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update cog doc with colab link

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Victor Hall 2024-03-24 10:02:38 -04:00
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# CogVLM captioning # CogVLM captioning
CogVLM [code](https://github.com/THUDM/CogVLM) [model](https://huggingface.co/THUDM/cogvlm-chat-hf) is, so far (Q1 2024), the best model for automatically generating captions. CogVLM ([code](https://github.com/THUDM/CogVLM)) ([model](https://huggingface.co/THUDM/cogvlm-chat-hf)) is, so far (Q1 2024), the best model for automatically generating captions.
The model uses about 13.5GB of VRAM due to 4bit inference with the default setting of 1 beam, and up to 4 or 5 beams is possible with a 24GB GPU meaning it is very capable on consumer hardware. It is slow, ~6-10 seconds on a RTX 3090, but the quality is worth it over other models. The model uses about 13.5GB of VRAM due to 4bit inference with the default setting of 1 beam, and up to 4 or 5 beams is possible with a 24GB GPU meaning it is very capable on consumer hardware. It is slow, ~6-10+ seconds on a RTX 3090, but the quality is worth it over other models.
It is capable of naming and identifying things with proper nouns and has a large vocabulary. It can also readily read text even for hard to read fonts, from oblique angles, or from curved surfaces. It is capable of naming and identifying things with proper nouns and has a large vocabulary. It can also readily read text even for hard to read fonts, from oblique angles, or from curved surfaces.
<a href="https://colab.research.google.com/github/nawnie/EveryDream2trainer/blob/main/CaptionCog.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Basics ## Basics
Run `python caption_cog.py --help` to get a list of options. Run `python caption_cog.py --help` to get a list of options.