├── .gitattributes ├── .github └── workflows │ └── publish.yml ├── .gitignore ├── LICENSE ├── README.md ├── __init__.py ├── examples ├── dataset_captioning_example.jpg ├── dataset_captioning_workflow.json ├── llama_vision_bounding_box_example.jpg ├── llama_vision_bounding_box_workflow.json ├── molmo_count_example.jpg ├── molmo_counting_workflow.json ├── molmo_multi_pointing_example.jpg ├── pixtral_caption_example.jpg ├── pixtral_caption_workflow.json └── pixtral_comparison_example.jpg ├── nodes.py ├── pyproject.toml └── requirements.txt /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.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 | jobs: 12 | publish-node: 13 | name: Publish Custom Node to registry 14 | runs-on: ubuntu-latest 15 | # if this is a forked repository. Skipping the workflow. 16 | if: github.event.repository.fork == false 17 | steps: 18 | - name: Check out code 19 | uses: actions/checkout@v4 20 | - name: Publish Custom Node 21 | uses: Comfy-Org/publish-node-action@main 22 | with: 23 | ## Add your own personal access token to your Github Repository secrets and reference it here. 24 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} 25 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. For the purposes of this definition, 18 | "control" means (i) the power, direct or indirect, to cause the 19 | direction or management of such entity, whether by contract or 20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 21 | outstanding shares, or (iii) beneficial ownership of such entity. 22 | 23 | "You" (or "Your") shall mean an individual or Legal Entity 24 | exercising permissions granted by this License. 25 | 26 | "Source" form shall mean the preferred form for making modifications, 27 | including but not limited to software source code, documentation 28 | source, and configuration files. 29 | 30 | "Object" form shall mean any form resulting from mechanical 31 | transformation or translation of a Source form, including but 32 | not limited to compiled object code, generated documentation, 33 | and conversions to other media types. 34 | 35 | "Work" shall mean the work of authorship, whether in Source or 36 | Object form, made available under the License, as indicated by a 37 | copyright notice that is included in or attached to the work 38 | (an example is provided in the Appendix below). 39 | 40 | "Derivative Works" shall mean any work, whether in Source or Object 41 | form, that is based on (or derived from) the Work and for which the 42 | editorial revisions, annotations, elaborations, or other modifications 43 | represent, as a whole, an original work of authorship. For the purposes 44 | of this License, Derivative Works shall not include works that remain 45 | separable from, or merely link (or bind by name) to the interfaces of, 46 | the Work and Derivative Works thereof. 47 | 48 | "Contribution" shall mean any work of authorship, including 49 | the original version of the Work and any modifications or additions 50 | to that Work or Derivative Works thereof, that is intentionally 51 | submitted to Licensor for inclusion in the Work by the copyright owner 52 | or by an individual or Legal Entity authorized to submit on behalf of 53 | the copyright owner. For the purposes of this definition, "submitted" 54 | means any form of electronic, verbal, or written communication sent 55 | to the Licensor or its representatives, including but not limited to 56 | communication on electronic mailing lists, source code control systems, 57 | and issue tracking systems that are managed by, or on behalf of, the 58 | Licensor for the purpose of discussing and improving the Work, but 59 | excluding communication that is conspicuously marked or otherwise 60 | designated in writing by the copyright owner as "Not a Contribution." 61 | 62 | "Contributor" shall mean Licensor and any individual or Legal Entity 63 | on behalf of whom a Contribution has been received by Licensor and 64 | subsequently incorporated within the Work. 65 | 66 | 2. Grant of Copyright License. Subject to the terms and conditions of 67 | this License, each Contributor hereby grants to You a perpetual, 68 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 69 | copyright license to reproduce, prepare Derivative Works of, 70 | publicly display, publicly perform, sublicense, and distribute the 71 | Work and such Derivative Works in Source or Object form. 72 | 73 | 3. Grant of Patent License. Subject to the terms and conditions of 74 | this License, each Contributor hereby grants to You a perpetual, 75 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 76 | (except as stated in this section) patent license to make, have made, 77 | use, offer to sell, sell, import, and otherwise transfer the Work, 78 | where such license applies only to those patent claims licensable 79 | by such Contributor that are necessarily infringed by their 80 | Contribution(s) alone or by combination of their Contribution(s) 81 | with the Work to which such Contribution(s) was submitted. If You 82 | institute patent litigation against any entity (including a 83 | cross-claim or counterclaim in a lawsuit) alleging that the Work 84 | or a Contribution incorporated within the Work constitutes direct 85 | or contributory patent infringement, then any patent licenses 86 | granted to You under this License for that Work shall terminate 87 | as of the date such litigation is filed. 88 | 89 | 4. Redistribution. You may reproduce and distribute copies of the 90 | Work or Derivative Works thereof in any medium, with or without 91 | modifications, and in Source or Object form, provided that You 92 | meet the following conditions: 93 | 94 | (a) You must give any other recipients of the Work or 95 | Derivative Works a copy of this License; and 96 | 97 | (b) You must cause any modified files to carry prominent notices 98 | stating that You changed the files; and 99 | 100 | (c) You must retain, in the Source form of any Derivative Works 101 | that You distribute, all copyright, patent, trademark, and 102 | attribution notices from the Source form of the Work, 103 | excluding those notices that do not pertain to any part of 104 | the Derivative Works; and 105 | 106 | (d) If the Work includes a "NOTICE" text file as part of its 107 | distribution, then any Derivative Works that You distribute must 108 | include a readable copy of the attribution notices contained 109 | within such NOTICE file, excluding those notices that do not 110 | pertain to any part of the Derivative Works, in at least one 111 | of the following places: within a NOTICE text file distributed 112 | as part of the Derivative Works; within the Source form or 113 | documentation, if provided along with the Derivative Works; or, 114 | within a display generated by the Derivative Works, if and 115 | wherever such third-party notices normally appear. The contents 116 | of the NOTICE file are for informational purposes only and 117 | do not modify the License. You may add Your own attribution 118 | notices within Derivative Works that You distribute, alongside 119 | or as an addendum to the NOTICE text from the Work, provided 120 | that such additional attribution notices cannot be construed 121 | as modifying the License. 122 | 123 | You may add Your own copyright statement to Your modifications and 124 | may provide additional or different license terms and conditions 125 | for use, reproduction, or distribution of Your modifications, or 126 | for any such Derivative Works as a whole, provided Your use, 127 | reproduction, and distribution of the Work otherwise complies with 128 | the conditions stated in this License. 129 | 130 | 5. Submission of Contributions. Unless You explicitly state otherwise, 131 | any Contribution intentionally submitted for inclusion in the Work 132 | by You to the Licensor shall be under the terms and conditions of 133 | this License, without any additional terms or conditions. 134 | Notwithstanding the above, nothing herein shall supersede or modify 135 | the terms of any separate license agreement you may have executed 136 | with Licensor regarding such Contributions. 137 | 138 | 6. Trademarks. This License does not grant permission to use the trade 139 | names, trademarks, service marks, or product names of the Licensor, 140 | except as required for reasonable and customary use in describing the 141 | origin of the Work and reproducing the content of the NOTICE file. 142 | 143 | 7. Disclaimer of Warranty. Unless required by applicable law or 144 | agreed to in writing, Licensor provides the Work (and each 145 | Contributor provides its Contributions) on an "AS IS" BASIS, 146 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 147 | implied, including, without limitation, any warranties or conditions 148 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 149 | PARTICULAR PURPOSE. You are solely responsible for determining the 150 | appropriateness of using or redistributing the Work and assume any 151 | risks associated with Your exercise of permissions under this License. 152 | 153 | 8. Limitation of Liability. In no event and under no legal theory, 154 | whether in tort (including negligence), contract, or otherwise, 155 | unless required by applicable law (such as deliberate and grossly 156 | negligent acts) or agreed to in writing, shall any Contributor be 157 | liable to You for damages, including any direct, indirect, special, 158 | incidental, or consequential damages of any character arising as a 159 | result of this License or out of the use or inability to use the 160 | Work (including but not limited to damages for loss of goodwill, 161 | work stoppage, computer failure or malfunction, or any and all 162 | other commercial damages or losses), even if such Contributor 163 | has been advised of the possibility of such damages. 