033[0m\033[3m - Roy T. Bennett',
+ '\033[93m"The only person you should try to be better than is the person you were yesterday."\033[0m\033[3m - Unknown',
+ '\033[93m"Success is not in what you have, but who you are."\033[0m\033[3m - Bo Bennett',
+ '\033[93m"Do one thing every day that scares you."\033[0m\033[3m - Eleanor Roosevelt',
+ '\033[93m"Failure is the opportunity to begin again more intelligently."\033[0m\033[3m - Henry Ford',
+ '\033[93m"Dream big and dare to fail."\033[0m\033[3m - Norman Vaughan',
+ '\033[93m"Everything you\'ve ever wanted is on the other side of fear."\033[0m\033[3m - George Addair',
+ '\033[93m"Believe you deserve it and the universe will serve it."\033[0m\033[3m - Unknown',
+ '\033[93m"Don\'t wait. The time will never be just right."\033[0m\033[3m - Napoleon Hill',
+ '\033[93m"The distance between insanity and genius is measured only by success."\033[0m\033[3m - Bruce Feirstein',
+ '\033[93m"Be the change that you wish to see in the world."\033[0m\033[3m - Mahatma Gandhi',
+ '\033[93m"Success is not about being better than someone else. It\'s about being better than you used to be."\033[0m\033[3m - Unknown',
+ '\033[93m"The best revenge is massive success."\033[0m\033[3m - Frank Sinatra',
+ '\033[93m"You have within you right now, everything you need to deal with whatever the world can throw at you."\033[0m\033[3m - Brian Tracy',
+ '\033[93m"Don\'t let yesterday take up too much of today."\033[0m\033[3m - Will Rogers',
+ '\033[93m"The biggest risk is not taking any risk. In a world that is changing quickly, the only strategy that is guaranteed to fail is not taking risks."\033[0m\033[3m - Mark Zuckerberg',
+ '\033[93m"The journey of a thousand miles begins with one step."\033[0m\033[3m - Lao Tzu',
+ '\033[93m"Every strike brings me closer to the next home run."\033[0m\033[3m - Babe Ruth',
+ ]
+ print(f'\n\t\033[3m{random.choice(art_quotes)}\033[0m\n')
diff --git a/was-node-suite-comfyui/__init__.py b/was-node-suite-comfyui/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b09dfa1a17a4d612937d01f7324e02e564a11806
--- /dev/null
+++ b/was-node-suite-comfyui/__init__.py
@@ -0,0 +1,3 @@
+from .WAS_Node_Suite import NODE_CLASS_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS']
diff --git a/was-node-suite-comfyui/install.bat b/was-node-suite-comfyui/install.bat
new file mode 100644
index 0000000000000000000000000000000000000000..23add63bc6dc8504320a24322657e24386807961
--- /dev/null
+++ b/was-node-suite-comfyui/install.bat
@@ -0,0 +1,20 @@
+@echo off
+
+set "requirements_txt=%~dp0\requirements.txt"
+set "python_exec=..\..\..\python_embeded\python.exe"
+
+echo Installing WAS-NS ...
+
+if exist "%python_exec%" (
+ echo Installing with ComfyUI Portable
+ for /f "delims=" %%i in (%requirements_txt%) do (
+ %python_exec% -s -m pip install "%%i"
+ )
+) else (
+ echo Installing with system Python
+ for /f "delims=" %%i in (%requirements_txt%) do (
+ pip install "%%i"
+ )
+)
+
+pause
\ No newline at end of file
diff --git a/was-node-suite-comfyui/requirements.txt b/was-node-suite-comfyui/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..49d5856a3dfc91ff0ddd08ac3387aa10b815cfad
--- /dev/null
+++ b/was-node-suite-comfyui/requirements.txt
@@ -0,0 +1,19 @@
+cmake
+fairscale>=0.4.4
+git+https://github.com/WASasquatch/img2texture.git
+gitpython
+imageio
+joblib
+matplotlib
+numba
+numpy<1.24>=1.18
+opencv-python-headless[ffmpeg]
+pilgram
+git+https://github.com/WASasquatch/ffmpy.git
+rembg
+scikit-image==0.20.0
+scikit-learn
+scipy
+timm>=0.4.12
+tqdm
+transformers==4.26.1
\ No newline at end of file
diff --git a/was-node-suite-comfyui/res/font.ttf b/was-node-suite-comfyui/res/font.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..5eaf3c73aa73f0a84d1fd524470951dcb1a47f9a
Binary files /dev/null and b/was-node-suite-comfyui/res/font.ttf differ
diff --git a/was-node-suite-comfyui/res/haarcascade_frontalface_alt.xml b/was-node-suite-comfyui/res/haarcascade_frontalface_alt.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ade4b2121a68e6967cc558f4393dc8d828cee60e
--- /dev/null
+++ b/was-node-suite-comfyui/res/haarcascade_frontalface_alt.xml
@@ -0,0 +1,24350 @@
+
+
+
+BOOST
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+ 0
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diff --git a/was-node-suite-comfyui/res/haarcascade_frontalface_alt2.xml b/was-node-suite-comfyui/res/haarcascade_frontalface_alt2.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b49cf5df3b0c561c9b3887d5e63a93b813847e18
--- /dev/null
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diff --git a/was-node-suite-comfyui/res/haarcascade_frontalface_alt_tree.xml b/was-node-suite-comfyui/res/haarcascade_frontalface_alt_tree.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ff638d5b19d7bc938321749aaee2ce8727dbeb5a
--- /dev/null
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diff --git a/was-node-suite-comfyui/res/haarcascade_frontalface_default.xml b/was-node-suite-comfyui/res/haarcascade_frontalface_default.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8dff079dac798e0b84f26aad876f3323d594c8fa
--- /dev/null
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diff --git a/was-node-suite-comfyui/res/haarcascade_profileface.xml b/was-node-suite-comfyui/res/haarcascade_profileface.xml
new file mode 100644
index 0000000000000000000000000000000000000000..486d8e3d83075578c446be96b75a2414efe75ecb
--- /dev/null
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diff --git a/was-node-suite-comfyui/res/haarcascade_upperbody.xml b/was-node-suite-comfyui/res/haarcascade_upperbody.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3c75aa6927762b1a54a104c9926cd0a8d4891e17
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diff --git a/was-node-suite-comfyui/res/lbpcascade_animeface.xml b/was-node-suite-comfyui/res/lbpcascade_animeface.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5a973ef6a1d2a77c23adafbfbf7fbf9de00e95c6
--- /dev/null
+++ b/was-node-suite-comfyui/res/lbpcascade_animeface.xml
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