├── .github └── workflows │ └── publish.yml ├── .gitignore ├── LICENSE ├── README.md ├── __init__.py ├── nodes.py ├── pyproject.toml ├── requirements.txt └── utils.py /.github/workflows/publish.yml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - main 7 | - master 8 | paths: 9 | - "pyproject.toml" 10 | 11 | permissions: 12 | issues: write 13 | 14 | jobs: 15 | publish-node: 16 | name: Publish Custom Node to registry 17 | runs-on: ubuntu-latest 18 | if: ${{ github.repository_owner == 'bedovyy' }} 19 | steps: 20 | - name: Check out code 21 | uses: actions/checkout@v4 22 | - name: Publish Custom Node 23 | uses: Comfy-Org/publish-node-action@main 24 | with: 25 | ## Add your own personal access token to your Github Repository secrets and reference it here. 26 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} 27 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ComfyUI_NAIDGenerator 2 | 3 | A [ComfyUI](https://github.com/comfyanonymous/ComfyUI) extension for generating image via NovelAI API. 4 | 5 | ## Installation 6 | 7 | - `git clone https://github.com/bedovyy/ComfyUI_NAIDGenerator` into the `custom_nodes` directory. 8 | - or 'Install via Git URL' from [Comfyui Manager](https://github.com/ltdrdata/ComfyUI-Manager) 9 | 10 | ## Setting up NAI account 11 | 12 | Before using the nodes, you should set NAI_ACCESS_TOKEN on `ComfyUI/.env` file. 13 | 14 | ``` 15 | NAI_ACCESS_TOKEN= 16 | ``` 17 | 18 | You can get persistent API token by **User Settings > Account > Get Persistent API Token** on NovelAI webpage. 19 | 20 | Otherwise, you can get access token which is valid for 30 days using [novelai-api](https://github.com/Aedial/novelai-api). 21 | 22 | ## Usage 23 | 24 | The nodes are located at `NovelAI` category. 25 | 26 | ![image](https://github.com/bedovyy/ComfyUI_NAIDGenerator/assets/137917911/8ab1ecc0-2ba8-4e38-8810-727e50a20923) 27 | 28 | ### Txt2img 29 | 30 | Simply connect `GenerateNAID` node and `SaveImage` node. 31 | 32 | ![generate](https://github.com/bedovyy/ComfyUI_NAIDGenerator/assets/137917911/1328896d-7d4b-4d47-8ec2-d1c4e8e2561c) 33 | 34 | Note that all generated images via `GeneratedNAID` node are saved as `output/NAI_autosave_12345_.png` for keeping original metadata. 35 | 36 | ### Img2img 37 | 38 | Connect `Img2ImgOptionNAID` node to `GenerateNAID` node and put original image. 39 | 40 | ![image](https://github.com/bedovyy/ComfyUI_NAIDGenerator/assets/137917911/15ff8961-4f6b-4f23-86bf-34b86ace45c0) 41 | 42 | Note that width and height of the source image will be resized to generation size. 43 | 44 | ### Inpainting 45 | 46 | Connect `InpaintingOptionNAID` node to `GenerateNAID` node and put original image and mask image. 47 | 48 | ![image](https://github.com/bedovyy/ComfyUI_NAIDGenerator/assets/137917911/5ed1ad77-b90e-46be-8c37-9a5ee0935a3d) 49 | 50 | Note that both source image and mask will be resized fit to generation size. 51 | 52 | (You don't need `MaskImageToNAID` node to convert mask image to NAID mask image.) 53 | 54 | ### Vibe Transfer 55 | 56 | Connect `VibeTransferOptionNAID` node to `GenerateNAID` node and put reference image. 57 | 58 | ![Comfy_workflow](https://github.com/bedovyy/ComfyUI_NAIDGenerator/assets/137917911/8c6c1c2e-f29d-42a1-b615-439155cb3164) 59 | 60 | You can also relay Img2ImgOption on it. 61 | 62 | ![image](https://github.com/bedovyy/ComfyUI_NAIDGenerator/assets/137917911/acf0496c-8c7c-48f4-9530-18e6a23669d5) 63 | 64 | Note that width and height of the source images will be resized to generation size. **This will change aspect ratio of source images.** 65 | 66 | #### Multiple Vibe Transfer 67 | 68 | Just connect multiple `VibeTransferOptionNAID` nodes to `GenerateNAID` node. 69 | 70 | ![preview_vibe_2](https://github.com/user-attachments/assets/2d56c0f7-bcd5-48ff-b436-012ea43604fe) 71 | 72 | ### ModelOption 73 | 74 | The default model of `GenerateNAID` node is `nai-diffusion-3`(NAI Diffusion Anime V3). 75 | 76 | If you want to change model, put `ModelOptionNAID` node to `GenerateNAID` node. 77 | 78 | ![ModelOption](https://github.com/bedovyy/ComfyUI_NAIDGenerator/assets/137917911/0b484edb-bcb5-428a-b2af-1372a9d7a34f) 79 | 80 | ### NetworkOption 81 | 82 | You can set timeout or retry option from `NetworkOption` node. 83 | Moreover, you can ignore error by `ignore_errors`. In that case, the result will be 1x1 size grayscale image. 84 | Without this node, the request never retry and wait response forever, and stop the queue when error occurs 85 | 86 | ![preview_network](https://github.com/user-attachments/assets/d82b0ff2-c57c-4870-9024-8d78261a8fea) 87 | 88 | **Note that if you set timeout too short, you may not get image but spend Anlas.** 89 | 90 | ### PromptToNAID 91 | 92 | ComfyUI use `()` or `(word:weight)` for emphasis, but NovelAI use `{}` and `[]`. This node convert ComfyUI's prompt to NovelAI's. 93 | 94 | Optionally, you can choose weight per brace. If you set `weight_per_brace` to 0.10, `(word:1.1)` will convert to `{word}` instead of `{{word}}`. 