164 | 165 | 9. Accepting Warranty or Additional Liability. While redistributing 166 | the Work or Derivative Works thereof, You may choose to offer, 167 | and charge a fee for, acceptance of support, warranty, indemnity, 168 | or other liability obligations and/or rights consistent with this 169 | License. However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | APPENDIX: How to apply the Apache License to your work. 179 | 180 | To apply the Apache License to your work, attach the following 181 | boilerplate notice, with the fields enclosed by brackets "[]" 182 | replaced with your own identifying information. (Don't include 183 | the brackets!) The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ComfyUI-PixtralLlamaMolmoVision 2 | 3 | For loading and running Pixtral, Llama 3.2 Vision, and Molmo models. 4 | 5 | Important change compared to last version: Models should now be placed in the `ComfyUI/models/LLM` folder for better compatibility with other custom nodes for LLM. I apologize for having to move your models around if you were using the previous version. 6 | 7 | Includes nodes for loading and running VLMs: 8 | - Load Vision Model 9 | - Load Pixtral Model 10 | - Load Llama Vision Model 11 | - Load Molmo Model 12 | - Generate Text with Pixtral 13 | - Generate Text with Llama Vision 14 | - Generate Text with Molmo 15 | 16 | Along with some utility nodes for working with text: 17 | - Parse Bounding Boxes 18 | - Parse Points 19 | - Plot Points 20 | - Regex Split String 21 | - Regex Search 22 | - Regex Find All 23 | - Regex Substitution 24 | - Join String 25 | - Select Index 26 | - Slice List 27 | 28 | The Load Vision Model node is able to load any of these model types, but it will also be able to load other models in the LLM folder. Other model types, like Florence2, will not work with these nodes, though. 29 | 30 | The other model loading nodes are for specific model types and will filter the list to just that model type. 31 | 32 | The text generation nodes are model-specific. Pixtral seems to be the only one out of these that currently supports repetition penalty. I plan to add some more nodes for creating prompts following a chat sequence. 33 | 34 | The Generate Text with Pixtral node can take the `[IMG]` special token in the prompt, and should include it for as many images as you want to process in a single prompt. If these tags aren't added, they will be automatically added to the beginning of your prompt. The Llama and Molmo models will add the images to the beginning of the prompt automatically, and while they do support processing multiple images at once, they don't support including multiple images in different places in the prompt like this. 35 | 36 | System prompts are optional. I didn't include them for Pixtral because the current setup is already using the `[INST]` special token, so the pixtral prompting is already like a system prompt rather than a user conversation. I might change this later. 37 | 38 | Use `trust_remote_code` at your own risk. (I think Molmo looks safe, though) 39 | 40 | ## Installation 41 | 42 | Available in [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) as ComfyUI-PixtralLlamaVision. When installed from ComfyUI-Manager, the required packages will be installed automatically. You may need to update your pytorch version. 43 | 44 | If you install by cloning this repo into your custom nodes folder, you'll need to install `transformers >= 4.45.0` to load Pixtral and Llama Vision models, and you'll also need to make sure `accelerate`, `bitsandbytes`, and `torchvision` are updated. You can install these in the windows portable version of ComfyUI with: 45 | `python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-PixtralLlamaVision\requirements.txt` 46 | 47 | Models should be placed in the `ComfyUI/models/LLM` folder, with each model inside a folder with the `model.safetensors` file along with any config files and the tokenizer. 48 | 49 | You can get a 4-bit quantized version of Pixtral-12B and/or Llama-3.2-11B-Vision-Instruct which is compatible with these custom nodes here: 50 | 51 | [https://huggingface.co/SeanScripts/pixtral-12b-nf4](https://huggingface.co/SeanScripts/pixtral-12b-nf4) 52 | 53 | [https://huggingface.co/SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4](https://huggingface.co/SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4) 54 | 55 | Unfortunately, the Pixtral nf4 model has considerably degraded performance on some tasks, like OCR. The Llama Vision model seems to be better for this task. 56 | 57 | ## Examples 58 | 59 | Example Pixtral image captioning (not saving the output to a text file in this example): 60 | ![Example Pixtral image captioning workflow](examples/pixtral_caption_example.jpg) 61 | 62 | All of these models should work very well for image captioning, even in 4-bit quantization. You can also customize your captioning instructions. Larger images might not work as well with Pixtral, so scaling them down to something like 512 x 512 before sending them to the text generation node might be a good idea. It's also worth noting that the nf4 Pixtral model has significantly degraded performance on images which consist of mainly text. 63 | 64 | Example Molmo dataset captioning for a LoRA: 65 | ![Example dataset captioning workflow](examples/dataset_captioning_example.jpg) 66 | 67 | This workflow sends a list of images to the image generation node to caption each of them sequentially, and creates images and text files in a folder with names `1.png`, `1.txt`, etc for easy LoRA training setup. 68 | 69 | Note that for captioning each image separately, this input should be a list, not a batch of images, because these models can take multiple images as input for a single generation. Currently these nodes don't support batched text generation, but I might add that in the future. Doing one text generation task at a time is probably better for people with normal amounts of VRAM though. 70 | 71 | Example Pixtral image comparison: 72 | ![Example Pixtral image comparison workflow](examples/pixtral_comparison_example.jpg) 73 | 74 | I haven't been able to get image comparison to work well at all with Llama Vision. It doesn't give any errors, but the multi-image understanding just isn't there. The image tokens have to be **before** the question/instruction and consecutive for the model to even be able to see both images at once (I found this out by looking at the image preprocessor cross-attention implementation), and even then, it seems to randomly mix up which is the first/second, left/right, the colors between them and other details. It doesn't seem usable for purposes involving two images in the same message, in my opinion. Not sure whether the non-quantized model is better at this. 75 | 76 | Since Pixtral directly tokenizes the input images, it's able to handle them inline in the context, with any number of images of any aspect ratio, but it's limited by token lengths, since each image can be around 1000 - 4000 tokens. 77 | 78 | Example Llama Vision object detection with bounding box: 79 | ![Example Llama Vision object detection with bounding box workflow](examples/llama_vision_bounding_box_example.jpg) 80 | 81 | Both Pixtral and Llama kind of work for this, but not that well. They definitely have some understanding of the positions of objects in the image, though. Maybe it needs a better prompt. Or a non-quantized model. Or a finetune. But it does sometimes work. Surprisingly, Molmo is pretty bad at this, though it is capable of pointing and counting. 82 | 83 | Example Molmo counting: 84 | ![Example Molmo counting workflow](examples/molmo_count_example.jpg) 85 | 86 | Example Molmo pointing, with labels: 87 | ![Example Molmo pointing workflow](examples/molmo_multi_pointing_example.jpg) 88 | 89 | I wasn't able to get it to point at both objects with a single prompt for some reason (it would just assign both labels to both points), but splitting it into two simple prompts like this isn't too bad. -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 2 | 3 | __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] -------------------------------------------------------------------------------- /examples/dataset_captioning_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SeanScripts/ComfyUI-PixtralLlamaMolmoVision/48fab4b9814f4602528bb2144f3fd0da8b9b8c36/examples/dataset_captioning_example.jpg -------------------------------------------------------------------------------- /examples/dataset_captioning_workflow.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 25, 3 | "last_link_id": 24, 4 | "nodes": [ 5 | { 6 | "id": 17, 7 | "type": "Note", 8 | "pos": { 9 | "0": 1864, 10 | "1": 871 11 | }, 12 | "size": { 13 | "0": 260.5903625488281, 14 | "1": 137.3493194580078 15 | }, 16 | "flags": {}, 17 | "order": 0, 18 | "mode": 0, 19 | "inputs": [], 20 | "outputs": [], 21 | "properties": {}, 22 | "widgets_values": [ 23 | "Folder within the ComfyUI output folder where you want the images + captions to be saved with the same name (1.png, 1.txt, etc.)" 24 | ], 25 | "color": "#432", 26 | "bgcolor": "#653" 27 | }, 28 | { 29 | "id": 16, 30 | "type": "Note", 31 | "pos": { 32 | "0": 1864, 33 | "1": 431 34 | }, 35 | "size": { 36 | "0": 282.4085388183594, 37 | "1": 132.3493194580078 38 | }, 39 | "flags": {}, 40 | "order": 1, 41 | "mode": 0, 42 | "inputs": [], 43 | "outputs": [], 44 | "properties": {}, 45 | "widgets_values": [ 46 | "Path here should be the same as the path below, but as a relative path from where you are running ComfyUI, or as an absolute path" 47 | ], 48 | "color": "#432", 49 | "bgcolor": "#653" 50 | }, 51 | { 52 | "id": 18, 53 | "type": "Note", 54 | "pos": { 55 | "0": 267, 56 | "1": 795 57 | }, 58 | "size": { 59 | "0": 279.317626953125, 60 | "1": 145.62205505371094 61 | }, 62 | "flags": {}, 63 | "order": 2, 64 | "mode": 0, 65 | "inputs": [], 66 | "outputs": [], 67 | "properties": {}, 68 | "widgets_values": [ 69 | "Path to a folder with the images you want to caption (filenames don't matter).