Do not clone it unless you want to test Molmo, this is for debug and test only, please clone it from SeanScripts/ComfyUI-PixtralLlamaVision For loading and running Pixtral and Llama 3.2 Vision models
https://huggingface.co/cyan2k/molmo-7B-D-bnb-4bit
Includes six nodes:
- Load Molmo Model
- Generate Text with Molmo
- Load Pixtral Model
- Generate Text with Pixtral
- Load Llama Vision Model
- Generate Text with Llama Vision
Along with some utility nodes for working with text:
- Parse Bounding Boxes
- Regex Split String
- Regex Search
- Regex Find All
- Regex Substitution
- Join String
- Select Index
- Slice List
Available in ComfyUI-Manager as ComfyUI-PixtralLlamaVision. When installed from ComfyUI-Manager, the required packages will be installed automatically.
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:
python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-PixtralLlamaVision\requirements.txt
Models should be placed in the ComfyUI/models/pixtral and ComfyUI/models/llama-vision folders, with each model inside a folder with the model.safetensors file along with any config files and the tokenizer.
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:
https://huggingface.co/SeanScripts/pixtral-12b-nf4
https://huggingface.co/SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4
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.
Example of Molmo D (based on Qwen2-7B) BNB (you need to install tensorflow)
Example Pixtral image captioning (not saving the output to a text file in this example):
Both models should work very well for image captioning, even in 4-bit quantization. You can also customize your captioning instructions.
Example Pixtral image comparison:
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.
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 tokens.
Example Llama Vision object detection with bounding box:
Both models 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.