95 | 96 | ![image](https://github.com/bedovyy/ComfyUI_NAIDGenerator/assets/137917911/25c48350-7268-4d6f-81fe-9eb080fc6e5a) 97 | 98 | ### Director Tools 99 | 100 | ![image](https://github.com/user-attachments/assets/e205a51e-59dc-4d5a-94c8-29715ed98739) 101 | 102 | You can find director tools like `LineArtNAID` or `EmotionNAID` on NovelAI > director_tools. 103 | 104 | ![augment_example](https://github.com/user-attachments/assets/5833e9fb-f92e-4d53-9069-58ca8503a3e7) 105 | 106 | ### V4 Support (Preview) 107 | 108 | The node now supports NAI's V4 architecture through the nai-diffusion-4-curated-preview model. This is a preview release of V4 with some limitations: 109 | 110 | - **Important Notes:** 111 | - This is a preview version of V4 and some features are limited 112 | - Inpainting will automatically use V3 model (but works with V4-generated images) 113 | - Vibe transfer is not yet supported with V4 preview (will be available with full V4 release) 114 | - Full V4 feature support will come with the official V4 release 115 | 116 | 117 | ### V4.5 Support (Curated Preview) 118 | 119 | Support has been added for **NAI Diffusion 4.5 Curated Preview**, an updated version of V4 with further improvements in detail, contrast, and prompt responsiveness. 120 | 121 | - **Model Name:** 122 | ```python 123 | model = "nai-diffusion-4-5-curated-preview" 124 | ``` 125 | 126 | - **Availability:** 127 | Selectable through `ModelOptionNAID` node under the name **NAI Diffusion 4.5 Curated Preview**. 128 | 129 | - **Compatibility Notes:** 130 | - Works the same as V4 preview, with the same limitations: 131 | - Inpainting will still default to V4 backend 132 | - Vibe transfer is not yet supported 133 | - Prompt formatting remains the same as for V4 (`V4BasePrompt` and `V4NegativePrompt` nodes are compatible) 134 | 135 | 136 | #### New Model Option 137 | 138 | NAI Diffusion V4 Curated Preview is now available in the ModelOptionNAID node: 139 | 140 | ```python 141 | model = "nai-diffusion-4-curated-preview" 142 | ``` 143 | 144 | #### V4 Prompt Handling 145 | 146 | Two new nodes have been added for V4 prompt handling: 147 | 148 | ##### V4BasePrompt 149 | 150 | A node for handling V4 positive prompts: 151 | 152 | ``` 153 | V4BasePrompt -----> positive 154 | GenerateNAID 155 | ``` 156 | 157 | ##### V4NegativePrompt 158 | 159 | A node for handling V4 negative prompts: 160 | 161 | ``` 162 | V4NegativePrompt -> negative 163 | GenerateNAID 164 | ``` 165 | 166 | #### Example V4 Workflow 167 | 168 | Here's a basic V4 setup: 169 | 170 | ``` 171 | V4BasePrompt -----> positive 172 | V4NegativePrompt -> negative GenerateNAID 173 | ModelOption ------> option 174 | ``` 175 | 176 | #### Work In Progress Features 177 | 178 | The following V4 features are currently in development: 179 | 180 | ```python 181 | """ 182 | - V4PromptConfig: Advanced prompt configuration 183 | - Coordinate-based prompting 184 | - Order-based prompting 185 | - V4CharacterCaption: Character-specific prompting with positioning 186 | """ 187 | ``` 188 | 189 | Note: Basic img2img functionality works with V4 preview. For inpainting, the node will automatically use V3 model but can still work on V4-generated images. Vibe transfer will be supported once V4 fully releases. 190 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import subprocess 3 | import importlib 4 | 5 | def ensure_package(package, install_package_name=None): 6 | # Try to import the package 7 | try: 8 | importlib.import_module(package) 9 | except ImportError: 10 | print(f"Package {package} is not installed. Installing now...") 11 | 12 | if "python_embeded" in sys.executable or "python_embedded" in sys.executable: 13 | pip_install = [sys.executable, "-s", "-m", "pip", "install"] 14 | else: 15 | pip_install = [sys.executable, "-m", "pip", "install"] 16 | 17 | subprocess.check_call(pip_install + [install_package_name or package]) 18 | # else: 19 | # print(f"Package {package} is already installed.") 20 | 21 | ensure_package("dotenv", "python-dotenv") 22 | ensure_package("argon2", "argon2-cffi") 23 | 24 | 25 | from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 26 | __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] 27 | -------------------------------------------------------------------------------- /nodes.py: -------------------------------------------------------------------------------- 1 | import copy 2 | import io 3 | from pathlib import Path 4 | import folder_paths 5 | import zipfile 6 | 7 | from .utils import * 8 | 9 | TOOLTIP_LIMIT_OPUS_FREE = "Limit image size and steps for free generation by Opus." 10 | 11 | class PromptToNAID: 12 | @classmethod 13 | def INPUT_TYPES(s): 14 | return { "required": { 15 | "text": ("STRING", { "forceInput":True, "multiline": True, "dynamicPrompts": False,}), 16 | "weight_per_brace": ("FLOAT", { "default": 0.05, "min": 0.05, "max": 0.10, "step": 0.05 }), 17 | "syntax_mode": (["brace", "numeric"], { "default": "brace" }), 18 | }} 19 | 20 | RETURN_TYPES = ("STRING",) 21 | FUNCTION = "convert" 22 | CATEGORY = "NovelAI/utils" 23 | def convert(self, text, weight_per_brace, syntax_mode): 24 | nai_prompt = prompt_to_nai(text, weight_per_brace, syntax_mode) 25 | return (nai_prompt,) 26 | 27 | class ImageToNAIMask: 28 | @classmethod 29 | def INPUT_TYPES(s): 30 | return { "required": { "image": ("IMAGE",) } } 31 | 32 | RETURN_TYPES = ("IMAGE",) 33 | FUNCTION = "convert" 34 | CATEGORY = "NovelAI/utils" 35 | def convert(self, image): 36 | s = resize_to_naimask(image) 37 | return (s,) 38 | 39 | class ModelOption: 40 | @classmethod 41 | def INPUT_TYPES(s): 42 | return { 43 | "required": { 44 | "model": (["nai-diffusion-2", "nai-diffusion-furry-3", "nai-diffusion-3", "nai-diffusion-4-curated-preview", "nai-diffusion-4-full", "nai-diffusion-4-5-curated", "nai-diffusion-4-5-full"], { "default": "nai-diffusion-4-5-full" }), 45 | }, 46 | "optional": { "option": ("NAID_OPTION",) }, 47 | } 48 | 49 | RETURN_TYPES = ("NAID_OPTION",) 50 | FUNCTION = "set_option" 51 | CATEGORY = "NovelAI" 52 | def set_option(self, model, option=None): 53 | option = copy.deepcopy(option) if option else {} 54 | option["model"] = model 55 | return (option,) 56 | 57 | class Img2ImgOption: 58 | @classmethod 59 | def INPUT_TYPES(s): 60 | return { 61 | "required": { 62 | "image": ("IMAGE",), 63 | "strength": ("FLOAT", { "default": 0.70, "min": 0.01, "max": 0.99, "step": 0.01, "display": "number" }), 64 | "noise": ("FLOAT", { "default": 0.00, "min": 0.00, "max": 0.99, "step": 0.02, "display": "number" }), 65 | }, 66 | } 67 | 68 | RETURN_TYPES = ("NAID_OPTION",) 69 | FUNCTION = "set_option" 70 | CATEGORY = "NovelAI" 71 | def set_option(self, image, strength, noise): 72 | option = {} 73 | option["img2img"] = (image, strength, noise) 74 | return (option,) 75 | 76 | class InpaintingOption: 77 | @classmethod 78 | def INPUT_TYPES(s): 79 | return { 80 | "required": { 81 | "image": ("IMAGE",), 82 | "mask": ("IMAGE",), 83 | "add_original_image": ("BOOLEAN", { "default": True }), 84 | }, 85 | } 86 | 87 | RETURN_TYPES = ("NAID_OPTION",) 88 | FUNCTION = "set_option" 89 | CATEGORY = "NovelAI" 90 | def set_option(self, image, mask, add_original_image): 91 | option = {} 92 | option["infill"] = (image, mask, add_original_image) 93 | return (option,) 94 | 95 | class VibeTransferOption: 96 | @classmethod 97 | def INPUT_TYPES(s): 98 | return { 99 | "required": { 100 | "image": ("IMAGE",), 101 | "information_extracted": ("FLOAT", { "default": 1.0, "min": 0.01, "max": 1.0, "step": 0.01, "display": "number" }), 102 | "strength": ("FLOAT", { "default": 0.6, "min": 0.01, "max": 1.0, "step": 0.01, "display": "number" }), 103 | }, 104 | "optional": { "option": ("NAID_OPTION",) }, 105 | } 106 | RETURN_TYPES = ("NAID_OPTION",) 107 | FUNCTION = "set_option" 108 | CATEGORY = "NovelAI" 109 | def set_option(self, image, information_extracted, strength, option=None): 110 | option = copy.deepcopy(option) if option else {} 111 | if "vibe" not in option: 112 | option["vibe"] = [] 113 | 114 | option["vibe"].append((image, information_extracted, strength)) 115 | return (option,) 116 | 117 | class NetworkOption: 118 | @classmethod 119 | def INPUT_TYPES(s): 120 | return { 121 | "required": { 122 | "ignore_errors": ("BOOLEAN", { "default": True }), 123 | "timeout_sec": ("INT", { "default": 120, "min": 30, "max": 3000, "step": 1, "display": "number" }), 124 | "retry": ("INT", { "default": 3, "min": 1, "max": 100, "step": 1, "display": "number" }), 125 | }, 126 | "optional": { "option": ("NAID_OPTION",) }, 127 | } 128 | RETURN_TYPES = ("NAID_OPTION",) 129 | FUNCTION = "set_option" 130 | CATEGORY = "NovelAI" 131 | def set_option(self, ignore_errors, timeout_sec, retry, option=None): 132 | option = copy.deepcopy(option) if option else {} 133 | option["ignore_errors"] = ignore_errors 134 | option["timeout"] = timeout_sec 135 | option["retry"] = retry 136 | return (option,) 137 | 138 | 139 | class GenerateNAID: 140 | def __init__(self): 141 | self.access_token = get_access_token() 142 | self.output_dir = folder_paths.get_output_directory() 143 | 144 | @classmethod 145 | def INPUT_TYPES(s): 146 | return { 147 | "required": { 148 | "limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }), 149 | "width": ("INT", { "default": 832, "min": 64, "max": 1600, "step": 64, "display": "number" }), 150 | "height": ("INT", { "default": 1216, "min": 64, "max": 1600, "step": 64, "display": "number" }), 151 | "positive": ("STRING", { "default": ", best quality, amazing quality, very aesthetic, absurdres", "multiline": True, "dynamicPrompts": False }), 152 | "negative": ("STRING", { "default": "lowres", "multiline": True, "dynamicPrompts": False }), 153 | "steps": ("INT", { "default": 28, "min": 0, "max": 50, "step": 1, "display": "number" }), 154 | "cfg": ("FLOAT", { "default": 5.0, "min": 0.0, "max": 10.0, "step": 