\nimage_load_cap = 0 will load all the images from the folder." 70 | ], 71 | "color": "#432", 72 | "bgcolor": "#653" 73 | }, 74 | { 75 | "id": 24, 76 | "type": "MolmoGenerateText", 77 | "pos": { 78 | "0": 990, 79 | "1": 401 80 | }, 81 | "size": { 82 | "0": 400, 83 | "1": 362 84 | }, 85 | "flags": {}, 86 | "order": 8, 87 | "mode": 0, 88 | "inputs": [ 89 | { 90 | "name": "molmo_model", 91 | "type": "VISION_MODEL", 92 | "link": 22 93 | }, 94 | { 95 | "name": "images", 96 | "type": "IMAGE", 97 | "link": 23 98 | } 99 | ], 100 | "outputs": [ 101 | { 102 | "name": "STRING", 103 | "type": "STRING", 104 | "links": [ 105 | 24 106 | ], 107 | "slot_index": 0, 108 | "shape": 3 109 | } 110 | ], 111 | "properties": { 112 | "Node name for S&R": "MolmoGenerateText" 113 | }, 114 | "widgets_values": [ 115 | "", 116 | "Describe this image in detail. ", 117 | 256, 118 | true, 119 | 0.3, 120 | 0.9, 121 | 40, 122 | "<|endoftext|>", 123 | 3561946545, 124 | "randomize", 125 | false 126 | ] 127 | }, 128 | { 129 | "id": 23, 130 | "type": "MolmoModelLoader", 131 | "pos": { 132 | "0": 572, 133 | "1": 407 134 | }, 135 | "size": { 136 | "0": 315, 137 | "1": 58 138 | }, 139 | "flags": {}, 140 | "order": 3, 141 | "mode": 0, 142 | "inputs": [], 143 | "outputs": [ 144 | { 145 | "name": "VISION_MODEL", 146 | "type": "VISION_MODEL", 147 | "links": [ 148 | 22 149 | ], 150 | "slot_index": 0, 151 | "shape": 3 152 | } 153 | ], 154 | "properties": { 155 | "Node name for S&R": "MolmoModelLoader" 156 | }, 157 | "widgets_values": [ 158 | "molmo-7B-D-bnb-4bit" 159 | ] 160 | }, 161 | { 162 | "id": 19, 163 | "type": "Note", 164 | "pos": { 165 | "0": 290, 166 | "1": 368 167 | }, 168 | "size": { 169 | "0": 264.54547119140625, 170 | "1": 134.54547119140625 171 | }, 172 | "flags": {}, 173 | "order": 4, 174 | "mode": 0, 175 | "inputs": [], 176 | "outputs": [], 177 | "properties": {}, 178 | "widgets_values": [ 179 | "Llama 3.2 11B Vision and Molmo are probably better quality than Pixtral for this" 180 | ], 181 | "color": "#432", 182 | "bgcolor": "#653" 183 | }, 184 | { 185 | "id": 8, 186 | "type": "Save Text File", 187 | "pos": { 188 | "0": 1537, 189 | "1": 407 190 | }, 191 | "size": { 192 | "0": 303.0448913574219, 193 | "1": 174 194 | }, 195 | "flags": {}, 196 | "order": 10, 197 | "mode": 0, 198 | "inputs": [ 199 | { 200 | "name": "text", 201 | "type": "STRING", 202 | "link": 24, 203 | "widget": { 204 | "name": "text" 205 | } 206 | }, 207 | { 208 | "name": "filename_prefix", 209 | "type": "STRING", 210 | "link": 9, 211 | "widget": { 212 | "name": "filename_prefix" 213 | } 214 | } 215 | ], 216 | "outputs": [], 217 | "properties": { 218 | "Node name for S&R": "Save Text File" 219 | }, 220 | "widgets_values": [ 221 | "", 222 | ".\\ComfyUI\\output\\images_with_captions", 223 | "", 224 | "", 225 | 0, 226 | ".txt", 227 | "utf-8" 228 | ] 229 | }, 230 | { 231 | "id": 4, 232 | "type": "ListCounter //Inspire", 233 | "pos": { 234 | "0": 954, 235 | "1": 864 236 | }, 237 | "size": { 238 | "0": 210, 239 | "1": 58 240 | }, 241 | "flags": {}, 242 | "order": 7, 243 | "mode": 0, 244 | "inputs": [ 245 | { 246 | "name": "signal", 247 | "type": "*", 248 | "link": 3 249 | } 250 | ], 251 | "outputs": [ 252 | { 253 | "name": "INT", 254 | "type": "INT", 255 | "links": [ 256 | 1 257 | ], 258 | "slot_index": 0, 259 | "shape": 3 260 | } 261 | ], 262 | "properties": { 263 | "Node name for S&R": "ListCounter //Inspire" 264 | }, 265 | "widgets_values": [ 266 | 1 267 | ] 268 | }, 269 | { 270 | "id": 6, 271 | "type": "SomethingToString", 272 | "pos": { 273 | "0": 1223, 274 | "1": 865 275 | }, 276 | "size": { 277 | "0": 210, 278 | "1": 82 279 | }, 280 | "flags": {}, 281 | "order": 9, 282 | "mode": 0, 283 | "inputs": [ 284 | { 285 | "name": "input", 286 | "type": "*", 287 | "link": 1 288 | } 289 | ], 290 | "outputs": [ 291 | { 292 | "name": "STRING", 293 | "type": "STRING", 294 | "links": [ 295 | 9, 296 | 14, 297 | 15 298 | ], 299 | "slot_index": 0, 300 | "shape": 3 301 | } 302 | ], 303 | "properties": { 304 | "Node name for S&R": "SomethingToString" 305 | }, 306 | "widgets_values": [ 307 | "", 308 | "" 309 | ] 310 | }, 311 | { 312 | "id": 1, 313 | "type": "LoadImageListFromDir //Inspire", 314 | "pos": { 315 | "0": 570, 316 | "1": 786 317 | }, 318 | "size": { 319 | "0": 315, 320 | "1": 170 321 | }, 322 | "flags": {}, 323 | "order": 5, 324 | "mode": 0, 325 | "inputs": [], 326 | "outputs": [ 327 | { 328 | "name": "IMAGE", 329 | "type": "IMAGE", 330 | "links": [ 331 | 3, 332 | 13, 333 | 23 334 | ], 335 | "slot_index": 0, 336 | "shape": 6 337 | }, 338 | { 339 | "name": "MASK", 340 | "type": "MASK", 341 | "links": null, 342 | "shape": 6 343 | }, 344 | { 345 | "name": "FILE PATH", 346 | "type": "STRING", 347 | "links": null, 348 | "shape": 6 349 | } 350 | ], 351 | "properties": { 352 | "Node name for S&R": "LoadImageListFromDir //Inspire" 353 | }, 354 | "widgets_values": [ 355 | "E:\\datasets\\example", 356 | 0, 357 | 0, 358 | false 359 | ] 360 | }, 361 | { 362 | "id": 13, 363 | "type": "> Save Image", 364 | "pos": { 365 | "0": 1540, 366 | "1": 790 367 | }, 368 | "size": { 369 | "0": 299.77215576171875, 370 | "1": 406 371 | }, 372 | "flags": {}, 373 | "order": 11, 374 | "mode": 0, 375 | "inputs": [ 376 | { 377 | "name": "images", 378 | "type": "IMAGE", 379 | "link": 13 380 | }, 381 | { 382 | "name": "filename_opt", 383 | "type": "STRING", 384 | "link": 14, 385 | "widget": { 386 | "name": "filename_opt" 387 | } 388 | }, 389 | { 390 | "name": "filename_prefix", 391 | "type": "STRING", 392 | "link": 15, 393 | "widget": { 394 | "name": "filename_prefix" 395 | } 396 | } 397 | ], 398 | "outputs": [], 399 | "properties": { 400 | "Node name for S&R": "> Save Image" 401 | }, 402 | "widgets_values": [ 403 | "", 404 | "images_with_captions", 405 | false, 406 | false, 407 | "png", 408 | 100, 409 | "" 410 | ] 411 | }, 412 | { 413 | "id": 25, 414 | "type": "Note", 415 | "pos": { 416 | "0": 1301, 417 | "1": 1068 418 | }, 419 | "size": [ 420 | 221.29719695532322, 421 | 122.35482607937593 422 | ], 423 | "flags": {}, 424 | "order": 6, 425 | "mode": 0, 426 | "inputs": [], 427 | "outputs": [], 428 | "properties": {}, 429 | "widgets_values": [ 430 | "This node is from ComfyUI_yanc, idk why \"Install Missing Custom Nodes\" doesn't work for this one" 431 | ], 432 | "color": "#432", 433 | "bgcolor": "#653" 434 | } 435 | ], 436 | "links": [ 437 | [ 438 | 1, 439 | 4, 440 | 0, 441 | 6, 442 | 0, 443 | "*" 444 | ], 445 | [ 446 | 3, 447 | 1, 448 | 0, 449 | 4, 450 | 0, 451 | "*" 452 | ], 453 | [ 454 | 9, 455 | 6, 456 | 0, 457 | 8, 458 | 1, 459 | "STRING" 460 | ], 461 | [ 462 | 13, 463 | 1, 464 | 0, 465 | 13, 466 | 0, 467 | "IMAGE" 468 | ], 469 | [ 470 | 14, 471 | 6, 472 | 0, 473 | 13, 474 | 1, 475 | "STRING" 476 | ], 477 | [ 478 | 15, 479 | 6, 480 | 0, 481 | 13, 482 | 2, 483 | "STRING" 484 | ], 485 | [ 486 | 22, 487 | 23, 488 | 0, 489 | 24, 490 | 0, 491 | "VISION_MODEL" 492 | ], 493 | [ 494 | 23, 495 | 1, 496 | 0, 497 | 24, 498 | 1, 499 | "IMAGE" 500 | ], 501 | [ 502 | 24, 503 | 24, 504 | 0, 505 | 8, 506 | 0, 507 | "STRING" 508 | ] 509 | ], 510 | "groups": [], 511 | "config": {}, 512 | "extra": { 513 | "ds": { 514 | "scale": 0.7513148009015777, 515 | "offset": [ 516 | -54.49319695532304, 517 | 20.897173920624354 518 | ] 519 | } 520 | }, 521 | "version": 0.4 522 | } -------------------------------------------------------------------------------- /examples/llama_vision_bounding_box_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SeanScripts/ComfyUI-PixtralLlamaMolmoVision/48fab4b9814f4602528bb2144f3fd0da8b9b8c36/examples/llama_vision_bounding_box_example.jpg -------------------------------------------------------------------------------- /examples/llama_vision_bounding_box_workflow.json: -------------------------------------------------------------------------------- 1 | {"last_node_id":48,"last_link_id":66,"nodes":[{"id":30,"type":"RegexSubstitution","pos":[480,370],"size":[210,102],"flags":{},"order":4,"mode":0,"inputs":[{"name":"string","type":"STRING","link":47,"widget":{"name":"string"}},{"name":"replace","type":"STRING","link":46,"widget":{"name":"replace"}}],"outputs":[{"name":"STRING","type":"STRING","links":[64],"slot_index":0,"shape":3}],"properties":{"Node name for S&R":"RegexSubstitution"},"widgets_values":["{object}","test {object} test","test","M"]},{"id":15,"type":"DF_Text_Box","pos":[75,550],"size":[247.62606811523438,92.8243179321289],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[{"name":"STRING","type":"STRING","links":[46],"slot_index":0,"shape":3}],"properties":{"Node name for S&R":"DF_Text_Box"},"widgets_values":["text"]},{"id":1,"type":"LoadImage","pos":[366,605],"size":[315,314],"flags":{},"order":1,"mode":0,"inputs":[],"outputs":[{"name":"IMAGE","type":"IMAGE","links":[7,12,63],"slot_index":0,"shape":3},{"name":"MASK","type":"MASK","links":null,"shape":3}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["CogVideoX-I2V_00006.png","image"]},{"id":10,"type":"ParseBoundingBoxes","pos":[1125,315],"size":[210,102],"flags":{},"order":6,"mode":0,"inputs":[{"name":"image","type":"IMAGE","link":12},{"name":"string","type":"STRING","link":65,"widget":{"name":"string"}}],"outputs":[{"name":"BBOX","type":"BBOX","links":[26,27],"slot_index":0,"shape":3}],"properties":{"Node name for S&R":"ParseBoundingBoxes"},"widgets_values":["",true,true]},{"id":12,"type":"Display Any (rgthree)","pos":[1418,251],"size":[248.57421875,85.7146224975586],"flags":{},"order":9,"mode":0,"inputs":[{"name":"source","type":"*","link":27,"dir":3}],"outputs":[],"properties":{"Node name for S&R":"Display Any (rgthree)"},"widgets_values":["[[36, 72, 648, 144]]"]},{"id":6,"type":"BboxVisualize","pos":[1431,410],"size":[210,78],"flags":{},"order":8,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":7},{"name":"bboxes","type":"BBOX","link":26}],"outputs":[{"name":"images","type":"IMAGE","links":[8],"slot_index":0,"shape":3}],"properties":{"Node name for S&R":"BboxVisualize"},"widgets_values":[3]},{"id":7,"type":"PreviewImage","pos":[1702,404],"size":[443,489],"flags":{},"order":10,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":8}],"outputs":[],"properties":{"Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":4,"type":"Display Any (rgthree)","pos":[1692,138],"size":[502.48602294921875,207.90098571777344],"flags":{},"order":7,"mode":0,"inputs":[{"name":"source","type":"*","link":66,"dir":3}],"outputs":[],"properties":{"Node name for S&R":"Display Any (rgthree)"},"widgets_values":["The bounding box coordinates of the text are: [(0.05, 0.15), (0.95, 0.45)]."]