Auto-generate caption (BLIP):
Using to automate img2img process (BLIP and Llava)
Requirements/Dependencies
For Llava
bitsandbytes>=0.43.0
accelerate>=0.3.0
For MiniCPM
transformers<=4.41.2
timm>=1.0.7
sentencepiece
Installation
cd into ComfyUI/custom_nodes directory
git clone this repo
cd img2txt-comfyui-nodes
pip install -r requirements.txt
Models will be automatically downloaded per-use. If you never toggle a model on in the UI, it will never be downloaded.
To ask a list of specific questions about the image, use the Llava or MiniPCM models. The questions are separated by line in the multiline text input box.
Support for Chinese
The MiniCPM model works with Chinese text input without any additional configuration. The output will also be in Chinese.
“MiniCPM-V 2.0 supports strong bilingual multimodal capabilities in both English and Chinese. This is enabled by generalizing multimodal capabilities across languages, a technique from VisCPM”
Please support the creators of MiniCPM here
Tips
The multi-line input can be used to ask any type of questions. You can even ask very specific or complex questions about images.
To get best results for a prompt that will be fed back into a txt2img or img2img prompt, usually it’s best to only ask one or two questions, asking for a general description of the image and the most salient features and styles.
Model Locations/Paths
Models are downloaded automatically using the Huggingface cache system and the transformers from_pretrained method so no manual installation of models is necessary.
If you really want to manually download the models, please refer to Huggingface’s documentation concerning the cache system. Here is the relevant except:
> Pretrained models are downloaded and locally cached at ~/.cache/huggingface/hub. This is the default directory given by the shell environment variable TRANSFORMERS_CACHE. On Windows, the default directory is given by C:\Users\username\.cache\huggingface\hub. You can change the shell environment variables shown below – in order of priority – to specify a different cache directory:
> – Shell environment variable (default): HUGGINGFACE_HUB_CACHE or TRANSFORMERS_CACHE.
> – Shell environment variable: HF_HOME.
> – Shell environment variable: XDG_CACHE_HOME + /huggingface.
Models
MiniCPM (Chinese & English)
Title: MiniCPM-V-2 – Strong multimodal large language model for efficient end-side deployment
Datasets: HuggingFaceM4VQAv2, RLHF-V-Dataset, LLaVA-Instruct-150K
Size: ~ 6.8GB
Salesforce – blip-image-captioning-base
Title: BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Size: ~ 2GB
Dataset: COCO (The MS COCO dataset is a large-scale object detection, image segmentation, and captioning dataset published by Microsoft)
llava – llava-1.5-7b-hf
Title: LLava: Large Language Models for Vision and Language Tasks
Size: ~ 15GB
Dataset: 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP, 158K GPT-generated multimodal instruction-following data, 450K academic-task-oriented VQA data mixture, 40K ShareGPT data.
Prompts
This is the guide for the format of an “ideal” txt2img prompt (using BLIP). Use as the basis for the questions to ask the img2txt models.
Subject – you can specify region, write the most about the subject
Medium – material used to make artwork. Some examples are illustration, oil painting, 3D rendering, and photography. Medium has a strong effect because one keyword alone can dramatically change the style.
Style – artistic style of the image. Examples include impressionist, surrealist, pop art, etc.
Artists – Artist names are strong modifiers. They allow you to dial in the exact style using a particular artist as a reference. It is also common to use multiple artist names to blend their styles. Now let’s add Stanley Artgerm Lau, a superhero comic artist, and Alphonse Mucha, a portrait painter in the 19th century.
Website – Niche graphic websites such as Artstation and Deviant Art aggregate many images of distinct genres. Using them in a prompt is a sure way to steer the image toward these styles.
Resolution – Resolution represents how sharp and detailed the image is. Let’s add keywords highly detailed and sharp focus
Enviornment
Additional Details and objects – Additional details are sweeteners added to modify an image. We will add sci-fi, stunningly beautiful and dystopian to add some vibe to the image.
Composition – camera type, detail, cinematography, blur, depth-of-field
Color/Warmth – You can control the overall color of the image by adding color keywords. The colors you specified may appear as a tone or in objects.
Lighting – Any photographer would tell you lighting is a key factor in creating successful images. Lighting keywords can have a huge effect on how the image looks. Let’s add cinematic lighting and dark to the prompt.