0.1, "display": "number" }), 155 | "variety" : ("BOOLEAN", { "default": False }), 156 | "decrisper": ("BOOLEAN", { "default": False }), 157 | "smea": (["none", "SMEA", "SMEA+DYN"], { "default": "none" }), 158 | "sampler": (["k_euler", "k_euler_ancestral", "k_dpmpp_2s_ancestral", "k_dpmpp_2m_sde", "k_dpmpp_2m", "k_dpmpp_sde", "ddim"], { "default": "k_euler" }), 159 | "scheduler": (["native", "karras", "exponential", "polyexponential"], { "default": "native" }), 160 | "seed": ("INT", { "default": 0, "min": 0, "max": 9999999999, "step": 1, "display": "number" }), 161 | "uncond_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 1.5, "step": 0.05, "display": "number" }), 162 | "cfg_rescale": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.02, "display": "number" }), 163 | "keep_alpha": ("BOOLEAN", { "default": True, "tooltip": "Disable to further process output images locally" }), 164 | }, 165 | "optional": { "option": ("NAID_OPTION",) }, 166 | } 167 | 168 | RETURN_TYPES = ("IMAGE",) 169 | FUNCTION = "generate" 170 | CATEGORY = "NovelAI" 171 | 172 | def generate(self, limit_opus_free, width, height, positive, negative, steps, cfg, decrisper, variety, smea, sampler, scheduler, seed, uncond_scale, cfg_rescale, keep_alpha, option=None): 173 | width, height = calculate_resolution(width*height, (width, height)) 174 | 175 | # ref. novelai_api.ImagePreset 176 | params = { 177 | "params_version": 1, 178 | "width": width, 179 | "height": height, 180 | "scale": cfg, 181 | "sampler": sampler, 182 | "steps": steps, 183 | "seed": seed, 184 | "n_samples": 1, 185 | "ucPreset": 3, 186 | "qualityToggle": False, 187 | "sm": (smea == "SMEA" or smea == "SMEA+DYN") and sampler != "ddim", 188 | "sm_dyn": smea == "SMEA+DYN" and sampler != "ddim", 189 | "dynamic_thresholding": decrisper, 190 | "skip_cfg_above_sigma": None, 191 | "controlnet_strength": 1.0, 192 | "legacy": False, 193 | "add_original_image": False, 194 | "cfg_rescale": cfg_rescale, 195 | "noise_schedule": scheduler, 196 | "legacy_v3_extend": False, 197 | "uncond_scale": uncond_scale, 198 | "negative_prompt": negative, 199 | "prompt": positive, 200 | "reference_image_multiple": [], 201 | "reference_information_extracted_multiple": [], 202 | "reference_strength_multiple": [], 203 | "extra_noise_seed": seed, 204 | "v4_prompt": { 205 | "use_coords": False, 206 | "use_order": False, 207 | "caption": { 208 | "base_caption": positive, 209 | "char_captions": [] 210 | } 211 | }, 212 | "v4_negative_prompt": { 213 | "use_coords": False, 214 | "use_order": False, 215 | "caption": { 216 | "base_caption": negative, 217 | "char_captions": [] 218 | } 219 | } 220 | } 221 | model = "nai-diffusion-4-5-full" 222 | action = "generate" 223 | 224 | if sampler == "k_euler_ancestral" and scheduler != "native": 225 | params["deliberate_euler_ancestral_bug"] = False 226 | params["prefer_brownian"] = True 227 | 228 | if option: 229 | if "img2img" in option: 230 | action = "img2img" 231 | image, strength, noise = option["img2img"] 232 | params["image"] = image_to_base64(resize_image(image, (width, height))) 233 | params["strength"] = strength 234 | params["noise"] = noise 235 | elif "infill" in option: 236 | action = "infill" 237 | image, mask, add_original_image = option["infill"] 238 | params["image"] = image_to_base64(resize_image(image, (width, height))) 239 | params["mask"] = naimask_to_base64(resize_to_naimask(mask, (width, height), "4" in model)) 240 | params["add_original_image"] = add_original_image 241 | 242 | if "vibe" in option: 243 | for vibe in option["vibe"]: 244 | image, information_extracted, strength = vibe 245 | params["reference_image_multiple"].append(image_to_base64(resize_image(image, (width, height)))) 246 | params["reference_information_extracted_multiple"].append(information_extracted) 247 | params["reference_strength_multiple"].append(strength) 248 | 249 | if "model" in option: 250 | model = option["model"] 251 | 252 | # Handle V4 options 253 | if "v4_prompt" in option: 254 | params["v4_prompt"].update(option["v4_prompt"]) 255 | 256 | timeout = option["timeout"] if option and "timeout" in option else None 257 | retry = option["retry"] if option and "retry" in option else None 258 | 259 | if limit_opus_free: 260 | pixel_limit = 1024*1024 261 | if width * height > pixel_limit: 262 | max_width, max_height = calculate_resolution(pixel_limit, (width, height)) 263 | params["width"] = max_width 264 | params["height"] = max_height 265 | if steps > 28: 266 | params["steps"] = 28 267 | 268 | if variety: 269 | params["skip_cfg_above_sigma"] = calculate_skip_cfg_above_sigma(params["width"], params["height"]) 270 | 271 | if sampler == "ddim" and model not in ("nai-diffusion-2"): 272 | params["sampler"] = "ddim_v3" 273 | 274 | if action == "infill" and model not in ("nai-diffusion-2"): 275 | model = f"{model}-inpainting" 276 | 277 | image = blank_image() 278 | try: 279 | zipped_bytes = generate_image(self.access_token, positive, model, action, params, timeout, retry) 280 | zipped = zipfile.ZipFile(io.BytesIO(zipped_bytes)) 281 | image_bytes = zipped.read(zipped.infolist()[0]) # only support one n_samples 282 | 283 | ## save original png to comfy output dir 284 | full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path("NAI_autosave", self.output_dir) 285 | file = f"{filename}_{counter:05}_.png" 286 | d = Path(full_output_folder) 287 | d.mkdir(exist_ok=True) 288 | (d / file).write_bytes(image_bytes) 289 | 290 | image = bytes_to_image(image_bytes, keep_alpha) 291 | except Exception as e: 292 | if "ignore_errors" in option and option["ignore_errors"]: 293 | print("ignore error:", e) 294 | else: 295 | raise e 296 | 297 | return (image,) 298 | 299 | 300 | def base_augment(access_token, output_dir, limit_opus_free, ignore_errors, req_type, image, options=None): 301 | image = image.movedim(-1, 1) 302 | w, h = (image.shape[3], image.shape[2]) 303 | image = image.movedim(1, -1) 304 | 305 | if limit_opus_free: 306 | pixel_limit = 1024 * 1024 307 | if w * h > pixel_limit: 308 | w, h = calculate_resolution(pixel_limit, (w, h)) 309 | base64_image = image_to_base64(resize_image(image, (w, h))) 310 | result_image = blank_image() 311 | try: 312 | # Build request based on NAI v4 API spec 313 | request = { 314 | "image": base64_image, 315 | "req_type": req_type, 316 | "width": w, 317 | "height": h 318 | } 319 | 320 | # Add optional parameters if provided 321 | if options: 322 | if "defry" in options: 323 | request["defry"] = options["defry"] 324 | if "prompt" in options: 325 | request["prompt"] = options["prompt"] 326 | 327 | zipped_bytes = augment_image(access_token, req_type, w, h, base64_image, options=options) 328 | zipped = zipfile.ZipFile(io.BytesIO(zipped_bytes)) 329 | image_bytes = zipped.read(zipped.infolist()[0]) # only support one n_samples 330 | 331 | ## save original png to comfy output dir 332 | full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path("NAI_autosave", output_dir) 333 | file = f"{filename}_{counter:05}_.png" 334 | d = Path(full_output_folder) 335 | d.mkdir(exist_ok=True) 336 | (d / file).write_bytes(image_bytes) 337 | 338 | result_image = bytes_to_image(image_bytes) 339 | except Exception as e: 340 | if ignore_errors: 341 | print("ignore error:", e) 342 | else: 343 | raise e 344 | 345 | return (result_image,) 346 | 347 | class RemoveBGAugment: 348 | def __init__(self): 349 | self.access_token = get_access_token() 350 | self.output_dir = folder_paths.get_output_directory() 351 | @classmethod 352 | def INPUT_TYPES(s): 353 | return { 354 | "required": { 355 | "image": ("IMAGE",), 356 | "limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }), 357 | "ignore_errors": ("BOOLEAN", { "default": False }), 358 | }, 359 | } 360 | RETURN_TYPES = ("IMAGE",) 361 | FUNCTION = "augment" 362 | CATEGORY = "NovelAI/director_tools" 363 | def augment(self, image, limit_opus_free, ignore_errors): 364 | return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "bg-removal", image) 365 | 366 | class LineArtAugment: 367 | def __init__(self): 368 | self.access_token = get_access_token() 369 | self.output_dir = folder_paths.get_output_directory() 370 | @classmethod 371 | def INPUT_TYPES(s): 372 | return { 373 | "required": { 374 | "image": ("IMAGE",), 375 | "limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }), 376 | "ignore_errors": ("BOOLEAN", { "default": False }), 377 | }, 378 | } 379 | RETURN_TYPES = ("IMAGE",) 380 | FUNCTION = "augment" 381 | CATEGORY = "NovelAI/director_tools" 382 | def augment(self, image, limit_opus_free, ignore_errors): 383 | return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "lineart", image) 384 | 385 | class SketchAugment: 386 | def __init__(self): 387 | self.access_token = get_access_token() 388 | self.output_dir = folder_paths.get_output_directory() 389 | @classmethod 390 | def INPUT_TYPES(s): 391 | return { 392 | "required": { 393 | "image": ("IMAGE",), 394 | "limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }), 395 | "ignore_errors": ("BOOLEAN", { "default": False }), 396 | }, 397 | } 398 | RETURN_TYPES = ("IMAGE",) 399 | FUNCTION = "augment" 400 | CATEGORY = "NovelAI/director_tools" 401 | def augment(self, image, limit_opus_free, ignore_errors): 402 | return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "sketch", image) 403 | 404 | class ColorizeAugment: 405 | def __init__(self): 406 | self.access_token = get_access_token() 407 | self.output_dir = folder_paths.get_output_directory() 408 | @classmethod 409 | def INPUT_TYPES(s): 410 | return { 411 | "required": { 412 | "image": ("IMAGE",), 413 | "limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }), 414 | "ignore_errors": ("BOOLEAN", { "default": False }), 415 | "defry": ("INT", { "default": 0, "min": 0, "max": 5, "step": 1, "display": "number" }), 416 | "prompt": ("STRING", { "default": "", "multiline": True, "dynamicPrompts": False }), 417 | }, 418 | } 419 | RETURN_TYPES = ("IMAGE",) 420 | FUNCTION = "augment" 421 | CATEGORY = "NovelAI/director_tools" 422 | def augment(self, image, limit_opus_free, ignore_errors, defry, prompt): 423 | return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "colorize", image, options={ "defry": defry, "prompt": prompt }) 424 | 425 | class EmotionAugment: 426 | def __init__(self): 427 | self.access_token = get_access_token() 428 | self.output_dir = folder_paths.get_output_directory() 429 | 430 | strength_list = ["normal", "slightly_weak", "weak", "even_weaker", "very_weak", "weakest"] 431 | @classmethod 432 | def INPUT_TYPES(s): 433 | return { 434 | "required": { 435 | "image": ("IMAGE",), 436 | "limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }), 437 | "ignore_errors": ("BOOLEAN", { "default": False }), 438 | "mood": (["neutral", "happy", "sad", "angry", "scared", 439 | "surprised", "tired", "excited", "nervous", "thinking", 440 | "confused", "shy", "disgusted", "smug", "bored", 441 | "laughing", "irritated", "aroused", "embarrassed", "worried", 442 | "love", "determined", "hurt", "playful"], { "default": "neutral" }), 443 | "strength": (s.strength_list, { "default": "normal" }), 444 | "prompt": ("STRING", { "default": "", "multiline": True, "dynamicPrompts": False }), 445 | }, 446 | } 447 | RETURN_TYPES = ("IMAGE",) 448 | FUNCTION = "augment" 449 | CATEGORY = "NovelAI/director_tools" 450 | def augment(self, image, limit_opus_free, ignore_errors, mood, strength, prompt): 451 | prompt = f"{mood};;{prompt}" 452 | defry = EmotionAugment.strength_list.index(strength) 453 | return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "emotion", image, options={ "defry": defry, "prompt": prompt }) 454 | 455 | class DeclutterAugment: 456 | def __init__(self): 457 | self.access_token = get_access_token() 458 | self.output_dir = folder_paths.get_output_directory() 459 | @classmethod 460 | def INPUT_TYPES(s): 461 | return { 462 | "required": { 463 | "image": ("IMAGE",), 464 | "limit_opus_free": ("BOOLEAN", { "default": True, "tooltip": TOOLTIP_LIMIT_OPUS_FREE }), 465 | "ignore_errors": ("BOOLEAN", { "default": False }), 466 | }, 467 | } 468 | RETURN_TYPES = ("IMAGE",) 469 | FUNCTION = "augment" 470 | CATEGORY = "NovelAI/director_tools" 471 | def augment(self, image, limit_opus_free, ignore_errors): 472 | return base_augment(self.access_token, self.output_dir, limit_opus_free, ignore_errors, "declutter", image) 473 | class V4BasePrompt: 474 | @classmethod 475 | def INPUT_TYPES(s): 476 | return { 477 | "required": { 478 | "base_caption": ("STRING", { "multiline": True }), 479 | } 480 | } 481 | 482 | RETURN_TYPES = ("STRING",) # Changed from NAID_OPTION to STRING 483 | FUNCTION = "convert" # Changed from set_option to convert 484 | CATEGORY = "NovelAI/v4" 485 | def convert(self, base_caption): 486 | return (base_caption,) # Simply returns the caption as a string 487 | 488 | """class V4PromptConfig: 489 | @classmethod 490 | def INPUT_TYPES(s): 491 | return { 492 | "required": { 493 | "use_coords": ("BOOLEAN", { "default": False }), 494 | "use_order": ("BOOLEAN", { "default": False }), 495 | }, 496 | "optional": { "option": ("NAID_OPTION",) }, 497 | } 498 | 499 | RETURN_TYPES = ("NAID_OPTION",) 500 | FUNCTION = "set_option" 501 | CATEGORY = "NovelAI/v4" 502 | def set_option(self, use_coords, use_order, option=None): 503 | option = copy.deepcopy(option) if option else {} 504 | if "v4_prompt" not in option: 505 | option["v4_prompt"] = {} 506 | option["v4_prompt"]["use_coords"] = use_coords 507 | option["v4_prompt"]["use_order"] = use_order 508 | return (option,) 509 | 510 | class V4CharacterCaption: 511 | @classmethod 512 | def INPUT_TYPES(s): 513 | return { 514 | "required": { 515 | "char_caption": ("STRING", { "multiline": True }), 516 | "x": ("FLOAT", { "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01 }), 517 | "y": ("FLOAT", { "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01 }), 518 | }, 519 | "optional": { "option": ("NAID_OPTION",) }, 520 | } 521 | 522 | RETURN_TYPES = ("NAID_OPTION",) 523 | FUNCTION = "set_option" 524 | CATEGORY = "NovelAI/v4" 525 | def set_option(self, char_caption, x, y, option=None): 526 | option = copy.deepcopy(option) if option else {} 527 | if "v4_prompt" not in option: 528 | option["v4_prompt"] = { 529 | "caption": { 530 | "base_caption": "", 531 | "char_captions": [] 532 | } 533 | } 534 | 535 | char_caption_obj = { 536 | "char_caption": char_caption, 537 | "centers": [{"x": x, "y": y}] 538 | } 539 | 540 | option["v4_prompt"]["caption"]["char_captions"].append(char_caption_obj) 541 | return (option,) 542 | """ 543 | class V4NegativePrompt: 544 | @classmethod 545 | def INPUT_TYPES(s): 546 | return { 547 | "required": { 548 | "negative_caption": ("STRING", { "multiline": True }), 549 | } 550 | } 551 | 552 | RETURN_TYPES = ("STRING",) 553 | FUNCTION = "convert" 554 | CATEGORY = "NovelAI/v4" 555 | def convert(self, negative_caption): 