},{"id":44,"type":"LlamaVisionModelLoader","pos":[489,175],"size":[351.37103271484375,58],"flags":{},"order":2,"mode":0,"inputs":[],"outputs":[{"name":"VISION_MODEL","type":"VISION_MODEL","links":[62],"slot_index":0,"shape":3}],"properties":{"Node name for S&R":"LlamaVisionModelLoader"},"widgets_values":["Llama-3.2-11B-Vision-Instruct-nf4"]},{"id":46,"type":"LlamaVisionGenerateText","pos":[722,293],"size":[383,384],"flags":{},"order":5,"mode":0,"inputs":[{"name":"llama_vision_model","type":"VISION_MODEL","link":62},{"name":"images","type":"IMAGE","link":63,"shape":7},{"name":"prompt","type":"STRING","link":64,"widget":{"name":"prompt"}}],"outputs":[{"name":"STRING","type":"STRING","links":[65,66]}],"properties":{"Node name for S&R":"LlamaVisionGenerateText"},"widgets_values":["","Caption this image.",256,true,0.3,0.9,40,"<|eot_id|>",929916883,"randomize",false,false]},{"id":14,"type":"DF_Text_Box","pos":[25,132],"size":[419.071044921875,355.2226867675781],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[{"name":"STRING","type":"STRING","links":[47],"slot_index":0,"shape":3}],"properties":{"Node name for S&R":"DF_Text_Box"},"widgets_values":["Create an approximate bounding box containing the {object} in this image. Your response should only include the coordinates of the upper-left and lower-right corners of the bounding box relative to the image size, e.g. [(0.0, 0.0), (1.0, 1.0)] is a bounding box that covers the entire image. The bounding box coordinates of the {object} are:"]}],"links":[[7,1,0,6,0,"IMAGE"],[8,6,0,7,0,"IMAGE"],[12,1,0,10,0,"IMAGE"],[26,10,0,6,1,"BBOX"],[27,10,0,12,0,"*"],[46,15,0,30,1,"STRING"],[47,14,0,30,0,"STRING"],[62,44,0,46,0,"VISION_MODEL"],[63,1,0,46,1,"IMAGE"],[64,30,0,46,2,"STRING"],[65,46,0,10,1,"STRING"],[66,46,0,4,0,"*"]],"groups":[],"config":{},"extra":{"ds":{"scale":1,"offset":[191.67246566216193,168.1576626509841]},"node_versions":{"ComfyUI-PixtralLlamaMolmoVision":"01728a16308eaa501dd025cb70f14a3b07a322a1","Derfuu_ComfyUI_ModdedNodes":"d0905bed31249f2bd0814c67585cf4fe3c77c015","comfy-core":"0.3.13","rgthree-comfy":"5d771b8b56a343c24a26e8cea1f0c87c3d58102f","ComfyUI-KJNodes":"2abf557e3d6ae6618456a190044a85a52f2a585a"},"VHS_latentpreview":false,"VHS_latentpreviewrate":0},"version":0.4} -------------------------------------------------------------------------------- /examples/molmo_count_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SeanScripts/ComfyUI-PixtralLlamaMolmoVision/48fab4b9814f4602528bb2144f3fd0da8b9b8c36/examples/molmo_count_example.jpg -------------------------------------------------------------------------------- /examples/molmo_counting_workflow.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 58, 3 | "last_link_id": 114, 4 | "nodes": [ 5 | { 6 | "id": 56, 7 | "type": "ParsePoints", 8 | "pos": { 9 | "0": 1086, 10 | "1": 335 11 | }, 12 | "size": [ 13 | 315, 14 | 122 15 | ], 16 | "flags": {}, 17 | "order": 4, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "string", 22 | "type": "STRING", 23 | "link": 111, 24 | "widget": { 25 | "name": "string" 26 | } 27 | } 28 | ], 29 | "outputs": [ 30 | { 31 | "name": "POINT", 32 | "type": "POINT", 33 | "links": [ 34 | 112 35 | ], 36 | "shape": 3, 37 | "slot_index": 0 38 | }, 39 | { 40 | "name": "STRING", 41 | "type": "STRING", 42 | "links": null, 43 | "shape": 3 44 | }, 45 | { 46 | "name": "STRING", 47 | "type": "STRING", 48 | "links": null, 49 | "shape": 3 50 | } 51 | ], 52 | "properties": { 53 | "Node name for S&R": "ParsePoints" 54 | }, 55 | "widgets_values": [ 56 | "", 57 | "" 58 | ] 59 | }, 60 | { 61 | "id": 54, 62 | "type": "MolmoModelLoader", 63 | "pos": { 64 | "0": 313, 65 | "1": 248 66 | }, 67 | "size": { 68 | "0": 315, 69 | "1": 58 70 | }, 71 | "flags": {}, 72 | "order": 0, 73 | "mode": 0, 74 | "inputs": [], 75 | "outputs": [ 76 | { 77 | "name": "VISION_MODEL", 78 | "type": "VISION_MODEL", 79 | "links": [ 80 | 108 81 | ], 82 | "shape": 3, 83 | "slot_index": 0 84 | } 85 | ], 86 | "properties": { 87 | "Node name for S&R": "MolmoModelLoader" 88 | }, 89 | "widgets_values": [ 90 | "molmo-7B-D-bnb-4bit" 91 | ] 92 | }, 93 | { 94 | "id": 58, 95 | "type": "PreviewImage", 96 | "pos": { 97 | "0": 1771, 98 | "1": 334 99 | }, 100 | "size": [ 101 | 382.1099039489918, 102 | 403.9147862091281 103 | ], 104 | "flags": {}, 105 | "order": 6, 106 | "mode": 0, 107 | "inputs": [ 108 | { 109 | "name": "images", 110 | "type": "IMAGE", 111 | "link": 113 112 | } 113 | ], 114 | "outputs": [], 115 | "properties": { 116 | "Node name for S&R": "PreviewImage" 117 | } 118 | }, 119 | { 120 | "id": 4, 121 | "type": "LoadImage", 122 | "pos": { 123 | "0": 324, 124 | "1": 358 125 | }, 126 | "size": { 127 | "0": 294.7367248535156, 128 | "1": 375.8291931152344 129 | }, 130 | "flags": {}, 131 | "order": 1, 132 | "mode": 0, 133 | "inputs": [], 134 | "outputs": [ 135 | { 136 | "name": "IMAGE", 137 | "type": "IMAGE", 138 | "links": [ 139 | 109, 140 | 114 141 | ], 142 | "slot_index": 0, 143 | "shape": 3 144 | }, 145 | { 146 | "name": "MASK", 147 | "type": "MASK", 148 | "links": null, 149 | "slot_index": 1, 150 | "shape": 3 151 | } 152 | ], 153 | "properties": { 154 | "Node name for S&R": "LoadImage" 155 | }, 156 | "widgets_values": [ 157 | "Flux_00207_ (1).png", 158 | "image" 159 | ] 160 | }, 161 | { 162 | "id": 57, 163 | "type": "PlotPoints", 164 | "pos": { 165 | "0": 1429, 166 | "1": 340 167 | }, 168 | "size": { 169 | "0": 315, 170 | "1": 150 171 | }, 172 | "flags": {}, 173 | "order": 5, 174 | "mode": 0, 175 | "inputs": [ 176 | { 177 | "name": "points", 178 | "type": "POINT", 179 | "link": 112 180 | }, 181 | { 182 | "name": "image", 183 | "type": "IMAGE", 184 | "link": 114 185 | } 186 | ], 187 | "outputs": [ 188 | { 189 | "name": "IMAGE", 190 | "type": "IMAGE", 191 | "links": [ 192 | 113 193 | ], 194 | "shape": 3, 195 | "slot_index": 0 196 | } 197 | ], 198 | "properties": { 199 | "Node name for S&R": "PlotPoints" 200 | }, 201 | "widgets_values": [ 202 | 10, 203 | 40, 204 | "#ff00ff", 205 | "" 206 | ] 207 | }, 208 | { 209 | "id": 55, 210 | "type": "MolmoGenerateText", 211 | "pos": { 212 | "0": 656, 213 | "1": 301 214 | }, 215 | "size": { 216 | "0": 400, 217 | "1": 312 218 | }, 219 | "flags": {}, 220 | "order": 2, 221 | "mode": 0, 222 | "inputs": [ 223 | { 224 | "name": "molmo_model", 225 | "type": "VISION_MODEL", 226 | "link": 108 227 | }, 228 | { 229 | "name": "images", 230 | "type": "IMAGE", 231 | "link": 109 232 | } 233 | ], 234 | "outputs": [ 235 | { 236 | "name": "STRING", 237 | "type": "STRING", 238 | "links": [ 239 | 110, 240 | 111 241 | ], 242 | "shape": 3, 243 | "slot_index": 0 244 | } 245 | ], 246 | "properties": { 247 | "Node name for S&R": "MolmoGenerateText" 248 | }, 249 | "widgets_values": [ 250 | "Count the people in this image.", 251 | 512, 252 | true, 253 | 0.3, 254 | 0.9, 255 | 40, 256 | "<|endoftext|>", 257 | 2130269981, 258 | "randomize", 259 | false 260 | ] 261 | }, 262 | { 263 | "id": 3, 264 | "type": "Display Any (rgthree)", 265 | "pos": { 266 | "0": 1109, 267 | "1": 6 268 | }, 269 | "size": [ 270 | 608.1341091642305, 271 | 278.6934670890276 272 | ], 273 | "flags": {}, 274 | "order": 3, 275 | "mode": 0, 276 | "inputs": [ 277 | { 278 | "name": "source", 279 | "type": "*", 280 | "link": 110, 281 | "dir": 3 282 | } 283 | ], 284 | "outputs": [], 285 | "properties": { 286 | "Node name for S&R": "Display Any (rgthree)" 287 | }, 288 | "widgets_values": [ 289 | "" 290 | ] 291 | } 292 | ], 293 | "links": [ 294 | [ 295 | 108, 296 | 54, 297 | 0, 298 | 55, 299 | 0, 300 | "VISION_MODEL" 301 | ], 302 | [ 303 | 109, 304 | 4, 305 | 0, 306 | 55, 307 | 1, 308 | "IMAGE" 309 | ], 310 | [ 311 | 110, 312 | 55, 313 | 0, 314 | 3, 315 | 0, 316 | "*" 317 | ], 318 | [ 319 | 111, 320 | 55, 321 | 0, 322 | 56, 323 | 0, 324 | "STRING" 325 | ], 326 | [ 327 | 112, 328 | 56, 329 | 0, 330 | 57, 331 | 0, 332 | "POINT" 333 | ], 334 | [ 335 | 113, 336 | 57, 337 | 0, 338 | 58, 339 | 0, 340 | "IMAGE" 341 | ], 342 | [ 343 | 114, 344 | 4, 345 | 0, 346 | 57, 347 | 1, 348 | "IMAGE" 349 | ] 350 | ], 351 | "groups": [], 352 | "config": {}, 353 | "extra": { 354 | "ds": { 355 | "scale": 1.2284597357367266, 356 | "offset": [ 357 | -254.51223899431685, 358 | 74.38924093465424 359 | ] 360 | } 361 | }, 362 | "version": 0.4 363 | } -------------------------------------------------------------------------------- /examples/molmo_multi_pointing_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SeanScripts/ComfyUI-PixtralLlamaMolmoVision/48fab4b9814f4602528bb2144f3fd0da8b9b8c36/examples/molmo_multi_pointing_example.jpg -------------------------------------------------------------------------------- /examples/pixtral_caption_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SeanScripts/ComfyUI-PixtralLlamaMolmoVision/48fab4b9814f4602528bb2144f3fd0da8b9b8c36/examples/pixtral_caption_example.jpg -------------------------------------------------------------------------------- /examples/pixtral_caption_workflow.