Instructions to use dataautogpt3/ProteusV0.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dataautogpt3/ProteusV0.4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dataautogpt3/ProteusV0.4", dtype=torch.bfloat16, device_map="cuda") prompt = "3 fish in a fish tank wearing adorable outfits, best quality, hd" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Can the gpt caption/prompt process be shared?
I would love to study the method you used with chat gpt so that I can recreate your caption methods and use them to create Lora’s for this model. I would also like to test your method using an alternative captioning model, llava 1.6.
So the prompting method used for gpt and maybe a couple examples of the images used and their proper outcomes would be great. That way I know I’m using the same method when expanding on this model.
I would love to study the method you used with chat gpt so that I can recreate your caption methods and use them to create Lora’s for this model. I would also like to test your method using an alternative captioning model, llava 1.6.
So the prompting method used for gpt and maybe a couple examples of the images used and their proper outcomes would be great. That way I know I’m using the same method when expanding on this model.
see https://huggingface.co/dataautogpt3/ProteusV0.3/discussions/1
Thank you so much!
Do you have a few examples of the images you used for gpt4v and their prompt output? I would like to compare it against a prompt I am working in with llava 1.6