556 | return (negative_caption,) 557 | 558 | 559 | NODE_CLASS_MAPPINGS = { 560 | "GenerateNAID": GenerateNAID, 561 | "ModelOptionNAID": ModelOption, 562 | "Img2ImgOptionNAID": Img2ImgOption, 563 | "InpaintingOptionNAID": InpaintingOption, 564 | "VibeTransferOptionNAID": VibeTransferOption, 565 | "NetworkOptionNAID": NetworkOption, 566 | "MaskImageToNAID": ImageToNAIMask, 567 | "PromptToNAID": PromptToNAID, 568 | "RemoveBGNAID": RemoveBGAugment, 569 | "LineArtNAID": LineArtAugment, 570 | "SketchNAID": SketchAugment, 571 | "ColorizeNAID": ColorizeAugment, 572 | "EmotionNAID": EmotionAugment, 573 | "DeclutterNAID": DeclutterAugment, 574 | "V4BasePrompt": V4BasePrompt, 575 | "V4NegativePrompt": V4NegativePrompt, 576 | } 577 | 578 | NODE_DISPLAY_NAME_MAPPINGS = { 579 | "GenerateNAID": "Generate ✒️🅝🅐🅘", 580 | "ModelOptionNAID": "ModelOption ✒️🅝🅐🅘", 581 | "Img2ImgOptionNAID": "Img2ImgOption ✒️🅝🅐🅘", 582 | "InpaintingOptionNAID": "InpaintingOption ✒️🅝🅐🅘", 583 | "VibeTransferOptionNAID": "VibeTransferOption ✒️🅝🅐🅘", 584 | "NetworkOptionNAID": "NetworkOption ✒️🅝🅐🅘", 585 | "MaskImageToNAID": "Convert Mask Image ✒️🅝🅐🅘", 586 | "PromptToNAID": "Convert Prompt ✒️🅝🅐🅘", 587 | "RemoveBGNAID": "Remove BG ✒️🅝🅐🅘", 588 | "LineArtNAID": "LineArt ✒️🅝🅐🅘", 589 | "SketchNAID": "Sketch ✒️🅝🅐🅘", 590 | "ColorizeNAID": "Colorize ✒️🅝🅐🅘", 591 | "EmotionNAID": "Emotion ✒️🅝🅐🅘", 592 | "DeclutterNAID": "Declutter ✒️🅝🅐🅘", 593 | "V4BasePrompt": "V4 Base Prompt ✒️🅝🅐🅘", 594 | "V4NegativePrompt": "V4 Negative Prompt ✒️🅝🅐🅘", 595 | } 596 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "comfyui_naidgenerator" 3 | description = "This extension helps generate images through NAI." 4 | version = "1.0.9" 5 | license = { file = "LICENSE" } 6 | dependencies = ["python-dotenv", "argon2-cffi"] 7 | 8 | [project.urls] 9 | Repository = "https://github.com/bedovyy/ComfyUI_NAIDGenerator" 10 | # Used by Comfy Registry https://comfyregistry.org 11 | 12 | [tool.comfy] 13 | PublisherId = "bedovyy" 14 | DisplayName = "ComfyUI_NAIDGenerator" 15 | Icon = "" 16 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | python-dotenv 2 | argon2-cffi 3 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | from hashlib import blake2b 2 | import argon2 3 | 4 | import base64 5 | import dotenv 6 | from os import environ as env 7 | import io 8 | import re 9 | import requests 10 | from requests.adapters import HTTPAdapter, Retry 11 | import comfy.utils 12 | 13 | import torch 14 | import numpy as np 15 | from PIL import Image, ImageOps 16 | 17 | # cherry-picked from novelai_api.utils 18 | def argon_hash(email: str, password: str, size: int, domain: str) -> str: 19 | pre_salt = f"{password[:6]}{email}{domain}" 20 | blake = blake2b(digest_size=16) 21 | blake.update(pre_salt.encode()) 22 | salt = blake.digest() 23 | raw = argon2.low_level.hash_secret_raw(password.encode(), salt, 2, int(2000000 / 1024), 1, size, argon2.low_level.Type.ID,) 24 | hashed = base64.urlsafe_b64encode(raw).decode() 25 | return hashed 26 | 27 | def get_access_key(email: str, password: str) -> str: 28 | return argon_hash(email, password, 64, "novelai_data_access_key")[:64] 29 | 30 | 31 | def login(key) -> str: 32 | response = requests.post(f"https://api.novelai.net/user/login", json={ "key": key }) 33 | response.raise_for_status() 34 | return response.json()["accessToken"] 35 | 36 | def get_access_token(): 37 | dotenv.load_dotenv() 38 | if "NAI_ACCESS_TOKEN" in env: 39 | access_token = env["NAI_ACCESS_TOKEN"] 40 | elif "NAI_ACCESS_KEY" in env: 41 | print("ComfyUI_NAIDGenerator: NAI_ACCESS_KEY is deprecated. use NAI_ACCESS_TOKEN instead.") 42 | access_key = env["NAI_ACCESS_KEY"] 43 | elif "NAI_USERNAME" in env and "NAI_PASSWORD" in env: 44 | print("ComfyUI_NAIDGenerator: NAI_USERNAME is deprecated. use NAI_ACCESS_TOKEN instead.") 45 | username = env["NAI_USERNAME"] 46 | password = env["NAI_PASSWORD"] 47 | access_key = get_access_key(username, password) 48 | else: 49 | raise RuntimeError("Please ensure that NAI_API_TOKEN is set in ComfyUI/.env file.") 50 | 51 | if not access_token: 52 | access_token = login(access_key) 53 | return access_token 54 | 55 | 56 | BASE_URL="https://image.novelai.net" 57 | def generate_image(access_token, prompt, model, action, parameters, timeout=None, retry=None): 58 | data = { "input": prompt, "model": model, "action": action, "parameters": parameters } 59 | 60 | request = requests 61 | if retry is not None and retry > 1: 62 | retries = Retry(total=retry, backoff_factor=1, status_forcelist=[429, 500, 502, 503, 504], allowed_methods=["POST"]) 63 | session = requests.Session() 64 | session.mount("https://", HTTPAdapter(max_retries=retries)) 65 | request = session 66 | 67 | response = request.post(f"{BASE_URL}/ai/generate-image", json=data, headers={ "Authorization": f"Bearer {access_token}" }, timeout=timeout) 68 | response.raise_for_status() 69 | return response.content 70 | 71 | def