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 19, 3 | "last_link_id": 38, 4 | "nodes": [ 5 | { 6 | "id": 10, 7 | "type": "PixtralModelLoader", 8 | "pos": { 9 | "0": 364, 10 | "1": 304 11 | }, 12 | "size": { 13 | "0": 315, 14 | "1": 58 15 | }, 16 | "flags": {}, 17 | "order": 0, 18 | "mode": 0, 19 | "inputs": [], 20 | "outputs": [ 21 | { 22 | "name": "PIXTRAL_MODEL", 23 | "type": "PIXTRAL_MODEL", 24 | "links": [ 25 | 36 26 | ], 27 | "slot_index": 0, 28 | "shape": 3 29 | } 30 | ], 31 | "properties": { 32 | "Node name for S&R": "PixtralModelLoader" 33 | }, 34 | "widgets_values": [ 35 | "pixtral-12b-nf4" 36 | ] 37 | }, 38 | { 39 | "id": 4, 40 | "type": "LoadImage", 41 | "pos": { 42 | "0": 362, 43 | "1": 463 44 | }, 45 | "size": { 46 | "0": 315, 47 | "1": 314 48 | }, 49 | "flags": {}, 50 | "order": 1, 51 | "mode": 0, 52 | "inputs": [], 53 | "outputs": [ 54 | { 55 | "name": "IMAGE", 56 | "type": "IMAGE", 57 | "links": [ 58 | 37 59 | ], 60 | "slot_index": 0, 61 | "shape": 3 62 | }, 63 | { 64 | "name": "MASK", 65 | "type": "MASK", 66 | "links": null, 67 | "shape": 3 68 | } 69 | ], 70 | "properties": { 71 | "Node name for S&R": "LoadImage" 72 | }, 73 | "widgets_values": [ 74 | "test.png", 75 | "image" 76 | ] 77 | }, 78 | { 79 | "id": 19, 80 | "type": "PixtralGenerateText", 81 | "pos": { 82 | "0": 835, 83 | "1": 380 84 | }, 85 | "size": [ 86 | 405.81818181818176, 87 | 258.090909090909 88 | ], 89 | "flags": {}, 90 | "order": 2, 91 | "mode": 0, 92 | "inputs": [ 93 | { 94 | "name": "pixtral_model", 95 | "type": "PIXTRAL_MODEL", 96 | "link": 36 97 | }, 98 | { 99 | "name": "images", 100 | "type": "IMAGE", 101 | "link": 37 102 | } 103 | ], 104 | "outputs": [ 105 | { 106 | "name": "STRING", 107 | "type": "STRING", 108 | "links": [ 109 | 38 110 | ], 111 | "shape": 3, 112 | "slot_index": 0 113 | } 114 | ], 115 | "properties": { 116 | "Node name for S&R": "PixtralGenerateText" 117 | }, 118 | "widgets_values": [ 119 | "[INST]Caption this image:\n[IMG][/INST]", 120 | 256, 121 | true, 122 | 0.5, 123 | 3722012111, 124 | "randomize" 125 | ] 126 | }, 127 | { 128 | "id": 3, 129 | "type": "Display Any (rgthree)", 130 | "pos": { 131 | "0": 1333, 132 | "1": 368 133 | }, 134 | "size": [ 135 | 397.81818181818176, 136 | 337.36363636363626 137 | ], 138 | "flags": {}, 139 | "order": 3, 140 | "mode": 0, 141 | "inputs": [ 142 | { 143 | "name": "source", 144 | "type": "*", 145 | "link": 38, 146 | "dir": 3 147 | } 148 | ], 149 | "outputs": [], 150 | "properties": { 151 | "Node name for S&R": "Display Any (rgthree)" 152 | }, 153 | "widgets_values": [ 154 | "" 155 | ] 156 | } 157 | ], 158 | "links": [ 159 | [ 160 | 36, 161 | 10, 162 | 0, 163 | 19, 164 | 0, 165 | "PIXTRAL_MODEL" 166 | ], 167 | [ 168 | 37, 169 | 4, 170 | 0, 171 | 19, 172 | 1, 173 | "IMAGE" 174 | ], 175 | [ 176 | 38, 177 | 19, 178 | 0, 179 | 3, 180 | 0, 181 | "*" 182 | ] 183 | ], 184 | "groups": [], 185 | "config": {}, 186 | "extra": { 187 | "ds": { 188 | "scale": 1.1, 189 | "offset": [ 190 | 60.090909090908944, 191 | 74.63636363636358 192 | ] 193 | } 194 | }, 195 | "version": 0.4 196 | } -------------------------------------------------------------------------------- /examples/pixtral_comparison_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SeanScripts/ComfyUI-PixtralLlamaMolmoVision/48fab4b9814f4602528bb2144f3fd0da8b9b8c36/examples/pixtral_comparison_example.jpg -------------------------------------------------------------------------------- /nodes.py: -------------------------------------------------------------------------------- 1 | import comfy.utils 2 | import comfy.model_management as mm 3 | #from comfy.model_patcher import ModelPatcher 4 | import folder_paths 5 | 6 | from transformers import ( 7 | LlavaForConditionalGeneration, 8 | MllamaForConditionalGeneration, 9 | AutoModelForCausalLM, 10 | AutoProcessor, 11 | BitsAndBytesConfig, 12 | GenerationConfig, 13 | StopStringCriteria, 14 | set_seed 15 | ) 16 | from torchvision.transforms.functional import to_pil_image 17 | import numpy as np 18 | import torch 19 | 20 | import json 21 | import os 22 | from pathlib import Path 23 | from PIL import Image, ImageDraw 24 | import re 25 | import time 26 | 27 | # Using folder ComfyUI/models/LLM -- Place each model inside its own folder here, e.g. ComfyUI/models/LLM/pixtral-12b-nf4/model.safetensors 28 | # Also include other config files and tokenizer files in that same folder 29 | llm_model_dir = os.path.join(folder_paths.models_dir, "LLM") 30 | # Add LLM folder if not present 31 | if not os.path.exists(llm_model_dir): 32 | os.makedirs(llm_model_dir) 33 | 34 | model_type_map = { 35 | "LlavaForConditionalGeneration": LlavaForConditionalGeneration, 36 | "MllamaForConditionalGeneration": MllamaForConditionalGeneration, 37 | "MolmoForCausalLM": AutoModelForCausalLM, 38 | # Other vision models can be added here as needed but will require importing 39 | "AutoModelForCausalLM": AutoModelForCausalLM, 40 | } 41 | 42 | def get_models_with_config(): 43 | models = [] 44 | for model_path in Path(llm_model_dir).iterdir(): 45 | if model_path.is_dir(): 46 | if os.path.exists(os.path.join(model_path, "config.json")): 47 | models.append(model_path.parts[-1]) 48 | return models 49 | 50 | def get_model_type(model_path): 51 | config_path = os.path.join(model_path, "config.json") 52 | if os.path.exists(config_path): 53 | with open(config_path, 'r') as config_file: 54 | config = json.load(config_file) 55 | return config["architectures"][0] 56 | print(f"Invalid model config for model {model_path}") 57 | return "Invalid model config" 58 | 59 | def get_models_of_type(model_type): 60 | models = [] 61 | for model_path in Path(llm_model_dir).iterdir(): 62 | if model_path.is_dir(): 63 | current_model_type = get_model_type(model_path) 64 | if current_model_type == model_type: 65 | models.append(model_path.parts[-1]) 66 | return models 67 | 68 | class PixtralModelLoader: 69 | """Loads a Pixtral model. Add models as folders inside the `ComfyUI/models/LLM` folder. Each model folder should contain a standard transformers loadable safetensors model along with a tokenizer and any config files needed.""" 70 | @classmethod 71 | def INPUT_TYPES(s): 72 | return { 73 | "required": { 74 | "model_name": (get_models_of_type("LlavaForConditionalGeneration"),), 75 | } 76 | } 77 | 78 | RETURN_TYPES = ("VISION_MODEL",) 79 | FUNCTION = "load_model" 80 | CATEGORY = "PixtralLlamaVision/Pixtral" 81 | TITLE = "Load Pixtral Model" 82 | 83 | def load_model(self, model_name): 84 | model_path = os.path.join(llm_model_dir, model_name) 85 | print(f"Setting Pixtral model: {model_name}") 86 | # Don't load the full model until needed for generation 87 | processor = AutoProcessor.from_pretrained(model_path) 88 | pixtral_model = { 89 | 'path': model_path, 90 | 'processor': processor, 91 | } 92 | return (pixtral_model,) 93 | 94 | 95 | class PixtralGenerateText: 96 | """Generates text using a Pixtral model. Takes a list of images and a string prompt as input. The prompt must contain an equal number of [IMG] tokens to the number of images passed in.""" 97 | @classmethod 98 | def INPUT_TYPES(s): 99 | return { 100 | "optional": { 101 | "images": ("IMAGE",), 102 | }, 103 | "required": { 104 | "pixtral_model": ("VISION_MODEL",), 105 | #"system_prompt": ("STRING", {"default": "", "multiline": True}), 106 | "prompt": ("STRING", {"default": "Caption this image:\n[IMG]", "multiline": True}), 107 | "max_new_tokens": ("INT", {"default": 256, "min": 1, "max": 4096}), 108 | "do_sample": ("BOOLEAN", {"default": True}), 109 | "temperature": ("FLOAT", {"default": 0.3, "min": 0, "step": 0.1}), 110 | "top_p": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.1}), 111 | "top_k": ("INT", {"default": 40, "min": 1}), 112 | "repetition_penalty": ("FLOAT", {"default": 1.1}), 113 | "stop_strings": ("STRING", {"default": ""}), 114 | "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffff}), 115 | "include_prompt_in_output": ("BOOLEAN", {"default": False}), 116 | "unload_after_generate": ("BOOLEAN", {"default": False}), 117 | } 118 | } 119 | 120 | RETURN_TYPES = ("STRING",) 121 | FUNCTION = "generate_text" 122 | CATEGORY = "PixtralLlamaVision/Pixtral" 123 | TITLE = "Generate Text with Pixtral" 124 | 125 | def generate_text(self, pixtral_model, images, prompt, max_new_tokens, do_sample, temperature, top_p, top_k, repetition_penalty, stop_strings, seed, include_prompt_in_output, unload_after_generate): 126 | # Load model now if needed 127 | device = mm.get_torch_device() 128 | if pixtral_model['path'] and 'model' not in pixtral_model: 129 | pixtral_model['model'] = LlavaForConditionalGeneration.from_pretrained( 130 | pixtral_model['path'], 131 | use_safetensors=True, 132 | device_map=device, 133 | ) 134 | 135 | # I'm sure there is a way to do this without converting back to numpy and then PIL... 136 | # Pixtral requires PIL input for some reason, and the to_pil_image function requires channels to be the first dimension for a Tensor but the last dimension for a numpy array... Yeah idk 137 | if images != None and len(images) > 0: 138 | print(f"Batch of {images.shape} images") 139 | image_list = [to_pil_image(image.numpy()) for image in images] 140 | 141 | # Process prompt 142 | # Example: [INST]Caption this image:\n[IMG][/INST] 143 | # Images can be placed anywhere, unlike the other models 144 | image_tag_count = prompt.count("[IMG]") 145 | added_image_tags = "" 146 | if image_tag_count > 0 and (images is None or len(images) == 0): 147 | print("Warning: Prompt contains image tags but no image") 148 | elif image_tag_count < len(images): 149 | added_image_tags = "[IMG]"*(len(images) - image_tag_count) 150 | print("Warning: Adding extra images to the beginning of the prompt") 151 | elif image_tag_count > len(images): 152 | print("Warning: Too many image tags") 153 | 154 | # Not sure how system vs user input differs for this model yet 155 | final_prompt = "" 156 | #if system_prompt != "": 157 | # final_prompt += f"[INST]{system_prompt}[/INST]" 158 | final_prompt += f"[INST]{added_image_tags}{prompt}[/INST]" 159 | 160 | inputs = pixtral_model['processor'](images=image_list, text=prompt, return_tensors="pt").to(device) 161 | prompt_tokens = len(inputs['input_ids'][0]) 162 | print(f"Prompt tokens: {prompt_tokens}") 163 | stop_strings_list = stop_strings.split(",") 164 | set_seed(seed) 165 | t0 = time.time() 166 | generate_ids = pixtral_model['model'].generate( 167 | **inputs, 168 | generation_config=GenerationConfig( 169 | max_new_tokens=max_new_tokens, 170 | do_sample=do_sample, 171 | temperature=temperature, 172 | top_p=top_p, 173 | top_k=top_k, 174 | repetition_penalty=repetition_penalty, 175 | ), 176 | stopping_criteria=[StopStringCriteria(tokenizer=pixtral_model['processor'].tokenizer, stop_strings=stop_strings_list)], 177 | ) 178 | t1 = time.time() 179 | total_time = t1 - t0 180 | generated_tokens = len(generate_ids[0]) - prompt_tokens 181 | time_per_token = generated_tokens/total_time 182 | print(f"Generated {generated_tokens} tokens in {total_time:.3f} s ({time_per_token:.3f} tok/s)") 183 | output_tokens = generate_ids[0] if include_prompt_in_output else generate_ids[0][prompt_tokens:] 184 | output = pixtral_model['processor'].decode(output_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=False) 185 | print(output) 186 | 187 | # Unload model 188 | if unload_after_generate: 189 | del pixtral_model['model'] 190 | torch.cuda.empty_cache() 191 | print("Pixtral model unloaded") 192 | 193 | return (output,) 194 | 195 | 196 | class LlamaVisionModelLoader: 197 | """Loads a Llama 3.2 Vision model. Add models as folders inside the `ComfyUI/models/LLM` folder. Each model folder should contain a standard transformers loadable safetensors model along with a tokenizer and any config files needed.""" 198 | @classmethod 199 | def INPUT_TYPES(s): 200 | return { 201 | "required": { 202 | "model_name": (get_models_of_type("MllamaForConditionalGeneration"),), 203 | } 204 | } 205 | 206 | RETURN_TYPES = ("VISION_MODEL",) 207 | FUNCTION = "load_model" 208 | CATEGORY = "PixtralLlamaVision/LlamaVision" 209 | TITLE = "Load Llama Vision Model" 210 | 211 | def load_model(self, model_name): 212 | model_path = os.path.join(llm_model_dir, model_name) 213 | print(f"Setting Llama Vision model: {model_name}") 214 | # Don't load the full model until needed for generation 215 | processor = AutoProcessor.from_pretrained(model_path) 216 | llama_vision_model = { 217 | 'path': model_path, 218 | 'processor': processor, 219 | } 220 | return (llama_vision_model,) 221 | 222 | 223 | class LlamaVisionGenerateText: 224 | """Generates text using a Llama 3.2 Vision model. The prompt must contain an equal number of <|image|> tokens to the number of images passed in. Image tokens must also be sequential and before the text you want them to apply to for the image attention to work as intended.""" 225 | @classmethod 226 | def INPUT_TYPES(s): 227 | return { 228 | "optional": { 229 | "images": ("IMAGE",), 230 | }, 231 | "required": { 232 | "llama_vision_model": ("VISION_MODEL",), 233 | "system_prompt": ("STRING", {"default": "", "multiline": True}), 234 | "prompt": ("STRING", {"default": "Caption this image.", "multiline": True}), 235 | "max_new_tokens": ("INT", {"default": 256, "min": 1, "max": 4096}), 236 | "do_sample": ("BOOLEAN", {"default": True}), 237 | "temperature": ("FLOAT", {"default": 0.3, "min": 0.0, "step": 0.1}), 238 | "top_p": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.1}), 239 | "top_k": ("INT", {"default": 40, "min": 1}), 240 | # For some reason, including this causes the CUDA kernel to fail catastrophically? Didn't have this issue with Pixtral 241 | #"repetition_penalty": ("FLOAT", {"default": 1.1}), 242 | "stop_strings": ("STRING", {"default": "<|eot_id|>"}), 243 | "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffff}), 244 | "include_prompt_in_output": ("BOOLEAN", {"default": False}), 245 | "unload_after_generate": ("BOOLEAN", {"default": False}), 246 | } 247 | } 248 | 249 | RETURN_TYPES = ("STRING",) 250 | FUNCTION = "generate_text" 251 | CATEGORY = "PixtralLlamaVision/LlamaVision" 252 | TITLE = "Generate Text with Llama Vision" 253 | 254 | # TODO: Support batching 255 | 256 | def generate_text(self, llama_vision_model, images, system_prompt, prompt, max_new_tokens, do_sample, temperature, top_p, top_k, stop_strings, seed, include_prompt_in_output, unload_after_generate): 257 | # Load model now if needed 258 | device = mm.get_torch_device() 259 | if llama_vision_model['path'] and 'model' not in llama_vision_model: 260 | llama_vision_model['model'] = MllamaForConditionalGeneration.from_pretrained( 261 | llama_vision_model['path'], 262 | use_safetensors=True, 263 | device_map=device, 264 | ) 265 | 266 | # I'm sure there is a way to do this without converting back to numpy and then PIL... 267 | # Llama Vision also requires PIL input for some reason, and the to_pil_image function requires channels to be the first dimension for a Tensor but the last dimension for a numpy array... Yeah idk 268 | 269 | if images != None and len(images) > 0: 270 | print(f"Batch of {images.shape} images") 271 | image_list = [to_pil_image(image.numpy()) for image in images] 272 | 273 | # Process prompt 274 | image_tags = "<|image|>"*len(images) 275 | final_prompt = "<|begin_of_text|>" 276 | if system_prompt != "": 277 | final_prompt += f"<|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|>\n\n" 278 | final_prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{image_tags}{prompt}<|eot_id|>\n\n" 279 | final_prompt += "<|start_header_id|>assistant<|end_header_id|>\n\n" 280 | 281 | inputs = llama_vision_model['processor'](images=image_list, text=final_prompt, return_tensors="pt").to(device) 282 | prompt_tokens = len(inputs['input_ids'][0]) 283 | print(f"Prompt tokens: {prompt_tokens}") 284 | stop_strings_list = stop_strings.split(",") 285 | set_seed(seed) 286 | t0 = time.time() 287 | generate_ids = llama_vision_model['model'].generate( 288 | **inputs, 289 | generation_config=GenerationConfig( 290 | max_new_tokens=max_new_tokens, 291 | do_sample=do_sample, 292 | temperature=temperature, 293 | top_p=top_p, 294 | top_k=top_k, 295 | #repetition_penalty=repetition_penalty, 296 | ), 297 | stopping_criteria=[StopStringCriteria(tokenizer=llama_vision_model['processor'].tokenizer, stop_strings=stop_strings_list)], 298 | ) 299 | t1 = time.time() 300 | total_time = t1 - t0 301 | generated_tokens = len(generate_ids[0]) - prompt_tokens 302 | time_per_token = generated_tokens/total_time 303 | print(f"Generated {generated_tokens} tokens in {total_time:.3f} s ({time_per_token:.3f} tok/s)") 304 | output_tokens = generate_ids[0] if include_prompt_in_output else generate_ids[0][prompt_tokens:] 305 | output = llama_vision_model['processor'].decode(output_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=False) 306 | print(output) 307 | 308 | # Unload model 309 | if unload_after_generate: 310 | del llama_vision_model['model'] 311 | torch.cuda.empty_cache() 312 | print("Llama vision model unloaded") 313 | 314 | return (output,) 315 | 316 | 317 | class MolmoModelLoader: 318 | """Loads a Molmo model. Add models as folders inside the `ComfyUI/models/LLM` folder. Each model folder should contain a standard transformers loadable safetensors model along with a tokenizer and any config files needed.""" 319 | @classmethod 320 | def INPUT_TYPES(s): 321 | return { 322 | "required": { 323 | "model_name": (get_models_of_type("MolmoForCausalLM"),), 324 | } 325 | } 326 | 327 | RETURN_TYPES = ("VISION_MODEL",) 328 | FUNCTION = "load_model" 329 | CATEGORY = "PixtralLlamaVision/Molmo" 330 | TITLE = "Load Molmo Model" 331 | 332 | def load_model(self, model_name): 333 | model_path = os.path.join(llm_model_dir, model_name) 334 | print(f"Setting Molmo model: {model_name}") 335 | # Don't load the full model until needed for generation 336 | processor = AutoProcessor.from_pretrained( 337 | model_path, 338 | torch_dtype="auto", 339 | trust_remote_code=True, 340 | ) 341 | molmo_model = { 342 | 'path': model_path, 343 | 'processor': processor, 344 | } 345 | return (molmo_model,) 346 | 347 | 348 | class MolmoGenerateText: 349 | """Generates text using a Molmo model. Takes a list of images and a string prompt as input. The prompt must contain an equal number of [IMG] tokens to the number of images passed in.""" 350 | @classmethod 351 | def INPUT_TYPES(s): 352 | return { 353 | "optional": { 354 | "images": ("IMAGE",), 355 | }, 356 | "required": { 357 | "molmo_model": ("VISION_MODEL",), 358 | "system_prompt": ("STRING", {"default": "", "multiline": True}), 359 | "prompt": ("STRING", {"default": "Describe this image. ", "multiline": True}), 360 | "max_new_tokens": ("INT", {"default": 256, "min": 1, "max": 4096}), 361 | "do_sample": ("BOOLEAN", {"default": True}), 362 | "temperature": ("FLOAT", {"default": 0.3, "min": 0, "step": 0.1}), 363 | "top_p": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.1}), 364 | "top_k": ("INT", {"default": 40, "min": 1}), 365 | # This doesn't seem to work for this model 366 | #"repetition_penalty": ("FLOAT", {"default": 1.1}), 367 | "stop_strings": ("STRING", {"default": "<|endoftext|>"}), 368 | "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffff}), 369 | "include_prompt_in_output": ("BOOLEAN", {"default": False}), 370 | "unload_after_generate": ("BOOLEAN", {"default": False}), 371 | } 372 | } 373 | 374 | RETURN_TYPES = ("STRING",) 375 | FUNCTION = "generate_text" 376 | CATEGORY = "PixtralLlamaVision/Molmo" 377 | TITLE = "Generate Text with Molmo" 378 | 379 | # TODO: Support batching 380 | 381 | def generate_text(self, molmo_model, images, system_prompt, prompt, max_new_tokens, do_sample, temperature, top_p, top_k, stop_strings, seed, include_prompt_in_output, unload_after_generate): 382 | # Load model now if needed 383 | device = mm.get_torch_device() 384 | if molmo_model['path'] and 'model' not in molmo_model: 385 | molmo_model['model'] = AutoModelForCausalLM.from_pretrained( 386 | molmo_model['path'], 387 | use_safetensors=True, 388 | device_map=device, 389 | torch_dtype="auto", 390 | trust_remote_code=True, 391 | ) 392 | 393 | if images != None and len(images) > 0: 394 | print(f"Batch of {images.shape} images") 395 | image_list = [to_pil_image(image.numpy()) for image in images] 396 | 397 | # Process prompt 398 | final_prompt = "" 399 | if system_prompt != "": 400 | final_prompt += f"<|im_start|>system\n{system_prompt}<|im_end|>\n" 401 | final_prompt += f"<|im_start|>user\n{prompt}<|im_end|>\n" 402 | final_prompt += "<|im_start|>assistant\n" 403 | 404 | inputs = molmo_model['processor'].process( 405 | images=image_list, 406 | #system_prompt=system_prompt, # Doesn't do anything 407 | text=final_prompt, 408 | message_format="none", 409 | always_start_with_space=False, 410 | ) 411 | inputs = {k: v.to(device).unsqueeze(0) for k, v in inputs.items()} 412 | 413 | prompt_tokens = inputs["input_ids"].size(1) 414 | print(f"Prompt tokens: {prompt_tokens}") 415 | 416 | stop_strings_list = stop_strings.split(",") 417 | 418 | set_seed(seed) 419 | t0 = time.time() 420 | output = molmo_model['model'].generate_from_batch( 421 | inputs, 422 | generation_config=GenerationConfig( 423 | max_new_tokens=max_new_tokens, 424 | do_sample=do_sample, 425 | temperature=temperature, 426 | top_p=top_p, 427 | top_k=top_k, 428 | #repetition_penalty=repetition_penalty, 429 | ), 430 | stopping_criteria=[StopStringCriteria(tokenizer=molmo_model['processor'].tokenizer, stop_strings=stop_strings_list)], 431 | tokenizer=molmo_model['processor'].tokenizer, 432 | ) 433 | t1 = time.time() 434 | 435 | total_time = t1 - t0 436 | generated_tokens = output.size(1) - prompt_tokens 437 | time_per_token = generated_tokens/total_time 438 | print(f"Generated {generated_tokens} tokens in {total_time:.3f} s ({time_per_token:.3f} tok/s)") 439 | 440 | output_tokens = output[0] if include_prompt_in_output else output[0, prompt_tokens:] 441 | generated_text = molmo_model['processor'].tokenizer.decode(output_tokens, skip_special_tokens=True) 442 | print(generated_text) 443 | 444 | # Unload model 445 | if unload_after_generate: 446 | del molmo_model['model'] 447 | torch.cuda.empty_cache() 448 | print("Molmo model unloaded") 449 | 450 | return (generated_text,) 451 | 452 | 453 | class AutoVisionModelLoader: 454 | """Loads a vision model. Add models as folders inside the `ComfyUI/models/LLM` folder. Each model folder should contain a standard transformers loadable safetensors model along with a tokenizer and any config files needed. Use `trust_remote_code` at your own risk.""" 455 | @classmethod 456 | def INPUT_TYPES(s): 457 | return { 458 | "required": { 459 | "model_name": (get_models_with_config(),), 460 | "trust_remote_code": ("BOOLEAN", {"default": False}), # No longer very useful. I can add a bit of code checking this when loading Pixtral/Llama Vision if there are custom finetunes of them in the future. 461 | } 462 | } 463 | 464 | RETURN_TYPES = ("VISION_MODEL",) 465 | FUNCTION = "load_model" 466 | CATEGORY = "PixtralLlamaVision/VLM" 467 | TITLE = "Load Vision Model" 468 | 469 | def load_model(self, model_name, trust_remote_code): 470 | model_path = os.path.join(llm_model_dir, model_name) 471 | device = mm.get_torch_device() 472 | # Don't load the full model until needed for generation 473 | try: 474 | model_type_name = get_model_type(model_path) 475 | print(f"Setting vision model: {model_name} of type {model_type_name}") 476 | ''' 477 | model_type = model_type_map.get(model_type_name, AutoModelForCausalLM) 478 | model = model_type.from_pretrained( 479 | model_path, 480 | use_safetensors=True, 481 | device_map=device, 482 | torch_dtype="auto", 483 | trust_remote_code=trust_remote_code, 484 | ) 485 | ''' 486 | processor = AutoProcessor.from_pretrained( 487 | model_path, 488 | torch_dtype="auto", 489 | trust_remote_code=trust_remote_code, 490 | ) 491 | vision_model = { 492 | 'path': model_path, 493 | 'model_type_name': model_type_name, # Not used yet 494 | 'trust_remote_code': trust_remote_code, # Not used yet 495 | 'processor': processor, 496 | } 497 | return (vision_model,) 498 | except Exception as e: 499 | print(f"Error loading vision model: {e}") 500 | raise 501 | 502 | 503 | # Utility for bounding boxes (I'm sure this has been done before but I just wanted to try it out to see how well Pixtral can do it) 504 | class ParseBoundingBoxes: 505 | """Uses a regular expression to find bounding boxes in a string, returning a list of bbox objects (compatible with mtb). `relative` means the bounding box uses float values between 0 and 1 if true and absolute image coordinates if false. `corners_only` means the bounding box is [(x1, y1), (x2, y2)] if true and [(x1, y1), (width, height)] if false. Parentheses are treated as optional.""" 506 | @classmethod 507 | def INPUT_TYPES(s): 508 | return { 509 | "required": { 510 | "image": ("IMAGE",), 511 | "string": ("STRING",), 512 | "relative": ("BOOLEAN", {"default": True}), 513 | "corners_only": ("BOOLEAN", {"default": True}), 514 | } 515 | } 516 | 517 | RETURN_TYPES = ("BBOX",) 518 | FUNCTION = "generate_bboxes" 519 | CATEGORY = "PixtralLlamaVision/Utility" 520 | TITLE = "Parse Bounding Boxes" 521 | 522 | def generate_bboxes(self, image, string, relative, corners_only): 523 | image_width = image.shape[2] 524 | image_height = image.shape[1] 525 | 526 | bboxes = [] 527 | # Ridiculous-looking regex 528 | for match in re.findall(r"""\[?\(?([0-9\.]+),\s*([0-9\.]+)\)?,\s*\(?([0-9\.]+),\s*([0-9\.]+)\)?\]?""", string, flags=re.M): 529 | try: 530 | x1_raw = float(match[0]) 531 | y1_raw = float(match[1]) 532 | x2_raw = float(match[2]) 533 | y2_raw = float(match[3]) 534 | 535 | if relative: 536 | x1 = int(image_width*x1_raw) 537 | y1 = int(image_height*y1_raw) 538 | x2 = int(image_width*x2_raw) 539 | y2 = int(image_height*y2_raw) 540 | else: 541 | x1 = int(x1_raw) 542 | y1 = int(y1_raw) 543 | x2 = int(x2_raw) 544 | y2 = int(y2_raw) 545 | 546 | if corners_only: 547 | width = x2 - x1 548 | height = y2 - y1 549 | else: 550 | width = x2 551 | height = y2 552 | 553 | if width <= 0 or width > image_width or height <= 0 or height > image_height: 554 | print(f"Invalid bbox: ({x1}, {y1}, {width}, {height})") 555 | continue 556 | bbox = (x1, y1, width, height) 557 | bboxes.append(bbox) 558 | except Exception as e: 559 | print(f"Failed to parse bbox: {match}") 560 | 561 | return (bboxes,) 562 | 563 | 564 | class ParsePoints: 565 | """eyes""" 566 | @classmethod 567 | def INPUT_TYPES(s): 568 | return { 569 | "required": { 570 | "string": ("STRING",), 571 | "filter": ("STRING",), 572 | } 573 | } 574 | 575 | RETURN_TYPES = ("POINT", "STRING", "STRING") 576 | FUNCTION = "generate_points" 577 | CATEGORY = "PixtralLlamaVision/Utility" 578 | TITLE = "Parse Points" 579 | 580 | def generate_points(self, string, filter): 581 | point_batches = [] 582 | label_batches = [] 583 | alt_label_batches = [] 584 | if type(string) != list: 585 | string = [string] # batch 1 586 | for s in string: 587 | points = [] 588 | labels = [] 589 | alt_labels = [] 590 | # Tried to design this regex in a way where even if the message gets cut off by the token limit, it finds the points 591 | # Another absolutely ridiculous looking regex 592 | for match in re.findall(r"""[<\[]?points?\s*([xy\d\.="\s]*?)\s*(?:alt="([^"]*)")?(?=>|]|$)[>\]]?([^<\[]*)""", s, flags=re.M): 593 | try: 594 | data = match[0] 595 | if len(match) > 1: 596 | alt = match[1] 597 | if len(match) > 2: 598 | inner = match[2] 599 | else: 600 | inner = "" 601 | else: 602 | alt = "" 603 | inner = "" 604 | 605 | # Roughly matching 606 | if alt == "" or filter.lower() in alt.lower() or filter.lower() in inner.lower(): 607 | data_parts = data.split(" ") 608 | for i in range(len(data_parts)//2): 609 | # Points from Molmo are expressed as percentages 610 | x = float(data_parts[2*i].split('"')[1])/100.0 611 | y = float(data_parts[2*i+1].split('"')[1])/100.0 612 | 613 | # Check for duplicates 614 | valid = True 615 | for point, label, alt_label in zip(points, labels, alt_labels): 616 | if point[0] == x and point[1] == y and label == inner and alt_label == alt: 617 | print(f"Duplicate point ({x}, {y}, {alt}, {inner})") 618 | valid = False 619 | break 620 | if valid: 621 | points.append([x, y]) 622 | labels.append(inner) 623 | alt_labels.append(alt) # I'm not really convinced alt even matters 624 | else: 625 | print(f"Non-matching filter for {match}") 626 | except Exception as e: 627 | print(f"Failed to parse points: {match}: {e}") 628 | point_batches.append(points) 629 | label_batches.append(labels) 630 | alt_label_batches.append(alt_labels) 631 | return (np.array(point_batches), np.array(label_batches), np.array(alt_label_batches)) 632 | 633 | 634 | class PlotPoints: 635 | @classmethod 636 | def INPUT_TYPES(s): 637 | return { 638 | "optional": { 639 | "labels": ("STRING",), 640 | }, 641 | "required": { 642 | "points": ("POINT",), 643 | "image": ("IMAGE",), 644 | "size": ("INT", {"default": 5, "min": 1, "step": 1}), 645 | "font_size": ("INT", {"default": 40}), 646 | "color": ("STRING", {"default": "#0000ff"}), 647 | } 648 | } 649 | 650 | RETURN_TYPES = ("IMAGE",) 651 | FUNCTION = "plot_points" 652 | CATEGORY = "PixtralLlamaVision/Utility" 653 | TITLE = "Plot Points" 654 | 655 | def plot_points(self, points, labels, image, size, font_size, color): 656 | image_width = image.shape[2] 657 | image_height = image.shape[1] 658 | 659 | if labels is None or len(labels) == 0 or font_size == 0: 660 | labels = np.array([["" for point in point_list] for point_list in points]) 661 | 662 | batch_size = image.shape[0] 663 | if len(points) != len(labels) or len(points) != image.shape[0]: 664 | print(f"Warning: Batch size mismatch: Image {image.shape}, points {points.shape}, labels {labels.shape}") 665 | batch_size = min(image.shape[0], len(points), len(labels)) 666 | 667 | # font = ImageFont.truetype("Pillow/Tests/fonts/FreeMono.ttf", font_size) 668 | # I might have overengineered this, it doesn't seem like the model can label separate objects in a single call. But you can concatenate the strings anyway. 