augment_image(access_token, req_type, width, height, image, options={}, timeout=None, retry=None): 72 | data = { "req_type": req_type, "width": width, "height": height, "image": image } 73 | if options: 74 | data.update(options) 75 | 76 | request = requests 77 | if retry is not None and retry > 1: 78 | retries = Retry(total=retry, backoff_factor=1, status_forcelist=[429, 500, 502, 503, 504], allowed_methods=["POST"]) 79 | session = requests.Session() 80 | session.mount("https://", HTTPAdapter(max_retries=retries)) 81 | request = session 82 | 83 | response = request.post(f"{BASE_URL}/ai/augment-image", json=data, headers={ "Authorization": f"Bearer {access_token}" }, timeout=timeout) 84 | response.raise_for_status() 85 | return response.content 86 | 87 | 88 | def image_to_base64(image): 89 | i = 255. * image[0].cpu().numpy() 90 | img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) 91 | image_bytesIO = io.BytesIO() 92 | img.save(image_bytesIO, format="png") 93 | return base64.b64encode(image_bytesIO.getvalue()).decode() 94 | 95 | def naimask_to_base64(image): 96 | i = 255. * image[0].cpu().numpy() 97 | i = np.clip(i, 0, 255).astype(np.uint8) 98 | alpha = np.sum(i, axis=-1) > 0 99 | alpha = np.uint8(alpha * 255) 100 | rgba = np.dstack((i, alpha)) 101 | img = Image.fromarray(rgba) 102 | image_bytesIO = io.BytesIO() 103 | img.save(image_bytesIO, format="png") 104 | return base64.b64encode(image_bytesIO.getvalue()).decode() 105 | 106 | def bytes_to_image(image_bytes, keep_alpha=True): 107 | i = Image.open(io.BytesIO(image_bytes)) 108 | i = ImageOps.exif_transpose(i) 109 | if not keep_alpha: 110 | i = i.convert("RGB") 111 | image = np.array(i).astype(np.float32) / 255.0 112 | return torch.from_numpy(image)[None,] 113 | 114 | def blank_image(): 115 | return torch.tensor([[[0]]]) 116 | 117 | def resize_image(image, size_to): 118 | samples = image.movedim(-1,1) 119 | w, h = size_to 120 | s = comfy.utils.common_upscale(samples, w, h, "bilinear", "disabled") 121 | s = s.movedim(1,-1) 122 | return s 123 | 124 | def resize_to_naimask(mask, image_size=None, is_v4=False): 125 | samples = mask.movedim(-1,1) 126 | w, h = (samples.shape[3], samples.shape[2]) if not image_size else image_size 127 | width = int(np.ceil(w / 64) * 8) 128 | height = int(np.ceil(h / 64) * 8) 129 | s = comfy.utils.common_upscale(samples, width, height, "nearest-exact", "disabled") 130 | if is_v4: 131 | s = comfy.utils.common_upscale(s, width*8, height*8, "nearest-exact", "disabled") 132 | s = s.movedim(1,-1) 133 | return s 134 | 135 | def calculate_resolution(pixel_count, aspect_ratio): 136 | pixel_count = pixel_count / 4096 137 | w, h = aspect_ratio 138 | k = (pixel_count * w / h) ** 0.5 139 | width = int(np.floor(k) * 64) 140 | height = int(np.floor(k * h / w) * 64) 141 | return width, height 142 | 143 | def calculate_skip_cfg_above_sigma(w, h): 144 | # 832 * 1216 145 | return (w * h / 1011712) ** 0.5 * 19 146 | 147 | 148 | def prompt_to_stack(sentence): 149 | result = [] 150 | current_str = "" 151 | stack = [{ "weight": 1.0, "data": result }] 152 | 153 | for i, c in enumerate(sentence): 154 | if c in '()': 155 | # current_str = current_str.strip() 156 | if c == '(': 157 | if current_str: stack[-1]["data"].append(current_str) 158 | stack[-1]["data"].append({ "weight": 1.0, "data": [] }); 159 | stack.append(stack[-1]["data"][-1]) 160 | elif c == ')': 161 | searched = re.search(r"^(.*):(-?[0-9\.]+)$", current_str) 162 | current_str, weight = searched.groups() if searched else (current_str, 1.1) 163 | if current_str: stack[-1]["data"].append(current_str) 164 | stack[-1]["weight"] = float(weight) 165 | if stack[-1]["data"] != result: 166 | stack.pop() 167 | else: # no more to pop 168 | print("error :", sentence); 169 | print(f"col {i:>3}:", " " * i + "^") 170 | # raise Exception('Error durring parsing parentheses', sentence, i, c) 171 | current_str = "" 172 | else: 173 | current_str += c 174 | 175 | if current_str: 176 | stack[-1]["data"].append(current_str) 177 | 178 | return result 179 | 180 | def prompt_stack_to_nai(l, weight_per_brace=0.05, syntax_mode="brace"): 181 | result = "" 182 | for el in l: 183 | if isinstance(el, dict): 184 | weight = el["weight"] 185 | prompt = prompt_stack_to_nai(el["data"], weight_per_brace, syntax_mode) 186 | if weight < 0: 187 | syntax_mode = "numeric" 188 | if syntax_mode == "brace": 189 | brace_count = round((weight - 1.0) / weight_per_brace) 190 | result += "{" * brace_count + "[" * -brace_count + prompt + "}" * brace_count + "]" * -brace_count 191 | elif syntax_mode == "numeric": 192 | result += f"{weight:g}::{prompt} ::" 193 | else: 194 | result += el 195 | return result 196 | 197 | def prompt_to_nai(prompt, weight_per_brace=0.05, syntax_mode="brace"): 198 | return prompt_stack_to_nai(prompt_to_stack(prompt.replace("\(", "（").replace("\)", "）")), weight_per_brace, syntax_mode).replace("（", "(").replace("）",")") 199 | --------------------------------------------------------------------------------