669 | colors = [color] 670 | if "," in color: 671 | colors = color.split(",") 672 | color_map = {"": colors[0]} 673 | for i, label in enumerate(np.unique(labels)): 674 | color_map[label] = colors[i%len(colors)] 675 | 676 | # Add points to image (which is a tensor of floats of shape (batch, height, width, channels) 677 | changed_images = [] 678 | for img, point_list, label_list in zip(image, points, labels): 679 | temp_image = to_pil_image(img.numpy()) 680 | draw = ImageDraw.Draw(temp_image) 681 | for point, label in zip(point_list, label_list): 682 | x = int(image_width*point[0]) 683 | y = int(image_height*point[1]) 684 | draw.circle((x, y), fill=color_map[label], outline=color_map[label], radius=size) 685 | if label != "": 686 | draw.text((x, y-size), label, fill=color_map[label], font_size=font_size, anchor='md') 687 | output_image = np.array(temp_image)/0xff 688 | changed_images.append(output_image) 689 | return (torch.Tensor(np.array(changed_images)),) 690 | 691 | 692 | def process_regex_flags(flags): 693 | # Workaround for Python 3.10 not having re.NOFLAG 694 | flag_value = 0 # re.NOFLAG 695 | if 'a' in flags.lower(): 696 | flag_value |= re.A 697 | if 'i' in flags.lower(): 698 | flag_value |= re.I 699 | if 'l' in flags.lower(): 700 | flag_value |= re.L 701 | if 'm' in flags.lower(): 702 | flag_value |= re.M 703 | if 's' in flags.lower(): 704 | flag_value |= re.S 705 | if 'u' in flags.lower(): # u for useless 706 | flag_value |= re.U 707 | if 'x' in flags.lower(): 708 | flag_value |= re.X 709 | return flag_value 710 | 711 | # Utility nodes that I couldn't find elsewhere, not sure why? 712 | class RegexSplitString: 713 | """Uses a regular expression to split in a string by a pattern into a list of strings""" 714 | @classmethod 715 | def INPUT_TYPES(s): 716 | return { 717 | "required": { 718 | "pattern": ("STRING",), 719 | "string": ("STRING",), 720 | "flags": ("STRING", {"default": "M"}), 721 | } 722 | } 723 | 724 | RETURN_TYPES = ("STRING",) 725 | FUNCTION = "split_string" 726 | CATEGORY = "PixtralLlamaVision/Utility" 727 | TITLE = "Regex Split String" 728 | 729 | def split_string(self, pattern, string, flags): 730 | return (re.split(pattern, string, flags=process_regex_flags(flags)),) 731 | 732 | 733 | class RegexSearch: 734 | """Uses a regular expression to search for the first occurrence of a pattern in a string, returning whether the pattern was found, the start and end positions if found, and the list of match groups if found""" 735 | @classmethod 736 | def INPUT_TYPES(s): 737 | return { 738 | "required": { 739 | "pattern": ("STRING",), 740 | "string": ("STRING",), 741 | "flags": ("STRING", {"default": "M"}), 742 | } 743 | } 744 | 745 | RETURN_TYPES = ("BOOLEAN", "INT", "INT", "STRING") 746 | FUNCTION = "search" 747 | CATEGORY = "PixtralLlamaVision/Utility" 748 | TITLE = "Regex Search" 749 | 750 | def search(self, pattern, string, flags): 751 | match = re.search(pattern, string, flags=process_regex_flags(flags)) 752 | if match: 753 | span = match.span() 754 | groups = list(match.groups()) 755 | return (True, span[0], span[1], groups) 756 | return (False, 0, 0, []) 757 | 758 | 759 | class RegexFindAll: 760 | """Uses a regular expression to find all matches of a pattern in a string, returning a list of match groups (which could be strings or tuples of strings if you have more than one match group)""" 761 | @classmethod 762 | def INPUT_TYPES(s): 763 | return { 764 | "required": { 765 | "pattern": ("STRING",), 766 | "string": ("STRING",), 767 | "flags": ("STRING", {"default": "M"}), 768 | } 769 | } 770 | 771 | RETURN_TYPES = ("STRING",) 772 | FUNCTION = "find_all" 773 | CATEGORY = "PixtralLlamaVision/Utility" 774 | TITLE = "Regex Find All" 775 | 776 | def find_all(self, pattern, string, flags): 777 | return (re.findall(pattern, string, flags=process_regex_flags(flags)),) 778 | 779 | 780 | # This one is also available in Derfuu_ComfyUI_ModdedNodes 781 | class RegexSubstitution: 782 | """Uses a regular expression to find and replace text in a string""" 783 | @classmethod 784 | def INPUT_TYPES(s): 785 | return { 786 | "required": { 787 | "pattern": ("STRING",), 788 | "string": ("STRING",), 789 | "replace": ("STRING",), 790 | "flags": ("STRING", {"default": "M"}), 791 | } 792 | } 793 | 794 | RETURN_TYPES = ("STRING",) 795 | FUNCTION = "sub" 796 | CATEGORY = "PixtralLlamaVision/Utility" 797 | TITLE = "Regex Substitution" 798 | 799 | def sub(self, pattern, string, replace, flags): 800 | return (re.sub(pattern, replace, string, flags=process_regex_flags(flags)),) 801 | 802 | 803 | class JoinString: 804 | """Joins a list of strings with a delimiter between them""" 805 | @classmethod 806 | def INPUT_TYPES(s): 807 | return { 808 | "required": { 809 | "string_list": ("STRING",), 810 | "delimiter": ("STRING", {"default": ", "}), 811 | } 812 | } 813 | 814 | RETURN_TYPES = ("STRING",) 815 | FUNCTION = "join_string" 816 | CATEGORY = "PixtralLlamaVision/Utility" 817 | TITLE = "Join String" 818 | 819 | def join_string(self, string_list, delimiter): 820 | # Convert to strings just in case? Or is this a bad idea? Well, it'll error if they're not strings, so I guess this will have to do 821 | return (delimiter.join([str(string) for string in string_list]),) 822 | 823 | 824 | # Arbitrary data type for list/tuple indexing 825 | class AnyType(str): 826 | def __ne__(self, __value: object) -> bool: 827 | return False 828 | 829 | ANY = AnyType("*") 830 | 831 | # These ones are especially weird to not be doable in ComfyUI base 832 | class SelectIndex: 833 | """Returns list[index]""" 834 | @classmethod 835 | def INPUT_TYPES(s): 836 | return { 837 | "required": { 838 | "list": (ANY,), 839 | "index": ("INT", {"default": 0}), 840 | } 841 | } 842 | 843 | RETURN_TYPES = (ANY,) 844 | FUNCTION = "select_index" 845 | CATEGORY = "PixtralLlamaVision/Utility" 846 | TITLE = "Select Index" 847 | 848 | def select_index(self, list, index): 849 | return (list[index],) 850 | 851 | class SliceList: 852 | """Returns list[start_index:end_index]""" 853 | @classmethod 854 | def INPUT_TYPES(s): 855 | return { 856 | "required": { 857 | "list": (ANY,), 858 | "start_index": ("INT", {"default": 0}), 859 | "end_index": ("INT", {"default": 1}), 860 | } 861 | } 862 | 863 | RETURN_TYPES = (ANY,) 864 | FUNCTION = "select_index" 865 | CATEGORY = "PixtralLlamaVision/Utility" 866 | TITLE = "Slice List" 867 | 868 | def select_index(self, list, start_index, end_index): 869 | return (list[start_index:end_index],) 870 | 871 | # Batch Count works for getting list length 872 | 873 | NODE_CLASS_MAPPINGS = { 874 | "PixtralModelLoader": PixtralModelLoader, 875 | "PixtralGenerateText": PixtralGenerateText, 876 | # Not really much need to work with the image tokenization directly for something like image captioning, but might be interesting later... 877 | #"PixtralImageEncode": PixtralImageEncode, 878 | #"PixtralTextEncode": PixtralTextEncode, 879 | "LlamaVisionModelLoader": LlamaVisionModelLoader, 880 | "LlamaVisionGenerateText": LlamaVisionGenerateText, 881 | "MolmoModelLoader": MolmoModelLoader, 882 | "MolmoGenerateText": MolmoGenerateText, 883 | "AutoVisionModelLoader": AutoVisionModelLoader, 884 | "RegexSplitString": RegexSplitString, 885 | "RegexSearch": RegexSearch, 886 | "RegexFindAll": RegexFindAll, 887 | "RegexSubstitution": RegexSubstitution, 888 | "JoinString": JoinString, 889 | "ParseBoundingBoxes": ParseBoundingBoxes, 890 | "ParsePoints": ParsePoints, 891 | "PlotPoints": PlotPoints, 892 | "SelectIndex": SelectIndex, 893 | "SliceList": SliceList, 894 | } 895 | 896 | NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()} -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "comfyui-pixtralllamamolmovision" 3 | description = "For loading and running Pixtral, Llama 3.2 Vision, and Molmo models. Put models in the models/LLM folder." 4 | version = "3.0.1" 5 | license = {file = "LICENSE"} 6 | 7 | [project.urls] 8 | Repository = "https://github.com/SeanScripts/ComfyUI-PixtralLlamaMolmoVision" 9 | # Used by Comfy Registry https://comfyregistry.org 10 | 11 | [tool.comfy] 12 | PublisherId = "seanscripts" 13 | DisplayName = "ComfyUI-PixtralLlamaMolmoVision" 14 | Icon = "" 15 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | transformers >= 4.45.0 2 | accelerate 3 | bitsandbytes 4 | torchvision >= 0.17 5 | --------------------------------------------------------------------------------